OrthoMCL: identification of ortholog groups for eukaryotic genomes. 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.

References in zbMATH (referenced in 14 articles )

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  1. Geiß, Manuela; Laffitte, Marcos E. González; Sánchez, Alitzel López; Valdivia, Dulce I.; Hellmuth, Marc; Rosales, Maribel Hernández; Stadler, Peter F.: Best match graphs and reconciliation of gene trees with species trees (2020)
  2. Hellmuth, Marc; Huber, Katharina T.; Moulton, Vincent: Reconciling event-labeled gene trees with MUL-trees and species networks (2019)
  3. Rasti, Saeid; Vogiatzis, Chrysafis: A survey of computational methods in protein-protein interaction networks (2019)
  4. Warnow, Tandy (ed.): Bioinformatics and phylogenetics. Seminal contributions of Bernard Moret (2019)
  5. Hellmuth, Marc; Hernandez-Rosales, Maribel; Huber, Katharina T.; Moulton, Vincent; Stadler, Peter F.; Wieseke, Nicolas: Orthology relations, symbolic ultrametrics, and cographs (2013)
  6. Linard, Benjamin; Thompson, Julie D.; Poch, Olivier; Lecompte, Odile: Orthoinspector: comprehensive orthology analysis and visual exploration (2011) ioport
  7. Min, Jeong Eun; Whiteside, Matthew D.; Brinkman, Fiona S. L.; McNeney, Brad; Graham, Jinko: A statistical approach to high-throughput screening of predicted orthologs (2011)
  8. Fokkens, Like; Botelho, Sandra M. C.; Boekhorst, Jos; Snel, Berend: Enrichment of homologs in insignificant BLAST hits by co-complex network alignment (2010) ioport
  9. Shi, Guanqun; Zhang, Liqing; Jiang, Tao: MSOAR 2.0: incorporating tandem duplications into ortholog assignment based on genome rearrangement (2010) ioport
  10. Towfic, Fadi; Vanderplas, Susan; Oliver, Casey A.; Couture, Oliver; Tuggle, Christopher K.; Greenlee, M. Heather West; Honavar, Vasant: Detection of gene orthology from gene co-expression and protein interaction networks (2010) ioport
  11. Blom, Jochen; Albaum, Stefan P.; Doppmeier, Daniel; Pühler, Alfred; Vorhölter, Frank-Jörg; Zakrzewski, Martha; Goesmann, Alexander: EDGAR: A software framework for the comparative analysis of prokaryotic genomes (2009) ioport
  12. Jiang, Tao: Some algorithmic challenges in genome-wide ortholog assignment (2009) ioport
  13. Schreiber, Fabian; Pick, Kerstin; Erpenbeck, Dirk; Wörheide, Gert; Morgenstern, Burkhard: Orthoselect: a protocol for selecting orthologous groups in phylogenomics (2009) ioport
  14. Zhang, Guoqing; Cao, Zhiwei; Luo, Qingming; Cai, Yudong; Li, Yixue: Operon prediction based on SVM (2006)