A data parallel strategy for aligning multiple biological sequences on multi-core computers

Authors:
Xiangyuan Zhu;Kenli Li;Ahmad Salah
Affiliations:
College of Information Science and Engineering, Hunan University, Changsha, Hunan 410082, China and Education Technology and Computer Center, Zhaoqing University, Zhaoqing, Guangdong 516061, China;College of Information Science and Engineering, Hunan University, Changsha, Hunan 410082, China and National Super-computer Center in Changsha, Hunan University, Changsha, Hunan 410082, China;College of Information Science and Engineering, Hunan University, Changsha, Hunan 410082, China and Department of Computer Science, Zagazig University, Zagazig, Sharkia 44519, Egypt
Venue:
Computers in Biology and Medicine
Year:
2013

Citing 14
Cited 0

Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences

Bioinformatics
Probalign: multiple sequence alignment using partition function posterior probabilities

Bioinformatics
Parallel strategies for the local biological sequence alignment in a cluster of workstations

Journal of Parallel and Distributed Computing
On the design of high-performance algorithms for aligning multiple protein sequences on mesh-based multiprocessor architectures

Journal of Parallel and Distributed Computing
Streaming Algorithms for Biological Sequence Alignment on GPUs

IEEE Transactions on Parallel and Distributed Systems
A domain decomposition strategy for alignment of multiple biological sequences on multiprocessor platforms

Journal of Parallel and Distributed Computing
Handling biological sequence alignments on networked computing systems: A divide-and-conquer approach

Journal of Parallel and Distributed Computing
Network-on-Chip Hardware Accelerators for Biological Sequence Alignment

IEEE Transactions on Computers
Next-generation bioinformatics

Bioinformatics
A Hardware Accelerator for the Fast Retrieval of DIALIGN Biological Sequence Alignments in Linear Space

IEEE Transactions on Computers
Parallel geometric algorithms for multi-core computers

Computational Geometry: Theory and Applications
SEED

Bioinformatics
High-quality sequence clustering guided by network topology and multiple alignment likelihood

Bioinformatics
Aligning biological sequences on distributed bus networks: a divisible load scheduling approach

IEEE Transactions on Information Technology in Biomedicine

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we address the large-scale biological sequence alignment problem, which has an increasing demand in computational biology. We employ data parallelism paradigm that is suitable for handling large-scale processing on multi-core computers to achieve a high degree of parallelism. Using the data parallelism paradigm, we propose a general strategy which can be used to speed up any multiple sequence alignment method. We applied five different clustering algorithms in our strategy and implemented rigorous tests on an 8-core computer using four traditional benchmarks and artificially generated sequences. The results show that our multi-core-based implementations can achieve up to 151-fold improvements in execution time while losing 2.19% accuracy on average. The source code of the proposed strategy, together with the test sets used in our analysis, is available on request.