Pairwise sequence alignment for very long sequences on GPUs.

Q4 Health Professions

International Journal of Bioinformatics Research and Applications Pub Date : 2014-01-01 DOI:10.1504/IJBRA.2014.062989

Junjie Li, Sanjay Ranka, Sartaj Sahni

引用次数: 29

Abstract

We develop novel single-GPU parallelisations of the Smith-Waterman algorithm for pairwise sequence alignment. Our algorithms, which are suitable for the alignment of a single pair of very long sequences, can be used to determine the alignment score as well as the actual alignment. Experimental results demonstrate an order of magnitude reduction in run time relative to competing GPU algorithms.

查看原文本刊更多论文

gpu上非常长的序列的成对序列对齐。

我们开发了新的单gpu并行史密斯-沃特曼算法成对序列对齐。我们的算法适用于单对超长序列的比对，可以用来确定比对得分和实际比对。实验结果表明，相对于竞争的GPU算法，该算法的运行时间减少了一个数量级。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

International Journal of Bioinformatics Research and Applications Health Professions-Health Information Management

CiteScore

0.60

自引率

0.00%

发文量

期刊介绍： Bioinformatics is an interdisciplinary research field that combines biology, computer science, mathematics and statistics into a broad-based field that will have profound impacts on all fields of biology. The emphasis of IJBRA is on basic bioinformatics research methods, tool development, performance evaluation and their applications in biology. IJBRA addresses the most innovative developments, research issues and solutions in bioinformatics and computational biology and their applications. Topics covered include Databases, bio-grid, system biology Biomedical image processing, modelling and simulation Bio-ontology and data mining, DNA assembly, clustering, mapping Computational genomics/proteomics Silico technology: computational intelligence, high performance computing E-health, telemedicine Gene expression, microarrays, identification, annotation Genetic algorithms, fuzzy logic, neural networks, data visualisation Hidden Markov models, machine learning, support vector machines Molecular evolution, phylogeny, modelling, simulation, sequence analysis Parallel algorithms/architectures, computational structural biology Phylogeny reconstruction algorithms, physiome, protein structure prediction Sequence assembly, search, alignment Signalling/computational biomedical data engineering Simulated annealing, statistical analysis, stochastic grammars.