Helmsman: fast and efficient mutation signature analysis for massive sequencing datasets
dc.contributor.author | Carlson, Jedidiah | |
dc.contributor.author | Li, Jun Z | |
dc.contributor.author | Zöllner, Sebastian | |
dc.date.accessioned | 2018-12-02T04:10:38Z | |
dc.date.available | 2018-12-02T04:10:38Z | |
dc.date.issued | 2018-11-28 | |
dc.identifier.citation | BMC Genomics. 2018 Nov 28;19(1):845 | |
dc.identifier.uri | https://doi.org/10.1186/s12864-018-5264-y | |
dc.identifier.uri | https://hdl.handle.net/2027.42/146537 | |
dc.description.abstract | 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 . | |
dc.title | Helmsman: fast and efficient mutation signature analysis for massive sequencing datasets | |
dc.type | Article | en_US |
dc.description.bitstreamurl | https://deepblue.lib.umich.edu/bitstream/2027.42/146537/1/12864_2018_Article_5264.pdf | |
dc.language.rfc3066 | en | |
dc.rights.holder | The Author(s). | |
dc.date.updated | 2018-12-02T04:10:39Z | |
dc.owningcollname | Interdisciplinary and Peer-Reviewed |
Files in this item
Remediation of Harmful Language
The University of Michigan Library aims to describe library materials in a way that respects the people and communities who create, use, and are represented in our collections. Report harmful or offensive language in catalog records, finding aids, or elsewhere in our collections anonymously through our metadata feedback form. More information at Remediation of Harmful Language.
Accessibility
If you are unable to use this file in its current format, please select the Contact Us link and we can modify it to make it more accessible to you.