An OpenMP-based tool for finding longest common subsequence in bioinformatics

Objective Finding the longest common subsequence (LCS) among sequences is NP-hard. This is an important problem in bioinformatics for DNA sequence alignment and pattern discovery. In this research, we propose new CPU-based parallel implementations that can provide significant advantages in terms of...

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Bibliographic Details
Published in:BMC research notes Vol. 12; no. 1; pp. 220 - 6
Main Authors: Shikder, Rayhan, Thulasiraman, Parimala, Irani, Pourang, Hu, Pingzhao
Format: Journal Article
Language:English
Published: London BioMed Central 11.04.2019
BioMed Central Ltd
Springer Nature B.V
BMC
Subjects:
Algorithms
Analysis
Animals
Base Sequence
Bees - genetics
Bioinformatics
Biomedical and Life Sciences
Biomedicine
Computational biology
Computational Biology - methods
Deoxyribonucleic acid
DNA
DNA sequence alignment
DNA sequencing
End users
Humans
LCS on MPI and OpenMP
Life Sciences
Longest common subsequence (LCS)
Medical research
Medicine/Public Health
Novels
Nucleotide sequence
Parallel algorithms for LCS
Research Note
Sequence Alignment
Sequence Analysis, DNA - statistics & numerical data
Sequence Homology, Nucleic Acid
Software
Strigiformes - genetics
Tool for finding LCS
Viruses - genetics
DNA sequence alignment
Parallel algorithms for LCS
Tool for finding LCS
LCS on MPI and OpenMP
Longest common subsequence (LCS)
ISSN:1756-0500, 1756-0500
Online Access:Get full text
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Summary:Objective Finding the longest common subsequence (LCS) among sequences is NP-hard. This is an important problem in bioinformatics for DNA sequence alignment and pattern discovery. In this research, we propose new CPU-based parallel implementations that can provide significant advantages in terms of execution times, monetary cost, and pervasiveness in finding LCS of DNA sequences in an environment where Graphics Processing Units are not available. For general purpose use, we also make the OpenMP-based tool publicly available to end users. Result In this study, we develop three novel parallel versions of the LCS algorithm on: (i) distributed memory machine using message passing interface (MPI); (ii) shared memory machine using OpenMP, and (iii) hybrid platform that utilizes both distributed and shared memory using MPI-OpenMP. The experimental results with both simulated and real DNA sequence data show that the shared memory OpenMP implementation provides at least two-times absolute speedup than the best sequential version of the algorithm and a relative speedup of almost 7. We provide a detailed comparison of the execution times among the implementations on different platforms with different versions of the algorithm. We also show that removing branch conditions negatively affects the performance of the CPU-based parallel algorithm on OpenMP platform.
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ISSN:1756-0500
1756-0500
DOI:10.1186/s13104-019-4256-6