Microbiome meta-analysis and cross-disease comparison enabled by the SIAMCAT machine learning toolbox
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 v...
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| Published in: | Genome Biology Vol. 22; no. 1; p. 93 |
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| Main Authors: | , , , , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
London
BioMed Central
30.03.2021
Springer Nature B.V BMC |
| Subjects: | |
| ISSN: | 1474-760X, 1474-7596, 1474-760X |
| Online Access: | Get full text |
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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 reveals some biomarkers to be disease-specific, with others shared across multiple conditions. SIAMCAT is freely available from
siamcat.embl.de
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| AbstractList | 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 reveals some biomarkers to be disease-specific, with others shared across multiple conditions. SIAMCAT is freely available from siamcat.embl.de. 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 reveals some biomarkers to be disease-specific, with others shared across multiple conditions. SIAMCAT is freely available from siamcat.embl.de .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 reveals some biomarkers to be disease-specific, with others shared across multiple conditions. SIAMCAT is freely available from siamcat.embl.de . 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 reveals some biomarkers to be disease-specific, with others shared across multiple conditions. SIAMCAT is freely available from siamcat.embl.de . 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 reveals some biomarkers to be disease-specific, with others shared across multiple conditions. SIAMCAT is freely available from siamcat.embl.de . |
| ArticleNumber | 93 |
| Author | Karcher, Nicolai Zeller, Georg Zych, Konrad Bork, Peer Essex, Morgan Kartal, Ece Salazar, Guillem Sunagawa, Shinichi Wirbel, Jakob |
| Author_xml | – sequence: 1 givenname: Jakob orcidid: 0000-0002-4073-3562 surname: Wirbel fullname: Wirbel, Jakob organization: Structural and Computational Biology Unit, European Molecular Biology Laboratory (EMBL) – sequence: 2 givenname: Konrad orcidid: 0000-0001-7426-0516 surname: Zych fullname: Zych, Konrad organization: Structural and Computational Biology Unit, European Molecular Biology Laboratory (EMBL), Present Address: Clinical Microbiomics A/S – sequence: 3 givenname: Morgan orcidid: 0000-0001-8758-7497 surname: Essex fullname: Essex, Morgan organization: Structural and Computational Biology Unit, European Molecular Biology Laboratory (EMBL), Present Address: Experimental and Clinical Research Center (ECRC) of the Max Delbrück Center for Molecular Medicine and Charité University Hospital – sequence: 4 givenname: Nicolai orcidid: 0000-0001-7894-8182 surname: Karcher fullname: Karcher, Nicolai organization: Structural and Computational Biology Unit, European Molecular Biology Laboratory (EMBL), Department CIBIO, University of Trento – sequence: 5 givenname: Ece orcidid: 0000-0002-7720-455X surname: Kartal fullname: Kartal, Ece organization: Structural and Computational Biology Unit, European Molecular Biology Laboratory (EMBL) – sequence: 6 givenname: Guillem orcidid: 0000-0002-9786-1493 surname: Salazar fullname: Salazar, Guillem organization: Department of Biology, Institute of Microbiology and Swiss Institute of Bioinformatics, ETH Zürich – sequence: 7 givenname: Peer orcidid: 0000-0002-2627-833X surname: Bork fullname: Bork, Peer organization: Structural and Computational Biology Unit, European Molecular Biology Laboratory (EMBL), Molecular Medicine Partnership Unit, Max Delbrück Centre for Molecular Medicine, Department of Bioinformatics, Biocenter, University of Würzburg – sequence: 8 givenname: Shinichi orcidid: 0000-0003-3065-0314 surname: Sunagawa fullname: Sunagawa, Shinichi organization: Department of Biology, Institute of Microbiology and Swiss Institute of Bioinformatics, ETH Zürich – sequence: 9 givenname: Georg orcidid: 0000-0003-1429-7485 surname: Zeller fullname: Zeller, Georg email: zeller@embl.de organization: Structural and Computational Biology Unit, European Molecular Biology Laboratory (EMBL) |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/33785070$$D View this record in MEDLINE/PubMed |
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| Keywords | Statistical modeling Microbiome data analysis Microbiome-wide association studies (MWAS) Machine learning Meta-analysis |
| Language | English |
| License | Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
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| Snippet | The human microbiome is increasingly mined for diagnostic and therapeutic biomarkers using machine learning (ML). However, metagenomics-specific software is... Abstract The human microbiome is increasingly mined for diagnostic and therapeutic biomarkers using machine learning (ML). However, metagenomics-specific... |
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| SubjectTerms | Algorithms Animal Genetics and Genomics Bioinformatics Biomarkers Biomedical and Life Sciences Computational Biology - methods computer software Confounding Factors, Epidemiologic Crohn Disease - etiology Databases, Genetic Datasets Evolutionary Biology Gastrointestinal Microbiome genome Human Genetics Humans Inflammatory bowel disease Learning algorithms Life Sciences Machine Learning Meta-analysis Meta-Analysis as Topic Metagenome Metagenomics Metagenomics - methods Microbial Genetics and Genomics microbiome Microbiome data analysis Microbiome-wide association studies (MWAS) Microbiomes Microbiota Models, Statistical Plant Genetics and Genomics ROC Curve Software Statistical analysis Statistical modeling Taxonomy therapeutics Variables Workflow |
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| Title | Microbiome meta-analysis and cross-disease comparison enabled by the SIAMCAT machine learning toolbox |
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