Please use this identifier to cite or link to this item: https://doi.org/10.1186/1471-2105-11-S1-S63
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dc.titleBidirectional best hit r-window gene clusters
dc.contributor.authorZhang, M.
dc.contributor.authorLeong, H.W.
dc.date.accessioned2013-07-04T07:45:38Z
dc.date.available2013-07-04T07:45:38Z
dc.date.issued2010
dc.identifier.citationZhang, M., Leong, H.W. (2010). Bidirectional best hit r-window gene clusters. BMC Bioinformatics 11 (SUPPLL.1). ScholarBank@NUS Repository. https://doi.org/10.1186/1471-2105-11-S1-S63
dc.identifier.issn14712105
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/39616
dc.description.abstractBackground: Conserved gene clusters are groups of genes that are located close to one another in the genomes of several species. They tend to code for proteins that have a functional interaction. The identification of conserved gene clusters is an important step towards understanding genome evolution and predicting gene function.Results: In this paper, we propose a novel pairwise gene cluster model that combines the notion of bidirectional best hits with the r-window model introduced in 2003 by Durand and Sankoff. The bidirectional best hit (BBH) constraint removes the need to specify the minimum number of shared genes in the r-window model and improves the relevance of the results. We design a subquadratic time algorithm to compute the set of BBH r-window gene clusters efficiently.Conclusion: We apply our cluster model to the comparative analysis of E. coli K-12 and B. subtilis and perform an extensive comparison between our new model and the gene teams model developed by Bergeron et al. As compared to the gene teams model, our new cluster model has a slightly lower recall but a higher precision at all levels of recall when the results were ranked using statistical tests. An analysis of the most significant BBH r-window gene cluster show that they correspond to known operons. © 2010 Zhang and Leong; licensee BioMed Central Ltd.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1186/1471-2105-11-S1-S63
dc.sourceScopus
dc.typeArticle
dc.contributor.departmentCOMPUTER SCIENCE
dc.description.doi10.1186/1471-2105-11-S1-S63
dc.description.sourcetitleBMC Bioinformatics
dc.description.volume11
dc.description.issueSUPPLL.1
dc.description.codenBBMIC
dc.identifier.isiut000277537900042
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