Parallel progressive multiple sequence alignment on reconfigurable meshes

BMC Genomics

Research Article

Yi Pan¹  Ge Nong²  Ken D Nguyen³ 

[1] Department of Computer Science, Georgia State University, 30303, Atlanta, GA, USA;Department of Computer Science, Sun Yat-sen University, P.R.C;Department of Information Technology, Clayton State University, 30260, Morrow, GA, USA;

关键词: Dynamic Programming;    Graphic Processing Unit;    Processing Unit;    Dynamic Programming Algorithm;    Longe Common Subsequence;   

DOI  :  10.1186/1471-2164-12-S5-S4

来源: Springer

【 摘 要 】

BackgroundOne of the most fundamental and challenging tasks in bio-informatics is to identify related sequences and their hidden biological significance. The most popular and proven best practice method to accomplish this task is aligning multiple sequences together. However, multiple sequence alignment is a computing extensive task. In addition, the advancement in DNA/RNA and Protein sequencing techniques has created a vast amount of sequences to be analyzed that exceeding the capability of traditional computing models. Therefore, an effective parallel multiple sequence alignment model capable of resolving these issues is in a great demand.ResultsWe design O(1) run-time solutions for both local and global dynamic programming pair-wise alignment algorithms on reconfigurable mesh computing model. To align m sequences with max length n, we combining the parallel pair-wise dynamic programming solutions with newly designed parallel components. We successfully reduce the progressive multiple sequence alignment algorithm's run-time complexity from O(m × n4) to O(m) using O(m × n3) processing units for scoring schemes that use three distinct values for match/mismatch/gap-extension. The general solution to multiple sequence alignment algorithm takes O(m × n4) processing units and completes in O(m) time.ConclusionsTo our knowledge, this is the first time the progressive multiple sequence alignment algorithm is completely parallelized with O(m) run-time. We also provide a new parallel algorithm for the Longest Common Subsequence (LCS) with O(1) run-time using O(n3) processing units. This is a big improvement over the current best constant-time algorithm that uses O(n4) processing units.

【 授权许可】

Unknown   
© Nguyen et al. licensee BioMed Central Ltd 2011. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.