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Multiple Sequence Alignment (MSA) is one of the most computationallyintensive tasks in Computational Biology. Existing best known solutions formultiple sequence alignment take several hours (in some cases days) ofcomputation time to align, for example, 2000 homologous sequences of averagelength 300. Inspired by the Sample Sort approach in parallel processing, inthis paper we propose a highly scalable multiprocessor solution for the MSAproblem in phylogenetically diverse sequences. Our method employs anintelligent scheme to partition the set of sequences into smaller subsets usingkmer count based similarity index, referred to as k-mer rank. Each subset isthen independently aligned in parallel using any sequential approach. Furtherfine tuning of the local alignments is achieved using constraints derived froma global ancestor of the entire set. The proposed Sample-Align-D Algorithm hasbeen implemented on a cluster of workstations using MPI message passinglibrary. The accuracy of the proposed solution has been tested on standardbenchmarks such as PREFAB. The accuracy of the alignment produced by ourmethods is comparable to that of well known sequential MSA techniques. We wereable to align 2000 randomly selected sequences from the Methanosarcinaacetivorans genome in less than 10 minutes using Sample-Align-D on a 16 nodecluster, compared to over 23 hours on sequential MUSCLE system running on asingle cluster node.

Sample-Align-D: A High Performance Multiple Sequence Alignment System using Phylogenetic Sampling and Domain Decomposition