http://wiki.metabolomicssociety.org/api.php?action=feedcontributions&user=Viniciusveri&feedformat=atomMetabolomics Society Wiki - User contributions [en]2024-03-28T10:23:37ZUser contributionsMediaWiki 1.28.0http://wiki.metabolomicssociety.org/index.php?title=Early-Career_Members_Network&diff=1640Early-Career Members Network2022-03-09T07:03:19Z<p>Viniciusveri: </p>
<hr />
<div>[[Image:Facebook_cover.jpg|thumb|The EMN Committee for 2021-2022]]<br />
=Current Early-career Members Network (EMN) Committee=<br />
<br />
The EMN aims to provide a forum for metabolomics researchers at the start of their professional career and serve the early-career members of the Metabolomics Society. Aspirations include, but are not limited to: strengthen communication and collaboration, encourage opportunities and invention, support developmental learning and enjoy professional growth.<br />
<br />
Detailed info about the members can be found [https://metabolomicssociety.org/board-committees/society-committees/ here]<br />
<br />
{|<br />
|Evelina Charidemou (Chair)<br />
|University of Cyprus (Cyprus)<br />
|-<br />
|Vinicius Verri Hernandes (Secretary)<br />
|Eurac Research (Italy)<br />
|-<br />
|Stefania Noerman (Treasurer)<br />
|Chalmers University (Sweeden)<br />
|-<br />
|Dimitrios E. Damalas<br />
|National and Kapodistrian University of Athens (Greece)<br />
|-<br />
|Dimitrios E. Damalas<br />
|National and Kapodistrian University of Athens (Greece)<br />
|-<br />
|Sandi Azab<br />
|McMaster University (Canada)<br />
|-<br />
|Purva Kulkarni<br />
|Radboud University Medical Center (Netherlands)<br />
|-<br />
|Fitri Amalia<br />
|Osaka University (Japan)<br />
|-<br />
|Michelle E. Reid<br />
|Institute of Molecular Systems Biology (Switzerland)<br />
|-<br />
|Sofina Begun<br />
|Harvard Medical School (USA)<br />
|-<br />
|Tee Khim Boon<br />
|Universiti Malaya and Ministry of Health (Malaysia)<br />
|-<br />
|Susana Alejandra Palma-Durán<br />
|Imperial College London (United Kingdom)<br />
|-<br />
|Johanna Jokioja<br />
|Afekta Technologies Ltd (Finland)<br />
|-<br />
|Álvaro Fernandez Ocho<br />
|Max Delbrück Center For Molecular Medicine (Germany)<br />
|-<br />
|Laimdota Zizmare<br />
|Eberhard Karls University of Tuebingen (Germany)<br />
|-<br />
|Marine Letertre (Past chair and Advisor)<br />
|CEISAM Institute, University of Nantes (France)<br />
|-<br />
|Natasa Giallourou (Advisor)<br />
|Imperial College London (UK)<br />
|}<br />
<br />
=Social Media=<br />
Follow us on [https://www.facebook.com/EMN.MetabolomicsSociety/ Facebook] and [https://twitter.com/EMN_MetSoc Twitter] for further updates and initiatives. <br />
<br />
=History & Role=<br />
The Early-career Members Network (EMN) was established in October 2013 through the Metabolomics Society Task Group as part of an initiative to develop activities and ideas and determine how to best serve her members – and especially her Early-career members. The EMN was initially run by nine early career researchers: Sasta Putri (EMN Chair), Vincent Asiago, David Liesenfeld, Thomas Payne, Nicholas Rattray, Ralf Weber, Evangelina Daskalaki, Justin van der Hooft, and Gabriel Valbuena. In 2015, the EMN expanded to a total of twelve international members across a variety of metabolomics fields<ref>Liesenfeld, D. B. et al. Activity update from the early career members network. Metabolomics 11, 247-248, doi:10.1007/s11306-015-0779-6 (2015)</ref>. The mission of the EMN was to recognize the value and importance of early career members, to ensure that their views are heard and acted upon, ultimately improving their experience of metabolomics science and the community as a whole <ref>Putri, S. P. Establishment of an Early-career Members Network (EMN) of the Metabolomics Society. Metabolomics 10, 1-2, doi:10.1007/s11306-013-0613-y (2014)</ref>. The EMN is dedicated to all Metabolomics Society members who are within 5 years of completing their higher degrees and comprises members from industry, government and academia. The EMN is active at the Metabolomics Society Conference through its workshops and Welcome Reception. The EMN workshops contain career development sessions but also scientifically based topics aimed at improving the general understanding of basic metabolomics principles. Outside of the conference, the EMN hosts well-visited Webinars, a Facebook page, a Bursary program, and now the newly live Wiki page. The EMN is always seeking new initiatives to better serve its early careers members and the Metabolomics Society as a whole. <br />
<br />
=References=<br />
<br />
Liesenfeld, D. B. et al. Activity update from the early career members network. Metabolomics 11, 247-248, doi:10.1007/s11306-015-0779-6 (2015)<br />
Putri, S. P. Establishment of an Early-career Members Network (EMN) of the Metabolomics Society. Metabolomics 10, 1-2, doi:10.1007/s11306-013-0613-y (2014)</div>Viniciusverihttp://wiki.metabolomicssociety.org/index.php?title=Early-Career_Members_Network&diff=1639Early-Career Members Network2022-03-09T07:02:49Z<p>Viniciusveri: </p>
<hr />
<div>[[Image:Facebook_cover.jpg|thumb|The EMN Committee for 2021-2022]]<br />
=Current Early-career Members Network (EMN) Committee=<br />
<br />
The EMN aims to provide a forum for metabolomics researchers at the start of their professional career and serve the early-career members of the Metabolomics Society. Aspirations include, but are not limited to: strengthen communication and collaboration, encourage opportunities and invention, support developmental learning and enjoy professional growth.<br />
<br />
Detailed info about the members can be found [https://metabolomicssociety.org/board-committees/society-committees/ here]<br />
<br />
{|<br />
|Evelina Charidemou (Chair)<br />
|University of Cyprus (Cyprus)<br />
|-<br />
|Vinicius Verri Hernandes (Secretary)<br />
|Eurac Research (Italy)<br />
|-<br />
|Stefania Noerman (Treasurer)<br />
|Chalmers University (Sweeden)<br />
|-<br />
|Dimitrios E. Damalas<br />
|National and Kapodistrian University of Athens (Greece)<br />
|-<br />
|Dimitrios E. Damalas<br />
|National and Kapodistrian University of Athens (Greece)<br />
|-<br />
|Sandi Azab<br />
|McMaster University (Canada)<br />
|-<br />
|Purva Kulkarni<br />
|Radboud University Medical Center (Netherlands)<br />
|-<br />
|Fitri Amalia<br />
|Osaka University (Japan)<br />
|-<br />
|Michelle E. Reid<br />
|Institute of Molecular Systems Biology (Switzerland)<br />
|-<br />
|Sofina Begun<br />
|Harvard Medical School (USA)<br />
|-<br />
|Tee Khim Boon<br />
|Universiti Malaya and Ministry of Health (Malaysia)<br />
|-<br />
|Susana Alejandra Palma-Durán<br />
|Imperial College London (United Kingdom)<br />
|-<br />
|Johanna Jokioja<br />
|Afekta Technologies Ltd (Finland)<br />
|-<br />
|Álvaro Fernandez Ocho<br />
|Max Delbrück Center For Molecular Medicine (Germany)<br />
|-<br />
|Laimdota Zizmare<br />
|Eberhard Karls University of Tuebingen (Germany)<br />
|-<br />
|Marine Letertre (Past chair and Advisor)<br />
|CEISAM Institute, University of Nantes (France)<br />
|-<br />
|Natasa Giallourou (Advisor)<br />
|Imperial College London (UK)<br />
|}<br />
<br />
=Social Media=<br />
Follow us on [https://www.facebook.com/EMN.MetabolomicsSociety/ Facebook] and [https://twitter.com/EMN_MetSoc Twitter] for further updates and initiatives. <br />
<br />
=History & Role=<br />
The Early-career Members Network (EMN) was established in October 2013 through the Metabolomics Society Task Group as part of an initiative to develop activities and ideas and determine how to best serve her members – and especially her Early-career members. The EMN was initially run by nine early career researchers: Sasta Putri (EMN Chair), Vincent Asiago, David Liesenfeld, Thomas Payne, Nicholas Rattray, Ralf Weber, Evangelina Daskalaki, Justin van der Hooft, and Gabriel Valbuena. In 2015, the EMN expanded to a total of twelve international members across a variety of metabolomics fields<ref>Liesenfeld, D. B. et al. Activity update from the early career members network. Metabolomics 11, 247-248, doi:10.1007/s11306-015-0779-6 (2015)</ref>. The mission of the EMN was to recognize the value and importance of early career members, to ensure that their views are heard and acted upon, ultimately improving their experience of metabolomics science and the community as a whole <ref>Putri, S. P. Establishment of an Early-career Members Network (EMN) of the Metabolomics Society. Metabolomics 10, 1-2, doi:10.1007/s11306-013-0613-y (2014)</ref>. The EMN is dedicated to all Metabolomics Society members who are within 5 years of completing their higher degrees and comprises members from industry, government and academia. The EMN is active at the Metabolomics Society Conference through its workshops and Welcome Reception. The EMN workshops contain career development sessions but also scientifically based topics aimed at improving the general understanding of basic metabolomics principles. Outside of the conference, the EMN hosts well-visited Webinars, a Facebook page, a Bursary program, and now the newly live Wiki page. The EMN is always seeking new initiatives to better serve its early careers members and the Metabolomics Society as a whole. <br />
<br />
=Links=<br />
[http://metabolomicssociety.org/board/society-committees/early-career-members-network-emn-committee EMN Committee on the Metabolomics Society web page]<br />
<br />
=References=<br />
<br />
Liesenfeld, D. B. et al. Activity update from the early career members network. Metabolomics 11, 247-248, doi:10.1007/s11306-015-0779-6 (2015)<br />
Putri, S. P. Establishment of an Early-career Members Network (EMN) of the Metabolomics Society. Metabolomics 10, 1-2, doi:10.1007/s11306-013-0613-y (2014)</div>Viniciusverihttp://wiki.metabolomicssociety.org/index.php?title=Early-Career_Members_Network&diff=1638Early-Career Members Network2022-03-09T07:02:22Z<p>Viniciusveri: </p>
<hr />
<div>[[Image:Facebook_cover.jpg|thumb|The EMN Committee for 2020-2021]]<br />
=Current Early-career Members Network (EMN) Committee=<br />
<br />
The EMN aims to provide a forum for metabolomics researchers at the start of their professional career and serve the early-career members of the Metabolomics Society. Aspirations include, but are not limited to: strengthen communication and collaboration, encourage opportunities and invention, support developmental learning and enjoy professional growth.<br />
<br />
Detailed info about the members can be found [https://metabolomicssociety.org/board-committees/society-committees/ here]<br />
<br />
{|<br />
|Evelina Charidemou (Chair)<br />
|University of Cyprus (Cyprus)<br />
|-<br />
|Vinicius Verri Hernandes (Secretary)<br />
|Eurac Research (Italy)<br />
|-<br />
|Stefania Noerman (Treasurer)<br />
|Chalmers University (Sweeden)<br />
|-<br />
|Dimitrios E. Damalas<br />
|National and Kapodistrian University of Athens (Greece)<br />
|-<br />
|Dimitrios E. Damalas<br />
|National and Kapodistrian University of Athens (Greece)<br />
|-<br />
|Sandi Azab<br />
|McMaster University (Canada)<br />
|-<br />
|Purva Kulkarni<br />
|Radboud University Medical Center (Netherlands)<br />
|-<br />
|Fitri Amalia<br />
|Osaka University (Japan)<br />
|-<br />
|Michelle E. Reid<br />
|Institute of Molecular Systems Biology (Switzerland)<br />
|-<br />
|Sofina Begun<br />
|Harvard Medical School (USA)<br />
|-<br />
|Tee Khim Boon<br />
|Universiti Malaya and Ministry of Health (Malaysia)<br />
|-<br />
|Susana Alejandra Palma-Durán<br />
|Imperial College London (United Kingdom)<br />
|-<br />
|Johanna Jokioja<br />
|Afekta Technologies Ltd (Finland)<br />
|-<br />
|Álvaro Fernandez Ocho<br />
|Max Delbrück Center For Molecular Medicine (Germany)<br />
|-<br />
|Laimdota Zizmare<br />
|Eberhard Karls University of Tuebingen (Germany)<br />
|-<br />
|Marine Letertre (Past chair and Advisor)<br />
|CEISAM Institute, University of Nantes (France)<br />
|-<br />
|Natasa Giallourou (Advisor)<br />
|Imperial College London (UK)<br />
|}<br />
<br />
=Social Media=<br />
Follow us on [https://www.facebook.com/EMN.MetabolomicsSociety/ Facebook] and [https://twitter.com/EMN_MetSoc Twitter] for further updates and initiatives. <br />
<br />
=History & Role=<br />
The Early-career Members Network (EMN) was established in October 2013 through the Metabolomics Society Task Group as part of an initiative to develop activities and ideas and determine how to best serve her members – and especially her Early-career members. The EMN was initially run by nine early career researchers: Sasta Putri (EMN Chair), Vincent Asiago, David Liesenfeld, Thomas Payne, Nicholas Rattray, Ralf Weber, Evangelina Daskalaki, Justin van der Hooft, and Gabriel Valbuena. In 2015, the EMN expanded to a total of twelve international members across a variety of metabolomics fields<ref>Liesenfeld, D. B. et al. Activity update from the early career members network. Metabolomics 11, 247-248, doi:10.1007/s11306-015-0779-6 (2015)</ref>. The mission of the EMN was to recognize the value and importance of early career members, to ensure that their views are heard and acted upon, ultimately improving their experience of metabolomics science and the community as a whole <ref>Putri, S. P. Establishment of an Early-career Members Network (EMN) of the Metabolomics Society. Metabolomics 10, 1-2, doi:10.1007/s11306-013-0613-y (2014)</ref>. The EMN is dedicated to all Metabolomics Society members who are within 5 years of completing their higher degrees and comprises members from industry, government and academia. The EMN is active at the Metabolomics Society Conference through its workshops and Welcome Reception. The EMN workshops contain career development sessions but also scientifically based topics aimed at improving the general understanding of basic metabolomics principles. Outside of the conference, the EMN hosts well-visited Webinars, a Facebook page, a Bursary program, and now the newly live Wiki page. The EMN is always seeking new initiatives to better serve its early careers members and the Metabolomics Society as a whole. <br />
<br />
=Links=<br />
[http://metabolomicssociety.org/board/society-committees/early-career-members-network-emn-committee EMN Committee on the Metabolomics Society web page]<br />
<br />
=References=<br />
<br />
Liesenfeld, D. B. et al. Activity update from the early career members network. Metabolomics 11, 247-248, doi:10.1007/s11306-015-0779-6 (2015)<br />
Putri, S. P. Establishment of an Early-career Members Network (EMN) of the Metabolomics Society. Metabolomics 10, 1-2, doi:10.1007/s11306-013-0613-y (2014)</div>Viniciusverihttp://wiki.metabolomicssociety.org/index.php?title=Early-Career_Members_Network&diff=1637Early-Career Members Network2022-03-09T07:02:07Z<p>Viniciusveri: </p>
<hr />
<div>[[Image:Facebook_cover.jpg|thumb|The EMN Committee for 2020-2021]]<br />
=Current Early-career Members Network (EMN) Committee=<br />
<br />
The EMN aims to provide a forum for metabolomics researchers at the start of their professional career and serve the early-career members of the Metabolomics Society. Aspirations include, but are not limited to: strengthen communication and collaboration, encourage opportunities and invention, support developmental learning and enjoy professional growth.<br />
<br />
Detailed info about the members can be found [https://metabolomicssociety.org/board-committees/society-committees/ here]<br />
<br />
{|<br />
|Evelina Charidemou (Chair)<br />
|University of Cyprus (Cyprus)<br />
|-<br />
|Vinicius Verri Hernandes (Secretary)<br />
|Eurac Research (Italy)<br />
|-<br />
|Stefania Noerman (Treasurer)<br />
|Chalmers University (Sweeden)<br />
|-<br />
|Dimitrios E. Damalas<br />
|National and Kapodistrian University of Athens (Greece)<br />
|-<br />
|[http://metabolomicssociety.org/site-map/articles/78-bios/326-dimitrios-e-damalas Dimitrios E. Damalas]<br />
|National and Kapodistrian University of Athens (Greece)<br />
|-<br />
|Sandi Azab<br />
|McMaster University (Canada)<br />
|-<br />
|Purva Kulkarni<br />
|Radboud University Medical Center (Netherlands)<br />
|-<br />
|Fitri Amalia<br />
|Osaka University (Japan)<br />
|-<br />
|Michelle E. Reid<br />
|Institute of Molecular Systems Biology (Switzerland)<br />
|-<br />
|Sofina Begun<br />
|Harvard Medical School (USA)<br />
|-<br />
|Tee Khim Boon<br />
|Universiti Malaya and Ministry of Health (Malaysia)<br />
|-<br />
|Susana Alejandra Palma-Durán<br />
|Imperial College London (United Kingdom)<br />
|-<br />
|Johanna Jokioja<br />
|Afekta Technologies Ltd (Finland)<br />
|-<br />
|Álvaro Fernandez Ocho<br />
|Max Delbrück Center For Molecular Medicine (Germany)<br />
|-<br />
|Laimdota Zizmare<br />
|Eberhard Karls University of Tuebingen (Germany)<br />
|-<br />
|Marine Letertre (Past chair and Advisor)<br />
|CEISAM Institute, University of Nantes (France)<br />
|-<br />
|Natasa Giallourou (Advisor)<br />
|Imperial College London (UK)<br />
|}<br />
<br />
=Social Media=<br />
Follow us on [https://www.facebook.com/EMN.MetabolomicsSociety/ Facebook] and [https://twitter.com/EMN_MetSoc Twitter] for further updates and initiatives. <br />
<br />
=History & Role=<br />
The Early-career Members Network (EMN) was established in October 2013 through the Metabolomics Society Task Group as part of an initiative to develop activities and ideas and determine how to best serve her members – and especially her Early-career members. The EMN was initially run by nine early career researchers: Sasta Putri (EMN Chair), Vincent Asiago, David Liesenfeld, Thomas Payne, Nicholas Rattray, Ralf Weber, Evangelina Daskalaki, Justin van der Hooft, and Gabriel Valbuena. In 2015, the EMN expanded to a total of twelve international members across a variety of metabolomics fields<ref>Liesenfeld, D. B. et al. Activity update from the early career members network. Metabolomics 11, 247-248, doi:10.1007/s11306-015-0779-6 (2015)</ref>. The mission of the EMN was to recognize the value and importance of early career members, to ensure that their views are heard and acted upon, ultimately improving their experience of metabolomics science and the community as a whole <ref>Putri, S. P. Establishment of an Early-career Members Network (EMN) of the Metabolomics Society. Metabolomics 10, 1-2, doi:10.1007/s11306-013-0613-y (2014)</ref>. The EMN is dedicated to all Metabolomics Society members who are within 5 years of completing their higher degrees and comprises members from industry, government and academia. The EMN is active at the Metabolomics Society Conference through its workshops and Welcome Reception. The EMN workshops contain career development sessions but also scientifically based topics aimed at improving the general understanding of basic metabolomics principles. Outside of the conference, the EMN hosts well-visited Webinars, a Facebook page, a Bursary program, and now the newly live Wiki page. The EMN is always seeking new initiatives to better serve its early careers members and the Metabolomics Society as a whole. <br />
<br />
=Links=<br />
[http://metabolomicssociety.org/board/society-committees/early-career-members-network-emn-committee EMN Committee on the Metabolomics Society web page]<br />
<br />
=References=<br />
<br />
Liesenfeld, D. B. et al. Activity update from the early career members network. Metabolomics 11, 247-248, doi:10.1007/s11306-015-0779-6 (2015)<br />
Putri, S. P. Establishment of an Early-career Members Network (EMN) of the Metabolomics Society. Metabolomics 10, 1-2, doi:10.1007/s11306-013-0613-y (2014)</div>Viniciusverihttp://wiki.metabolomicssociety.org/index.php?title=Early-Career_Members_Network&diff=1636Early-Career Members Network2022-03-09T06:54:32Z<p>Viniciusveri: </p>
<hr />
<div>[[Image:Facebook_cover.jpg|thumb|The EMN Committee for 2020-2021]]<br />
=Current Early-career Members Network (EMN) Committee=<br />
<br />
The EMN aims to provide a forum for metabolomics researchers at the start of their professional career and serve the early-career members of the Metabolomics Society. Aspirations include, but are not limited to: strengthen communication and collaboration, encourage opportunities and invention, support developmental learning and enjoy professional growth.<br />
<br />
Detailed info about the members can be found [https://metabolomicssociety.org/board-committees/society-committees/ here]<br />
<br />
{|<br />
|Marine Letertre (Chair)<br />
|Corsaire metabolomics platform, Biogenouest (France)<br />
|-<br />
|[http://metabolomicssociety.org/board/society-committees/early-career-members-network-emn-committee Caroline Birer (Secretary)]<br />
|Université de Tours (France)<br />
|-<br />
|[http://metabolomicssociety.org/site-map/articles/78-bios/301-alexandra-george Alexandra George (Treasurer)]<br />
|Baker Heart and Diabetes Institute (Australia)<br />
|-<br />
|[http://metabolomicssociety.org/site-map/articles/78-bios/325-evelina-charidemou Evelina Charidemou]<br />
|University of Cyprus (Cyprus)<br />
|-<br />
|[http://metabolomicssociety.org/site-map/articles/78-bios/326-dimitrios-e-damalas Dimitrios E. Damalas]<br />
|National and Kapodistrian University of Athens (Greece)<br />
|-<br />
|[http://metabolomicssociety.org/site-map/articles/78-bios/327-kehau-a-hagiwara Kehau A. Hagiwara]<br />
|National Institute Of Standards And Technology ( USA)<br />
|-<br />
|[http://metabolomicssociety.org/site-map/articles/78-bios/328-purva-kulkarni Purva Kulkarni]<br />
|Radboud University Medical Center (Netherlands)<br />
|-<br />
|[http://metabolomicssociety.org/site-map/articles/78-bios/329-stefania-noerman Stefania Noerman]<br />
|University Of Eastern Finland (Finland)<br />
|-<br />
|[http://metabolomicssociety.org/site-map/articles/78-bios/330-michelle-e-reid Michelle E. Reid]<br />
|Institute of Molecular Systems Biology (Switzerland)<br />
|-<br />
|[http://metabolomicssociety.org/site-map/articles/78-bios/331-vinicius-veri-hernandes Vinicius Veri Hernandes]<br />
|Eurac Research (Italy)<br />
|-<br />
|[http://metabolomicssociety.org/site-map/articles/78-bios/303-jennifer-l-matthews Jennifer Matthews]<br />
|University of Technology Sydney (Australia)<br />
|-<br />
|[http://metabolomicssociety.org/site-map/articles/78-bios/304-msizi-mhlongo Msizi Mhlongo]<br />
|University of Johannesburg (South Africa)<br />
|-<br />
|[http://metabolomicssociety.org/site-map/articles/78-bios/292-natasa-giallourou Natasa Giallourou (Past Chair)]<br />
|Imperial College London (UK)<br />
|}<br />
<br />
=Social Media=<br />
Follow us on [https://www.facebook.com/EMN.MetabolomicsSociety/ Facebook] and [https://twitter.com/EMN_MetSoc Twitter] for further updates and initiatives. <br />
<br />
=History & Role=<br />
The Early-career Members Network (EMN) was established in October 2013 through the Metabolomics Society Task Group as part of an initiative to develop activities and ideas and determine how to best serve her members – and especially her Early-career members. The EMN was initially run by nine early career researchers: Sasta Putri (EMN Chair), Vincent Asiago, David Liesenfeld, Thomas Payne, Nicholas Rattray, Ralf Weber, Evangelina Daskalaki, Justin van der Hooft, and Gabriel Valbuena. In 2015, the EMN expanded to a total of twelve international members across a variety of metabolomics fields<ref>Liesenfeld, D. B. et al. Activity update from the early career members network. Metabolomics 11, 247-248, doi:10.1007/s11306-015-0779-6 (2015)</ref>. The mission of the EMN was to recognize the value and importance of early career members, to ensure that their views are heard and acted upon, ultimately improving their experience of metabolomics science and the community as a whole <ref>Putri, S. P. Establishment of an Early-career Members Network (EMN) of the Metabolomics Society. Metabolomics 10, 1-2, doi:10.1007/s11306-013-0613-y (2014)</ref>. The EMN is dedicated to all Metabolomics Society members who are within 5 years of completing their higher degrees and comprises members from industry, government and academia. The EMN is active at the Metabolomics Society Conference through its workshops and Welcome Reception. The EMN workshops contain career development sessions but also scientifically based topics aimed at improving the general understanding of basic metabolomics principles. Outside of the conference, the EMN hosts well-visited Webinars, a Facebook page, a Bursary program, and now the newly live Wiki page. The EMN is always seeking new initiatives to better serve its early careers members and the Metabolomics Society as a whole. <br />
<br />
=Links=<br />
[http://metabolomicssociety.org/board/society-committees/early-career-members-network-emn-committee EMN Committee on the Metabolomics Society web page]<br />
<br />
=References=<br />
<br />
Liesenfeld, D. B. et al. Activity update from the early career members network. Metabolomics 11, 247-248, doi:10.1007/s11306-015-0779-6 (2015)<br />
Putri, S. P. Establishment of an Early-career Members Network (EMN) of the Metabolomics Society. Metabolomics 10, 1-2, doi:10.1007/s11306-013-0613-y (2014)</div>Viniciusverihttp://wiki.metabolomicssociety.org/index.php?title=Early-Career_Members_Network&diff=1635Early-Career Members Network2022-03-09T06:51:55Z<p>Viniciusveri: </p>
<hr />
<div>[[Image:Facebook_cover.jpg|thumb|The EMN Committee for 2020-2021]]<br />
=Current Early-career Members Network (EMN) Committee=<br />
<br />
The EMN aims to provide a forum for metabolomics researchers at the start of their professional career and serve the early-career members of the Metabolomics Society. Aspirations include, but are not limited to: strengthen communication and collaboration, encourage opportunities and invention, support developmental learning and enjoy professional growth.<br />
<br />
{|<br />
|[http://metabolomicssociety.org/site-map/articles/78-bios/307-marine-letertre Marine Letertre (Chair)]<br />
|Corsaire metabolomics platform, Biogenouest (France)<br />
|-<br />
|[http://metabolomicssociety.org/board/society-committees/early-career-members-network-emn-committee Caroline Birer (Secretary)]<br />
|Université de Tours (France)<br />
|-<br />
|[http://metabolomicssociety.org/site-map/articles/78-bios/301-alexandra-george Alexandra George (Treasurer)]<br />
|Baker Heart and Diabetes Institute (Australia)<br />
|-<br />
|[http://metabolomicssociety.org/site-map/articles/78-bios/325-evelina-charidemou Evelina Charidemou]<br />
|University of Cyprus (Cyprus)<br />
|-<br />
|[http://metabolomicssociety.org/site-map/articles/78-bios/326-dimitrios-e-damalas Dimitrios E. Damalas]<br />
|National and Kapodistrian University of Athens (Greece)<br />
|-<br />
|[http://metabolomicssociety.org/site-map/articles/78-bios/327-kehau-a-hagiwara Kehau A. Hagiwara]<br />
|National Institute Of Standards And Technology ( USA)<br />
|-<br />
|[http://metabolomicssociety.org/site-map/articles/78-bios/328-purva-kulkarni Purva Kulkarni]<br />
|Radboud University Medical Center (Netherlands)<br />
|-<br />
|[http://metabolomicssociety.org/site-map/articles/78-bios/329-stefania-noerman Stefania Noerman]<br />
|University Of Eastern Finland (Finland)<br />
|-<br />
|[http://metabolomicssociety.org/site-map/articles/78-bios/330-michelle-e-reid Michelle E. Reid]<br />
|Institute of Molecular Systems Biology (Switzerland)<br />
|-<br />
|[http://metabolomicssociety.org/site-map/articles/78-bios/331-vinicius-veri-hernandes Vinicius Veri Hernandes]<br />
|Eurac Research (Italy)<br />
|-<br />
|[http://metabolomicssociety.org/site-map/articles/78-bios/303-jennifer-l-matthews Jennifer Matthews]<br />
|University of Technology Sydney (Australia)<br />
|-<br />
|[http://metabolomicssociety.org/site-map/articles/78-bios/304-msizi-mhlongo Msizi Mhlongo]<br />
|University of Johannesburg (South Africa)<br />
|-<br />
|[http://metabolomicssociety.org/site-map/articles/78-bios/292-natasa-giallourou Natasa Giallourou (Past Chair)]<br />
|Imperial College London (UK)<br />
|}<br />
<br />
=Social Media=<br />
Follow us on [https://www.facebook.com/EMN.MetabolomicsSociety/ Facebook] and [https://twitter.com/EMN_MetSoc Twitter] for further updates and initiatives. <br />
<br />
=History & Role=<br />
The Early-career Members Network (EMN) was established in October 2013 through the Metabolomics Society Task Group as part of an initiative to develop activities and ideas and determine how to best serve her members – and especially her Early-career members. The EMN was initially run by nine early career researchers: Sasta Putri (EMN Chair), Vincent Asiago, David Liesenfeld, Thomas Payne, Nicholas Rattray, Ralf Weber, Evangelina Daskalaki, Justin van der Hooft, and Gabriel Valbuena. In 2015, the EMN expanded to a total of twelve international members across a variety of metabolomics fields<ref>Liesenfeld, D. B. et al. Activity update from the early career members network. Metabolomics 11, 247-248, doi:10.1007/s11306-015-0779-6 (2015)</ref>. The mission of the EMN was to recognize the value and importance of early career members, to ensure that their views are heard and acted upon, ultimately improving their experience of metabolomics science and the community as a whole <ref>Putri, S. P. Establishment of an Early-career Members Network (EMN) of the Metabolomics Society. Metabolomics 10, 1-2, doi:10.1007/s11306-013-0613-y (2014)</ref>. The EMN is dedicated to all Metabolomics Society members who are within 5 years of completing their higher degrees and comprises members from industry, government and academia. The EMN is active at the Metabolomics Society Conference through its workshops and Welcome Reception. The EMN workshops contain career development sessions but also scientifically based topics aimed at improving the general understanding of basic metabolomics principles. Outside of the conference, the EMN hosts well-visited Webinars, a Facebook page, a Bursary program, and now the newly live Wiki page. The EMN is always seeking new initiatives to better serve its early careers members and the Metabolomics Society as a whole. <br />
<br />
=Links=<br />
[http://metabolomicssociety.org/board/society-committees/early-career-members-network-emn-committee EMN Committee on the Metabolomics Society web page]<br />
<br />
=References=<br />
<br />
Liesenfeld, D. B. et al. Activity update from the early career members network. Metabolomics 11, 247-248, doi:10.1007/s11306-015-0779-6 (2015)<br />
Putri, S. P. Establishment of an Early-career Members Network (EMN) of the Metabolomics Society. Metabolomics 10, 1-2, doi:10.1007/s11306-013-0613-y (2014)</div>Viniciusverihttp://wiki.metabolomicssociety.org/index.php?title=File:Facebook_cover.jpg&diff=1634File:Facebook cover.jpg2022-03-09T06:51:28Z<p>Viniciusveri: </p>
<hr />
<div></div>Viniciusverihttp://wiki.metabolomicssociety.org/index.php?title=Main_Page&diff=1607Main Page2022-01-10T14:50:54Z<p>Viniciusveri: </p>
<hr />
<div>__NOTOC____NOEDITSECTION__{{notitle}}<div style="position: relative; top: -30px; z-index: 100; font-size:100%;"><br />
{|cellpadding="5" cellspacing="0"|<br />
<br />
|style="border: 1px solid #DDDDDD;font-size:120%"|<br />
Welcome to the '''Early-Career Members Network (EMN) Webpage''', a resource curated by [[Early-Career_Members_Network | Early-Career Members Network of the Metabolomics Society]]. This wiki-styled page is designed to be a focal point for educational resources and online tools related to all facets of metabolomics, aiming to reach mainly young researchers of the field.<br />
<br />
<!-- NOTES FOR THE CATEGORIES TABLE:<br />
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* There is *NO* paragraph break after the <br /> tags, only a single space.<br />
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<span id="Highlights"></span><br />
<center><br />
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{| width=90% style="border: 2px solid #DDDDDD; background-color:rgb(250,250,255); margin-top:10px" cellspacing=20<br />
!colspan=2 style="text-align:center; font-size:100%;"|'''Highlights'''<br />
|-<br />
!colspan=2 style="text-align:center;"|<br />
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|-<br />
|style="width:25%; font-size:80%; vertical-align:center; text-align:center;padding-left:1em;"|<br />
<h3><br />
[[Image: TimEbbels.jpg|x140px|border|link= Tim Ebbels]]<br /><br /> <br />
This month Expert Opinion comes from Dr [[Tim Ebbels| Tim Ebbels!]]<br /><br /><br />
</h3><br />
|style="width:25%; font-size:80%; vertical-align:center; text-align:center;"|<br />
<h3><br />
[[Image:Conference_2022.png|x150px|border|link=Upcoming Events]]<br /><br /> <br />
Do not miss the Metabolomics Conference 2022. Click [https://www.metabolomics2022.org/ here] to know more! <br /><br /><br />
|}<br />
<br />
<span id="Regions"></span><br />
<br />
{|width=80% style="border: 2px solid #DDDDDD; margin-top:1px" cellspacing=1;"<br />
!colspan=6 style="text-align:center; font-size:100%;"|'''Finding Metabolomics Communities'''<br />
|-<br />
!colspan=6 style="text-align:center;"|<br />
----<br />
|-<br />
|style="width:16,66%; font-size:95%; vertical-align:bottom; text-align:center;"|<br />
[[Image:128px-Blank Map-Africa.svg.png|link= Metabolomics Communities#Africa|x100px]]<br /><br /> <br />
[[Metabolomics_Communities#Africa|'''Africa''']] <br /><br /> <br />
</h3><br />
|style="width:16,66%; font-size:80%; vertical-align:bottom; text-align:center;"|<br />
<h3><br />
[[Image:128px-Location Map Asia.svg.png|link= Metabolomics Communities#Asia|x100px]]<br /><br /> <br />
[[Metabolomics_Communities#Asia|'''Asia''']] <br /><br /> <br />
</h3><br />
|style="width:16,66%; font-size:80%; vertical-align:bottom; text-align:center;"|<br />
<h3><br />
[[Image:BlankMap-Europe-v4.png|link= Metabolomics Communities#Europe|x100px]]<br /><br /><br />
[[Metabolomics_Communities#Europe|'''Europe''']] <br /><br /> <br />
</h3><br />
|style="width:16,66%; font-size:80%; vertical-align:bottom; text-align:center;"|<br />
<h3><br />
[[Image:128px-America_Blank.svg.png|link=Metabolomics Communities#North and Central America|x100px]]<br /><br /> <br />
[[Metabolomics_Communities#North and Central America|'''North & Central America''']] <br /><br /> <br />
</h3><br />
|style="width:16,66%; font-size:80%; vertical-align:bottom; text-align:center;"|<br />
<h3><br />
[[Image:Blank Map Oceania3.svg.png|link=Metabolomics Communities#Oceania|x100px]]<br /><br /> <br />
[[Metabolomics_Communities#Oceania|'''Oceania''']] <br /><br /> <br />
</h3><br />
|style="width:16,66%; font-size:80%; vertical-align:bottom; text-align:center;"|<br />
<h3><br />
[[Image:128px-BlankMap-South-America.png|link= Communities#South America|x100px]]<br /><br /><br />
[[Metabolomics_Communities#South America|'''South America''']] <br /><br /> <br />
|}<br />
<br />
<span id="Keep updated! Follow us"></span><br />
{| width=90% style="border: 2px solid #DDDDDD; ; margin-top:20px" cellspacing=6<br />
!colspan=3 style="text-align:center; font-size:100%;"|'''Keep updated! Follow us on social medias!'''<br />
|-<br />
!colspan=3 style="text-align:center;"|<br />
----<br />
|-<br />
|style="width:40%; font-size:95%; vertical-align:center; text-align:right; padding-left:1em;"|<br />
[[Image: Facebook.png|70px|link=https://www.facebook.com/EMN.MetabolomicsSociety]]<br /><br /> <br />
|style="width:20%; font-size:95%; vertical-align:center; text-align:left;"|<br />
|style="width:40%; font-size:95%; vertical-align:center; text-align:left;"|<br />
[[Image:Twitter.png|70px|link=https://twitter.com/emn_metsoc?lang=pt]]<br /><br /> <br />
</h3><br />
|}<br />
If you would like to suggest content, please contact the current EMN committee at ''info.emn@metabolomicssociety.org''<br />
|}</div>Viniciusverihttp://wiki.metabolomicssociety.org/index.php?title=Tim_Ebbels&diff=1606Tim Ebbels2022-01-10T14:49:14Z<p>Viniciusveri: </p>
<hr />
<div>[[Image: TimEbbels.jpg|thumb| Tim Ebbels ]]<br />
<br />
==Short Biography==<br />
<br />
''' Biography''' <br />
<br />
Dr. Michael Witting studied Applied Chemistry with a functional direction into biochemistry at the Georg-Simon-Ohm University of Applied Science, Nuremberg, Germany and obtained his PhD in 2013 from the Technical University of Munich. He is a current member of the Metabolomics Society Board of Directors and since 1st of January he is heading the metabolomics section of the Metabolomics and Proteomics Core at the Helmholtz Zentrum München. His main research interests are LC-MS based metabolomics method development and application, as well as metabolite identification improvement by retention time prediction.<br />
<br />
==Expert Opinion==<br />
===Question 1===<br />
<br />
''' 1. Q'''<br />
<br />
Answer<br />
<br />
===Question 2===<br />
<br />
''' 2. Q '''<br />
<br />
Answer<br />
<br />
===Question 3===<br />
<br />
''' 3. Q '''<br />
<br />
Answer<br />
<br />
===Question 4===<br />
<br />
''' 4. Q '''<br />
<br />
Answer<br />
<br />
===Question 5===<br />
<br />
''' 5. Q '''<br />
<br />
Answer<br />
<br />
<br />
===References===<br />
<br />
<br />
1. Witting, M., et al., DI-ICR-FT-MS-based high-throughput deep metabotyping: a case study of the Caenorhabditis elegans–Pseudomonas aeruginosa infection model. Analytical and Bioanalytical Chemistry, 2015. 407(4): p. 1059-1073.<br />
<br />
2. Salzer, L. and M. Witting, Quo Vadis Caenorhabditis elegans Metabolomics—A Review of Current Methods and Applications to Explore Metabolism in the Nematode. Metabolites, 2021. 11(5): p. 284.<br />
<br />
3. Witting, M. and S. Böcker, Current status of retention time prediction in metabolite identification. Journal of Separation Science, 2020. 43(9-10): p. 1746-1754.<br />
<br />
4. Dirksen, P., et al., CeMbio - The <em>Caenorhabditis elegans</em> Microbiome Resource. G3: Genes|Genomes|Genetics, 2020. 10(9): p. 3025-3039.<br />
<br />
5. Rappez, L., et al., SpaceM reveals metabolic states of single cells. Nature Methods, 2021. 18(7): p. 799-805.<br />
<br />
==See also==<br />
<br />
[[Category:Expert Opinion]]</div>Viniciusverihttp://wiki.metabolomicssociety.org/index.php?title=Tim_Ebbels&diff=1605Tim Ebbels2022-01-10T14:48:56Z<p>Viniciusveri: </p>
<hr />
<div>[[Image: TimEbbels.jpg|thumb| Michael Witting ]]<br />
<br />
==Short Biography==<br />
<br />
''' Biography''' <br />
<br />
Dr. Michael Witting studied Applied Chemistry with a functional direction into biochemistry at the Georg-Simon-Ohm University of Applied Science, Nuremberg, Germany and obtained his PhD in 2013 from the Technical University of Munich. He is a current member of the Metabolomics Society Board of Directors and since 1st of January he is heading the metabolomics section of the Metabolomics and Proteomics Core at the Helmholtz Zentrum München. His main research interests are LC-MS based metabolomics method development and application, as well as metabolite identification improvement by retention time prediction.<br />
<br />
==Expert Opinion==<br />
===Question 1===<br />
<br />
''' 1. Q'''<br />
<br />
Answer<br />
<br />
===Question 2===<br />
<br />
''' 2. Q '''<br />
<br />
Answer<br />
<br />
===Question 3===<br />
<br />
''' 3. Q '''<br />
<br />
Answer<br />
<br />
===Question 4===<br />
<br />
''' 4. Q '''<br />
<br />
Answer<br />
<br />
===Question 5===<br />
<br />
''' 5. Q '''<br />
<br />
Answer<br />
<br />
<br />
===References===<br />
<br />
<br />
1. Witting, M., et al., DI-ICR-FT-MS-based high-throughput deep metabotyping: a case study of the Caenorhabditis elegans–Pseudomonas aeruginosa infection model. Analytical and Bioanalytical Chemistry, 2015. 407(4): p. 1059-1073.<br />
<br />
2. Salzer, L. and M. Witting, Quo Vadis Caenorhabditis elegans Metabolomics—A Review of Current Methods and Applications to Explore Metabolism in the Nematode. Metabolites, 2021. 11(5): p. 284.<br />
<br />
3. Witting, M. and S. Böcker, Current status of retention time prediction in metabolite identification. Journal of Separation Science, 2020. 43(9-10): p. 1746-1754.<br />
<br />
4. Dirksen, P., et al., CeMbio - The <em>Caenorhabditis elegans</em> Microbiome Resource. G3: Genes|Genomes|Genetics, 2020. 10(9): p. 3025-3039.<br />
<br />
5. Rappez, L., et al., SpaceM reveals metabolic states of single cells. Nature Methods, 2021. 18(7): p. 799-805.<br />
<br />
==See also==<br />
<br />
[[Category:Expert Opinion]]</div>Viniciusverihttp://wiki.metabolomicssociety.org/index.php?title=Tim_Ebbels&diff=1604Tim Ebbels2022-01-10T14:48:42Z<p>Viniciusveri: Created page with " Michael Witting ==Short Biography== ''' Biography''' Dr. Michael Witting studied Applied Chemistry with a functional direction into bioch..."</p>
<hr />
<div>[[Image: TimEbbelsa.jpg|thumb| Michael Witting ]]<br />
<br />
==Short Biography==<br />
<br />
''' Biography''' <br />
<br />
Dr. Michael Witting studied Applied Chemistry with a functional direction into biochemistry at the Georg-Simon-Ohm University of Applied Science, Nuremberg, Germany and obtained his PhD in 2013 from the Technical University of Munich. He is a current member of the Metabolomics Society Board of Directors and since 1st of January he is heading the metabolomics section of the Metabolomics and Proteomics Core at the Helmholtz Zentrum München. His main research interests are LC-MS based metabolomics method development and application, as well as metabolite identification improvement by retention time prediction.<br />
<br />
==Expert Opinion==<br />
===Question 1===<br />
<br />
''' 1. Q'''<br />
<br />
Answer<br />
<br />
===Question 2===<br />
<br />
''' 2. Q '''<br />
<br />
Answer<br />
<br />
===Question 3===<br />
<br />
''' 3. Q '''<br />
<br />
Answer<br />
<br />
===Question 4===<br />
<br />
''' 4. Q '''<br />
<br />
Answer<br />
<br />
===Question 5===<br />
<br />
''' 5. Q '''<br />
<br />
Answer<br />
<br />
<br />
===References===<br />
<br />
<br />
1. Witting, M., et al., DI-ICR-FT-MS-based high-throughput deep metabotyping: a case study of the Caenorhabditis elegans–Pseudomonas aeruginosa infection model. Analytical and Bioanalytical Chemistry, 2015. 407(4): p. 1059-1073.<br />
<br />
2. Salzer, L. and M. Witting, Quo Vadis Caenorhabditis elegans Metabolomics—A Review of Current Methods and Applications to Explore Metabolism in the Nematode. Metabolites, 2021. 11(5): p. 284.<br />
<br />
3. Witting, M. and S. Böcker, Current status of retention time prediction in metabolite identification. Journal of Separation Science, 2020. 43(9-10): p. 1746-1754.<br />
<br />
4. Dirksen, P., et al., CeMbio - The <em>Caenorhabditis elegans</em> Microbiome Resource. G3: Genes|Genomes|Genetics, 2020. 10(9): p. 3025-3039.<br />
<br />
5. Rappez, L., et al., SpaceM reveals metabolic states of single cells. Nature Methods, 2021. 18(7): p. 799-805.<br />
<br />
==See also==<br />
<br />
[[Category:Expert Opinion]]</div>Viniciusverihttp://wiki.metabolomicssociety.org/index.php?title=Expert_Opinion&diff=1603Expert Opinion2022-01-10T14:46:24Z<p>Viniciusveri: </p>
<hr />
<div>The '''Expert Opinion''' is an initiative from the [[Early-Career Members Network|Early-Career Members Network (EMN) Committee]] that intends to publish career feedback from leading researchers in the metabolomics field. With that, early career researchers can get to know different specialists on the field and, more importantly, get insights and tips on how to build their on career.<br />
<br />
<br />
[[Image: TimEbbels.jpg|75px|link= Tim Ebbels]] [[Tim Ebbels| Dr. Tim Ebbels (January, 2022)]]<br />
<br />
[[Image: Michael Witting.jpg|75px|link= Michael Witting]] [[Michael Witting| Dr. Michael Witting (October, 2021)]]<br />
<br />
[[Image: Candice Ulmer.jpg|75px|link= Candice Ulmer]] [[Candice Ulmer| Dr. Candice Z. Ulmer (August, 2021)]]<br />
<br />
[[Image: Kati_Hanhineva2.jpg|75px|link= Kati Hanhineva]] [[Kati Hanhineva| Dr. Kati Hanhineva (May, 2021)]]<br />
<br />
[[Image: Justine_Bertrand-Michel.jpg|75px|link= Justine Bertrand-Michel]] [[Justine Bertrand-Michel|Dr. Justine Bertrand-Michel (April, 2021)]]<br />
<br />
[[Image:Pieter_Dorrestein.jpg|75px|link= Pieter Dorrestein]] [[Pieter Dorrestein|Professor Pieter Dorrestein (March, 2021)]]<br />
<br />
[[Image:Roy_Goodacre.png|75px|link= Roy Goodacre]] [[Roy Goodacre|Professor Roy Goodacre (February, 2021)]]<br />
<br />
[[Image: Kazuki_Saito.jpg|75px|link= Kazuki Saito]] [[Kazuki Saito|Dr. Kazuki Saito (January, 2021)]]<br />
<br />
[[Image: Augustin_Scalbert.jpg|75px|link= Augustin Scalbert]] [[Augustin Scalbert|Dr. Augustin Scalbert (December, 2020)]]<br />
<br />
[[Image: Jessica_LaskySu.jpg|75px|link= Jessica Lasky-Su]] [[Jessica Lasky-Su|Associate Professor Jessica Lasky-Su (February, 2020)]]<br />
<br />
[[Image: Nichole_Reisdorph.png|75px|link= Nichole Reisdorph]] [[Nichole Reisdorph|Dr Nichole Reisdorph (September, 2019)]]<br />
<br />
[[Image:RickDunn.png|75px|link= Rick Dunn]] [[Rick Dunn|Professor Warwick (Rick) Dunn (July, 2019)]]<br />
<br />
[[Image:Mark_R_Viant.png|75px|link= Mark Viant]] [[Mark Viant|Professor Mark Viant (April, 2019)]]<br />
<br />
[[Image:StaceyReinke.jpg|75px|link= Stacey Reinke]] [[Stacey Reinke|Dr Stacey Reinke (March, 2019)]]<br />
<br />
[[Image:Antonio.jpg|75px|link= Carla Antonio]] [[Carla_Antonio|Dr Carla Antonio (July, 2018)]]<br />
<br />
[[Image:Vanderhooft.jpg|75px|link= Justin van der Hooft]] [[Justin_van_der_Hooft|Dr Justin J.J. van der Hooft (February, 2018)]]</div>Viniciusverihttp://wiki.metabolomicssociety.org/index.php?title=File:TimEbbels.jpg&diff=1602File:TimEbbels.jpg2022-01-10T14:45:25Z<p>Viniciusveri: </p>
<hr />
<div></div>Viniciusverihttp://wiki.metabolomicssociety.org/index.php?title=Main_Page&diff=1601Main Page2021-12-06T15:31:54Z<p>Viniciusveri: </p>
<hr />
<div>__NOTOC____NOEDITSECTION__{{notitle}}<div style="position: relative; top: -30px; z-index: 100; font-size:100%;"><br />
{|cellpadding="5" cellspacing="0"|<br />
<br />
|style="border: 1px solid #DDDDDD;font-size:120%"|<br />
Welcome to the '''Early-Career Members Network (EMN) Webpage''', a resource curated by [[Early-Career_Members_Network | Early-Career Members Network of the Metabolomics Society]]. This wiki-styled page is designed to be a focal point for educational resources and online tools related to all facets of metabolomics, aiming to reach mainly young researchers of the field.<br />
<br />
<!-- NOTES FOR THE CATEGORIES TABLE:<br />
<br />
* There is *NO* paragraph break after the <br /> tags, only a single space.<br />
* Do not enter empty lines anywhere between <span...> and </center> (breaks the formatting).<br />
* Images are taken from http://en.wikipedia.org/wiki/Wikipedia:Categorical_index<br />
<br />
<br />
--><br />
<span id="Highlights"></span><br />
<center><br />
<br />
{| width=90% style="border: 2px solid #DDDDDD; background-color:rgb(250,250,255); margin-top:10px" cellspacing=20<br />
!colspan=2 style="text-align:center; font-size:100%;"|'''Highlights'''<br />
|-<br />
!colspan=2 style="text-align:center;"|<br />
----<br />
|-<br />
|style="width:25%; font-size:80%; vertical-align:center; text-align:center;padding-left:1em;"|<br />
<h3><br />
[[Image: Michael_Witting_cropped.jpg|x140px|border|link= Michael Witting]]<br /><br /> <br />
This month Expert Opinion comes from Dr [[Michael Witting| Michael Witting!]]<br /><br /><br />
</h3><br />
|style="width:25%; font-size:80%; vertical-align:center; text-align:center;"|<br />
<h3><br />
[[Image:Conference_2022.png|x150px|border|link=Upcoming Events]]<br /><br /> <br />
Do not miss the Metabolomics Conference 2022. Click [https://www.metabolomics2022.org/ here] to know more! <br /><br /><br />
|}<br />
<br />
<span id="Regions"></span><br />
<br />
{|width=80% style="border: 2px solid #DDDDDD; margin-top:1px" cellspacing=1;"<br />
!colspan=6 style="text-align:center; font-size:100%;"|'''Finding Metabolomics Communities'''<br />
|-<br />
!colspan=6 style="text-align:center;"|<br />
----<br />
|-<br />
|style="width:16,66%; font-size:95%; vertical-align:bottom; text-align:center;"|<br />
[[Image:128px-Blank Map-Africa.svg.png|link= Metabolomics Communities#Africa|x100px]]<br /><br /> <br />
[[Metabolomics_Communities#Africa|'''Africa''']] <br /><br /> <br />
</h3><br />
|style="width:16,66%; font-size:80%; vertical-align:bottom; text-align:center;"|<br />
<h3><br />
[[Image:128px-Location Map Asia.svg.png|link= Metabolomics Communities#Asia|x100px]]<br /><br /> <br />
[[Metabolomics_Communities#Asia|'''Asia''']] <br /><br /> <br />
</h3><br />
|style="width:16,66%; font-size:80%; vertical-align:bottom; text-align:center;"|<br />
<h3><br />
[[Image:BlankMap-Europe-v4.png|link= Metabolomics Communities#Europe|x100px]]<br /><br /><br />
[[Metabolomics_Communities#Europe|'''Europe''']] <br /><br /> <br />
</h3><br />
|style="width:16,66%; font-size:80%; vertical-align:bottom; text-align:center;"|<br />
<h3><br />
[[Image:128px-America_Blank.svg.png|link=Metabolomics Communities#North and Central America|x100px]]<br /><br /> <br />
[[Metabolomics_Communities#North and Central America|'''North & Central America''']] <br /><br /> <br />
</h3><br />
|style="width:16,66%; font-size:80%; vertical-align:bottom; text-align:center;"|<br />
<h3><br />
[[Image:Blank Map Oceania3.svg.png|link=Metabolomics Communities#Oceania|x100px]]<br /><br /> <br />
[[Metabolomics_Communities#Oceania|'''Oceania''']] <br /><br /> <br />
</h3><br />
|style="width:16,66%; font-size:80%; vertical-align:bottom; text-align:center;"|<br />
<h3><br />
[[Image:128px-BlankMap-South-America.png|link= Communities#South America|x100px]]<br /><br /><br />
[[Metabolomics_Communities#South America|'''South America''']] <br /><br /> <br />
|}<br />
<br />
<span id="Keep updated! Follow us"></span><br />
{| width=90% style="border: 2px solid #DDDDDD; ; margin-top:20px" cellspacing=6<br />
!colspan=3 style="text-align:center; font-size:100%;"|'''Keep updated! Follow us on social medias!'''<br />
|-<br />
!colspan=3 style="text-align:center;"|<br />
----<br />
|-<br />
|style="width:40%; font-size:95%; vertical-align:center; text-align:right; padding-left:1em;"|<br />
[[Image: Facebook.png|70px|link=https://www.facebook.com/EMN.MetabolomicsSociety]]<br /><br /> <br />
|style="width:20%; font-size:95%; vertical-align:center; text-align:left;"|<br />
|style="width:40%; font-size:95%; vertical-align:center; text-align:left;"|<br />
[[Image:Twitter.png|70px|link=https://twitter.com/emn_metsoc?lang=pt]]<br /><br /> <br />
</h3><br />
|}<br />
If you would like to suggest content, please contact the current EMN committee at ''info.emn@metabolomicssociety.org''<br />
|}</div>Viniciusverihttp://wiki.metabolomicssociety.org/index.php?title=Main_Page&diff=1600Main Page2021-12-06T15:30:40Z<p>Viniciusveri: </p>
<hr />
<div>__NOTOC____NOEDITSECTION__{{notitle}}<div style="position: relative; top: -30px; z-index: 100; font-size:100%;"><br />
{|cellpadding="5" cellspacing="0"|<br />
<br />
|style="border: 1px solid #DDDDDD;font-size:120%"|<br />
Welcome to the '''Early-Career Members Network (EMN) Webpage''', a resource curated by [[Early-Career_Members_Network | Early-Career Members Network of the Metabolomics Society]]. This wiki-styled page is designed to be a focal point for educational resources and online tools related to all facets of metabolomics, aiming to reach mainly young researchers of the field.<br />
<br />
<!-- NOTES FOR THE CATEGORIES TABLE:<br />
<br />
* There is *NO* paragraph break after the <br /> tags, only a single space.<br />
* Do not enter empty lines anywhere between <span...> and </center> (breaks the formatting).<br />
* Images are taken from http://en.wikipedia.org/wiki/Wikipedia:Categorical_index<br />
<br />
<br />
--><br />
<span id="Highlights"></span><br />
<center><br />
<br />
{| width=90% style="border: 2px solid #DDDDDD; background-color:rgb(250,250,255); margin-top:10px" cellspacing=20<br />
!colspan=2 style="text-align:center; font-size:100%;"|'''Highlights'''<br />
|-<br />
!colspan=2 style="text-align:center;"|<br />
----<br />
|-<br />
|style="width:25%; font-size:80%; vertical-align:center; text-align:center;padding-left:1em;"|<br />
<h3><br />
[[Image: Michael_Witting_cropped.jpg|x140px|border|link= Michael Witting]]<br /><br /> <br />
This month Expert Opinion comes from Dr [[Michael Witting| Michael Witting!]]<br /><br /><br />
</h3><br />
|style="width:25%; font-size:80%; vertical-align:center; text-align:center;"|<br />
<h3><br />
[[Image:Conference_2022.png|x150px|border|link=Upcoming Events]]<br /><br /> <br />
Do not miss the Metabolomics Conference 2022. Click [https://www.metabolomics2022.org/| here] to know more! <br /><br /><br />
|}<br />
<br />
<span id="Regions"></span><br />
<br />
{|width=80% style="border: 2px solid #DDDDDD; margin-top:1px" cellspacing=1;"<br />
!colspan=6 style="text-align:center; font-size:100%;"|'''Finding Metabolomics Communities'''<br />
|-<br />
!colspan=6 style="text-align:center;"|<br />
----<br />
|-<br />
|style="width:16,66%; font-size:95%; vertical-align:bottom; text-align:center;"|<br />
[[Image:128px-Blank Map-Africa.svg.png|link= Metabolomics Communities#Africa|x100px]]<br /><br /> <br />
[[Metabolomics_Communities#Africa|'''Africa''']] <br /><br /> <br />
</h3><br />
|style="width:16,66%; font-size:80%; vertical-align:bottom; text-align:center;"|<br />
<h3><br />
[[Image:128px-Location Map Asia.svg.png|link= Metabolomics Communities#Asia|x100px]]<br /><br /> <br />
[[Metabolomics_Communities#Asia|'''Asia''']] <br /><br /> <br />
</h3><br />
|style="width:16,66%; font-size:80%; vertical-align:bottom; text-align:center;"|<br />
<h3><br />
[[Image:BlankMap-Europe-v4.png|link= Metabolomics Communities#Europe|x100px]]<br /><br /><br />
[[Metabolomics_Communities#Europe|'''Europe''']] <br /><br /> <br />
</h3><br />
|style="width:16,66%; font-size:80%; vertical-align:bottom; text-align:center;"|<br />
<h3><br />
[[Image:128px-America_Blank.svg.png|link=Metabolomics Communities#North and Central America|x100px]]<br /><br /> <br />
[[Metabolomics_Communities#North and Central America|'''North & Central America''']] <br /><br /> <br />
</h3><br />
|style="width:16,66%; font-size:80%; vertical-align:bottom; text-align:center;"|<br />
<h3><br />
[[Image:Blank Map Oceania3.svg.png|link=Metabolomics Communities#Oceania|x100px]]<br /><br /> <br />
[[Metabolomics_Communities#Oceania|'''Oceania''']] <br /><br /> <br />
</h3><br />
|style="width:16,66%; font-size:80%; vertical-align:bottom; text-align:center;"|<br />
<h3><br />
[[Image:128px-BlankMap-South-America.png|link= Communities#South America|x100px]]<br /><br /><br />
[[Metabolomics_Communities#South America|'''South America''']] <br /><br /> <br />
|}<br />
<br />
<span id="Keep updated! Follow us"></span><br />
{| width=90% style="border: 2px solid #DDDDDD; ; margin-top:20px" cellspacing=6<br />
!colspan=3 style="text-align:center; font-size:100%;"|'''Keep updated! Follow us on social medias!'''<br />
|-<br />
!colspan=3 style="text-align:center;"|<br />
----<br />
|-<br />
|style="width:40%; font-size:95%; vertical-align:center; text-align:right; padding-left:1em;"|<br />
[[Image: Facebook.png|70px|link=https://www.facebook.com/EMN.MetabolomicsSociety]]<br /><br /> <br />
|style="width:20%; font-size:95%; vertical-align:center; text-align:left;"|<br />
|style="width:40%; font-size:95%; vertical-align:center; text-align:left;"|<br />
[[Image:Twitter.png|70px|link=https://twitter.com/emn_metsoc?lang=pt]]<br /><br /> <br />
</h3><br />
|}<br />
If you would like to suggest content, please contact the current EMN committee at ''info.emn@metabolomicssociety.org''<br />
|}</div>Viniciusverihttp://wiki.metabolomicssociety.org/index.php?title=Main_Page&diff=1599Main Page2021-12-06T15:30:19Z<p>Viniciusveri: </p>
<hr />
<div>__NOTOC____NOEDITSECTION__{{notitle}}<div style="position: relative; top: -30px; z-index: 100; font-size:100%;"><br />
{|cellpadding="5" cellspacing="0"|<br />
<br />
|style="border: 1px solid #DDDDDD;font-size:120%"|<br />
Welcome to the '''Early-Career Members Network (EMN) Webpage''', a resource curated by [[Early-Career_Members_Network | Early-Career Members Network of the Metabolomics Society]]. This wiki-styled page is designed to be a focal point for educational resources and online tools related to all facets of metabolomics, aiming to reach mainly young researchers of the field.<br />
<br />
<!-- NOTES FOR THE CATEGORIES TABLE:<br />
<br />
* There is *NO* paragraph break after the <br /> tags, only a single space.<br />
* Do not enter empty lines anywhere between <span...> and </center> (breaks the formatting).<br />
* Images are taken from http://en.wikipedia.org/wiki/Wikipedia:Categorical_index<br />
<br />
<br />
--><br />
<span id="Highlights"></span><br />
<center><br />
<br />
{| width=90% style="border: 2px solid #DDDDDD; background-color:rgb(250,250,255); margin-top:10px" cellspacing=20<br />
!colspan=2 style="text-align:center; font-size:100%;"|'''Highlights'''<br />
|-<br />
!colspan=2 style="text-align:center;"|<br />
----<br />
|-<br />
|style="width:25%; font-size:80%; vertical-align:center; text-align:center;padding-left:1em;"|<br />
<h3><br />
[[Image: Michael_Witting_cropped.jpg|x140px|border|link= Michael Witting]]<br /><br /> <br />
This month Expert Opinion comes from Dr [[Michael Witting| Michael Witting!]]<br /><br /><br />
</h3><br />
|style="width:25%; font-size:80%; vertical-align:center; text-align:center;"|<br />
<h3><br />
[[Image:Conference_2022.png|x150px|border|link=Upcoming Events]]<br /><br /> <br />
Do not miss the Metabolomics Conference 2022. Click [https://www.metabolomics2022.org/| here!] to know more <br /><br /><br />
|}<br />
<br />
<span id="Regions"></span><br />
<br />
{|width=80% style="border: 2px solid #DDDDDD; margin-top:1px" cellspacing=1;"<br />
!colspan=6 style="text-align:center; font-size:100%;"|'''Finding Metabolomics Communities'''<br />
|-<br />
!colspan=6 style="text-align:center;"|<br />
----<br />
|-<br />
|style="width:16,66%; font-size:95%; vertical-align:bottom; text-align:center;"|<br />
[[Image:128px-Blank Map-Africa.svg.png|link= Metabolomics Communities#Africa|x100px]]<br /><br /> <br />
[[Metabolomics_Communities#Africa|'''Africa''']] <br /><br /> <br />
</h3><br />
|style="width:16,66%; font-size:80%; vertical-align:bottom; text-align:center;"|<br />
<h3><br />
[[Image:128px-Location Map Asia.svg.png|link= Metabolomics Communities#Asia|x100px]]<br /><br /> <br />
[[Metabolomics_Communities#Asia|'''Asia''']] <br /><br /> <br />
</h3><br />
|style="width:16,66%; font-size:80%; vertical-align:bottom; text-align:center;"|<br />
<h3><br />
[[Image:BlankMap-Europe-v4.png|link= Metabolomics Communities#Europe|x100px]]<br /><br /><br />
[[Metabolomics_Communities#Europe|'''Europe''']] <br /><br /> <br />
</h3><br />
|style="width:16,66%; font-size:80%; vertical-align:bottom; text-align:center;"|<br />
<h3><br />
[[Image:128px-America_Blank.svg.png|link=Metabolomics Communities#North and Central America|x100px]]<br /><br /> <br />
[[Metabolomics_Communities#North and Central America|'''North & Central America''']] <br /><br /> <br />
</h3><br />
|style="width:16,66%; font-size:80%; vertical-align:bottom; text-align:center;"|<br />
<h3><br />
[[Image:Blank Map Oceania3.svg.png|link=Metabolomics Communities#Oceania|x100px]]<br /><br /> <br />
[[Metabolomics_Communities#Oceania|'''Oceania''']] <br /><br /> <br />
</h3><br />
|style="width:16,66%; font-size:80%; vertical-align:bottom; text-align:center;"|<br />
<h3><br />
[[Image:128px-BlankMap-South-America.png|link= Communities#South America|x100px]]<br /><br /><br />
[[Metabolomics_Communities#South America|'''South America''']] <br /><br /> <br />
|}<br />
<br />
<span id="Keep updated! Follow us"></span><br />
{| width=90% style="border: 2px solid #DDDDDD; ; margin-top:20px" cellspacing=6<br />
!colspan=3 style="text-align:center; font-size:100%;"|'''Keep updated! Follow us on social medias!'''<br />
|-<br />
!colspan=3 style="text-align:center;"|<br />
----<br />
|-<br />
|style="width:40%; font-size:95%; vertical-align:center; text-align:right; padding-left:1em;"|<br />
[[Image: Facebook.png|70px|link=https://www.facebook.com/EMN.MetabolomicsSociety]]<br /><br /> <br />
|style="width:20%; font-size:95%; vertical-align:center; text-align:left;"|<br />
|style="width:40%; font-size:95%; vertical-align:center; text-align:left;"|<br />
[[Image:Twitter.png|70px|link=https://twitter.com/emn_metsoc?lang=pt]]<br /><br /> <br />
</h3><br />
|}<br />
If you would like to suggest content, please contact the current EMN committee at ''info.emn@metabolomicssociety.org''<br />
|}</div>Viniciusverihttp://wiki.metabolomicssociety.org/index.php?title=Main_Page&diff=1598Main Page2021-12-06T15:29:59Z<p>Viniciusveri: </p>
<hr />
<div>__NOTOC____NOEDITSECTION__{{notitle}}<div style="position: relative; top: -30px; z-index: 100; font-size:100%;"><br />
{|cellpadding="5" cellspacing="0"|<br />
<br />
