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Microbiome meta-analysis and cross-disease comparison enabled by the SIAMCAT machine-learning toolbox

View ORCID ProfileJakob Wirbel, Konrad Zych, View ORCID ProfileMorgan Essex, View ORCID ProfileNicolai Karcher, View ORCID ProfileEce Kartal, View ORCID ProfileGuillem Salazar, View ORCID ProfilePeer Bork, View ORCID ProfileShinichi Sunagawa, View ORCID ProfileGeorg Zeller
doi: https://doi.org/10.1101/2020.02.06.931808
Jakob Wirbel
1Structural and Computational Biology Unit, European Molecular Biology Laboratory (EMBL), 69117 Heidelberg, Germany
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Konrad Zych
1Structural and Computational Biology Unit, European Molecular Biology Laboratory (EMBL), 69117 Heidelberg, Germany
2Clinical Microbiomics A/S, Ole Maaløes Vej 3, 2200 København, Danmark
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Morgan Essex
1Structural and Computational Biology Unit, European Molecular Biology Laboratory (EMBL), 69117 Heidelberg, Germany
3Experimental and Clinical Research Center (ECRC) of the Max Delbrück Center for Molecular Medicine and Charité University Hospital, 13125 Berlin, Germany
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Nicolai Karcher
1Structural and Computational Biology Unit, European Molecular Biology Laboratory (EMBL), 69117 Heidelberg, Germany
4Department CIBIO, University of Trento, Trento 38123, Italy
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Ece Kartal
1Structural and Computational Biology Unit, European Molecular Biology Laboratory (EMBL), 69117 Heidelberg, Germany
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Guillem Salazar
5Department of Biology, Institute of Microbiology and Swiss Institute of Bioinformatics, ETH Zürich, Zürich 8093, Switzerland
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Peer Bork
1Structural and Computational Biology Unit, European Molecular Biology Laboratory (EMBL), 69117 Heidelberg, Germany
6Molecular Medicine Partnership Unit, Heidelberg, Germany
7Max Delbrück Centre for Molecular Medicine, Berlin, Germany
8Department of Bioinformatics, Biocenter, University of Würzburg, Würzburg, Germany
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Shinichi Sunagawa
5Department of Biology, Institute of Microbiology and Swiss Institute of Bioinformatics, ETH Zürich, Zürich 8093, Switzerland
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Georg Zeller
9Structural and Computational Biology Unit, European Molecular Biology Laboratory (EMBL), Meyerhofstr 1, 69117 Heidelberg, Germany
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  • ORCID record for Georg Zeller
  • For correspondence: zeller@embl.de
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Abstract

The human microbiome is increasingly mined for diagnostic and therapeutic biomarkers using machine learning (ML). However, metagenomics-specific software is scarce and overoptimistic evaluation and limited cross-study generalization are prevailing issues. To address these, we developed SIAMCAT, a versatile R toolbox for ML-based comparative metagenomics. We demonstrate its capabilities in a meta-analysis of fecal metagenomic studies (10,803 samples). When naively transferred across studies, ML models lost accuracy and disease specificity, which could however be resolved by a novel training set augmentation strategy. This revealed some biomarkers to be disease-specific, others shared across multiple conditions. SIAMCAT is freely available from siamcat.embl.de.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • jakob.wirbel{at}embl.de, konrad.zych{at}embl.de, morgan.essex{at}mdc-berlin.de, nicolai.karcher{at}embl.de, ece.kartal{at}embl.de, guillems{at}ethz.ch, bork{at}embl.de, ssunagawa{at}ethz.ch

  • https://siamcat.embl.de/

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 4.0 International license.
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Posted November 05, 2020.
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Microbiome meta-analysis and cross-disease comparison enabled by the SIAMCAT machine-learning toolbox
Jakob Wirbel, Konrad Zych, Morgan Essex, Nicolai Karcher, Ece Kartal, Guillem Salazar, Peer Bork, Shinichi Sunagawa, Georg Zeller
bioRxiv 2020.02.06.931808; doi: https://doi.org/10.1101/2020.02.06.931808
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Microbiome meta-analysis and cross-disease comparison enabled by the SIAMCAT machine-learning toolbox
Jakob Wirbel, Konrad Zych, Morgan Essex, Nicolai Karcher, Ece Kartal, Guillem Salazar, Peer Bork, Shinichi Sunagawa, Georg Zeller
bioRxiv 2020.02.06.931808; doi: https://doi.org/10.1101/2020.02.06.931808

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