title: A fast hierarchical clustering algorithm for large-scale protein sequence data sets.
abstract: TRIBE-MCL is a Markov clustering algorithm that operates on a graph built from pairwise similarity information of the input data. Edge weights stored in the stochastic similarity matrix are alternately fed to the two main operations, inflation and expansion, and are normalized in each main loop to maintain the probabilistic constraint. In this paper we propose an efficient implementation of the TRIBE-MCL clustering algorithm, suitable for fast and accurate grouping of protein sequences. A modified sparse matrix structure is introduced that can efficiently handle most operations of the main loop. Taking advantage of the symmetry of the similarity matrix, a fast matrix squaring formula is also introduced to facilitate the time consuming expansion. The proposed algorithm was tested on protein sequence databases like SCOP95. In terms of efficiency, the proposed solution improves execution speed by two orders of magnitude, compared to recently published efficient solutions, reducing the total runtime well below 1min in the case of the 11,944proteins of SCOP95. This improvement in computation time is reached without losing anything from the partition quality. Convergence is generally reached in approximately 50 iterations. The efficient execution enabled us to perform a thorough evaluation of classification results and to formulate recommendations regarding the choice of the algorithm׳s parameter values.
authors.affiliation: Petru Maior University, Department of Informatics, Str. Nicolae Iorga Nr. 1, 540088 Tîrgu Mureş, Romania. Electronic address: szsandor72@yahoo.com.
authors.forename: Sándor M
authors.initials: SM
authors.lastname: Szilágyi
authors.affiliation: Budapest University of Technology and Economics, Department of Control Engineering and Information Technology, Magyar tudósok krt. 2, H-1117 Budapest, Hungary; Sapientia University of Transylvania, Faculty of Technical and Human Sciences, Şoseaua Sighişoarei 1/C, 540485 Tîrgu Mureş, Romania. Electronic address: lalo@ms.sapientia.ro.
authors.forename: László
authors.initials: L
authors.lastname: Szilágyi
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D008390:Markov Chains; D011506:Proteins / Q000737:chemistry / Q000145:classification; D020539:Sequence Analysis, Protein / Q000379:methods*
chemical_list: D011506:Proteins
keywords: Efficient computing; Markov clustering; Markov processes; Protein sequence clustering; Sparse matrix