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Extensive complementarity between gene function prediction methods

Vidulin, Vedrana; Šmuc, Tomislav; Supek, Fran (2016) Extensive complementarity between gene function prediction methods. Bioinformatics, 32 (23). pp. 3645-3653. ISSN 1367-4803

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Abstract

Motivation: The number of sequenced genomes rises steadily, but we still lack the knowledge about the biological roles of many genes. Automated function prediction (AFP) is thus a necessity. We hypothesize that AFP approaches which draw on distinct genome features may be useful for predicting different types of gene functions, motivating a systematic analysis of the benefits gained by obtaining and integrating such predictions. Results: Our pipeline amalgamates 5, 133, 543 genes from 2, 071 genomes in a single massive analysis that evaluates five established genomic AFP methodologies. While 1, 227 Gene Ontology terms yielded reliable predictions, the majority of these functions were accessible to only one or two of the methods. Moreover, different methods tend to assign a GO term to non-overlapping sets of genes. Thus, inferences made by diverse AFP methods display a striking complementary, both gene-wise and function-wise. Because of this, a viable integration strategy is to rely on a single most- confident prediction per gene/function, instead of enforcing agreement across multiple AFP methods. Using an information-theoretic approach, we estimate that current databases contain 29.2 bits/gene of known E. coli gene functions. This can be increased by up to 5.5 bits/gene using individual AFP methods, or by 11 additional bits/gene upon integration, thereby providing a highly-ranking predictor on the CAFA2 community benchmark. Availability of more sequenced genomes boosts the predictive accuracy of AFP approaches and also the benefit from integrating them.

Item Type: Article
Additional Information: This is a pre-copyedited, author-produced version of an article accepted for publication in Bioinformatics following peer review. The version of record Biomater. Sci., 2016,4, 1412-1416 is available online at: http://pubs.rsc.org/en/Content/ArticleLanding/2016/BM/C6BM00287K#!divAbstract; DOI: 10.1093/bioinformatics/btw532.
Uncontrolled Keywords: gene function prediction; comparative genomics; gene ontology; random forest
Subjects: NATURAL SCIENCES > Biology
NATURAL SCIENCES > Biology > Genetics, Evolution and Phylogenetics
TECHNICAL SCIENCES > Computing > Data Processing
BIOTECHNICAL SCIENCES > Biotechnology > Bioinformatics
Divisions: Division of Electronics
Projects:
Project titleProject leaderProject codeProject type
Postupci strojnog učenja za dubinsku analizu složenih struktura podataka-DescriptiveInductionDragan GambergerIP-2013-11-9623HRZZ
Mulitidisciplinarni pristup otkriću lijekova s ciljanim djelovanjem na matične stanice tumora – uloga transporta kalija-MultiCaSTMarijeta KraljIP-2013-11-5660HRZZ
Foundational Research on MULTIlevel comPLEX networks and systems-MULTIPLEXUNSPECIFIED317532EK
Enhancement of the Innovation Potential in SEE through new Molecular Solutions in Research and Development-INNOMOLUNSPECIFIED316289EK
Learning from Massive, Incompletely annotated, and Structured Data-MAESTRAUNSPECIFIED612944EK
Depositing User: Phd Tomislav Šmuc
Date Deposited: 08 Dec 2017 13:13
Last Modified: 08 Dec 2017 13:28
URI: http://fulir.irb.hr/id/eprint/3715
DOI: 10.1093/bioinformatics/btw532

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