|style="border: 1px solid #DDDDDD;font-size:120%"|<br />
Welcome to the '''Early-Career Members Network (EMN) Webpage''', a resource curated by [[Early-Career_Members_Network | Early-Career Members Network of the Metabolomics Society]]. This wiki-styled page is designed to be a focal point for educational resources and online tools related to all facets of metabolomics, aiming to reach mainly young researchers of the field.<br />
<br />
<!-- NOTES FOR THE CATEGORIES TABLE:<br />
<br />
* There is *NO* paragraph break after the <br /> tags, only a single space.<br />
* Do not enter empty lines anywhere between <span...> and </center> (breaks the formatting).<br />
* Images are taken from http://en.wikipedia.org/wiki/Wikipedia:Categorical_index<br />
<br />
<br />
--><br />
<span id="Highlights"></span><br />
<center><br />
<br />
{| width=90% style="border: 2px solid #DDDDDD; background-color:rgb(250,250,255); margin-top:10px" cellspacing=20<br />
!colspan=2 style="text-align:center; font-size:100%;"|'''Highlights'''<br />
|-<br />
!colspan=2 style="text-align:center;"|<br />
----<br />
|-<br />
|style="width:25%; font-size:80%; vertical-align:center; text-align:center;padding-left:1em;"|<br />
<h3><br />
[[Image: Michael_Witting_cropped.jpg|x140px|border|link= Michael Witting]]<br /><br /> <br />
This month Expert Opinion comes from Dr [[Michael Witting| Michael Witting!]]<br /><br /><br />
</h3><br />
|style="width:25%; font-size:80%; vertical-align:center; text-align:center;"|<br />
<h3><br />
[[Image:Conference_2022.png|x150px|border|link=Upcoming Events]]<br /><br /> <br />
Do not miss the Metabolomics Conference 2022. Click here [https://www.metabolomics2022.org/| here!]] to know more <br /><br /><br />
|}<br />
<br />
<span id="Regions"></span><br />
<br />
{|width=80% style="border: 2px solid #DDDDDD; margin-top:1px" cellspacing=1;"<br />
!colspan=6 style="text-align:center; font-size:100%;"|'''Finding Metabolomics Communities'''<br />
|-<br />
!colspan=6 style="text-align:center;"|<br />
----<br />
|-<br />
|style="width:16,66%; font-size:95%; vertical-align:bottom; text-align:center;"|<br />
[[Image:128px-Blank Map-Africa.svg.png|link= Metabolomics Communities#Africa|x100px]]<br /><br /> <br />
[[Metabolomics_Communities#Africa|'''Africa''']] <br /><br /> <br />
</h3><br />
|style="width:16,66%; font-size:80%; vertical-align:bottom; text-align:center;"|<br />
<h3><br />
[[Image:128px-Location Map Asia.svg.png|link= Metabolomics Communities#Asia|x100px]]<br /><br /> <br />
[[Metabolomics_Communities#Asia|'''Asia''']] <br /><br /> <br />
</h3><br />
|style="width:16,66%; font-size:80%; vertical-align:bottom; text-align:center;"|<br />
<h3><br />
[[Image:BlankMap-Europe-v4.png|link= Metabolomics Communities#Europe|x100px]]<br /><br /><br />
[[Metabolomics_Communities#Europe|'''Europe''']] <br /><br /> <br />
</h3><br />
|style="width:16,66%; font-size:80%; vertical-align:bottom; text-align:center;"|<br />
<h3><br />
[[Image:128px-America_Blank.svg.png|link=Metabolomics Communities#North and Central America|x100px]]<br /><br /> <br />
[[Metabolomics_Communities#North and Central America|'''North & Central America''']] <br /><br /> <br />
</h3><br />
|style="width:16,66%; font-size:80%; vertical-align:bottom; text-align:center;"|<br />
<h3><br />
[[Image:Blank Map Oceania3.svg.png|link=Metabolomics Communities#Oceania|x100px]]<br /><br /> <br />
[[Metabolomics_Communities#Oceania|'''Oceania''']] <br /><br /> <br />
</h3><br />
|style="width:16,66%; font-size:80%; vertical-align:bottom; text-align:center;"|<br />
<h3><br />
[[Image:128px-BlankMap-South-America.png|link= Communities#South America|x100px]]<br /><br /><br />
[[Metabolomics_Communities#South America|'''South America''']] <br /><br /> <br />
|}<br />
<br />
<span id="Keep updated! Follow us"></span><br />
{| width=90% style="border: 2px solid #DDDDDD; ; margin-top:20px" cellspacing=6<br />
!colspan=3 style="text-align:center; font-size:100%;"|'''Keep updated! Follow us on social medias!'''<br />
|-<br />
!colspan=3 style="text-align:center;"|<br />
----<br />
|-<br />
|style="width:40%; font-size:95%; vertical-align:center; text-align:right; padding-left:1em;"|<br />
[[Image: Facebook.png|70px|link=https://www.facebook.com/EMN.MetabolomicsSociety]]<br /><br /> <br />
|style="width:20%; font-size:95%; vertical-align:center; text-align:left;"|<br />
|style="width:40%; font-size:95%; vertical-align:center; text-align:left;"|<br />
[[Image:Twitter.png|70px|link=https://twitter.com/emn_metsoc?lang=pt]]<br /><br /> <br />
</h3><br />
|}<br />
If you would like to suggest content, please contact the current EMN committee at ''info.emn@metabolomicssociety.org''<br />
|}</div>Viniciusverihttp://wiki.metabolomicssociety.org/index.php?title=Upcoming_Events&diff=1597Upcoming Events2021-12-06T15:28:41Z<p>Viniciusveri: Replaced content with "''Be sure to check out Metabonews for more upcoming events!''"</p>
<hr />
<div>''Be sure to check out [[Metabonews]] for more upcoming events!''</div>Viniciusverihttp://wiki.metabolomicssociety.org/index.php?title=Main_Page&diff=1596Main Page2021-12-06T15:27:56Z<p>Viniciusveri: </p>
<hr />
<div>__NOTOC____NOEDITSECTION__{{notitle}}<div style="position: relative; top: -30px; z-index: 100; font-size:100%;"><br />
{|cellpadding="5" cellspacing="0"|<br />
<br />
|style="border: 1px solid #DDDDDD;font-size:120%"|<br />
Welcome to the '''Early-Career Members Network (EMN) Webpage''', a resource curated by [[Early-Career_Members_Network | Early-Career Members Network of the Metabolomics Society]]. This wiki-styled page is designed to be a focal point for educational resources and online tools related to all facets of metabolomics, aiming to reach mainly young researchers of the field.<br />
<br />
<!-- NOTES FOR THE CATEGORIES TABLE:<br />
<br />
* There is *NO* paragraph break after the <br /> tags, only a single space.<br />
* Do not enter empty lines anywhere between <span...> and </center> (breaks the formatting).<br />
* Images are taken from http://en.wikipedia.org/wiki/Wikipedia:Categorical_index<br />
<br />
<br />
--><br />
<span id="Highlights"></span><br />
<center><br />
<br />
{| width=90% style="border: 2px solid #DDDDDD; background-color:rgb(250,250,255); margin-top:10px" cellspacing=20<br />
!colspan=2 style="text-align:center; font-size:100%;"|'''Highlights'''<br />
|-<br />
!colspan=2 style="text-align:center;"|<br />
----<br />
|-<br />
|style="width:25%; font-size:80%; vertical-align:center; text-align:center;padding-left:1em;"|<br />
<h3><br />
[[Image: Michael_Witting_cropped.jpg|x140px|border|link= Michael Witting]]<br /><br /> <br />
This month Expert Opinion comes from Dr [[Michael Witting| Michael Witting!]]<br /><br /><br />
</h3><br />
|style="width:25%; font-size:80%; vertical-align:center; text-align:center;"|<br />
<h3><br />
[[Image:Conference_2022.png|x150px|border|link=Upcoming Events]]<br /><br /> <br />
Do not miss the upcoming events in our community (including MetSoc Conference 2022!) Check all [[Upcoming Events| here!]]<br /><br /><br />
|}<br />
<br />
<span id="Regions"></span><br />
<br />
{|width=80% style="border: 2px solid #DDDDDD; margin-top:1px" cellspacing=1;"<br />
!colspan=6 style="text-align:center; font-size:100%;"|'''Finding Metabolomics Communities'''<br />
|-<br />
!colspan=6 style="text-align:center;"|<br />
----<br />
|-<br />
|style="width:16,66%; font-size:95%; vertical-align:bottom; text-align:center;"|<br />
[[Image:128px-Blank Map-Africa.svg.png|link= Metabolomics Communities#Africa|x100px]]<br /><br /> <br />
[[Metabolomics_Communities#Africa|'''Africa''']] <br /><br /> <br />
</h3><br />
|style="width:16,66%; font-size:80%; vertical-align:bottom; text-align:center;"|<br />
<h3><br />
[[Image:128px-Location Map Asia.svg.png|link= Metabolomics Communities#Asia|x100px]]<br /><br /> <br />
[[Metabolomics_Communities#Asia|'''Asia''']] <br /><br /> <br />
</h3><br />
|style="width:16,66%; font-size:80%; vertical-align:bottom; text-align:center;"|<br />
<h3><br />
[[Image:BlankMap-Europe-v4.png|link= Metabolomics Communities#Europe|x100px]]<br /><br /><br />
[[Metabolomics_Communities#Europe|'''Europe''']] <br /><br /> <br />
</h3><br />
|style="width:16,66%; font-size:80%; vertical-align:bottom; text-align:center;"|<br />
<h3><br />
[[Image:128px-America_Blank.svg.png|link=Metabolomics Communities#North and Central America|x100px]]<br /><br /> <br />
[[Metabolomics_Communities#North and Central America|'''North & Central America''']] <br /><br /> <br />
</h3><br />
|style="width:16,66%; font-size:80%; vertical-align:bottom; text-align:center;"|<br />
<h3><br />
[[Image:Blank Map Oceania3.svg.png|link=Metabolomics Communities#Oceania|x100px]]<br /><br /> <br />
[[Metabolomics_Communities#Oceania|'''Oceania''']] <br /><br /> <br />
</h3><br />
|style="width:16,66%; font-size:80%; vertical-align:bottom; text-align:center;"|<br />
<h3><br />
[[Image:128px-BlankMap-South-America.png|link= Communities#South America|x100px]]<br /><br /><br />
[[Metabolomics_Communities#South America|'''South America''']] <br /><br /> <br />
|}<br />
<br />
<span id="Keep updated! Follow us"></span><br />
{| width=90% style="border: 2px solid #DDDDDD; ; margin-top:20px" cellspacing=6<br />
!colspan=3 style="text-align:center; font-size:100%;"|'''Keep updated! Follow us on social medias!'''<br />
|-<br />
!colspan=3 style="text-align:center;"|<br />
----<br />
|-<br />
|style="width:40%; font-size:95%; vertical-align:center; text-align:right; padding-left:1em;"|<br />
[[Image: Facebook.png|70px|link=https://www.facebook.com/EMN.MetabolomicsSociety]]<br /><br /> <br />
|style="width:20%; font-size:95%; vertical-align:center; text-align:left;"|<br />
|style="width:40%; font-size:95%; vertical-align:center; text-align:left;"|<br />
[[Image:Twitter.png|70px|link=https://twitter.com/emn_metsoc?lang=pt]]<br /><br /> <br />
</h3><br />
|}<br />
If you would like to suggest content, please contact the current EMN committee at ''info.emn@metabolomicssociety.org''<br />
|}</div>Viniciusverihttp://wiki.metabolomicssociety.org/index.php?title=File:Michael_Witting_cropped.jpg&diff=1595File:Michael Witting cropped.jpg2021-12-06T15:27:35Z<p>Viniciusveri: </p>
<hr />
<div></div>Viniciusverihttp://wiki.metabolomicssociety.org/index.php?title=Main_Page&diff=1594Main Page2021-12-06T15:25:57Z<p>Viniciusveri: </p>
<hr />
<div>__NOTOC____NOEDITSECTION__{{notitle}}<div style="position: relative; top: -30px; z-index: 100; font-size:100%;"><br />
{|cellpadding="5" cellspacing="0"|<br />
<br />
|style="border: 1px solid #DDDDDD;font-size:120%"|<br />
Welcome to the '''Early-Career Members Network (EMN) Webpage''', a resource curated by [[Early-Career_Members_Network | Early-Career Members Network of the Metabolomics Society]]. This wiki-styled page is designed to be a focal point for educational resources and online tools related to all facets of metabolomics, aiming to reach mainly young researchers of the field.<br />
<br />
<!-- NOTES FOR THE CATEGORIES TABLE:<br />
<br />
* There is *NO* paragraph break after the <br /> tags, only a single space.<br />
* Do not enter empty lines anywhere between <span...> and </center> (breaks the formatting).<br />
* Images are taken from http://en.wikipedia.org/wiki/Wikipedia:Categorical_index<br />
<br />
<br />
--><br />
<span id="Highlights"></span><br />
<center><br />
<br />
{| width=90% style="border: 2px solid #DDDDDD; background-color:rgb(250,250,255); margin-top:10px" cellspacing=20<br />
!colspan=2 style="text-align:center; font-size:100%;"|'''Highlights'''<br />
|-<br />
!colspan=2 style="text-align:center;"|<br />
----<br />
|-<br />
|style="width:25%; font-size:80%; vertical-align:center; text-align:center;padding-left:1em;"|<br />
<h3><br />
[[Image: Michael Witting.jpg|x140px|border|link= Michael Witting]]<br /><br /> <br />
This month Expert Opinion comes from Dr [[Michael Witting| Michael Witting!]]<br /><br /><br />
</h3><br />
|style="width:25%; font-size:80%; vertical-align:center; text-align:center;"|<br />
<h3><br />
[[Image:Conference_2022.png|x150px|border|link=Upcoming Events]]<br /><br /> <br />
Do not miss the upcoming events in our community (including MetSoc Conference 2022!) Check all [[Upcoming Events| here!]]<br /><br /><br />
|}<br />
<br />
<span id="Regions"></span><br />
<br />
{|width=80% style="border: 2px solid #DDDDDD; margin-top:1px" cellspacing=1;"<br />
!colspan=6 style="text-align:center; font-size:100%;"|'''Finding Metabolomics Communities'''<br />
|-<br />
!colspan=6 style="text-align:center;"|<br />
----<br />
|-<br />
|style="width:16,66%; font-size:95%; vertical-align:bottom; text-align:center;"|<br />
[[Image:128px-Blank Map-Africa.svg.png|link= Metabolomics Communities#Africa|x100px]]<br /><br /> <br />
[[Metabolomics_Communities#Africa|'''Africa''']] <br /><br /> <br />
</h3><br />
|style="width:16,66%; font-size:80%; vertical-align:bottom; text-align:center;"|<br />
<h3><br />
[[Image:128px-Location Map Asia.svg.png|link= Metabolomics Communities#Asia|x100px]]<br /><br /> <br />
[[Metabolomics_Communities#Asia|'''Asia''']] <br /><br /> <br />
</h3><br />
|style="width:16,66%; font-size:80%; vertical-align:bottom; text-align:center;"|<br />
<h3><br />
[[Image:BlankMap-Europe-v4.png|link= Metabolomics Communities#Europe|x100px]]<br /><br /><br />
[[Metabolomics_Communities#Europe|'''Europe''']] <br /><br /> <br />
</h3><br />
|style="width:16,66%; font-size:80%; vertical-align:bottom; text-align:center;"|<br />
<h3><br />
[[Image:128px-America_Blank.svg.png|link=Metabolomics Communities#North and Central America|x100px]]<br /><br /> <br />
[[Metabolomics_Communities#North and Central America|'''North & Central America''']] <br /><br /> <br />
</h3><br />
|style="width:16,66%; font-size:80%; vertical-align:bottom; text-align:center;"|<br />
<h3><br />
[[Image:Blank Map Oceania3.svg.png|link=Metabolomics Communities#Oceania|x100px]]<br /><br /> <br />
[[Metabolomics_Communities#Oceania|'''Oceania''']] <br /><br /> <br />
</h3><br />
|style="width:16,66%; font-size:80%; vertical-align:bottom; text-align:center;"|<br />
<h3><br />
[[Image:128px-BlankMap-South-America.png|link= Communities#South America|x100px]]<br /><br /><br />
[[Metabolomics_Communities#South America|'''South America''']] <br /><br /> <br />
|}<br />
<br />
<span id="Keep updated! Follow us"></span><br />
{| width=90% style="border: 2px solid #DDDDDD; ; margin-top:20px" cellspacing=6<br />
!colspan=3 style="text-align:center; font-size:100%;"|'''Keep updated! Follow us on social medias!'''<br />
|-<br />
!colspan=3 style="text-align:center;"|<br />
----<br />
|-<br />
|style="width:40%; font-size:95%; vertical-align:center; text-align:right; padding-left:1em;"|<br />
[[Image: Facebook.png|70px|link=https://www.facebook.com/EMN.MetabolomicsSociety]]<br /><br /> <br />
|style="width:20%; font-size:95%; vertical-align:center; text-align:left;"|<br />
|style="width:40%; font-size:95%; vertical-align:center; text-align:left;"|<br />
[[Image:Twitter.png|70px|link=https://twitter.com/emn_metsoc?lang=pt]]<br /><br /> <br />
</h3><br />
|}<br />
If you would like to suggest content, please contact the current EMN committee at ''info.emn@metabolomicssociety.org''<br />
|}</div>Viniciusverihttp://wiki.metabolomicssociety.org/index.php?title=Main_Page&diff=1593Main Page2021-12-06T15:25:01Z<p>Viniciusveri: </p>
<hr />
<div>__NOTOC____NOEDITSECTION__{{notitle}}<div style="position: relative; top: -30px; z-index: 100; font-size:100%;"><br />
{|cellpadding="5" cellspacing="0"|<br />
<br />
|style="border: 1px solid #DDDDDD;font-size:120%"|<br />
Welcome to the '''Early-Career Members Network (EMN) Webpage''', a resource curated by [[Early-Career_Members_Network | Early-Career Members Network of the Metabolomics Society]]. This wiki-styled page is designed to be a focal point for educational resources and online tools related to all facets of metabolomics, aiming to reach mainly young researchers of the field.<br />
<br />
<!-- NOTES FOR THE CATEGORIES TABLE:<br />
<br />
* There is *NO* paragraph break after the <br /> tags, only a single space.<br />
* Do not enter empty lines anywhere between <span...> and </center> (breaks the formatting).<br />
* Images are taken from http://en.wikipedia.org/wiki/Wikipedia:Categorical_index<br />
<br />
<br />
--><br />
<span id="Highlights"></span><br />
<center><br />
<br />
{| width=90% style="border: 2px solid #DDDDDD; background-color:rgb(250,250,255); margin-top:10px" cellspacing=20<br />
!colspan=2 style="text-align:center; font-size:100%;"|'''Highlights'''<br />
|-<br />
!colspan=2 style="text-align:center;"|<br />
----<br />
|-<br />
|style="width:25%; font-size:80%; vertical-align:center; text-align:center;padding-left:1em;"|<br />
<h3><br />
[[Image: Michael Witting.jpg|x140px|border|link= Michael Witting]]<br /><br /> <br />
This month Expert Opinion comes from Dr [[Michael Witting| Michael Witting!]]<br /><br /><br />
</h3><br />
|style="width:25%; font-size:80%; vertical-align:center; text-align:center;"|<br />
<h3><br />
|style="width:25%; font-size:80%; vertical-align:center; text-align:center;"|<br />
<h3><br />
[[Image:Conference_2022.png|x150px|border|link=Upcoming Events]]<br /><br /> <br />
Do not miss the upcoming events in our community (including MetSoc Conference 2022!) Check all [[Upcoming Events| here!]]<br /><br /><br />
|}<br />
<br />
<span id="Regions"></span><br />
<br />
{|width=80% style="border: 2px solid #DDDDDD; margin-top:1px" cellspacing=1;"<br />
!colspan=6 style="text-align:center; font-size:100%;"|'''Finding Metabolomics Communities'''<br />
|-<br />
!colspan=6 style="text-align:center;"|<br />
----<br />
|-<br />
|style="width:16,66%; font-size:95%; vertical-align:bottom; text-align:center;"|<br />
[[Image:128px-Blank Map-Africa.svg.png|link= Metabolomics Communities#Africa|x100px]]<br /><br /> <br />
[[Metabolomics_Communities#Africa|'''Africa''']] <br /><br /> <br />
</h3><br />
|style="width:16,66%; font-size:80%; vertical-align:bottom; text-align:center;"|<br />
<h3><br />
[[Image:128px-Location Map Asia.svg.png|link= Metabolomics Communities#Asia|x100px]]<br /><br /> <br />
[[Metabolomics_Communities#Asia|'''Asia''']] <br /><br /> <br />
</h3><br />
|style="width:16,66%; font-size:80%; vertical-align:bottom; text-align:center;"|<br />
<h3><br />
[[Image:BlankMap-Europe-v4.png|link= Metabolomics Communities#Europe|x100px]]<br /><br /><br />
[[Metabolomics_Communities#Europe|'''Europe''']] <br /><br /> <br />
</h3><br />
|style="width:16,66%; font-size:80%; vertical-align:bottom; text-align:center;"|<br />
<h3><br />
[[Image:128px-America_Blank.svg.png|link=Metabolomics Communities#North and Central America|x100px]]<br /><br /> <br />
[[Metabolomics_Communities#North and Central America|'''North & Central America''']] <br /><br /> <br />
</h3><br />
|style="width:16,66%; font-size:80%; vertical-align:bottom; text-align:center;"|<br />
<h3><br />
[[Image:Blank Map Oceania3.svg.png|link=Metabolomics Communities#Oceania|x100px]]<br /><br /> <br />
[[Metabolomics_Communities#Oceania|'''Oceania''']] <br /><br /> <br />
</h3><br />
|style="width:16,66%; font-size:80%; vertical-align:bottom; text-align:center;"|<br />
<h3><br />
[[Image:128px-BlankMap-South-America.png|link= Communities#South America|x100px]]<br /><br /><br />
[[Metabolomics_Communities#South America|'''South America''']] <br /><br /> <br />
|}<br />
<br />
<span id="Keep updated! Follow us"></span><br />
{| width=90% style="border: 2px solid #DDDDDD; ; margin-top:20px" cellspacing=6<br />
!colspan=3 style="text-align:center; font-size:100%;"|'''Keep updated! Follow us on social medias!'''<br />
|-<br />
!colspan=3 style="text-align:center;"|<br />
----<br />
|-<br />
|style="width:40%; font-size:95%; vertical-align:center; text-align:right; padding-left:1em;"|<br />
[[Image: Facebook.png|70px|link=https://www.facebook.com/EMN.MetabolomicsSociety]]<br /><br /> <br />
|style="width:20%; font-size:95%; vertical-align:center; text-align:left;"|<br />
|style="width:40%; font-size:95%; vertical-align:center; text-align:left;"|<br />
[[Image:Twitter.png|70px|link=https://twitter.com/emn_metsoc?lang=pt]]<br /><br /> <br />
</h3><br />
|}<br />
If you would like to suggest content, please contact the current EMN committee at ''info.emn@metabolomicssociety.org''<br />
|}</div>Viniciusverihttp://wiki.metabolomicssociety.org/index.php?title=Main_Page&diff=1592Main Page2021-12-06T15:24:31Z<p>Viniciusveri: </p>
<hr />
<div>__NOTOC____NOEDITSECTION__{{notitle}}<div style="position: relative; top: -30px; z-index: 100; font-size:100%;"><br />
{|cellpadding="5" cellspacing="0"|<br />
<br />
|style="border: 1px solid #DDDDDD;font-size:120%"|<br />
Welcome to the '''Early-Career Members Network (EMN) Webpage''', a resource curated by [[Early-Career_Members_Network | Early-Career Members Network of the Metabolomics Society]]. This wiki-styled page is designed to be a focal point for educational resources and online tools related to all facets of metabolomics, aiming to reach mainly young researchers of the field.<br />
<br />
<!-- NOTES FOR THE CATEGORIES TABLE:<br />
<br />
* There is *NO* paragraph break after the <br /> tags, only a single space.<br />
* Do not enter empty lines anywhere between <span...> and </center> (breaks the formatting).<br />
* Images are taken from http://en.wikipedia.org/wiki/Wikipedia:Categorical_index<br />
<br />
<br />
--><br />
<span id="Highlights"></span><br />
<center><br />
<br />
{| width=90% style="border: 2px solid #DDDDDD; background-color:rgb(250,250,255); margin-top:10px" cellspacing=20<br />
!colspan=3 style="text-align:center; font-size:100%;"|'''Highlights'''<br />
|-<br />
!colspan=3 style="text-align:center;"|<br />
----<br />
|-<br />
|style="width:25%; font-size:80%; vertical-align:center; text-align:center;padding-left:1em;"|<br />
<h3><br />
[[Image: Michael Witting.jpg|x140px|border|link= Michael Witting]]<br /><br /> <br />
This month Expert Opinion comes from Dr [[Michael Witting| Michael Witting!]]<br /><br /><br />
</h3><br />
|style="width:25%; font-size:80%; vertical-align:center; text-align:center;"|<br />
<h3><br />
|style="width:25%; font-size:80%; vertical-align:center; text-align:center;"|<br />
<h3><br />
[[Image:Conference_2022.png|x150px|border|link=Upcoming Events]]<br /><br /> <br />
Do not miss the upcoming events in our community (including MetSoc Conference 2022!) Check all [[Upcoming Events| here!]]<br /><br /><br />
|}<br />
<br />
<span id="Regions"></span><br />
<br />
{|width=80% style="border: 2px solid #DDDDDD; margin-top:1px" cellspacing=1;"<br />
!colspan=6 style="text-align:center; font-size:100%;"|'''Finding Metabolomics Communities'''<br />
|-<br />
!colspan=6 style="text-align:center;"|<br />
----<br />
|-<br />
|style="width:16,66%; font-size:95%; vertical-align:bottom; text-align:center;"|<br />
[[Image:128px-Blank Map-Africa.svg.png|link= Metabolomics Communities#Africa|x100px]]<br /><br /> <br />
[[Metabolomics_Communities#Africa|'''Africa''']] <br /><br /> <br />
</h3><br />
|style="width:16,66%; font-size:80%; vertical-align:bottom; text-align:center;"|<br />
<h3><br />
[[Image:128px-Location Map Asia.svg.png|link= Metabolomics Communities#Asia|x100px]]<br /><br /> <br />
[[Metabolomics_Communities#Asia|'''Asia''']] <br /><br /> <br />
</h3><br />
|style="width:16,66%; font-size:80%; vertical-align:bottom; text-align:center;"|<br />
<h3><br />
[[Image:BlankMap-Europe-v4.png|link= Metabolomics Communities#Europe|x100px]]<br /><br /><br />
[[Metabolomics_Communities#Europe|'''Europe''']] <br /><br /> <br />
</h3><br />
|style="width:16,66%; font-size:80%; vertical-align:bottom; text-align:center;"|<br />
<h3><br />
[[Image:128px-America_Blank.svg.png|link=Metabolomics Communities#North and Central America|x100px]]<br /><br /> <br />
[[Metabolomics_Communities#North and Central America|'''North & Central America''']] <br /><br /> <br />
</h3><br />
|style="width:16,66%; font-size:80%; vertical-align:bottom; text-align:center;"|<br />
<h3><br />
[[Image:Blank Map Oceania3.svg.png|link=Metabolomics Communities#Oceania|x100px]]<br /><br /> <br />
[[Metabolomics_Communities#Oceania|'''Oceania''']] <br /><br /> <br />
</h3><br />
|style="width:16,66%; font-size:80%; vertical-align:bottom; text-align:center;"|<br />
<h3><br />
[[Image:128px-BlankMap-South-America.png|link= Communities#South America|x100px]]<br /><br /><br />
[[Metabolomics_Communities#South America|'''South America''']] <br /><br /> <br />
|}<br />
<br />
<span id="Keep updated! Follow us"></span><br />
{| width=90% style="border: 2px solid #DDDDDD; ; margin-top:20px" cellspacing=6<br />
!colspan=3 style="text-align:center; font-size:100%;"|'''Keep updated! Follow us on social medias!'''<br />
|-<br />
!colspan=3 style="text-align:center;"|<br />
----<br />
|-<br />
|style="width:40%; font-size:95%; vertical-align:center; text-align:right; padding-left:1em;"|<br />
[[Image: Facebook.png|70px|link=https://www.facebook.com/EMN.MetabolomicsSociety]]<br /><br /> <br />
|style="width:20%; font-size:95%; vertical-align:center; text-align:left;"|<br />
|style="width:40%; font-size:95%; vertical-align:center; text-align:left;"|<br />
[[Image:Twitter.png|70px|link=https://twitter.com/emn_metsoc?lang=pt]]<br /><br /> <br />
</h3><br />
|}<br />
If you would like to suggest content, please contact the current EMN committee at ''info.emn@metabolomicssociety.org''<br />
|}</div>Viniciusverihttp://wiki.metabolomicssociety.org/index.php?title=Main_Page&diff=1591Main Page2021-12-06T15:23:40Z<p>Viniciusveri: </p>
<hr />
<div>__NOTOC____NOEDITSECTION__{{notitle}}<div style="position: relative; top: -30px; z-index: 100; font-size:100%;"><br />
{|cellpadding="5" cellspacing="0"|<br />
<br />
|style="border: 1px solid #DDDDDD;font-size:120%"|<br />
Welcome to the '''Early-Career Members Network (EMN) Webpage''', a resource curated by [[Early-Career_Members_Network | Early-Career Members Network of the Metabolomics Society]]. This wiki-styled page is designed to be a focal point for educational resources and online tools related to all facets of metabolomics, aiming to reach mainly young researchers of the field.<br />
<br />
<!-- NOTES FOR THE CATEGORIES TABLE:<br />
<br />
* There is *NO* paragraph break after the <br /> tags, only a single space.<br />
* Do not enter empty lines anywhere between <span...> and </center> (breaks the formatting).<br />
* Images are taken from http://en.wikipedia.org/wiki/Wikipedia:Categorical_index<br />
<br />
<br />
--><br />
<span id="Highlights"></span><br />
<center><br />
<br />
{| width=90% style="border: 2px solid #DDDDDD; background-color:rgb(250,250,255); margin-top:10px" cellspacing=20<br />
!colspan=3 style="text-align:center; font-size:100%;"|'''Highlights'''<br />
|-<br />
!colspan=3 style="text-align:center;"|<br />
----<br />
|-<br />
|style="width:25%; font-size:80%; vertical-align:center; text-align:center;padding-left:1em;"|<br />
<h3><br />
[[Image: Michael Witting.jpg|x140px|border|link= Michael Witting]]<br /><br /> <br />
This month Expert Opinion comes from Dr [[Michael Witting| Michael Witting!]]<br /><br /><br />
</h3><br />
|style="width:25%; font-size:80%; vertical-align:center; text-align:center;"|<br />
<h3><br />
[[Image:Dan_Fausto3.png|x150px|border|link=http://metabolomicssociety.org/site-map/articles/88-videos/306-2020-emn-webinars-public]]<br /><br /><br />
Check out our last EMN Webinar on new bio-statistical methods for metabolomics!<br /><br /><br />
</h3><br />
|style="width:25%; font-size:80%; vertical-align:center; text-align:center;"|<br />
<h3><br />
[[Image:Conference_2022.png|x150px|border|link=Upcoming Events]]<br /><br /> <br />
Do not miss the upcoming events in our community (including MetSoc Conference 2022!) Check all [[Upcoming Events| here!]]<br /><br /><br />
|}<br />
<br />
<span id="Regions"></span><br />
<br />
{|width=80% style="border: 2px solid #DDDDDD; margin-top:1px" cellspacing=1;"<br />
!colspan=6 style="text-align:center; font-size:100%;"|'''Finding Metabolomics Communities'''<br />
|-<br />
!colspan=6 style="text-align:center;"|<br />
----<br />
|-<br />
|style="width:16,66%; font-size:95%; vertical-align:bottom; text-align:center;"|<br />
[[Image:128px-Blank Map-Africa.svg.png|link= Metabolomics Communities#Africa|x100px]]<br /><br /> <br />
[[Metabolomics_Communities#Africa|'''Africa''']] <br /><br /> <br />
</h3><br />
|style="width:16,66%; font-size:80%; vertical-align:bottom; text-align:center;"|<br />
<h3><br />
[[Image:128px-Location Map Asia.svg.png|link= Metabolomics Communities#Asia|x100px]]<br /><br /> <br />
[[Metabolomics_Communities#Asia|'''Asia''']] <br /><br /> <br />
</h3><br />
|style="width:16,66%; font-size:80%; vertical-align:bottom; text-align:center;"|<br />
<h3><br />
[[Image:BlankMap-Europe-v4.png|link= Metabolomics Communities#Europe|x100px]]<br /><br /><br />
[[Metabolomics_Communities#Europe|'''Europe''']] <br /><br /> <br />
</h3><br />
|style="width:16,66%; font-size:80%; vertical-align:bottom; text-align:center;"|<br />
<h3><br />
[[Image:128px-America_Blank.svg.png|link=Metabolomics Communities#North and Central America|x100px]]<br /><br /> <br />
[[Metabolomics_Communities#North and Central America|'''North & Central America''']] <br /><br /> <br />
</h3><br />
|style="width:16,66%; font-size:80%; vertical-align:bottom; text-align:center;"|<br />
<h3><br />
[[Image:Blank Map Oceania3.svg.png|link=Metabolomics Communities#Oceania|x100px]]<br /><br /> <br />
[[Metabolomics_Communities#Oceania|'''Oceania''']] <br /><br /> <br />
</h3><br />
|style="width:16,66%; font-size:80%; vertical-align:bottom; text-align:center;"|<br />
<h3><br />
[[Image:128px-BlankMap-South-America.png|link= Communities#South America|x100px]]<br /><br /><br />
[[Metabolomics_Communities#South America|'''South America''']] <br /><br /> <br />
|}<br />
<br />
<span id="Keep updated! Follow us"></span><br />
{| width=90% style="border: 2px solid #DDDDDD; ; margin-top:20px" cellspacing=6<br />
!colspan=3 style="text-align:center; font-size:100%;"|'''Keep updated! Follow us on social medias!'''<br />
|-<br />
!colspan=3 style="text-align:center;"|<br />
----<br />
|-<br />
|style="width:40%; font-size:95%; vertical-align:center; text-align:right; padding-left:1em;"|<br />
[[Image: Facebook.png|70px|link=https://www.facebook.com/EMN.MetabolomicsSociety]]<br /><br /> <br />
|style="width:20%; font-size:95%; vertical-align:center; text-align:left;"|<br />
|style="width:40%; font-size:95%; vertical-align:center; text-align:left;"|<br />
[[Image:Twitter.png|70px|link=https://twitter.com/emn_metsoc?lang=pt]]<br /><br /> <br />
</h3><br />
|}<br />
If you would like to suggest content, please contact the current EMN committee at ''info.emn@metabolomicssociety.org''<br />
|}</div>Viniciusverihttp://wiki.metabolomicssociety.org/index.php?title=Main_Page&diff=1590Main Page2021-12-06T15:21:47Z<p>Viniciusveri: </p>
<hr />
<div>__NOTOC____NOEDITSECTION__{{notitle}}<div style="position: relative; top: -30px; z-index: 100; font-size:100%;"><br />
{|cellpadding="5" cellspacing="0"|<br />
<br />
|style="border: 1px solid #DDDDDD;font-size:120%"|<br />
Welcome to the '''Early-Career Members Network (EMN) Webpage''', a resource curated by [[Early-Career_Members_Network | Early-Career Members Network of the Metabolomics Society]]. This wiki-styled page is designed to be a focal point for educational resources and online tools related to all facets of metabolomics, aiming to reach mainly young researchers of the field.<br />
<br />
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[[Image: Michael Witting.jpg|x140px|border|link= Michael Witting]]<br /><br /> <br />
This month Expert Opinion comes from Dr Michael Witting! Check it out [[Michael Witting| here!]]<br /><br /><br />
</h3><br />
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<h3><br />
[[Image:Dan_Fausto3.png|x150px|border|link=http://metabolomicssociety.org/site-map/articles/88-videos/306-2020-emn-webinars-public]]<br /><br /><br />
Check out our last EMN Webinar on new bio-statistical methods for metabolomics!<br /><br /><br />
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[[Image:Conference_2022.png|x150px|border|link=Upcoming Events]]<br /><br /> <br />
Do not miss the upcoming events in our community (including MetSoc Conference 2022!) Check all [[Upcoming Events| here!]]<br /><br /><br />
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{|width=80% style="border: 2px solid #DDDDDD; margin-top:1px" cellspacing=1;"<br />
!colspan=6 style="text-align:center; font-size:100%;"|'''Finding Metabolomics Communities'''<br />
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[[Metabolomics_Communities#Africa|'''Africa''']] <br /><br /> <br />
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[[Image:128px-Location Map Asia.svg.png|link= Metabolomics Communities#Asia|x100px]]<br /><br /> <br />
[[Metabolomics_Communities#Asia|'''Asia''']] <br /><br /> <br />
</h3><br />
|style="width:16,66%; font-size:80%; vertical-align:bottom; text-align:center;"|<br />
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[[Image:BlankMap-Europe-v4.png|link= Metabolomics Communities#Europe|x100px]]<br /><br /><br />
[[Metabolomics_Communities#Europe|'''Europe''']] <br /><br /> <br />
</h3><br />
|style="width:16,66%; font-size:80%; vertical-align:bottom; text-align:center;"|<br />
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[[Image:128px-America_Blank.svg.png|link=Metabolomics Communities#North and Central America|x100px]]<br /><br /> <br />
[[Metabolomics_Communities#North and Central America|'''North & Central America''']] <br /><br /> <br />
</h3><br />
|style="width:16,66%; font-size:80%; vertical-align:bottom; text-align:center;"|<br />
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[[Image:Blank Map Oceania3.svg.png|link=Metabolomics Communities#Oceania|x100px]]<br /><br /> <br />
[[Metabolomics_Communities#Oceania|'''Oceania''']] <br /><br /> <br />
</h3><br />
|style="width:16,66%; font-size:80%; vertical-align:bottom; text-align:center;"|<br />
<h3><br />
[[Image:128px-BlankMap-South-America.png|link= Communities#South America|x100px]]<br /><br /><br />
[[Metabolomics_Communities#South America|'''South America''']] <br /><br /> <br />
|}<br />
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|}</div>Viniciusverihttp://wiki.metabolomicssociety.org/index.php?title=File:Conference_2022.png&diff=1589File:Conference 2022.png2021-12-06T15:21:21Z<p>Viniciusveri: </p>
<hr />
<div></div>Viniciusverihttp://wiki.metabolomicssociety.org/index.php?title=Main_Page&diff=1588Main Page2021-12-06T15:19:30Z<p>Viniciusveri: </p>
<hr />
<div>__NOTOC____NOEDITSECTION__{{notitle}}<div style="position: relative; top: -30px; z-index: 100; font-size:100%;"><br />
{|cellpadding="5" cellspacing="0"|<br />
<br />
|style="border: 1px solid #DDDDDD;font-size:120%"|<br />
Welcome to the '''Early-Career Members Network (EMN) Webpage''', a resource curated by [[Early-Career_Members_Network | Early-Career Members Network of the Metabolomics Society]]. This wiki-styled page is designed to be a focal point for educational resources and online tools related to all facets of metabolomics, aiming to reach mainly young researchers of the field.<br />
<br />
<!-- NOTES FOR THE CATEGORIES TABLE:<br />
<br />
* There is *NO* paragraph break after the <br /> tags, only a single space.<br />
* Do not enter empty lines anywhere between <span...> and </center> (breaks the formatting).<br />
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<span id="Highlights"></span><br />
<center><br />
<br />
{| width=90% style="border: 2px solid #DDDDDD; background-color:rgb(250,250,255); margin-top:10px" cellspacing=20<br />
!colspan=3 style="text-align:center; font-size:100%;"|'''Highlights'''<br />
|-<br />
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----<br />
|-<br />
|style="width:25%; font-size:80%; vertical-align:center; text-align:center;padding-left:1em;"|<br />
<h3><br />
[[Image: Michael Witting.jpg|x140px|border|link= Michael Witting]]<br /><br /> <br />
This month Expert Opinion comes from Dr Michael Witting! Check it out [[Michael Witting| here!]]<br /><br /><br />
</h3><br />
|style="width:25%; font-size:80%; vertical-align:center; text-align:center;"|<br />
<h3><br />
[[Image:Dan_Fausto3.png|x150px|border|link=http://metabolomicssociety.org/site-map/articles/88-videos/306-2020-emn-webinars-public]]<br /><br /><br />
Check out our last EMN Webinar on new bio-statistical methods for metabolomics!<br /><br /><br />
</h3><br />
|style="width:25%; font-size:80%; vertical-align:center; text-align:center;"|<br />
<h3><br />
[[Image:conference_2022.jpeg|x150px|border|link=Upcoming Events]]<br /><br /> <br />
Do not miss the upcoming events in our community (including MetSoc Conference 2022!) Check all [[Upcoming Events| here!]]<br /><br /><br />
|}<br />
<br />
<span id="Regions"></span><br />
<br />
{|width=80% style="border: 2px solid #DDDDDD; margin-top:1px" cellspacing=1;"<br />
!colspan=6 style="text-align:center; font-size:100%;"|'''Finding Metabolomics Communities'''<br />
|-<br />
!colspan=6 style="text-align:center;"|<br />
----<br />
|-<br />
|style="width:16,66%; font-size:95%; vertical-align:bottom; text-align:center;"|<br />
[[Image:128px-Blank Map-Africa.svg.png|link= Metabolomics Communities#Africa|x100px]]<br /><br /> <br />
[[Metabolomics_Communities#Africa|'''Africa''']] <br /><br /> <br />
</h3><br />
|style="width:16,66%; font-size:80%; vertical-align:bottom; text-align:center;"|<br />
<h3><br />
[[Image:128px-Location Map Asia.svg.png|link= Metabolomics Communities#Asia|x100px]]<br /><br /> <br />
[[Metabolomics_Communities#Asia|'''Asia''']] <br /><br /> <br />
</h3><br />
|style="width:16,66%; font-size:80%; vertical-align:bottom; text-align:center;"|<br />
<h3><br />
[[Image:BlankMap-Europe-v4.png|link= Metabolomics Communities#Europe|x100px]]<br /><br /><br />
[[Metabolomics_Communities#Europe|'''Europe''']] <br /><br /> <br />
</h3><br />
|style="width:16,66%; font-size:80%; vertical-align:bottom; text-align:center;"|<br />
<h3><br />
[[Image:128px-America_Blank.svg.png|link=Metabolomics Communities#North and Central America|x100px]]<br /><br /> <br />
[[Metabolomics_Communities#North and Central America|'''North & Central America''']] <br /><br /> <br />
</h3><br />
|style="width:16,66%; font-size:80%; vertical-align:bottom; text-align:center;"|<br />
<h3><br />
[[Image:Blank Map Oceania3.svg.png|link=Metabolomics Communities#Oceania|x100px]]<br /><br /> <br />
[[Metabolomics_Communities#Oceania|'''Oceania''']] <br /><br /> <br />
</h3><br />
|style="width:16,66%; font-size:80%; vertical-align:bottom; text-align:center;"|<br />
<h3><br />
[[Image:128px-BlankMap-South-America.png|link= Communities#South America|x100px]]<br /><br /><br />
[[Metabolomics_Communities#South America|'''South America''']] <br /><br /> <br />
|}<br />
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<span id="Keep updated! Follow us"></span><br />
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[[Image: Facebook.png|70px|link=https://www.facebook.com/EMN.MetabolomicsSociety]]<br /><br /> <br />
|style="width:20%; font-size:95%; vertical-align:center; text-align:left;"|<br />
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[[Image:Twitter.png|70px|link=https://twitter.com/emn_metsoc?lang=pt]]<br /><br /> <br />
</h3><br />
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If you would like to suggest content, please contact the current EMN committee at ''info.emn@metabolomicssociety.org''<br />
|}</div>Viniciusverihttp://wiki.metabolomicssociety.org/index.php?title=Software&diff=1566Software2021-07-16T09:28:40Z<p>Viniciusveri: </p>
<hr />
<div>This page contains a list of the most widely used freely available software and tools that are used primarily in metabolomics based on the review article by Spicer et al<ref>Spicer R, Salek RM, Moreno P, Cañueto D, Steinbeck C., "Navigating freely-available software tools for metabolomics analysis", Metabolomics. 2017;13(9):106. doi: 10.1007/s11306-017-1242-7.</ref>.<br />
<br />
=Software tools for data preprocessing=<br />
[http://bioconductor.org/packages/release/bioc/html/xcms.html XCMS] for LC-MS and GC-MS<br />
<br />
[http://www.wageningenur.nl/en/show/MetAlign-1.htm MetAlign] for LC-MS<br />
<br />
[http://prime.psc.riken.jp/Metabolomics_Software/MS-DIAL/ MS-DIAL] for LC-MS <br />
<br />
[http://mzmatch.sourceforge.net/index.php mzMatch] for LC-MS<br />
<br />
[http://mzmatch.sourceforge.net/ideom.php IDEOM] for LC-MS<br />
<br />
[http://chemdata.nist.gov/dokuwiki/doku.php?id=chemdata:amdis AMDIS] for GC-MS<br />
<br />
[http://md.tu-bs.de MetaboliteDetector] for GC-MS<br />
<br />
[https://meltdb.cebitec.uni-bielefeld.de/cgi-bin/login.cgi MeltDB] for LC-MS and GC-MS<br />
<br />
[http://bioconductor.org/packages/release/bioc/html/metaMS.html metaMS] for GC-MS<br />
<br />
[http://spectconnect.mit.edu SpectConnect] for GC-MS<br />
<br />
[http://rnmr.nmrfam.wisc.edu rNMR] for NMR<br />
<br />
[https://git-r3lab.uni.lu/eci/shinyscreen shinyscreen] for MS<br />
<br />
=Software tools for data post-processing=<br />
[https://gitlab.com/CarlBrunius/batchCorr batchCorr] for LC-MS<br />
<br />
[https://cran.r-project.org/web/packages/crmn/ crmn] for LC-MS and GC-MS<br />
<br />
[https://sourceforge.net/projects/eigenms EigenMS] for LC-MS<br />
<br />
[https://cran.r-project.org/web/packages/KMDA/ KMDA] for MS<br />
<br />
[https://www.bioconductor.org/packages/release/bioc/html/metabomxtr.html metabomxtr] for LC-MS and GC-MS<br />
<br />
[https://sourceforge.net/projects/metabnorm Metabnorm] for NMR<br />
<br />
[http://metabr.r-forge.r-project.org/ MetabR] for LC-MS<br />
<br />
[https://cran.r-project.org/web/packages/MetNorm/ MetNorm] for LC-MS, GC-MS and NMR<br />
<br />
[https://sourceforge.net/projects/msprep/ MSPrep] for LC-MS<br />
<br />
[https://cran.r-project.org/web/packages/muma/ muma] for MS and NMR<br />
<br />
<br />
=Software tools for statistical analysis=<br />
[https://sourceforge.net/projects/ionwinze Ionwinze] for LC-MS<br />
<br />
[https://cran.r-project.org/web/packages/MetabolAnalyze MetabolAnalyze] for MS and NMR<br />
<br />
[https://cran.r-project.org/web/packages/muma/ muma] for MS and NMR<br />
<br />
[https://www.bioconductor.org/packages/release/bioc/html/ropls.html ropls] for MS and NMR<br />
<br />
[http://www.metaboanalyst.ca/ MetaboAnalyst] for LC-MS and NMR<br />
<br />
<br />
=Software tools for metabolite annotation=<br />
[http://bioconductor.org/packages/release/bioc/html/CAMERA.html CAMERA], Level 4<br />
<br />
[http://bioconductor.org/packages/release/bioc/html/Rdisop.html Rdisop], Level 4<br />
<br />
[https://bio.informatik.uni-jena.de/software/sirius SIRIUS and CSI Finger ID], Level 4<br />
<br />
[http://labpib.fmrp.usp.br/methods/probmetab ProbMetab], Level 3<br />
<br />
[http://mzmatch.sourceforge.net/index.php MetAssign–mzMatch], Level 3<br />
<br />
[http://ipb-halle.github.io/MetFrag/ MetFrag], Level 2a<br />
<br />
[https://sourceforge.net/projects/cfm-id/ CFM-ID], Level 2a<br />
<br />
[https://github.com/icdishb/fingerid FingerID], Level 2a<br />
<br />
[http://www.emetabolomics.org/magma MAGMa], Level 2a<br />
<br />
[http://mycompoundid.org/mycompoundid_IsoMS MyCompoundID], Level 2a<br />
<br />
[http://batman.r-forge.r-project.org BATMAN], NMR<br />
<br />
[http://bayesil.ca Bayesil], NMR<br />
<br />
[http://wishart.biology.ualberta.ca/metabominer MetaboMiner], NMR<br />
<br />
[http://prime.psc.riken.jp/?action=nmr_search SpinAssign], NMR<br />
<br />
[http://spin.ccic.ohio-state.edu/index.php/colmar COLMAR], NMR<br />
<br />
[http://www.massbank.jp/Search MassBank]<br />
<br />
[https://msbi.ipb-halle.de/MetFusion/ MetFusion]<br />
<br />
[http://cfmid.wishartlab.com/ CDM-ID]<br />
<br />
[http://ms2lda.org MS2LDA]<br />
<br />
[https://gnps.ucsd.edu/ProteoSAFe/static/gnps-splash.jsp GNPS]<br />
<br />
<br />
=Workflows for the analysis of metabolomics data=<br />
[https://github.com/Viant-Metabolomics/Galaxy-M Galaxy-M] for LC-MS<br />
<br />
[https://gnps.ucsd.edu/ProteoSAFe/static/gnps-splash.jsp GNPS] for LC-MS<br />
<br />
[http://www.metaboanalyst.ca MetaboAnalyst 4.0] for LC-MS and NMR<br />
<br />
[http://genomics-pubs.princeton.edu/mzroll/index.php MAVEN] for LC-MS<br />
<br />
[https://www.bioconductor.org/packages/release/bioc/html/MAIT.html MAIT] for LC-MS<br />
<br />
[http://mzmine.github.io/ MZmine 2] for LC-MS<br />
<br />
[http://workflow4metabolomics.org Workflow4metabolomics] for LC-MS, GC-MS and NMR<br />
<br />
[https://xcmsonline.scripps.edu/landing_page.php?pgcontent=mainPage XCMS Online] for LC-MS and GC-MS<br />
<br />
<br />
<br />
<br />
''"Disclaimer: The links provided here are for informational purposes only. They do not constitute an endorsement or recommendation by the Metabolomics Society. The Metabolomics Society bears no responsibility for the accuracy, timeliness or completeness of information contained on a linked website".''<br />
<br />
<br />
=References=</div>Viniciusverihttp://wiki.metabolomicssociety.org/index.php?title=Metabolomics_Communities&diff=1565Metabolomics Communities2021-07-16T07:52:25Z<p>Viniciusveri: </p>
<hr />
<div>The current list of affiliates of the Metabolomics Society can be found [http://metabolomicssociety.org/international-affiliations/current-affiliates here]<br />
<br />
=Africa=<br />
<br />
'''Current Affiliates to the Metabolomics Society'''<br />
<br />
[https://twitter.com/metabolomics_sa Metabolomics South Africa]<br />
<br />
<br />
'''Other Metabolomics Communities'''<br />
<br />
[http://natural-sciences.nwu.ac.za/human-metabolomics Centre for Human Metabolomics (CHM)]<br />
<br />
<br />
=Asia=<br />
<br />
'''Current Affiliates to the Metabolomics Society'''<br />
<br />
[http://www.komets.or.kr 사단법인 한국대사체학회 Korea Metabolomics Society (KoMetS)] <br />
<br />
<br />
'''Other Metabolomics Communities'''<br />
<br />
''Indonesia'' <br /><br />
[http://biofarmaka.ipb.ac.id/news/news/293-partisipasi-trop-brc-dalam-fgd-pendidikan-tinggi-dan-vokasional-indonesia-belanda-2016 Biopharmaca Biofarmaka Partisipasi (Trop BRC)]<br />
<br />
''Japan'' <br /><br />
[https://www.sbj.or.jp/division/division_metabo.html メタボロミクス研究部会 (Metabolomics within the Society for Biotechnology, Japan)]<br />
<br />
[https://sites.google.com/site/esitomonokai/ ESI友の会:メタボロミクスの発展を目指して (ESI Tomonokai)]<br />
<br />
[http://metabolo2017.kenkyuukai.jp/special/?id=23186 Annual metabolome symposium (in Japanese) 2017 Meeting]<br />
<br />
''Malaysia''<br />
<br />
[http://www.inbiosis.ukm.my/metabolomics/ INBIOSIS, Universiti Kebangsaan Malaysia]<br />
<br />
[https://www.iium.edu.my/bpmeru/ Bioprocess and Molecular Engineering Research Unit (BPMERU), International Islamic University of Malaysia]<br />
<br />
''Singapore''<br />
<br />
[https://www.duke-nus.edu.sg/research/research-facilities/metabolomics-facility Duke-NUS Metabolomics Facility]<br />
<br />
[http://www.scelse.sg/Page/metabolomics-capacity Singapore Centre for Environmental Life Sciences Engineering Metabolomics]<br />
<br />
''Thailand''<br />
<br />
[https://www.si.mahidol.ac.th/th/research-academics/research/simpc/ Siriraj Metabolomics and Phenomics Center] <br />
<br />
[http://www.biotec.or.th/en/index.php/research/overview1 ศูนย์พันธุวิศวกรรมและเทคโนโลยีชีวภาพแห่งชาติ (National Center for Genetic Engineering and Biotechnology)]<br />
<br />
<br />
=Europe=<br />
<br />
'''Current Affiliates to the Metabolomics Society'''<br />
<br />
[http://www.rfmf.fr/ Réseau Francophone de Métabolomique et Fluxomique (French speaking Metabolomics and Fluxomics Network)] <br />
<br />
[http://www.metabolomicscentre.nl/ Netherlands Metabolomics Centre]<br />
<br />
[http://www.scottishmetabolomics.net Scottish Metabolomics Network]<br />
<br />
[http://www.nordicmetsoc.org/ Nordic Metabolomics Society]<br />
<br />
[http://www.swiss-metabolomics.ch/ Swiss Metabolomics Society] <br />
<br />
<br />
'''Other Metabolomics Communities'''<br />
<br />
''Austria''<br />
<br />
[http://metabolomics.univie.ac.at/ Vienna Metabolomics Center (VIME)]<br />
<br />
''Belgium''<br />
<br />
[https://elixir-europe.org/communities/metabolomics ELIXIR Metbolomics Community]<br />
<br />
[https://corefacilities.vib.be/metabcore Vlaams Instituut voor Biotechnologie Metabolomics Core]<br />
<br />
''Czechia''<br />
<br />
[http://www.fgu.cas.cz/en/departments/metabolomics Metabolomics Service Department]<br />
<br />
''Denmark''<br />
<br />
[http://clime.dk/ Danish Clinical Metabolomics Network]<br />
<br />
''Finland''<br />
<br />
[http://www.uef.fi/en/web/metabolomics-center Biocenter Kuopio LC-MS Metabolomics Center]<br />
<br />
[https://www.fimm.fi/en/services/technology-centre/metabolomics FIMM Metabolomics/Lipidomics/Fluxomics Unit]<br />
<br />
[https://www.oru.se/english/about-us/conferences/nordic-metabolomics-conference/ Nordic Metabolomics Society]<br />
<br />
[https://www.btk.fi/metabolomics/ Turku Metabolomics Facility]<br />
<br />
[https://www.helsinki.fi/en/infrastructures/metabolomics/infrastructures/viikki-metabolomics-unit-vimu Viikki Metabolomics Unit (ViMU)]<br />
<br />
''France''<br />
<br />
[https://www.rfmf.fr/rfmf-junior/ French-speaking Metabolomics and Fluxomics Junior Network]<br />
<br />
''Germany''<br />
<br />
[https://www.embl.de/services/core_facilities/metabolomics/ European Molecular Biology Laboratory (EMBL) Metabolomics Core Facility]<br />
<br />
[https://www.helmholtz-muenchen.de/ibis/index.html Institute of Bioinformatics and Systems Biology Metabolomics Group, Helmholtz Zentrum München] <br />
<br />
''Ireland''<br />
<br />
[http://www.ucdplantscience.com/facilities/metabolomic-analysis-suite/ Metabolomic Analysis Suite, UCD Plant Science]<br />
<br />
''Italy''<br />
<br />
[https://www.itb.cnr.it/en/laboratories/omics-technologies-bari/ Proteomics and Metabolomics Laboratory, CNR-ITB]<br />
<br />
[http://www.sysbio.it/isbe-service/metabolomics-facility/ SYSBIO/ISBE.IT metabolomics facility] <br />
<br />
''Netherlands''<br />
<br />
[https://www.bbmri.nl/omics-metabolomics/ Biobanking and BioMolecular resources Research and Infrastructure (BBMRI) Metabolomics Consortium]<br />
<br />
[https://www.universiteitleiden.nl/en/research/research-facilities/science/metabolomics-facility Metabolomics Facility Leiden]<br />
<br />
[https://www.wur.nl/en/show/Metabolomics-Wageningen-Genomics-Facility-1.htm Wageningen Omics Facility]<br />
<br />
''Norway''<br />
<br />
[http://www.uib.no/en/med/metabolomics Core Facility for Metabolomics, University of Bergen]<br />
<br />
[https://www.ntnu.edu/nv-mslab/metabolomics Norwegian University of Science and Technology Mass Spectrometry based Metabolomics, Lipidomics, and Fluxomics]<br />
<br />
''Poland''<br />
<br />
[http://konferencje.pwr.edu.pl/konferencje/kalendarz-konferencji/metabolomics-circle-2017-51.html Metabolomics Circle]<br />
<br />
''Portugal''<br />
<br />
[http://healthportugal.com/tris-hcp/searching-for-specialised-services/metabolomics Health Cluster Portugal Metabolomics]<br />
<br />
''Spain''<br />
<br />
[https://cembio.uspceu.es/ Centre of Metabolomics and Bioanalysis (CEMBIO), San Pablo CEU]<br />
<br />
[http://metabolomicsplatform.com/ RV and CIBERDEM Metabolomics Platform]<br />
<br />
''Sweden''<br />
<br />
[https://www.swedishmetabolomicscentre.se/ Swedish Metabolomics Centre]<br />
<br />
<br />
<br />
=North and Central America=<br />
<br />
'''Current Affiliates to the Metabolomics Society'''<br />
<br />
[https://metabolomicsna.org/ Metabolomics Association of North America]<br />
<br />
<br />
'''Other Metabolomics Communities'''<br />
<br />
''USA''<br />
<br />
[http://www.metabolomicsworkbench.org/ Metabolomics Workbench]<br />
<br />
[https://masspec.scripps.edu/ Scripps Center for Metabolomics]<br />
<br />
[http://www.ucdenver.edu/massspec University of Colorado SSPPS MS Core]<br />
<br />
[http://metabolomics.ucdavis.edu/ West Coast Metabolomics Center (UC Davis)]<br />
<br />
<br />
''Canada''<br />
<br />
[https://www.metabolomicscentre.ca/ The Metabolomics Innovation Centre]<br />
<br />
<br />
=Oceania=<br />
<br />
'''Current Affiliates to the Metabolomics Society'''<br />
<br />
[https://sites.google.com/site/anzmn2/ The Australia New Zealand Metabolomics Network]<br />
<br />
<br />
'''Other Metabolomics Communities'''<br />
<br />
''Australia''<br />
<br />
[http://www.metabolomics.net.au/ Metabolomics Australia]<br />
<br />
<br />
=South America=<br />
'''Current Affiliates to the Metabolomics Society'''<br />
<br />
-<br />
<br />
<br />
'''Other Metabolomics Communities'''<br />
<br />
[http://jwist.github.io/lamps/ Latin American Metabolic Profiling Society]</div>Viniciusverihttp://wiki.metabolomicssociety.org/index.php?title=EMN_Webinars&diff=1564EMN Webinars2021-06-21T06:42:23Z<p>Viniciusveri: </p>
<hr />
<div>The [[Early-Career Members Network#EMN Committee|Early-Career Members Network (EMN) Committee]] has organized webinars since 2015 on a variety of metabolomics-related topics. This page provides a summary of all webinars to date with links to the webinars (when available).<br />
<br />
To participate on the live webinars, follow us on [https://twitter.com/EMN_MetSoc Twitter] and [https://www.facebook.com/EMN.MetabolomicsSociety Facebook] to get all updates from the EMN or subscribe to the Metabolomics Society website to receive the email invitation.<br />
<br />
<br />
=2021=<br />
<br />
==Metabolic subphenotypes and colon cancer prognosis (Link will be soon available)==<br />
<br />
Date: May 20, 2021<br />
<br />
'''Metabolic subphenotypes and colon cancer prognosis<br />
<br />
By: ''Prof Caroline Johnson<br />
<br />
Cancer metabolism is dependent on both genetic and environmental influences that can affect patient prognosis and drug responses. Using untargeted mass spectrometry-based metabolomics, we show that tumor tissue metabolites from colon cancer patients differ in their abundance by tumor stage, sex of the patient, oncogenes, and location of the tumor in the colon. In addition, we observe that these metabolic phenotypes associate with patient prognosis. Therefore, we show the importance of identifying metabolic phenotypes within a disease, to identify patient subgroups that may have differential responses to therapeutics, and thus clinical outcomes.<br />
<br />
<br />
'''‘Normalizing Untargeted Periconceptional Urinary Metabolomics Data’<br />
<br />
By: ''Ms Ana Rosen Vollmar<br />
<br />
Metabolomics studies of the early-life exposome often use maternal urine specimens to investigate critical developmental windows, including the periconceptional period and early pregnancy. During these windows, changes in kidney function impact urine concentration, making accounting for differential urinary dilution across samples a challenge. We compared the performance of three common approaches to this problem, creatinine adjustment, specific gravity adjustment, and probabilistic quotient normalization (PQN), and found that specific gravity and PQN are reliable methods. We then applied this finding to our research on whether parabens, endocrine-disrupting chemicals, alter the periconceptional urinary metabolome. <br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/334-2021-emn-webinars-public TidyMS: a tool for preprocessing and Improving data quality in metabolomics]==<br />
<br />
Date: April 27, 2021<br />
<br />
'''TidyMS: a tool for preprocessing and Improving data quality in metabolomics<br />
<br />
By: ''Dr. María Eugenia Monge<br />
<br />
One of the challenges in untargeted metabolomics workflows is preprocessing data in a reproducible and robust way. In liquid chromatography-mass spectrometry (LC-MS), data curation involves removing biologically non-relevant features (retention time, m/z pairs) and retaining only those analytically robust enough to allow subsequent data analysis and interpretation. In this presentation, I will share the strategies that we used for quality control (QC) purposes in LC-MS-based untargeted studies that interrogated samples from in vitro models and serum samples as well as in an ambient-MS-based approach. In addition, I will introduce “TidyMS”, which is a new Python library for LC-MS data preprocessing that has recently been developed in our research group with the aim of performing QC procedures in a rapid and reproducible workflow. TidyMS includes tools to work with both raw MS data and data matrices. It provides functionality to build customized pipelines for data curation to obtain cleaned matrices from untargeted metabolomics and lipidomics studies for subsequent statistical analysis, and aims at ensuring accuracy and reliability in LC–MS measurements. The capabilities of TidyMS will be illustrated with pipelines for an LC-MS system suitability check, system conditioning, signal drift evaluation, and data curation. These applications were implemented to successfully preprocess data from a new suite of candidate human plasma reference materials currently under development by the National Institute of Standards and Technology (NIST; hypertriglyceridemic, diabetic, and African-American plasma pools), in addition to NIST SRM 1950 Metabolites in Frozen Human Plasma to be used in metabolomics and lipidomics studies. Overall, TidyMS can be used in an automated or semi-automated way, and it is an open and free tool available to all users.<br />
<br />
<br />
'''Improving data quality in metabolomics workflows: A Clear Cell Renal Cell Carcinoma (ccRCC) case study<br />
<br />
By: ''Mr. Nicolás Zabalegui<br />
<br />
Liquid chromatography-mass spectrometry (LC-MS)-based untargeted metabolomics studies involving biological samples may lead to the detection of tens of thousands of potential metabolic features (retention time, m/z pairs) at initial stages of the workflow. However, data needs to be preprocessed in a reproducible way to remove biologically non-relevant features and thereafter obtain cleaned matrices suitable for subsequent statistical analysis.Kidney cancer is accepted to be a metabolic disease. More than 50% of cases of renal cell carcinoma (RCC) are incidentally diagnosed; being clear cell RCC (ccRCC) the most common histological subtype (75% of cases), and considered a glycolytic and lipogenic tumor. Since the disease is inherently resistant to chemotherapy and radiotherapy, surgery is the most promising treatment for curation when the disease is detected at earlier stages.In this study, serum samples from a cohort that included patients with different ccRCC stages (I, II, III, IV), which were collected before (n=113) and after surgery (n=56), as well as samples from controls (n=52), were interrogated with a discovery-based metabolomics approach using UPLC-QTOF-MS. LC-MS data were preprocessed with TidyMS, a Python package used to retain only high-quality data for subsequent analysis and interpretation. As well, additional experiments were conducted to account for metabolite stability over time and non-linearity in instrumental responses, and were utilized to improve data quality before performing statistical multivariate analysis.<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/334-2021-emn-webinars-public Viral infection in algal blooms and The glycosphingolipid-based arms race]==<br />
<br />
Date: March 22, 2021<br />
<br />
'''Viral infection in algal blooms and The glycosphingolipid-based arms race<br />
<br />
By: ''Prof. Assaf Vardi & Dr. Guy Schleyer<br />
<br />
In this webinar, we will present how we utilize the recent advances in the field of chemical ecology (metabolomics and mass spectrometry imaging) combined with single-cell imaging and transcriptomic approaches to track host-pathogen interactions at the microscale.<br />
<br />
=2020=<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/306-2020-emn-webinars-public New bio-statistical methods for metabolomics and metabolite annotation]== <br />
<br />
Date: December 22, 2020<br />
<br />
'''New bio-statistical methods for metabolomics<br />
<br />
By: ''Dr. Daniel Raftery<br />
<br />
Highly complex biological samples present challenging analysis problems for the field of metabolomics. Ideally, platforms that provide broad metabolome coverage and high data quality allow the opportunity for deep insights into biological problems. However, this goal can be difficult to achieve on a routine basis because the highly complex data are subject to matrix effects and complicated correlative relationships among many metabolites. As such metabolite identification, biomarker identification and validation can be very challenging. Advanced statistical methods are needed to deal with these issues for improved biomarker discovery, unknown identification and biological interpretation. We have pursued the development of a number of approaches that try to unravel the complex and multidimensional structure of metabolomics datasets, with some successes and some failures along the way. In this talk, I will provide some examples of where even non-experts in biostatistics can make progress in developing advanced analysis approaches and discuss some areas that provide significant challenges for future work.<br />
