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Cited by 7Genomics and Phylogenetic StudiesMachine Learning in BioinformaticsMicrobial Community Ecology and Physiology

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DeepToA: an ensemble deep-learning approach to predicting the theater of activity of a microbiome

Open access
Zeng, Wenhuan, Gautam, Anupam, Huson, Daniel H·2022·Bioinformatics
DOI 10.1093/bioinformatics/btac584

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Abstract

Motivation: Metagenomics is the study of microbiomes using DNA sequencing. A microbiome consists of an assemblage of microbes that is associated with a ‘theater of activity’ (ToA). An important question is, to what degree does the taxonomic and functional content of the former depend on the (details of the) latter? Here, we investigate a related technical question: Given a taxonomic and/or functional profile estimated from metagenomic sequencing data, how to predict the associated ToA? We present a deep-learning approach to this question. We use both taxonomic and functional profiles as input. We apply node2vec to embed hierarchical taxonomic profiles into numerical vectors. We then perform dimension reduction using clustering, to address the sparseness of the taxonomic data and thus make the problem more amenable to deep-learning algorithms. Functional features are combined with textual descriptions of protein families or domains. We present an ensemble deep-learning framework DeepToA for predicting the ToA of amicrobial community, based on taxonomic and functional profiles. We use SHAP (SHapley Additive exPlanations) values to determine which taxonomic and functional features are important for the prediction. Results: Based on 7560 metagenomic profiles downloaded from MGnify, classified into 10 different theaters of activity, we demonstrate that DeepToA has an accuracy of 98.30%. We show that adding textual information to functional features increases the accuracy. Availability and implementation: Our approach is available at http://ab.inf.uni-tuebingen.de/software/deeptoa. Supplementary information: Supplementary data are available at Bioinformatics online.

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Metadata

Authors
Zeng, W., Gautam, A., & Huson, D. H.
Affiliations
  • University of Tübingen (DE)
  • Max Planck Institute for Biology (DE)
Corresponding author
Huson, Daniel H
ORCID
Venue
Bioinformatics
Publisher
Oxford University Press (OUP)
Published
2022-08-27
Volume
38
Issue
20
Pages
4670-4676
ISSN
  • 1367-4803 (ISSN-L)
  • 1367-4811
DOI
10.1093/bioinformatics/btac584
OpenAlex
W4293363887
Cited by
7
References
5
Citations by year
2026: 2 · 2025: 2 · 2023: 2 · 2022: 1
FWCI
0.41
Type
journal article
Language
en
Open access
Licence
Locations
  • Bioinformatics (publishedVersion)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc)
  • PubMed (publishedVersion)
Funders
  • Deutsche Forschungsgemeinschaft (INST 37/935-1, 031A533B, 031A537C, 37/935-1, 031A537D, INST~37/935-1 FUGG, 37/935-1 FUGG, 031A534A, 031A537B, 031A535A, 031A538A, 031A537A, 031A532B, 031A533A)
  • Eberhard Karls Universität Tübingen
  • Bundesministerium für Bildung und Forschung (031A537C, 031A533A, 031A532B, 031A533A, 031A533B, 031A534A, 031A535A, 031A537A, 031A537B, 031A537C, 031A537D, 031A538A, 031A535A, 031A537A, 031A538A, 031A537D, 031A534A, 031A532B, 031A533B, 031A537B)
  • German Network for Bioinformatics Infrastructure (031A533A, 031A534A, 031A538A, 031A537 A, 031A537D, 031 A535 A, 031 A537B, 031 A532B, 031 A533B, #031A537C, 031A532B,031A533A,031A533B,031A534A,031A535A,031A537A,031A537B,031A537C,031A537D,031A538A)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
  • Physical Sciences › Environmental Science › Ecology › Microbial Community Ecology and Physiology
Keywords
microbiome, metagenomics, taxonomic profiles, functional profile, ensemble deep learning, node2vec, SHAP feature importance, microbial community prediction
MeSH terms
Deep Learning, Algorithms, Sequence Analysis, DNA, Metagenome, Metagenomics, Microbiota

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