{"title":"MUSET: set of utilities for constructing abundance unitig matrices from sequencing data.","authors":"Riccardo Vicedomini, Francesco Andreace, Yoann Dufresne, Rayan Chikhi, Camila Duitama González","doi":"10.1093/bioinformatics/btaf054","DOIUrl":null,"url":null,"abstract":"<p><strong>Summary: </strong>MUSET is a novel set of utilities designed to efficiently construct abundance unitig matrices from sequencing data. Unitig matrices extend the concept of k-mer matrices by merging overlapping k-mers that unambiguously belong to the same sequence. MUSET addresses the limitations of current software by integrating k-mer counting and unitig extraction to generate unitig matrices containing abundance values, as opposed to only presence-absence in previous tools. These matrices preserve variations between samples while reducing disk space and the number of rows compared to k-mer matrices. We evaluated MUSET's performance using datasets derived from a 618-GB collection of ancient oral sequencing samples, producing a filtered unitig matrix that records abundances in <10 h and 20 GB memory.</p><p><strong>Availability and implementation: </strong>MUSET is open source and publicly available under the AGPL-3.0 licence in GitHub at https://github.com/CamilaDuitama/muset. Source code is implemented in C++ and provided with kmat_tools, a collection of tools for processing k-mer matrices. Version v0.5.1 is available on Zenodo with DOI 10.5281/zenodo.14164801.</p>","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11897428/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Bioinformatics (Oxford, England)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1093/bioinformatics/btaf054","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Summary: MUSET is a novel set of utilities designed to efficiently construct abundance unitig matrices from sequencing data. Unitig matrices extend the concept of k-mer matrices by merging overlapping k-mers that unambiguously belong to the same sequence. MUSET addresses the limitations of current software by integrating k-mer counting and unitig extraction to generate unitig matrices containing abundance values, as opposed to only presence-absence in previous tools. These matrices preserve variations between samples while reducing disk space and the number of rows compared to k-mer matrices. We evaluated MUSET's performance using datasets derived from a 618-GB collection of ancient oral sequencing samples, producing a filtered unitig matrix that records abundances in <10 h and 20 GB memory.
Availability and implementation: MUSET is open source and publicly available under the AGPL-3.0 licence in GitHub at https://github.com/CamilaDuitama/muset. Source code is implemented in C++ and provided with kmat_tools, a collection of tools for processing k-mer matrices. Version v0.5.1 is available on Zenodo with DOI 10.5281/zenodo.14164801.