<br />
<br />
'''Expanding Automated Metabolite Annotation in Untargeted Metabolomics through Mass Spectral Networks<br />
<br />
By: ''Dr. Fausto Carnevale Neto<br />
<br />
The major goal of metabolomics is to interrogate complex biological extracts for the purposes of metabolic exploration and biomarker discovery. Liquid chromatography (LC) coupled with tandem mass spectrometry (MS/MS or MS2) is widely accepted as a powerful strategy to explore the chemical constituency of complex mixtures. It offers accurate masses of detected ions and the structural information derived from fragmentation reactions in the gas-phase. While global LC-MS/MS profiling provides the comprehensive measurement of metabolites in complex biological samples, structure annotation remains a challenge, and computational approaches are necessary to translate the molecular composition into biological knowledge. Recently, global natural products social molecular networking (GNPS) has emerged as superb mining tool to assist the interpretation of large MS/MS datasets in the context of metabolomics. It integrates spectral database matching, unsupervised molecular substructure discovery, in silico fragmentation prediction, and automated chemical classification into a network topology. By embedding independent experimental and predictive annotation outputs on to the multi-informative molecular network, we can expand the automated chemical structural annotation within complex metabolic mixtures. <br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/306-2020-emn-webinars-public Metabolic networks]== <br />
<br />
Date: November 26, 2020<br />
<br />
'''Metabolic networks to enrich and interpret metabolic fingerprints<br />
<br />
By: ''Dr. Fabien Jourdan<br />
<br />
Metabolic modulation is a cornerstone cellular response to genetic or environmental stresses. This plasticity is going beyond central metabolism and may involve complex processes spanning several metabolic pathways. Hence, it is a key challenge to be able to decipher metabolic modulations in a systemic and global perspective.<br />
The aim of the computational methods and tools which will be presented is thus to consider the full complexity of metabolism. To do so, all metabolic reactions the cell is able to achieve are gathered in a single mathematical model call “genome scale metabolic network”. Based on this model, it is then possible to identify metabolic modulations associated to metabolic fingerprints or suggest metabolites of interest to enrich biochemical interpretation.<br />
<br />
<br />
<br />
'''Improving metabolic studies with diverse context-specific metabolic networks<br />
<br />
By: ''Dr. Pablo Rodriguez Mier<br />
<br />
Understanding deregulations of metabolism based on isolated measures of gene expression, protein or metabolite concentrations is a challenging task due to the interconnection of multiple processes. One solution is to extract, from generic genome-scale metabolic networks, the specific sub-network which is modulated in the studied condition. Many algorithms have been proposed for such context-specific network extraction based on experimental measurements. However, this process is subject to some randomness and variability, since multiple metabolic networks can model the metabolic state in a similarly adequate manner for the same experimental data. This means that for some condition and reconstruction method, there are usually multiple possible sub-networks that can explain the same experimental data, but most current methods ignore this fact and return a single sub-network instead. Ignoring this variability can not only lead to incorrect or incomplete explanations of the biological experiment, but also causes valuable information to be lost that could be used to improve predictions. In this talk we will see what context-specific metabolic sub-networks are, some of the limitations of the current methods, and how we can get a diverse set of sub-networks to improve predictions and obtain better mechanistic insights about the metabolism.<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/306-2020-emn-webinars-public Decomposing Complex Metabolite Mixtures...& Unraveling the neonatal metabolome]== <br />
<br />
Date: June 19, 2020<br />
<br />
'''Decomposing Complex Metabolite Mixtures through Substructure-based Metabolomics Workflows<br />
<br />
By: ''Dr. Justin J.J. van der Hooft<br />
<br />
In this webinar, Dr. Justin J.J. van der Hooft will start with an introduction on the challenges of metabolite annotation and identification in untargeted metabolomics experiments of complex mixtures typically encountered in natural products and food research. He will also show how MS2LDA has been successfully used to capture chemical knowledge from diverse plant, food, and microbial-related data sets. Dr. Justin J.J. van der Hooft will finish by highlighting the advantages of combining metabolome mining and annotation tools in public data of different plant and microbial-related studies.<br />
<br />
<br />
'''Unraveling the neonatal metabolome using mass spectral data mining tools<br />
<br />
By: ''Madeleine Ernst<br />
<br />
In this webinar, Dr. Madeleine Ernst will explain how mass spectral data mining tools, such as molecular networking through the community platform GNPS, MS2LDA, in silico structure prediction (e.g. Network Annotation Propagation) and ClassyFire can significantly enhance chemical structural annotation retrieved in clinical mass spectrometry-based metabolomics studies. Dr. Madeleine Ernst will also elucidate how metabolic signatures of neonatal health and disease can be unraveled, significantly enhancing biological interpretation and hypothesis generation in metabolomics studies.<br />
<br />
==Plant Metabolomics - Elucidating biochemicals mechanisms underlying plant-environment interactions== <br />
<br />
By: ''Dr. Fidele Tugizimana<br />
<br />
Date: May 1, 2020<br />
<br />
In this webinar, Dr. Fidele will provide a snapshot of applications of metabolomics in plant sciences, particularly in plant-environment interactions research. The webinar will highlight some examples of the use of metabolomics to elucidate hypothetical frameworks that describe the biochemistry underlying naïve and primed-plant responses to microbial infections. Furthermore, one of the novel (emerging) strategies for sustainable food production and food security is the use of biostimulants in agriculture industry. Application of metabolomics in decoding and understanding plant-biostimulant interactions will be also highlighted.<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/306-2020-emn-webinars-public Human metabolic individuality and its role in multi-omics based studies]== <br />
<br />
By: ''Dr. Karsten Suhre<br />
<br />
Date: February 4, 2020<br />
<br />
In this webinar, Dr. Suhre will discuss Genome-wide association studies with clinically relevant intermediate traits, such as gene expression, proteomics, and metabolomics, which unravelled numerous pathophysiological pathways and generated many hypotheses regarding the functional basis of complex disorders. More recently, similar approaches linked variation in epigenetic modifications, especially differential methylation of chromosomal CpG-pairs, to changes in gene expression and blood circulating metabolites. These large-scale population and patient cohort studies reflect experimental data obtained from naturally occurring variance of the general population where each individual may be viewed as an experiment conducted by Nature. The next and most challenging step on the way to a truly personalized approach to medicine is to translate the results from these large-scale omics studies to applications at the patient level. In this presentation,<br />
<br />
=2019=<br />
<br />
==Metabolomics as a tool for elucidating plant growth regulation== <br />
<br />
By: ''Dr. Camila Caldana<br />
Date: November 20, 2019<br />
<br />
Rising demand for food and fuels makes it crucial to develop breeding strategies for increasing crop yield/biomass. Plant biomass production is tightly associated with growth and relies on a tight regulation of a complex signaling network that integrates external and internal stimuli. The main goal of our group is to elucidate the processes underlying plant growth and production of biomass by combining physiology, metabolomics, and gene expression analyses. In my presentation, I will provide examples of i) how the evolutionary conserved Target of Rapamycin pathway fine-tunes metabolic homeostasis to promote biosynthetic growth in plants; ii) the potential of metabolite profiles to predict plant performance as biomarkers.<br />
<br />
==Discovering Metabolites that Alter Physiology, an Omics Perspective== <br />
<br />
By: ''Dr. Gary Siuzdak<br />
Date: September 18, 2019<br />
<br />
Metabolomics and the comprehensive analysis of the metabolome and lipidome has traditionally been pursued with the aim of identifying biomarkers in the diagnosis and prediction of disease. However, the value of metabolomics has been redefined from a simple biomarker identification tool to a technology for the discovery of active drivers of biological processes. It is now clear that the metabolome affects cellular physiology through modulation of other ‘omics’ levels, including the genome, epigenome, transcriptome and proteome. In this presentation, I will focus on our recent progress in using metabolomics to understand how the metabolome influences other omics and, by extension, to reveal the active role of metabolites in physiology and disease. This concept of utilizing metabolomics to perform activity screens to identify biologically active metabolites — which we term activity metabolomics — is already having a broad impact on biology.<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/298-2019-emn-webinars-public Lipidomics and epilipidomics – from analysis to data integration]== <br />
<br />
By: ''Dr. Maria Fedorova<br />
Date: July 17, 2019<br />
<br />
Lipidomics is a large-scale study of diversified molecular species of lipids aiming to address the identity, quantities, cellular and tissue distribution of lipids as well as related signalling and metabolic pathways. In addition to the native lipidome, lipid species derived from enzymatic and non-enzymatic modifications (the epilipidome) further increase regulatory capacity of the biological systems. High diversity of physico-chemical properties as well as large dynamic range of lipid concentrations in native lipidomes makes significantly challenge their analysis. For omics-wide high-throughput identification of lipid species from complex biological samples, several crucial analytical steps including extraction, chromatographic separation and mass spectrometry analysis need to be carefully considered and validated. The webinar will review current analytical strategies used in contemporary lipidomics and epilipidomics with the focus on optimization of LC-MS/MS based workflows for “discovery” lipidomics including sample preparation, lipid fractionation, separation using different chromatography techniques and high-throughput identification solutions. Available bioinformatics tools for identification of native and modified lipids will be described and compared as well as possible lipidomics data integration strategies.<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/298-2019-emn-webinars-public Metabolomics 2019: Professional Career Development: The Survival Kit]== <br />
<br />
By: ''Cathy Delhanty<br />
<br />
Date: June 23, 2019<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/298-2019-emn-webinars-public A Combined NMR and MS Metabolomics Approach to Study Neurodegenerative Diseases]== <br />
<br />
By: ''Dr. Robert Powers<br />
<br />
Date: May 24, 2019<br />
<br />
The metabolome captures how the system responds to drug treatment, disease state, or genetic modification. In this regards, metabolomics is an invaluable approach to easily and rapidly diagnose human disease and to assist in personalized medicine by monitoring a patient’s response to treatments. But, metabolomics is deceivingly complex with numerous sources of errors and technical challenges at every step of the process. One specific challenge is achieving a complete and accurate coverage of the metabolome, which can be addressed by combining NMR and mass spectrometry. Our metabolomics protocols and MVAPACK software for integrating NMR and mass spectral data for the analysis of neurodegenerative disease will be discussed. Our investigation into the molecular mechanisms of Parkinson’s disease and the identification of biomarkers for multiple sclerosis will be highlighted.<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/298-2019-emn-webinars-public Computational mass spectrometry in metabolomics to deepen the understanding of metabolisms]== <br />
<br />
By: ''Dr. Hiroshi Tsugawa<br />
<br />
Date: April 23, 2019<br />
<br />
Computational mass spectrometry is a growing research field to process mass spectrometry data, assist the interpretation of mass fragmentations, and elucidate unknown structures with metabolome databases and repositories for the global identification of metabolomes in various living organisms. In this talk, Dr Tsugawa will introduce three metabolomics software programs which include (1) MS-DIAL for untargeted metabolomics, (2) MS-FINDER for structure elucidations of unknowns, and (3) MRMPROBS for targeted metabolomics. These programs are demonstrated to perform the comprehensive analyses of primary metabolites, lipids, and plant specialized metabolites where unknown metabolites are also untangled with various methodologies including stable isotope labeled organisms, metabolite class recommendations, and integrated metabolome network analyses. In addition, a computational workflow to link untargeted- and targeted metabolomics is also highlighted in this talk.<br />
<br />
==Multidimensional Chromatography, what is the synergy with untargeted metabolomics profiling?== <br />
<br />
By: ''Dr. Pierre-Hugues Stefanuto<br />
<br />
Date: March 28, 2019<br />
<br />
In this webinar, Dr Pierre-Hugues Stefanuto will discuss the new development of multidimensional chromatography and the synergy with metabolomics. This presentation will be broadcasted in the context of the Multidimensional Chromatography Workshop held in Liège last January (http://multidimensionalchromatography.com). During this event, four focus group discussions were organized: 1) data processing for untargeted screening, 2) minimum reporting information for QC and compound validation, 3) hyphenation of MDGC with high-resolution MS, 4) and the general acceptance of MDC techniques. He will illustrate these topics through some ongoing medical research articulated around volatile organic compound (VOC) measurements in human breath and in vitro in metabolomic applications.<br />
<br />
==Untargeted metabolomics reveals smokers' characteristic profiles== <br />
<br />
By: ''Dr. Ping-Ching Hsu<br />
<br />
Date: March 1, 2019<br />
<br />
=2018=<br />
<br />
==Recent advances towards integrating metabolomics in kinetic models of large metabolic networks== <br />
<br />
By: ''Prof. Lars Nielsen<br />
<br />
Date: October 15, 2018<br />
<br />
Lars Nielsen is Chair of Biological Engineering at the Australian Institute for Bioengineering &Nanotechnology and Scientific Director for the Section for Quantitative Modelling of Cellular Metabolism at the Novo Nordisk Foundation Center for Biosustainability in Denmark. He is Director of the Bioplatforms Australia Queensland Node for Metabolomics and Proteomics, which provides systems and synthetic biology support to design and build cell factories for the production of fuels, chemicals and pharmaceuticals. His core research interest is modelling of cellular metabolism and his team has made many contributions to the formulation and use of genome scale models. He recently received a Novo Nordisk Foundation Laureate Research Grant to develop large scale, mathematical models to explore and explain the molecular basis for homeostasis–the self-regulating processes evolved to maintain metabolic equilibrium. Studying homeostasis is relevant for the understanding and treatment of complex diseases, particular with the emergence of personalized medicine. It is equally important when we seek to repurpose the cellular machinery for the production of desired chemicals, materials and pharmaceuticals.<br />
<br />
==Metabolomics-based Elucidation of Plant Specialized Metabolism== <br />
<br />
By: ''Prof. Kazuki Saito<br />
<br />
Date: July 25, 2018<br />
<br />
The recent advances of genomics and metabolomics in plant science accelerate our understanding about the mechanism, regulation and evolution of biosynthesis of plant specialized products. We can now address the questions how the metabolomic diversity of plants is originated at the levels of genome (phytochemical genomics) and how we should apply this knowledge to drug discovery, industry and agriculture. In this presentation, at first, technological developments of metabolomic analysis will be discussed forthe better understanding chemical diversity of plants. Then, a couple of examples of application of metabolomics to functional genomics of specialized metabolism in a model plant Arabidopsis thalianawill be presented, focusing on the biosynthesis of phenylpropanoids and lipids. The further extension to crops and medicinal plants producing a variety of specialized metabolites will be presented.<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/277-2018-emn-webinars-public Activity Metabolomics: Technologies to identify metabolites that modulate phenotype]== <br />
<br />
By: ''Prof. Gary Siuzdak<br />
<br />
Date: May 29, 2018<br />
<br />
Metabolomics is broadly acknowledged to be the omics discipline closest tothe phenotype and therefore widely used for biomarker discovery. However,metabolomics can also be designed to identify active metabolites thatalter a cell’s or an organism's phenotype.This "Activity Metabolomics”concept integrates metabolomics data analysis with pathway and systemsbiology data, ultimately to select endogenous metabolites that can bescreened for functionality. A growing literature reports the use ofmetabolites to modulate diverse processes including stem celldifferentiation, oligodendrocytematuration, insulin signaling,T-cellsurvival and macrophage immune responses. We have developed XCMS Online(xcmsonline.scripps.edu) and the newly expanded METLIN database (now withover 50,000 standards containing MS/MS data) to perform untargeted andtargeted metabolomics, as well as pathway analysis and systems biologydata integration.Metabolomics Activity Screening (MAS) has beenimplemented within XCMS Online to help achieve this integration goal foridentifying active metabolites. Because metabolites are often readilyavailable, activity metabolomics is uniquely positioning its practitionersto move beyond biomarkers, and become active participants in thebiological endgame of modulating phenotype. (for more information seeNature Biotechnology 2018 nature.com/articles/nbt.4101)<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/277-2018-emn-webinars-public Use better software: MS-DIAL instead of XCMS, ChemRich/MetDA instead of Metaboanalyst]== <br />
<br />
By: ''Dr. Oliver Fiehn<br />
<br />
Date: April 24, 2018<br />
<br />
XCMS and MetaboAnalystare the two most popular tools used by metabolomics researchers. But are these the best options? The West Coast Metabolomics Center at UC Davis has collaborated with RIKEN/NGI in Japan to release MS-DIAL vs.2 that yields far fewer false positive peak detections in untargeted LC-MS/MS runs than XCMS, with superior integration of compound identification software. MS-DIAL now also works on low or high resolution GC-MS data, making it the tool of choice for raw data processing of any mass spectrometry-based metabolomics study.We have also developed alternative software suites for statistical analysis of final result data. ChemRich uses all identified metabolites, including complex lipids, for set enrichment statistics. In comparison, MetaboAnalyst cannot perform pathway enrichment statistics on more than half of all identified metabolites, because it relies on KEGG pathways only. Moreover, we have developed new software for improved data normalization and statistics workflows in MetDA web-based analyses that will be presented on published example data.<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/277-2018-emn-webinars-public Can we capture an accurate view of cell or tissue specific metabolism from an expression profile?]== <br />
<br />
By: ''Dr. Nathan Lewis<br />
<br />
Date: March 26, 2018<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/277-2018-emn-webinars-public Metabolomics as a hypothesis generator for understanding metabolic regulation]== <br />
<br />
By: ''Prof. Uwe Sauer<br />
<br />
Date: February 14, 2018<br />
<br />
Prof. Uwe Sauer focuses on two conceptual problems: i) discovery of functionally important regulation mechanisms and ii) understanding which of the many known mechnisns actually matter for a given adaption. On the discovery side, he illustrates the use of coarse-grained kinetic models to extract mechnistic hypotheses from dynamic metabolomics data. For learning the coordination mechanisms, he presents an approach that hypothesizes the dynamically important mechnism from the much fewer steady state measurements in the bacterium E. coli. The surprising result is that only very few regulation events appear to be required for a given transition, typically involving less than a handful of active regulators.<br />
=2017=<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/258-2017-emn-webinars-public Introduction to Stable Isotope Resolved Metabolomics (SIRM) and Applications to Cancer Metabolism]==<br />
<br />
By: ''Dr. Andrew Lane<br />
<br />
Date: December 14, 2017<br />
<br />
Stable isotope resolved metabolomics (SIRM), for pathway tracing, represents an important new approach to obtaining metabolic parameters. SIRM allows the generation of atomic fate maps in cells and tissues, which provides the necessary information and data for metabolic flux analyis. This powerful new approach has already provided important new insights into metabolic adaptations in lung cancer cells.<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/258-2017-emn-webinars-public Using PhenoMeNal for your metabolomics data analysis on the cloud]==<br />
<br />
By: ''Dr. Pablo Moreno<br />
<br />
Date: October 24, 2017<br />
<br />
PhenoMeNal aims to bridge the gap between cloud computing and metabolomics researchers by providing the ability to create Cloud Research Environments (CRE) for metabolomics data analysis. A PhenoMeNal CRE is a small cluster of computers with popular metabolomics data analysis tools already installed. These tools are ready to be run and are accessible through a user friendly Galaxy workflow environment reducing the need for in-house bioinformatics. The PhenoMeNal CRE not only includes data analysis tools, but also example workflows where some of these tools are used together. You can also make your own workflows inside the CRE. In this webinar we will explain the main components of PhenoMeNal. We will demonstrate how to register, access existing tools and workflows, create a new PhenoMeNal CRE on Amazon, and execute a workflow on a PhenoMeNal CRE.<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/258-2017-emn-webinars-public Machine learning powered metabolomic network analysis]==<br />
<br />
By: ''Dr. Dmitry Grapov <br />
<br />
Date: May 30, 2017<br />
<br />
Metabolomic network analysis can be used to interpret experimental results within a variety of contexts including: biochemical relationships, structural and spectral similarity and empirical correlation. Machine learning is useful for modeling relationships in the context of pattern recognition, clustering, classification and regression based predictive modeling. The combination of developed metabolomic networks and machine learning based predictive models offer a unique method to visualize empirical relationships while testing key experimental hypotheses. The following presentation focuses on data analysis, visualization, machine learning and network mapping approaches used to create richly mapped metabolomic networks.<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/258-2017-emn-webinars-public How well do I quantify? Concepts for method validation and evaluation of measurement uncertainty in metabolomics]==<br />
<br />
By: ''Assoc. Prof. Stephan Hann<br />
<br />
Date: March 24, 2017<br />
<br />
This webinar will give an introduction to the basic terminology and principles of validation and measurement uncertainty in metabolomics. It will be demonstrated how validation parameters are determined in selected examples (e.g. LC-MS/MS, GC-MS/MS) for quantitative metabolomic analysis. Different quantification approaches will be overviewed in detail, and tips on choosing the most appropriate analytical strategies to answer metabolome-level hypotheses will be given. Finally, a workflow for estimating measurement uncertainty in metabolomics will be presented and discussed.<br />
<br />
=2016=<br />
<br />
== [http://metabolomicssociety.org/site-map/articles/88-videos/218-2016-emn-webinars-public Smarter ways to clean LC-MS] ==<br />
<br />
By: ''Assoc. Prof. Carl Brunius<br />
<br />
Date: November 17, 2016<br />
<br />
LC-MS is the most frequently used technique for untargeted metabolomics owing to broad coverage of metabolites, high sensitivity and simple sample preparation. However, instrument data tends to have high noise contribution from drift in signal intensity, mass accuracy and retention times. This noise has both within batch and between batch contributions and results in reduced measurement repeatability and reproducibility. The power to detect biological responses may thus be decreased and interpretations consequently obscured. Dr. Carl Brunius (the speaker) is involved in developing procedures to address and correct for within- and between-batch variability in processing multiple-batch untargeted LC-MS metabolomics data to increase their quality. This webinar session will talk about (i) alignment and merging of LC-MS features that are systematically misaligned between batches and (ii) within batch intensity drift correction that allows multiple drift patterns within batch. These algorithms will be applied on authentic data, resulting in improved peak picking performance and decreased noise in the dataset. All algorithms are developed as open source and are, together with example data, freely available as an R package from https://GitLab.com/CarlBrunius/batchCorr.<br />
== [http://metabolomicssociety.org/site-map/articles/88-videos/218-2016-emn-webinars-public Mass Spectral Libraries] ==<br />
<br />
By: ''Dr. Emma Schymanski<br />
<br />
Date: October 6, 2016<br />
<br />
Mass spectrometry is applied in diverse ways in metabolomics research and the mass spectrum of a small molecule can act as a fingerprint for identification. Just as the scientific questions in metabolomics vary, there is a diverse set of mass spectral libraries available to assist in the identification of metabolites and other small molecules. This webinar aims to provide listeners with a brief overview of several different mass spectral<br />
resources, including a personal view on pros and cons of the different options – providing a basis for listeners to choose the resource(s) that may best suit their investigation and needs. Additional information about substance overlap, spectral matching, identification confidence and spectral exchange will also be given – as well as some factors to consider carefully. Finally, some perspectives towards in silico identification without spectral libraries will be given to lead into a topic for a future webinar.<br />
<br />
== [http://metabolomicssociety.org/site-map/articles/88-videos/218-2016-emn-webinars-public Targeted High-throughput Lipidomics Platform: Development and Application] ==<br />
<br />
By: ''Dr. Peter Meikle<br />
<br />
Date: July 27, 2016<br />
<br />
Lipidomics, full analysis of lipid species and their biological roles with respect to health and diseases, has attracted increasing attention of biological and analytical scientific community. Knowing and understanding steps involved in lipidomics experimental workflow is essential for successful outcome. The Metabolomics Laboratory at Baker IDI Heart and Diabetes Institute (Melbourne, Australia) has a focus on the dyslipidemia and altered lipid metabolism associated with obesity, diabetes and cardiovascular disease and its relationship to the pathogenesis of these disease states. The laboratory has developed a targeted lipidomics platform that is able to quantify over 500 lipid species in 15 minutes using liquid chromatography coupled to electrospray ionisation tandem mass spectrometry. This platform is being applied to identify new approaches to early diagnosis and risk assessment as well as the development of new lipid modulating therapies for chronic disease. With illustration from this well-established lipidomics platform, this webinar will discuss the development of targeted high-throughput lipidomics platform, including selection and characterisation of lipid species, development of chromatography and quality control in the analysis of large sample sets. The presentation will draw on specific examples to highlight the application to large cohort studies and the Institute’s work to develop new therapeutic strategies for cardiovascular disease.<br />
<br />
== [http://metabolomicssociety.org/site-map/articles/88-videos/218-2016-emn-webinars-public From Features to Compounds: How To Get Started and Move Forward]==<br />
<br />
By: ''Dr. Jan Stanstrup<br />
<br />
Date: May 27, 2016<br />
<br />
In the untargeted analysis of complex mixtures the identification of compounds is the fundamental step enabling the results to be put in biological context. It is therefore of crucial importance for early career scientists approaching the field of metabolomics to familiarize themselves with this step. Many tools have been developed to aid identification; however, compound identification still constitutes one of the main bottlenecks in metabolomics and still requires substantial amounts of manual work. This webinar will go through the basic concepts used in MS-based compound identification and will introduce a number of relevant tools and databases allowing researchers to approach identification in a systematic way. The webinar is in particular dedicated to those researchers coming into the field of metabolomics that are not familiar with the methods used for compound identification and for which getting started can be a daunting and time-consuming challenge.<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/218-2016-emn-webinars-public Chromatography and Metabolomics: What’s the Point?] ==<br />
<br />
By: ''Dr. Karl Burgess<br />
<br />
Date: April 29, 2016<br />
<br />
The key to both chromatography and mass spectrometry is the separation of chemical species based on their physicochemical properties. As metabolomics researchers, we look to improved chromatography so enable us to<br />
detect compounds we previously had trouble with, to reduce the enormous complexity of the samples we analyse, and to clean up samples before or during analysis. Advances in mass spectrometry bring us greater sensitivity, better selectivity and a toolbox of techniques to aid in identification of biochemicals. With all these advantages come many disadvantages - poor reproducibility, compound bias and contamination. In this webinar, I'll explore chromatography and mass spectrometry with a critical eye. How can we improve them? Do we need them in every experiment? And really, what's the point of metabolomics at all?<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/218-2016-emn-webinars-public How close are we to metabolomics reproducibility? Data sharing, data standards and workflows for metabolomics]==<br />
<br />
By: ''Dr. Reza Salek<br />
<br />
Date: March 24, 2016<br />
<br />
=2015=<br />
<br />
== [http://metabolomicssociety.org/site-map/articles/88-videos/187-2015-emn-webinars-public Metabolomics and Beyond: Challenges and Strategies for Next-gen Omic Analyses] ==<br />
<br />
By: ''Dr. Dmitry Grapov<br />
<br />
Date: September 15, 2015<br />
<br />
Robust interpretation of experimental results measuring discreet biological domains remains a significant challenge in the face of complex biochemical regulation processes such as organismal versus tissue versus<br />
cellular metabolism, epigenetics, and protein post-translational modification. Integration of analyses carried out across multiple measurement or omic platforms is an emerging approach to help address these<br />
challenges. The following presentation will focus on key challenges faced by metabolomics researchers in the areas large-scale studies data normalization, multivariate analysis, visualization and omics data integration.<br />
<br />
== [http://metabolomicssociety.org/site-map/articles/88-videos/187-2015-emn-webinars-public Metabolomics with high resolution mass spectrometry: from spectral databases to biomarker discovery] ==<br />
<br />
By: ''Dr. Christophe Junot<br />
<br />
Date: 12 June 2015<br />
<br />
Since the middle of the 2000s, high resolution mass spectrometry (HRMS) is widely used in metabolomics, mainly because the detection and identification of metabolites are improved compared to low resolution instruments. Thanks to their versatility, HRMS instruments are the most appropriate to achieve an optimal metabolome coverage, at the border of other omics fields such as lipidomics and glycomics. The aim of this talk will be to present HRMS based tools for metabolomics and lipidomics developed at the laboratory, and their relevance to the field of biomarker discovery for the diagnosis and follow-up of pathologies.<br />
<br />
== [http://metabolomicssociety.org/site-map/articles/88-videos/187-2015-emn-webinars-public#|: Metabolomics and systems biology: models as guide and natural integrator]==<br />
<br />
By: ''Prof. Bas Teusink<br />
<br />
Date: 14 April 2015<br />
<br />
In this webinar, I will discuss the use of mathematical models in guiding targeted and (semi-)untargeted metabolomics efforts. I will show how genome-scale metabolic models can be used as a data integration platform - also for metabolomics data. I will provide an example from medium optimisation in a biotechnological context. My message is that the use of models upfront combined with quantitative and well-timed metabolomics is often much more effective than simply generating lots of data and subsequent statistical analysis.<br />
<br />
== [http://metabolomicssociety.org/site-map/articles/88-videos/187-2015-emn-webinars-public Large-scale, computational and empirical UHPLC-MS-SPE-NMR annotation of plant metabolomes] ==<br />
<br />
By: ''Dr. Lloyd Sumner<br />
<br />
Date: 5 March 2015<br />
<br />
Integrated metabolomics is a revolutionary systems biology tool for understanding plant metabolism and elucidating gene function. Although the vast utility of metabolomics is well documented in the literature, its<br />
full scientific promise has not yet been realized due to multiple technical challenges. The number one, grand challenge of metabolomics is the large-scale confident chemical identification of metabolites. To address<br />
this challenge, we have developed sophisticated computational and empirical metabolomics tools for the systematic and biological directed annotation of plant metabolomes. This presentation will introduce novel<br />
software entitled Plant Metabolite Annotation Toolbox (PlantMAT) and a sophisticated UHPLC-MS-SPENMR instrumental ensemble that are being used for ‘sequencing’ the first plant metabolomes of the model plant systems Arabidopsis and Medicago truncatula.<br />
<br />
== [http://metabolomicssociety.org/site-map/articles/88-videos/187-2015-emn-webinars-public Metabolomics: only suitable for multidisciplinary teams] ==<br />
<br />
By: ''Dr. Oscar Yanes<br />
<br />
Date: 29 Jan 2015<br />
<br />
Metabolomics is defined as the comprehensive and quantitative analysis of metabolites in living organisms. Among the omic sciences, metabolomics is possibly the most multidisciplinary of all, involving knowledge<br />
about electronic engineering and signal processing, analytical and organic chemistry, biostatistics and statistical physics, and biochemistry and cell metabolism. Here an untargeted metabolomics workflow will<br />
be detailed that provides examples of this multidisciplinarity.</div>Viniciusverihttp://wiki.metabolomicssociety.org/index.php?title=EMN_Webinars&diff=1563EMN Webinars2021-06-21T06:38:04Z<p>Viniciusveri: </p>
<hr />
<div>The [[Early-Career Members Network#EMN Committee|Early-Career Members Network (EMN) Committee]] has organized webinars since 2015 on a variety of metabolomics-related topics. This page provides a summary of all webinars to date with links to the webinars (when available).<br />
<br />
To participate on the live webinars, follow us on [https://twitter.com/EMN_MetSoc Twitter] and [https://www.facebook.com/EMN.MetabolomicsSociety Facebook] to get all updates from the EMN or subscribe to the Metabolomics Society website to receive the email invitation.<br />
<br />
<br />
=2021=<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/334-2021-emn-webinars-public TidyMS: a tool for preprocessing and Improving data quality in metabolomics]==<br />
<br />
Date: April 27, 2021<br />
<br />
'''TidyMS: a tool for preprocessing and Improving data quality in metabolomics<br />
<br />
By: ''Dr. María Eugenia Monge<br />
<br />
One of the challenges in untargeted metabolomics workflows is preprocessing data in a reproducible and robust way. In liquid chromatography-mass spectrometry (LC-MS), data curation involves removing biologically non-relevant features (retention time, m/z pairs) and retaining only those analytically robust enough to allow subsequent data analysis and interpretation. In this presentation, I will share the strategies that we used for quality control (QC) purposes in LC-MS-based untargeted studies that interrogated samples from in vitro models and serum samples as well as in an ambient-MS-based approach. In addition, I will introduce “TidyMS”, which is a new Python library for LC-MS data preprocessing that has recently been developed in our research group with the aim of performing QC procedures in a rapid and reproducible workflow. TidyMS includes tools to work with both raw MS data and data matrices. It provides functionality to build customized pipelines for data curation to obtain cleaned matrices from untargeted metabolomics and lipidomics studies for subsequent statistical analysis, and aims at ensuring accuracy and reliability in LC–MS measurements. The capabilities of TidyMS will be illustrated with pipelines for an LC-MS system suitability check, system conditioning, signal drift evaluation, and data curation. These applications were implemented to successfully preprocess data from a new suite of candidate human plasma reference materials currently under development by the National Institute of Standards and Technology (NIST; hypertriglyceridemic, diabetic, and African-American plasma pools), in addition to NIST SRM 1950 Metabolites in Frozen Human Plasma to be used in metabolomics and lipidomics studies. Overall, TidyMS can be used in an automated or semi-automated way, and it is an open and free tool available to all users.<br />
<br />
<br />
'''Improving data quality in metabolomics workflows: A Clear Cell Renal Cell Carcinoma (ccRCC) case study<br />
<br />
By: ''Mr. Nicolás Zabalegui<br />
<br />
Liquid chromatography-mass spectrometry (LC-MS)-based untargeted metabolomics studies involving biological samples may lead to the detection of tens of thousands of potential metabolic features (retention time, m/z pairs) at initial stages of the workflow. However, data needs to be preprocessed in a reproducible way to remove biologically non-relevant features and thereafter obtain cleaned matrices suitable for subsequent statistical analysis.Kidney cancer is accepted to be a metabolic disease. More than 50% of cases of renal cell carcinoma (RCC) are incidentally diagnosed; being clear cell RCC (ccRCC) the most common histological subtype (75% of cases), and considered a glycolytic and lipogenic tumor. Since the disease is inherently resistant to chemotherapy and radiotherapy, surgery is the most promising treatment for curation when the disease is detected at earlier stages.In this study, serum samples from a cohort that included patients with different ccRCC stages (I, II, III, IV), which were collected before (n=113) and after surgery (n=56), as well as samples from controls (n=52), were interrogated with a discovery-based metabolomics approach using UPLC-QTOF-MS. LC-MS data were preprocessed with TidyMS, a Python package used to retain only high-quality data for subsequent analysis and interpretation. As well, additional experiments were conducted to account for metabolite stability over time and non-linearity in instrumental responses, and were utilized to improve data quality before performing statistical multivariate analysis.<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/334-2021-emn-webinars-public Viral infection in algal blooms and The glycosphingolipid-based arms race]==<br />
<br />
Date: March 22, 2021<br />
<br />
'''Viral infection in algal blooms and The glycosphingolipid-based arms race<br />
<br />
By: ''Prof. Assaf Vardi & Dr. Guy Schleyer<br />
<br />
In this webinar, we will present how we utilize the recent advances in the field of chemical ecology (metabolomics and mass spectrometry imaging) combined with single-cell imaging and transcriptomic approaches to track host-pathogen interactions at the microscale.<br />
<br />
=2020=<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/306-2020-emn-webinars-public New bio-statistical methods for metabolomics and metabolite annotation]== <br />
<br />
Date: December 22, 2020<br />
<br />
'''New bio-statistical methods for metabolomics<br />
<br />
By: ''Dr. Daniel Raftery<br />
<br />
Highly complex biological samples present challenging analysis problems for the field of metabolomics. Ideally, platforms that provide broad metabolome coverage and high data quality allow the opportunity for deep insights into biological problems. However, this goal can be difficult to achieve on a routine basis because the highly complex data are subject to matrix effects and complicated correlative relationships among many metabolites. As such metabolite identification, biomarker identification and validation can be very challenging. Advanced statistical methods are needed to deal with these issues for improved biomarker discovery, unknown identification and biological interpretation. We have pursued the development of a number of approaches that try to unravel the complex and multidimensional structure of metabolomics datasets, with some successes and some failures along the way. In this talk, I will provide some examples of where even non-experts in biostatistics can make progress in developing advanced analysis approaches and discuss some areas that provide significant challenges for future work.<br />
<br />
<br />
'''Expanding Automated Metabolite Annotation in Untargeted Metabolomics through Mass Spectral Networks<br />
<br />
By: ''Dr. Fausto Carnevale Neto<br />
<br />
The major goal of metabolomics is to interrogate complex biological extracts for the purposes of metabolic exploration and biomarker discovery. Liquid chromatography (LC) coupled with tandem mass spectrometry (MS/MS or MS2) is widely accepted as a powerful strategy to explore the chemical constituency of complex mixtures. It offers accurate masses of detected ions and the structural information derived from fragmentation reactions in the gas-phase. While global LC-MS/MS profiling provides the comprehensive measurement of metabolites in complex biological samples, structure annotation remains a challenge, and computational approaches are necessary to translate the molecular composition into biological knowledge. Recently, global natural products social molecular networking (GNPS) has emerged as superb mining tool to assist the interpretation of large MS/MS datasets in the context of metabolomics. It integrates spectral database matching, unsupervised molecular substructure discovery, in silico fragmentation prediction, and automated chemical classification into a network topology. By embedding independent experimental and predictive annotation outputs on to the multi-informative molecular network, we can expand the automated chemical structural annotation within complex metabolic mixtures. <br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/306-2020-emn-webinars-public Metabolic networks]== <br />
<br />
Date: November 26, 2020<br />
<br />
'''Metabolic networks to enrich and interpret metabolic fingerprints<br />
<br />
By: ''Dr. Fabien Jourdan<br />
<br />
Metabolic modulation is a cornerstone cellular response to genetic or environmental stresses. This plasticity is going beyond central metabolism and may involve complex processes spanning several metabolic pathways. Hence, it is a key challenge to be able to decipher metabolic modulations in a systemic and global perspective.<br />
The aim of the computational methods and tools which will be presented is thus to consider the full complexity of metabolism. To do so, all metabolic reactions the cell is able to achieve are gathered in a single mathematical model call “genome scale metabolic network”. Based on this model, it is then possible to identify metabolic modulations associated to metabolic fingerprints or suggest metabolites of interest to enrich biochemical interpretation.<br />
<br />
<br />
<br />
'''Improving metabolic studies with diverse context-specific metabolic networks<br />
<br />
By: ''Dr. Pablo Rodriguez Mier<br />
<br />
Understanding deregulations of metabolism based on isolated measures of gene expression, protein or metabolite concentrations is a challenging task due to the interconnection of multiple processes. One solution is to extract, from generic genome-scale metabolic networks, the specific sub-network which is modulated in the studied condition. Many algorithms have been proposed for such context-specific network extraction based on experimental measurements. However, this process is subject to some randomness and variability, since multiple metabolic networks can model the metabolic state in a similarly adequate manner for the same experimental data. This means that for some condition and reconstruction method, there are usually multiple possible sub-networks that can explain the same experimental data, but most current methods ignore this fact and return a single sub-network instead. Ignoring this variability can not only lead to incorrect or incomplete explanations of the biological experiment, but also causes valuable information to be lost that could be used to improve predictions. In this talk we will see what context-specific metabolic sub-networks are, some of the limitations of the current methods, and how we can get a diverse set of sub-networks to improve predictions and obtain better mechanistic insights about the metabolism.<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/306-2020-emn-webinars-public Decomposing Complex Metabolite Mixtures...& Unraveling the neonatal metabolome]== <br />
<br />
Date: June 19, 2020<br />
<br />
'''Decomposing Complex Metabolite Mixtures through Substructure-based Metabolomics Workflows<br />
<br />
By: ''Dr. Justin J.J. van der Hooft<br />
<br />
In this webinar, Dr. Justin J.J. van der Hooft will start with an introduction on the challenges of metabolite annotation and identification in untargeted metabolomics experiments of complex mixtures typically encountered in natural products and food research. He will also show how MS2LDA has been successfully used to capture chemical knowledge from diverse plant, food, and microbial-related data sets. Dr. Justin J.J. van der Hooft will finish by highlighting the advantages of combining metabolome mining and annotation tools in public data of different plant and microbial-related studies.<br />
<br />
<br />
'''Unraveling the neonatal metabolome using mass spectral data mining tools<br />
<br />
By: ''Madeleine Ernst<br />
<br />
In this webinar, Dr. Madeleine Ernst will explain how mass spectral data mining tools, such as molecular networking through the community platform GNPS, MS2LDA, in silico structure prediction (e.g. Network Annotation Propagation) and ClassyFire can significantly enhance chemical structural annotation retrieved in clinical mass spectrometry-based metabolomics studies. Dr. Madeleine Ernst will also elucidate how metabolic signatures of neonatal health and disease can be unraveled, significantly enhancing biological interpretation and hypothesis generation in metabolomics studies.<br />
<br />
==Plant Metabolomics - Elucidating biochemicals mechanisms underlying plant-environment interactions== <br />
<br />
By: ''Dr. Fidele Tugizimana<br />
<br />
Date: May 1, 2020<br />
<br />
In this webinar, Dr. Fidele will provide a snapshot of applications of metabolomics in plant sciences, particularly in plant-environment interactions research. The webinar will highlight some examples of the use of metabolomics to elucidate hypothetical frameworks that describe the biochemistry underlying naïve and primed-plant responses to microbial infections. Furthermore, one of the novel (emerging) strategies for sustainable food production and food security is the use of biostimulants in agriculture industry. Application of metabolomics in decoding and understanding plant-biostimulant interactions will be also highlighted.<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/306-2020-emn-webinars-public Human metabolic individuality and its role in multi-omics based studies]== <br />
<br />
By: ''Dr. Karsten Suhre<br />
<br />
Date: February 4, 2020<br />
<br />
In this webinar, Dr. Suhre will discuss Genome-wide association studies with clinically relevant intermediate traits, such as gene expression, proteomics, and metabolomics, which unravelled numerous pathophysiological pathways and generated many hypotheses regarding the functional basis of complex disorders. More recently, similar approaches linked variation in epigenetic modifications, especially differential methylation of chromosomal CpG-pairs, to changes in gene expression and blood circulating metabolites. These large-scale population and patient cohort studies reflect experimental data obtained from naturally occurring variance of the general population where each individual may be viewed as an experiment conducted by Nature. The next and most challenging step on the way to a truly personalized approach to medicine is to translate the results from these large-scale omics studies to applications at the patient level. In this presentation,<br />
<br />
=2019=<br />
<br />
==Metabolomics as a tool for elucidating plant growth regulation== <br />
<br />
By: ''Dr. Camila Caldana<br />
Date: November 20, 2019<br />
<br />
Rising demand for food and fuels makes it crucial to develop breeding strategies for increasing crop yield/biomass. Plant biomass production is tightly associated with growth and relies on a tight regulation of a complex signaling network that integrates external and internal stimuli. The main goal of our group is to elucidate the processes underlying plant growth and production of biomass by combining physiology, metabolomics, and gene expression analyses. In my presentation, I will provide examples of i) how the evolutionary conserved Target of Rapamycin pathway fine-tunes metabolic homeostasis to promote biosynthetic growth in plants; ii) the potential of metabolite profiles to predict plant performance as biomarkers.<br />
<br />
==Discovering Metabolites that Alter Physiology, an Omics Perspective== <br />
<br />
By: ''Dr. Gary Siuzdak<br />
Date: September 18, 2019<br />
<br />
Metabolomics and the comprehensive analysis of the metabolome and lipidome has traditionally been pursued with the aim of identifying biomarkers in the diagnosis and prediction of disease. However, the value of metabolomics has been redefined from a simple biomarker identification tool to a technology for the discovery of active drivers of biological processes. It is now clear that the metabolome affects cellular physiology through modulation of other ‘omics’ levels, including the genome, epigenome, transcriptome and proteome. In this presentation, I will focus on our recent progress in using metabolomics to understand how the metabolome influences other omics and, by extension, to reveal the active role of metabolites in physiology and disease. This concept of utilizing metabolomics to perform activity screens to identify biologically active metabolites — which we term activity metabolomics — is already having a broad impact on biology.<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/298-2019-emn-webinars-public Lipidomics and epilipidomics – from analysis to data integration]== <br />
<br />
By: ''Dr. Maria Fedorova<br />
Date: July 17, 2019<br />
<br />
Lipidomics is a large-scale study of diversified molecular species of lipids aiming to address the identity, quantities, cellular and tissue distribution of lipids as well as related signalling and metabolic pathways. In addition to the native lipidome, lipid species derived from enzymatic and non-enzymatic modifications (the epilipidome) further increase regulatory capacity of the biological systems. High diversity of physico-chemical properties as well as large dynamic range of lipid concentrations in native lipidomes makes significantly challenge their analysis. For omics-wide high-throughput identification of lipid species from complex biological samples, several crucial analytical steps including extraction, chromatographic separation and mass spectrometry analysis need to be carefully considered and validated. The webinar will review current analytical strategies used in contemporary lipidomics and epilipidomics with the focus on optimization of LC-MS/MS based workflows for “discovery” lipidomics including sample preparation, lipid fractionation, separation using different chromatography techniques and high-throughput identification solutions. Available bioinformatics tools for identification of native and modified lipids will be described and compared as well as possible lipidomics data integration strategies.<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/298-2019-emn-webinars-public Metabolomics 2019: Professional Career Development: The Survival Kit]== <br />
<br />
By: ''Cathy Delhanty<br />
<br />
Date: June 23, 2019<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/298-2019-emn-webinars-public A Combined NMR and MS Metabolomics Approach to Study Neurodegenerative Diseases]== <br />
<br />
By: ''Dr. Robert Powers<br />
<br />
Date: May 24, 2019<br />
<br />
The metabolome captures how the system responds to drug treatment, disease state, or genetic modification. In this regards, metabolomics is an invaluable approach to easily and rapidly diagnose human disease and to assist in personalized medicine by monitoring a patient’s response to treatments. But, metabolomics is deceivingly complex with numerous sources of errors and technical challenges at every step of the process. One specific challenge is achieving a complete and accurate coverage of the metabolome, which can be addressed by combining NMR and mass spectrometry. Our metabolomics protocols and MVAPACK software for integrating NMR and mass spectral data for the analysis of neurodegenerative disease will be discussed. Our investigation into the molecular mechanisms of Parkinson’s disease and the identification of biomarkers for multiple sclerosis will be highlighted.<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/298-2019-emn-webinars-public Computational mass spectrometry in metabolomics to deepen the understanding of metabolisms]== <br />
<br />
By: ''Dr. Hiroshi Tsugawa<br />
<br />
Date: April 23, 2019<br />
<br />
Computational mass spectrometry is a growing research field to process mass spectrometry data, assist the interpretation of mass fragmentations, and elucidate unknown structures with metabolome databases and repositories for the global identification of metabolomes in various living organisms. In this talk, Dr Tsugawa will introduce three metabolomics software programs which include (1) MS-DIAL for untargeted metabolomics, (2) MS-FINDER for structure elucidations of unknowns, and (3) MRMPROBS for targeted metabolomics. These programs are demonstrated to perform the comprehensive analyses of primary metabolites, lipids, and plant specialized metabolites where unknown metabolites are also untangled with various methodologies including stable isotope labeled organisms, metabolite class recommendations, and integrated metabolome network analyses. In addition, a computational workflow to link untargeted- and targeted metabolomics is also highlighted in this talk.<br />
<br />
==Multidimensional Chromatography, what is the synergy with untargeted metabolomics profiling?== <br />
<br />
By: ''Dr. Pierre-Hugues Stefanuto<br />
<br />
Date: March 28, 2019<br />
<br />
In this webinar, Dr Pierre-Hugues Stefanuto will discuss the new development of multidimensional chromatography and the synergy with metabolomics. This presentation will be broadcasted in the context of the Multidimensional Chromatography Workshop held in Liège last January (http://multidimensionalchromatography.com). During this event, four focus group discussions were organized: 1) data processing for untargeted screening, 2) minimum reporting information for QC and compound validation, 3) hyphenation of MDGC with high-resolution MS, 4) and the general acceptance of MDC techniques. He will illustrate these topics through some ongoing medical research articulated around volatile organic compound (VOC) measurements in human breath and in vitro in metabolomic applications.<br />
<br />
==Untargeted metabolomics reveals smokers' characteristic profiles== <br />
<br />
By: ''Dr. Ping-Ching Hsu<br />
<br />
Date: March 1, 2019<br />
<br />
=2018=<br />
<br />
==Recent advances towards integrating metabolomics in kinetic models of large metabolic networks== <br />
<br />
By: ''Prof. Lars Nielsen<br />
<br />
Date: October 15, 2018<br />
<br />
Lars Nielsen is Chair of Biological Engineering at the Australian Institute for Bioengineering &Nanotechnology and Scientific Director for the Section for Quantitative Modelling of Cellular Metabolism at the Novo Nordisk Foundation Center for Biosustainability in Denmark. He is Director of the Bioplatforms Australia Queensland Node for Metabolomics and Proteomics, which provides systems and synthetic biology support to design and build cell factories for the production of fuels, chemicals and pharmaceuticals. His core research interest is modelling of cellular metabolism and his team has made many contributions to the formulation and use of genome scale models. He recently received a Novo Nordisk Foundation Laureate Research Grant to develop large scale, mathematical models to explore and explain the molecular basis for homeostasis–the self-regulating processes evolved to maintain metabolic equilibrium. Studying homeostasis is relevant for the understanding and treatment of complex diseases, particular with the emergence of personalized medicine. It is equally important when we seek to repurpose the cellular machinery for the production of desired chemicals, materials and pharmaceuticals.<br />
<br />
==Metabolomics-based Elucidation of Plant Specialized Metabolism== <br />
<br />
By: ''Prof. Kazuki Saito<br />
<br />
Date: July 25, 2018<br />
<br />
