RT Journal Article
SR Electronic
T1 Automation and Evaluation of the SOWH Test with SOWHAT
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 005264
DO 10.1101/005264
A1 Samuel H. Church
A1 Joseph F. Ryan
A1 Casey W. Dunn
YR 2015
UL http://biorxiv.org/content/early/2015/05/07/005264.abstract
AB 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 and choice of likelihood software 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.