ArticleBMC bioinformatics2008
Clustering of cognate proteins among distinct proteomes derived from multiple links to a single seed sequence.
Article in BMC bioinformatics, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed, 8 citations in OpenAlex.
- BOWS (bioinformatics open web services) to centralize bioinformatics tools in web services.BMC research notes · 2015Article
- Amino acids biosynthesis and nitrogen assimilation pathways: a great genomic deletion during eukaryotes evolution.BMC genomics · 2011Article
- Preimplantation development regulatory pathway construction through a text-mining approach.BMC genomics · 2011Article
- Filling the gap between biology and computer science.BioData mining · 2008Article
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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