A divide-and-conquer method for scalable phylogenetic network inference from multilocus data
| dc.citation.firstpage | i370 | en_US |
| dc.citation.issueNumber | 14 | en_US |
| dc.citation.journalTitle | Bioinformatics | en_US |
| dc.citation.lastpage | i378 | en_US |
| dc.citation.volumeNumber | 35 | en_US |
| dc.contributor.author | Zhu, Jiafan | en_US |
| dc.contributor.author | Liu, Xinhao | en_US |
| dc.contributor.author | Ogilvie, Huw A. | en_US |
| dc.contributor.author | Nakhleh, Luay K. | en_US |
| dc.date.accessioned | 2019-08-28T16:10:11Z | en_US |
| dc.date.available | 2019-08-28T16:10:11Z | en_US |
| dc.date.issued | 2019 | en_US |
| dc.description.abstract | Motivation: Reticulate evolutionary histories, such as those arising in the presence of hybridization, are best modeled as phylogenetic networks. Recently developed methods allow for statistical inference of phylogenetic networks while also accounting for other processes, such as incomplete lineage sorting. However, these methods can only handle a small number of loci from a handful of genomes. Results: In this article, we introduce a novel two-step method for scalable inference of phylogenetic networks from the sequence alignments of multiple, unlinked loci. The method infers networks on subproblems and then merges them into a network on the full set of taxa. To reduce the number of trinets to infer, we formulate a Hitting Set version of the problem of finding a small number of subsets, and implement a simple heuristic to solve it. We studied their performance, in terms of both running time and accuracy, on simulated as well as on biological datasets. The two-step method accurately infers phylogenetic networks at a scale that is infeasible with existing methods. The results are a significant and promising step towards accurate, large-scale phylogenetic network inference. | en_US |
| dc.identifier.citation | Zhu, Jiafan, Liu, Xinhao, Ogilvie, Huw A., et al.. "A divide-and-conquer method for scalable phylogenetic network inference from multilocus data." <i>Bioinformatics,</i> 35, no. 14 (2019) Oxford University Press: i370-i378. https://doi.org/10.1093/bioinformatics/btz359. | en_US |
| dc.identifier.doi | https://doi.org/10.1093/bioinformatics/btz359 | en_US |
| dc.identifier.uri | https://hdl.handle.net/1911/107367 | en_US |
| dc.language.iso | eng | en_US |
| dc.publisher | Oxford University Press | en_US |
| dc.rights | This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. | en_US |
| dc.rights.uri | https://creativecommons.org/licenses/by-nc/4.0/ | en_US |
| dc.title | A divide-and-conquer method for scalable phylogenetic network inference from multilocus data | en_US |
| dc.type | Journal article | en_US |
| dc.type.dcmi | Text | en_US |
| dc.type.publication | publisher version | en_US |
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