OrthoMCL: Identification of Ortholog Groups for Eukaryotic Genomes
Open accessAbstract
The identification of orthologous groups is useful for genome annotation, studies on gene/protein evolution, comparative genomics, and the identification of taxonomically restricted sequences. Methods successfully exploited for prokaryotic genome analysis have proved difficult to apply to eukaryotes, however, as larger genomes may contain multiple paralogous genes, and sequence information is often incomplete. OrthoMCL provides a scalable method for constructing orthologous groups across multiple eukaryotic taxa, using a Markov Cluster algorithm to group (putative) orthologs and paralogs. This method performs similarly to the INPARANOID algorithm when applied to two genomes, but can be extended to cluster orthologs from multiple species. OrthoMCL clusters are coherent with groups identified by EGO, but improved recognition of “recent” paralogs permits overlapping EGO groups representing the same gene to be merged. Comparison with previously assigned EC annotations suggests a high degree of reliability, implying utility for automated eukaryotic genome annotation. OrthoMCL has been applied to the proteome data set from seven publicly available genomes (human, fly, worm, yeast, Arabidopsis, the malaria parasite Plasmodium falciparum , and Escherichia coli ). A Web interface allows queries based on individual genes or user-defined phylogenetic patterns ( http://www.cbil.upenn.edu/gene-family ). Analysis of clusters incorporating P. falciparum genes identifies numerous enzymes that were incompletely annotated in first-pass annotation of the parasite genome.
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Graph-based methods cluster genes into pairs or groups of orthologs based on pairwise sequence similarity such as reciprocal best alignment hits.
“Graph-based methods cluster genes into pairs or groups of orthologs based on pairwise sequence similarity such as (reciprocal) best alignment hits (8–12).”
From Integrating gene annotation with orthology inference at scale (2023) · cites this paper as [8]
Heuristic analysis of pairwise sequence similarity scores is based on the premise that higher scoring sequence pairs are likely to have diverged more recently than lower scoring sequence pairs.
“The central premise underlying their use is that higher scoring sequence pairs are likely to have diverged more recently than lower scoring sequence pairs.”
From OrthoFinder: phylogenetic orthology inference for comparative genomics (2019) · cites this paper as [8]
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- Authors
- Li, L., Stoeckert, C. J., & Roos, D. S.
- Affiliations
- University of Pennsylvania (US)
- Venue
- Genome Research
- Publisher
- Cold Spring Harbor Laboratory
- Published
- 2003-09
- Volume
- 13
- Issue
- 9
- Pages
- 2178-2189
- ISSN
- 1088-9051 (ISSN-L)
- PMC
- PMC403725
- OpenAlex
- W2008856488
- Cited by
- 5,707
- References
- 3
- Citations by year
- 2026: 122 · 2025: 199 · 2024: 279 · 2023: 277 · 2022: 397 · 2021: 511 · 2020: 523 · 2019: 463 · 2018: 448 · 2017: 455 · 2016: 408 · 2015: 324 · 2014: 301 · 2013: 246 · 2012: 193
- Type
- posted content
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Genome Research (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health
- 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 › Agricultural and Biological Sciences › Plant Science › Chromosomal and Genetic Variations
- Keywords
- ortholog identification, orthogroups, eukaryotic genomes, Markov cluster algorithm, paralogs, genome annotation, comparative genomics, gene family clustering, Plasmodium falciparum, ego, InParanoid, enzyme annotation
- MeSH terms
- Animals, Drosophila melanogaster, Eukaryotic Cells, Humans, Plasmodium falciparum, Saccharomyces cerevisiae, Sequence Homology, Nucleic Acid, Software, Genome, Genome, Fungal, Caenorhabditis elegans, Arabidopsis, Genome, Protozoan, Genome, Plant, Computational Biology, Internet
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