Title: On the Use of Gene Ontology Annotations to Assess Functional Similarity among Orthologs and Paralogs: A Short Report

Abstract

A recent paper (Nehrt et al., PLoS Comput. Biol. 7:e1002073, 2011) has proposed a metric for the ‘‘functional similarity’’ between two genes that uses only the Gene Ontology (GO) annotations directly derived from published experimental results. Applying this metric, the authors concluded that paralogous genes within the mouse genome or the human genome are more functionally similar on average than orthologous genes between these genomes, an unexpected result with broad implications if true. We suggest, based on both theoretical and empirical considerations, that this proposed metric should not be interpreted as a functional similarity, and therefore cannot be used to support any conclusions about the ‘‘ortholog conjecture’’ (or, more properly, the ‘‘ortholog functional conservation hypothesis’’). First, we reexamine the case studies presented by Nehrt et al. as examples of orthologs with divergent functions, and come to a very different conclusion: they actually exemplify how GO annotations for orthologous genes provide complementary information about conserved biological functions. We then show that there is a global ascertainment bias in the experiment-based GO annotations for human and mouse genes: particular types of experiments tend to be performed in different model organisms. We conclude that the reported statistical differences in annotations between pairs ofmore » orthologous genes do not reflect differences in biological function, but rather complementarity in experimental approaches. Our results underscore two general considerations for researchers proposing novel types of analysis based on the GO: 1) that GO annotations are often incomplete, potentially in a biased manner, and subject to an ‘‘open world assumption’’ (absence of an annotation does not imply absence of a function), and 2) that conclusions drawn from a novel, large-scale GO analysis should whenever possible be supported by careful, in-depth examination of examples, to help ensure the conclusions have a justifiable biological basis.« less

Authors:

Thomas, Paul D. ^[1]; Wood, Valerie ^[2]; Mungall, Christopher J. ^[3]; Lewis, Suzanna E. ^[3]; Blake, Judith A. ^[4]

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+ Show Author Affiliations

1. Univ. of California, Los Angeles, CA (United States). Dept. of Preventive Medicine. Division of Bioinformatics
2. Univ. of Cambridge (United Kingdom). Dept. of Biochemistry. Cambridge Systems Biology Centre
3. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Genomics Division
4. The Jackson Lab., Bar Harbor, ME (United States). Bioinformatics and Computational Biology

Publication Date:

Thu Feb 16 00:00:00 EST 2012

Research Org.:

Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC)

Sponsoring Org.:

USDOE Office of Science (SC), Biological and Environmental Research (BER). Biological Systems Science Division; National Institutes of Health (NIH)

OSTI Identifier:

1627221

Grant/Contract Number:  

AC02-05CH11231; P41 HG002273; R01 GM081084

Resource Type:

Accepted Manuscript

Journal Name:

PLoS Computational Biology (Online)

Additional Journal Information:

Journal Name: PLoS Computational Biology (Online); Journal Volume: 8; Journal Issue: 2; Journal ID: ISSN 1553-7358

Publisher:

Public Library of Science

Country of Publication:

United States

Language:

English

Subject:

59 BASIC BIOLOGICAL SCIENCES; Biochemistry & Molecular Biology; Mathematical & Computational Biology