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dc.contributor.authorPark, Hyun Jung
Nakhleh, Luay
dc.date.accessioned 2013-03-19T17:51:13Z
dc.date.available 2013-03-19T17:51:13Z
dc.date.issued 2012
dc.identifier.citation Park, Hyun Jung and Nakhleh, Luay. "Inference of reticulate evolutionary histories by maximum likelihood: the performance of information criteria." BMC Bioinformatics, 13, no. Suppl 19 (2012) BioMed Central: S12. https://doi.org/10.1186/1471-2105-13-S19-S12.
dc.identifier.urihttps://hdl.handle.net/1911/70724
dc.description.abstract Background: Maximum likelihood has been widely used for over three decades to infer phylogenetic trees from molecular data. When reticulate evolutionary events occur, several genomic regions may have conflicting evolutionary histories, and a phylogenetic network may provide a more adequate model for representing the evolutionary history of the genomes or species. A maximum likelihood (ML) model has been proposed for this case and accounts for both mutation within a genomic region and reticulation across the regions. However, the performance of this model in terms of inferring information about reticulate evolution and properties that affect this performance have not been studied. Results: In this paper, we study the effect of the evolutionary diameter and height of a reticulation event on its identifiability under ML. We find both of them, particularly the diameter, have a significant effect. Further, we find that the number of genes (which can be generalized to the concept of "non-recombining genomic regions") that are transferred across a reticulation edge affects its detectability. Last but not least, a fundamental challenge with phylogenetic networks is that they allow an arbitrary level of complexity, giving rise to the model selection problem. We investigate the performance of two information criteria, the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC), for addressing this problem. We find that BIC performs well in general for controlling the model complexity and preventing ML from grossly overestimating the number of reticulation events. Conclusion: Our results demonstrate that BIC provides a good framework for inferring reticulate evolutionary histories. Nevertheless, the results call for caution when interpreting the accuracy of the inference particularly for data sets with particular evolutionary features.
dc.language.iso eng
dc.publisher BioMed Central
dc.rightsThis article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
dc.rights.urihttps://creativecommons.org/licenses/by/2.0/
dc.title Inference of reticulate evolutionary histories by maximum likelihood: the performance of information criteria
dc.type Journal article
dc.contributor.funder National Science Foundation
dc.contributor.funder U.S. National Library of Medicine
dc.contributor.funder Alfred P. Sloan Foundation
dc.contributor.funder Sun Microsystems
dc.contributor.funder Sigma Solutions, Inc.
dc.citation.journalTitle BMC Bioinformatics
dc.citation.volumeNumber 13
dc.citation.issueNumber Suppl 19
dc.citation.conferenceName Tenth Annual Research in Computational Molecular Biology (RECOMB) Satellite Workshop on Comparative Genomics
dc.citation.conferenceDate 2012-10-17
dc.embargo.terms none
dc.type.dcmi Text
dc.identifier.doihttps://doi.org/10.1186/1471-2105-13-S19-S12
dc.identifier.pmcid PMC3526433
dc.identifier.pmid 23281614
dc.identifier.grantID DBI-1062463 (National Science Foundation)
dc.identifier.grantID R01LM009494 (U.S. National Library of Medicine)
dc.identifier.grantID Research Fellowship (Alfred P. Sloan Foundation)
dc.identifier.grantID EIA-0216467 (National Science Foundation)
dc.type.publication publisher version
dc.citation.firstpage S12


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This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Except where otherwise noted, this item's license is described as This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.