OrthoMCL: identification of ortholog groups for eukaryotic genomes.

By Li Li, Christian J. Stoeckert Jr., David S. Roos. Genome Research, 2003

5,762 citations recorded by the source corpus.

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.

Subfields: Molecular Biology

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Citations in the corpus

antiSMASH: rapid identification, annotation and analysis of secondary metabolite biosynthesis gene clusters in bacterial and fungal genome sequences (2011, methodology): “To construct the smCOGs, clustering of all gene cluster proteins was performed using OrthoMCL (21), and consensus annotations were manually assigned based on the frequencies of the ﬁve most prevalent annotations of each smCOG in GenBank.”
Double Digest RADseq: An Inexpensive Method for De Novo SNP Discovery and Genotyping in Model and Non-Model Species (2012, positive): “We then employ the MCL (Markov Cluster Learning) graph clustering algorithm to discover groups of unusually similar sequences, analogous to the OrthoMCL phylogenetic ortholog finding approach [27].”
The genome of Theobroma cacao (2011, neutral): “Cacao chromosome Arabidopsis chromosome C1(1321) A1(63)-A2(85)-A3(69)-A4(116)-A5(64) C2(935) A1(89)-A2(30)-A4(61)-A5(10) C3(1018) A1(70)-A3(17)-A4(63)-A5(130) C4(837) A1(21)-A2(9)-A3(94)-A4(36)-A5(44) C5(820) A1(24)-A2(51)-A3(78)-A5(37) C6(616) A1(59)-A2(15)-A4(66)-A5(26) C7(287) A1(5)-A5(32) C8(605”
The human phylome (2007, methodology): “Extensions of the BRH approach include the definition of 'triangular' BRH relationships across a minimum of three species [48], and recent implementations thereof, such as Inparanoid [74] or OrthoMCL [75], that include closely related paralogs in the orthologous groups.”
Phylotranscriptomic analysis of the origin and early diversification of land plants (2014, methodology): “To sort assembled transcripts into gene families, we constructed an a priori set of gene families by clustering the protein sequences of 25 sequenced plant genomes using orthoMCL (54).”
Insights into secondary metabolism from a global analysis of prokaryotic biosynthetic gene clusters. (2014, methodology): “We then constructed a modified variant of the cluster in which the ermE* Cell 158, 412–421, July 17, 2014 ª2014 Elsevier Inc. 415 (A) Heat map and dendrogram of all 1,021 detected APE family gene clusters, based on Clusters of Orthologous Groups generated by OrthoMCL (Li et al., 2003) using our adap”
Ultrafast clustering algorithms for metagenomic sequence analysis (2012, methodology): “In the past, many available clustering programs were used for clustering protein sequences such as ProtoMap [18], ProtoNet [19], RSDB [20], GeneRAGE [21], TribeMCL [22], ProClust [23], UniqueProt [24], OrthMCL [25], MC-UPGMA [26], Blastclust [27] and CD-HIT [28–31].”
Using MCL to extract clusters from networks. (2012, methodology): “It has subsequently been used, among others, for detecting orthologous groups [6], predicting protein complexes from protein interaction networks [7,8], clustering of genes according to their expression profiles [9–11], and clustering of scientific documents [12].”
eggNOG: automated construction and annotation of orthologous groups of genes (2007, methodology): “Numerous methods have been developed to derive orthologs and orthologous groups, ranging from the simple reciprocal-best-hit approach, via InParanoid (5), MultiParanoid (6), identification best-hit triangles (2,7,8) and clustering-based approaches ( 9 ), to tree-based methods (10–13).”
PlantTFDB 3.0: a portal for the functional and evolutionary study of plant transcription factors (2013, background): “After that, proteins within the same cluster were assigned into orthologous groups by OrthoMCL (30).”
Proteinortho: Detection of (Co-)orthologs in large-scale analysis (2011, methodology): “It was more recently extended e.g. in OrthoMCL [16] and MultiParanoid [17] to detect (co-)orthologs within multiple species.”
EDGAR: A software framework for the comparative analysis of prokaryotic genomes (2009, background): “Various more sophisticated orthology identification approaches have been developed in the past, e.g. Clusters of Orthologous Genes (COG), InParanoid, OrthoMCL, Ensembl Compara, Homolo-gene, RoundUp, EggNOG, or OMA [17-24].”