Show simple item record

dc.contributor.authorZhang, Liqingen_US
dc.contributor.authorWatson, Layne Ten_US
dc.contributor.authorHeath, Lenwood S.en_US
dc.identifier.citationBMC Bioinformatics. 2011 May 23;12(1):191en_US
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.en_US
dc.rightsAttribution 4.0 United States*
dc.titleA Network of SCOP Hidden Markov Models and Its Analysisen_US
dc.typeJournal articleen_US
dc.description.versionPeer Revieweden_US
dc.rights.holderLiqing Zhang et al.; licensee BioMed Central Ltd.en_US

Files in this item


This item appears in the following Collection(s)

  • BioMed Central [379]
    BioMed Central publications by Virginia Tech authors.

Show simple item record

Attribution 4.0 United States
Except where otherwise noted, this item's license is described as Attribution 4.0 United States