gsufsort: constructing suffix arrays, LCP arrays and BWTs for string collections.

IF 1.5 4区 生物学 Q4 BIOCHEMICAL RESEARCH METHODS
Algorithms for Molecular Biology Pub Date : 2020-09-22 eCollection Date: 2020-01-01 DOI:10.1186/s13015-020-00177-y
Felipe A Louza, Guilherme P Telles, Simon Gog, Nicola Prezza, Giovanna Rosone
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引用次数: 11

Abstract

Background: The construction of a suffix array for a collection of strings is a fundamental task in Bioinformatics and in many other applications that process strings. Related data structures, as the Longest Common Prefix array, the Burrows-Wheeler transform, and the document array, are often needed to accompany the suffix array to efficiently solve a wide variety of problems. While several algorithms have been proposed to construct the suffix array for a single string, less emphasis has been put on algorithms to construct suffix arrays for string collections.

Result: In this paper we introduce gsufsort, an open source software for constructing the suffix array and related data indexing structures for a string collection with N symbols in O(N) time. Our tool is written in ANSI/C and is based on the algorithm gSACA-K (Louza et al. in Theor Comput Sci 678:22-39, 2017), the fastest algorithm to construct suffix arrays for string collections. The tool supports large fasta, fastq and text files with multiple strings as input. Experiments have shown very good performance on different types of strings.

Conclusions: gsufsort is a fast, portable, and lightweight tool for constructing the suffix array and additional data structures for string collections.

Abstract Image

为字符串集合构造后缀数组、LCP数组和bwt。
背景:在生物信息学和许多其他处理字符串的应用中,为字符串集合构建后缀数组是一项基本任务。相关的数据结构,如最长公共前缀数组、Burrows-Wheeler转换和文档数组,通常需要与后缀数组一起有效地解决各种各样的问题。虽然已经提出了几种算法来为单个字符串构造后缀数组,但很少强调为字符串集合构造后缀数组的算法。结果:本文介绍了一个开源软件gsufsort,用于在O(N)时间内为N个字符的字符串集合构建后缀数组和相关的数据索引结构。我们的工具是用ANSI/C编写的,基于算法gSACA-K (Louza等人在《理论计算科学》678:22- 39,2017),这是为字符串集合构建后缀数组的最快算法。该工具支持使用多个字符串作为输入的大型fasta、fastq和文本文件。实验表明,该方法在不同类型的弦上都具有良好的性能。结论:gsufsort是一个快速、便携和轻量级的工具,用于为字符串集合构造后缀数组和其他数据结构。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Algorithms for Molecular Biology
Algorithms for Molecular Biology 生物-生化研究方法
CiteScore
2.40
自引率
10.00%
发文量
16
审稿时长
>12 weeks
期刊介绍: Algorithms for Molecular Biology publishes articles on novel algorithms for biological sequence and structure analysis, phylogeny reconstruction, and combinatorial algorithms and machine learning. Areas of interest include but are not limited to: algorithms for RNA and protein structure analysis, gene prediction and genome analysis, comparative sequence analysis and alignment, phylogeny, gene expression, machine learning, and combinatorial algorithms. Where appropriate, manuscripts should describe applications to real-world data. However, pure algorithm papers are also welcome if future applications to biological data are to be expected, or if they address complexity or approximation issues of novel computational problems in molecular biology. Articles about novel software tools will be considered for publication if they contain some algorithmically interesting aspects.
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