Year: 2025
Type: article
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.
Source: Bioinformatics
Institutions: Centre National de la Recherche Scientifique, Institut national de recherche en sciences et technologies du numérique, Institut de Recherche en Informatique et Systèmes Aléatoires, GENSCALE: Algorithmes pour les données génomiques : passage à l'échelle, précision, frugalité, Université de Rennes, Institut Pasteur, Université Paris Cité, Sorbonne Université
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $3,989
Awards: G2059998499, G2259702365, G3010527862, G4196955921, Processing-in-Memory for Genomics, G5038421102, Institut Convergences pour l’étude de l’Emergence des Pathologies au Travers des Individus et des populatiONs, Search engine for environmental genomic sequencing data, G5386688974, PaRis Artificial Intelligence Research InstitutE, Unbiased massive RNA data-mining for medical applications, G7379917482, G7774630463, G7949288910, G8711373613
OpenAlex ID: W4407100609