The recent advances of genomics and metabolomics in plant science accelerate our understanding about the mechanism, regulation and evolution of biosynthesis of plant specialized products. We can now address the questions how the metabolomic diversity of plants is originated at the levels of genome (phytochemical genomics) and how we should apply this knowledge to drug discovery, industry and agriculture. In this presentation, at first, technological developments of metabolomic analysis will be discussed forthe better understanding chemical diversity of plants. Then, a couple of examples of application of metabolomics to functional genomics of specialized metabolism in a model plant Arabidopsis thalianawill be presented, focusing on the biosynthesis of phenylpropanoids and lipids. The further extension to crops and medicinal plants producing a variety of specialized metabolites will be presented.<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/277-2018-emn-webinars-public Activity Metabolomics: Technologies to identify metabolites that modulate phenotype]== <br />
<br />
By: ''Prof. Gary Siuzdak<br />
<br />
Date: May 29, 2018<br />
<br />
Metabolomics is broadly acknowledged to be the omics discipline closest tothe phenotype and therefore widely used for biomarker discovery. However,metabolomics can also be designed to identify active metabolites thatalter a cell’s or an organism's phenotype.This "Activity Metabolomics”concept integrates metabolomics data analysis with pathway and systemsbiology data, ultimately to select endogenous metabolites that can bescreened for functionality. A growing literature reports the use ofmetabolites to modulate diverse processes including stem celldifferentiation, oligodendrocytematuration, insulin signaling,T-cellsurvival and macrophage immune responses. We have developed XCMS Online(xcmsonline.scripps.edu) and the newly expanded METLIN database (now withover 50,000 standards containing MS/MS data) to perform untargeted andtargeted metabolomics, as well as pathway analysis and systems biologydata integration.Metabolomics Activity Screening (MAS) has beenimplemented within XCMS Online to help achieve this integration goal foridentifying active metabolites. Because metabolites are often readilyavailable, activity metabolomics is uniquely positioning its practitionersto move beyond biomarkers, and become active participants in thebiological endgame of modulating phenotype. (for more information seeNature Biotechnology 2018 nature.com/articles/nbt.4101)<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/277-2018-emn-webinars-public Use better software: MS-DIAL instead of XCMS, ChemRich/MetDA instead of Metaboanalyst]== <br />
<br />
By: ''Dr. Oliver Fiehn<br />
<br />
Date: April 24, 2018<br />
<br />
XCMS and MetaboAnalystare the two most popular tools used by metabolomics researchers. But are these the best options? The West Coast Metabolomics Center at UC Davis has collaborated with RIKEN/NGI in Japan to release MS-DIAL vs.2 that yields far fewer false positive peak detections in untargeted LC-MS/MS runs than XCMS, with superior integration of compound identification software. MS-DIAL now also works on low or high resolution GC-MS data, making it the tool of choice for raw data processing of any mass spectrometry-based metabolomics study.We have also developed alternative software suites for statistical analysis of final result data. ChemRich uses all identified metabolites, including complex lipids, for set enrichment statistics. In comparison, MetaboAnalyst cannot perform pathway enrichment statistics on more than half of all identified metabolites, because it relies on KEGG pathways only. Moreover, we have developed new software for improved data normalization and statistics workflows in MetDA web-based analyses that will be presented on published example data.<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/277-2018-emn-webinars-public Can we capture an accurate view of cell or tissue specific metabolism from an expression profile?]== <br />
<br />
By: ''Dr. Nathan Lewis<br />
<br />
Date: March 26, 2018<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/277-2018-emn-webinars-public Metabolomics as a hypothesis generator for understanding metabolic regulation]== <br />
<br />
By: ''Prof. Uwe Sauer<br />
<br />
Date: February 14, 2018<br />
<br />
Prof. Uwe Sauer focuses on two conceptual problems: i) discovery of functionally important regulation mechanisms and ii) understanding which of the many known mechnisns actually matter for a given adaption. On the discovery side, he illustrates the use of coarse-grained kinetic models to extract mechnistic hypotheses from dynamic metabolomics data. For learning the coordination mechanisms, he presents an approach that hypothesizes the dynamically important mechnism from the much fewer steady state measurements in the bacterium E. coli. The surprising result is that only very few regulation events appear to be required for a given transition, typically involving less than a handful of active regulators.<br />
=2017=<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/258-2017-emn-webinars-public Introduction to Stable Isotope Resolved Metabolomics (SIRM) and Applications to Cancer Metabolism]==<br />
<br />
By: ''Dr. Andrew Lane<br />
<br />
Date: December 14, 2017<br />
<br />
Stable isotope resolved metabolomics (SIRM), for pathway tracing, represents an important new approach to obtaining metabolic parameters. SIRM allows the generation of atomic fate maps in cells and tissues, which provides the necessary information and data for metabolic flux analyis. This powerful new approach has already provided important new insights into metabolic adaptations in lung cancer cells.<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/258-2017-emn-webinars-public Using PhenoMeNal for your metabolomics data analysis on the cloud]==<br />
<br />
By: ''Dr. Pablo Moreno<br />
<br />
Date: October 24, 2017<br />
<br />
PhenoMeNal aims to bridge the gap between cloud computing and metabolomics researchers by providing the ability to create Cloud Research Environments (CRE) for metabolomics data analysis. A PhenoMeNal CRE is a small cluster of computers with popular metabolomics data analysis tools already installed. These tools are ready to be run and are accessible through a user friendly Galaxy workflow environment reducing the need for in-house bioinformatics. The PhenoMeNal CRE not only includes data analysis tools, but also example workflows where some of these tools are used together. You can also make your own workflows inside the CRE. In this webinar we will explain the main components of PhenoMeNal. We will demonstrate how to register, access existing tools and workflows, create a new PhenoMeNal CRE on Amazon, and execute a workflow on a PhenoMeNal CRE.<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/258-2017-emn-webinars-public Machine learning powered metabolomic network analysis]==<br />
<br />
By: ''Dr. Dmitry Grapov <br />
<br />
Date: May 30, 2017<br />
<br />
Metabolomic network analysis can be used to interpret experimental results within a variety of contexts including: biochemical relationships, structural and spectral similarity and empirical correlation. Machine learning is useful for modeling relationships in the context of pattern recognition, clustering, classification and regression based predictive modeling. The combination of developed metabolomic networks and machine learning based predictive models offer a unique method to visualize empirical relationships while testing key experimental hypotheses. The following presentation focuses on data analysis, visualization, machine learning and network mapping approaches used to create richly mapped metabolomic networks.<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/258-2017-emn-webinars-public How well do I quantify? Concepts for method validation and evaluation of measurement uncertainty in metabolomics]==<br />
<br />
By: ''Assoc. Prof. Stephan Hann<br />
<br />
Date: March 24, 2017<br />
<br />
This webinar will give an introduction to the basic terminology and principles of validation and measurement uncertainty in metabolomics. It will be demonstrated how validation parameters are determined in selected examples (e.g. LC-MS/MS, GC-MS/MS) for quantitative metabolomic analysis. Different quantification approaches will be overviewed in detail, and tips on choosing the most appropriate analytical strategies to answer metabolome-level hypotheses will be given. Finally, a workflow for estimating measurement uncertainty in metabolomics will be presented and discussed.<br />
<br />
=2016=<br />
<br />
== [http://metabolomicssociety.org/site-map/articles/88-videos/218-2016-emn-webinars-public Smarter ways to clean LC-MS] ==<br />
<br />
By: ''Assoc. Prof. Carl Brunius<br />
<br />
Date: November 17, 2016<br />
<br />
LC-MS is the most frequently used technique for untargeted metabolomics owing to broad coverage of metabolites, high sensitivity and simple sample preparation. However, instrument data tends to have high noise contribution from drift in signal intensity, mass accuracy and retention times. This noise has both within batch and between batch contributions and results in reduced measurement repeatability and reproducibility. The power to detect biological responses may thus be decreased and interpretations consequently obscured. Dr. Carl Brunius (the speaker) is involved in developing procedures to address and correct for within- and between-batch variability in processing multiple-batch untargeted LC-MS metabolomics data to increase their quality. This webinar session will talk about (i) alignment and merging of LC-MS features that are systematically misaligned between batches and (ii) within batch intensity drift correction that allows multiple drift patterns within batch. These algorithms will be applied on authentic data, resulting in improved peak picking performance and decreased noise in the dataset. All algorithms are developed as open source and are, together with example data, freely available as an R package from https://GitLab.com/CarlBrunius/batchCorr.<br />
== [http://metabolomicssociety.org/site-map/articles/88-videos/218-2016-emn-webinars-public Mass Spectral Libraries] ==<br />
<br />
By: ''Dr. Emma Schymanski<br />
<br />
Date: October 6, 2016<br />
<br />
Mass spectrometry is applied in diverse ways in metabolomics research and the mass spectrum of a small molecule can act as a fingerprint for identification. Just as the scientific questions in metabolomics vary, there is a diverse set of mass spectral libraries available to assist in the identification of metabolites and other small molecules. This webinar aims to provide listeners with a brief overview of several different mass spectral<br />
resources, including a personal view on pros and cons of the different options – providing a basis for listeners to choose the resource(s) that may best suit their investigation and needs. Additional information about substance overlap, spectral matching, identification confidence and spectral exchange will also be given – as well as some factors to consider carefully. Finally, some perspectives towards in silico identification without spectral libraries will be given to lead into a topic for a future webinar.<br />
<br />
== [http://metabolomicssociety.org/site-map/articles/88-videos/218-2016-emn-webinars-public Targeted High-throughput Lipidomics Platform: Development and Application] ==<br />
<br />
By: ''Dr. Peter Meikle<br />
<br />
Date: July 27, 2016<br />
<br />
Lipidomics, full analysis of lipid species and their biological roles with respect to health and diseases, has attracted increasing attention of biological and analytical scientific community. Knowing and understanding steps involved in lipidomics experimental workflow is essential for successful outcome. The Metabolomics Laboratory at Baker IDI Heart and Diabetes Institute (Melbourne, Australia) has a focus on the dyslipidemia and altered lipid metabolism associated with obesity, diabetes and cardiovascular disease and its relationship to the pathogenesis of these disease states. The laboratory has developed a targeted lipidomics platform that is able to quantify over 500 lipid species in 15 minutes using liquid chromatography coupled to electrospray ionisation tandem mass spectrometry. This platform is being applied to identify new approaches to early diagnosis and risk assessment as well as the development of new lipid modulating therapies for chronic disease. With illustration from this well-established lipidomics platform, this webinar will discuss the development of targeted high-throughput lipidomics platform, including selection and characterisation of lipid species, development of chromatography and quality control in the analysis of large sample sets. The presentation will draw on specific examples to highlight the application to large cohort studies and the Institute’s work to develop new therapeutic strategies for cardiovascular disease.<br />
<br />
== [http://metabolomicssociety.org/site-map/articles/88-videos/218-2016-emn-webinars-public From Features to Compounds: How To Get Started and Move Forward]==<br />
<br />
By: ''Dr. Jan Stanstrup<br />
<br />
Date: May 27, 2016<br />
<br />
In the untargeted analysis of complex mixtures the identification of compounds is the fundamental step enabling the results to be put in biological context. It is therefore of crucial importance for early career scientists approaching the field of metabolomics to familiarize themselves with this step. Many tools have been developed to aid identification; however, compound identification still constitutes one of the main bottlenecks in metabolomics and still requires substantial amounts of manual work. This webinar will go through the basic concepts used in MS-based compound identification and will introduce a number of relevant tools and databases allowing researchers to approach identification in a systematic way. The webinar is in particular dedicated to those researchers coming into the field of metabolomics that are not familiar with the methods used for compound identification and for which getting started can be a daunting and time-consuming challenge.<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/218-2016-emn-webinars-public Chromatography and Metabolomics: What’s the Point?] ==<br />
<br />
By: ''Dr. Karl Burgess<br />
<br />
Date: April 29, 2016<br />
<br />
The key to both chromatography and mass spectrometry is the separation of chemical species based on their physicochemical properties. As metabolomics researchers, we look to improved chromatography so enable us to<br />
detect compounds we previously had trouble with, to reduce the enormous complexity of the samples we analyse, and to clean up samples before or during analysis. Advances in mass spectrometry bring us greater sensitivity, better selectivity and a toolbox of techniques to aid in identification of biochemicals. With all these advantages come many disadvantages - poor reproducibility, compound bias and contamination. In this webinar, I'll explore chromatography and mass spectrometry with a critical eye. How can we improve them? Do we need them in every experiment? And really, what's the point of metabolomics at all?<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/218-2016-emn-webinars-public How close are we to metabolomics reproducibility? Data sharing, data standards and workflows for metabolomics]==<br />
<br />
By: ''Dr. Reza Salek<br />
<br />
Date: March 24, 2016<br />
<br />
=2015=<br />
<br />
== [http://metabolomicssociety.org/site-map/articles/88-videos/187-2015-emn-webinars-public Metabolomics and Beyond: Challenges and Strategies for Next-gen Omic Analyses] ==<br />
<br />
By: ''Dr. Dmitry Grapov<br />
<br />
Date: September 15, 2015<br />
<br />
Robust interpretation of experimental results measuring discreet biological domains remains a significant challenge in the face of complex biochemical regulation processes such as organismal versus tissue versus<br />
cellular metabolism, epigenetics, and protein post-translational modification. Integration of analyses carried out across multiple measurement or omic platforms is an emerging approach to help address these<br />
challenges. The following presentation will focus on key challenges faced by metabolomics researchers in the areas large-scale studies data normalization, multivariate analysis, visualization and omics data integration.<br />
<br />
== [http://metabolomicssociety.org/site-map/articles/88-videos/187-2015-emn-webinars-public Metabolomics with high resolution mass spectrometry: from spectral databases to biomarker discovery] ==<br />
<br />
By: ''Dr. Christophe Junot<br />
<br />
Date: 12 June 2015<br />
<br />
Since the middle of the 2000s, high resolution mass spectrometry (HRMS) is widely used in metabolomics, mainly because the detection and identification of metabolites are improved compared to low resolution instruments. Thanks to their versatility, HRMS instruments are the most appropriate to achieve an optimal metabolome coverage, at the border of other omics fields such as lipidomics and glycomics. The aim of this talk will be to present HRMS based tools for metabolomics and lipidomics developed at the laboratory, and their relevance to the field of biomarker discovery for the diagnosis and follow-up of pathologies.<br />
<br />
== [http://metabolomicssociety.org/site-map/articles/88-videos/187-2015-emn-webinars-public#|: Metabolomics and systems biology: models as guide and natural integrator]==<br />
<br />
By: ''Prof. Bas Teusink<br />
<br />
Date: 14 April 2015<br />
<br />
In this webinar, I will discuss the use of mathematical models in guiding targeted and (semi-)untargeted metabolomics efforts. I will show how genome-scale metabolic models can be used as a data integration platform - also for metabolomics data. I will provide an example from medium optimisation in a biotechnological context. My message is that the use of models upfront combined with quantitative and well-timed metabolomics is often much more effective than simply generating lots of data and subsequent statistical analysis.<br />
<br />
== [http://metabolomicssociety.org/site-map/articles/88-videos/187-2015-emn-webinars-public Large-scale, computational and empirical UHPLC-MS-SPE-NMR annotation of plant metabolomes] ==<br />
<br />
By: ''Dr. Lloyd Sumner<br />
<br />
Date: 5 March 2015<br />
<br />
Integrated metabolomics is a revolutionary systems biology tool for understanding plant metabolism and elucidating gene function. Although the vast utility of metabolomics is well documented in the literature, its<br />
full scientific promise has not yet been realized due to multiple technical challenges. The number one, grand challenge of metabolomics is the large-scale confident chemical identification of metabolites. To address<br />
this challenge, we have developed sophisticated computational and empirical metabolomics tools for the systematic and biological directed annotation of plant metabolomes. This presentation will introduce novel<br />
software entitled Plant Metabolite Annotation Toolbox (PlantMAT) and a sophisticated UHPLC-MS-SPENMR instrumental ensemble that are being used for ‘sequencing’ the first plant metabolomes of the model plant systems Arabidopsis and Medicago truncatula.<br />
<br />
== [http://metabolomicssociety.org/site-map/articles/88-videos/187-2015-emn-webinars-public Metabolomics: only suitable for multidisciplinary teams] ==<br />
<br />
By: ''Dr. Oscar Yanes<br />
<br />
Date: 29 Jan 2015<br />
<br />
Metabolomics is defined as the comprehensive and quantitative analysis of metabolites in living organisms. Among the omic sciences, metabolomics is possibly the most multidisciplinary of all, involving knowledge<br />
about electronic engineering and signal processing, analytical and organic chemistry, biostatistics and statistical physics, and biochemistry and cell metabolism. Here an untargeted metabolomics workflow will<br />
be detailed that provides examples of this multidisciplinarity.</div>Viniciusverihttp://wiki.metabolomicssociety.org/index.php?title=EMN_Webinars&diff=1562EMN Webinars2021-06-21T06:36:23Z<p>Viniciusveri: </p>
<hr />
<div>The [[Early-Career Members Network#EMN Committee|Early-Career Members Network (EMN) Committee]] has organized webinars since 2015 on a variety of metabolomics-related topics. This page provides a summary of all webinars to date with links to the webinars (when available).<br />
<br />
To participate on the live webinars, follow us on [https://twitter.com/EMN_MetSoc Twitter] and [https://www.facebook.com/EMN.MetabolomicsSociety Facebook] to get all updates from the EMN or subscribe to the Metabolomics Society website to receive the email invitation.<br />
<br />
<br />
=2021=<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/334-2021-emn-webinars-public TidyMS: a tool for preprocessing and Improving data quality in metabolomics]==<br />
<br />
Date: April 27, 2021<br />
<br />
'''TidyMS: a tool for preprocessing and Improving data quality in metabolomics<br />
<br />
By: ''Dr. María Eugenia Monge<br />
<br />
One of the challenges in untargeted metabolomics workflows is preprocessing data in a reproducible and robust way. In liquid chromatography-mass spectrometry (LC-MS), data curation involves removing biologically non-relevant features (retention time, m/z pairs) and retaining only those analytically robust enough to allow subsequent data analysis and interpretation. In this presentation, I will share the strategies that we used for quality control (QC) purposes in LC-MS-based untargeted studies that interrogated samples from in vitro models and serum samples as well as in an ambient-MS-based approach. In addition, I will introduce “TidyMS”, which is a new Python library for LC-MS data preprocessing that has recently been developed in our research group with the aim of performing QC procedures in a rapid and reproducible workflow. TidyMS includes tools to work with both raw MS data and data matrices. It provides functionality to build customized pipelines for data curation to obtain cleaned matrices from untargeted metabolomics and lipidomics studies for subsequent statistical analysis, and aims at ensuring accuracy and reliability in LC–MS measurements. The capabilities of TidyMS will be illustrated with pipelines for an LC-MS system suitability check, system conditioning, signal drift evaluation, and data curation. These applications were implemented to successfully preprocess data from a new suite of candidate human plasma reference materials currently under development by the National Institute of Standards and Technology (NIST; hypertriglyceridemic, diabetic, and African-American plasma pools), in addition to NIST SRM 1950 Metabolites in Frozen Human Plasma to be used in metabolomics and lipidomics studies. Overall, TidyMS can be used in an automated or semi-automated way, and it is an open and free tool available to all users.<br />
<br />
<br />
'''Improving data quality in metabolomics workflows: A Clear Cell Renal Cell Carcinoma (ccRCC) case study<br />
<br />
By: ''Mr. Nicolás Zabalegui<br />
<br />
Liquid chromatography-mass spectrometry (LC-MS)-based untargeted metabolomics studies involving biological samples may lead to the detection of tens of thousands of potential metabolic features (retention time, m/z pairs) at initial stages of the workflow. However, data needs to be preprocessed in a reproducible way to remove biologically non-relevant features and thereafter obtain cleaned matrices suitable for subsequent statistical analysis.Kidney cancer is accepted to be a metabolic disease. More than 50% of cases of renal cell carcinoma (RCC) are incidentally diagnosed; being clear cell RCC (ccRCC) the most common histological subtype (75% of cases), and considered a glycolytic and lipogenic tumor. Since the disease is inherently resistant to chemotherapy and radiotherapy, surgery is the most promising treatment for curation when the disease is detected at earlier stages.In this study, serum samples from a cohort that included patients with different ccRCC stages (I, II, III, IV), which were collected before (n=113) and after surgery (n=56), as well as samples from controls (n=52), were interrogated with a discovery-based metabolomics approach using UPLC-QTOF-MS. LC-MS data were preprocessed with TidyMS, a Python package used to retain only high-quality data for subsequent analysis and interpretation. As well, additional experiments were conducted to account for metabolite stability over time and non-linearity in instrumental responses, and were utilized to improve data quality before performing statistical multivariate analysis.<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/334-2021-emn-webinars-public Viral infection in algal blooms and The glycosphingolipid-based arms race]==<br />
<br />
Date: March 22, 2021<br />
<br />
'''Viral infection in algal blooms and The glycosphingolipid-based arms race<br />
<br />
By: Prof. Assaf Vardi & Dr. Guy Schleyer<br />
<br />
In this webinar, we will present how we utilize the recent advances in the field of chemical ecology (metabolomics and mass spectrometry imaging) combined with single-cell imaging and transcriptomic approaches to track host-pathogen interactions at the microscale.<br />
<br />
=2020=<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/306-2020-emn-webinars-public New bio-statistical methods for metabolomics and metabolite annotation]== <br />
<br />
Date: December 22, 2020<br />
<br />
'''New bio-statistical methods for metabolomics<br />
<br />
By: Dr. Daniel Raftery<br />
<br />
Highly complex biological samples present challenging analysis problems for the field of metabolomics. Ideally, platforms that provide broad metabolome coverage and high data quality allow the opportunity for deep insights into biological problems. However, this goal can be difficult to achieve on a routine basis because the highly complex data are subject to matrix effects and complicated correlative relationships among many metabolites. As such metabolite identification, biomarker identification and validation can be very challenging. Advanced statistical methods are needed to deal with these issues for improved biomarker discovery, unknown identification and biological interpretation. We have pursued the development of a number of approaches that try to unravel the complex and multidimensional structure of metabolomics datasets, with some successes and some failures along the way. In this talk, I will provide some examples of where even non-experts in biostatistics can make progress in developing advanced analysis approaches and discuss some areas that provide significant challenges for future work.<br />
<br />
<br />
'''Expanding Automated Metabolite Annotation in Untargeted Metabolomics through Mass Spectral Networks<br />
<br />
By: Dr. Fausto Carnevale Neto<br />
<br />
The major goal of metabolomics is to interrogate complex biological extracts for the purposes of metabolic exploration and biomarker discovery. Liquid chromatography (LC) coupled with tandem mass spectrometry (MS/MS or MS2) is widely accepted as a powerful strategy to explore the chemical constituency of complex mixtures. It offers accurate masses of detected ions and the structural information derived from fragmentation reactions in the gas-phase. While global LC-MS/MS profiling provides the comprehensive measurement of metabolites in complex biological samples, structure annotation remains a challenge, and computational approaches are necessary to translate the molecular composition into biological knowledge. Recently, global natural products social molecular networking (GNPS) has emerged as superb mining tool to assist the interpretation of large MS/MS datasets in the context of metabolomics. It integrates spectral database matching, unsupervised molecular substructure discovery, in silico fragmentation prediction, and automated chemical classification into a network topology. By embedding independent experimental and predictive annotation outputs on to the multi-informative molecular network, we can expand the automated chemical structural annotation within complex metabolic mixtures. <br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/306-2020-emn-webinars-public Metabolic networks]== <br />
<br />
Date: November 26, 2020<br />
<br />
'''Metabolic networks to enrich and interpret metabolic fingerprints<br />
<br />
By: Dr. Fabien Jourdan<br />
<br />
Metabolic modulation is a cornerstone cellular response to genetic or environmental stresses. This plasticity is going beyond central metabolism and may involve complex processes spanning several metabolic pathways. Hence, it is a key challenge to be able to decipher metabolic modulations in a systemic and global perspective.<br />
The aim of the computational methods and tools which will be presented is thus to consider the full complexity of metabolism. To do so, all metabolic reactions the cell is able to achieve are gathered in a single mathematical model call “genome scale metabolic network”. Based on this model, it is then possible to identify metabolic modulations associated to metabolic fingerprints or suggest metabolites of interest to enrich biochemical interpretation.<br />
<br />
<br />
<br />
'''Improving metabolic studies with diverse context-specific metabolic networks<br />
<br />
By: Dr. Pablo Rodriguez Mier<br />
<br />
Understanding deregulations of metabolism based on isolated measures of gene expression, protein or metabolite concentrations is a challenging task due to the interconnection of multiple processes. One solution is to extract, from generic genome-scale metabolic networks, the specific sub-network which is modulated in the studied condition. Many algorithms have been proposed for such context-specific network extraction based on experimental measurements. However, this process is subject to some randomness and variability, since multiple metabolic networks can model the metabolic state in a similarly adequate manner for the same experimental data. This means that for some condition and reconstruction method, there are usually multiple possible sub-networks that can explain the same experimental data, but most current methods ignore this fact and return a single sub-network instead. Ignoring this variability can not only lead to incorrect or incomplete explanations of the biological experiment, but also causes valuable information to be lost that could be used to improve predictions. In this talk we will see what context-specific metabolic sub-networks are, some of the limitations of the current methods, and how we can get a diverse set of sub-networks to improve predictions and obtain better mechanistic insights about the metabolism.<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/306-2020-emn-webinars-public Decomposing Complex Metabolite Mixtures...& Unraveling the neonatal metabolome]== <br />
<br />
Date: June 19, 2020<br />
<br />
'''Decomposing Complex Metabolite Mixtures through Substructure-based Metabolomics Workflows<br />
<br />
By: Justin J.J. van der Hooft<br />
<br />
In this webinar, Dr. Justin J.J. van der Hooft will start with an introduction on the challenges of metabolite annotation and identification in untargeted metabolomics experiments of complex mixtures typically encountered in natural products and food research. He will also show how MS2LDA has been successfully used to capture chemical knowledge from diverse plant, food, and microbial-related data sets. Dr. Justin J.J. van der Hooft will finish by highlighting the advantages of combining metabolome mining and annotation tools in public data of different plant and microbial-related studies.<br />
<br />
<br />
'''Unraveling the neonatal metabolome using mass spectral data mining tools<br />
<br />
By: Madeleine Ernst<br />
<br />
In this webinar, Dr. Madeleine Ernst will explain how mass spectral data mining tools, such as molecular networking through the community platform GNPS, MS2LDA, in silico structure prediction (e.g. Network Annotation Propagation) and ClassyFire can significantly enhance chemical structural annotation retrieved in clinical mass spectrometry-based metabolomics studies. Dr. Madeleine Ernst will also elucidate how metabolic signatures of neonatal health and disease can be unraveled, significantly enhancing biological interpretation and hypothesis generation in metabolomics studies.<br />
<br />
==Plant Metabolomics - Elucidating biochemicals mechanisms underlying plant-environment interactions== <br />
<br />
By: Dr. Fidele Tugizimana<br />
<br />
Date: May 1, 2020<br />
<br />
In this webinar, Dr. Fidele will provide a snapshot of applications of metabolomics in plant sciences, particularly in plant-environment interactions research. The webinar will highlight some examples of the use of metabolomics to elucidate hypothetical frameworks that describe the biochemistry underlying naïve and primed-plant responses to microbial infections. Furthermore, one of the novel (emerging) strategies for sustainable food production and food security is the use of biostimulants in agriculture industry. Application of metabolomics in decoding and understanding plant-biostimulant interactions will be also highlighted.<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/306-2020-emn-webinars-public Human metabolic individuality and its role in multi-omics based studies]== <br />
<br />
By: Dr. Karsten Suhre<br />
<br />
Date: February 4, 2020<br />
<br />
In this webinar, Dr. Suhre will discuss Genome-wide association studies with clinically relevant intermediate traits, such as gene expression, proteomics, and metabolomics, which unravelled numerous pathophysiological pathways and generated many hypotheses regarding the functional basis of complex disorders. More recently, similar approaches linked variation in epigenetic modifications, especially differential methylation of chromosomal CpG-pairs, to changes in gene expression and blood circulating metabolites. These large-scale population and patient cohort studies reflect experimental data obtained from naturally occurring variance of the general population where each individual may be viewed as an experiment conducted by Nature. The next and most challenging step on the way to a truly personalized approach to medicine is to translate the results from these large-scale omics studies to applications at the patient level. In this presentation,<br />
<br />
=2019=<br />
<br />
==Metabolomics as a tool for elucidating plant growth regulation== <br />
<br />
By: Dr. Camila Caldana<br />
Date: November 20, 2019<br />
<br />
Rising demand for food and fuels makes it crucial to develop breeding strategies for increasing crop yield/biomass. Plant biomass production is tightly associated with growth and relies on a tight regulation of a complex signaling network that integrates external and internal stimuli. The main goal of our group is to elucidate the processes underlying plant growth and production of biomass by combining physiology, metabolomics, and gene expression analyses. In my presentation, I will provide examples of i) how the evolutionary conserved Target of Rapamycin pathway fine-tunes metabolic homeostasis to promote biosynthetic growth in plants; ii) the potential of metabolite profiles to predict plant performance as biomarkers.<br />
<br />
==Discovering Metabolites that Alter Physiology, an Omics Perspective== <br />
<br />
By: Dr. Gary Siuzdak<br />
Date: September 18, 2019<br />
<br />
Metabolomics and the comprehensive analysis of the metabolome and lipidome has traditionally been pursued with the aim of identifying biomarkers in the diagnosis and prediction of disease. However, the value of metabolomics has been redefined from a simple biomarker identification tool to a technology for the discovery of active drivers of biological processes. It is now clear that the metabolome affects cellular physiology through modulation of other ‘omics’ levels, including the genome, epigenome, transcriptome and proteome. In this presentation, I will focus on our recent progress in using metabolomics to understand how the metabolome influences other omics and, by extension, to reveal the active role of metabolites in physiology and disease. This concept of utilizing metabolomics to perform activity screens to identify biologically active metabolites — which we term activity metabolomics — is already having a broad impact on biology.<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/298-2019-emn-webinars-public Lipidomics and epilipidomics – from analysis to data integration]== <br />
<br />
By: Dr. Maria Fedorova<br />
Date: July 17, 2019<br />
<br />
Lipidomics is a large-scale study of diversified molecular species of lipids aiming to address the identity, quantities, cellular and tissue distribution of lipids as well as related signalling and metabolic pathways. In addition to the native lipidome, lipid species derived from enzymatic and non-enzymatic modifications (the epilipidome) further increase regulatory capacity of the biological systems. High diversity of physico-chemical properties as well as large dynamic range of lipid concentrations in native lipidomes makes significantly challenge their analysis. For omics-wide high-throughput identification of lipid species from complex biological samples, several crucial analytical steps including extraction, chromatographic separation and mass spectrometry analysis need to be carefully considered and validated. The webinar will review current analytical strategies used in contemporary lipidomics and epilipidomics with the focus on optimization of LC-MS/MS based workflows for “discovery” lipidomics including sample preparation, lipid fractionation, separation using different chromatography techniques and high-throughput identification solutions. Available bioinformatics tools for identification of native and modified lipids will be described and compared as well as possible lipidomics data integration strategies.<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/298-2019-emn-webinars-public Metabolomics 2019: Professional Career Development: The Survival Kit]== <br />
<br />
By: Cathy Delhanty<br />
<br />
Date: June 23, 2019<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/298-2019-emn-webinars-public A Combined NMR and MS Metabolomics Approach to Study Neurodegenerative Diseases]== <br />
<br />
By: Dr. Robert Powers<br />
<br />
Date: May 24, 2019<br />
<br />
The metabolome captures how the system responds to drug treatment, disease state, or genetic modification. In this regards, metabolomics is an invaluable approach to easily and rapidly diagnose human disease and to assist in personalized medicine by monitoring a patient’s response to treatments. But, metabolomics is deceivingly complex with numerous sources of errors and technical challenges at every step of the process. One specific challenge is achieving a complete and accurate coverage of the metabolome, which can be addressed by combining NMR and mass spectrometry. Our metabolomics protocols and MVAPACK software for integrating NMR and mass spectral data for the analysis of neurodegenerative disease will be discussed. Our investigation into the molecular mechanisms of Parkinson’s disease and the identification of biomarkers for multiple sclerosis will be highlighted.<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/298-2019-emn-webinars-public Computational mass spectrometry in metabolomics to deepen the understanding of metabolisms]== <br />
<br />
By: Dr. Hiroshi Tsugawa<br />
<br />
Date: April 23, 2019<br />
<br />
Computational mass spectrometry is a growing research field to process mass spectrometry data, assist the interpretation of mass fragmentations, and elucidate unknown structures with metabolome databases and repositories for the global identification of metabolomes in various living organisms. In this talk, Dr Tsugawa will introduce three metabolomics software programs which include (1) MS-DIAL for untargeted metabolomics, (2) MS-FINDER for structure elucidations of unknowns, and (3) MRMPROBS for targeted metabolomics. These programs are demonstrated to perform the comprehensive analyses of primary metabolites, lipids, and plant specialized metabolites where unknown metabolites are also untangled with various methodologies including stable isotope labeled organisms, metabolite class recommendations, and integrated metabolome network analyses. In addition, a computational workflow to link untargeted- and targeted metabolomics is also highlighted in this talk.<br />
<br />
==Multidimensional Chromatography, what is the synergy with untargeted metabolomics profiling?== <br />
<br />
By: Dr. Pierre-Hugues Stefanuto<br />
<br />
Date: March 28, 2019<br />
<br />
In this webinar, Dr Pierre-Hugues Stefanuto will discuss the new development of multidimensional chromatography and the synergy with metabolomics. This presentation will be broadcasted in the context of the Multidimensional Chromatography Workshop held in Liège last January (http://multidimensionalchromatography.com). During this event, four focus group discussions were organized: 1) data processing for untargeted screening, 2) minimum reporting information for QC and compound validation, 3) hyphenation of MDGC with high-resolution MS, 4) and the general acceptance of MDC techniques. He will illustrate these topics through some ongoing medical research articulated around volatile organic compound (VOC) measurements in human breath and in vitro in metabolomic applications.<br />
<br />
==Untargeted metabolomics reveals smokers' characteristic profiles== <br />
<br />
By: Dr. Ping-Ching Hsu<br />
<br />
Date: March 1, 2019<br />
<br />
=2018=<br />
<br />
==Recent advances towards integrating metabolomics in kinetic models of large metabolic networks== <br />
<br />
By: Prof. Lars Nielsen<br />
<br />
Date: October 15, 2018<br />
<br />
Lars Nielsen is Chair of Biological Engineering at the Australian Institute for Bioengineering &Nanotechnology and Scientific Director for the Section for Quantitative Modelling of Cellular Metabolism at the Novo Nordisk Foundation Center for Biosustainability in Denmark. He is Director of the Bioplatforms Australia Queensland Node for Metabolomics and Proteomics, which provides systems and synthetic biology support to design and build cell factories for the production of fuels, chemicals and pharmaceuticals. His core research interest is modelling of cellular metabolism and his team has made many contributions to the formulation and use of genome scale models. He recently received a Novo Nordisk Foundation Laureate Research Grant to develop large scale, mathematical models to explore and explain the molecular basis for homeostasis–the self-regulating processes evolved to maintain metabolic equilibrium. Studying homeostasis is relevant for the understanding and treatment of complex diseases, particular with the emergence of personalized medicine. It is equally important when we seek to repurpose the cellular machinery for the production of desired chemicals, materials and pharmaceuticals.<br />
<br />
==Metabolomics-based Elucidation of Plant Specialized Metabolism== <br />
<br />
By: Prof. Kazuki Saito<br />
<br />
Date: July 25, 2018<br />
<br />
The recent advances of genomics and metabolomics in plant science accelerate our understanding about the mechanism, regulation and evolution of biosynthesis of plant specialized products. We can now address the questions how the metabolomic diversity of plants is originated at the levels of genome (phytochemical genomics) and how we should apply this knowledge to drug discovery, industry and agriculture. In this presentation, at first, technological developments of metabolomic analysis will be discussed forthe better understanding chemical diversity of plants. Then, a couple of examples of application of metabolomics to functional genomics of specialized metabolism in a model plant Arabidopsis thalianawill be presented, focusing on the biosynthesis of phenylpropanoids and lipids. The further extension to crops and medicinal plants producing a variety of specialized metabolites will be presented.<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/277-2018-emn-webinars-public Activity Metabolomics: Technologies to identify metabolites that modulate phenotype]== <br />
<br />
By: Prof. Gary Siuzdak<br />
<br />
Date: May 29, 2018<br />
<br />
Metabolomics is broadly acknowledged to be the omics discipline closest tothe phenotype and therefore widely used for biomarker discovery. However,metabolomics can also be designed to identify active metabolites thatalter a cell’s or an organism's phenotype.This "Activity Metabolomics”concept integrates metabolomics data analysis with pathway and systemsbiology data, ultimately to select endogenous metabolites that can bescreened for functionality. A growing literature reports the use ofmetabolites to modulate diverse processes including stem celldifferentiation, oligodendrocytematuration, insulin signaling,T-cellsurvival and macrophage immune responses. We have developed XCMS Online(xcmsonline.scripps.edu) and the newly expanded METLIN database (now withover 50,000 standards containing MS/MS data) to perform untargeted andtargeted metabolomics, as well as pathway analysis and systems biologydata integration.Metabolomics Activity Screening (MAS) has beenimplemented within XCMS Online to help achieve this integration goal foridentifying active metabolites. Because metabolites are often readilyavailable, activity metabolomics is uniquely positioning its practitionersto move beyond biomarkers, and become active participants in thebiological endgame of modulating phenotype. (for more information seeNature Biotechnology 2018 nature.com/articles/nbt.4101)<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/277-2018-emn-webinars-public Use better software: MS-DIAL instead of XCMS, ChemRich/MetDA instead of Metaboanalyst]== <br />
<br />
By: Dr. Oliver Fiehn<br />
<br />
Date: April 24, 2018<br />
<br />
XCMS and MetaboAnalystare the two most popular tools used by metabolomics researchers. But are these the best options? The West Coast Metabolomics Center at UC Davis has collaborated with RIKEN/NGI in Japan to release MS-DIAL vs.2 that yields far fewer false positive peak detections in untargeted LC-MS/MS runs than XCMS, with superior integration of compound identification software. MS-DIAL now also works on low or high resolution GC-MS data, making it the tool of choice for raw data processing of any mass spectrometry-based metabolomics study.We have also developed alternative software suites for statistical analysis of final result data. ChemRich uses all identified metabolites, including complex lipids, for set enrichment statistics. In comparison, MetaboAnalyst cannot perform pathway enrichment statistics on more than half of all identified metabolites, because it relies on KEGG pathways only. Moreover, we have developed new software for improved data normalization and statistics workflows in MetDA web-based analyses that will be presented on published example data.<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/277-2018-emn-webinars-public Can we capture an accurate view of cell or tissue specific metabolism from an expression profile?]== <br />
<br />
By: Dr. Nathan Lewis<br />
<br />
Date: March 26, 2018<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/277-2018-emn-webinars-public Metabolomics as a hypothesis generator for understanding metabolic regulation]== <br />
<br />
By: Prof. Uwe Sauer<br />
<br />
Date: February 14, 2018<br />
<br />
Prof. Uwe Sauer focuses on two conceptual problems: i) discovery of functionally important regulation mechanisms and ii) understanding which of the many known mechnisns actually matter for a given adaption. On the discovery side, he illustrates the use of coarse-grained kinetic models to extract mechnistic hypotheses from dynamic metabolomics data. For learning the coordination mechanisms, he presents an approach that hypothesizes the dynamically important mechnism from the much fewer steady state measurements in the bacterium E. coli. The surprising result is that only very few regulation events appear to be required for a given transition, typically involving less than a handful of active regulators.<br />
=2017=<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/258-2017-emn-webinars-public Introduction to Stable Isotope Resolved Metabolomics (SIRM) and Applications to Cancer Metabolism]==<br />
<br />
By: Dr. Andrew Lane<br />
<br />
Date: December 14, 2017<br />
<br />
Stable isotope resolved metabolomics (SIRM), for pathway tracing, represents an important new approach to obtaining metabolic parameters. SIRM allows the generation of atomic fate maps in cells and tissues, which provides the necessary information and data for metabolic flux analyis. This powerful new approach has already provided important new insights into metabolic adaptations in lung cancer cells.<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/258-2017-emn-webinars-public Using PhenoMeNal for your metabolomics data analysis on the cloud]==<br />
<br />
By: Dr. Pablo Moreno<br />
<br />
Date: October 24, 2017<br />
<br />
PhenoMeNal aims to bridge the gap between cloud computing and metabolomics researchers by providing the ability to create Cloud Research Environments (CRE) for metabolomics data analysis. A PhenoMeNal CRE is a small cluster of computers with popular metabolomics data analysis tools already installed. These tools are ready to be run and are accessible through a user friendly Galaxy workflow environment reducing the need for in-house bioinformatics. The PhenoMeNal CRE not only includes data analysis tools, but also example workflows where some of these tools are used together. You can also make your own workflows inside the CRE. In this webinar we will explain the main components of PhenoMeNal. We will demonstrate how to register, access existing tools and workflows, create a new PhenoMeNal CRE on Amazon, and execute a workflow on a PhenoMeNal CRE.<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/258-2017-emn-webinars-public Machine learning powered metabolomic network analysis]==<br />
<br />
By: Dr. Dmitry Grapov <br />
<br />
Date: May 30, 2017<br />
<br />
Metabolomic network analysis can be used to interpret experimental results within a variety of contexts including: biochemical relationships, structural and spectral similarity and empirical correlation. Machine learning is useful for modeling relationships in the context of pattern recognition, clustering, classification and regression based predictive modeling. The combination of developed metabolomic networks and machine learning based predictive models offer a unique method to visualize empirical relationships while testing key experimental hypotheses. The following presentation focuses on data analysis, visualization, machine learning and network mapping approaches used to create richly mapped metabolomic networks.<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/258-2017-emn-webinars-public How well do I quantify? Concepts for method validation and evaluation of measurement uncertainty in metabolomics]==<br />
<br />
By: Assoc. Prof. Stephan Hann<br />
<br />
Date: March 24, 2017<br />
<br />
This webinar will give an introduction to the basic terminology and principles of validation and measurement uncertainty in metabolomics. It will be demonstrated how validation parameters are determined in selected examples (e.g. LC-MS/MS, GC-MS/MS) for quantitative metabolomic analysis. Different quantification approaches will be overviewed in detail, and tips on choosing the most appropriate analytical strategies to answer metabolome-level hypotheses will be given. Finally, a workflow for estimating measurement uncertainty in metabolomics will be presented and discussed.<br />
<br />
=2016=<br />
<br />
== [http://metabolomicssociety.org/site-map/articles/88-videos/218-2016-emn-webinars-public Smarter ways to clean LC-MS] ==<br />
<br />
By: Assoc. Prof. Carl Brunius<br />
<br />
Date: November 17, 2016<br />
<br />
LC-MS is the most frequently used technique for untargeted metabolomics owing to broad coverage of metabolites, high sensitivity and simple sample preparation. However, instrument data tends to have high noise contribution from drift in signal intensity, mass accuracy and retention times. This noise has both within batch and between batch contributions and results in reduced measurement repeatability and reproducibility. The power to detect biological responses may thus be decreased and interpretations consequently obscured. Dr. Carl Brunius (the speaker) is involved in developing procedures to address and correct for within- and between-batch variability in processing multiple-batch untargeted LC-MS metabolomics data to increase their quality. This webinar session will talk about (i) alignment and merging of LC-MS features that are systematically misaligned between batches and (ii) within batch intensity drift correction that allows multiple drift patterns within batch. These algorithms will be applied on authentic data, resulting in improved peak picking performance and decreased noise in the dataset. All algorithms are developed as open source and are, together with example data, freely available as an R package from https://GitLab.com/CarlBrunius/batchCorr.<br />
== [http://metabolomicssociety.org/site-map/articles/88-videos/218-2016-emn-webinars-public Mass Spectral Libraries] ==<br />
<br />
By: Dr. Emma Schymanski<br />
<br />
Date: October 6, 2016<br />
<br />
Mass spectrometry is applied in diverse ways in metabolomics research and the mass spectrum of a small molecule can act as a fingerprint for identification. Just as the scientific questions in metabolomics vary, there is a diverse set of mass spectral libraries available to assist in the identification of metabolites and other small molecules. This webinar aims to provide listeners with a brief overview of several different mass spectral<br />
resources, including a personal view on pros and cons of the different options – providing a basis for listeners to choose the resource(s) that may best suit their investigation and needs. Additional information about substance overlap, spectral matching, identification confidence and spectral exchange will also be given – as well as some factors to consider carefully. Finally, some perspectives towards in silico identification without spectral libraries will be given to lead into a topic for a future webinar.<br />
<br />
== [http://metabolomicssociety.org/site-map/articles/88-videos/218-2016-emn-webinars-public Targeted High-throughput Lipidomics Platform: Development and Application] ==<br />
<br />
By: Dr. Peter Meikle<br />
<br />
Date: July 27, 2016<br />
<br />
Lipidomics, full analysis of lipid species and their biological roles with respect to health and diseases, has attracted increasing attention of biological and analytical scientific community. Knowing and understanding steps involved in lipidomics experimental workflow is essential for successful outcome. The Metabolomics Laboratory at Baker IDI Heart and Diabetes Institute (Melbourne, Australia) has a focus on the dyslipidemia and altered lipid metabolism associated with obesity, diabetes and cardiovascular disease and its relationship to the pathogenesis of these disease states. The laboratory has developed a targeted lipidomics platform that is able to quantify over 500 lipid species in 15 minutes using liquid chromatography coupled to electrospray ionisation tandem mass spectrometry. This platform is being applied to identify new approaches to early diagnosis and risk assessment as well as the development of new lipid modulating therapies for chronic disease. With illustration from this well-established lipidomics platform, this webinar will discuss the development of targeted high-throughput lipidomics platform, including selection and characterisation of lipid species, development of chromatography and quality control in the analysis of large sample sets. The presentation will draw on specific examples to highlight the application to large cohort studies and the Institute’s work to develop new therapeutic strategies for cardiovascular disease.<br />
<br />
== [http://metabolomicssociety.org/site-map/articles/88-videos/218-2016-emn-webinars-public From Features to Compounds: How To Get Started and Move Forward]==<br />
<br />
By: Dr. Jan Stanstrup<br />
<br />
Date: May 27, 2016<br />
<br />
In the untargeted analysis of complex mixtures the identification of compounds is the fundamental step enabling the results to be put in biological context. It is therefore of crucial importance for early career scientists approaching the field of metabolomics to familiarize themselves with this step. Many tools have been developed to aid identification; however, compound identification still constitutes one of the main bottlenecks in metabolomics and still requires substantial amounts of manual work. This webinar will go through the basic concepts used in MS-based compound identification and will introduce a number of relevant tools and databases allowing researchers to approach identification in a systematic way. The webinar is in particular dedicated to those researchers coming into the field of metabolomics that are not familiar with the methods used for compound identification and for which getting started can be a daunting and time-consuming challenge.<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/218-2016-emn-webinars-public Chromatography and Metabolomics: What’s the Point?] ==<br />
<br />
By: Dr. Karl Burgess<br />
<br />
Date: April 29, 2016<br />
<br />
The key to both chromatography and mass spectrometry is the separation of chemical species based on their physicochemical properties. As metabolomics researchers, we look to improved chromatography so enable us to<br />
detect compounds we previously had trouble with, to reduce the enormous complexity of the samples we analyse, and to clean up samples before or during analysis. Advances in mass spectrometry bring us greater sensitivity, better selectivity and a toolbox of techniques to aid in identification of biochemicals. With all these advantages come many disadvantages - poor reproducibility, compound bias and contamination. In this webinar, I'll explore chromatography and mass spectrometry with a critical eye. How can we improve them? Do we need them in every experiment? And really, what's the point of metabolomics at all?<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/218-2016-emn-webinars-public How close are we to metabolomics reproducibility? Data sharing, data standards and workflows for metabolomics]==<br />
<br />
By: Dr. Reza Salek<br />
<br />
Date: March 24, 2016<br />
<br />
=2015=<br />
<br />
== [http://metabolomicssociety.org/site-map/articles/88-videos/187-2015-emn-webinars-public Metabolomics and Beyond: Challenges and Strategies for Next-gen Omic Analyses] ==<br />
<br />
By: Dr. Dmitry Grapov<br />
<br />
Date: September 15, 2015<br />
<br />
Robust interpretation of experimental results measuring discreet biological domains remains a significant challenge in the face of complex biochemical regulation processes such as organismal versus tissue versus<br />
cellular metabolism, epigenetics, and protein post-translational modification. Integration of analyses carried out across multiple measurement or omic platforms is an emerging approach to help address these<br />
challenges. The following presentation will focus on key challenges faced by metabolomics researchers in the areas large-scale studies data normalization, multivariate analysis, visualization and omics data integration.<br />
<br />
== [http://metabolomicssociety.org/site-map/articles/88-videos/187-2015-emn-webinars-public Metabolomics with high resolution mass spectrometry: from spectral databases to biomarker discovery] ==<br />
<br />
By: Dr. Christophe Junot<br />
<br />
Date: 12 June 2015<br />
<br />
Since the middle of the 2000s, high resolution mass spectrometry (HRMS) is widely used in metabolomics, mainly because the detection and identification of metabolites are improved compared to low resolution instruments. Thanks to their versatility, HRMS instruments are the most appropriate to achieve an optimal metabolome coverage, at the border of other omics fields such as lipidomics and glycomics. The aim of this talk will be to present HRMS based tools for metabolomics and lipidomics developed at the laboratory, and their relevance to the field of biomarker discovery for the diagnosis and follow-up of pathologies.<br />
<br />
