A Modified Markov Clustering Approach for Protein Sequence Clustering

In this paper we propose a modified Markov clustering algorithm for efficient clustering of large protein sequence databases, based on previously evaluated sequence similarity criteria. The proposed alteration consists in an exponentially decreasing inflation rate, which aims at helping the quick creation of the hard structure of clusters by using a strong inflation in the beginning, and at producing fine partitions with a weaker inflation thereafter. The algorithm, which was tested and validated using the whole SCOP95 database, or randomly selected 10-50% sections, generally converges within 12-14 iteration cycles and provides clusters of high quality. Furthermore, a novel generalized formula is given for the inflation operation, and an efficient matrix symmetrization technique is presented, in order to improve the partition quality with relatively low amount of extra computations. A large graph layout technique is also employed for the efficient visualization of the obtained clusters.