高度相似序列的快速Boyer-Moore模式匹配算法

IF 0.2 4区 生物学 Q4 MATHEMATICAL & COMPUTATIONAL BIOLOGY International Journal of Data Mining and Bioinformatics Pub Date : 2015-09-01 DOI:10.1504/IJDMB.2015.072101
Nadia Ben Nsira, T. Lecroq, M. Elloumi
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引用次数: 7

摘要

在过去的十年中,生物学和医学经历了根本性的变化:与传统的Sanger方法相比,下一代测序(NGS)技术能够以非常快的速度和低成本获得基因组序列。因此,这些NGS技术可以收集同一物种个体的基因组序列(基因、外显子组甚至全基因组)。后面这些序列99%以上是相同的。因此,迫切需要高效的算法来对这些特定的序列集进行索引和执行快速模式匹配。在本文中,我们提出了一种非常有效的算法来解决一组高度相似的DNA序列中只有模式可以预处理的精确模式匹配问题。这个新算法扩展了Boyer-Moore精确字符串匹配算法的变体。实验结果表明,该方法在实际应用中具有较好的性能。
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A fast Boyer-Moore type pattern matching algorithm for highly similar sequences
In the last decade, biology and medicine have undergone a fundamental change: next generation sequencing (NGS) technologies have enabled to obtain genomic sequences very quickly and at small costs compared to the traditional Sanger method. These NGS technologies have thus permitted to collect genomic sequences (genes, exomes or even full genomes) of individuals of the same species. These latter sequences are identical to more than 99%. There is thus a strong need for efficient algorithms for indexing and performing fast pattern matching in such specific sets of sequences. In this paper we propose a very efficient algorithm that solves the exact pattern matching problem in a set of highly similar DNA sequences where only the pattern can be pre-processed. This new algorithm extends variants of the Boyer-Moore exact string matching algorithm. Experimental results show that it exhibits the best performances in practice.
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来源期刊
CiteScore
1.00
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0.00%
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0
审稿时长
>12 weeks
期刊介绍: Mining bioinformatics data is an emerging area at the intersection between bioinformatics and data mining. The objective of IJDMB is to facilitate collaboration between data mining researchers and bioinformaticians by presenting cutting edge research topics and methodologies in the area of data mining for bioinformatics. This perspective acknowledges the inter-disciplinary nature of research in data mining and bioinformatics and provides a unified forum for researchers/practitioners/students/policy makers to share the latest research and developments in this fast growing multi-disciplinary research area.
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