Resolving the Ortholog Conjecture: Orthologs Tend to Be Weakly, but Significantly, More Similar in Function than Paralogs
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The function of most proteins is not determined experimentally, but is extrapolated from homologs. According to the "ortholog conjecture", or standard model of phylogenomics, protein function changes rapidly after duplication, leading to paralogs with different functions, while orthologs retain the ancestral function. We report here that a comparison of experimentally supported functional annotations among homologs from 13 genomes mostly supports this model. We show that to analyze GO annotation effectively, several confounding factors need to be controlled: authorship bias, variation of GO term frequency among species, variation of background similarity among species pairs, and propagated annotation bias. After controlling for these biases, we observe that orthologs have generally more similar functional annotations than paralogs. This is especially strong for sub-cellular localization. We observe only a weak decrease in functional similarity with increasing sequence divergence. These findings hold over a large diversity of species; notably orthologs from model organisms such as E. coli, yeast or mouse have conserved function with human proteins.
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Distinguishing orthologs and paralogs is a fundamental problem in evolutionary and molecular biology and a prerequisite for many genomic analyses.
“Distinguishing orthologs and paralogs is a fundamental problem in evolutionary and molecular biology (1) and a prerequisite for many genomic analyses, including reconstructing phylogenetic trees, predicting gene function, investigating molecular and genome evolution, and discovering differences in genes that underlie phenotypes of the sequenced species (2–6).”
From Integrating gene annotation with orthology inference at scale (2023) · cites this paper as [3]
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Self-citation
- Authors
- Altenhoff, A. M., Studer, R. A., Robinson-Rechavi, M., & Dessimoz, C.
- Affiliations
- SIB Swiss Institute of Bioinformatics (CH)
- Corresponding author
- Dessimoz, Christophe
- Venue
- PLoS Computational Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2012-05-17
- Volume
- 8
- Issue
- 5
- Pages
- e1002514
- ISSN
- 1553-734X (ISSN-L)
- 1553-7358
- PMC
- PMC3355068
- OpenAlex
- W1968637495
- Cited by
- 248
- References
- 0
- Citations by year
- 2026: 11 · 2025: 11 · 2024: 17 · 2023: 12 · 2022: 17 · 2021: 29 · 2020: 37 · 2019: 16 · 2018: 19 · 2017: 10 · 2016: 18 · 2015: 9 · 2014: 20 · 2013: 16 · 2012: 6
- FWCI
- 6.14
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PLoS Computational Biology (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- IRIS (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- UCL Discovery (University College London) (submittedVersion)
- Figshare (submittedVersion, cc-by)
- SERVAL (Université de Lausanne) (submittedVersion, other-oa)
- Repository for Publications and Research Data (ETH Zurich) (submittedVersion, cc-by)
- ETH Zürich Research Collection (cc-by)
- Funders
- National Science Foundation
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (136461, 133011, 132476, 136477)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
- Keywords
- orthologs, paralogs, gene duplication, functional annotation, Gene Ontology, subcellular localization, sequence divergence, phylogenomics, annotation bias, authorship bias
- MeSH terms
- Amino Acid Sequence, Computer Simulation, Models, Chemical, Models, Genetic, Molecular Sequence Data, Proteins, Structure-Activity Relationship, Sequence Homology, Amino Acid, Evolution, Molecular, Sequence Analysis, Protein
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