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dc.contributor.authorZhang, Liqing
dc.contributor.authorWatson, Layne T
dc.contributor.authorHeath, Lenwood S
dc.date.accessioned2012-08-24T11:05:44Z
dc.date.available2012-08-24T11:05:44Z
dc.date.issued2011-05-23
dc.identifier.citationBMC Bioinformatics. 2011 May 23;12(1):191
dc.identifier.urihttp://hdl.handle.net/10919/18809
dc.description.abstractAbstract Background The Structural Classification of Proteins (SCOP) database uses a large number of hidden Markov models (HMMs) to represent families and superfamilies composed of proteins that presumably share the same evolutionary origin. However, how the HMMs are related to one another has not been examined before. Results In this work, taking into account the processes used to build the HMMs, we propose a working hypothesis to examine the relationships between HMMs and the families and superfamilies that they represent. Specifically, we perform an all-against-all HMM comparison using the HHsearch program (similar to BLAST) and construct a network where the nodes are HMMs and the edges connect similar HMMs. We hypothesize that the HMMs in a connected component belong to the same family or superfamily more often than expected under a random network connection model. Results show a pattern consistent with this working hypothesis. Moreover, the HMM network possesses features distinctly different from the previously documented biological networks, exemplified by the exceptionally high clustering coefficient and the large number of connected components. Conclusions The current finding may provide guidance in devising computational methods to reduce the degree of overlaps between the HMMs representing the same superfamilies, which may in turn enable more efficient large-scale sequence searches against the database of HMMs.
dc.titleA Network of SCOP Hidden Markov Models and Its Analysis
dc.typeJournal Article
dc.date.updated2012-08-24T11:05:44Z
dc.description.versionPeer Reviewed
dc.language.rfc3066en
dc.rights.holderLiqing Zhang et al.; licensee BioMed Central Ltd.
dc.identifier.doihttp://dx.doi.org/10.1186/1471-2105-12-191


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