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Multiple sequence alignment (MSA) is one of the most computationally intensive tasks in Computational Biology. Existing best known solutions for multiple sequence alignment take several hours (in some cases days) of computation time to align, for example, 2000 homologous sequences of average length 300. Inspired by the Sample Sort approach in parallel processing, in this paper we propose a highly scalable multiprocessor solution for the MSA problem in phylogenetically diverse sequences. Our method employs an intelligent scheme to partition the set of sequences into smaller subsets using k- mer count based similarity index, referred to as k-mer rank. Each subset is then independently aligned in parallel using any sequential approach. Further fine tuning of the local alignments is achieved using constraints derived from a global ancestor of the entire set. The proposed sample-align-D algorithm has been implemented on a cluster of workstations using MPI message passing library. The accuracy of the proposed solution has been tested on standard benchmarks such as PREFAB. The accuracy of the alignment produced by our methods is comparable to that of well known sequential MSA techniques. We were able to align 2000 randomly selected sequences from the Methanosarcina acetivorans genome in less than 10 minutes using sample-align-D on a 16 node cluster, compared to over 23 hours on sequential MUSCLE system running on a single cluster node.
Date of Conference: 14-18 April 2008