@article{mannino_wisotsky_pond_muse_2020, title={Equiprobable discrete models of site-specific substitution rates underestimate the extent of rate variability}, volume={15}, ISSN={["1932-6203"]}, DOI={10.1371/journal.pone.0229493}, abstractNote={It is standard practice to model site-to-site variability of substitution rates by discretizing a continuous distribution into a small number, K, of equiprobable rate categories. We demonstrate that the variance of this discretized distribution has an upper bound determined solely by the choice of K and the mean of the distribution. This bound can introduce biases into statistical inference, especially when estimating parameters governing site-to-site variability of substitution rates. Applications to two large collections of sequence alignments demonstrate that this upper bound is often reached in analyses of real data. When parameter estimation is of primary interest, additional rate categories or more flexible modeling methods should be considered.}, number={3}, journal={PLOS ONE}, author={Mannino, Frank and Wisotsky, Sadie and Pond, Sergei L. Kosakovsky and Muse, Spencer V}, year={2020}, month={Mar} }
 @article{wisotsky_pond_shank_muse_2020, title={Synonymous Site-to-Site Substitution Rate Variation Dramatically Inflates False Positive Rates of Selection Analyses: Ignore at Your Own Peril}, volume={37}, ISSN={["1537-1719"]}, DOI={10.1093/molbev/msaa037}, abstractNote={AbstractMost molecular evolutionary studies of natural selection maintain the decades-old assumption that synonymous substitution rate variation (SRV) across sites within genes occurs at levels that are either nonexistent or negligible. However, numerous studies challenge this assumption from a biological perspective and show that SRV is comparable in magnitude to that of nonsynonymous substitution rate variation. We evaluated the impact of this assumption on methods for inferring selection at the molecular level by incorporating SRV into an existing method (BUSTED) for detecting signatures of episodic diversifying selection in genes. Using simulated data we found that failing to account for even moderate levels of SRV in selection testing is likely to produce intolerably high false positive rates. To evaluate the effect of the SRV assumption on actual inferences we compared results of tests with and without the assumption in an empirical analysis of over 13,000 Euteleostomi (bony vertebrate) gene alignments from the Selectome database. This exercise reveals that close to 50% of positive results (i.e., evidence for selection) in empirical analyses disappear when SRV is modeled as part of the statistical analysis and are thus candidates for being false positives. The results from this work add to a growing literature establishing that tests of selection are much more sensitive to certain model assumptions than previously believed.}, number={8}, journal={MOLECULAR BIOLOGY AND EVOLUTION}, author={Wisotsky, Sadie R. and Pond, Sergei L. Kosakovsky and Shank, Stephen D. and Muse, Spencer V}, year={2020}, month={Aug}, pages={2430–2439} }
 @article{pond_poon_velazquez_weaver_hepler_murrell_shank_magalis_bouvier_nekrutenko_et al._2020, title={HyPhy 2.5-A Customizable Platform for Evolutionary Hypothesis Testing Using Phylogenies}, volume={37}, ISSN={["1537-1719"]}, DOI={10.1093/molbev/msz197}, abstractNote={Abstract
               HYpothesis testing using PHYlogenies (HyPhy) is a scriptable, open-source package for fitting a broad range of evolutionary models to multiple sequence alignments, and for conducting subsequent parameter estimation and hypothesis testing, primarily in the maximum likelihood statistical framework. It has become a popular choice for characterizing various aspects of the evolutionary process: natural selection, evolutionary rates, recombination, and coevolution. The 2.5 release (available from www.hyphy.org) includes a completely re-engineered computational core and analysis library that introduces new classes of evolutionary models and statistical tests, delivers substantial performance and stability enhancements, improves usability, streamlines end-to-end analysis workflows, makes it easier to develop custom analyses, and is mostly backward compatible with previous HyPhy releases.}, number={1}, journal={MOLECULAR BIOLOGY AND EVOLUTION}, author={Pond, Sergei L. Kosakovsky and Poon, Art F. Y. and Velazquez, Ryan and Weaver, Steven and Hepler, N. Lance and Murrell, Ben and Shank, Stephen D. and Magalis, Brittany Rife and Bouvier, Dave and Nekrutenko, Anton and et al.}, year={2020}, month={Jan}, pages={295–299} }