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KwARG : Parsimonious reconstruction of ancestral recombination graphs with recurrent mutation
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Ignatieva, Anastasia, Lyngsø, Rune B., Jenkins, Paul and Hein, Jotun (2021) KwARG : Parsimonious reconstruction of ancestral recombination graphs with recurrent mutation. Bioinformatics, 37 (19). pp. 3277-3284. btab351. doi:10.1093/bioinformatics/btab351 ISSN 1460-2059.
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WRAP-KwARG-parsimonious-reconstruction-ancestral-graphs-recurrent-2021.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (889Kb) | Preview |
Official URL: https://doi.org/10.1093/bioinformatics/btab351
Abstract
Motivation The reconstruction of possible histories given a sample of genetic data in the presence of recombination and recurrent mutation is a challenging problem, but can provide key insights into the evolution of a population. We present KwARG, which implements a parsimony-based greedy heuristic algorithm for finding plausible genealogical histories (ancestral recombination graphs) that are minimal or near-minimal in the number of posited recombination and mutation events. Results Given an input dataset of aligned sequences, KwARG outputs a list of possible candidate solutions, each comprising a list of mutation and recombination events that could have generated the dataset; the relative proportion of recombinations and recurrent mutations in a solution can be controlled via specifying a set of ‘cost’ parameters. We demonstrate that the algorithm performs well when compared against existing methods. Availability The software is available at https://github.com/a-ignatieva/kwarg. Supplementary information Supplementary materials are available at Bioinformatics online.
| Item Type: | Journal Article | |||||||||||||||
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| Subjects: | C Auxiliary Sciences of History > CS Genealogy Q Science > QH Natural history |
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| Divisions: | Faculty of Science, Engineering and Medicine > Science > Statistics | |||||||||||||||
| SWORD Depositor: | Library Publications Router | |||||||||||||||
| Library of Congress Subject Headings (LCSH): | Population genetics, Population genetics -- Mathematical models, Genealogy -- Statistical methods | |||||||||||||||
| Journal or Publication Title: | Bioinformatics | |||||||||||||||
| Publisher: | Oxford University Press (OUP) | |||||||||||||||
| ISSN: | 1460-2059 | |||||||||||||||
| Official Date: | 1 October 2021 | |||||||||||||||
| Dates: |
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| Volume: | 37 | |||||||||||||||
| Number: | 19 | |||||||||||||||
| Page Range: | pp. 3277-3284 | |||||||||||||||
| Article Number: | btab351 | |||||||||||||||
| DOI: | 10.1093/bioinformatics/btab351 | |||||||||||||||
| Status: | Peer Reviewed | |||||||||||||||
| Publication Status: | Published | |||||||||||||||
| Access rights to Published version: | Open Access (Creative Commons) | |||||||||||||||
| Date of first compliant deposit: | 21 May 2021 | |||||||||||||||
| Date of first compliant Open Access: | 24 May 2021 | |||||||||||||||
| RIOXX Funder/Project Grant: |
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