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Helmsman: fast and efficient mutation signature analysis for massive sequencing datasets
BACKGROUND: The spectrum of somatic single-nucleotide variants in cancer genomes often reflects the signatures of multiple distinct mutational processes, which can provide clinically actionable insights into cancer etiology. Existing software tools for identifying and evaluating these mutational sig...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6263557/ https://www.ncbi.nlm.nih.gov/pubmed/30486787 http://dx.doi.org/10.1186/s12864-018-5264-y |
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author | Carlson, Jedidiah Li, Jun Z. Zöllner, Sebastian |
author_facet | Carlson, Jedidiah Li, Jun Z. Zöllner, Sebastian |
author_sort | Carlson, Jedidiah |
collection | PubMed |
description | BACKGROUND: The spectrum of somatic single-nucleotide variants in cancer genomes often reflects the signatures of multiple distinct mutational processes, which can provide clinically actionable insights into cancer etiology. Existing software tools for identifying and evaluating these mutational signatures do not scale to analyze large datasets containing thousands of individuals or millions of variants. RESULTS: We introduce Helmsman, a program designed to perform mutation signature analysis on arbitrarily large sequencing datasets. Helmsman is up to 300 times faster than existing software. Helmsman’s memory usage is independent of the number of variants, resulting in a small enough memory footprint to analyze datasets that would otherwise exceed the memory limitations of other programs. CONCLUSIONS: Helmsman is a computationally efficient tool that enables users to evaluate mutational signatures in massive sequencing datasets that are otherwise intractable with existing software. Helmsman is freely available at https://github.com/carjed/helmsman. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12864-018-5264-y) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-6263557 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-62635572018-12-05 Helmsman: fast and efficient mutation signature analysis for massive sequencing datasets Carlson, Jedidiah Li, Jun Z. Zöllner, Sebastian BMC Genomics Software BACKGROUND: The spectrum of somatic single-nucleotide variants in cancer genomes often reflects the signatures of multiple distinct mutational processes, which can provide clinically actionable insights into cancer etiology. Existing software tools for identifying and evaluating these mutational signatures do not scale to analyze large datasets containing thousands of individuals or millions of variants. RESULTS: We introduce Helmsman, a program designed to perform mutation signature analysis on arbitrarily large sequencing datasets. Helmsman is up to 300 times faster than existing software. Helmsman’s memory usage is independent of the number of variants, resulting in a small enough memory footprint to analyze datasets that would otherwise exceed the memory limitations of other programs. CONCLUSIONS: Helmsman is a computationally efficient tool that enables users to evaluate mutational signatures in massive sequencing datasets that are otherwise intractable with existing software. Helmsman is freely available at https://github.com/carjed/helmsman. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12864-018-5264-y) contains supplementary material, which is available to authorized users. BioMed Central 2018-11-28 /pmc/articles/PMC6263557/ /pubmed/30486787 http://dx.doi.org/10.1186/s12864-018-5264-y Text en © The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Software Carlson, Jedidiah Li, Jun Z. Zöllner, Sebastian Helmsman: fast and efficient mutation signature analysis for massive sequencing datasets |
title | Helmsman: fast and efficient mutation signature analysis for massive sequencing datasets |
title_full | Helmsman: fast and efficient mutation signature analysis for massive sequencing datasets |
title_fullStr | Helmsman: fast and efficient mutation signature analysis for massive sequencing datasets |
title_full_unstemmed | Helmsman: fast and efficient mutation signature analysis for massive sequencing datasets |
title_short | Helmsman: fast and efficient mutation signature analysis for massive sequencing datasets |
title_sort | helmsman: fast and efficient mutation signature analysis for massive sequencing datasets |
topic | Software |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6263557/ https://www.ncbi.nlm.nih.gov/pubmed/30486787 http://dx.doi.org/10.1186/s12864-018-5264-y |
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