Markov Analysis of Protein Sequence Similarities
In our previous work, we explored the use of graph-theoretic spectral methods for clustering protein sequences . The nodes of the graph represent a set of proteins to be clustered into families and/or super-families. Edges between nodes are undirected and weighted by the similarities between proteins. We constructed a novel similarity function based on BLAST scores. The similarity values are in turn used to construct a Markov matrix representing transition probabilities between every pair of connected proteins. By analyzing the perturbations to the stationary distribution of the Markov matrix (as in [6,4]), we partition the graph into clusters. In this paper, we compare our method with TribeMCL, which modifies random walks, by reinforcing strong edges and pruning weak ones, such that clusters emerge naturally from the graph . We compare these two methods with respect to their ease of use and the quality of the resulting clusters.
KeywordsStationary Distribution Singular Value Decomposition Spectral Cluster Transition Probability Matrix Strong Edge
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