== [http://metabolomicssociety.org/site-map/articles/88-videos/187-2015-emn-webinars-public#|: Metabolomics and systems biology: models as guide and natural integrator]==<br />
<br />
By: Prof. Bas Teusink<br />
<br />
Date: 14 April 2015<br />
<br />
In this webinar, I will discuss the use of mathematical models in guiding targeted and (semi-)untargeted metabolomics efforts. I will show how genome-scale metabolic models can be used as a data integration platform - also for metabolomics data. I will provide an example from medium optimisation in a biotechnological context. My message is that the use of models upfront combined with quantitative and well-timed metabolomics is often much more effective than simply generating lots of data and subsequent statistical analysis.<br />
<br />
== [http://metabolomicssociety.org/site-map/articles/88-videos/187-2015-emn-webinars-public Large-scale, computational and empirical UHPLC-MS-SPE-NMR annotation of plant metabolomes] ==<br />
<br />
By: Dr. Lloyd Sumner<br />
<br />
Date: 5 March 2015<br />
<br />
Integrated metabolomics is a revolutionary systems biology tool for understanding plant metabolism and elucidating gene function. Although the vast utility of metabolomics is well documented in the literature, its<br />
full scientific promise has not yet been realized due to multiple technical challenges. The number one, grand challenge of metabolomics is the large-scale confident chemical identification of metabolites. To address<br />
this challenge, we have developed sophisticated computational and empirical metabolomics tools for the systematic and biological directed annotation of plant metabolomes. This presentation will introduce novel<br />
software entitled Plant Metabolite Annotation Toolbox (PlantMAT) and a sophisticated UHPLC-MS-SPENMR instrumental ensemble that are being used for ‘sequencing’ the first plant metabolomes of the model plant systems Arabidopsis and Medicago truncatula.<br />
<br />
== [http://metabolomicssociety.org/site-map/articles/88-videos/187-2015-emn-webinars-public Metabolomics: only suitable for multidisciplinary teams] ==<br />
<br />
By: Dr. Oscar Yanes<br />
<br />
Date: 29 Jan 2015<br />
<br />
Metabolomics is defined as the comprehensive and quantitative analysis of metabolites in living organisms. Among the omic sciences, metabolomics is possibly the most multidisciplinary of all, involving knowledge<br />
about electronic engineering and signal processing, analytical and organic chemistry, biostatistics and statistical physics, and biochemistry and cell metabolism. Here an untargeted metabolomics workflow will<br />
be detailed that provides examples of this multidisciplinarity.</div>Viniciusverihttp://wiki.metabolomicssociety.org/index.php?title=EMN_Webinars&diff=1561EMN Webinars2021-06-21T06:35:41Z<p>Viniciusveri: </p>
<hr />
<div>The [[Early-Career Members Network#EMN Committee|Early-Career Members Network (EMN) Committee]] has organized webinars since 2015 on a variety of metabolomics-related topics. This page provides a summary of all webinars to date with links to the webinars (when available).<br />
<br />
To participate on the live webinars, follow us on [https://twitter.com/EMN_MetSoc Twitter] and [https://www.facebook.com/EMN.MetabolomicsSociety Facebook] to get all updates from the EMN or subscribe to the Metabolomics Society website to receive the email invitation.<br />
<br />
<br />
=2021=<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/334-2021-emn-webinars-public TidyMS: a tool for preprocessing and Improving data quality in metabolomics]==<br />
<br />
Date: April 27, 2021<br />
<br />
'''TidyMS: a tool for preprocessing and Improving data quality in metabolomics<br />
<br />
By: Dr. María Eugenia Monge<br />
<br />
One of the challenges in untargeted metabolomics workflows is preprocessing data in a reproducible and robust way. In liquid chromatography-mass spectrometry (LC-MS), data curation involves removing biologically non-relevant features (retention time, m/z pairs) and retaining only those analytically robust enough to allow subsequent data analysis and interpretation. In this presentation, I will share the strategies that we used for quality control (QC) purposes in LC-MS-based untargeted studies that interrogated samples from in vitro models and serum samples as well as in an ambient-MS-based approach. In addition, I will introduce “TidyMS”, which is a new Python library for LC-MS data preprocessing that has recently been developed in our research group with the aim of performing QC procedures in a rapid and reproducible workflow. TidyMS includes tools to work with both raw MS data and data matrices. It provides functionality to build customized pipelines for data curation to obtain cleaned matrices from untargeted metabolomics and lipidomics studies for subsequent statistical analysis, and aims at ensuring accuracy and reliability in LC–MS measurements. The capabilities of TidyMS will be illustrated with pipelines for an LC-MS system suitability check, system conditioning, signal drift evaluation, and data curation. These applications were implemented to successfully preprocess data from a new suite of candidate human plasma reference materials currently under development by the National Institute of Standards and Technology (NIST; hypertriglyceridemic, diabetic, and African-American plasma pools), in addition to NIST SRM 1950 Metabolites in Frozen Human Plasma to be used in metabolomics and lipidomics studies. Overall, TidyMS can be used in an automated or semi-automated way, and it is an open and free tool available to all users.<br />
<br />
<br />
'''Improving data quality in metabolomics workflows: A Clear Cell Renal Cell Carcinoma (ccRCC) case study<br />
<br />
By: Mr. Nicolás Zabalegui<br />
<br />
Liquid chromatography-mass spectrometry (LC-MS)-based untargeted metabolomics studies involving biological samples may lead to the detection of tens of thousands of potential metabolic features (retention time, m/z pairs) at initial stages of the workflow. However, data needs to be preprocessed in a reproducible way to remove biologically non-relevant features and thereafter obtain cleaned matrices suitable for subsequent statistical analysis.Kidney cancer is accepted to be a metabolic disease. More than 50% of cases of renal cell carcinoma (RCC) are incidentally diagnosed; being clear cell RCC (ccRCC) the most common histological subtype (75% of cases), and considered a glycolytic and lipogenic tumor. Since the disease is inherently resistant to chemotherapy and radiotherapy, surgery is the most promising treatment for curation when the disease is detected at earlier stages.In this study, serum samples from a cohort that included patients with different ccRCC stages (I, II, III, IV), which were collected before (n=113) and after surgery (n=56), as well as samples from controls (n=52), were interrogated with a discovery-based metabolomics approach using UPLC-QTOF-MS. LC-MS data were preprocessed with TidyMS, a Python package used to retain only high-quality data for subsequent analysis and interpretation. As well, additional experiments were conducted to account for metabolite stability over time and non-linearity in instrumental responses, and were utilized to improve data quality before performing statistical multivariate analysis.<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/334-2021-emn-webinars-public Viral infection in algal blooms and The glycosphingolipid-based arms race]==<br />
<br />
Date: March 22, 2021<br />
<br />
'''Viral infection in algal blooms and The glycosphingolipid-based arms race<br />
<br />
By: Prof. Assaf Vardi & Dr. Guy Schleyer<br />
<br />
In this webinar, we will present how we utilize the recent advances in the field of chemical ecology (metabolomics and mass spectrometry imaging) combined with single-cell imaging and transcriptomic approaches to track host-pathogen interactions at the microscale.<br />
<br />
=2020=<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/306-2020-emn-webinars-public New bio-statistical methods for metabolomics and metabolite annotation]== <br />
<br />
Date: December 22, 2020<br />
<br />
'''New bio-statistical methods for metabolomics<br />
<br />
By: Dr. Daniel Raftery<br />
<br />
Highly complex biological samples present challenging analysis problems for the field of metabolomics. Ideally, platforms that provide broad metabolome coverage and high data quality allow the opportunity for deep insights into biological problems. However, this goal can be difficult to achieve on a routine basis because the highly complex data are subject to matrix effects and complicated correlative relationships among many metabolites. As such metabolite identification, biomarker identification and validation can be very challenging. Advanced statistical methods are needed to deal with these issues for improved biomarker discovery, unknown identification and biological interpretation. We have pursued the development of a number of approaches that try to unravel the complex and multidimensional structure of metabolomics datasets, with some successes and some failures along the way. In this talk, I will provide some examples of where even non-experts in biostatistics can make progress in developing advanced analysis approaches and discuss some areas that provide significant challenges for future work.<br />
<br />
<br />
'''Expanding Automated Metabolite Annotation in Untargeted Metabolomics through Mass Spectral Networks<br />
<br />
By: Dr. Fausto Carnevale Neto<br />
<br />
The major goal of metabolomics is to interrogate complex biological extracts for the purposes of metabolic exploration and biomarker discovery. Liquid chromatography (LC) coupled with tandem mass spectrometry (MS/MS or MS2) is widely accepted as a powerful strategy to explore the chemical constituency of complex mixtures. It offers accurate masses of detected ions and the structural information derived from fragmentation reactions in the gas-phase. While global LC-MS/MS profiling provides the comprehensive measurement of metabolites in complex biological samples, structure annotation remains a challenge, and computational approaches are necessary to translate the molecular composition into biological knowledge. Recently, global natural products social molecular networking (GNPS) has emerged as superb mining tool to assist the interpretation of large MS/MS datasets in the context of metabolomics. It integrates spectral database matching, unsupervised molecular substructure discovery, in silico fragmentation prediction, and automated chemical classification into a network topology. By embedding independent experimental and predictive annotation outputs on to the multi-informative molecular network, we can expand the automated chemical structural annotation within complex metabolic mixtures. <br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/306-2020-emn-webinars-public Metabolic networks]== <br />
<br />
Date: November 26, 2020<br />
<br />
'''Metabolic networks to enrich and interpret metabolic fingerprints<br />
<br />
By: Dr. Fabien Jourdan<br />
<br />
Metabolic modulation is a cornerstone cellular response to genetic or environmental stresses. This plasticity is going beyond central metabolism and may involve complex processes spanning several metabolic pathways. Hence, it is a key challenge to be able to decipher metabolic modulations in a systemic and global perspective.<br />
The aim of the computational methods and tools which will be presented is thus to consider the full complexity of metabolism. To do so, all metabolic reactions the cell is able to achieve are gathered in a single mathematical model call “genome scale metabolic network”. Based on this model, it is then possible to identify metabolic modulations associated to metabolic fingerprints or suggest metabolites of interest to enrich biochemical interpretation.<br />
<br />
<br />
<br />
'''Improving metabolic studies with diverse context-specific metabolic networks<br />
<br />
By: Dr. Pablo Rodriguez Mier<br />
<br />
Understanding deregulations of metabolism based on isolated measures of gene expression, protein or metabolite concentrations is a challenging task due to the interconnection of multiple processes. One solution is to extract, from generic genome-scale metabolic networks, the specific sub-network which is modulated in the studied condition. Many algorithms have been proposed for such context-specific network extraction based on experimental measurements. However, this process is subject to some randomness and variability, since multiple metabolic networks can model the metabolic state in a similarly adequate manner for the same experimental data. This means that for some condition and reconstruction method, there are usually multiple possible sub-networks that can explain the same experimental data, but most current methods ignore this fact and return a single sub-network instead. Ignoring this variability can not only lead to incorrect or incomplete explanations of the biological experiment, but also causes valuable information to be lost that could be used to improve predictions. In this talk we will see what context-specific metabolic sub-networks are, some of the limitations of the current methods, and how we can get a diverse set of sub-networks to improve predictions and obtain better mechanistic insights about the metabolism.<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/306-2020-emn-webinars-public Decomposing Complex Metabolite Mixtures...& Unraveling the neonatal metabolome]== <br />
<br />
Date: June 19, 2020<br />
<br />
'''Decomposing Complex Metabolite Mixtures through Substructure-based Metabolomics Workflows<br />
<br />
By: Justin J.J. van der Hooft<br />
<br />
In this webinar, Dr. Justin J.J. van der Hooft will start with an introduction on the challenges of metabolite annotation and identification in untargeted metabolomics experiments of complex mixtures typically encountered in natural products and food research. He will also show how MS2LDA has been successfully used to capture chemical knowledge from diverse plant, food, and microbial-related data sets. Dr. Justin J.J. van der Hooft will finish by highlighting the advantages of combining metabolome mining and annotation tools in public data of different plant and microbial-related studies.<br />
<br />
<br />
'''Unraveling the neonatal metabolome using mass spectral data mining tools<br />
<br />
By: Madeleine Ernst<br />
<br />
In this webinar, Dr. Madeleine Ernst will explain how mass spectral data mining tools, such as molecular networking through the community platform GNPS, MS2LDA, in silico structure prediction (e.g. Network Annotation Propagation) and ClassyFire can significantly enhance chemical structural annotation retrieved in clinical mass spectrometry-based metabolomics studies. Dr. Madeleine Ernst will also elucidate how metabolic signatures of neonatal health and disease can be unraveled, significantly enhancing biological interpretation and hypothesis generation in metabolomics studies.<br />
<br />
==Plant Metabolomics - Elucidating biochemicals mechanisms underlying plant-environment interactions== <br />
<br />
By: Dr. Fidele Tugizimana<br />
<br />
Date: May 1, 2020<br />
<br />
In this webinar, Dr. Fidele will provide a snapshot of applications of metabolomics in plant sciences, particularly in plant-environment interactions research. The webinar will highlight some examples of the use of metabolomics to elucidate hypothetical frameworks that describe the biochemistry underlying naïve and primed-plant responses to microbial infections. Furthermore, one of the novel (emerging) strategies for sustainable food production and food security is the use of biostimulants in agriculture industry. Application of metabolomics in decoding and understanding plant-biostimulant interactions will be also highlighted.<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/306-2020-emn-webinars-public Human metabolic individuality and its role in multi-omics based studies]== <br />
<br />
By: Dr. Karsten Suhre<br />
<br />
Date: February 4, 2020<br />
<br />
In this webinar, Dr. Suhre will discuss Genome-wide association studies with clinically relevant intermediate traits, such as gene expression, proteomics, and metabolomics, which unravelled numerous pathophysiological pathways and generated many hypotheses regarding the functional basis of complex disorders. More recently, similar approaches linked variation in epigenetic modifications, especially differential methylation of chromosomal CpG-pairs, to changes in gene expression and blood circulating metabolites. These large-scale population and patient cohort studies reflect experimental data obtained from naturally occurring variance of the general population where each individual may be viewed as an experiment conducted by Nature. The next and most challenging step on the way to a truly personalized approach to medicine is to translate the results from these large-scale omics studies to applications at the patient level. In this presentation,<br />
<br />
=2019=<br />
<br />
==Metabolomics as a tool for elucidating plant growth regulation== <br />
<br />
By: Dr. Camila Caldana<br />
Date: November 20, 2019<br />
<br />
Rising demand for food and fuels makes it crucial to develop breeding strategies for increasing crop yield/biomass. Plant biomass production is tightly associated with growth and relies on a tight regulation of a complex signaling network that integrates external and internal stimuli. The main goal of our group is to elucidate the processes underlying plant growth and production of biomass by combining physiology, metabolomics, and gene expression analyses. In my presentation, I will provide examples of i) how the evolutionary conserved Target of Rapamycin pathway fine-tunes metabolic homeostasis to promote biosynthetic growth in plants; ii) the potential of metabolite profiles to predict plant performance as biomarkers.<br />
<br />
==Discovering Metabolites that Alter Physiology, an Omics Perspective== <br />
<br />
By: Dr. Gary Siuzdak<br />
Date: September 18, 2019<br />
<br />
Metabolomics and the comprehensive analysis of the metabolome and lipidome has traditionally been pursued with the aim of identifying biomarkers in the diagnosis and prediction of disease. However, the value of metabolomics has been redefined from a simple biomarker identification tool to a technology for the discovery of active drivers of biological processes. It is now clear that the metabolome affects cellular physiology through modulation of other ‘omics’ levels, including the genome, epigenome, transcriptome and proteome. In this presentation, I will focus on our recent progress in using metabolomics to understand how the metabolome influences other omics and, by extension, to reveal the active role of metabolites in physiology and disease. This concept of utilizing metabolomics to perform activity screens to identify biologically active metabolites — which we term activity metabolomics — is already having a broad impact on biology.<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/298-2019-emn-webinars-public Lipidomics and epilipidomics – from analysis to data integration]== <br />
<br />
By: Dr. Maria Fedorova<br />
Date: July 17, 2019<br />
<br />
Lipidomics is a large-scale study of diversified molecular species of lipids aiming to address the identity, quantities, cellular and tissue distribution of lipids as well as related signalling and metabolic pathways. In addition to the native lipidome, lipid species derived from enzymatic and non-enzymatic modifications (the epilipidome) further increase regulatory capacity of the biological systems. High diversity of physico-chemical properties as well as large dynamic range of lipid concentrations in native lipidomes makes significantly challenge their analysis. For omics-wide high-throughput identification of lipid species from complex biological samples, several crucial analytical steps including extraction, chromatographic separation and mass spectrometry analysis need to be carefully considered and validated. The webinar will review current analytical strategies used in contemporary lipidomics and epilipidomics with the focus on optimization of LC-MS/MS based workflows for “discovery” lipidomics including sample preparation, lipid fractionation, separation using different chromatography techniques and high-throughput identification solutions. Available bioinformatics tools for identification of native and modified lipids will be described and compared as well as possible lipidomics data integration strategies.<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/298-2019-emn-webinars-public Metabolomics 2019: Professional Career Development: The Survival Kit]== <br />
<br />
By: Cathy Delhanty<br />
<br />
Date: June 23, 2019<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/298-2019-emn-webinars-public A Combined NMR and MS Metabolomics Approach to Study Neurodegenerative Diseases]== <br />
<br />
By: Dr. Robert Powers<br />
<br />
Date: May 24, 2019<br />
<br />
The metabolome captures how the system responds to drug treatment, disease state, or genetic modification. In this regards, metabolomics is an invaluable approach to easily and rapidly diagnose human disease and to assist in personalized medicine by monitoring a patient’s response to treatments. But, metabolomics is deceivingly complex with numerous sources of errors and technical challenges at every step of the process. One specific challenge is achieving a complete and accurate coverage of the metabolome, which can be addressed by combining NMR and mass spectrometry. Our metabolomics protocols and MVAPACK software for integrating NMR and mass spectral data for the analysis of neurodegenerative disease will be discussed. Our investigation into the molecular mechanisms of Parkinson’s disease and the identification of biomarkers for multiple sclerosis will be highlighted.<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/298-2019-emn-webinars-public Computational mass spectrometry in metabolomics to deepen the understanding of metabolisms]== <br />
<br />
By: Dr. Hiroshi Tsugawa<br />
<br />
Date: April 23, 2019<br />
<br />
Computational mass spectrometry is a growing research field to process mass spectrometry data, assist the interpretation of mass fragmentations, and elucidate unknown structures with metabolome databases and repositories for the global identification of metabolomes in various living organisms. In this talk, Dr Tsugawa will introduce three metabolomics software programs which include (1) MS-DIAL for untargeted metabolomics, (2) MS-FINDER for structure elucidations of unknowns, and (3) MRMPROBS for targeted metabolomics. These programs are demonstrated to perform the comprehensive analyses of primary metabolites, lipids, and plant specialized metabolites where unknown metabolites are also untangled with various methodologies including stable isotope labeled organisms, metabolite class recommendations, and integrated metabolome network analyses. In addition, a computational workflow to link untargeted- and targeted metabolomics is also highlighted in this talk.<br />
<br />
==Multidimensional Chromatography, what is the synergy with untargeted metabolomics profiling?== <br />
<br />
By: Dr. Pierre-Hugues Stefanuto<br />
<br />
Date: March 28, 2019<br />
<br />
In this webinar, Dr Pierre-Hugues Stefanuto will discuss the new development of multidimensional chromatography and the synergy with metabolomics. This presentation will be broadcasted in the context of the Multidimensional Chromatography Workshop held in Liège last January (http://multidimensionalchromatography.com). During this event, four focus group discussions were organized: 1) data processing for untargeted screening, 2) minimum reporting information for QC and compound validation, 3) hyphenation of MDGC with high-resolution MS, 4) and the general acceptance of MDC techniques. He will illustrate these topics through some ongoing medical research articulated around volatile organic compound (VOC) measurements in human breath and in vitro in metabolomic applications.<br />
<br />
==Untargeted metabolomics reveals smokers' characteristic profiles== <br />
<br />
By: Dr. Ping-Ching Hsu<br />
<br />
Date: March 1, 2019<br />
<br />
=2018=<br />
<br />
==Recent advances towards integrating metabolomics in kinetic models of large metabolic networks== <br />
<br />
By: Prof. Lars Nielsen<br />
<br />
Date: October 15, 2018<br />
<br />
Lars Nielsen is Chair of Biological Engineering at the Australian Institute for Bioengineering &Nanotechnology and Scientific Director for the Section for Quantitative Modelling of Cellular Metabolism at the Novo Nordisk Foundation Center for Biosustainability in Denmark. He is Director of the Bioplatforms Australia Queensland Node for Metabolomics and Proteomics, which provides systems and synthetic biology support to design and build cell factories for the production of fuels, chemicals and pharmaceuticals. His core research interest is modelling of cellular metabolism and his team has made many contributions to the formulation and use of genome scale models. He recently received a Novo Nordisk Foundation Laureate Research Grant to develop large scale, mathematical models to explore and explain the molecular basis for homeostasis–the self-regulating processes evolved to maintain metabolic equilibrium. Studying homeostasis is relevant for the understanding and treatment of complex diseases, particular with the emergence of personalized medicine. It is equally important when we seek to repurpose the cellular machinery for the production of desired chemicals, materials and pharmaceuticals.<br />
<br />
==Metabolomics-based Elucidation of Plant Specialized Metabolism== <br />
<br />
By: Prof. Kazuki Saito<br />
<br />
Date: July 25, 2018<br />
<br />
The recent advances of genomics and metabolomics in plant science accelerate our understanding about the mechanism, regulation and evolution of biosynthesis of plant specialized products. We can now address the questions how the metabolomic diversity of plants is originated at the levels of genome (phytochemical genomics) and how we should apply this knowledge to drug discovery, industry and agriculture. In this presentation, at first, technological developments of metabolomic analysis will be discussed forthe better understanding chemical diversity of plants. Then, a couple of examples of application of metabolomics to functional genomics of specialized metabolism in a model plant Arabidopsis thalianawill be presented, focusing on the biosynthesis of phenylpropanoids and lipids. The further extension to crops and medicinal plants producing a variety of specialized metabolites will be presented.<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/277-2018-emn-webinars-public Activity Metabolomics: Technologies to identify metabolites that modulate phenotype]== <br />
<br />
By: Prof. Gary Siuzdak<br />
<br />
Date: May 29, 2018<br />
<br />
Metabolomics is broadly acknowledged to be the omics discipline closest tothe phenotype and therefore widely used for biomarker discovery. However,metabolomics can also be designed to identify active metabolites thatalter a cell’s or an organism's phenotype.This "Activity Metabolomics”concept integrates metabolomics data analysis with pathway and systemsbiology data, ultimately to select endogenous metabolites that can bescreened for functionality. A growing literature reports the use ofmetabolites to modulate diverse processes including stem celldifferentiation, oligodendrocytematuration, insulin signaling,T-cellsurvival and macrophage immune responses. We have developed XCMS Online(xcmsonline.scripps.edu) and the newly expanded METLIN database (now withover 50,000 standards containing MS/MS data) to perform untargeted andtargeted metabolomics, as well as pathway analysis and systems biologydata integration.Metabolomics Activity Screening (MAS) has beenimplemented within XCMS Online to help achieve this integration goal foridentifying active metabolites. Because metabolites are often readilyavailable, activity metabolomics is uniquely positioning its practitionersto move beyond biomarkers, and become active participants in thebiological endgame of modulating phenotype. (for more information seeNature Biotechnology 2018 nature.com/articles/nbt.4101)<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/277-2018-emn-webinars-public Use better software: MS-DIAL instead of XCMS, ChemRich/MetDA instead of Metaboanalyst]== <br />
<br />
By: Dr. Oliver Fiehn<br />
<br />
Date: April 24, 2018<br />
<br />
XCMS and MetaboAnalystare the two most popular tools used by metabolomics researchers. But are these the best options? The West Coast Metabolomics Center at UC Davis has collaborated with RIKEN/NGI in Japan to release MS-DIAL vs.2 that yields far fewer false positive peak detections in untargeted LC-MS/MS runs than XCMS, with superior integration of compound identification software. MS-DIAL now also works on low or high resolution GC-MS data, making it the tool of choice for raw data processing of any mass spectrometry-based metabolomics study.We have also developed alternative software suites for statistical analysis of final result data. ChemRich uses all identified metabolites, including complex lipids, for set enrichment statistics. In comparison, MetaboAnalyst cannot perform pathway enrichment statistics on more than half of all identified metabolites, because it relies on KEGG pathways only. Moreover, we have developed new software for improved data normalization and statistics workflows in MetDA web-based analyses that will be presented on published example data.<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/277-2018-emn-webinars-public Can we capture an accurate view of cell or tissue specific metabolism from an expression profile?]== <br />
<br />
By: Dr. Nathan Lewis<br />
<br />
Date: March 26, 2018<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/277-2018-emn-webinars-public Metabolomics as a hypothesis generator for understanding metabolic regulation]== <br />
<br />
By: Prof. Uwe Sauer<br />
<br />
Date: February 14, 2018<br />
<br />
Prof. Uwe Sauer focuses on two conceptual problems: i) discovery of functionally important regulation mechanisms and ii) understanding which of the many known mechnisns actually matter for a given adaption. On the discovery side, he illustrates the use of coarse-grained kinetic models to extract mechnistic hypotheses from dynamic metabolomics data. For learning the coordination mechanisms, he presents an approach that hypothesizes the dynamically important mechnism from the much fewer steady state measurements in the bacterium E. coli. The surprising result is that only very few regulation events appear to be required for a given transition, typically involving less than a handful of active regulators.<br />
=2017=<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/258-2017-emn-webinars-public Introduction to Stable Isotope Resolved Metabolomics (SIRM) and Applications to Cancer Metabolism]==<br />
<br />
By: Dr. Andrew Lane<br />
<br />
Date: December 14, 2017<br />
<br />
Stable isotope resolved metabolomics (SIRM), for pathway tracing, represents an important new approach to obtaining metabolic parameters. SIRM allows the generation of atomic fate maps in cells and tissues, which provides the necessary information and data for metabolic flux analyis. This powerful new approach has already provided important new insights into metabolic adaptations in lung cancer cells.<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/258-2017-emn-webinars-public Using PhenoMeNal for your metabolomics data analysis on the cloud]==<br />
<br />
By: Dr. Pablo Moreno<br />
<br />
Date: October 24, 2017<br />
<br />
PhenoMeNal aims to bridge the gap between cloud computing and metabolomics researchers by providing the ability to create Cloud Research Environments (CRE) for metabolomics data analysis. A PhenoMeNal CRE is a small cluster of computers with popular metabolomics data analysis tools already installed. These tools are ready to be run and are accessible through a user friendly Galaxy workflow environment reducing the need for in-house bioinformatics. The PhenoMeNal CRE not only includes data analysis tools, but also example workflows where some of these tools are used together. You can also make your own workflows inside the CRE. In this webinar we will explain the main components of PhenoMeNal. We will demonstrate how to register, access existing tools and workflows, create a new PhenoMeNal CRE on Amazon, and execute a workflow on a PhenoMeNal CRE.<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/258-2017-emn-webinars-public Machine learning powered metabolomic network analysis]==<br />
<br />
By: Dr. Dmitry Grapov <br />
<br />
Date: May 30, 2017<br />
<br />
Metabolomic network analysis can be used to interpret experimental results within a variety of contexts including: biochemical relationships, structural and spectral similarity and empirical correlation. Machine learning is useful for modeling relationships in the context of pattern recognition, clustering, classification and regression based predictive modeling. The combination of developed metabolomic networks and machine learning based predictive models offer a unique method to visualize empirical relationships while testing key experimental hypotheses. The following presentation focuses on data analysis, visualization, machine learning and network mapping approaches used to create richly mapped metabolomic networks.<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/258-2017-emn-webinars-public How well do I quantify? Concepts for method validation and evaluation of measurement uncertainty in metabolomics]==<br />
<br />
By: Assoc. Prof. Stephan Hann<br />
<br />
Date: March 24, 2017<br />
<br />
This webinar will give an introduction to the basic terminology and principles of validation and measurement uncertainty in metabolomics. It will be demonstrated how validation parameters are determined in selected examples (e.g. LC-MS/MS, GC-MS/MS) for quantitative metabolomic analysis. Different quantification approaches will be overviewed in detail, and tips on choosing the most appropriate analytical strategies to answer metabolome-level hypotheses will be given. Finally, a workflow for estimating measurement uncertainty in metabolomics will be presented and discussed.<br />
<br />
=2016=<br />
<br />
== [http://metabolomicssociety.org/site-map/articles/88-videos/218-2016-emn-webinars-public Smarter ways to clean LC-MS] ==<br />
<br />
By: Assoc. Prof. Carl Brunius<br />
<br />
Date: November 17, 2016<br />
<br />
LC-MS is the most frequently used technique for untargeted metabolomics owing to broad coverage of metabolites, high sensitivity and simple sample preparation. However, instrument data tends to have high noise contribution from drift in signal intensity, mass accuracy and retention times. This noise has both within batch and between batch contributions and results in reduced measurement repeatability and reproducibility. The power to detect biological responses may thus be decreased and interpretations consequently obscured. Dr. Carl Brunius (the speaker) is involved in developing procedures to address and correct for within- and between-batch variability in processing multiple-batch untargeted LC-MS metabolomics data to increase their quality. This webinar session will talk about (i) alignment and merging of LC-MS features that are systematically misaligned between batches and (ii) within batch intensity drift correction that allows multiple drift patterns within batch. These algorithms will be applied on authentic data, resulting in improved peak picking performance and decreased noise in the dataset. All algorithms are developed as open source and are, together with example data, freely available as an R package from https://GitLab.com/CarlBrunius/batchCorr.<br />
== [http://metabolomicssociety.org/site-map/articles/88-videos/218-2016-emn-webinars-public Mass Spectral Libraries] ==<br />
<br />
By: Dr. Emma Schymanski<br />
<br />
Date: October 6, 2016<br />
<br />
Mass spectrometry is applied in diverse ways in metabolomics research and the mass spectrum of a small molecule can act as a fingerprint for identification. Just as the scientific questions in metabolomics vary, there is a diverse set of mass spectral libraries available to assist in the identification of metabolites and other small molecules. This webinar aims to provide listeners with a brief overview of several different mass spectral<br />
resources, including a personal view on pros and cons of the different options – providing a basis for listeners to choose the resource(s) that may best suit their investigation and needs. Additional information about substance overlap, spectral matching, identification confidence and spectral exchange will also be given – as well as some factors to consider carefully. Finally, some perspectives towards in silico identification without spectral libraries will be given to lead into a topic for a future webinar.<br />
<br />
== [http://metabolomicssociety.org/site-map/articles/88-videos/218-2016-emn-webinars-public Targeted High-throughput Lipidomics Platform: Development and Application] ==<br />
<br />
By: Dr. Peter Meikle<br />
<br />
Date: July 27, 2016<br />
<br />
Lipidomics, full analysis of lipid species and their biological roles with respect to health and diseases, has attracted increasing attention of biological and analytical scientific community. Knowing and understanding steps involved in lipidomics experimental workflow is essential for successful outcome. The Metabolomics Laboratory at Baker IDI Heart and Diabetes Institute (Melbourne, Australia) has a focus on the dyslipidemia and altered lipid metabolism associated with obesity, diabetes and cardiovascular disease and its relationship to the pathogenesis of these disease states. The laboratory has developed a targeted lipidomics platform that is able to quantify over 500 lipid species in 15 minutes using liquid chromatography coupled to electrospray ionisation tandem mass spectrometry. This platform is being applied to identify new approaches to early diagnosis and risk assessment as well as the development of new lipid modulating therapies for chronic disease. With illustration from this well-established lipidomics platform, this webinar will discuss the development of targeted high-throughput lipidomics platform, including selection and characterisation of lipid species, development of chromatography and quality control in the analysis of large sample sets. The presentation will draw on specific examples to highlight the application to large cohort studies and the Institute’s work to develop new therapeutic strategies for cardiovascular disease.<br />
<br />
== [http://metabolomicssociety.org/site-map/articles/88-videos/218-2016-emn-webinars-public From Features to Compounds: How To Get Started and Move Forward]==<br />
<br />
By: Dr. Jan Stanstrup<br />
<br />
Date: May 27, 2016<br />
<br />
In the untargeted analysis of complex mixtures the identification of compounds is the fundamental step enabling the results to be put in biological context. It is therefore of crucial importance for early career scientists approaching the field of metabolomics to familiarize themselves with this step. Many tools have been developed to aid identification; however, compound identification still constitutes one of the main bottlenecks in metabolomics and still requires substantial amounts of manual work. This webinar will go through the basic concepts used in MS-based compound identification and will introduce a number of relevant tools and databases allowing researchers to approach identification in a systematic way. The webinar is in particular dedicated to those researchers coming into the field of metabolomics that are not familiar with the methods used for compound identification and for which getting started can be a daunting and time-consuming challenge.<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/218-2016-emn-webinars-public Chromatography and Metabolomics: What’s the Point?] ==<br />
<br />
By: Dr. Karl Burgess<br />
<br />
Date: April 29, 2016<br />
<br />
The key to both chromatography and mass spectrometry is the separation of chemical species based on their physicochemical properties. As metabolomics researchers, we look to improved chromatography so enable us to<br />
detect compounds we previously had trouble with, to reduce the enormous complexity of the samples we analyse, and to clean up samples before or during analysis. Advances in mass spectrometry bring us greater sensitivity, better selectivity and a toolbox of techniques to aid in identification of biochemicals. With all these advantages come many disadvantages - poor reproducibility, compound bias and contamination. In this webinar, I'll explore chromatography and mass spectrometry with a critical eye. How can we improve them? Do we need them in every experiment? And really, what's the point of metabolomics at all?<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/218-2016-emn-webinars-public How close are we to metabolomics reproducibility? Data sharing, data standards and workflows for metabolomics]==<br />
<br />
By: Dr. Reza Salek<br />
<br />
Date: March 24, 2016<br />
<br />
=2015=<br />
<br />
== [http://metabolomicssociety.org/site-map/articles/88-videos/187-2015-emn-webinars-public Metabolomics and Beyond: Challenges and Strategies for Next-gen Omic Analyses] ==<br />
<br />
By: Dr. Dmitry Grapov<br />
<br />
Date: September 15, 2015<br />
<br />
Robust interpretation of experimental results measuring discreet biological domains remains a significant challenge in the face of complex biochemical regulation processes such as organismal versus tissue versus<br />
cellular metabolism, epigenetics, and protein post-translational modification. Integration of analyses carried out across multiple measurement or omic platforms is an emerging approach to help address these<br />
challenges. The following presentation will focus on key challenges faced by metabolomics researchers in the areas large-scale studies data normalization, multivariate analysis, visualization and omics data integration.<br />
<br />
== [http://metabolomicssociety.org/site-map/articles/88-videos/187-2015-emn-webinars-public Metabolomics with high resolution mass spectrometry: from spectral databases to biomarker discovery] ==<br />
<br />
By: Dr. Christophe Junot<br />
<br />
Date: 12 June 2015<br />
<br />
Since the middle of the 2000s, high resolution mass spectrometry (HRMS) is widely used in metabolomics, mainly because the detection and identification of metabolites are improved compared to low resolution instruments. Thanks to their versatility, HRMS instruments are the most appropriate to achieve an optimal metabolome coverage, at the border of other omics fields such as lipidomics and glycomics. The aim of this talk will be to present HRMS based tools for metabolomics and lipidomics developed at the laboratory, and their relevance to the field of biomarker discovery for the diagnosis and follow-up of pathologies.<br />
<br />
== [http://metabolomicssociety.org/site-map/articles/88-videos/187-2015-emn-webinars-public#|: Metabolomics and systems biology: models as guide and natural integrator]==<br />
<br />
By: Prof. Bas Teusink<br />
<br />
Date: 14 April 2015<br />
<br />
In this webinar, I will discuss the use of mathematical models in guiding targeted and (semi-)untargeted metabolomics efforts. I will show how genome-scale metabolic models can be used as a data integration platform - also for metabolomics data. I will provide an example from medium optimisation in a biotechnological context. My message is that the use of models upfront combined with quantitative and well-timed metabolomics is often much more effective than simply generating lots of data and subsequent statistical analysis.<br />
<br />
== [http://metabolomicssociety.org/site-map/articles/88-videos/187-2015-emn-webinars-public Large-scale, computational and empirical UHPLC-MS-SPE-NMR annotation of plant metabolomes] ==<br />
<br />
By: Dr. Lloyd Sumner<br />
<br />
Date: 5 March 2015<br />
<br />
Integrated metabolomics is a revolutionary systems biology tool for understanding plant metabolism and elucidating gene function. Although the vast utility of metabolomics is well documented in the literature, its<br />
full scientific promise has not yet been realized due to multiple technical challenges. The number one, grand challenge of metabolomics is the large-scale confident chemical identification of metabolites. To address<br />
this challenge, we have developed sophisticated computational and empirical metabolomics tools for the systematic and biological directed annotation of plant metabolomes. This presentation will introduce novel<br />
software entitled Plant Metabolite Annotation Toolbox (PlantMAT) and a sophisticated UHPLC-MS-SPENMR instrumental ensemble that are being used for ‘sequencing’ the first plant metabolomes of the model plant systems Arabidopsis and Medicago truncatula.<br />
<br />
== [http://metabolomicssociety.org/site-map/articles/88-videos/187-2015-emn-webinars-public Metabolomics: only suitable for multidisciplinary teams] ==<br />
<br />
By: Dr. Oscar Yanes<br />
<br />
Date: 29 Jan 2015<br />
<br />
Metabolomics is defined as the comprehensive and quantitative analysis of metabolites in living organisms. Among the omic sciences, metabolomics is possibly the most multidisciplinary of all, involving knowledge<br />
about electronic engineering and signal processing, analytical and organic chemistry, biostatistics and statistical physics, and biochemistry and cell metabolism. Here an untargeted metabolomics workflow will<br />
be detailed that provides examples of this multidisciplinarity.</div>Viniciusverihttp://wiki.metabolomicssociety.org/index.php?title=EMN_Webinars&diff=1560EMN Webinars2021-06-21T06:34:32Z<p>Viniciusveri: </p>
<hr />
<div>The [[Early-Career Members Network#EMN Committee|Early-Career Members Network (EMN) Committee]] has organized webinars since 2015 on a variety of metabolomics-related topics. This page provides a summary of all webinars to date with links to the webinars (when available).<br />
<br />
To participate on the live webinars, follow us on [https://twitter.com/EMN_MetSoc Twitter] and [https://www.facebook.com/EMN.MetabolomicsSociety Facebook] to get all updates from the EMN or subscribe to the Metabolomics Society website to receive the email invitation.<br />
<br />
<br />
=2021=<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/334-2021-emn-webinars-public TidyMS: a tool for preprocessing and Improving data quality in metabolomics]==<br />
<br />
Date: April 27, 2021<br />
<br />
'''TidyMS: a tool for preprocessing and Improving data quality in metabolomics<br />
<br />
By: Dr. María Eugenia Monge<br />
<br />
One of the challenges in untargeted metabolomics workflows is preprocessing data in a reproducible and robust way. In liquid chromatography-mass spectrometry (LC-MS), data curation involves removing biologically non-relevant features (retention time, m/z pairs) and retaining only those analytically robust enough to allow subsequent data analysis and interpretation. In this presentation, I will share the strategies that we used for quality control (QC) purposes in LC-MS-based untargeted studies that interrogated samples from in vitro models and serum samples as well as in an ambient-MS-based approach. In addition, I will introduce “TidyMS”, which is a new Python library for LC-MS data preprocessing that has recently been developed in our research group with the aim of performing QC procedures in a rapid and reproducible workflow. TidyMS includes tools to work with both raw MS data and data matrices. It provides functionality to build customized pipelines for data curation to obtain cleaned matrices from untargeted metabolomics and lipidomics studies for subsequent statistical analysis, and aims at ensuring accuracy and reliability in LC–MS measurements. The capabilities of TidyMS will be illustrated with pipelines for an LC-MS system suitability check, system conditioning, signal drift evaluation, and data curation. These applications were implemented to successfully preprocess data from a new suite of candidate human plasma reference materials currently under development by the National Institute of Standards and Technology (NIST; hypertriglyceridemic, diabetic, and African-American plasma pools), in addition to NIST SRM 1950 Metabolites in Frozen Human Plasma to be used in metabolomics and lipidomics studies. Overall, TidyMS can be used in an automated or semi-automated way, and it is an open and free tool available to all users.<br />
<br />
'''Improving data quality in metabolomics workflows: A Clear Cell Renal Cell Carcinoma (ccRCC) case study<br />
<br />
By: Mr. Nicolás Zabalegui<br />
<br />
Liquid chromatography-mass spectrometry (LC-MS)-based untargeted metabolomics studies involving biological samples may lead to the detection of tens of thousands of potential metabolic features (retention time, m/z pairs) at initial stages of the workflow. However, data needs to be preprocessed in a reproducible way to remove biologically non-relevant features and thereafter obtain cleaned matrices suitable for subsequent statistical analysis.Kidney cancer is accepted to be a metabolic disease. More than 50% of cases of renal cell carcinoma (RCC) are incidentally diagnosed; being clear cell RCC (ccRCC) the most common histological subtype (75% of cases), and considered a glycolytic and lipogenic tumor. Since the disease is inherently resistant to chemotherapy and radiotherapy, surgery is the most promising treatment for curation when the disease is detected at earlier stages.In this study, serum samples from a cohort that included patients with different ccRCC stages (I, II, III, IV), which were collected before (n=113) and after surgery (n=56), as well as samples from controls (n=52), were interrogated with a discovery-based metabolomics approach using UPLC-QTOF-MS. LC-MS data were preprocessed with TidyMS, a Python package used to retain only high-quality data for subsequent analysis and interpretation. As well, additional experiments were conducted to account for metabolite stability over time and non-linearity in instrumental responses, and were utilized to improve data quality before performing statistical multivariate analysis.<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/334-2021-emn-webinars-public Viral infection in algal blooms and The glycosphingolipid-based arms race]==<br />
<br />
Date: March 22, 2021<br />
<br />
'''Viral infection in algal blooms and The glycosphingolipid-based arms race<br />
<br />
By: Prof. Assaf Vardi & Dr. Guy Schleyer<br />
<br />
<br />
=2020=<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/306-2020-emn-webinars-public New bio-statistical methods for metabolomics and metabolite annotation]== <br />
<br />
Date: December 22, 2020<br />
<br />
'''New bio-statistical methods for metabolomics<br />
<br />
By: Dr. Daniel Raftery<br />
<br />
Highly complex biological samples present challenging analysis problems for the field of metabolomics. Ideally, platforms that provide broad metabolome coverage and high data quality allow the opportunity for deep insights into biological problems. However, this goal can be difficult to achieve on a routine basis because the highly complex data are subject to matrix effects and complicated correlative relationships among many metabolites. As such metabolite identification, biomarker identification and validation can be very challenging. Advanced statistical methods are needed to deal with these issues for improved biomarker discovery, unknown identification and biological interpretation. We have pursued the development of a number of approaches that try to unravel the complex and multidimensional structure of metabolomics datasets, with some successes and some failures along the way. In this talk, I will provide some examples of where even non-experts in biostatistics can make progress in developing advanced analysis approaches and discuss some areas that provide significant challenges for future work.<br />
<br />
'''Expanding Automated Metabolite Annotation in Untargeted Metabolomics through Mass Spectral Networks<br />
<br />
By: Dr. Fausto Carnevale Neto<br />
<br />
The major goal of metabolomics is to interrogate complex biological extracts for the purposes of metabolic exploration and biomarker discovery. Liquid chromatography (LC) coupled with tandem mass spectrometry (MS/MS or MS2) is widely accepted as a powerful strategy to explore the chemical constituency of complex mixtures. It offers accurate masses of detected ions and the structural information derived from fragmentation reactions in the gas-phase. While global LC-MS/MS profiling provides the comprehensive measurement of metabolites in complex biological samples, structure annotation remains a challenge, and computational approaches are necessary to translate the molecular composition into biological knowledge. Recently, global natural products social molecular networking (GNPS) has emerged as superb mining tool to assist the interpretation of large MS/MS datasets in the context of metabolomics. It integrates spectral database matching, unsupervised molecular substructure discovery, in silico fragmentation prediction, and automated chemical classification into a network topology. By embedding independent experimental and predictive annotation outputs on to the multi-informative molecular network, we can expand the automated chemical structural annotation within complex metabolic mixtures. <br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/306-2020-emn-webinars-public Metabolic networks]== <br />
<br />
Date: November 26, 2020<br />
<br />
'''Metabolic networks to enrich and interpret metabolic fingerprints<br />
<br />
By: Dr. Fabien Jourdan<br />
<br />
Metabolic modulation is a cornerstone cellular response to genetic or environmental stresses. This plasticity is going beyond central metabolism and may involve complex processes spanning several metabolic pathways. Hence, it is a key challenge to be able to decipher metabolic modulations in a systemic and global perspective.<br />
The aim of the computational methods and tools which will be presented is thus to consider the full complexity of metabolism. To do so, all metabolic reactions the cell is able to achieve are gathered in a single mathematical model call “genome scale metabolic network”. Based on this model, it is then possible to identify metabolic modulations associated to metabolic fingerprints or suggest metabolites of interest to enrich biochemical interpretation.<br />
<br />
<br />
<br />
'''Improving metabolic studies with diverse context-specific metabolic networks<br />
<br />
By: Dr. Pablo Rodriguez Mier<br />
<br />
Understanding deregulations of metabolism based on isolated measures of gene expression, protein or metabolite concentrations is a challenging task due to the interconnection of multiple processes. One solution is to extract, from generic genome-scale metabolic networks, the specific sub-network which is modulated in the studied condition. Many algorithms have been proposed for such context-specific network extraction based on experimental measurements. However, this process is subject to some randomness and variability, since multiple metabolic networks can model the metabolic state in a similarly adequate manner for the same experimental data. This means that for some condition and reconstruction method, there are usually multiple possible sub-networks that can explain the same experimental data, but most current methods ignore this fact and return a single sub-network instead. Ignoring this variability can not only lead to incorrect or incomplete explanations of the biological experiment, but also causes valuable information to be lost that could be used to improve predictions. In this talk we will see what context-specific metabolic sub-networks are, some of the limitations of the current methods, and how we can get a diverse set of sub-networks to improve predictions and obtain better mechanistic insights about the metabolism.<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/306-2020-emn-webinars-public Decomposing Complex Metabolite Mixtures...& Unraveling the neonatal metabolome]== <br />
<br />
Date: June 19, 2020<br />
<br />
'''Decomposing Complex Metabolite Mixtures through Substructure-based Metabolomics Workflows<br />
<br />
By: Justin J.J. van der Hooft<br />
<br />
In this webinar, Dr. Justin J.J. van der Hooft will start with an introduction on the challenges of metabolite annotation and identification in untargeted metabolomics experiments of complex mixtures typically encountered in natural products and food research. He will also show how MS2LDA has been successfully used to capture chemical knowledge from diverse plant, food, and microbial-related data sets. Dr. Justin J.J. van der Hooft will finish by highlighting the advantages of combining metabolome mining and annotation tools in public data of different plant and microbial-related studies.<br />
<br />
<br />
'''Unraveling the neonatal metabolome using mass spectral data mining tools<br />
<br />
By: Madeleine Ernst<br />
<br />
In this webinar, Dr. Madeleine Ernst will explain how mass spectral data mining tools, such as molecular networking through the community platform GNPS, MS2LDA, in silico structure prediction (e.g. Network Annotation Propagation) and ClassyFire can significantly enhance chemical structural annotation retrieved in clinical mass spectrometry-based metabolomics studies. Dr. Madeleine Ernst will also elucidate how metabolic signatures of neonatal health and disease can be unraveled, significantly enhancing biological interpretation and hypothesis generation in metabolomics studies.<br />
<br />
==Plant Metabolomics - Elucidating biochemicals mechanisms underlying plant-environment interactions== <br />
<br />
By: Dr. Fidele Tugizimana<br />
<br />
Date: May 1, 2020<br />
<br />
In this webinar, Dr. Fidele will provide a snapshot of applications of metabolomics in plant sciences, particularly in plant-environment interactions research. The webinar will highlight some examples of the use of metabolomics to elucidate hypothetical frameworks that describe the biochemistry underlying naïve and primed-plant responses to microbial infections. Furthermore, one of the novel (emerging) strategies for sustainable food production and food security is the use of biostimulants in agriculture industry. Application of metabolomics in decoding and understanding plant-biostimulant interactions will be also highlighted.<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/306-2020-emn-webinars-public Human metabolic individuality and its role in multi-omics based studies]== <br />
<br />
By: Dr. Karsten Suhre<br />
<br />
Date: February 4, 2020<br />
<br />
In this webinar, Dr. Suhre will discuss Genome-wide association studies with clinically relevant intermediate traits, such as gene expression, proteomics, and metabolomics, which unravelled numerous pathophysiological pathways and generated many hypotheses regarding the functional basis of complex disorders. More recently, similar approaches linked variation in epigenetic modifications, especially differential methylation of chromosomal CpG-pairs, to changes in gene expression and blood circulating metabolites. These large-scale population and patient cohort studies reflect experimental data obtained from naturally occurring variance of the general population where each individual may be viewed as an experiment conducted by Nature. The next and most challenging step on the way to a truly personalized approach to medicine is to translate the results from these large-scale omics studies to applications at the patient level. In this presentation,<br />
<br />
=2019=<br />
<br />
==Metabolomics as a tool for elucidating plant growth regulation== <br />
<br />
By: Dr. Camila Caldana<br />
Date: November 20, 2019<br />
<br />
Rising demand for food and fuels makes it crucial to develop breeding strategies for increasing crop yield/biomass. Plant biomass production is tightly associated with growth and relies on a tight regulation of a complex signaling network that integrates external and internal stimuli. The main goal of our group is to elucidate the processes underlying plant growth and production of biomass by combining physiology, metabolomics, and gene expression analyses. In my presentation, I will provide examples of i) how the evolutionary conserved Target of Rapamycin pathway fine-tunes metabolic homeostasis to promote biosynthetic growth in plants; ii) the potential of metabolite profiles to predict plant performance as biomarkers.<br />
