updated with SysBio bibtex file

sowhat.bibtex

@@ -1,16 +1,10 @@
-@article {
-        author = {Samuel H. Church and Joseph F. Ryan and Casey W. Dunn},
-        title = {Automation and Evaluation of the SOWH Test with SOWHAT},
-        year = {2015},
-        doi = {10.1101/005264},
-        abstract = {Samuel H. Church1,3 (samuel_church{at}brown.edu), Joseph F. Ryan2 (joseph.ryan{at}whitney.ufl.edu) and Casey W. Dunn1 (casey_dunn{at}brown.edu)
-                                1 Brown University;
-
-                                2 Whitney Laboratory for Marine Biosciences
-                        ↵*
-                                         Corresponding author; email: samuel_church{at}brown.edu
-                                AbstractThe Swofford-Olsen-Waddell-Hillis (SOWH) test is a method to evaluate incongruent phylogenetic topologies. It is used, for example, when an investigator wishes to know if the maximum likelihood tree recovered in their analysis is significantly different than an alternative phylogenetic hypothesis. The SOWH test compares the observed difference in likelihood between the topologies to a null distribution of differences in likelihood generated by parametric resampling. The SOWH test is a well-established and important phylogenetic method, but it can be difficult to implement and its sensitivity to various factors is not well understood. We wrote SOWHAT, a program that automates the SOWH test. In test analyses, we find that variation in parameter estimation as well as the use of a more complex model of parameter estimation have little impact on results, but that results can be inconsistent when an insufficient number of replicates are used to estimate the null distribution. We provide methods of analyzing the sampling as well as a simple stopping criteria for sufficient bootstrap replicates, which increase the overall reliability of the approach. Applications of the SOWH test should include explicit evaluations of sampling adequacy. SOWHAT is available for download from https://github.com/josephryan/SOWHAT.Received May 16, 2014.Accepted May 19, 2014.{\textcopyright} 2014, Published by Cold Spring Harbor Laboratory PressThis pre-print is available under a Creative Commons License (Attribution-NonCommercial-NoDerivs 4.0 International), CC BY-NC-ND 4.0, as described at http://creativecommons.org/licenses/by-nc-nd/4.0/},
-        URL = {http://biorxiv.org/content/early/2015/06/15/005264},
-        eprint = {http://biorxiv.org/content/early/2015/06/15/005264.full.pdf},
-        journal = {bioRxiv}
+@article{Church30072015,
+author = {Church, Samuel H. and Ryan, Joseph F. and Dunn, Casey W.}, 
+title = {Automation and Evaluation of the SOWH Test with SOWHAT},
+year = {2015}, 
+doi = {10.1093/sysbio/syv055}, 
+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 log-likelihood between two topologies to a null distribution of differences in log-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 several 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 number of sample replicates, 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 of the datasets tested here. These results will help others implement and evaluate the SOWH test and allow us to provide recommendations for future applications of the SOWH test. SOWHAT is available for download from https://github.com/josephryan/SOWHAT.}, 
+URL = {http://sysbio.oxfordjournals.org/content/early/2015/07/30/sysbio.syv055.abstract}, 
+eprint = {http://sysbio.oxfordjournals.org/content/early/2015/07/30/sysbio.syv055.full.pdf+html}, 
+journal = {Systematic Biology} 
 }