Show simple item record

dc.contributor.authorYu, Yun
Jermaine, Christopher
Nakhleh, Luay
dc.date.accessioned 2016-11-11T17:03:14Z
dc.date.available 2016-11-11T17:03:14Z
dc.date.issued 2016
dc.identifier.citation Yu, Yun, Jermaine, Christopher and Nakhleh, Luay. "Exploring phylogenetic hypotheses via Gibbs sampling on evolutionary networks." BMC Genomics, (2016) http://dx.doi.org/10.1186/s12864-016-3099-y.
dc.identifier.urihttps://hdl.handle.net/1911/92707
dc.description.abstract Abstract Background Phylogenetic networks are leaf-labeled graphs used to model and display complex evolutionary relationships that do not fit a single tree. There are two classes of phylogenetic networks: Data-display networks and evolutionary networks. While data-display networks are very commonly used to explore data, they are not amenable to incorporating probabilistic models of gene and genome evolution. Evolutionary networks, on the other hand, can accommodate such probabilistic models, but they are not commonly used for exploration. Results In this work, we show how to turn evolutionary networks into a tool for statistical exploration of phylogenetic hypotheses via a novel application of Gibbs sampling. We demonstrate the utility of our work on two recently available genomic data sets, one from a group of mosquitos and the other from a group of modern birds. We demonstrate that our method allows the use of evolutionary networks not only for explicit modeling of reticulate evolutionary histories, but also for exploring conflicting treelike hypotheses. We further demonstrate the performance of the method on simulated data sets, where the true evolutionary histories are known. Conclusion We introduce an approach to explore phylogenetic hypotheses over evolutionary phylogenetic networks using Gibbs sampling. The hypotheses could involve reticulate and non-reticulate evolutionary processes simultaneously as we illustrate on mosquito and modern bird genomic data sets.
dc.rightsThis 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.
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.title Exploring phylogenetic hypotheses via Gibbs sampling on evolutionary networks
dc.type Journal article
dc.citation.journalTitle BMC Genomics
dc.contributor.publisher BioMed Central
dc.date.updated 2016-11-11T17:03:14Z
dc.type.dcmi Text
dc.identifier.doihttp://dx.doi.org/10.1186/s12864-016-3099-y
dc.language.rfc3066 en
dc.type.publication publisher version
dc.rights.holder The Author(s)
local.sword.agent BioMed Central


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record

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.
Except where otherwise noted, this item's license is described as 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.