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The evolutionary signal in metagenome phyletic profiles predicts many gene functions

Vidulin, Vedrana; Šmuc, Tomislav; Džeroski, Sašo; Supek, Fran (2018) The evolutionary signal in metagenome phyletic profiles predicts many gene functions. Microbiome, 6 . ISSN 2049-2618

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Abstract

Background. The function of many genes is still not known even in model organisms. An increasing availability of microbiome DNA sequencing data provides an opportunity to infer gene function in a systematic manner. Results. We evaluated if the evolutionary signal contained in metagenome phyletic profiles (MPP) is predictive of a broad array of gene functions. The MPPs are an encoding of environmental DNA sequencing data that consists of relative abundances of gene families across metagenomes. We find that such MPPs can accurately predict 826 Gene Ontology functional categories, while drawing on human gut microbiomes, ocean metagenomes, and DNA sequences from various other engineered and natural environments. Overall, in this task, the MPPs are highly accurate, and moreover they provide coverage for a set of Gene Ontology terms largely complementary to standard phylogenetic profiles, derived from fully sequenced genomes. We also find that metagenomes approximated from taxon relative abundance obtained via 16S rRNA gene sequencing may provide surprisingly useful predictive models. Crucially, the MPPs derived from different types of environments can infer distinct, non-overlapping sets of gene functions and therefore complement each other. Consistently, simulations on > 5000 metagenomes indicate that the amount of data is not in itself critical for maximizing predictive accuracy, while the diversity of sampled environments appears to be the critical factor for obtaining robust models. Conclusions. In past work, metagenomics has provided invaluable insight into ecology of various habitats, into diversity of microbial life and also into human health and disease mechanisms. We propose that environmental DNA sequencing additionally constitutes a useful tool to predict biological roles of genes, yielding inferences out of reach for existing comparative genomics approaches.

Item Type: Article
Uncontrolled Keywords: genomics ; bacteria ; gene function
Subjects: NATURAL SCIENCES > Biology
NATURAL SCIENCES > Interdisciplinary Natural Sciences
TECHNICAL SCIENCES > Computing
Divisions: Division of Electronics
Projects:
Project titleProject leaderProject codeProject type
Learning from Massive, Incompletely annotated, and Structured Data-MAESTRAUNSPECIFIED612944EK
Depositing User: Bojan Macan
Date Deposited: 01 Aug 2018 11:35
Last Modified: 01 Aug 2018 11:35
URI: http://fulir.irb.hr/id/eprint/4115
DOI: 10.1186/s40168-018-0506-4

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