Open AccessHyPhy 2.5—A Customizable Platform for Evolutionary Hypothesis Testing Using Phylogenies
Author(s) -
Sergei L. Kosakovsky Pond,
Art F. Y. Poon,
Ryan Velazquez,
Steven Weaver,
N. Lance Hepler,
Ben Murrell,
Stephen D. Shank,
Brittany Rife Magalis,
Dave Bouvier,
Anton Nekrutenko,
Sadie R Wisotsky,
Stephanie J. Spielman,
Simon D. W. Frost,
Spencer V. Muse
Publication year - 2019
Publication title -
molecular biology and evolution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.637
H-Index - 218
eISSN - 1537-1719
pISSN - 0737-4038
DOI - 10.1093/molbev/msz197
Subject(s) - biology , statistical hypothesis testing , coevolution , workflow , range (aeronautics) , usability , selection (genetic algorithm) , sequence (biology) , process (computing) , evolutionary biology , computer science , machine learning , statistics , genetics , mathematics , operating system , human–computer interaction , database , materials science , composite material
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.
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