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      The Ortholog Conjecture Is Untestable by the Current Gene Ontology but Is Supported by RNA Sequencing Data

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      PLoS Computational Biology
      Public Library of Science

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          Abstract

          The ortholog conjecture posits that orthologous genes are functionally more similar than paralogous genes. This conjecture is a cornerstone of phylogenomics and is used daily by both computational and experimental biologists in predicting, interpreting, and understanding gene functions. A recent study, however, challenged the ortholog conjecture on the basis of experimentally derived Gene Ontology (GO) annotations and microarray gene expression data in human and mouse. It instead proposed that the functional similarity of homologous genes is primarily determined by the cellular context in which the genes act, explaining why a greater functional similarity of (within-species) paralogs than (between-species) orthologs was observed. Here we show that GO-based functional similarity between human and mouse orthologs, relative to that between paralogs, has been increasing in the last five years. Further, compared with paralogs, orthologs are less likely to be included in the same study, causing an underestimation in their functional similarity. A close examination of functional studies of homologs with identical protein sequences reveals experimental biases, annotation errors, and homology-based functional inferences that are labeled in GO as experimental. These problems and the temporary nature of the GO-based finding make the current GO inappropriate for testing the ortholog conjecture. RNA sequencing (RNA-Seq) is known to be superior to microarray for comparing the expressions of different genes or in different species. Our analysis of a large RNA-Seq dataset of multiple tissues from eight mammals and the chicken shows that the expression similarity between orthologs is significantly higher than that between within-species paralogs, supporting the ortholog conjecture and refuting the cellular context hypothesis for gene expression. We conclude that the ortholog conjecture remains largely valid to the extent that it has been tested, but further scrutiny using more and better functional data is needed.

          Author Summary

          Today's exceedingly high speed of genome sequencing, compared with the generally slow pace of functional assay, means that the functions of most genes identified from genome sequences will be annotated only through computational prediction. The primary source of information for this prediction is the functions of orthologous genes in model organisms, because orthologs are widely believed to be functionally similar, especially when compared with paralogs. This belief, known as the ortholog conjecture, was recently challenged on the basis of experimentally derived Gene Ontology (GO) annotations and microarray gene expression data, because these data revealed greater functional and expressional similarities of paralogs than orthologs. Here we show that GO-based estimates of functional similarities are temporary and unreliable, due to experimental biases, annotation errors, and homology-based functional inferences that are incorrectly labeled as experimental in GO. RNA sequencing (RNA-Seq) is superior to microarray for comparing the expressions of different genes or in different species, and our analysis of a large RNA-Seq dataset provides strong support to the ortholog conjecture for gene expression. We conclude that the ortholog conjecture remains largely valid to the extent that it has been tested, but further scrutiny using more and better functional data is needed.

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          Gene Ontology: tool for the unification of biology

          Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.
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            The evolution of gene expression levels in mammalian organs.

            Changes in gene expression are thought to underlie many of the phenotypic differences between species. However, large-scale analyses of gene expression evolution were until recently prevented by technological limitations. Here we report the sequencing of polyadenylated RNA from six organs across ten species that represent all major mammalian lineages (placentals, marsupials and monotremes) and birds (the evolutionary outgroup), with the goal of understanding the dynamics of mammalian transcriptome evolution. We show that the rate of gene expression evolution varies among organs, lineages and chromosomes, owing to differences in selective pressures: transcriptome change was slow in nervous tissues and rapid in testes, slower in rodents than in apes and monotremes, and rapid for the X chromosome right after its formation. Although gene expression evolution in mammals was strongly shaped by purifying selection, we identify numerous potentially selectively driven expression switches, which occurred at different rates across lineages and tissues and which probably contributed to the specific organ biology of various mammals.
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              Orthologs, paralogs, and evolutionary genomics.

              Orthologs and paralogs are two fundamentally different types of homologous genes that evolved, respectively, by vertical descent from a single ancestral gene and by duplication. Orthology and paralogy are key concepts of evolutionary genomics. A clear distinction between orthologs and paralogs is critical for the construction of a robust evolutionary classification of genes and reliable functional annotation of newly sequenced genomes. Genome comparisons show that orthologous relationships with genes from taxonomically distant species can be established for the majority of the genes from each sequenced genome. This review examines in depth the definitions and subtypes of orthologs and paralogs, outlines the principal methodological approaches employed for identification of orthology and paralogy, and considers evolutionary and functional implications of these concepts.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS Comput Biol
                PLoS Comput. Biol
                plos
                ploscomp
                PLoS Computational Biology
                Public Library of Science (San Francisco, USA )
                1553-734X
                1553-7358
                November 2012
                November 2012
                29 November 2012
                : 8
                : 11
                : e1002784
                Affiliations
                [1]Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, Michigan, United States of America
                The Centre for Research and Technology, Hellas, Greece
                Author notes

                The authors have declared that no competing interests exist.

                Conceived and designed the experiments: JZ XC. Performed the experiments: XC. Analyzed the data: XC. Contributed reagents/materials/analysis tools: JZ. Wrote the paper: JZ XC.

                Article
                PCOMPBIOL-D-12-00786
                10.1371/journal.pcbi.1002784
                3510086
                23209392
                1f52e812-68cc-493e-8e42-54ec30e9a30f
                Copyright @ 2012

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 14 May 2012
                : 2 October 2012
                Page count
                Pages: 13
                Funding
                This work was supported by the U.S. National Institutes of Health research grant R01GM67030 to JZ. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology
                Computational Biology
                Bio-Ontologies
                Evolutionary Biology
                Comparative Genomics
                Evolutionary Genetics
                Genomic Evolution
                Genomics
                Comparative Genomics
                Functional Genomics
                Genome Expression Analysis

                Quantitative & Systems biology
                Quantitative & Systems biology

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