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  1. IPB Halle
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    • Trenner 0
    • Molecular Signal Processing
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  • 1

Printed publications

Nothias, L. F.; Petras, D.; Schmid, R.; Dührkop, K.; Rainer, J.; Sarvepalli, A.; Protsyuk, I.; Ernst, M.; Tsugawa, H.; Fleischauer, M.; Aicheler, F.; Aksenov, A. A.; Alka, O.; Allard, P.-M.; Barsch, A.; Cachet, X.; Caraballo, M.; Da Silva, R. R.; Dang, T.; Garg, N.; Gauglitz, J. M.; Gurevich, A.; Isaac, G.; Jarmusch, A. K.; Kameník, Z.; Kang, K. B.; Kessler, N.; Koester, I.; Korf, A.; Gouellec, A. L.; Ludwig, M.; Christian, M. H.; McCall, L.-I.; McSayles, J.; Meyer, S. W.; Mohimani, H.; Morsy, M.; Moyne, O.; Neumann, S.; Neuweger, H.; Nguyen, N. H.; Nothias-Esposito, M.; Paolini, J.; Phelan, V. V.; Pluskal, T.; Quinn, R. A.; Rogers, S.; Shrestha, B.; Tripathi, A.; van der Hooft, J. J. J.; Vargas, F.; Weldon, K. C.; Witting, M.; Yang, H.; Zhang, Z.; Zubeil, F.; Kohlbacher, O.; Böcker, S.; Alexandrov, T.; Bandeira, N.; Wang, M.; Dorrestein, P. C.; Feature-based Molecular Networking in the GNPS Analysis Environment bioRxiv (2019) DOI: 10.1101/812404
  • Abstract
  • BibText
  • RIS

Molecular networking has become a key method used to visualize and annotate the chemical space in non-targeted mass spectrometry-based experiments. However, distinguishing isomeric compounds and quantitative interpretation are currently limited. Therefore, we created Feature-based Molecular Networking (FBMN) as a new analysis method in the Global Natural Products Social Molecular Networking (GNPS) infrastructure. FBMN leverages feature detection and alignment tools to enhance quantitative analyses and isomer distinction, including from ion-mobility spectrometry experiments, in molecular networks.

Printed publications

Nothias, L. F.; Petras, D.; Schmid, R.; Dührkop, K.; Rainer, J.; Sarvepalli, A.; Protsyuk, I.; Ernst, M.; Tsugawa, H.; Fleischauer, M.; Aicheler, F.; Aksenov, A. A.; Alka, O.; Allard, P.-M.; Barsch, A.; Cachet, X.; Caraballo, M.; Da Silva, R. R.; Dang, T.; Garg, N.; Gauglitz, J. M.; Gurevich, A.; Isaac, G.; Jarmusch, A. K.; Kameník, Z.; Kang, K. B.; Kessler, N.; Koester, I.; Korf, A.; Gouellec, A. L.; Ludwig, M.; Christian, M. H.; McCall, L.-I.; McSayles, J.; Meyer, S. W.; Mohimani, H.; Morsy, M.; Moyne, O.; Neumann, S.; Neuweger, H.; Nguyen, N. H.; Nothias-Esposito, M.; Paolini, J.; Phelan, V. V.; Pluskal, T.; Quinn, R. A.; Rogers, S.; Shrestha, B.; Tripathi, A.; van der Hooft, J. J. J.; Vargas, F.; Weldon, K. C.; Witting, M.; Yang, H.; Zhang, Z.; Zubeil, F.; Kohlbacher, O.; Böcker, S.; Alexandrov, T.; Bandeira, N.; Wang, M.; Dorrestein, P. C.; Feature-based Molecular Networking in the GNPS Analysis Environment bioRxiv (2019) DOI: 10.1101/812404
  • Abstract
  • BibText
  • RIS

Molecular networking has become a key method used to visualize and annotate the chemical space in non-targeted mass spectrometry-based experiments. However, distinguishing isomeric compounds and quantitative interpretation are currently limited. Therefore, we created Feature-based Molecular Networking (FBMN) as a new analysis method in the Global Natural Products Social Molecular Networking (GNPS) infrastructure. FBMN leverages feature detection and alignment tools to enhance quantitative analyses and isomer distinction, including from ion-mobility spectrometry experiments, in molecular networks.

