Clustering of cognate proteins among distinct proteomes derived from multiple links to a single seed sequence

BackgroundModern proteomes evolved by modification of pre-existing ones. It is extremely important to comparative biology that related proteins be identified as members of the same cognate group, since a characterized putative homolog could be used to find clues about the function of uncharacterized proteins from the same group. Typically, databases of related proteins focus on those from completely-sequenced genomes. Unfortunately, relatively few organisms have had their genomes fully sequenced; accordingly, many proteins are ignored by the currently available databases of cognate proteins, despite the high amount of important genes that are functionally described only for these incomplete proteomes.ResultsWe have developed a method to cluster cognate proteins from multiple organisms beginning with only one sequence, through connectivity saturation with that Seed sequence. We show that the generated clusters are in agreement with some other approaches based on full genome comparison.ConclusionThe method produced results that are as reliable as those produced by conventional clustering approaches. Generating clusters based only on individual proteins of interest is less time consuming than generating clusters for whole proteomes.

[1]  Michael Y. Galperin,et al.  The COG database: new developments in phylogenetic classification of proteins from complete genomes , 2001, Nucleic Acids Res..

[2]  G. Pertea,et al.  Cross-referencing eukaryotic genomes: TIGR Orthologous Gene Alignments (TOGA). , 2002, Genome research.

[3]  A. Hilliker,et al.  Comparative genomic analysis of equilibrative nucleoside transporters suggests conserved protein structure despite limited sequence identity. , 2002, Nucleic acids research.

[4]  Tin Wee Tan,et al.  Supporting the Curation of Biological Databases with Reusable Text Mining , 2005 .

[5]  E. Koonin,et al.  Orthology, paralogy and proposed classification for paralog subtypes. , 2002, Trends in genetics : TIG.

[6]  C. Stoeckert,et al.  OrthoMCL: identification of ortholog groups for eukaryotic genomes. , 2003, Genome research.

[7]  Emily Dimmer,et al.  The Gene Ontology Annotation (GOA) Database - An integrated resource of GO annotations to the UniProt Knowledgebase , 2003, Silico Biol..

[8]  Feng Chen,et al.  OrthoMCL-DB: querying a comprehensive multi-species collection of ortholog groups , 2005, Nucleic Acids Res..

[9]  Erik L. L. Sonnhammer,et al.  Inparanoid: a comprehensive database of eukaryotic orthologs , 2004, Nucleic Acids Res..

[10]  Darren A. Natale,et al.  The COG database: an updated version includes eukaryotes , 2003, BMC Bioinformatics.

[11]  E. Sonnhammer,et al.  Classification of transmembrane protein families in the Caenorhabditis elegans genome and identification of human orthologs. , 2000, Genome research.

[12]  Tao Cai,et al.  Automated genome annotation and pathway identification using the KEGG Orthology (KO) as a controlled vocabulary , 2005, Bioinform..

[13]  Cathy H. Wu,et al.  UniProt: the Universal Protein knowledgebase , 2004, Nucleic Acids Res..

[14]  Gang Liu,et al.  Automatic clustering of orthologs and inparalogs shared by multiple proteomes , 2006, ISMB.

[15]  Christian E. V. Storm,et al.  Automatic clustering of orthologs and in-paralogs from pairwise species comparisons. , 2001, Journal of molecular biology.