<br />
==Discovering Metabolites that Alter Physiology, an Omics Perspective== <br />
<br />
By: Dr. Gary Siuzdak<br />
Date: September 18, 2019<br />
<br />
Metabolomics and the comprehensive analysis of the metabolome and lipidome has traditionally been pursued with the aim of identifying biomarkers in the diagnosis and prediction of disease. However, the value of metabolomics has been redefined from a simple biomarker identification tool to a technology for the discovery of active drivers of biological processes. It is now clear that the metabolome affects cellular physiology through modulation of other ‘omics’ levels, including the genome, epigenome, transcriptome and proteome. In this presentation, I will focus on our recent progress in using metabolomics to understand how the metabolome influences other omics and, by extension, to reveal the active role of metabolites in physiology and disease. This concept of utilizing metabolomics to perform activity screens to identify biologically active metabolites — which we term activity metabolomics — is already having a broad impact on biology.<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/298-2019-emn-webinars-public Lipidomics and epilipidomics – from analysis to data integration]== <br />
<br />
By: Dr. Maria Fedorova<br />
Date: July 17, 2019<br />
<br />
Lipidomics is a large-scale study of diversified molecular species of lipids aiming to address the identity, quantities, cellular and tissue distribution of lipids as well as related signalling and metabolic pathways. In addition to the native lipidome, lipid species derived from enzymatic and non-enzymatic modifications (the epilipidome) further increase regulatory capacity of the biological systems. High diversity of physico-chemical properties as well as large dynamic range of lipid concentrations in native lipidomes makes significantly challenge their analysis. For omics-wide high-throughput identification of lipid species from complex biological samples, several crucial analytical steps including extraction, chromatographic separation and mass spectrometry analysis need to be carefully considered and validated. The webinar will review current analytical strategies used in contemporary lipidomics and epilipidomics with the focus on optimization of LC-MS/MS based workflows for “discovery” lipidomics including sample preparation, lipid fractionation, separation using different chromatography techniques and high-throughput identification solutions. Available bioinformatics tools for identification of native and modified lipids will be described and compared as well as possible lipidomics data integration strategies.<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/298-2019-emn-webinars-public Metabolomics 2019: Professional Career Development: The Survival Kit]== <br />
<br />
By: Cathy Delhanty<br />
<br />
Date: June 23, 2019<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/298-2019-emn-webinars-public A Combined NMR and MS Metabolomics Approach to Study Neurodegenerative Diseases]== <br />
<br />
By: Dr. Robert Powers<br />
<br />
Date: May 24, 2019<br />
<br />
The metabolome captures how the system responds to drug treatment, disease state, or genetic modification. In this regards, metabolomics is an invaluable approach to easily and rapidly diagnose human disease and to assist in personalized medicine by monitoring a patient’s response to treatments. But, metabolomics is deceivingly complex with numerous sources of errors and technical challenges at every step of the process. One specific challenge is achieving a complete and accurate coverage of the metabolome, which can be addressed by combining NMR and mass spectrometry. Our metabolomics protocols and MVAPACK software for integrating NMR and mass spectral data for the analysis of neurodegenerative disease will be discussed. Our investigation into the molecular mechanisms of Parkinson’s disease and the identification of biomarkers for multiple sclerosis will be highlighted.<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/298-2019-emn-webinars-public Computational mass spectrometry in metabolomics to deepen the understanding of metabolisms]== <br />
<br />
By: Dr. Hiroshi Tsugawa<br />
<br />
Date: April 23, 2019<br />
<br />
Computational mass spectrometry is a growing research field to process mass spectrometry data, assist the interpretation of mass fragmentations, and elucidate unknown structures with metabolome databases and repositories for the global identification of metabolomes in various living organisms. In this talk, Dr Tsugawa will introduce three metabolomics software programs which include (1) MS-DIAL for untargeted metabolomics, (2) MS-FINDER for structure elucidations of unknowns, and (3) MRMPROBS for targeted metabolomics. These programs are demonstrated to perform the comprehensive analyses of primary metabolites, lipids, and plant specialized metabolites where unknown metabolites are also untangled with various methodologies including stable isotope labeled organisms, metabolite class recommendations, and integrated metabolome network analyses. In addition, a computational workflow to link untargeted- and targeted metabolomics is also highlighted in this talk.<br />
<br />
==Multidimensional Chromatography, what is the synergy with untargeted metabolomics profiling?== <br />
<br />
By: Dr. Pierre-Hugues Stefanuto<br />
<br />
Date: March 28, 2019<br />
<br />
In this webinar, Dr Pierre-Hugues Stefanuto will discuss the new development of multidimensional chromatography and the synergy with metabolomics. This presentation will be broadcasted in the context of the Multidimensional Chromatography Workshop held in Liège last January (http://multidimensionalchromatography.com). During this event, four focus group discussions were organized: 1) data processing for untargeted screening, 2) minimum reporting information for QC and compound validation, 3) hyphenation of MDGC with high-resolution MS, 4) and the general acceptance of MDC techniques. He will illustrate these topics through some ongoing medical research articulated around volatile organic compound (VOC) measurements in human breath and in vitro in metabolomic applications.<br />
<br />
==Untargeted metabolomics reveals smokers' characteristic profiles== <br />
<br />
By: Dr. Ping-Ching Hsu<br />
<br />
Date: March 1, 2019<br />
<br />
=2018=<br />
<br />
==Recent advances towards integrating metabolomics in kinetic models of large metabolic networks== <br />
<br />
By: Prof. Lars Nielsen<br />
<br />
Date: October 15, 2018<br />
<br />
Lars Nielsen is Chair of Biological Engineering at the Australian Institute for Bioengineering &Nanotechnology and Scientific Director for the Section for Quantitative Modelling of Cellular Metabolism at the Novo Nordisk Foundation Center for Biosustainability in Denmark. He is Director of the Bioplatforms Australia Queensland Node for Metabolomics and Proteomics, which provides systems and synthetic biology support to design and build cell factories for the production of fuels, chemicals and pharmaceuticals. His core research interest is modelling of cellular metabolism and his team has made many contributions to the formulation and use of genome scale models. He recently received a Novo Nordisk Foundation Laureate Research Grant to develop large scale, mathematical models to explore and explain the molecular basis for homeostasis–the self-regulating processes evolved to maintain metabolic equilibrium. Studying homeostasis is relevant for the understanding and treatment of complex diseases, particular with the emergence of personalized medicine. It is equally important when we seek to repurpose the cellular machinery for the production of desired chemicals, materials and pharmaceuticals.<br />
<br />
==Metabolomics-based Elucidation of Plant Specialized Metabolism== <br />
<br />
By: Prof. Kazuki Saito<br />
<br />
Date: July 25, 2018<br />
<br />
The recent advances of genomics and metabolomics in plant science accelerate our understanding about the mechanism, regulation and evolution of biosynthesis of plant specialized products. We can now address the questions how the metabolomic diversity of plants is originated at the levels of genome (phytochemical genomics) and how we should apply this knowledge to drug discovery, industry and agriculture. In this presentation, at first, technological developments of metabolomic analysis will be discussed forthe better understanding chemical diversity of plants. Then, a couple of examples of application of metabolomics to functional genomics of specialized metabolism in a model plant Arabidopsis thalianawill be presented, focusing on the biosynthesis of phenylpropanoids and lipids. The further extension to crops and medicinal plants producing a variety of specialized metabolites will be presented.<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/277-2018-emn-webinars-public Activity Metabolomics: Technologies to identify metabolites that modulate phenotype]== <br />
<br />
By: Prof. Gary Siuzdak<br />
<br />
Date: May 29, 2018<br />
<br />
Metabolomics is broadly acknowledged to be the omics discipline closest tothe phenotype and therefore widely used for biomarker discovery. However,metabolomics can also be designed to identify active metabolites thatalter a cell’s or an organism's phenotype.This "Activity Metabolomics”concept integrates metabolomics data analysis with pathway and systemsbiology data, ultimately to select endogenous metabolites that can bescreened for functionality. A growing literature reports the use ofmetabolites to modulate diverse processes including stem celldifferentiation, oligodendrocytematuration, insulin signaling,T-cellsurvival and macrophage immune responses. We have developed XCMS Online(xcmsonline.scripps.edu) and the newly expanded METLIN database (now withover 50,000 standards containing MS/MS data) to perform untargeted andtargeted metabolomics, as well as pathway analysis and systems biologydata integration.Metabolomics Activity Screening (MAS) has beenimplemented within XCMS Online to help achieve this integration goal foridentifying active metabolites. Because metabolites are often readilyavailable, activity metabolomics is uniquely positioning its practitionersto move beyond biomarkers, and become active participants in thebiological endgame of modulating phenotype. (for more information seeNature Biotechnology 2018 nature.com/articles/nbt.4101)<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/277-2018-emn-webinars-public Use better software: MS-DIAL instead of XCMS, ChemRich/MetDA instead of Metaboanalyst]== <br />
<br />
By: Dr. Oliver Fiehn<br />
<br />
Date: April 24, 2018<br />
<br />
XCMS and MetaboAnalystare the two most popular tools used by metabolomics researchers. But are these the best options? The West Coast Metabolomics Center at UC Davis has collaborated with RIKEN/NGI in Japan to release MS-DIAL vs.2 that yields far fewer false positive peak detections in untargeted LC-MS/MS runs than XCMS, with superior integration of compound identification software. MS-DIAL now also works on low or high resolution GC-MS data, making it the tool of choice for raw data processing of any mass spectrometry-based metabolomics study.We have also developed alternative software suites for statistical analysis of final result data. ChemRich uses all identified metabolites, including complex lipids, for set enrichment statistics. In comparison, MetaboAnalyst cannot perform pathway enrichment statistics on more than half of all identified metabolites, because it relies on KEGG pathways only. Moreover, we have developed new software for improved data normalization and statistics workflows in MetDA web-based analyses that will be presented on published example data.<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/277-2018-emn-webinars-public Can we capture an accurate view of cell or tissue specific metabolism from an expression profile?]== <br />
<br />
By: Dr. Nathan Lewis<br />
<br />
Date: March 26, 2018<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/277-2018-emn-webinars-public Metabolomics as a hypothesis generator for understanding metabolic regulation]== <br />
<br />
By: Prof. Uwe Sauer<br />
<br />
Date: February 14, 2018<br />
<br />
Prof. Uwe Sauer focuses on two conceptual problems: i) discovery of functionally important regulation mechanisms and ii) understanding which of the many known mechnisns actually matter for a given adaption. On the discovery side, he illustrates the use of coarse-grained kinetic models to extract mechnistic hypotheses from dynamic metabolomics data. For learning the coordination mechanisms, he presents an approach that hypothesizes the dynamically important mechnism from the much fewer steady state measurements in the bacterium E. coli. The surprising result is that only very few regulation events appear to be required for a given transition, typically involving less than a handful of active regulators.<br />
=2017=<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/258-2017-emn-webinars-public Introduction to Stable Isotope Resolved Metabolomics (SIRM) and Applications to Cancer Metabolism]==<br />
<br />
By: Dr. Andrew Lane<br />
<br />
Date: December 14, 2017<br />
<br />
Stable isotope resolved metabolomics (SIRM), for pathway tracing, represents an important new approach to obtaining metabolic parameters. SIRM allows the generation of atomic fate maps in cells and tissues, which provides the necessary information and data for metabolic flux analyis. This powerful new approach has already provided important new insights into metabolic adaptations in lung cancer cells.<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/258-2017-emn-webinars-public Using PhenoMeNal for your metabolomics data analysis on the cloud]==<br />
<br />
By: Dr. Pablo Moreno<br />
<br />
Date: October 24, 2017<br />
<br />
PhenoMeNal aims to bridge the gap between cloud computing and metabolomics researchers by providing the ability to create Cloud Research Environments (CRE) for metabolomics data analysis. A PhenoMeNal CRE is a small cluster of computers with popular metabolomics data analysis tools already installed. These tools are ready to be run and are accessible through a user friendly Galaxy workflow environment reducing the need for in-house bioinformatics. The PhenoMeNal CRE not only includes data analysis tools, but also example workflows where some of these tools are used together. You can also make your own workflows inside the CRE. In this webinar we will explain the main components of PhenoMeNal. We will demonstrate how to register, access existing tools and workflows, create a new PhenoMeNal CRE on Amazon, and execute a workflow on a PhenoMeNal CRE.<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/258-2017-emn-webinars-public Machine learning powered metabolomic network analysis]==<br />
<br />
By: Dr. Dmitry Grapov <br />
<br />
Date: May 30, 2017<br />
<br />
Metabolomic network analysis can be used to interpret experimental results within a variety of contexts including: biochemical relationships, structural and spectral similarity and empirical correlation. Machine learning is useful for modeling relationships in the context of pattern recognition, clustering, classification and regression based predictive modeling. The combination of developed metabolomic networks and machine learning based predictive models offer a unique method to visualize empirical relationships while testing key experimental hypotheses. The following presentation focuses on data analysis, visualization, machine learning and network mapping approaches used to create richly mapped metabolomic networks.<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/258-2017-emn-webinars-public How well do I quantify? Concepts for method validation and evaluation of measurement uncertainty in metabolomics]==<br />
<br />
By: Assoc. Prof. Stephan Hann<br />
<br />
Date: March 24, 2017<br />
<br />
This webinar will give an introduction to the basic terminology and principles of validation and measurement uncertainty in metabolomics. It will be demonstrated how validation parameters are determined in selected examples (e.g. LC-MS/MS, GC-MS/MS) for quantitative metabolomic analysis. Different quantification approaches will be overviewed in detail, and tips on choosing the most appropriate analytical strategies to answer metabolome-level hypotheses will be given. Finally, a workflow for estimating measurement uncertainty in metabolomics will be presented and discussed.<br />
<br />
=2016=<br />
<br />
== [http://metabolomicssociety.org/site-map/articles/88-videos/218-2016-emn-webinars-public Smarter ways to clean LC-MS] ==<br />
<br />
By: Assoc. Prof. Carl Brunius<br />
<br />
Date: November 17, 2016<br />
<br />
LC-MS is the most frequently used technique for untargeted metabolomics owing to broad coverage of metabolites, high sensitivity and simple sample preparation. However, instrument data tends to have high noise contribution from drift in signal intensity, mass accuracy and retention times. This noise has both within batch and between batch contributions and results in reduced measurement repeatability and reproducibility. The power to detect biological responses may thus be decreased and interpretations consequently obscured. Dr. Carl Brunius (the speaker) is involved in developing procedures to address and correct for within- and between-batch variability in processing multiple-batch untargeted LC-MS metabolomics data to increase their quality. This webinar session will talk about (i) alignment and merging of LC-MS features that are systematically misaligned between batches and (ii) within batch intensity drift correction that allows multiple drift patterns within batch. These algorithms will be applied on authentic data, resulting in improved peak picking performance and decreased noise in the dataset. All algorithms are developed as open source and are, together with example data, freely available as an R package from https://GitLab.com/CarlBrunius/batchCorr.<br />
== [http://metabolomicssociety.org/site-map/articles/88-videos/218-2016-emn-webinars-public Mass Spectral Libraries] ==<br />
<br />
By: Dr. Emma Schymanski<br />
<br />
Date: October 6, 2016<br />
<br />
Mass spectrometry is applied in diverse ways in metabolomics research and the mass spectrum of a small molecule can act as a fingerprint for identification. Just as the scientific questions in metabolomics vary, there is a diverse set of mass spectral libraries available to assist in the identification of metabolites and other small molecules. This webinar aims to provide listeners with a brief overview of several different mass spectral<br />
resources, including a personal view on pros and cons of the different options – providing a basis for listeners to choose the resource(s) that may best suit their investigation and needs. Additional information about substance overlap, spectral matching, identification confidence and spectral exchange will also be given – as well as some factors to consider carefully. Finally, some perspectives towards in silico identification without spectral libraries will be given to lead into a topic for a future webinar.<br />
<br />
== [http://metabolomicssociety.org/site-map/articles/88-videos/218-2016-emn-webinars-public Targeted High-throughput Lipidomics Platform: Development and Application] ==<br />
<br />
By: Dr. Peter Meikle<br />
<br />
Date: July 27, 2016<br />
<br />
Lipidomics, full analysis of lipid species and their biological roles with respect to health and diseases, has attracted increasing attention of biological and analytical scientific community. Knowing and understanding steps involved in lipidomics experimental workflow is essential for successful outcome. The Metabolomics Laboratory at Baker IDI Heart and Diabetes Institute (Melbourne, Australia) has a focus on the dyslipidemia and altered lipid metabolism associated with obesity, diabetes and cardiovascular disease and its relationship to the pathogenesis of these disease states. The laboratory has developed a targeted lipidomics platform that is able to quantify over 500 lipid species in 15 minutes using liquid chromatography coupled to electrospray ionisation tandem mass spectrometry. This platform is being applied to identify new approaches to early diagnosis and risk assessment as well as the development of new lipid modulating therapies for chronic disease. With illustration from this well-established lipidomics platform, this webinar will discuss the development of targeted high-throughput lipidomics platform, including selection and characterisation of lipid species, development of chromatography and quality control in the analysis of large sample sets. The presentation will draw on specific examples to highlight the application to large cohort studies and the Institute’s work to develop new therapeutic strategies for cardiovascular disease.<br />
<br />
== [http://metabolomicssociety.org/site-map/articles/88-videos/218-2016-emn-webinars-public From Features to Compounds: How To Get Started and Move Forward]==<br />
<br />
By: Dr. Jan Stanstrup<br />
<br />
Date: May 27, 2016<br />
<br />
In the untargeted analysis of complex mixtures the identification of compounds is the fundamental step enabling the results to be put in biological context. It is therefore of crucial importance for early career scientists approaching the field of metabolomics to familiarize themselves with this step. Many tools have been developed to aid identification; however, compound identification still constitutes one of the main bottlenecks in metabolomics and still requires substantial amounts of manual work. This webinar will go through the basic concepts used in MS-based compound identification and will introduce a number of relevant tools and databases allowing researchers to approach identification in a systematic way. The webinar is in particular dedicated to those researchers coming into the field of metabolomics that are not familiar with the methods used for compound identification and for which getting started can be a daunting and time-consuming challenge.<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/218-2016-emn-webinars-public Chromatography and Metabolomics: What’s the Point?] ==<br />
<br />
By: Dr. Karl Burgess<br />
<br />
Date: April 29, 2016<br />
<br />
The key to both chromatography and mass spectrometry is the separation of chemical species based on their physicochemical properties. As metabolomics researchers, we look to improved chromatography so enable us to<br />
detect compounds we previously had trouble with, to reduce the enormous complexity of the samples we analyse, and to clean up samples before or during analysis. Advances in mass spectrometry bring us greater sensitivity, better selectivity and a toolbox of techniques to aid in identification of biochemicals. With all these advantages come many disadvantages - poor reproducibility, compound bias and contamination. In this webinar, I'll explore chromatography and mass spectrometry with a critical eye. How can we improve them? Do we need them in every experiment? And really, what's the point of metabolomics at all?<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/218-2016-emn-webinars-public How close are we to metabolomics reproducibility? Data sharing, data standards and workflows for metabolomics]==<br />
<br />
By: Dr. Reza Salek<br />
<br />
Date: March 24, 2016<br />
<br />
=2015=<br />
<br />
== [http://metabolomicssociety.org/site-map/articles/88-videos/187-2015-emn-webinars-public Metabolomics and Beyond: Challenges and Strategies for Next-gen Omic Analyses] ==<br />
<br />
By: Dr. Dmitry Grapov<br />
<br />
Date: September 15, 2015<br />
<br />
Robust interpretation of experimental results measuring discreet biological domains remains a significant challenge in the face of complex biochemical regulation processes such as organismal versus tissue versus<br />
cellular metabolism, epigenetics, and protein post-translational modification. Integration of analyses carried out across multiple measurement or omic platforms is an emerging approach to help address these<br />
challenges. The following presentation will focus on key challenges faced by metabolomics researchers in the areas large-scale studies data normalization, multivariate analysis, visualization and omics data integration.<br />
<br />
== [http://metabolomicssociety.org/site-map/articles/88-videos/187-2015-emn-webinars-public Metabolomics with high resolution mass spectrometry: from spectral databases to biomarker discovery] ==<br />
<br />
By: Dr. Christophe Junot<br />
<br />
Date: 12 June 2015<br />
<br />
Since the middle of the 2000s, high resolution mass spectrometry (HRMS) is widely used in metabolomics, mainly because the detection and identification of metabolites are improved compared to low resolution instruments. Thanks to their versatility, HRMS instruments are the most appropriate to achieve an optimal metabolome coverage, at the border of other omics fields such as lipidomics and glycomics. The aim of this talk will be to present HRMS based tools for metabolomics and lipidomics developed at the laboratory, and their relevance to the field of biomarker discovery for the diagnosis and follow-up of pathologies.<br />
<br />
== [http://metabolomicssociety.org/site-map/articles/88-videos/187-2015-emn-webinars-public#|: Metabolomics and systems biology: models as guide and natural integrator]==<br />
<br />
By: Prof. Bas Teusink<br />
<br />
Date: 14 April 2015<br />
<br />
In this webinar, I will discuss the use of mathematical models in guiding targeted and (semi-)untargeted metabolomics efforts. I will show how genome-scale metabolic models can be used as a data integration platform - also for metabolomics data. I will provide an example from medium optimisation in a biotechnological context. My message is that the use of models upfront combined with quantitative and well-timed metabolomics is often much more effective than simply generating lots of data and subsequent statistical analysis.<br />
<br />
== [http://metabolomicssociety.org/site-map/articles/88-videos/187-2015-emn-webinars-public Large-scale, computational and empirical UHPLC-MS-SPE-NMR annotation of plant metabolomes] ==<br />
<br />
By: Dr. Lloyd Sumner<br />
<br />
Date: 5 March 2015<br />
<br />
Integrated metabolomics is a revolutionary systems biology tool for understanding plant metabolism and elucidating gene function. Although the vast utility of metabolomics is well documented in the literature, its<br />
full scientific promise has not yet been realized due to multiple technical challenges. The number one, grand challenge of metabolomics is the large-scale confident chemical identification of metabolites. To address<br />
this challenge, we have developed sophisticated computational and empirical metabolomics tools for the systematic and biological directed annotation of plant metabolomes. This presentation will introduce novel<br />
software entitled Plant Metabolite Annotation Toolbox (PlantMAT) and a sophisticated UHPLC-MS-SPENMR instrumental ensemble that are being used for ‘sequencing’ the first plant metabolomes of the model plant systems Arabidopsis and Medicago truncatula.<br />
<br />
== [http://metabolomicssociety.org/site-map/articles/88-videos/187-2015-emn-webinars-public Metabolomics: only suitable for multidisciplinary teams] ==<br />
<br />
By: Dr. Oscar Yanes<br />
<br />
Date: 29 Jan 2015<br />
<br />
Metabolomics is defined as the comprehensive and quantitative analysis of metabolites in living organisms. Among the omic sciences, metabolomics is possibly the most multidisciplinary of all, involving knowledge<br />
about electronic engineering and signal processing, analytical and organic chemistry, biostatistics and statistical physics, and biochemistry and cell metabolism. Here an untargeted metabolomics workflow will<br />
be detailed that provides examples of this multidisciplinarity.</div>Viniciusverihttp://wiki.metabolomicssociety.org/index.php?title=EMN_Webinars&diff=1559EMN Webinars2021-06-21T06:28:45Z<p>Viniciusveri: </p>
<hr />
<div>The [[Early-Career Members Network#EMN Committee|Early-Career Members Network (EMN) Committee]] has organized webinars since 2015 on a variety of metabolomics-related topics. This page provides a summary of all webinars to date with links to the webinars (when available).<br />
<br />
To participate on the live webinars, follow us on [https://twitter.com/EMN_MetSoc Twitter] and [https://www.facebook.com/EMN.MetabolomicsSociety Facebook] to get all updates from the EMN or subscribe to the Metabolomics Society website to receive the email invitation.<br />
<br />
<br />
=2021=<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/334-2021-emn-webinars-public TidyMS: a tool for preprocessing and Improving data quality in metabolomics]==<br />
<br />
Date: April 27, 2021<br />
<br />
'''TidyMS: a tool for preprocessing and Improving data quality in metabolomics<br />
<br />
By: Dr. María Eugenia Monge<br />
<br />
One of the challenges in untargeted metabolomics workflows is preprocessing data in a reproducible and robust way. In liquid chromatography-mass spectrometry (LC-MS), data curation involves removing biologically non-relevant features (retention time, m/z pairs) and retaining only those analytically robust enough to allow subsequent data analysis and interpretation. In this presentation, I will share the strategies that we used for quality control (QC) purposes in LC-MS-based untargeted studies that interrogated samples from in vitro models and serum samples as well as in an ambient-MS-based approach. In addition, I will introduce “TidyMS”, which is a new Python library for LC-MS data preprocessing that has recently been developed in our research group with the aim of performing QC procedures in a rapid and reproducible workflow. TidyMS includes tools to work with both raw MS data and data matrices. It provides functionality to build customized pipelines for data curation to obtain cleaned matrices from untargeted metabolomics and lipidomics studies for subsequent statistical analysis, and aims at ensuring accuracy and reliability in LC–MS measurements. The capabilities of TidyMS will be illustrated with pipelines for an LC-MS system suitability check, system conditioning, signal drift evaluation, and data curation. These applications were implemented to successfully preprocess data from a new suite of candidate human plasma reference materials currently under development by the National Institute of Standards and Technology (NIST; hypertriglyceridemic, diabetic, and African-American plasma pools), in addition to NIST SRM 1950 Metabolites in Frozen Human Plasma to be used in metabolomics and lipidomics studies. Overall, TidyMS can be used in an automated or semi-automated way, and it is an open and free tool available to all users.<br />
<br />
'''Improving data quality in metabolomics workflows: A Clear Cell Renal Cell Carcinoma (ccRCC) case study<br />
<br />
By: Mr. Nicolás Zabalegui<br />
<br />
Liquid chromatography-mass spectrometry (LC-MS)-based untargeted metabolomics studies involving biological samples may lead to the detection of tens of thousands of potential metabolic features (retention time, m/z pairs) at initial stages of the workflow. However, data needs to be preprocessed in a reproducible way to remove biologically non-relevant features and thereafter obtain cleaned matrices suitable for subsequent statistical analysis.Kidney cancer is accepted to be a metabolic disease. More than 50% of cases of renal cell carcinoma (RCC) are incidentally diagnosed; being clear cell RCC (ccRCC) the most common histological subtype (75% of cases), and considered a glycolytic and lipogenic tumor. Since the disease is inherently resistant to chemotherapy and radiotherapy, surgery is the most promising treatment for curation when the disease is detected at earlier stages.In this study, serum samples from a cohort that included patients with different ccRCC stages (I, II, III, IV), which were collected before (n=113) and after surgery (n=56), as well as samples from controls (n=52), were interrogated with a discovery-based metabolomics approach using UPLC-QTOF-MS. LC-MS data were preprocessed with TidyMS, a Python package used to retain only high-quality data for subsequent analysis and interpretation. As well, additional experiments were conducted to account for metabolite stability over time and non-linearity in instrumental responses, and were utilized to improve data quality before performing statistical multivariate analysis.<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/334-2021-emn-webinars-public Viral infection in algal blooms and The glycosphingolipid-based arms race]==<br />
<br />
Date: March 22, 2021<br />
<br />
'''Viral infection in algal blooms and The glycosphingolipid-based arms race<br />
<br />
By: Prof. Assaf Vardi & Dr. Guy Schleyer<br />
<br />
<br />
=2020=<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/306-2020-emn-webinars-public New bio-statistical methods for metabolomics and metabolite annotation]== <br />
<br />
Date: December 22, 2020<br />
<br />
'''New bio-statistical methods for metabolomics<br />
<br />
By: Dr. Daniel Raftery<br />
<br />
Highly complex biological samples present challenging analysis problems for the field of metabolomics. Ideally, platforms that provide broad metabolome coverage and high data quality allow the opportunity for deep insights into biological problems. However, this goal can be difficult to achieve on a routine basis because the highly complex data are subject to matrix effects and complicated correlative relationships among many metabolites. As such metabolite identification, biomarker identification and validation can be very challenging. Advanced statistical methods are needed to deal with these issues for improved biomarker discovery, unknown identification and biological interpretation. We have pursued the development of a number of approaches that try to unravel the complex and multidimensional structure of metabolomics datasets, with some successes and some failures along the way. In this talk, I will provide some examples of where even non-experts in biostatistics can make progress in developing advanced analysis approaches and discuss some areas that provide significant challenges for future work.<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/306-2020-emn-webinars-public Metabolic networks]== <br />
<br />
Date: November 26, 2020<br />
<br />
'''Metabolic networks to enrich and interpret metabolic fingerprints<br />
<br />
By: Dr. Fabien Jourdan<br />
<br />
Metabolic modulation is a cornerstone cellular response to genetic or environmental stresses. This plasticity is going beyond central metabolism and may involve complex processes spanning several metabolic pathways. Hence, it is a key challenge to be able to decipher metabolic modulations in a systemic and global perspective.<br />
The aim of the computational methods and tools which will be presented is thus to consider the full complexity of metabolism. To do so, all metabolic reactions the cell is able to achieve are gathered in a single mathematical model call “genome scale metabolic network”. Based on this model, it is then possible to identify metabolic modulations associated to metabolic fingerprints or suggest metabolites of interest to enrich biochemical interpretation.<br />
<br />
<br />
<br />
'''Improving metabolic studies with diverse context-specific metabolic networks<br />
<br />
By: Dr. Pablo Rodriguez Mier<br />
<br />
Understanding deregulations of metabolism based on isolated measures of gene expression, protein or metabolite concentrations is a challenging task due to the interconnection of multiple processes. One solution is to extract, from generic genome-scale metabolic networks, the specific sub-network which is modulated in the studied condition. Many algorithms have been proposed for such context-specific network extraction based on experimental measurements. However, this process is subject to some randomness and variability, since multiple metabolic networks can model the metabolic state in a similarly adequate manner for the same experimental data. This means that for some condition and reconstruction method, there are usually multiple possible sub-networks that can explain the same experimental data, but most current methods ignore this fact and return a single sub-network instead. Ignoring this variability can not only lead to incorrect or incomplete explanations of the biological experiment, but also causes valuable information to be lost that could be used to improve predictions. In this talk we will see what context-specific metabolic sub-networks are, some of the limitations of the current methods, and how we can get a diverse set of sub-networks to improve predictions and obtain better mechanistic insights about the metabolism.<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/306-2020-emn-webinars-public Decomposing Complex Metabolite Mixtures...& Unraveling the neonatal metabolome]== <br />
<br />
Date: June 19, 2020<br />
<br />
'''Decomposing Complex Metabolite Mixtures through Substructure-based Metabolomics Workflows<br />
<br />
By: Justin J.J. van der Hooft<br />
<br />
In this webinar, Dr. Justin J.J. van der Hooft will start with an introduction on the challenges of metabolite annotation and identification in untargeted metabolomics experiments of complex mixtures typically encountered in natural products and food research. He will also show how MS2LDA has been successfully used to capture chemical knowledge from diverse plant, food, and microbial-related data sets. Dr. Justin J.J. van der Hooft will finish by highlighting the advantages of combining metabolome mining and annotation tools in public data of different plant and microbial-related studies.<br />
<br />
<br />
'''Unraveling the neonatal metabolome using mass spectral data mining tools<br />
<br />
By: Madeleine Ernst<br />
<br />
In this webinar, Dr. Madeleine Ernst will explain how mass spectral data mining tools, such as molecular networking through the community platform GNPS, MS2LDA, in silico structure prediction (e.g. Network Annotation Propagation) and ClassyFire can significantly enhance chemical structural annotation retrieved in clinical mass spectrometry-based metabolomics studies. Dr. Madeleine Ernst will also elucidate how metabolic signatures of neonatal health and disease can be unraveled, significantly enhancing biological interpretation and hypothesis generation in metabolomics studies.<br />
<br />
==Plant Metabolomics - Elucidating biochemicals mechanisms underlying plant-environment interactions== <br />
<br />
By: Dr. Fidele Tugizimana<br />
<br />
Date: May 1, 2020<br />
<br />
In this webinar, Dr. Fidele will provide a snapshot of applications of metabolomics in plant sciences, particularly in plant-environment interactions research. The webinar will highlight some examples of the use of metabolomics to elucidate hypothetical frameworks that describe the biochemistry underlying naïve and primed-plant responses to microbial infections. Furthermore, one of the novel (emerging) strategies for sustainable food production and food security is the use of biostimulants in agriculture industry. Application of metabolomics in decoding and understanding plant-biostimulant interactions will be also highlighted.<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/306-2020-emn-webinars-public Human metabolic individuality and its role in multi-omics based studies]== <br />
<br />
By: Dr. Karsten Suhre<br />
<br />
Date: February 4, 2020<br />
<br />
In this webinar, Dr. Suhre will discuss Genome-wide association studies with clinically relevant intermediate traits, such as gene expression, proteomics, and metabolomics, which unravelled numerous pathophysiological pathways and generated many hypotheses regarding the functional basis of complex disorders. More recently, similar approaches linked variation in epigenetic modifications, especially differential methylation of chromosomal CpG-pairs, to changes in gene expression and blood circulating metabolites. These large-scale population and patient cohort studies reflect experimental data obtained from naturally occurring variance of the general population where each individual may be viewed as an experiment conducted by Nature. The next and most challenging step on the way to a truly personalized approach to medicine is to translate the results from these large-scale omics studies to applications at the patient level. In this presentation,<br />
<br />
=2019=<br />
<br />
==Metabolomics as a tool for elucidating plant growth regulation== <br />
<br />
By: Dr. Camila Caldana<br />
Date: November 20, 2019<br />
<br />
Rising demand for food and fuels makes it crucial to develop breeding strategies for increasing crop yield/biomass. Plant biomass production is tightly associated with growth and relies on a tight regulation of a complex signaling network that integrates external and internal stimuli. The main goal of our group is to elucidate the processes underlying plant growth and production of biomass by combining physiology, metabolomics, and gene expression analyses. In my presentation, I will provide examples of i) how the evolutionary conserved Target of Rapamycin pathway fine-tunes metabolic homeostasis to promote biosynthetic growth in plants; ii) the potential of metabolite profiles to predict plant performance as biomarkers.<br />
<br />
==Discovering Metabolites that Alter Physiology, an Omics Perspective== <br />
<br />
By: Dr. Gary Siuzdak<br />
Date: September 18, 2019<br />
<br />
Metabolomics and the comprehensive analysis of the metabolome and lipidome has traditionally been pursued with the aim of identifying biomarkers in the diagnosis and prediction of disease. However, the value of metabolomics has been redefined from a simple biomarker identification tool to a technology for the discovery of active drivers of biological processes. It is now clear that the metabolome affects cellular physiology through modulation of other ‘omics’ levels, including the genome, epigenome, transcriptome and proteome. In this presentation, I will focus on our recent progress in using metabolomics to understand how the metabolome influences other omics and, by extension, to reveal the active role of metabolites in physiology and disease. This concept of utilizing metabolomics to perform activity screens to identify biologically active metabolites — which we term activity metabolomics — is already having a broad impact on biology.<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/298-2019-emn-webinars-public Lipidomics and epilipidomics – from analysis to data integration]== <br />
<br />
By: Dr. Maria Fedorova<br />
Date: July 17, 2019<br />
<br />
Lipidomics is a large-scale study of diversified molecular species of lipids aiming to address the identity, quantities, cellular and tissue distribution of lipids as well as related signalling and metabolic pathways. In addition to the native lipidome, lipid species derived from enzymatic and non-enzymatic modifications (the epilipidome) further increase regulatory capacity of the biological systems. High diversity of physico-chemical properties as well as large dynamic range of lipid concentrations in native lipidomes makes significantly challenge their analysis. For omics-wide high-throughput identification of lipid species from complex biological samples, several crucial analytical steps including extraction, chromatographic separation and mass spectrometry analysis need to be carefully considered and validated. The webinar will review current analytical strategies used in contemporary lipidomics and epilipidomics with the focus on optimization of LC-MS/MS based workflows for “discovery” lipidomics including sample preparation, lipid fractionation, separation using different chromatography techniques and high-throughput identification solutions. Available bioinformatics tools for identification of native and modified lipids will be described and compared as well as possible lipidomics data integration strategies.<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/298-2019-emn-webinars-public Metabolomics 2019: Professional Career Development: The Survival Kit]== <br />
<br />
By: Cathy Delhanty<br />
<br />
Date: June 23, 2019<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/298-2019-emn-webinars-public A Combined NMR and MS Metabolomics Approach to Study Neurodegenerative Diseases]== <br />
<br />
By: Dr. Robert Powers<br />
<br />
Date: May 24, 2019<br />
<br />
The metabolome captures how the system responds to drug treatment, disease state, or genetic modification. In this regards, metabolomics is an invaluable approach to easily and rapidly diagnose human disease and to assist in personalized medicine by monitoring a patient’s response to treatments. But, metabolomics is deceivingly complex with numerous sources of errors and technical challenges at every step of the process. One specific challenge is achieving a complete and accurate coverage of the metabolome, which can be addressed by combining NMR and mass spectrometry. Our metabolomics protocols and MVAPACK software for integrating NMR and mass spectral data for the analysis of neurodegenerative disease will be discussed. Our investigation into the molecular mechanisms of Parkinson’s disease and the identification of biomarkers for multiple sclerosis will be highlighted.<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/298-2019-emn-webinars-public Computational mass spectrometry in metabolomics to deepen the understanding of metabolisms]== <br />
<br />
By: Dr. Hiroshi Tsugawa<br />
<br />
Date: April 23, 2019<br />
<br />
Computational mass spectrometry is a growing research field to process mass spectrometry data, assist the interpretation of mass fragmentations, and elucidate unknown structures with metabolome databases and repositories for the global identification of metabolomes in various living organisms. In this talk, Dr Tsugawa will introduce three metabolomics software programs which include (1) MS-DIAL for untargeted metabolomics, (2) MS-FINDER for structure elucidations of unknowns, and (3) MRMPROBS for targeted metabolomics. These programs are demonstrated to perform the comprehensive analyses of primary metabolites, lipids, and plant specialized metabolites where unknown metabolites are also untangled with various methodologies including stable isotope labeled organisms, metabolite class recommendations, and integrated metabolome network analyses. In addition, a computational workflow to link untargeted- and targeted metabolomics is also highlighted in this talk.<br />
<br />
==Multidimensional Chromatography, what is the synergy with untargeted metabolomics profiling?== <br />
<br />
By: Dr. Pierre-Hugues Stefanuto<br />
<br />
Date: March 28, 2019<br />
<br />
In this webinar, Dr Pierre-Hugues Stefanuto will discuss the new development of multidimensional chromatography and the synergy with metabolomics. This presentation will be broadcasted in the context of the Multidimensional Chromatography Workshop held in Liège last January (http://multidimensionalchromatography.com). During this event, four focus group discussions were organized: 1) data processing for untargeted screening, 2) minimum reporting information for QC and compound validation, 3) hyphenation of MDGC with high-resolution MS, 4) and the general acceptance of MDC techniques. He will illustrate these topics through some ongoing medical research articulated around volatile organic compound (VOC) measurements in human breath and in vitro in metabolomic applications.<br />
<br />
==Untargeted metabolomics reveals smokers' characteristic profiles== <br />
<br />
By: Dr. Ping-Ching Hsu<br />
<br />
Date: March 1, 2019<br />
<br />
=2018=<br />
<br />
==Recent advances towards integrating metabolomics in kinetic models of large metabolic networks== <br />
<br />
By: Prof. Lars Nielsen<br />
<br />
Date: October 15, 2018<br />
<br />
Lars Nielsen is Chair of Biological Engineering at the Australian Institute for Bioengineering &Nanotechnology and Scientific Director for the Section for Quantitative Modelling of Cellular Metabolism at the Novo Nordisk Foundation Center for Biosustainability in Denmark. He is Director of the Bioplatforms Australia Queensland Node for Metabolomics and Proteomics, which provides systems and synthetic biology support to design and build cell factories for the production of fuels, chemicals and pharmaceuticals. His core research interest is modelling of cellular metabolism and his team has made many contributions to the formulation and use of genome scale models. He recently received a Novo Nordisk Foundation Laureate Research Grant to develop large scale, mathematical models to explore and explain the molecular basis for homeostasis–the self-regulating processes evolved to maintain metabolic equilibrium. Studying homeostasis is relevant for the understanding and treatment of complex diseases, particular with the emergence of personalized medicine. It is equally important when we seek to repurpose the cellular machinery for the production of desired chemicals, materials and pharmaceuticals.<br />
<br />
==Metabolomics-based Elucidation of Plant Specialized Metabolism== <br />
<br />
By: Prof. Kazuki Saito<br />
<br />
Date: July 25, 2018<br />
<br />
The recent advances of genomics and metabolomics in plant science accelerate our understanding about the mechanism, regulation and evolution of biosynthesis of plant specialized products. We can now address the questions how the metabolomic diversity of plants is originated at the levels of genome (phytochemical genomics) and how we should apply this knowledge to drug discovery, industry and agriculture. In this presentation, at first, technological developments of metabolomic analysis will be discussed forthe better understanding chemical diversity of plants. Then, a couple of examples of application of metabolomics to functional genomics of specialized metabolism in a model plant Arabidopsis thalianawill be presented, focusing on the biosynthesis of phenylpropanoids and lipids. The further extension to crops and medicinal plants producing a variety of specialized metabolites will be presented.<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/277-2018-emn-webinars-public Activity Metabolomics: Technologies to identify metabolites that modulate phenotype]== <br />
<br />
By: Prof. Gary Siuzdak<br />
<br />
Date: May 29, 2018<br />
<br />
Metabolomics is broadly acknowledged to be the omics discipline closest tothe phenotype and therefore widely used for biomarker discovery. However,metabolomics can also be designed to identify active metabolites thatalter a cell’s or an organism's phenotype.This "Activity Metabolomics”concept integrates metabolomics data analysis with pathway and systemsbiology data, ultimately to select endogenous metabolites that can bescreened for functionality. A growing literature reports the use ofmetabolites to modulate diverse processes including stem celldifferentiation, oligodendrocytematuration, insulin signaling,T-cellsurvival and macrophage immune responses. We have developed XCMS Online(xcmsonline.scripps.edu) and the newly expanded METLIN database (now withover 50,000 standards containing MS/MS data) to perform untargeted andtargeted metabolomics, as well as pathway analysis and systems biologydata integration.Metabolomics Activity Screening (MAS) has beenimplemented within XCMS Online to help achieve this integration goal foridentifying active metabolites. Because metabolites are often readilyavailable, activity metabolomics is uniquely positioning its practitionersto move beyond biomarkers, and become active participants in thebiological endgame of modulating phenotype. (for more information seeNature Biotechnology 2018 nature.com/articles/nbt.4101)<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/277-2018-emn-webinars-public Use better software: MS-DIAL instead of XCMS, ChemRich/MetDA instead of Metaboanalyst]== <br />
<br />
By: Dr. Oliver Fiehn<br />
<br />
Date: April 24, 2018<br />
<br />
XCMS and MetaboAnalystare the two most popular tools used by metabolomics researchers. But are these the best options? The West Coast Metabolomics Center at UC Davis has collaborated with RIKEN/NGI in Japan to release MS-DIAL vs.2 that yields far fewer false positive peak detections in untargeted LC-MS/MS runs than XCMS, with superior integration of compound identification software. MS-DIAL now also works on low or high resolution GC-MS data, making it the tool of choice for raw data processing of any mass spectrometry-based metabolomics study.We have also developed alternative software suites for statistical analysis of final result data. ChemRich uses all identified metabolites, including complex lipids, for set enrichment statistics. In comparison, MetaboAnalyst cannot perform pathway enrichment statistics on more than half of all identified metabolites, because it relies on KEGG pathways only. Moreover, we have developed new software for improved data normalization and statistics workflows in MetDA web-based analyses that will be presented on published example data.<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/277-2018-emn-webinars-public Can we capture an accurate view of cell or tissue specific metabolism from an expression profile?]== <br />
<br />
By: Dr. Nathan Lewis<br />
<br />
Date: March 26, 2018<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/277-2018-emn-webinars-public Metabolomics as a hypothesis generator for understanding metabolic regulation]== <br />
<br />
By: Prof. Uwe Sauer<br />
<br />
Date: February 14, 2018<br />
<br />
Prof. Uwe Sauer focuses on two conceptual problems: i) discovery of functionally important regulation mechanisms and ii) understanding which of the many known mechnisns actually matter for a given adaption. On the discovery side, he illustrates the use of coarse-grained kinetic models to extract mechnistic hypotheses from dynamic metabolomics data. For learning the coordination mechanisms, he presents an approach that hypothesizes the dynamically important mechnism from the much fewer steady state measurements in the bacterium E. coli. The surprising result is that only very few regulation events appear to be required for a given transition, typically involving less than a handful of active regulators.<br />
=2017=<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/258-2017-emn-webinars-public Introduction to Stable Isotope Resolved Metabolomics (SIRM) and Applications to Cancer Metabolism]==<br />
<br />
By: Dr. Andrew Lane<br />
<br />
Date: December 14, 2017<br />
<br />
Stable isotope resolved metabolomics (SIRM), for pathway tracing, represents an important new approach to obtaining metabolic parameters. SIRM allows the generation of atomic fate maps in cells and tissues, which provides the necessary information and data for metabolic flux analyis. This powerful new approach has already provided important new insights into metabolic adaptations in lung cancer cells.<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/258-2017-emn-webinars-public Using PhenoMeNal for your metabolomics data analysis on the cloud]==<br />
<br />
By: Dr. Pablo Moreno<br />
<br />
Date: October 24, 2017<br />
<br />
PhenoMeNal aims to bridge the gap between cloud computing and metabolomics researchers by providing the ability to create Cloud Research Environments (CRE) for metabolomics data analysis. A PhenoMeNal CRE is a small cluster of computers with popular metabolomics data analysis tools already installed. These tools are ready to be run and are accessible through a user friendly Galaxy workflow environment reducing the need for in-house bioinformatics. The PhenoMeNal CRE not only includes data analysis tools, but also example workflows where some of these tools are used together. You can also make your own workflows inside the CRE. In this webinar we will explain the main components of PhenoMeNal. We will demonstrate how to register, access existing tools and workflows, create a new PhenoMeNal CRE on Amazon, and execute a workflow on a PhenoMeNal CRE.<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/258-2017-emn-webinars-public Machine learning powered metabolomic network analysis]==<br />
<br />
By: Dr. Dmitry Grapov <br />
<br />
Date: May 30, 2017<br />
<br />
Metabolomic network analysis can be used to interpret experimental results within a variety of contexts including: biochemical relationships, structural and spectral similarity and empirical correlation. Machine learning is useful for modeling relationships in the context of pattern recognition, clustering, classification and regression based predictive modeling. The combination of developed metabolomic networks and machine learning based predictive models offer a unique method to visualize empirical relationships while testing key experimental hypotheses. The following presentation focuses on data analysis, visualization, machine learning and network mapping approaches used to create richly mapped metabolomic networks.<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/258-2017-emn-webinars-public How well do I quantify? Concepts for method validation and evaluation of measurement uncertainty in metabolomics]==<br />
<br />
By: Assoc. Prof. Stephan Hann<br />
<br />
Date: March 24, 2017<br />
<br />
This webinar will give an introduction to the basic terminology and principles of validation and measurement uncertainty in metabolomics. It will be demonstrated how validation parameters are determined in selected examples (e.g. LC-MS/MS, GC-MS/MS) for quantitative metabolomic analysis. Different quantification approaches will be overviewed in detail, and tips on choosing the most appropriate analytical strategies to answer metabolome-level hypotheses will be given. Finally, a workflow for estimating measurement uncertainty in metabolomics will be presented and discussed.<br />
<br />
=2016=<br />
<br />
== [http://metabolomicssociety.org/site-map/articles/88-videos/218-2016-emn-webinars-public Smarter ways to clean LC-MS] ==<br />
<br />
By: Assoc. Prof. Carl Brunius<br />
<br />
Date: November 17, 2016<br />
<br />
LC-MS is the most frequently used technique for untargeted metabolomics owing to broad coverage of metabolites, high sensitivity and simple sample preparation. However, instrument data tends to have high noise contribution from drift in signal intensity, mass accuracy and retention times. This noise has both within batch and between batch contributions and results in reduced measurement repeatability and reproducibility. The power to detect biological responses may thus be decreased and interpretations consequently obscured. Dr. Carl Brunius (the speaker) is involved in developing procedures to address and correct for within- and between-batch variability in processing multiple-batch untargeted LC-MS metabolomics data to increase their quality. This webinar session will talk about (i) alignment and merging of LC-MS features that are systematically misaligned between batches and (ii) within batch intensity drift correction that allows multiple drift patterns within batch. These algorithms will be applied on authentic data, resulting in improved peak picking performance and decreased noise in the dataset. All algorithms are developed as open source and are, together with example data, freely available as an R package from https://GitLab.com/CarlBrunius/batchCorr.<br />
== [http://metabolomicssociety.org/site-map/articles/88-videos/218-2016-emn-webinars-public Mass Spectral Libraries] ==<br />
<br />
By: Dr. Emma Schymanski<br />
<br />
Date: October 6, 2016<br />
<br />
Mass spectrometry is applied in diverse ways in metabolomics research and the mass spectrum of a small molecule can act as a fingerprint for identification. Just as the scientific questions in metabolomics vary, there is a diverse set of mass spectral libraries available to assist in the identification of metabolites and other small molecules. This webinar aims to provide listeners with a brief overview of several different mass spectral<br />
