MAFcounter: an efficient tool for counting the occurrences of k-mers in MAF files.

IF 3.3 3区生物学 Q2 BIOCHEMICAL RESEARCH METHODS

BMC Bioinformatics Pub Date : 2025-05-30 DOI:10.1186/s12859-025-06172-7

Michail Patsakis, Kimonas Provatas, Aris Karatzikos, Charalampos Koilakos, Ioannis Mouratidis, Ilias Georgakopoulos-Soares

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引用次数: 0

Abstract

Motivation: With the rapid expansion of large-scale biological datasets, DNA and protein sequence alignments have become essential for comparative genomics and proteomics. These alignments facilitate the exploration of sequence similarity patterns, providing valuable insights into sequence conservation, evolutionary relationships and for functional analyses. Typically, sequence alignments are stored in formats such as the Multiple Alignment Format (MAF). Counting k-mer occurrences is a crucial task in many computational biology applications, but currently, there is no algorithm designed for k-mer counting in alignment files.

Results: We have developed MAFcounter, the first k-mer counter dedicated to alignment files. MAFcounter is multithreaded, fast, and memory efficient, enabling k-mer counting in DNA and protein sequence alignment files with a wide variety of features for k-mer analysis.

Availability: MAFcounter is released under GPL license as a suite of binary C++ applications and is available at: https://github.com/Georgakopoulos-Soares-lab/MAFcounter .

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MAFcounter：一个计算在MAF文件中k-mers出现次数的有效工具。

动机：随着大规模生物数据集的快速扩展，DNA和蛋白质序列比对已经成为比较基因组学和蛋白质组学的必要条件。这些比对促进了序列相似性模式的探索，为序列保护、进化关系和功能分析提供了有价值的见解。通常，序列对齐以多重对齐格式（Multiple Alignment Format， MAF）等格式存储。在许多计算生物学应用中，计算k-mer的出现次数是一项至关重要的任务，但目前，还没有为排列文件中的k-mer计数设计的算法。结果：研制出了国内第一个用于比对文件的k-mer计数器MAFcounter。MAFcounter是多线程的，快速的，内存高效的，使k-mer计数在DNA和蛋白质序列比对文件具有各种各样的k-mer分析功能。可用性：MAFcounter在GPL许可下作为一套二进制c++应用程序发布，可在：https://github.com/Georgakopoulos-Soares-lab/MAFcounter获得。

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

BMC Bioinformatics 生物-生化研究方法

CiteScore

5.70

自引率

3.30%

发文量

506

审稿时长

4.3 months

期刊介绍： BMC Bioinformatics is an open access, peer-reviewed journal that considers articles on all aspects of the development, testing and novel application of computational and statistical methods for the modeling and analysis of all kinds of biological data, as well as other areas of computational biology. BMC Bioinformatics is part of the BMC series which publishes subject-specific journals focused on the needs of individual research communities across all areas of biology and medicine. We offer an efficient, fair and friendly peer review service, and are committed to publishing all sound science, provided that there is some advance in knowledge presented by the work.