A Partitioning Strategy for Nonuniform Problems on Multiprocessors
IEEE Transactions on Computers
Parallel sorting by regular sampling
Journal of Parallel and Distributed Computing
Introduction to parallel computing: design and analysis of algorithms
Introduction to parallel computing: design and analysis of algorithms
Communication operations on coarse-grained mesh architectures
Parallel Computing
Dynamic Partitioning of Non-Uniform Structured Workloads with Spacefilling Curves
IEEE Transactions on Parallel and Distributed Systems
C3: a parallel model for coarse-grained machines
Journal of Parallel and Distributed Computing
Partitioning Unstructured Computational Graphs for Nonuniform and Adaptive Environments
IEEE Parallel & Distributed Technology: Systems & Technology
Strategies for Dynamic Load Balancing on Highly Parallel Computers
IEEE Transactions on Parallel and Distributed Systems
Non-uniform 2-D grid partitioning for heterogeneous parallel architectures
IPPS '95 Proceedings of the 9th International Symposium on Parallel Processing
Binary Dissection: Variants & Applications
Binary Dissection: Variants & Applications
Multiple alignment by sequence annealing
Bioinformatics
Parallel CLUSTAL W for PC clusters
ICCSA'03 Proceedings of the 2003 international conference on Computational science and its applications: PartII
Parallel implementation and performance characterization of MUSCLE
IPDPS'06 Proceedings of the 20th international conference on Parallel and distributed processing
Multiple Sequence Alignment System for Pyrosequencing Reads
BICoB '09 Proceedings of the 1st International Conference on Bioinformatics and Computational Biology
Journal of Parallel and Distributed Computing
A data parallel strategy for aligning multiple biological sequences on multi-core computers
Computers in Biology and Medicine
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
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Multiple Sequences Alignment (MSA) of biological sequences is a fundamental problem in computational biology due to its critical significance in wide ranging applications including haplotype reconstruction, sequence homology, phylogenetic analysis, and prediction of evolutionary origins. The MSA problem is considered NP-hard and known heuristics for the problem do not scale well with increasing numbers of sequences. On the other hand, with the advent of a new breed of fast sequencing techniques it is now possible to generate thousands of sequences very quickly. For rapid sequence analysis, it is therefore desirable to develop fast MSA algorithms that scale well with an increase in the dataset size. In this paper, we present a novel domain decomposition based technique to solve the MSA problem on multiprocessing platforms. The domain decomposition based technique, in addition to yielding better quality, gives enormous advantages in terms of execution time and memory requirements. The proposed strategy allows one to decrease the time complexity of any known heuristic of O(N)^x complexity by a factor of O(1/p)^x, where N is the number of sequences, x depends on the underlying heuristic approach, and p is the number of processing nodes. In particular, we propose a highly scalable algorithm, Sample-Align-D, for aligning biological sequences using Muscle system as the underlying heuristic. The proposed algorithm has been implemented on a cluster of workstations using the MPI library. Experimental results for different problem sizes are analyzed in terms of quality of alignment, execution time and speed-up.