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A network of SCOP hidden Markov models and its analysis.

Zhang L, Watson LT, Heath LS - BMC Bioinformatics (2011)

Bottom Line: 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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Computer Science, Virginia Tech, Blacksburg, VA 24061, USA. lqzhang@cs.vt.edu

ABSTRACT

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.

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Related in: MedlinePlus

The HMM network.
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Figure 1: The HMM network.

Mentions: The entire HMM network is shown in Figure 1, where the e-value cutoff is 0.001. There are altogether 151,461 edges for the 11,929 vertices. A significant property shown in Figure 1 is that the entire network is highly disconnected, with many much smaller connected components. In fact, there are altogether 1524 connected components (CCs). The size distribution of CCs is shown in Figure 2. The smallest CC contains two vertices, the largest 590 vertices, 566/1524 = 37% contain only two vertices and about 73% contain five or fewer vertices. The median CC size is 3 and the mean 7.8. The top 20 largest CCs are listed in Table 2.


A network of SCOP hidden Markov models and its analysis.

Zhang L, Watson LT, Heath LS - BMC Bioinformatics (2011)

The HMM network.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3119068&req=5

Figure 1: The HMM network.
Mentions: The entire HMM network is shown in Figure 1, where the e-value cutoff is 0.001. There are altogether 151,461 edges for the 11,929 vertices. A significant property shown in Figure 1 is that the entire network is highly disconnected, with many much smaller connected components. In fact, there are altogether 1524 connected components (CCs). The size distribution of CCs is shown in Figure 2. The smallest CC contains two vertices, the largest 590 vertices, 566/1524 = 37% contain only two vertices and about 73% contain five or fewer vertices. The median CC size is 3 and the mean 7.8. The top 20 largest CCs are listed in Table 2.

Bottom Line: 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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Computer Science, Virginia Tech, Blacksburg, VA 24061, USA. lqzhang@cs.vt.edu

ABSTRACT

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.

Show MeSH
Related in: MedlinePlus