Analyzing Relationships in Protein Classification Using HMMs
Author Information
Author(s): Zhang Liqing, Watson Layne T, Heath Lenwood S
Primary Institution: Virginia Tech
Hypothesis
The HMMs in a connected component belong to the same family or superfamily more often than expected under a random network connection model.
Conclusion
The study found that HMMs representing the same family or superfamily tend to cluster together in the network.
Supporting Evidence
- More than 77% of connected components have only members from the same family.
- Over 95% of connected components have only members from the same superfamily.
- The clustering coefficient of the HMM network is 0.85, indicating high clustering.
Takeaway
The researchers looked at how different protein models are related and found that similar models often group together, which helps in understanding protein families.
Methodology
An all-against-all comparison of HMMs was performed using the HHsearch program to construct a network of HMMs based on their similarities.
Limitations
The study primarily focuses on the relationships between HMMs without addressing the biological implications of these relationships.
Statistical Information
P-Value
< 2.2 ยท 10-16
Statistical Significance
p<0.05
Digital Object Identifier (DOI)
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