Publications

Hoffmann, N.; Rein, J.; Sachsenberg, T.; Hartler, J.; Haug, K.; Mayer, G.; Alka, O.; Dayalan, S.; Pearce, J. T. M.; Rocca-Serra, P.; Qi, D.; Eisenacher, M.; Perez-Riverol, Y.; Vizcaíno, J. A.; Salek, R. M.; Neumann, S.; Jones, A. R.; mzTab-M: A Data Standard for Sharing Quantitative Results in Mass Spectrometry Metabolomics Anal. Chem. 91 3302-3310 (2019) DOI: 10.1021/acs.analchem.8b04310
  • Abstract
  • BibText
  • RIS

Mass spectrometry (MS) is one of the primary techniques used for large-scale analysis of small molecules in metabolomics studies. To date, there has been little data format standardization in this field, as different software packages export results in different formats represented in XML or plain text, making data sharing, database deposition, and reanalysis highly challenging. Working within the consortia of the Metabolomics Standards Initiative, Proteomics Standards Initiative, and the Metabolomics Society, we have created mzTab-M to act as a common output format from analytical approaches using MS on small molecules. The format has been developed over several years, with input from a wide range of stakeholders. mzTab-M is a simple tab-separated text format, but importantly, the structure is highly standardized through the design of a detailed specification document, tightly coupled to validation software, and a mandatory controlled vocabulary of terms to populate it. The format is able to represent final quantification values from analyses, as well as the evidence trail in terms of features measured directly from MS (e.g., LC-MS, GC-MS, DIMS, etc.) and different types of approaches used to identify molecules. mzTab-M allows for ambiguity in the identification of molecules to be communicated clearly to readers of the files (both people and software). There are several implementations of the format available, and we anticipate widespread adoption in the field.

Printed publications

Nothias, L. F.; Petras, D.; Schmid, R.; Dührkop, K.; Rainer, J.; Sarvepalli, A.; Protsyuk, I.; Ernst, M.; Tsugawa, H.; Fleischauer, M.; Aicheler, F.; Aksenov, A. A.; Alka, O.; Allard, P.-M.; Barsch, A.; Cachet, X.; Caraballo, M.; Da Silva, R. R.; Dang, T.; Garg, N.; Gauglitz, J. M.; Gurevich, A.; Isaac, G.; Jarmusch, A. K.; Kameník, Z.; Kang, K. B.; Kessler, N.; Koester, I.; Korf, A.; Gouellec, A. L.; Ludwig, M.; Christian, M. H.; McCall, L.-I.; McSayles, J.; Meyer, S. W.; Mohimani, H.; Morsy, M.; Moyne, O.; Neumann, S.; Neuweger, H.; Nguyen, N. H.; Nothias-Esposito, M.; Paolini, J.; Phelan, V. V.; Pluskal, T.; Quinn, R. A.; Rogers, S.; Shrestha, B.; Tripathi, A.; van der Hooft, J. J. J.; Vargas, F.; Weldon, K. C.; Witting, M.; Yang, H.; Zhang, Z.; Zubeil, F.; Kohlbacher, O.; Böcker, S.; Alexandrov, T.; Bandeira, N.; Wang, M.; Dorrestein, P. C.; Feature-based Molecular Networking in the GNPS Analysis Environment bioRxiv (2019) DOI: 10.1101/812404
  • Abstract
  • BibText
  • RIS

Molecular networking has become a key method used to visualize and annotate the chemical space in non-targeted mass spectrometry-based experiments. However, distinguishing isomeric compounds and quantitative interpretation are currently limited. Therefore, we created Feature-based Molecular Networking (FBMN) as a new analysis method in the Global Natural Products Social Molecular Networking (GNPS) infrastructure. FBMN leverages feature detection and alignment tools to enhance quantitative analyses and isomer distinction, including from ion-mobility spectrometry experiments, in molecular networks.

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