Enhanced Compression of k-Mer Sets with Counters via de Bruijn Graphs.

IF 1.4 4区 生物学 Q4 BIOCHEMICAL RESEARCH METHODS Journal of Computational Biology Pub Date : 2024-06-01 Epub Date: 2024-05-31 DOI:10.1089/cmb.2024.0530
Enrico Rossignolo, Matteo Comin
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Abstract

An essential task in computational genomics involves transforming input sequences into their constituent k-mers. The quest for an efficient representation of k-mer sets is crucial for enhancing the scalability of bioinformatic analyses. One widely used method involves converting the k-mer set into a de Bruijn graph (dBG), followed by seeking a compact graph representation via the smallest path cover. This study introduces USTAR* (Unitig STitch Advanced constRuction), a tool designed to compress both a set of k-mers and their associated counts. USTAR leverages the connectivity and density of dBGs, enabling a more efficient path selection for constructing the path cover. The efficacy of USTAR is demonstrated through its application in compressing real read data sets. USTAR improves the compression achieved by UST (Unitig STitch), the best algorithm, by percentages ranging from 2.3% to 26.4%, depending on the k-mer size, and it is up to 7× times faster.

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通过 de Bruijn 图增强带有计数器的 k-Mer 集的压缩。
计算基因组学的一项基本任务是将输入序列转化为其组成的 k-聚合物。要提高生物信息分析的可扩展性,就必须寻求一种高效的 k-mer 集表示方法。一种广泛使用的方法是将 k-mer 集转换成 de Bruijn 图(dBG),然后通过最小路径覆盖寻求紧凑的图表示。本研究介绍了 USTAR*(Unitig STitch Advanced constRuction),这是一种旨在压缩 k-聚合物集及其相关计数的工具。USTAR 利用 dBGs 的连接性和密度,为构建路径覆盖提供了更有效的路径选择。USTAR 在压缩真实读取数据集中的应用证明了它的功效。USTAR 比最佳算法 UST(Unitig STitch)的压缩率提高了 2.3% 到 26.4%,具体取决于 k-mer 的大小,而且速度快达 7 倍。
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来源期刊
Journal of Computational Biology
Journal of Computational Biology 生物-计算机:跨学科应用
CiteScore
3.60
自引率
5.90%
发文量
113
审稿时长
6-12 weeks
期刊介绍: Journal of Computational Biology is the leading peer-reviewed journal in computational biology and bioinformatics, publishing in-depth statistical, mathematical, and computational analysis of methods, as well as their practical impact. Available only online, this is an essential journal for scientists and students who want to keep abreast of developments in bioinformatics. Journal of Computational Biology coverage includes: -Genomics -Mathematical modeling and simulation -Distributed and parallel biological computing -Designing biological databases -Pattern matching and pattern detection -Linking disparate databases and data -New tools for computational biology -Relational and object-oriented database technology for bioinformatics -Biological expert system design and use -Reasoning by analogy, hypothesis formation, and testing by machine -Management of biological databases
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