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 sign...
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| Published in | BMC genomics Vol. 19; no. 1; pp. 845 - 5 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
London
BioMed Central
28.11.2018
BioMed Central Ltd Springer Nature B.V BMC |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1471-2164 1471-2164 |
| DOI | 10.1186/s12864-018-5264-y |
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| Abstract | 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
. |
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| AbstractList | 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. 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. 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. 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.BACKGROUNDThe 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.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.RESULTSWe 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.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 .CONCLUSIONSHelmsman 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 . 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 . Abstract 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. 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. 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. 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 . 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. 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 Keywords: Mutational signatures, Cancer genomics, Python, Single nucleotide variants |
| ArticleNumber | 845 |
| Audience | Academic |
| Author | Zöllner, Sebastian Carlson, Jedidiah Li, Jun Z. |
| Author_xml | – sequence: 1 givenname: Jedidiah orcidid: 0000-0002-1363-872X surname: Carlson fullname: Carlson, Jedidiah email: jedidiah@umich.edu organization: Department of Computational Medicine & Bioinformatics, University of Michigan, Department of Genome Sciences, University of Washington – sequence: 2 givenname: Jun Z. surname: Li fullname: Li, Jun Z. organization: Department of Computational Medicine & Bioinformatics, University of Michigan, Department of Human Genetics, University of Michigan – sequence: 3 givenname: Sebastian surname: Zöllner fullname: Zöllner, Sebastian organization: Department of Biostatistics, University of Michigan, Department of Psychiatry, University of Michigan |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/30486787$$D View this record in MEDLINE/PubMed |
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| CitedBy_id | crossref_primary_10_1038_s41598_021_04207_6 crossref_primary_10_1093_gbe_evab104 crossref_primary_10_1093_narcan_zcaa026 crossref_primary_10_1186_s12864_019_6041_2 crossref_primary_10_1016_j_meegid_2021_104805 crossref_primary_10_1158_1055_9965_EPI_23_0728 crossref_primary_10_1038_s43018_023_00643_7 crossref_primary_10_1007_s10549_020_05630_5 crossref_primary_10_3390_cancers15071958 crossref_primary_10_1371_journal_pone_0221235 crossref_primary_10_21105_joss_05227 |
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| Copyright | The Author(s). 2018 COPYRIGHT 2018 BioMed Central Ltd. Copyright © 2018. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
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| Keywords | Mutational signatures Single nucleotide variants Cancer genomics Python |
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The spectrum of somatic single-nucleotide variants in cancer genomes often reflects the signatures of multiple distinct mutational processes, which... The spectrum of somatic single-nucleotide variants in cancer genomes often reflects the signatures of multiple distinct mutational processes, which can provide... Background The spectrum of somatic single-nucleotide variants in cancer genomes often reflects the signatures of multiple distinct mutational processes, which... Abstract Background The spectrum of somatic single-nucleotide variants in cancer genomes often reflects the signatures of multiple distinct mutational... |
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| SubjectTerms | Analysis Animal Genetics and Genomics Bioinformatics Biomedical and Life Sciences Cancer Cancer genetics Cancer genomics Computer memory Computer programs Datasets DNA Mutational Analysis - methods DNA repair Etiology Gene mutation Gene sequencing Genetic algorithms Genomes Genomics Genotype & phenotype High-Throughput Nucleotide Sequencing - methods Human and rodent genomics Life Sciences Microarrays Microbial Genetics and Genomics Mutation Mutation - genetics Mutational signatures Parameter estimation Plant Genetics and Genomics Proteomics Python Reproducibility of Results Sample size Scientific software Signature analysis Signatures Single nucleotide polymorphisms Single nucleotide variants Software Software development tools |
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| Title | Helmsman: fast and efficient mutation signature analysis for massive sequencing datasets |
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