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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

Log-log degree distribution. The log base is 2. The best fitting quadratic curve is 3.2481 - 0.176557x - 0.133088x2.
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Figure 4: Log-log degree distribution. The log base is 2. The best fitting quadratic curve is 3.2481 - 0.176557x - 0.133088x2.

Mentions: The distribution of the degrees of the HMM network is shown in Figure 3. Degree ranges from 1 to 268, with the average of 26 and median of 10. The log-log degree distribution is also shown (Figure 4). It is evident that a power law distribution does not fit the data. The best fitting quadratic curve is also plotted with the data. It provides a relatively good fit for the smaller values of log(degree), and then towards the larger degrees, the fit is not so good.


A network of SCOP hidden Markov models and its analysis.

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

Log-log degree distribution. The log base is 2. The best fitting quadratic curve is 3.2481 - 0.176557x - 0.133088x2.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 4: Log-log degree distribution. The log base is 2. The best fitting quadratic curve is 3.2481 - 0.176557x - 0.133088x2.
Mentions: The distribution of the degrees of the HMM network is shown in Figure 3. Degree ranges from 1 to 268, with the average of 26 and median of 10. The log-log degree distribution is also shown (Figure 4). It is evident that a power law distribution does not fit the data. The best fitting quadratic curve is also plotted with the data. It provides a relatively good fit for the smaller values of log(degree), and then towards the larger degrees, the fit is not so good.

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