resources, including a personal view on pros and cons of the different options – providing a basis for listeners to choose the resource(s) that may best suit their investigation and needs. Additional information about substance overlap, spectral matching, identification confidence and spectral exchange will also be given – as well as some factors to consider carefully. Finally, some perspectives towards in silico identification without spectral libraries will be given to lead into a topic for a future webinar.<br />
<br />
== [http://metabolomicssociety.org/site-map/articles/88-videos/218-2016-emn-webinars-public Targeted High-throughput Lipidomics Platform: Development and Application] ==<br />
<br />
By: Dr. Peter Meikle<br />
<br />
Date: July 27, 2016<br />
<br />
Lipidomics, full analysis of lipid species and their biological roles with respect to health and diseases, has attracted increasing attention of biological and analytical scientific community. Knowing and understanding steps involved in lipidomics experimental workflow is essential for successful outcome. The Metabolomics Laboratory at Baker IDI Heart and Diabetes Institute (Melbourne, Australia) has a focus on the dyslipidemia and altered lipid metabolism associated with obesity, diabetes and cardiovascular disease and its relationship to the pathogenesis of these disease states. The laboratory has developed a targeted lipidomics platform that is able to quantify over 500 lipid species in 15 minutes using liquid chromatography coupled to electrospray ionisation tandem mass spectrometry. This platform is being applied to identify new approaches to early diagnosis and risk assessment as well as the development of new lipid modulating therapies for chronic disease. With illustration from this well-established lipidomics platform, this webinar will discuss the development of targeted high-throughput lipidomics platform, including selection and characterisation of lipid species, development of chromatography and quality control in the analysis of large sample sets. The presentation will draw on specific examples to highlight the application to large cohort studies and the Institute’s work to develop new therapeutic strategies for cardiovascular disease.<br />
<br />
== [http://metabolomicssociety.org/site-map/articles/88-videos/218-2016-emn-webinars-public From Features to Compounds: How To Get Started and Move Forward]==<br />
<br />
By: Dr. Jan Stanstrup<br />
<br />
Date: May 27, 2016<br />
<br />
In the untargeted analysis of complex mixtures the identification of compounds is the fundamental step enabling the results to be put in biological context. It is therefore of crucial importance for early career scientists approaching the field of metabolomics to familiarize themselves with this step. Many tools have been developed to aid identification; however, compound identification still constitutes one of the main bottlenecks in metabolomics and still requires substantial amounts of manual work. This webinar will go through the basic concepts used in MS-based compound identification and will introduce a number of relevant tools and databases allowing researchers to approach identification in a systematic way. The webinar is in particular dedicated to those researchers coming into the field of metabolomics that are not familiar with the methods used for compound identification and for which getting started can be a daunting and time-consuming challenge.<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/218-2016-emn-webinars-public Chromatography and Metabolomics: What’s the Point?] ==<br />
<br />
By: Dr. Karl Burgess<br />
<br />
Date: April 29, 2016<br />
<br />
The key to both chromatography and mass spectrometry is the separation of chemical species based on their physicochemical properties. As metabolomics researchers, we look to improved chromatography so enable us to<br />
detect compounds we previously had trouble with, to reduce the enormous complexity of the samples we analyse, and to clean up samples before or during analysis. Advances in mass spectrometry bring us greater sensitivity, better selectivity and a toolbox of techniques to aid in identification of biochemicals. With all these advantages come many disadvantages - poor reproducibility, compound bias and contamination. In this webinar, I'll explore chromatography and mass spectrometry with a critical eye. How can we improve them? Do we need them in every experiment? And really, what's the point of metabolomics at all?<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/218-2016-emn-webinars-public How close are we to metabolomics reproducibility? Data sharing, data standards and workflows for metabolomics]==<br />
<br />
By: Dr. Reza Salek<br />
<br />
Date: March 24, 2016<br />
<br />
=2015=<br />
<br />
== [http://metabolomicssociety.org/site-map/articles/88-videos/187-2015-emn-webinars-public Metabolomics and Beyond: Challenges and Strategies for Next-gen Omic Analyses] ==<br />
<br />
By: Dr. Dmitry Grapov<br />
<br />
Date: September 15, 2015<br />
<br />
Robust interpretation of experimental results measuring discreet biological domains remains a significant challenge in the face of complex biochemical regulation processes such as organismal versus tissue versus<br />
cellular metabolism, epigenetics, and protein post-translational modification. Integration of analyses carried out across multiple measurement or omic platforms is an emerging approach to help address these<br />
challenges. The following presentation will focus on key challenges faced by metabolomics researchers in the areas large-scale studies data normalization, multivariate analysis, visualization and omics data integration.<br />
<br />
== [http://metabolomicssociety.org/site-map/articles/88-videos/187-2015-emn-webinars-public Metabolomics with high resolution mass spectrometry: from spectral databases to biomarker discovery] ==<br />
<br />
By: Dr. Christophe Junot<br />
<br />
Date: 12 June 2015<br />
<br />
Since the middle of the 2000s, high resolution mass spectrometry (HRMS) is widely used in metabolomics, mainly because the detection and identification of metabolites are improved compared to low resolution instruments. Thanks to their versatility, HRMS instruments are the most appropriate to achieve an optimal metabolome coverage, at the border of other omics fields such as lipidomics and glycomics. The aim of this talk will be to present HRMS based tools for metabolomics and lipidomics developed at the laboratory, and their relevance to the field of biomarker discovery for the diagnosis and follow-up of pathologies.<br />
<br />
== [http://metabolomicssociety.org/site-map/articles/88-videos/187-2015-emn-webinars-public#|: Metabolomics and systems biology: models as guide and natural integrator]==<br />
<br />
By: Prof. Bas Teusink<br />
<br />
Date: 14 April 2015<br />
<br />
In this webinar, I will discuss the use of mathematical models in guiding targeted and (semi-)untargeted metabolomics efforts. I will show how genome-scale metabolic models can be used as a data integration platform - also for metabolomics data. I will provide an example from medium optimisation in a biotechnological context. My message is that the use of models upfront combined with quantitative and well-timed metabolomics is often much more effective than simply generating lots of data and subsequent statistical analysis.<br />
<br />
== [http://metabolomicssociety.org/site-map/articles/88-videos/187-2015-emn-webinars-public Large-scale, computational and empirical UHPLC-MS-SPE-NMR annotation of plant metabolomes] ==<br />
<br />
By: Dr. Lloyd Sumner<br />
<br />
Date: 5 March 2015<br />
<br />
Integrated metabolomics is a revolutionary systems biology tool for understanding plant metabolism and elucidating gene function. Although the vast utility of metabolomics is well documented in the literature, its<br />
full scientific promise has not yet been realized due to multiple technical challenges. The number one, grand challenge of metabolomics is the large-scale confident chemical identification of metabolites. To address<br />
this challenge, we have developed sophisticated computational and empirical metabolomics tools for the systematic and biological directed annotation of plant metabolomes. This presentation will introduce novel<br />
software entitled Plant Metabolite Annotation Toolbox (PlantMAT) and a sophisticated UHPLC-MS-SPENMR instrumental ensemble that are being used for ‘sequencing’ the first plant metabolomes of the model plant systems Arabidopsis and Medicago truncatula.<br />
<br />
== [http://metabolomicssociety.org/site-map/articles/88-videos/187-2015-emn-webinars-public Metabolomics: only suitable for multidisciplinary teams] ==<br />
<br />
By: Dr. Oscar Yanes<br />
<br />
Date: 29 Jan 2015<br />
<br />
Metabolomics is defined as the comprehensive and quantitative analysis of metabolites in living organisms. Among the omic sciences, metabolomics is possibly the most multidisciplinary of all, involving knowledge<br />
about electronic engineering and signal processing, analytical and organic chemistry, biostatistics and statistical physics, and biochemistry and cell metabolism. Here an untargeted metabolomics workflow will<br />
be detailed that provides examples of this multidisciplinarity.</div>Viniciusverihttp://wiki.metabolomicssociety.org/index.php?title=EMN_Webinars&diff=1558EMN Webinars2021-06-21T06:26:35Z<p>Viniciusveri: </p>
<hr />
<div>The [[Early-Career Members Network#EMN Committee|Early-Career Members Network (EMN) Committee]] has organized webinars since 2015 on a variety of metabolomics-related topics. This page provides a summary of all webinars to date with links to the webinars (when available).<br />
<br />
To participate on the live webinars, follow us on [https://twitter.com/EMN_MetSoc Twitter] and [https://www.facebook.com/EMN.MetabolomicsSociety Facebook] to get all updates from the EMN or subscribe to the Metabolomics Society website to receive the email invitation.<br />
<br />
<br />
=2021=<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/334-2021-emn-webinars-public TidyMS: a tool for preprocessing and Improving data quality in metabolomics]==<br />
<br />
Date: April 27, 2021<br />
<br />
'''TidyMS: a tool for preprocessing and Improving data quality in metabolomics<br />
<br />
By: Dr. María Eugenia Monge and Mr. Nicolás Zabalegui<br />
<br />
One of the challenges in untargeted metabolomics workflows is preprocessing data in a reproducible and robust way. In liquid chromatography-mass spectrometry (LC-MS), data curation involves removing biologically non-relevant features (retention time, m/z pairs) and retaining only those analytically robust enough to allow subsequent data analysis and interpretation. In this presentation, I will share the strategies that we used for quality control (QC) purposes in LC-MS-based untargeted studies that interrogated samples from in vitro models and serum samples as well as in an ambient-MS-based approach. In addition, I will introduce “TidyMS”, which is a new Python library for LC-MS data preprocessing that has recently been developed in our research group with the aim of performing QC procedures in a rapid and reproducible workflow. TidyMS includes tools to work with both raw MS data and data matrices. It provides functionality to build customized pipelines for data curation to obtain cleaned matrices from untargeted metabolomics and lipidomics studies for subsequent statistical analysis, and aims at ensuring accuracy and reliability in LC–MS measurements. The capabilities of TidyMS will be illustrated with pipelines for an LC-MS system suitability check, system conditioning, signal drift evaluation, and data curation. These applications were implemented to successfully preprocess data from a new suite of candidate human plasma reference materials currently under development by the National Institute of Standards and Technology (NIST; hypertriglyceridemic, diabetic, and African-American plasma pools), in addition to NIST SRM 1950 Metabolites in Frozen Human Plasma to be used in metabolomics and lipidomics studies. Overall, TidyMS can be used in an automated or semi-automated way, and it is an open and free tool available to all users.<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/334-2021-emn-webinars-public Viral infection in algal blooms and The glycosphingolipid-based arms race]==<br />
<br />
Date: March 22, 2021<br />
<br />
'''Viral infection in algal blooms and The glycosphingolipid-based arms race<br />
<br />
By: Prof. Assaf Vardi & Dr. Guy Schleyer<br />
<br />
<br />
=2020=<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/306-2020-emn-webinars-public New bio-statistical methods for metabolomics and metabolite annotation]== <br />
<br />
Date: December 22, 2020<br />
<br />
'''New bio-statistical methods for metabolomics<br />
<br />
By: Dr. Daniel Raftery<br />
<br />
Highly complex biological samples present challenging analysis problems for the field of metabolomics. Ideally, platforms that provide broad metabolome coverage and high data quality allow the opportunity for deep insights into biological problems. However, this goal can be difficult to achieve on a routine basis because the highly complex data are subject to matrix effects and complicated correlative relationships among many metabolites. As such metabolite identification, biomarker identification and validation can be very challenging. Advanced statistical methods are needed to deal with these issues for improved biomarker discovery, unknown identification and biological interpretation. We have pursued the development of a number of approaches that try to unravel the complex and multidimensional structure of metabolomics datasets, with some successes and some failures along the way. In this talk, I will provide some examples of where even non-experts in biostatistics can make progress in developing advanced analysis approaches and discuss some areas that provide significant challenges for future work.<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/306-2020-emn-webinars-public Metabolic networks]== <br />
<br />
Date: November 26, 2020<br />
<br />
'''Metabolic networks to enrich and interpret metabolic fingerprints<br />
<br />
By: Dr. Fabien Jourdan<br />
<br />
Metabolic modulation is a cornerstone cellular response to genetic or environmental stresses. This plasticity is going beyond central metabolism and may involve complex processes spanning several metabolic pathways. Hence, it is a key challenge to be able to decipher metabolic modulations in a systemic and global perspective.<br />
The aim of the computational methods and tools which will be presented is thus to consider the full complexity of metabolism. To do so, all metabolic reactions the cell is able to achieve are gathered in a single mathematical model call “genome scale metabolic network”. Based on this model, it is then possible to identify metabolic modulations associated to metabolic fingerprints or suggest metabolites of interest to enrich biochemical interpretation.<br />
<br />
<br />
<br />
'''Improving metabolic studies with diverse context-specific metabolic networks<br />
<br />
By: Dr. Pablo Rodriguez Mier<br />
<br />
Understanding deregulations of metabolism based on isolated measures of gene expression, protein or metabolite concentrations is a challenging task due to the interconnection of multiple processes. One solution is to extract, from generic genome-scale metabolic networks, the specific sub-network which is modulated in the studied condition. Many algorithms have been proposed for such context-specific network extraction based on experimental measurements. However, this process is subject to some randomness and variability, since multiple metabolic networks can model the metabolic state in a similarly adequate manner for the same experimental data. This means that for some condition and reconstruction method, there are usually multiple possible sub-networks that can explain the same experimental data, but most current methods ignore this fact and return a single sub-network instead. Ignoring this variability can not only lead to incorrect or incomplete explanations of the biological experiment, but also causes valuable information to be lost that could be used to improve predictions. In this talk we will see what context-specific metabolic sub-networks are, some of the limitations of the current methods, and how we can get a diverse set of sub-networks to improve predictions and obtain better mechanistic insights about the metabolism.<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/306-2020-emn-webinars-public Decomposing Complex Metabolite Mixtures...& Unraveling the neonatal metabolome]== <br />
<br />
Date: June 19, 2020<br />
<br />
'''Decomposing Complex Metabolite Mixtures through Substructure-based Metabolomics Workflows<br />
<br />
By: Justin J.J. van der Hooft<br />
<br />
In this webinar, Dr. Justin J.J. van der Hooft will start with an introduction on the challenges of metabolite annotation and identification in untargeted metabolomics experiments of complex mixtures typically encountered in natural products and food research. He will also show how MS2LDA has been successfully used to capture chemical knowledge from diverse plant, food, and microbial-related data sets. Dr. Justin J.J. van der Hooft will finish by highlighting the advantages of combining metabolome mining and annotation tools in public data of different plant and microbial-related studies.<br />
<br />
<br />
'''Unraveling the neonatal metabolome using mass spectral data mining tools<br />
<br />
By: Madeleine Ernst<br />
<br />
In this webinar, Dr. Madeleine Ernst will explain how mass spectral data mining tools, such as molecular networking through the community platform GNPS, MS2LDA, in silico structure prediction (e.g. Network Annotation Propagation) and ClassyFire can significantly enhance chemical structural annotation retrieved in clinical mass spectrometry-based metabolomics studies. Dr. Madeleine Ernst will also elucidate how metabolic signatures of neonatal health and disease can be unraveled, significantly enhancing biological interpretation and hypothesis generation in metabolomics studies.<br />
<br />
==Plant Metabolomics - Elucidating biochemicals mechanisms underlying plant-environment interactions== <br />
<br />
By: Dr. Fidele Tugizimana<br />
<br />
Date: May 1, 2020<br />
<br />
In this webinar, Dr. Fidele will provide a snapshot of applications of metabolomics in plant sciences, particularly in plant-environment interactions research. The webinar will highlight some examples of the use of metabolomics to elucidate hypothetical frameworks that describe the biochemistry underlying naïve and primed-plant responses to microbial infections. Furthermore, one of the novel (emerging) strategies for sustainable food production and food security is the use of biostimulants in agriculture industry. Application of metabolomics in decoding and understanding plant-biostimulant interactions will be also highlighted.<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/306-2020-emn-webinars-public Human metabolic individuality and its role in multi-omics based studies]== <br />
<br />
By: Dr. Karsten Suhre<br />
<br />
Date: February 4, 2020<br />
<br />
In this webinar, Dr. Suhre will discuss Genome-wide association studies with clinically relevant intermediate traits, such as gene expression, proteomics, and metabolomics, which unravelled numerous pathophysiological pathways and generated many hypotheses regarding the functional basis of complex disorders. More recently, similar approaches linked variation in epigenetic modifications, especially differential methylation of chromosomal CpG-pairs, to changes in gene expression and blood circulating metabolites. These large-scale population and patient cohort studies reflect experimental data obtained from naturally occurring variance of the general population where each individual may be viewed as an experiment conducted by Nature. The next and most challenging step on the way to a truly personalized approach to medicine is to translate the results from these large-scale omics studies to applications at the patient level. In this presentation,<br />
<br />
=2019=<br />
<br />
==Metabolomics as a tool for elucidating plant growth regulation== <br />
<br />
By: Dr. Camila Caldana<br />
Date: November 20, 2019<br />
<br />
Rising demand for food and fuels makes it crucial to develop breeding strategies for increasing crop yield/biomass. Plant biomass production is tightly associated with growth and relies on a tight regulation of a complex signaling network that integrates external and internal stimuli. The main goal of our group is to elucidate the processes underlying plant growth and production of biomass by combining physiology, metabolomics, and gene expression analyses. In my presentation, I will provide examples of i) how the evolutionary conserved Target of Rapamycin pathway fine-tunes metabolic homeostasis to promote biosynthetic growth in plants; ii) the potential of metabolite profiles to predict plant performance as biomarkers.<br />
<br />
==Discovering Metabolites that Alter Physiology, an Omics Perspective== <br />
<br />
By: Dr. Gary Siuzdak<br />
Date: September 18, 2019<br />
<br />
Metabolomics and the comprehensive analysis of the metabolome and lipidome has traditionally been pursued with the aim of identifying biomarkers in the diagnosis and prediction of disease. However, the value of metabolomics has been redefined from a simple biomarker identification tool to a technology for the discovery of active drivers of biological processes. It is now clear that the metabolome affects cellular physiology through modulation of other ‘omics’ levels, including the genome, epigenome, transcriptome and proteome. In this presentation, I will focus on our recent progress in using metabolomics to understand how the metabolome influences other omics and, by extension, to reveal the active role of metabolites in physiology and disease. This concept of utilizing metabolomics to perform activity screens to identify biologically active metabolites — which we term activity metabolomics — is already having a broad impact on biology.<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/298-2019-emn-webinars-public Lipidomics and epilipidomics – from analysis to data integration]== <br />
<br />
By: Dr. Maria Fedorova<br />
Date: July 17, 2019<br />
<br />
Lipidomics is a large-scale study of diversified molecular species of lipids aiming to address the identity, quantities, cellular and tissue distribution of lipids as well as related signalling and metabolic pathways. In addition to the native lipidome, lipid species derived from enzymatic and non-enzymatic modifications (the epilipidome) further increase regulatory capacity of the biological systems. High diversity of physico-chemical properties as well as large dynamic range of lipid concentrations in native lipidomes makes significantly challenge their analysis. For omics-wide high-throughput identification of lipid species from complex biological samples, several crucial analytical steps including extraction, chromatographic separation and mass spectrometry analysis need to be carefully considered and validated. The webinar will review current analytical strategies used in contemporary lipidomics and epilipidomics with the focus on optimization of LC-MS/MS based workflows for “discovery” lipidomics including sample preparation, lipid fractionation, separation using different chromatography techniques and high-throughput identification solutions. Available bioinformatics tools for identification of native and modified lipids will be described and compared as well as possible lipidomics data integration strategies.<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/298-2019-emn-webinars-public Metabolomics 2019: Professional Career Development: The Survival Kit]== <br />
<br />
By: Cathy Delhanty<br />
<br />
Date: June 23, 2019<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/298-2019-emn-webinars-public A Combined NMR and MS Metabolomics Approach to Study Neurodegenerative Diseases]== <br />
<br />
By: Dr. Robert Powers<br />
<br />
Date: May 24, 2019<br />
<br />
The metabolome captures how the system responds to drug treatment, disease state, or genetic modification. In this regards, metabolomics is an invaluable approach to easily and rapidly diagnose human disease and to assist in personalized medicine by monitoring a patient’s response to treatments. But, metabolomics is deceivingly complex with numerous sources of errors and technical challenges at every step of the process. One specific challenge is achieving a complete and accurate coverage of the metabolome, which can be addressed by combining NMR and mass spectrometry. Our metabolomics protocols and MVAPACK software for integrating NMR and mass spectral data for the analysis of neurodegenerative disease will be discussed. Our investigation into the molecular mechanisms of Parkinson’s disease and the identification of biomarkers for multiple sclerosis will be highlighted.<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/298-2019-emn-webinars-public Computational mass spectrometry in metabolomics to deepen the understanding of metabolisms]== <br />
<br />
By: Dr. Hiroshi Tsugawa<br />
<br />
Date: April 23, 2019<br />
<br />
Computational mass spectrometry is a growing research field to process mass spectrometry data, assist the interpretation of mass fragmentations, and elucidate unknown structures with metabolome databases and repositories for the global identification of metabolomes in various living organisms. In this talk, Dr Tsugawa will introduce three metabolomics software programs which include (1) MS-DIAL for untargeted metabolomics, (2) MS-FINDER for structure elucidations of unknowns, and (3) MRMPROBS for targeted metabolomics. These programs are demonstrated to perform the comprehensive analyses of primary metabolites, lipids, and plant specialized metabolites where unknown metabolites are also untangled with various methodologies including stable isotope labeled organisms, metabolite class recommendations, and integrated metabolome network analyses. In addition, a computational workflow to link untargeted- and targeted metabolomics is also highlighted in this talk.<br />
<br />
==Multidimensional Chromatography, what is the synergy with untargeted metabolomics profiling?== <br />
<br />
By: Dr. Pierre-Hugues Stefanuto<br />
<br />
Date: March 28, 2019<br />
<br />
In this webinar, Dr Pierre-Hugues Stefanuto will discuss the new development of multidimensional chromatography and the synergy with metabolomics. This presentation will be broadcasted in the context of the Multidimensional Chromatography Workshop held in Liège last January (http://multidimensionalchromatography.com). During this event, four focus group discussions were organized: 1) data processing for untargeted screening, 2) minimum reporting information for QC and compound validation, 3) hyphenation of MDGC with high-resolution MS, 4) and the general acceptance of MDC techniques. He will illustrate these topics through some ongoing medical research articulated around volatile organic compound (VOC) measurements in human breath and in vitro in metabolomic applications.<br />
<br />
==Untargeted metabolomics reveals smokers' characteristic profiles== <br />
<br />
By: Dr. Ping-Ching Hsu<br />
<br />
Date: March 1, 2019<br />
<br />
=2018=<br />
<br />
==Recent advances towards integrating metabolomics in kinetic models of large metabolic networks== <br />
<br />
By: Prof. Lars Nielsen<br />
<br />
Date: October 15, 2018<br />
<br />
Lars Nielsen is Chair of Biological Engineering at the Australian Institute for Bioengineering &Nanotechnology and Scientific Director for the Section for Quantitative Modelling of Cellular Metabolism at the Novo Nordisk Foundation Center for Biosustainability in Denmark. He is Director of the Bioplatforms Australia Queensland Node for Metabolomics and Proteomics, which provides systems and synthetic biology support to design and build cell factories for the production of fuels, chemicals and pharmaceuticals. His core research interest is modelling of cellular metabolism and his team has made many contributions to the formulation and use of genome scale models. He recently received a Novo Nordisk Foundation Laureate Research Grant to develop large scale, mathematical models to explore and explain the molecular basis for homeostasis–the self-regulating processes evolved to maintain metabolic equilibrium. Studying homeostasis is relevant for the understanding and treatment of complex diseases, particular with the emergence of personalized medicine. It is equally important when we seek to repurpose the cellular machinery for the production of desired chemicals, materials and pharmaceuticals.<br />
<br />
==Metabolomics-based Elucidation of Plant Specialized Metabolism== <br />
<br />
By: Prof. Kazuki Saito<br />
<br />
Date: July 25, 2018<br />
<br />
The recent advances of genomics and metabolomics in plant science accelerate our understanding about the mechanism, regulation and evolution of biosynthesis of plant specialized products. We can now address the questions how the metabolomic diversity of plants is originated at the levels of genome (phytochemical genomics) and how we should apply this knowledge to drug discovery, industry and agriculture. In this presentation, at first, technological developments of metabolomic analysis will be discussed forthe better understanding chemical diversity of plants. Then, a couple of examples of application of metabolomics to functional genomics of specialized metabolism in a model plant Arabidopsis thalianawill be presented, focusing on the biosynthesis of phenylpropanoids and lipids. The further extension to crops and medicinal plants producing a variety of specialized metabolites will be presented.<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/277-2018-emn-webinars-public Activity Metabolomics: Technologies to identify metabolites that modulate phenotype]== <br />
<br />
By: Prof. Gary Siuzdak<br />
<br />
Date: May 29, 2018<br />
<br />
Metabolomics is broadly acknowledged to be the omics discipline closest tothe phenotype and therefore widely used for biomarker discovery. However,metabolomics can also be designed to identify active metabolites thatalter a cell’s or an organism's phenotype.This "Activity Metabolomics”concept integrates metabolomics data analysis with pathway and systemsbiology data, ultimately to select endogenous metabolites that can bescreened for functionality. A growing literature reports the use ofmetabolites to modulate diverse processes including stem celldifferentiation, oligodendrocytematuration, insulin signaling,T-cellsurvival and macrophage immune responses. We have developed XCMS Online(xcmsonline.scripps.edu) and the newly expanded METLIN database (now withover 50,000 standards containing MS/MS data) to perform untargeted andtargeted metabolomics, as well as pathway analysis and systems biologydata integration.Metabolomics Activity Screening (MAS) has beenimplemented within XCMS Online to help achieve this integration goal foridentifying active metabolites. Because metabolites are often readilyavailable, activity metabolomics is uniquely positioning its practitionersto move beyond biomarkers, and become active participants in thebiological endgame of modulating phenotype. (for more information seeNature Biotechnology 2018 nature.com/articles/nbt.4101)<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/277-2018-emn-webinars-public Use better software: MS-DIAL instead of XCMS, ChemRich/MetDA instead of Metaboanalyst]== <br />
<br />
By: Dr. Oliver Fiehn<br />
<br />
Date: April 24, 2018<br />
<br />
XCMS and MetaboAnalystare the two most popular tools used by metabolomics researchers. But are these the best options? The West Coast Metabolomics Center at UC Davis has collaborated with RIKEN/NGI in Japan to release MS-DIAL vs.2 that yields far fewer false positive peak detections in untargeted LC-MS/MS runs than XCMS, with superior integration of compound identification software. MS-DIAL now also works on low or high resolution GC-MS data, making it the tool of choice for raw data processing of any mass spectrometry-based metabolomics study.We have also developed alternative software suites for statistical analysis of final result data. ChemRich uses all identified metabolites, including complex lipids, for set enrichment statistics. In comparison, MetaboAnalyst cannot perform pathway enrichment statistics on more than half of all identified metabolites, because it relies on KEGG pathways only. Moreover, we have developed new software for improved data normalization and statistics workflows in MetDA web-based analyses that will be presented on published example data.<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/277-2018-emn-webinars-public Can we capture an accurate view of cell or tissue specific metabolism from an expression profile?]== <br />
<br />
By: Dr. Nathan Lewis<br />
<br />
Date: March 26, 2018<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/277-2018-emn-webinars-public Metabolomics as a hypothesis generator for understanding metabolic regulation]== <br />
<br />
By: Prof. Uwe Sauer<br />
<br />
Date: February 14, 2018<br />
<br />
Prof. Uwe Sauer focuses on two conceptual problems: i) discovery of functionally important regulation mechanisms and ii) understanding which of the many known mechnisns actually matter for a given adaption. On the discovery side, he illustrates the use of coarse-grained kinetic models to extract mechnistic hypotheses from dynamic metabolomics data. For learning the coordination mechanisms, he presents an approach that hypothesizes the dynamically important mechnism from the much fewer steady state measurements in the bacterium E. coli. The surprising result is that only very few regulation events appear to be required for a given transition, typically involving less than a handful of active regulators.<br />
=2017=<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/258-2017-emn-webinars-public Introduction to Stable Isotope Resolved Metabolomics (SIRM) and Applications to Cancer Metabolism]==<br />
<br />
By: Dr. Andrew Lane<br />
<br />
Date: December 14, 2017<br />
<br />
Stable isotope resolved metabolomics (SIRM), for pathway tracing, represents an important new approach to obtaining metabolic parameters. SIRM allows the generation of atomic fate maps in cells and tissues, which provides the necessary information and data for metabolic flux analyis. This powerful new approach has already provided important new insights into metabolic adaptations in lung cancer cells.<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/258-2017-emn-webinars-public Using PhenoMeNal for your metabolomics data analysis on the cloud]==<br />
<br />
By: Dr. Pablo Moreno<br />
<br />
Date: October 24, 2017<br />
<br />
PhenoMeNal aims to bridge the gap between cloud computing and metabolomics researchers by providing the ability to create Cloud Research Environments (CRE) for metabolomics data analysis. A PhenoMeNal CRE is a small cluster of computers with popular metabolomics data analysis tools already installed. These tools are ready to be run and are accessible through a user friendly Galaxy workflow environment reducing the need for in-house bioinformatics. The PhenoMeNal CRE not only includes data analysis tools, but also example workflows where some of these tools are used together. You can also make your own workflows inside the CRE. In this webinar we will explain the main components of PhenoMeNal. We will demonstrate how to register, access existing tools and workflows, create a new PhenoMeNal CRE on Amazon, and execute a workflow on a PhenoMeNal CRE.<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/258-2017-emn-webinars-public Machine learning powered metabolomic network analysis]==<br />
<br />
By: Dr. Dmitry Grapov <br />
<br />
Date: May 30, 2017<br />
<br />
Metabolomic network analysis can be used to interpret experimental results within a variety of contexts including: biochemical relationships, structural and spectral similarity and empirical correlation. Machine learning is useful for modeling relationships in the context of pattern recognition, clustering, classification and regression based predictive modeling. The combination of developed metabolomic networks and machine learning based predictive models offer a unique method to visualize empirical relationships while testing key experimental hypotheses. The following presentation focuses on data analysis, visualization, machine learning and network mapping approaches used to create richly mapped metabolomic networks.<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/258-2017-emn-webinars-public How well do I quantify? Concepts for method validation and evaluation of measurement uncertainty in metabolomics]==<br />
<br />
By: Assoc. Prof. Stephan Hann<br />
<br />
Date: March 24, 2017<br />
<br />
This webinar will give an introduction to the basic terminology and principles of validation and measurement uncertainty in metabolomics. It will be demonstrated how validation parameters are determined in selected examples (e.g. LC-MS/MS, GC-MS/MS) for quantitative metabolomic analysis. Different quantification approaches will be overviewed in detail, and tips on choosing the most appropriate analytical strategies to answer metabolome-level hypotheses will be given. Finally, a workflow for estimating measurement uncertainty in metabolomics will be presented and discussed.<br />
<br />
=2016=<br />
<br />
== [http://metabolomicssociety.org/site-map/articles/88-videos/218-2016-emn-webinars-public Smarter ways to clean LC-MS] ==<br />
<br />
By: Assoc. Prof. Carl Brunius<br />
<br />
Date: November 17, 2016<br />
<br />
LC-MS is the most frequently used technique for untargeted metabolomics owing to broad coverage of metabolites, high sensitivity and simple sample preparation. However, instrument data tends to have high noise contribution from drift in signal intensity, mass accuracy and retention times. This noise has both within batch and between batch contributions and results in reduced measurement repeatability and reproducibility. The power to detect biological responses may thus be decreased and interpretations consequently obscured. Dr. Carl Brunius (the speaker) is involved in developing procedures to address and correct for within- and between-batch variability in processing multiple-batch untargeted LC-MS metabolomics data to increase their quality. This webinar session will talk about (i) alignment and merging of LC-MS features that are systematically misaligned between batches and (ii) within batch intensity drift correction that allows multiple drift patterns within batch. These algorithms will be applied on authentic data, resulting in improved peak picking performance and decreased noise in the dataset. All algorithms are developed as open source and are, together with example data, freely available as an R package from https://GitLab.com/CarlBrunius/batchCorr.<br />
== [http://metabolomicssociety.org/site-map/articles/88-videos/218-2016-emn-webinars-public Mass Spectral Libraries] ==<br />
<br />
By: Dr. Emma Schymanski<br />
<br />
Date: October 6, 2016<br />
<br />
Mass spectrometry is applied in diverse ways in metabolomics research and the mass spectrum of a small molecule can act as a fingerprint for identification. Just as the scientific questions in metabolomics vary, there is a diverse set of mass spectral libraries available to assist in the identification of metabolites and other small molecules. This webinar aims to provide listeners with a brief overview of several different mass spectral<br />
resources, including a personal view on pros and cons of the different options – providing a basis for listeners to choose the resource(s) that may best suit their investigation and needs. Additional information about substance overlap, spectral matching, identification confidence and spectral exchange will also be given – as well as some factors to consider carefully. Finally, some perspectives towards in silico identification without spectral libraries will be given to lead into a topic for a future webinar.<br />
<br />
== [http://metabolomicssociety.org/site-map/articles/88-videos/218-2016-emn-webinars-public Targeted High-throughput Lipidomics Platform: Development and Application] ==<br />
<br />
By: Dr. Peter Meikle<br />
<br />
Date: July 27, 2016<br />
<br />
Lipidomics, full analysis of lipid species and their biological roles with respect to health and diseases, has attracted increasing attention of biological and analytical scientific community. Knowing and understanding steps involved in lipidomics experimental workflow is essential for successful outcome. The Metabolomics Laboratory at Baker IDI Heart and Diabetes Institute (Melbourne, Australia) has a focus on the dyslipidemia and altered lipid metabolism associated with obesity, diabetes and cardiovascular disease and its relationship to the pathogenesis of these disease states. The laboratory has developed a targeted lipidomics platform that is able to quantify over 500 lipid species in 15 minutes using liquid chromatography coupled to electrospray ionisation tandem mass spectrometry. This platform is being applied to identify new approaches to early diagnosis and risk assessment as well as the development of new lipid modulating therapies for chronic disease. With illustration from this well-established lipidomics platform, this webinar will discuss the development of targeted high-throughput lipidomics platform, including selection and characterisation of lipid species, development of chromatography and quality control in the analysis of large sample sets. The presentation will draw on specific examples to highlight the application to large cohort studies and the Institute’s work to develop new therapeutic strategies for cardiovascular disease.<br />
<br />
== [http://metabolomicssociety.org/site-map/articles/88-videos/218-2016-emn-webinars-public From Features to Compounds: How To Get Started and Move Forward]==<br />
<br />
By: Dr. Jan Stanstrup<br />
<br />
Date: May 27, 2016<br />
<br />
In the untargeted analysis of complex mixtures the identification of compounds is the fundamental step enabling the results to be put in biological context. It is therefore of crucial importance for early career scientists approaching the field of metabolomics to familiarize themselves with this step. Many tools have been developed to aid identification; however, compound identification still constitutes one of the main bottlenecks in metabolomics and still requires substantial amounts of manual work. This webinar will go through the basic concepts used in MS-based compound identification and will introduce a number of relevant tools and databases allowing researchers to approach identification in a systematic way. The webinar is in particular dedicated to those researchers coming into the field of metabolomics that are not familiar with the methods used for compound identification and for which getting started can be a daunting and time-consuming challenge.<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/218-2016-emn-webinars-public Chromatography and Metabolomics: What’s the Point?] ==<br />
<br />
By: Dr. Karl Burgess<br />
<br />
Date: April 29, 2016<br />
<br />
The key to both chromatography and mass spectrometry is the separation of chemical species based on their physicochemical properties. As metabolomics researchers, we look to improved chromatography so enable us to<br />
detect compounds we previously had trouble with, to reduce the enormous complexity of the samples we analyse, and to clean up samples before or during analysis. Advances in mass spectrometry bring us greater sensitivity, better selectivity and a toolbox of techniques to aid in identification of biochemicals. With all these advantages come many disadvantages - poor reproducibility, compound bias and contamination. In this webinar, I'll explore chromatography and mass spectrometry with a critical eye. How can we improve them? Do we need them in every experiment? And really, what's the point of metabolomics at all?<br />
<br />
==[http://metabolomicssociety.org/site-map/articles/88-videos/218-2016-emn-webinars-public How close are we to metabolomics reproducibility? Data sharing, data standards and workflows for metabolomics]==<br />
<br />
By: Dr. Reza Salek<br />
<br />
Date: March 24, 2016<br />
<br />
=2015=<br />
<br />
== [http://metabolomicssociety.org/site-map/articles/88-videos/187-2015-emn-webinars-public Metabolomics and Beyond: Challenges and Strategies for Next-gen Omic Analyses] ==<br />
<br />
By: Dr. Dmitry Grapov<br />
<br />
Date: September 15, 2015<br />
<br />
Robust interpretation of experimental results measuring discreet biological domains remains a significant challenge in the face of complex biochemical regulation processes such as organismal versus tissue versus<br />
cellular metabolism, epigenetics, and protein post-translational modification. Integration of analyses carried out across multiple measurement or omic platforms is an emerging approach to help address these<br />
challenges. The following presentation will focus on key challenges faced by metabolomics researchers in the areas large-scale studies data normalization, multivariate analysis, visualization and omics data integration.<br />
<br />
== [http://metabolomicssociety.org/site-map/articles/88-videos/187-2015-emn-webinars-public Metabolomics with high resolution mass spectrometry: from spectral databases to biomarker discovery] ==<br />
<br />
By: Dr. Christophe Junot<br />
<br />
Date: 12 June 2015<br />
<br />
Since the middle of the 2000s, high resolution mass spectrometry (HRMS) is widely used in metabolomics, mainly because the detection and identification of metabolites are improved compared to low resolution instruments. Thanks to their versatility, HRMS instruments are the most appropriate to achieve an optimal metabolome coverage, at the border of other omics fields such as lipidomics and glycomics. The aim of this talk will be to present HRMS based tools for metabolomics and lipidomics developed at the laboratory, and their relevance to the field of biomarker discovery for the diagnosis and follow-up of pathologies.<br />
<br />
== [http://metabolomicssociety.org/site-map/articles/88-videos/187-2015-emn-webinars-public#|: Metabolomics and systems biology: models as guide and natural integrator]==<br />
<br />
By: Prof. Bas Teusink<br />
<br />
Date: 14 April 2015<br />
<br />
In this webinar, I will discuss the use of mathematical models in guiding targeted and (semi-)untargeted metabolomics efforts. I will show how genome-scale metabolic models can be used as a data integration platform - also for metabolomics data. I will provide an example from medium optimisation in a biotechnological context. My message is that the use of models upfront combined with quantitative and well-timed metabolomics is often much more effective than simply generating lots of data and subsequent statistical analysis.<br />
<br />
== [http://metabolomicssociety.org/site-map/articles/88-videos/187-2015-emn-webinars-public Large-scale, computational and empirical UHPLC-MS-SPE-NMR annotation of plant metabolomes] ==<br />
<br />
By: Dr. Lloyd Sumner<br />
<br />
Date: 5 March 2015<br />
<br />
Integrated metabolomics is a revolutionary systems biology tool for understanding plant metabolism and elucidating gene function. Although the vast utility of metabolomics is well documented in the literature, its<br />
full scientific promise has not yet been realized due to multiple technical challenges. The number one, grand challenge of metabolomics is the large-scale confident chemical identification of metabolites. To address<br />
this challenge, we have developed sophisticated computational and empirical metabolomics tools for the systematic and biological directed annotation of plant metabolomes. This presentation will introduce novel<br />
software entitled Plant Metabolite Annotation Toolbox (PlantMAT) and a sophisticated UHPLC-MS-SPENMR instrumental ensemble that are being used for ‘sequencing’ the first plant metabolomes of the model plant systems Arabidopsis and Medicago truncatula.<br />
<br />
== [http://metabolomicssociety.org/site-map/articles/88-videos/187-2015-emn-webinars-public Metabolomics: only suitable for multidisciplinary teams] ==<br />
<br />
By: Dr. Oscar Yanes<br />
<br />
Date: 29 Jan 2015<br />
<br />
Metabolomics is defined as the comprehensive and quantitative analysis of metabolites in living organisms. Among the omic sciences, metabolomics is possibly the most multidisciplinary of all, involving knowledge<br />
about electronic engineering and signal processing, analytical and organic chemistry, biostatistics and statistical physics, and biochemistry and cell metabolism. Here an untargeted metabolomics workflow will<br />
be detailed that provides examples of this multidisciplinarity.</div>Viniciusverihttp://wiki.metabolomicssociety.org/index.php?title=Software&diff=1557Software2021-06-21T05:56:22Z<p>Viniciusveri: </p>
<hr />
<div>This page contains a list of the most widely used freely available software and tools that are used primarily in metabolomics based on the review article by Spicer et al<ref>Spicer R, Salek RM, Moreno P, Cañueto D, Steinbeck C., "Navigating freely-available software tools for metabolomics analysis", Metabolomics. 2017;13(9):106. doi: 10.1007/s11306-017-1242-7.</ref>.<br />
<br />
=Software tools for data preprocessing=<br />
[http://bioconductor.org/packages/release/bioc/html/xcms.html XCMS] for LC-MS and GC-MS<br />
<br />
[http://www.wageningenur.nl/en/show/MetAlign-1.htm MetAlign] for LC-MS<br />
<br />
[http://prime.psc.riken.jp/Metabolomics_Software/MS-DIAL/ MS-DIAL] for LC-MS <br />
<br />
[http://mzmatch.sourceforge.net/index.php mzMatch] for LC-MS<br />
<br />
[http://mzmatch.sourceforge.net/ideom.php IDEOM] for LC-MS<br />
<br />
[http://chemdata.nist.gov/dokuwiki/doku.php?id=chemdata:amdis AMDIS] for GC-MS<br />
<br />
[http://md.tu-bs.de MetaboliteDetector] for GC-MS<br />
<br />
[https://meltdb.cebitec.uni-bielefeld.de/cgi-bin/login.cgi MeltDB] for LC-MS and GC-MS<br />
<br />
[http://bioconductor.org/packages/release/bioc/html/metaMS.html metaMS] for GC-MS<br />
<br />
[http://spectconnect.mit.edu SpectConnect] for GC-MS<br />
<br />
[http://rnmr.nmrfam.wisc.edu rNMR] for NMR<br />
<br />
=Software tools for data post-processing=<br />
[https://gitlab.com/CarlBrunius/batchCorr batchCorr] for LC-MS<br />
<br />
[https://cran.r-project.org/web/packages/crmn/ crmn] for LC-MS and GC-MS<br />
<br />
[https://sourceforge.net/projects/eigenms EigenMS] for LC-MS<br />
<br />
[https://cran.r-project.org/web/packages/KMDA/ KMDA] for MS<br />
<br />
[https://www.bioconductor.org/packages/release/bioc/html/metabomxtr.html metabomxtr] for LC-MS and GC-MS<br />
<br />
[https://sourceforge.net/projects/metabnorm Metabnorm] for NMR<br />
<br />
[http://metabr.r-forge.r-project.org/ MetabR] for LC-MS<br />
<br />
[https://cran.r-project.org/web/packages/MetNorm/ MetNorm] for LC-MS, GC-MS and NMR<br />
<br />
[https://sourceforge.net/projects/msprep/ MSPrep] for LC-MS<br />
<br />
[https://cran.r-project.org/web/packages/muma/ muma] for MS and NMR<br />
<br />
<br />
=Software tools for statistical analysis=<br />
[https://sourceforge.net/projects/ionwinze Ionwinze] for LC-MS<br />
<br />
[https://cran.r-project.org/web/packages/MetabolAnalyze MetabolAnalyze] for MS and NMR<br />
<br />
[https://cran.r-project.org/web/packages/muma/ muma] for MS and NMR<br />
<br />
[https://www.bioconductor.org/packages/release/bioc/html/ropls.html ropls] for MS and NMR<br />
<br />
[http://www.metaboanalyst.ca/ MetaboAnalyst] for LC-MS and NMR<br />
<br />
<br />
=Software tools for metabolite annotation=<br />
[http://bioconductor.org/packages/release/bioc/html/CAMERA.html CAMERA], Level 4<br />
<br />
[http://bioconductor.org/packages/release/bioc/html/Rdisop.html Rdisop], Level 4<br />
<br />
[https://bio.informatik.uni-jena.de/software/sirius SIRIUS and CSI Finger ID], Level 4<br />
<br />
[http://labpib.fmrp.usp.br/methods/probmetab ProbMetab], Level 3<br />
<br />
[http://mzmatch.sourceforge.net/index.php MetAssign–mzMatch], Level 3<br />
<br />
[http://ipb-halle.github.io/MetFrag/ MetFrag], Level 2a<br />
<br />
[https://sourceforge.net/projects/cfm-id/ CFM-ID], Level 2a<br />
<br />
[https://github.com/icdishb/fingerid FingerID], Level 2a<br />
<br />
[http://www.emetabolomics.org/magma MAGMa], Level 2a<br />
<br />
[http://mycompoundid.org/mycompoundid_IsoMS MyCompoundID], Level 2a<br />
<br />
[http://batman.r-forge.r-project.org BATMAN], NMR<br />
<br />
[http://bayesil.ca Bayesil], NMR<br />
<br />
[http://wishart.biology.ualberta.ca/metabominer MetaboMiner], NMR<br />
<br />
[http://prime.psc.riken.jp/?action=nmr_search SpinAssign], NMR<br />
<br />
[http://spin.ccic.ohio-state.edu/index.php/colmar COLMAR], NMR<br />
<br />
[http://www.massbank.jp/Search MassBank]<br />
<br />
[https://msbi.ipb-halle.de/MetFusion/ MetFusion]<br />
<br />
[http://cfmid.wishartlab.com/ CDM-ID]<br />
<br />
[http://ms2lda.org MS2LDA]<br />
<br />
[https://gnps.ucsd.edu/ProteoSAFe/static/gnps-splash.jsp GNPS]<br />
<br />
<br />
=Workflows for the analysis of metabolomics data=<br />
[https://github.com/Viant-Metabolomics/Galaxy-M Galaxy-M] for LC-MS<br />
<br />
[https://gnps.ucsd.edu/ProteoSAFe/static/gnps-splash.jsp GNPS] for LC-MS<br />
<br />
[http://www.metaboanalyst.ca MetaboAnalyst 4.0] for LC-MS and NMR<br />
<br />
[http://genomics-pubs.princeton.edu/mzroll/index.php MAVEN] for LC-MS<br />
<br />
[https://www.bioconductor.org/packages/release/bioc/html/MAIT.html MAIT] for LC-MS<br />
<br />
[http://mzmine.github.io/ MZmine 2] for LC-MS<br />
<br />
[http://workflow4metabolomics.org Workflow4metabolomics] for LC-MS, GC-MS and NMR<br />
<br />
[https://xcmsonline.scripps.edu/landing_page.php?pgcontent=mainPage XCMS Online] for LC-MS and GC-MS<br />
<br />
<br />
<br />
<br />
''"Disclaimer: The links provided here are for informational purposes only. They do not constitute an endorsement or recommendation by the Metabolomics Society. The Metabolomics Society bears no responsibility for the accuracy, timeliness or completeness of information contained on a linked website".''<br />
<br />
<br />
=References=</div>Viniciusverihttp://wiki.metabolomicssociety.org/index.php?title=Software&diff=1556Software2021-06-21T05:56:08Z<p>Viniciusveri: </p>
<hr />
<div>This page contains a list of the most widely used freely available software and tools that are used primarily in metabolomics based on the review article by Spicer et al<ref>Spicer R, Salek RM, Moreno P, Cañueto D, Steinbeck C., "Navigating freely-available software tools for metabolomics analysis", Metabolomics. 2017;13(9):106. doi: 10.1007/s11306-017-1242-7.</ref>.<br />
<br />
=Software tools for data preprocessing=<br />
[http://bioconductor.org/packages/release/bioc/html/xcms.html XCMS] for LC-MS and GC-MS<br />
<br />
[http://www.wageningenur.nl/en/show/MetAlign-1.htm MetAlign] for LC-MS<br />
<br />
[http://prime.psc.riken.jp/Metabolomics_Software/MS-DIAL/ MS-DIAL] for LC-MS <br />
<br />
[http://mzmatch.sourceforge.net/index.php mzMatch] for LC-MS<br />
<br />
[http://mzmatch.sourceforge.net/ideom.php IDEOM] for LC-MS<br />
<br />
[http://chemdata.nist.gov/dokuwiki/doku.php?id=chemdata:amdis AMDIS] for GC-MS<br />
<br />
[http://md.tu-bs.de MetaboliteDetector] for GC-MS<br />
<br />
[https://meltdb.cebitec.uni-bielefeld.de/cgi-bin/login.cgi MeltDB] for LC-MS and GC-MS<br />
<br />
[http://bioconductor.org/packages/release/bioc/html/metaMS.html metaMS] for GC-MS<br />
<br />
[http://spectconnect.mit.edu SpectConnect] for GC-MS<br />
<br />
[http://rnmr.nmrfam.wisc.edu rNMR] for NMR<br />
<br />
=Software tools for data post-processing=<br />
[https://gitlab.com/CarlBrunius/batchCorr batchCorr] for LC-MS<br />
<br />
[https://cran.r-project.org/web/packages/crmn/ crmn] for LC-MS and GC-MS<br />
<br />
[https://sourceforge.net/projects/eigenms EigenMS] for LC-MS<br />
<br />
[https://cran.r-project.org/web/packages/KMDA/ KMDA] for MS<br />
<br />
[https://www.bioconductor.org/packages/release/bioc/html/metabomxtr.html metabomxtr] for LC-MS and GC-MS<br />
<br />
[https://sourceforge.net/projects/metabnorm Metabnorm] for NMR<br />
<br />
[http://metabr.r-forge.r-project.org/ MetabR] for LC-MS<br />
<br />
[https://cran.r-project.org/web/packages/MetNorm/ MetNorm] for LC-MS, GC-MS and NMR<br />
<br />
[https://sourceforge.net/projects/msprep/ MSPrep] for LC-MS<br />
<br />
[https://cran.r-project.org/web/packages/muma/ muma] for MS and NMR<br />
<br />
<br />
=Software tools for statistical analysis=<br />
[https://sourceforge.net/projects/ionwinze Ionwinze] for LC-MS<br />
<br />
[https://cran.r-project.org/web/packages/MetabolAnalyze MetabolAnalyze] for MS and NMR<br />
<br />
[https://cran.r-project.org/web/packages/muma/ muma] for MS and NMR<br />
<br />
[https://www.bioconductor.org/packages/release/bioc/html/ropls.html ropls] for MS and NMR<br />
<br />
[http://www.metaboanalyst.ca/ MetaboAnalyst] for LC-MS and NMR<br />
<br />
<br />
=Software tools for metabolite annotation=<br />
[http://bioconductor.org/packages/release/bioc/html/CAMERA.html CAMERA], Level 4<br />
<br />
[http://bioconductor.org/packages/release/bioc/html/Rdisop.html Rdisop], Level 4<br />
<br />
[https://bio.informatik.uni-jena.de/software/sirius SIRIUS and CSI Finger ID], Level 4<br />
<br />
[http://labpib.fmrp.usp.br/methods/probmetab ProbMetab], Level 3<br />
<br />
[http://mzmatch.sourceforge.net/index.php MetAssign–mzMatch], Level 3<br />
<br />
[http://ipb-halle.github.io/MetFrag/ MetFrag], Level 2a<br />
<br />
[https://sourceforge.net/projects/cfm-id/ CFM-ID], Level 2a<br />
<br />
[https://github.com/icdishb/fingerid FingerID], Level 2a<br />
<br />
[http://www.emetabolomics.org/magma MAGMa], Level 2a<br />
<br />
[http://mycompoundid.org/mycompoundid_IsoMS MyCompoundID], Level 2a<br />
<br />
[http://batman.r-forge.r-project.org BATMAN], NMR<br />
<br />
[http://bayesil.ca Bayesil], NMR<br />
<br />
[http://wishart.biology.ualberta.ca/metabominer MetaboMiner], NMR<br />
<br />
[http://prime.psc.riken.jp/?action=nmr_search SpinAssign], NMR<br />
<br />
[http://spin.ccic.ohio-state.edu/index.php/colmar COLMAR], NMR<br />
<br />
[http://www.massbank.jp/Search MassBank]<br />
<br />
[https://msbi.ipb-halle.de/MetFusion/ MetFusion]<br />
<br />
[http://cfmid.wishartlab.com/ CDM-ID]<br />
<br />
[http://ms2lda.org MS2LDA]<br />
<br />
[https://gnps.ucsd.edu/ProteoSAFe/static/gnps-splash.jsp GNPS]<br />
<br />
<br />
=Workflows for the analysis of metabolomics data=<br />
[https://github.com/Viant-Metabolomics/Galaxy-M Galaxy-M] for LC-MS<br />
<br />
[https://gnps.ucsd.edu/ProteoSAFe/static/gnps-splash.jsp GNPS] for LC-MS<br />
<br />
[http://www.metaboanalyst.ca MetaboAnalyst 4.0] for LC-MS and NMR<br />
<br />
[http://genomics-pubs.princeton.edu/mzroll/index.php MAVEN] for LC-MS<br />
<br />
[https://www.bioconductor.org/packages/release/bioc/html/MAIT.html MAIT] for LC-MS<br />
<br />
[http://mzmine.github.io/ MZmine 2] for LC-MS<br />
<br />
[http://workflow4metabolomics.org Workflow4metabolomics] for LC-MS, GC-MS and NMR<br />
<br />
[https://xcmsonline.scripps.edu/landing_page.php?pgcontent=mainPage XCMS Online] for LC-MS and GC-MS<br />
<br />
<br />
<br />
<br />
''"Disclaimer: The links provided here are for informational purposes only. They do not constitute an endorsement or recommendation by the Metabolomics Society. The Metabolomics Society bears no responsibility for the accuracy, timeliness or completeness of information contained on a linked website".''<br />
<br />
<br />
<br />
=References=</div>Viniciusverihttp://wiki.metabolomicssociety.org/index.php?title=Software&diff=1555Software2021-06-21T05:55:31Z<p>Viniciusveri: </p>
<hr />
<div>This page contains a list of the most widely used freely available software and tools that are used primarily in metabolomics based on the review article by Spicer et al<ref>Spicer R, Salek RM, Moreno P, Cañueto D, Steinbeck C., "Navigating freely-available software tools for metabolomics analysis", Metabolomics. 2017;13(9):106. doi: 10.1007/s11306-017-1242-7.</ref>.<br />
<br />
=Software tools for data preprocessing=<br />
[http://bioconductor.org/packages/release/bioc/html/xcms.html XCMS] for LC-MS and GC-MS<br />
