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Automation and Evaluation of the SOWH Test with SOWHAT

Samuel H. Church, Joseph F. Ryan, Casey W. Dunn
doi: https://doi.org/10.1101/005264
Samuel H. Church
1Department of Ecology and Evolutionary Biology, Brown University, Providence, Rhode Island, United States of America
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Joseph F. Ryan
2Whitney Laboratory for Marine Biosciences, St. Augustine, Florida, United States of America
3Sars International Centre For Marine Molecular Biology, Bergen, Norway
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Casey W. Dunn
1Department of Ecology and Evolutionary Biology, Brown University, Providence, Rhode Island, United States of America
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Abstract

The Swofford-Olsen-Waddell-Hillis (SOWH) test evaluates statistical support for incongruent phylogenetic topologies. It is commonly applied to determine if the maximum likelihood tree in a phylogenetic analysis is significantly different than an alternative hypothesis. The SOWH test compares the observed difference in likelihood between two topologies to a null distribution of differences in likelihood generated by parametric resampling. The test is a well-established phylogenetic method for topology testing, but is is sensitive to model misspecification, it is computationally burdensome to perform, and its implementation requires the investigator to make multiple decisions that each have the potential to affect the outcome of the test. We analyzed the effects of multiple factors using seven datasets to which the SOWH test was previously applied. These factors include bootstrap sample size, likelihood software, the introduction of gaps to simulated data, the use of distinct models of evolution for data simulation and likelihood inference, and a suggested test correction wherein an unresolved “zero-constrained” tree is used to simulate sequence data. In order to facilitate these analyses and future applications of the SOWH test, we wrote SOWHAT, a program that automates the SOWH test. We find that inadequate bootstrap sampling can change the outcome of the SOWH test. The results also show that using a zero-constrained tree for data simulation can result in a wider null distribution and higher p-values, but does not change the outcome of the SOWH test for most datasets. These results will help others implement and evaluate the SOWH test and allow us to provide recommendation for future applications of the SOWH test. SOWHAT is available for download from https://github.com/josephryan/SOWHAT.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted June 15, 2015.
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Automation and Evaluation of the SOWH Test with SOWHAT
Samuel H. Church, Joseph F. Ryan, Casey W. Dunn
bioRxiv 005264; doi: https://doi.org/10.1101/005264
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Automation and Evaluation of the SOWH Test with SOWHAT
Samuel H. Church, Joseph F. Ryan, Casey W. Dunn
bioRxiv 005264; doi: https://doi.org/10.1101/005264

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