Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

Classes for the masses: Systematic classification of unknowns using fragmentation spectra

View ORCID ProfileKai Dührkop, View ORCID ProfileLouis Felix Nothias, View ORCID ProfileMarkus Fleischauer, View ORCID ProfileMarcus Ludwig, View ORCID ProfileMartin A. Hoffmann, View ORCID ProfileJuho Rousu, View ORCID ProfilePieter C. Dorrestein, View ORCID ProfileSebastian Böcker
doi: https://doi.org/10.1101/2020.04.17.046672
Kai Dührkop
1Chair for Bioinformatics, Friedrich-Schiller-University, Jena, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Kai Dührkop
Louis Felix Nothias
2Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Louis Felix Nothias
Markus Fleischauer
1Chair for Bioinformatics, Friedrich-Schiller-University, Jena, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Markus Fleischauer
Marcus Ludwig
1Chair for Bioinformatics, Friedrich-Schiller-University, Jena, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Marcus Ludwig
Martin A. Hoffmann
1Chair for Bioinformatics, Friedrich-Schiller-University, Jena, Germany
3International Max Planck Research School “Exploration of Ecological Interactions with Molecular and Chemical Techniques”, Max Planck Institute for Chemical Ecology, Jena, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Martin A. Hoffmann
Juho Rousu
4Helsinki institute for Information Technology HIIT, Department of Computer Science, Aalto University, Espoo, Finland
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Juho Rousu
Pieter C. Dorrestein
2Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Pieter C. Dorrestein
Sebastian Böcker
1Chair for Bioinformatics, Friedrich-Schiller-University, Jena, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Sebastian Böcker
  • For correspondence: sebastian.boecker@uni-jena.de
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Preview PDF
Loading

ABSTRACT

Metabolomics experiments can employ non-targeted tandem mass spectrometry to detect hundreds to thousands of molecules in a biological sample. Structural annotation of molecules is typically carried out by searching their fragmentation spectra in spectral libraries or, recently, in structure databases. Annotations are limited to structures present in the library or database employed, prohibiting a thorough utilization of the experimental data. We present a computational tool for systematic compound class annotation: CANOPUS uses a deep neural network to predict 1,270 compound classes from fragmentation spectra, and explicitly targets compounds where neither spectral nor structural reference data are available. CANOPUS even predicts classes for which no MS/MS training data are available. We demonstrate the broad utility of CANOPUS by investigating the effect of the microbial colonization in the digestive system in mice, and through analysis of the chemodiversity of different Euphorbia plants; both uniquely revealing biological insights at the compound class level.

Competing Interest Statement

SB, KD, ML, MF, and MAH are co-founders of Bright Giant GmbH. PCD is scientific advisor for Sirenas LLC.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
Back to top
PreviousNext
Posted April 18, 2020.
Download PDF
Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
Classes for the masses: Systematic classification of unknowns using fragmentation spectra
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
Classes for the masses: Systematic classification of unknowns using fragmentation spectra
Kai Dührkop, Louis Felix Nothias, Markus Fleischauer, Marcus Ludwig, Martin A. Hoffmann, Juho Rousu, Pieter C. Dorrestein, Sebastian Böcker
bioRxiv 2020.04.17.046672; doi: https://doi.org/10.1101/2020.04.17.046672
Reddit logo Twitter logo Facebook logo LinkedIn logo Mendeley logo
Citation Tools
Classes for the masses: Systematic classification of unknowns using fragmentation spectra
Kai Dührkop, Louis Felix Nothias, Markus Fleischauer, Marcus Ludwig, Martin A. Hoffmann, Juho Rousu, Pieter C. Dorrestein, Sebastian Böcker
bioRxiv 2020.04.17.046672; doi: https://doi.org/10.1101/2020.04.17.046672

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Bioinformatics
Subject Areas
All Articles
  • Animal Behavior and Cognition (4397)
  • Biochemistry (9632)
  • Bioengineering (7123)
  • Bioinformatics (24940)
  • Biophysics (12671)
  • Cancer Biology (9997)
  • Cell Biology (14405)
  • Clinical Trials (138)
  • Developmental Biology (7989)
  • Ecology (12148)
  • Epidemiology (2067)
  • Evolutionary Biology (16026)
  • Genetics (10953)
  • Genomics (14778)
  • Immunology (9907)
  • Microbiology (23739)
  • Molecular Biology (9508)
  • Neuroscience (51055)
  • Paleontology (370)
  • Pathology (1545)
  • Pharmacology and Toxicology (2693)
  • Physiology (4038)
  • Plant Biology (8696)
  • Scientific Communication and Education (1512)
  • Synthetic Biology (2404)
  • Systems Biology (6459)
  • Zoology (1350)