<br />
[http://www.wageningenur.nl/en/show/MetAlign-1.htm MetAlign] for LC-MS<br />
<br />
[http://prime.psc.riken.jp/Metabolomics_Software/MS-DIAL/ MS-DIAL] for LC-MS <br />
<br />
[http://mzmatch.sourceforge.net/index.php mzMatch] for LC-MS<br />
<br />
[http://mzmatch.sourceforge.net/ideom.php IDEOM] for LC-MS<br />
<br />
[http://chemdata.nist.gov/dokuwiki/doku.php?id=chemdata:amdis AMDIS] for GC-MS<br />
<br />
[http://md.tu-bs.de MetaboliteDetector] for GC-MS<br />
<br />
[https://meltdb.cebitec.uni-bielefeld.de/cgi-bin/login.cgi MeltDB] for LC-MS and GC-MS<br />
<br />
[http://bioconductor.org/packages/release/bioc/html/metaMS.html metaMS] for GC-MS<br />
<br />
[http://spectconnect.mit.edu SpectConnect] for GC-MS<br />
<br />
[http://rnmr.nmrfam.wisc.edu rNMR] for NMR<br />
<br />
=Software tools for data post-processing=<br />
[https://gitlab.com/CarlBrunius/batchCorr batchCorr] for LC-MS<br />
<br />
[https://cran.r-project.org/web/packages/crmn/ crmn] for LC-MS and GC-MS<br />
<br />
[https://sourceforge.net/projects/eigenms EigenMS] for LC-MS<br />
<br />
[https://cran.r-project.org/web/packages/KMDA/ KMDA] for MS<br />
<br />
[https://www.bioconductor.org/packages/release/bioc/html/metabomxtr.html metabomxtr] for LC-MS and GC-MS<br />
<br />
[https://sourceforge.net/projects/metabnorm Metabnorm] for NMR<br />
<br />
[http://metabr.r-forge.r-project.org/ MetabR] for LC-MS<br />
<br />
[https://cran.r-project.org/web/packages/MetNorm/ MetNorm] for LC-MS, GC-MS and NMR<br />
<br />
[https://sourceforge.net/projects/msprep/ MSPrep] for LC-MS<br />
<br />
[https://cran.r-project.org/web/packages/muma/ muma] for MS and NMR<br />
<br />
<br />
=Software tools for statistical analysis=<br />
[https://sourceforge.net/projects/ionwinze Ionwinze] for LC-MS<br />
<br />
[https://cran.r-project.org/web/packages/MetabolAnalyze MetabolAnalyze] for MS and NMR<br />
<br />
[https://cran.r-project.org/web/packages/muma/ muma] for MS and NMR<br />
<br />
[https://www.bioconductor.org/packages/release/bioc/html/ropls.html ropls] for MS and NMR<br />
<br />
[http://www.metaboanalyst.ca/ MetaboAnalyst] for LC-MS and NMR<br />
<br />
<br />
=Software tools for metabolite annotation=<br />
[http://bioconductor.org/packages/release/bioc/html/CAMERA.html CAMERA], Level 4<br />
<br />
[http://bioconductor.org/packages/release/bioc/html/Rdisop.html Rdisop], Level 4<br />
<br />
[https://bio.informatik.uni-jena.de/software/sirius SIRIUS and CSI Finger ID], Level 4<br />
<br />
[http://labpib.fmrp.usp.br/methods/probmetab ProbMetab], Level 3<br />
<br />
[http://mzmatch.sourceforge.net/index.php MetAssign–mzMatch], Level 3<br />
<br />
[http://ipb-halle.github.io/MetFrag/ MetFrag], Level 2a<br />
<br />
[https://sourceforge.net/projects/cfm-id/ CFM-ID], Level 2a<br />
<br />
[https://github.com/icdishb/fingerid FingerID], Level 2a<br />
<br />
[http://www.emetabolomics.org/magma MAGMa], Level 2a<br />
<br />
[http://mycompoundid.org/mycompoundid_IsoMS MyCompoundID], Level 2a<br />
<br />
[http://batman.r-forge.r-project.org BATMAN], NMR<br />
<br />
[http://bayesil.ca Bayesil], NMR<br />
<br />
[http://wishart.biology.ualberta.ca/metabominer MetaboMiner], NMR<br />
<br />
[http://prime.psc.riken.jp/?action=nmr_search SpinAssign], NMR<br />
<br />
[http://spin.ccic.ohio-state.edu/index.php/colmar COLMAR], NMR<br />
<br />
[http://www.massbank.jp/Search MassBank]<br />
<br />
[https://msbi.ipb-halle.de/MetFusion/ MetFusion]<br />
<br />
[http://cfmid.wishartlab.com/ CDM-ID]<br />
<br />
[http://ms2lda.org MS2LDA]<br />
<br />
[https://gnps.ucsd.edu/ProteoSAFe/static/gnps-splash.jsp GNPS]<br />
<br />
<br />
=Workflows for the analysis of metabolomics data=<br />
[https://github.com/Viant-Metabolomics/Galaxy-M Galaxy-M] for LC-MS<br />
<br />
[https://gnps.ucsd.edu/ProteoSAFe/static/gnps-splash.jsp GNPS] for LC-MS<br />
<br />
[http://www.metaboanalyst.ca MetaboAnalyst 4.0] for LC-MS and NMR<br />
<br />
[http://genomics-pubs.princeton.edu/mzroll/index.php MAVEN] for LC-MS<br />
<br />
[https://www.bioconductor.org/packages/release/bioc/html/MAIT.html MAIT] for LC-MS<br />
<br />
[http://mzmine.github.io/ MZmine 2] for LC-MS<br />
<br />
[http://workflow4metabolomics.org Workflow4metabolomics] for LC-MS, GC-MS and NMR<br />
<br />
[https://xcmsonline.scripps.edu/landing_page.php?pgcontent=mainPage XCMS Online] for LC-MS and GC-MS<br />
<br />
<br />
=References=<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
''"Disclaimer: The links provided here are for informational purposes only. They do not constitute an endorsement or recommendation by the Metabolomics Society. The Metabolomics Society bears no responsibility for the accuracy, timeliness or completeness of information contained on a linked website".''</div>Viniciusverihttp://wiki.metabolomicssociety.org/index.php?title=Software&diff=1554Software2021-06-21T05:55:12Z<p>Viniciusveri: </p>
<hr />
<div>This page contains a list of the most widely used freely available software and tools that are used primarily in metabolomics based on the review article by Spicer et al<ref>Spicer R, Salek RM, Moreno P, Cañueto D, Steinbeck C., "Navigating freely-available software tools for metabolomics analysis", Metabolomics. 2017;13(9):106. doi: 10.1007/s11306-017-1242-7.</ref>.<br />
<br />
=Software tools for data preprocessing=<br />
[http://bioconductor.org/packages/release/bioc/html/xcms.html XCMS] for LC-MS and GC-MS<br />
<br />
[http://www.wageningenur.nl/en/show/MetAlign-1.htm MetAlign] for LC-MS<br />
<br />
[http://prime.psc.riken.jp/Metabolomics_Software/MS-DIAL/ MS-DIAL] for LC-MS <br />
<br />
[http://mzmatch.sourceforge.net/index.php mzMatch] for LC-MS<br />
<br />
[http://mzmatch.sourceforge.net/ideom.php IDEOM] for LC-MS<br />
<br />
[http://chemdata.nist.gov/dokuwiki/doku.php?id=chemdata:amdis AMDIS] for GC-MS<br />
<br />
[http://md.tu-bs.de MetaboliteDetector] for GC-MS<br />
<br />
[https://meltdb.cebitec.uni-bielefeld.de/cgi-bin/login.cgi MeltDB] for LC-MS and GC-MS<br />
<br />
[http://bioconductor.org/packages/release/bioc/html/metaMS.html metaMS] for GC-MS<br />
<br />
[http://spectconnect.mit.edu SpectConnect] for GC-MS<br />
<br />
[http://rnmr.nmrfam.wisc.edu rNMR] for NMR<br />
<br />
=Software tools for data post-processing=<br />
[https://gitlab.com/CarlBrunius/batchCorr batchCorr] for LC-MS<br />
<br />
[https://cran.r-project.org/web/packages/crmn/ crmn] for LC-MS and GC-MS<br />
<br />
[https://sourceforge.net/projects/eigenms EigenMS] for LC-MS<br />
<br />
[https://cran.r-project.org/web/packages/KMDA/ KMDA] for MS<br />
<br />
[https://www.bioconductor.org/packages/release/bioc/html/metabomxtr.html metabomxtr] for LC-MS and GC-MS<br />
<br />
[https://sourceforge.net/projects/metabnorm Metabnorm] for NMR<br />
<br />
[http://metabr.r-forge.r-project.org/ MetabR] for LC-MS<br />
<br />
[https://cran.r-project.org/web/packages/MetNorm/ MetNorm] for LC-MS, GC-MS and NMR<br />
<br />
[https://sourceforge.net/projects/msprep/ MSPrep] for LC-MS<br />
<br />
[https://cran.r-project.org/web/packages/muma/ muma] for MS and NMR<br />
<br />
<br />
=Software tools for statistical analysis=<br />
[https://sourceforge.net/projects/ionwinze Ionwinze] for LC-MS<br />
<br />
[https://cran.r-project.org/web/packages/MetabolAnalyze MetabolAnalyze] for MS and NMR<br />
<br />
[https://cran.r-project.org/web/packages/muma/ muma] for MS and NMR<br />
<br />
[https://www.bioconductor.org/packages/release/bioc/html/ropls.html ropls] for MS and NMR<br />
<br />
[http://www.metaboanalyst.ca/ MetaboAnalyst] for LC-MS and NMR<br />
<br />
<br />
=Software tools for metabolite annotation=<br />
[http://bioconductor.org/packages/release/bioc/html/CAMERA.html CAMERA], Level 4<br />
<br />
[http://bioconductor.org/packages/release/bioc/html/Rdisop.html Rdisop], Level 4<br />
<br />
[https://bio.informatik.uni-jena.de/software/sirius SIRIUS and CSI Finger ID], Level 4<br />
<br />
[http://labpib.fmrp.usp.br/methods/probmetab ProbMetab], Level 3<br />
<br />
[http://mzmatch.sourceforge.net/index.php MetAssign–mzMatch], Level 3<br />
<br />
[http://ipb-halle.github.io/MetFrag/ MetFrag], Level 2a<br />
<br />
[https://sourceforge.net/projects/cfm-id/ CFM-ID], Level 2a<br />
<br />
[https://github.com/icdishb/fingerid FingerID], Level 2a<br />
<br />
[http://www.emetabolomics.org/magma MAGMa], Level 2a<br />
<br />
[http://mycompoundid.org/mycompoundid_IsoMS MyCompoundID], Level 2a<br />
<br />
[http://batman.r-forge.r-project.org BATMAN], NMR<br />
<br />
[http://bayesil.ca Bayesil], NMR<br />
<br />
[http://wishart.biology.ualberta.ca/metabominer MetaboMiner], NMR<br />
<br />
[http://prime.psc.riken.jp/?action=nmr_search SpinAssign], NMR<br />
<br />
[http://spin.ccic.ohio-state.edu/index.php/colmar COLMAR], NMR<br />
<br />
[http://www.massbank.jp/Search MassBank]<br />
<br />
[https://msbi.ipb-halle.de/MetFusion/ MetFusion]<br />
<br />
[http://cfmid.wishartlab.com/ CDM-ID]<br />
<br />
[http://ms2lda.org MS2LDA]<br />
<br />
[https://gnps.ucsd.edu/ProteoSAFe/static/gnps-splash.jsp GNPS]<br />
<br />
<br />
=Workflows for the analysis of metabolomics data=<br />
[https://github.com/Viant-Metabolomics/Galaxy-M Galaxy-M] for LC-MS<br />
<br />
[https://gnps.ucsd.edu/ProteoSAFe/static/gnps-splash.jsp GNPS] for LC-MS<br />
<br />
[http://www.metaboanalyst.ca MetaboAnalyst 4.0] for LC-MS and NMR<br />
<br />
[http://genomics-pubs.princeton.edu/mzroll/index.php MAVEN] for LC-MS<br />
<br />
[https://www.bioconductor.org/packages/release/bioc/html/MAIT.html MAIT] for LC-MS<br />
<br />
[http://mzmine.github.io/ MZmine 2] for LC-MS<br />
<br />
[http://workflow4metabolomics.org Workflow4metabolomics] for LC-MS, GC-MS and NMR<br />
<br />
[https://xcmsonline.scripps.edu/landing_page.php?pgcontent=mainPage XCMS Online] for LC-MS and GC-MS<br />
<br />
<br />
''"Disclaimer: The links provided here are for informational purposes only. They do not constitute an endorsement or recommendation by the Metabolomics Society. The Metabolomics Society bears no responsibility for the accuracy, timeliness or completeness of information contained on a linked website".''<br />
<br />
=References=</div>Viniciusverihttp://wiki.metabolomicssociety.org/index.php?title=Software&diff=1553Software2021-06-21T05:54:51Z<p>Viniciusveri: </p>
<hr />
<div>This page contains a list of the most widely used freely available software and tools that are used primarily in metabolomics based on the review article by Spicer et al<ref>Spicer R, Salek RM, Moreno P, Cañueto D, Steinbeck C., "Navigating freely-available software tools for metabolomics analysis", Metabolomics. 2017;13(9):106. doi: 10.1007/s11306-017-1242-7.</ref>.<br />
<br />
=Software tools for data preprocessing=<br />
[http://bioconductor.org/packages/release/bioc/html/xcms.html XCMS] for LC-MS and GC-MS<br />
<br />
[http://www.wageningenur.nl/en/show/MetAlign-1.htm MetAlign] for LC-MS<br />
<br />
[http://prime.psc.riken.jp/Metabolomics_Software/MS-DIAL/ MS-DIAL] for LC-MS <br />
<br />
[http://mzmatch.sourceforge.net/index.php mzMatch] for LC-MS<br />
<br />
[http://mzmatch.sourceforge.net/ideom.php IDEOM] for LC-MS<br />
<br />
[http://chemdata.nist.gov/dokuwiki/doku.php?id=chemdata:amdis AMDIS] for GC-MS<br />
<br />
[http://md.tu-bs.de MetaboliteDetector] for GC-MS<br />
<br />
[https://meltdb.cebitec.uni-bielefeld.de/cgi-bin/login.cgi MeltDB] for LC-MS and GC-MS<br />
<br />
[http://bioconductor.org/packages/release/bioc/html/metaMS.html metaMS] for GC-MS<br />
<br />
[http://spectconnect.mit.edu SpectConnect] for GC-MS<br />
<br />
[http://rnmr.nmrfam.wisc.edu rNMR] for NMR<br />
<br />
=Software tools for data post-processing=<br />
[https://gitlab.com/CarlBrunius/batchCorr batchCorr] for LC-MS<br />
<br />
[https://cran.r-project.org/web/packages/crmn/ crmn] for LC-MS and GC-MS<br />
<br />
[https://sourceforge.net/projects/eigenms EigenMS] for LC-MS<br />
<br />
[https://cran.r-project.org/web/packages/KMDA/ KMDA] for MS<br />
<br />
[https://www.bioconductor.org/packages/release/bioc/html/metabomxtr.html metabomxtr] for LC-MS and GC-MS<br />
<br />
[https://sourceforge.net/projects/metabnorm Metabnorm] for NMR<br />
<br />
[http://metabr.r-forge.r-project.org/ MetabR] for LC-MS<br />
<br />
[https://cran.r-project.org/web/packages/MetNorm/ MetNorm] for LC-MS, GC-MS and NMR<br />
<br />
[https://sourceforge.net/projects/msprep/ MSPrep] for LC-MS<br />
<br />
[https://cran.r-project.org/web/packages/muma/ muma] for MS and NMR<br />
<br />
<br />
=Software tools for statistical analysis=<br />
[https://sourceforge.net/projects/ionwinze Ionwinze] for LC-MS<br />
<br />
[https://cran.r-project.org/web/packages/MetabolAnalyze MetabolAnalyze] for MS and NMR<br />
<br />
[https://cran.r-project.org/web/packages/muma/ muma] for MS and NMR<br />
<br />
[https://www.bioconductor.org/packages/release/bioc/html/ropls.html ropls] for MS and NMR<br />
<br />
[http://www.metaboanalyst.ca/ MetaboAnalyst] for LC-MS and NMR<br />
<br />
<br />
=Software tools for metabolite annotation=<br />
[http://bioconductor.org/packages/release/bioc/html/CAMERA.html CAMERA], Level 4<br />
<br />
[http://bioconductor.org/packages/release/bioc/html/Rdisop.html Rdisop], Level 4<br />
<br />
[https://bio.informatik.uni-jena.de/software/sirius SIRIUS and CSI Finger ID], Level 4<br />
<br />
[http://labpib.fmrp.usp.br/methods/probmetab ProbMetab], Level 3<br />
<br />
[http://mzmatch.sourceforge.net/index.php MetAssign–mzMatch], Level 3<br />
<br />
[http://ipb-halle.github.io/MetFrag/ MetFrag], Level 2a<br />
<br />
[https://sourceforge.net/projects/cfm-id/ CFM-ID], Level 2a<br />
<br />
[https://github.com/icdishb/fingerid FingerID], Level 2a<br />
<br />
[http://www.emetabolomics.org/magma MAGMa], Level 2a<br />
<br />
[http://mycompoundid.org/mycompoundid_IsoMS MyCompoundID], Level 2a<br />
<br />
[http://batman.r-forge.r-project.org BATMAN], NMR<br />
<br />
[http://bayesil.ca Bayesil], NMR<br />
<br />
[http://wishart.biology.ualberta.ca/metabominer MetaboMiner], NMR<br />
<br />
[http://prime.psc.riken.jp/?action=nmr_search SpinAssign], NMR<br />
<br />
[http://spin.ccic.ohio-state.edu/index.php/colmar COLMAR], NMR<br />
<br />
[http://www.massbank.jp/Search MassBank]<br />
<br />
[https://msbi.ipb-halle.de/MetFusion/ MetFusion]<br />
<br />
[http://cfmid.wishartlab.com/ CDM-ID]<br />
<br />
[http://ms2lda.org MS2LDA]<br />
<br />
[https://gnps.ucsd.edu/ProteoSAFe/static/gnps-splash.jsp GNPS]<br />
<br />
<br />
=Workflows for the analysis of metabolomics data=<br />
[https://github.com/Viant-Metabolomics/Galaxy-M Galaxy-M] for LC-MS<br />
<br />
[https://gnps.ucsd.edu/ProteoSAFe/static/gnps-splash.jsp GNPS] for LC-MS<br />
<br />
[http://www.metaboanalyst.ca MetaboAnalyst 4.0] for LC-MS and NMR<br />
<br />
[http://genomics-pubs.princeton.edu/mzroll/index.php MAVEN] for LC-MS<br />
<br />
[https://www.bioconductor.org/packages/release/bioc/html/MAIT.html MAIT] for LC-MS<br />
<br />
[http://mzmine.github.io/ MZmine 2] for LC-MS<br />
<br />
[http://workflow4metabolomics.org Workflow4metabolomics] for LC-MS, GC-MS and NMR<br />
<br />
[https://xcmsonline.scripps.edu/landing_page.php?pgcontent=mainPage XCMS Online] for LC-MS and GC-MS<br />
<br />
=References=<br />
<br />
<br />
''"Disclaimer: The links provided here are for informational purposes only. They do not constitute an endorsement or recommendation by the Metabolomics Society. The Metabolomics Society bears no responsibility for the accuracy, timeliness or completeness of information contained on a linked website".''</div>Viniciusverihttp://wiki.metabolomicssociety.org/index.php?title=Databases&diff=1552Databases2021-06-21T05:54:29Z<p>Viniciusveri: </p>
<hr />
<div>This page lists open-access metabolomics-related databases. Databases are roughly categorized whether they are a repository for raw/processed metabolomics data, provide reference spectra (experimentally or computationally derived) or principally provide curation/ontology of structures/pathways.<br />
<br />
=Metabolomic repositories=<br />
<br />
[https://massive.ucsd.edu/ProteoSAFe/static/massive.jsp '''MassIVE''']: a community resource developed by the NIH-funded Center for Computational Mass Spectrometry to promote the global, free exchange of mass spectrometry data.<br />
<br />
[https://www.ebi.ac.uk/metabolights/ '''MetaboLights''']: an open access repository for storage of metabolomics data. Users are encouraged to upload data from multiple species and techniques.<br />
<br />
<br />
=Reference databases=<br />
<br />
[https://bmrb.io/metabolomics/ '''Biological Magnetic Resonance Data Bank''']: a repository for data from NMR spectroscopy on proteins, peptides, nucleic acids, and other biomolecules. Developed by the University of Wisconsin.<br />
<br />
[http://www.bml-nmr.org/ '''Birmingham Metabolite Library''']: contains >3000 experimental 1D and 2D J-resolved NMR spectra of 208 metabolite standards. This resource was established by the University of Birmingham, UK, and was funded by the BBSRC.<br />
<br />
[https://jcggdb.jp/rcmg/glycodb/Ms_ResultSearch '''Glycan Mass Spectral Database''']:database of MS spectra data for N- and O-linked glycans and glycolipids glycans along with their partial chemical structures.<br />
<br />
<br />
[http://gmd.mpimp-golm.mpg.de/ '''Golm Metabolome Database''']: GC-MS metabolomics library developed and maintained in a collaboration between the Root Metabolism Group and the Bioinformatics Group of the Max Planck Institute for Molecular Plant Physiology, Germany.<br />
<br />
[https://hmdb.ca/ '''Human Metabolome Database''']: a comprehensive, high-quality, freely accessible, online database of small molecule metabolites found in the human body.<br />
<br />
[http://www.massbank.jp/ '''MassBank Japan''']: one of the largest open databases of mass spectral data and covers numerous different instrument types. MassBank was initiated by the Institute for Advanced Biosciences, in Keio University, Tsuruoka City, Yamagata, Japan.<br />
<br />
[https://massbank.eu//MassBank/ '''MassBank Bank: Europe Mirror''']:one of the largest open databases of mass spectral data and covers numerous different instrument types<br />
<br />
[https://mona.fiehnlab.ucdavis.edu/ '''MassBank of North America''']:a metadata-centric, auto-curating repository designed for efficient storage and querying of mass spectral records.<br />
<br />
[https://metlin.scripps.edu/landing_page.php?pgcontent=mainPage '''METLIN''']: is a repository of metabolite information as well as tandem mass spectrometry data.<br />
<br />
[https://www.mzcloud.org/ '''MzCloud''']: a web-based mass spectral database that comprises a curated collection of high and low resolution tandem mass spectra acquired under a number of experimental conditions which address the problem of spectra reproducibility.<br />
<br />
[https://www.nist.gov/srd/nist-standard-reference-database-1a-v17 '''NIST Standard Reference Database''']: extensive collection of data sets (EI MS, MS/MS, Replicate spectra, Retention index).<br />
<br />
[http://phenol-explorer.eu/ '''Phenol-Explorer''']: a database on natural phenols and polyphenols including food composition, food processing, and polyphenol metabolites in human and experimental animals.<br />
<br />
[https://www.library.ucsb.edu/node/6522 '''Spectral Database for Organic Compounds''']: repository of spectral database of organic compound; Variety of data sets (MS, NMR, IR, Raman, ESR).<br />
<br />
<br />
=Currated structures/pathways=<br />
<br />
[http://bigg.ucsd.edu/ '''BiGG''']: is a knowledgebase of genome-scale metabolic network reconstructions.<br />
<br />
[https://www.ebi.ac.uk/biomodels/ '''BioModels''']: a repository of mathematical models of biological and biomedical systems. It hosts a vast selection of existing literature-based physiologically and pharmaceutically relevant mechanistic models in standard formats.<br />
<br />
[https://www.ebi.ac.uk/chebi/ '''ChEBI''']: a database and ontology of molecular entities focused on 'small' chemical compounds, that is part of the Open Biomedical Ontologies effort.<br />
<br />
[http://www.chemspider.com/ '''Chemspider''']: a database of chemicals. ChemSpider is owned by the Royal Society of Chemistry.<br />
<br />
[https://hmdb.ca/ '''Human Metabolome Database''']: a comprehensive, high-quality, freely accessible, online database of small molecule metabolites found in the human body.<br />
<br />
[https://www.genome.jp/kegg/ '''KEGG''']: a collection of databases dealing with genomes, biological pathways, diseases, drugs, and chemical substances.<br />
<br />
[https://www.lipidmaps.org/data/proteome/LMPD.php '''LIPID MAPS Proteome Database''']: lipid-associated protein sequences with annotations from UniProt, EntrezGene, ENZYME, GO, KEGG and other public resources. Browse or search by species, lipid class association, and/or keywords.<br />
<br />
[https://www.lipidmaps.org/data/structure/index.php '''LIPID MAPS Structure Database''']: The LIPID MAPS Structure Database (LMSD) is comprised of structures and annotations of biologically relevant lipids, and includes representative examples from each category of the LIPID MAPS Lipid Classification System.<br />
<br />
[https://metacyc.org/ '''MetaCyc''']: a curated database of experimentally elucidated metabolic pathways from all domains of life.<br />
<br />
[https://zenodo.org/record/3693783#.Xn4NJ4hKjb0 '''OpenFoodTox''']: a structured database summarising the outcomes of hazard identification and characterisation for the human health, animal health and the environment.<br />
<br />
[http://phenol-explorer.eu/ '''Phenol-Explorer''']: a database on natural phenols and polyphenols including food composition, food processing, and polyphenol metabolites in human and experimental animals.<br />
<br />
[http://phytohub.eu/ '''PhytoHub''']: a freely available electronic database containing detailed information about dietary phytochemicals and their human and animal metabolites.<br />
<br />
[https://pubchem.ncbi.nlm.nih.gov/ '''PubChem''']: a database of chemical molecules and their activities against biological assays.<br />
<br />
[https://www.swisslipids.org/ '''SwissLipids''']: an expert curated database of lipids and their biological functions.<br />
<br />
[https://www.wikipathways.org/index.php/WikiPathways '''WikiPathways''']: a database of biological pathways maintained by and for the scientific community.<br />
<br />
<br />
''"Disclaimer: The links provided here are for informational purposes only. They do not constitute an endorsement or recommendation by the Metabolomics Society. The Metabolomics Society bears no responsibility for the accuracy, timeliness or completeness of information contained on a linked website".''</div>Viniciusverihttp://wiki.metabolomicssociety.org/index.php?title=Databases&diff=1551Databases2021-06-21T05:54:14Z<p>Viniciusveri: </p>
<hr />
<div>This page lists open-access metabolomics-related databases. Databases are roughly categorized whether they are a repository for raw/processed metabolomics data, provide reference spectra (experimentally or computationally derived) or principally provide curation/ontology of structures/pathways.<br />
<br />
=Metabolomic repositories=<br />
<br />
[https://massive.ucsd.edu/ProteoSAFe/static/massive.jsp '''MassIVE''']: a community resource developed by the NIH-funded Center for Computational Mass Spectrometry to promote the global, free exchange of mass spectrometry data.<br />
<br />
[https://www.ebi.ac.uk/metabolights/ '''MetaboLights''']: an open access repository for storage of metabolomics data. Users are encouraged to upload data from multiple species and techniques.<br />
<br />
<br />
=Reference databases=<br />
<br />
[https://bmrb.io/metabolomics/ '''Biological Magnetic Resonance Data Bank''']: a repository for data from NMR spectroscopy on proteins, peptides, nucleic acids, and other biomolecules. Developed by the University of Wisconsin.<br />
<br />
[http://www.bml-nmr.org/ '''Birmingham Metabolite Library''']: contains >3000 experimental 1D and 2D J-resolved NMR spectra of 208 metabolite standards. This resource was established by the University of Birmingham, UK, and was funded by the BBSRC.<br />
<br />
[https://jcggdb.jp/rcmg/glycodb/Ms_ResultSearch '''Glycan Mass Spectral Database''']:database of MS spectra data for N- and O-linked glycans and glycolipids glycans along with their partial chemical structures.<br />
<br />
<br />
[http://gmd.mpimp-golm.mpg.de/ '''Golm Metabolome Database''']: GC-MS metabolomics library developed and maintained in a collaboration between the Root Metabolism Group and the Bioinformatics Group of the Max Planck Institute for Molecular Plant Physiology, Germany.<br />
<br />
[https://hmdb.ca/ '''Human Metabolome Database''']: a comprehensive, high-quality, freely accessible, online database of small molecule metabolites found in the human body.<br />
<br />
[http://www.massbank.jp/ '''MassBank Japan''']: one of the largest open databases of mass spectral data and covers numerous different instrument types. MassBank was initiated by the Institute for Advanced Biosciences, in Keio University, Tsuruoka City, Yamagata, Japan.<br />
<br />
[https://massbank.eu//MassBank/ '''MassBank Bank: Europe Mirror''']:one of the largest open databases of mass spectral data and covers numerous different instrument types<br />
<br />
[https://mona.fiehnlab.ucdavis.edu/ '''MassBank of North America''']:a metadata-centric, auto-curating repository designed for efficient storage and querying of mass spectral records.<br />
<br />
[https://metlin.scripps.edu/landing_page.php?pgcontent=mainPage '''METLIN''']: is a repository of metabolite information as well as tandem mass spectrometry data.<br />
<br />
[https://www.mzcloud.org/ '''MzCloud''']: a web-based mass spectral database that comprises a curated collection of high and low resolution tandem mass spectra acquired under a number of experimental conditions which address the problem of spectra reproducibility.<br />
<br />
[https://www.nist.gov/srd/nist-standard-reference-database-1a-v17 '''NIST Standard Reference Database''']: extensive collection of data sets (EI MS, MS/MS, Replicate spectra, Retention index).<br />
<br />
[http://phenol-explorer.eu/ '''Phenol-Explorer''']: a database on natural phenols and polyphenols including food composition, food processing, and polyphenol metabolites in human and experimental animals.<br />
<br />
[https://www.library.ucsb.edu/node/6522 '''Spectral Database for Organic Compounds''']: repository of spectral database of organic compound; Variety of data sets (MS, NMR, IR, Raman, ESR).<br />
<br />
<br />
=Currated structures/pathways=<br />
<br />
[http://bigg.ucsd.edu/ '''BiGG''']: is a knowledgebase of genome-scale metabolic network reconstructions.<br />
<br />
[https://www.ebi.ac.uk/biomodels/ '''BioModels''']: a repository of mathematical models of biological and biomedical systems. It hosts a vast selection of existing literature-based physiologically and pharmaceutically relevant mechanistic models in standard formats.<br />
<br />
[https://www.ebi.ac.uk/chebi/ '''ChEBI''']: a database and ontology of molecular entities focused on 'small' chemical compounds, that is part of the Open Biomedical Ontologies effort.<br />
<br />
[http://www.chemspider.com/ '''Chemspider''']: a database of chemicals. ChemSpider is owned by the Royal Society of Chemistry.<br />
<br />
[https://hmdb.ca/ '''Human Metabolome Database''']: a comprehensive, high-quality, freely accessible, online database of small molecule metabolites found in the human body.<br />
<br />
[https://www.genome.jp/kegg/ '''KEGG''']: a collection of databases dealing with genomes, biological pathways, diseases, drugs, and chemical substances.<br />
<br />
[https://www.lipidmaps.org/data/proteome/LMPD.php '''LIPID MAPS Proteome Database''']: lipid-associated protein sequences with annotations from UniProt, EntrezGene, ENZYME, GO, KEGG and other public resources. Browse or search by species, lipid class association, and/or keywords.<br />
<br />
[https://www.lipidmaps.org/data/structure/index.php '''LIPID MAPS Structure Database''']: The LIPID MAPS Structure Database (LMSD) is comprised of structures and annotations of biologically relevant lipids, and includes representative examples from each category of the LIPID MAPS Lipid Classification System.<br />
<br />
[https://metacyc.org/ '''MetaCyc''']: a curated database of experimentally elucidated metabolic pathways from all domains of life.<br />
<br />
[https://zenodo.org/record/3693783#.Xn4NJ4hKjb0 '''OpenFoodTox''']: a structured database summarising the outcomes of hazard identification and characterisation for the human health, animal health and the environment.<br />
<br />
[http://phenol-explorer.eu/ '''Phenol-Explorer''']: a database on natural phenols and polyphenols including food composition, food processing, and polyphenol metabolites in human and experimental animals.<br />
<br />
[http://phytohub.eu/ '''PhytoHub''']: a freely available electronic database containing detailed information about dietary phytochemicals and their human and animal metabolites.<br />
<br />
[https://pubchem.ncbi.nlm.nih.gov/ '''PubChem''']: a database of chemical molecules and their activities against biological assays.<br />
<br />
[https://www.swisslipids.org/ '''SwissLipids''']: an expert curated database of lipids and their biological functions.<br />
<br />
[https://www.wikipathways.org/index.php/WikiPathways '''WikiPathways''']: a database of biological pathways maintained by and for the scientific community.<br />
<br />
<br />
''''"Disclaimer: The links provided here are for informational purposes only. They do not constitute an endorsement or recommendation by the Metabolomics Society. The Metabolomics Society bears no responsibility for the accuracy, timeliness or completeness of information contained on a linked website".''''</div>Viniciusverihttp://wiki.metabolomicssociety.org/index.php?title=Databases&diff=1550Databases2021-06-21T05:54:01Z<p>Viniciusveri: </p>
<hr />
<div>This page lists open-access metabolomics-related databases. Databases are roughly categorized whether they are a repository for raw/processed metabolomics data, provide reference spectra (experimentally or computationally derived) or principally provide curation/ontology of structures/pathways.<br />
<br />
=Metabolomic repositories=<br />
<br />
[https://massive.ucsd.edu/ProteoSAFe/static/massive.jsp '''MassIVE''']: a community resource developed by the NIH-funded Center for Computational Mass Spectrometry to promote the global, free exchange of mass spectrometry data.<br />
<br />
[https://www.ebi.ac.uk/metabolights/ '''MetaboLights''']: an open access repository for storage of metabolomics data. Users are encouraged to upload data from multiple species and techniques.<br />
<br />
<br />
=Reference databases=<br />
<br />
[https://bmrb.io/metabolomics/ '''Biological Magnetic Resonance Data Bank''']: a repository for data from NMR spectroscopy on proteins, peptides, nucleic acids, and other biomolecules. Developed by the University of Wisconsin.<br />
<br />
[http://www.bml-nmr.org/ '''Birmingham Metabolite Library''']: contains >3000 experimental 1D and 2D J-resolved NMR spectra of 208 metabolite standards. This resource was established by the University of Birmingham, UK, and was funded by the BBSRC.<br />
<br />
[https://jcggdb.jp/rcmg/glycodb/Ms_ResultSearch '''Glycan Mass Spectral Database''']:database of MS spectra data for N- and O-linked glycans and glycolipids glycans along with their partial chemical structures.<br />
<br />
<br />
[http://gmd.mpimp-golm.mpg.de/ '''Golm Metabolome Database''']: GC-MS metabolomics library developed and maintained in a collaboration between the Root Metabolism Group and the Bioinformatics Group of the Max Planck Institute for Molecular Plant Physiology, Germany.<br />
<br />
[https://hmdb.ca/ '''Human Metabolome Database''']: a comprehensive, high-quality, freely accessible, online database of small molecule metabolites found in the human body.<br />
<br />
[http://www.massbank.jp/ '''MassBank Japan''']: one of the largest open databases of mass spectral data and covers numerous different instrument types. MassBank was initiated by the Institute for Advanced Biosciences, in Keio University, Tsuruoka City, Yamagata, Japan.<br />
<br />
[https://massbank.eu//MassBank/ '''MassBank Bank: Europe Mirror''']:one of the largest open databases of mass spectral data and covers numerous different instrument types<br />
<br />
[https://mona.fiehnlab.ucdavis.edu/ '''MassBank of North America''']:a metadata-centric, auto-curating repository designed for efficient storage and querying of mass spectral records.<br />
<br />
[https://metlin.scripps.edu/landing_page.php?pgcontent=mainPage '''METLIN''']: is a repository of metabolite information as well as tandem mass spectrometry data.<br />
<br />
[https://www.mzcloud.org/ '''MzCloud''']: a web-based mass spectral database that comprises a curated collection of high and low resolution tandem mass spectra acquired under a number of experimental conditions which address the problem of spectra reproducibility.<br />
<br />
[https://www.nist.gov/srd/nist-standard-reference-database-1a-v17 '''NIST Standard Reference Database''']: extensive collection of data sets (EI MS, MS/MS, Replicate spectra, Retention index).<br />
<br />
[http://phenol-explorer.eu/ '''Phenol-Explorer''']: a database on natural phenols and polyphenols including food composition, food processing, and polyphenol metabolites in human and experimental animals.<br />
<br />
[https://www.library.ucsb.edu/node/6522 '''Spectral Database for Organic Compounds''']: repository of spectral database of organic compound; Variety of data sets (MS, NMR, IR, Raman, ESR).<br />
<br />
<br />
=Currated structures/pathways=<br />
<br />
[http://bigg.ucsd.edu/ '''BiGG''']: is a knowledgebase of genome-scale metabolic network reconstructions.<br />
<br />
[https://www.ebi.ac.uk/biomodels/ '''BioModels''']: a repository of mathematical models of biological and biomedical systems. It hosts a vast selection of existing literature-based physiologically and pharmaceutically relevant mechanistic models in standard formats.<br />
<br />
[https://www.ebi.ac.uk/chebi/ '''ChEBI''']: a database and ontology of molecular entities focused on 'small' chemical compounds, that is part of the Open Biomedical Ontologies effort.<br />
<br />
[http://www.chemspider.com/ '''Chemspider''']: a database of chemicals. ChemSpider is owned by the Royal Society of Chemistry.<br />
<br />
[https://hmdb.ca/ '''Human Metabolome Database''']: a comprehensive, high-quality, freely accessible, online database of small molecule metabolites found in the human body.<br />
<br />
[https://www.genome.jp/kegg/ '''KEGG''']: a collection of databases dealing with genomes, biological pathways, diseases, drugs, and chemical substances.<br />
<br />
[https://www.lipidmaps.org/data/proteome/LMPD.php '''LIPID MAPS Proteome Database''']: lipid-associated protein sequences with annotations from UniProt, EntrezGene, ENZYME, GO, KEGG and other public resources. Browse or search by species, lipid class association, and/or keywords.<br />
<br />
[https://www.lipidmaps.org/data/structure/index.php '''LIPID MAPS Structure Database''']: The LIPID MAPS Structure Database (LMSD) is comprised of structures and annotations of biologically relevant lipids, and includes representative examples from each category of the LIPID MAPS Lipid Classification System.<br />
<br />
[https://metacyc.org/ '''MetaCyc''']: a curated database of experimentally elucidated metabolic pathways from all domains of life.<br />
<br />
[https://zenodo.org/record/3693783#.Xn4NJ4hKjb0 '''OpenFoodTox''']: a structured database summarising the outcomes of hazard identification and characterisation for the human health, animal health and the environment.<br />
<br />
[http://phenol-explorer.eu/ '''Phenol-Explorer''']: a database on natural phenols and polyphenols including food composition, food processing, and polyphenol metabolites in human and experimental animals.<br />
<br />
[http://phytohub.eu/ '''PhytoHub''']: a freely available electronic database containing detailed information about dietary phytochemicals and their human and animal metabolites.<br />
<br />
[https://pubchem.ncbi.nlm.nih.gov/ '''PubChem''']: a database of chemical molecules and their activities against biological assays.<br />
<br />
[https://www.swisslipids.org/ '''SwissLipids''']: an expert curated database of lipids and their biological functions.<br />
<br />
[https://www.wikipathways.org/index.php/WikiPathways '''WikiPathways''']: a database of biological pathways maintained by and for the scientific community.<br />
<br />
<br />
'''"Disclaimer: The links provided here are for informational purposes only. They do not constitute an endorsement or recommendation by the Metabolomics Society. The Metabolomics Society bears no responsibility for the accuracy, timeliness or completeness of information contained on a linked website".'''</div>Viniciusverihttp://wiki.metabolomicssociety.org/index.php?title=Databases&diff=1549Databases2021-06-21T05:53:25Z<p>Viniciusveri: </p>
<hr />
<div>This page lists open-access metabolomics-related databases. Databases are roughly categorized whether they are a repository for raw/processed metabolomics data, provide reference spectra (experimentally or computationally derived) or principally provide curation/ontology of structures/pathways.<br />
<br />
=Metabolomic repositories=<br />
<br />
[https://massive.ucsd.edu/ProteoSAFe/static/massive.jsp '''MassIVE''']: a community resource developed by the NIH-funded Center for Computational Mass Spectrometry to promote the global, free exchange of mass spectrometry data.<br />
<br />
[https://www.ebi.ac.uk/metabolights/ '''MetaboLights''']: an open access repository for storage of metabolomics data. Users are encouraged to upload data from multiple species and techniques.<br />
<br />
<br />
=Reference databases=<br />
<br />
[https://bmrb.io/metabolomics/ '''Biological Magnetic Resonance Data Bank''']: a repository for data from NMR spectroscopy on proteins, peptides, nucleic acids, and other biomolecules. Developed by the University of Wisconsin.<br />
<br />
[http://www.bml-nmr.org/ '''Birmingham Metabolite Library''']: contains >3000 experimental 1D and 2D J-resolved NMR spectra of 208 metabolite standards. This resource was established by the University of Birmingham, UK, and was funded by the BBSRC.<br />
<br />
[https://jcggdb.jp/rcmg/glycodb/Ms_ResultSearch '''Glycan Mass Spectral Database''']:database of MS spectra data for N- and O-linked glycans and glycolipids glycans along with their partial chemical structures.<br />
<br />
<br />
[http://gmd.mpimp-golm.mpg.de/ '''Golm Metabolome Database''']: GC-MS metabolomics library developed and maintained in a collaboration between the Root Metabolism Group and the Bioinformatics Group of the Max Planck Institute for Molecular Plant Physiology, Germany.<br />
<br />
[https://hmdb.ca/ '''Human Metabolome Database''']: a comprehensive, high-quality, freely accessible, online database of small molecule metabolites found in the human body.<br />
<br />
[http://www.massbank.jp/ '''MassBank Japan''']: one of the largest open databases of mass spectral data and covers numerous different instrument types. MassBank was initiated by the Institute for Advanced Biosciences, in Keio University, Tsuruoka City, Yamagata, Japan.<br />
<br />
[https://massbank.eu//MassBank/ '''MassBank Bank: Europe Mirror''']:one of the largest open databases of mass spectral data and covers numerous different instrument types<br />
<br />
[https://mona.fiehnlab.ucdavis.edu/ '''MassBank of North America''']:a metadata-centric, auto-curating repository designed for efficient storage and querying of mass spectral records.<br />
<br />
[https://metlin.scripps.edu/landing_page.php?pgcontent=mainPage '''METLIN''']: is a repository of metabolite information as well as tandem mass spectrometry data.<br />
<br />
[https://www.mzcloud.org/ '''MzCloud''']: a web-based mass spectral database that comprises a curated collection of high and low resolution tandem mass spectra acquired under a number of experimental conditions which address the problem of spectra reproducibility.<br />
<br />
[https://www.nist.gov/srd/nist-standard-reference-database-1a-v17 '''NIST Standard Reference Database''']: extensive collection of data sets (EI MS, MS/MS, Replicate spectra, Retention index).<br />
<br />
[http://phenol-explorer.eu/ '''Phenol-Explorer''']: a database on natural phenols and polyphenols including food composition, food processing, and polyphenol metabolites in human and experimental animals.<br />
<br />
[https://www.library.ucsb.edu/node/6522 '''Spectral Database for Organic Compounds''']: repository of spectral database of organic compound; Variety of data sets (MS, NMR, IR, Raman, ESR).<br />
<br />
<br />
=Currated structures/pathways=<br />
<br />
[http://bigg.ucsd.edu/ '''BiGG''']: is a knowledgebase of genome-scale metabolic network reconstructions.<br />
<br />
[https://www.ebi.ac.uk/biomodels/ '''BioModels''']: a repository of mathematical models of biological and biomedical systems. It hosts a vast selection of existing literature-based physiologically and pharmaceutically relevant mechanistic models in standard formats.<br />
<br />
[https://www.ebi.ac.uk/chebi/ '''ChEBI''']: a database and ontology of molecular entities focused on 'small' chemical compounds, that is part of the Open Biomedical Ontologies effort.<br />
<br />
[http://www.chemspider.com/ '''Chemspider''']: a database of chemicals. ChemSpider is owned by the Royal Society of Chemistry.<br />
<br />
[https://hmdb.ca/ '''Human Metabolome Database''']: a comprehensive, high-quality, freely accessible, online database of small molecule metabolites found in the human body.<br />
<br />
[https://www.genome.jp/kegg/ '''KEGG''']: a collection of databases dealing with genomes, biological pathways, diseases, drugs, and chemical substances.<br />
<br />
[https://www.lipidmaps.org/data/proteome/LMPD.php '''LIPID MAPS Proteome Database''']: lipid-associated protein sequences with annotations from UniProt, EntrezGene, ENZYME, GO, KEGG and other public resources. Browse or search by species, lipid class association, and/or keywords.<br />
<br />
[https://www.lipidmaps.org/data/structure/index.php '''LIPID MAPS Structure Database''']: The LIPID MAPS Structure Database (LMSD) is comprised of structures and annotations of biologically relevant lipids, and includes representative examples from each category of the LIPID MAPS Lipid Classification System.<br />
<br />
[https://metacyc.org/ '''MetaCyc''']: a curated database of experimentally elucidated metabolic pathways from all domains of life.<br />
<br />
[https://zenodo.org/record/3693783#.Xn4NJ4hKjb0 '''OpenFoodTox''']: a structured database summarising the outcomes of hazard identification and characterisation for the human health, animal health and the environment.<br />
<br />
[http://phenol-explorer.eu/ '''Phenol-Explorer''']: a database on natural phenols and polyphenols including food composition, food processing, and polyphenol metabolites in human and experimental animals.<br />
<br />
[http://phytohub.eu/ '''PhytoHub''']: a freely available electronic database containing detailed information about dietary phytochemicals and their human and animal metabolites.<br />
<br />
[https://pubchem.ncbi.nlm.nih.gov/ '''PubChem''']: a database of chemical molecules and their activities against biological assays.<br />
<br />
[https://www.swisslipids.org/ '''SwissLipids''']: an expert curated database of lipids and their biological functions.<br />
<br />
[https://www.wikipathways.org/index.php/WikiPathways '''WikiPathways''']: a database of biological pathways maintained by and for the scientific community.<br />
<br />
<br />
''Disclaimer: The links provided here are for informational purposes only. They do not constitute an endorsement or recommendation by the Metabolomics Society. The Metabolomics Society bears no responsibility for the accuracy, timeliness or completeness of information contained on a linked website.''</div>Viniciusverihttp://wiki.metabolomicssociety.org/index.php?title=Databases&diff=1548Databases2021-06-21T05:53:10Z<p>Viniciusveri: </p>
<hr />
<div>This page lists open-access metabolomics-related databases. Databases are roughly categorized whether they are a repository for raw/processed metabolomics data, provide reference spectra (experimentally or computationally derived) or principally provide curation/ontology of structures/pathways.<br />
<br />
=Metabolomic repositories=<br />
<br />
[https://massive.ucsd.edu/ProteoSAFe/static/massive.jsp '''MassIVE''']: a community resource developed by the NIH-funded Center for Computational Mass Spectrometry to promote the global, free exchange of mass spectrometry data.<br />
<br />
[https://www.ebi.ac.uk/metabolights/ '''MetaboLights''']: an open access repository for storage of metabolomics data. Users are encouraged to upload data from multiple species and techniques.<br />
<br />
<br />
=Reference databases=<br />
<br />
[https://bmrb.io/metabolomics/ '''Biological Magnetic Resonance Data Bank''']: a repository for data from NMR spectroscopy on proteins, peptides, nucleic acids, and other biomolecules. Developed by the University of Wisconsin.<br />
<br />
[http://www.bml-nmr.org/ '''Birmingham Metabolite Library''']: contains >3000 experimental 1D and 2D J-resolved NMR spectra of 208 metabolite standards. This resource was established by the University of Birmingham, UK, and was funded by the BBSRC.<br />
<br />
[https://jcggdb.jp/rcmg/glycodb/Ms_ResultSearch '''Glycan Mass Spectral Database''']:database of MS spectra data for N- and O-linked glycans and glycolipids glycans along with their partial chemical structures.<br />
<br />
<br />
[http://gmd.mpimp-golm.mpg.de/ '''Golm Metabolome Database''']: GC-MS metabolomics library developed and maintained in a collaboration between the Root Metabolism Group and the Bioinformatics Group of the Max Planck Institute for Molecular Plant Physiology, Germany.<br />
<br />
[https://hmdb.ca/ '''Human Metabolome Database''']: a comprehensive, high-quality, freely accessible, online database of small molecule metabolites found in the human body.<br />
<br />
[http://www.massbank.jp/ '''MassBank Japan''']: one of the largest open databases of mass spectral data and covers numerous different instrument types. MassBank was initiated by the Institute for Advanced Biosciences, in Keio University, Tsuruoka City, Yamagata, Japan.<br />
<br />
[https://massbank.eu//MassBank/ '''MassBank Bank: Europe Mirror''']:one of the largest open databases of mass spectral data and covers numerous different instrument types<br />
<br />
[https://mona.fiehnlab.ucdavis.edu/ '''MassBank of North America''']:a metadata-centric, auto-curating repository designed for efficient storage and querying of mass spectral records.<br />
<br />
[https://metlin.scripps.edu/landing_page.php?pgcontent=mainPage '''METLIN''']: is a repository of metabolite information as well as tandem mass spectrometry data.<br />
<br />
[https://www.mzcloud.org/ '''MzCloud''']: a web-based mass spectral database that comprises a curated collection of high and low resolution tandem mass spectra acquired under a number of experimental conditions which address the problem of spectra reproducibility.<br />
<br />
[https://www.nist.gov/srd/nist-standard-reference-database-1a-v17 '''NIST Standard Reference Database''']: extensive collection of data sets (EI MS, MS/MS, Replicate spectra, Retention index).<br />
<br />
[http://phenol-explorer.eu/ '''Phenol-Explorer''']: a database on natural phenols and polyphenols including food composition, food processing, and polyphenol metabolites in human and experimental animals.<br />
<br />
[https://www.library.ucsb.edu/node/6522 '''Spectral Database for Organic Compounds''']: repository of spectral database of organic compound; Variety of data sets (MS, NMR, IR, Raman, ESR).<br />
<br />
<br />
=Currated structures/pathways=<br />
<br />
[http://bigg.ucsd.edu/ '''BiGG''']: is a knowledgebase of genome-scale metabolic network reconstructions.<br />
<br />
[https://www.ebi.ac.uk/biomodels/ '''BioModels''']: a repository of mathematical models of biological and biomedical systems. It hosts a vast selection of existing literature-based physiologically and pharmaceutically relevant mechanistic models in standard formats.<br />
<br />
[https://www.ebi.ac.uk/chebi/ '''ChEBI''']: a database and ontology of molecular entities focused on 'small' chemical compounds, that is part of the Open Biomedical Ontologies effort.<br />
<br />
[http://www.chemspider.com/ '''Chemspider''']: a database of chemicals. ChemSpider is owned by the Royal Society of Chemistry.<br />
<br />
[https://hmdb.ca/ '''Human Metabolome Database''']: a comprehensive, high-quality, freely accessible, online database of small molecule metabolites found in the human body.<br />
<br />
[https://www.genome.jp/kegg/ '''KEGG''']: a collection of databases dealing with genomes, biological pathways, diseases, drugs, and chemical substances.<br />
<br />
[https://www.lipidmaps.org/data/proteome/LMPD.php '''LIPID MAPS Proteome Database''']: lipid-associated protein sequences with annotations from UniProt, EntrezGene, ENZYME, GO, KEGG and other public resources. Browse or search by species, lipid class association, and/or keywords.<br />
<br />
[https://www.lipidmaps.org/data/structure/index.php '''LIPID MAPS Structure Database''']: The LIPID MAPS Structure Database (LMSD) is comprised of structures and annotations of biologically relevant lipids, and includes representative examples from each category of the LIPID MAPS Lipid Classification System.<br />
<br />
[https://metacyc.org/ '''MetaCyc''']: a curated database of experimentally elucidated metabolic pathways from all domains of life.<br />
<br />
[https://zenodo.org/record/3693783#.Xn4NJ4hKjb0 '''OpenFoodTox''']: a structured database summarising the outcomes of hazard identification and characterisation for the human health, animal health and the environment.<br />
<br />
[http://phenol-explorer.eu/ '''Phenol-Explorer''']: a database on natural phenols and polyphenols including food composition, food processing, and polyphenol metabolites in human and experimental animals.<br />
<br />
[http://phytohub.eu/ '''PhytoHub''']: a freely available electronic database containing detailed information about dietary phytochemicals and their human and animal metabolites.<br />
<br />
[https://pubchem.ncbi.nlm.nih.gov/ '''PubChem''']: a database of chemical molecules and their activities against biological assays.<br />
<br />
[https://www.swisslipids.org/ '''SwissLipids''']: an expert curated database of lipids and their biological functions.<br />
<br />
[https://www.wikipathways.org/index.php/WikiPathways '''WikiPathways''']: a database of biological pathways maintained by and for the scientific community.<br />
<br />
''Disclaimer: The links provided here are for informational purposes only. They do not constitute an endorsement or recommendation by the Metabolomics Society. The Metabolomics Society bears no responsibility for the accuracy, timeliness or completeness of information contained on a linked website.''</div>Viniciusverihttp://wiki.metabolomicssociety.org/index.php?title=Metabolomics_Society_Workshops&diff=1547Metabolomics Society Workshops2021-06-14T08:45:21Z<p>Viniciusveri: </p>
<hr />
<div>The Metabolomics Society holds workshops at the annual meeting each year. Below you can have access to previous conference workshops. <br />
<br />
<br />
===2019 - The Hague, The Netherlands===<br />
<br />
[http://metabolomicssociety.org/site-map/articles/88-videos/299-2019-conference-workshop-presentations Workshops from Metabolomics 2019 - The Hague, The Netherlands]<br />
<br />
===2018 - Seattle, USA===<br />
<br />
[http://metabolomicssociety.org/site-map/articles/88-videos/278-2018-conference-workshop-presentations Workshops from Metabolomics 2018 - Seattle, USA]<br />
<br />
===2017 - Brisbane, Australia===<br />
<br />
[http://metabolomicssociety.org/site-map/articles/88-videos/262-2017-conference-workshop-videos-public Workshops from Metabolomics 2017 - Brisbane, Australia]<br />
<br />
===2015 - San Francisco, USA===<br />
<br />
[http://metabolomicssociety.org/site-map/articles/88-videos/202-2015-conference-workshop-videos-public Workshops from Metabolomics 2015 - San Francisco, USA]<br />
<br />
===2014 - Tsuroka, Japan===<br />
<br />
[http://metabolomicssociety.org/site-map/articles/88-videos/177-2014-conference-workshop-videos-public Workshops from Metabolomics 2014 - Tsuroka, Japan]</div>Viniciusverihttp://wiki.metabolomicssociety.org/index.php?title=Metabolomics_Society_Workshops&diff=1546Metabolomics Society Workshops2021-06-14T08:43:11Z<p>Viniciusveri: </p>
<hr />
<div>The Metabolomics Society holds workshops at the annual meeting each year. Below you can have access to previous conference workshops. <br />
<br />
<br />
===2019 - The Hague, The Netherlands===<br />
<br />
[http://metabolomicssociety.org/site-map/articles/88-videos/299-2019-conference-workshop-presentations Workshops from Metabolomics 2019 - The Hague, The Netherlands]<br />
<br />
===2018 - Seattle, USA===<br />
<br />
[http://metabolomicssociety.org/site-map/articles/88-videos/278-2018-conference-workshop-presentations Workshops from Metabolomics 2018 - Seattle, USA]<br />
<br />
===Brisbane, Australia===<br />
<br />
[http://metabolomicssociety.org/site-map/articles/88-videos/262-2017-conference-workshop-videos-public Workshops from Metabolomics 2017 - Brisbane, Australia]<br />
<br />
===2015 - San Francisco, USA===<br />
<br />
[http://metabolomicssociety.org/site-map/articles/88-videos/202-2015-conference-workshop-videos-public Workshops from Metabolomics 2015 - San Francisco, USA]<br />
<br />
===2014 - Tsuroka, Japan===<br />
<br />
[http://metabolomicssociety.org/site-map/articles/88-videos/177-2014-conference-workshop-videos-public Workshops from Metabolomics 2014 - Tsuroka, Japan]</div>Viniciusverihttp://wiki.metabolomicssociety.org/index.php?title=Metabolomics_Society_Workshops&diff=1545Metabolomics Society Workshops2021-06-14T08:40:09Z<p>Viniciusveri: </p>
<hr />
<div>The Metabolomics Society holds workshops at the annual meeting each year. In addition, you may be interested in attending one of the external workshops that are available all over the world and online. <br />
<br />
<br />
===2019 - The Hague, The Netherlands===<br />
<br />
[http://metabolomicssociety.org/site-map/articles/88-videos/299-2019-conference-workshop-presentations Workshops from Metabolomics 2019 - The Hague, The Netherlands]<br />
<br />
===2018 - Seattle, USA===<br />
<br />
[http://metabolomicssociety.org/site-map/articles/88-videos/278-2018-conference-workshop-presentations Workshops from Metabolomics 2018 - Seattle, USA]<br />
<br />
===Brisbane, Australia===<br />
<br />
[http://metabolomicssociety.org/site-map/articles/88-videos/262-2017-conference-workshop-videos-public Workshops from Metabolomics 2017 - Brisbane, Australia]<br />
<br />
===2015 - San Francisco, USA===<br />
<br />
[http://metabolomicssociety.org/site-map/articles/88-videos/202-2015-conference-workshop-videos-public Workshops from Metabolomics 2015 - San Francisco, USA]<br />
<br />
===2014 - Tsuroka, Japan===<br />
<br />
[http://metabolomicssociety.org/site-map/articles/88-videos/177-2014-conference-workshop-videos-public Workshops from Metabolomics 2014 - Tsuroka, Japan]</div>Viniciusverihttp://wiki.metabolomicssociety.org/index.php?title=Main_Page&diff=1544Main Page2021-06-14T08:29:56Z<p>Viniciusveri: </p>
<hr />
<div>__NOTOC____NOEDITSECTION__{{notitle}}<div style="position: relative; top: -30px; z-index: 100; font-size:100%;"><br />
{|cellpadding="5" cellspacing="0"|<br />
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|style="border: 1px solid #DDDDDD;font-size:120%"|<br />
Welcome to the '''Early-Career Members Network (EMN) Webpage''', a resource curated by [[Early-Career_Members_Network | Early-Career Members Network of the Metabolomics Society]]. This wiki-styled page is designed to be a focal point for educational resources and online tools related to all facets of metabolomics, aiming to reach mainly young researchers of the field.<br />
<br />
<!-- NOTES FOR THE CATEGORIES TABLE:<br />
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<span id="Highlights"></span><br />
<center><br />
<br />
{| width=90% style="border: 2px solid #DDDDDD; background-color:rgb(250,250,255); margin-top:10px" cellspacing=20<br />
!colspan=3 style="text-align:center; font-size:100%;"|'''Highlights'''<br />
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<h3><br />
[[Image: Kati_Hanhineva2.jpg|x140px|border|link= Kati Hanhineva]]<br /><br /> <br />
This month Expert Opinion comes from Prof Kati Hanhineva! Check it out [[Kati Hanhineva| here!]]<br /><br /><br />
</h3><br />
|style="width:25%; font-size:80%; vertical-align:center; text-align:center;"|<br />
<h3><br />
[[Image:Dan_Fausto3.png|x150px|border|link=http://metabolomicssociety.org/site-map/articles/88-videos/306-2020-emn-webinars-public]]<br /><br /><br />
Check out our last EMN Webinar on new bio-statistical methods for metabolomics!<br /><br /><br />
</h3><br />
|style="width:25%; font-size:80%; vertical-align:center; text-align:center;"|<br />
<h3><br />
[[Image:MetSocConf2021_2.png|x150px|border|link=Upcoming Events]]<br /><br /> <br />
Do not miss the upcoming events in our community (including MetSoc Conference 2021!) Check all [[Upcoming Events| here!]]<br /><br /><br />
|}<br />
<br />
<span id="Regions"></span><br />
<br />
{|width=80% style="border: 2px solid #DDDDDD; margin-top:1px" cellspacing=1;"<br />
!colspan=6 style="text-align:center; font-size:100%;"|'''Finding Metabolomics Communities'''<br />
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|style="width:16,66%; font-size:95%; vertical-align:bottom; text-align:center;"|<br />
[[Image:128px-Blank Map-Africa.svg.png|link= Metabolomics Communities#Africa|x100px]]<br /><br /> <br />
[[Metabolomics_Communities#Africa|'''Africa''']] <br /><br /> <br />
</h3><br />
|style="width:16,66%; font-size:80%; vertical-align:bottom; text-align:center;"|<br />
<h3><br />
[[Image:128px-Location Map Asia.svg.png|link= Metabolomics Communities#Asia|x100px]]<br /><br /> <br />
[[Metabolomics_Communities#Asia|'''Asia''']] <br /><br /> <br />
</h3><br />
|style="width:16,66%; font-size:80%; vertical-align:bottom; text-align:center;"|<br />
<h3><br />
[[Image:BlankMap-Europe-v4.png|link= Metabolomics Communities#Europe|x100px]]<br /><br /><br />
[[Metabolomics_Communities#Europe|'''Europe''']] <br /><br /> <br />
</h3><br />
|style="width:16,66%; font-size:80%; vertical-align:bottom; text-align:center;"|<br />
<h3><br />
[[Image:128px-America_Blank.svg.png|link=Metabolomics Communities#North and Central America|x100px]]<br /><br /> <br />
[[Metabolomics_Communities#North and Central America|'''North & Central America''']] <br /><br /> <br />
</h3><br />
|style="width:16,66%; font-size:80%; vertical-align:bottom; text-align:center;"|<br />
<h3><br />
[[Image:Blank Map Oceania3.svg.png|link=Metabolomics Communities#Oceania|x100px]]<br /><br /> <br />
[[Metabolomics_Communities#Oceania|'''Oceania''']] <br /><br /> <br />
</h3><br />
|style="width:16,66%; font-size:80%; vertical-align:bottom; text-align:center;"|<br />
<h3><br />
[[Image:128px-BlankMap-South-America.png|link= Communities#South America|x100px]]<br /><br /><br />
[[Metabolomics_Communities#South America|'''South America''']] <br /><br /> <br />
|}<br />
<br />
<span id="Keep updated! Follow us"></span><br />
{| width=90% style="border: 2px solid #DDDDDD; ; margin-top:20px" cellspacing=6<br />
!colspan=3 style="text-align:center; font-size:100%;"|'''Keep updated! Follow us on social medias!'''<br />
|-<br />
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|-<br />
|style="width:40%; font-size:95%; vertical-align:center; text-align:right; padding-left:1em;"|<br />
[[Image: Facebook.png|70px|link=https://www.facebook.com/EMN.MetabolomicsSociety]]<br /><br /> <br />
|style="width:20%; font-size:95%; vertical-align:center; text-align:left;"|<br />
|style="width:40%; font-size:95%; vertical-align:center; text-align:left;"|<br />
[[Image:Twitter.png|70px|link=https://twitter.com/emn_metsoc?lang=pt]]<br /><br /> <br />
</h3><br />
|}<br />
If you would like to suggest content, please contact the current EMN committee at ''info.emn@metabolomicssociety.org''<br />
|}</div>Viniciusveri