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Relative evolutionary rate inference in HyPhy with LEISR
Author(s) -
Stephanie J. Spielman,
Sergei L. Kosakovsky Pond
Publication year - 2018
Publication title -
peerj
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.927
H-Index - 70
ISSN - 2167-8359
DOI - 10.7717/peerj.4339
Subject(s) - inference , computer science , evolutionary algorithm , data mining , machine learning , artificial intelligence
We introduce LEISR (Likehood Estimation of Individual Site Rates, pronounced “laser”), a tool to infer relative evolutionary rates from protein and nucleotide data, implemented in HyPhy. LEISR is based on the popular Rate4Site ( Pupko et al., 2002 ) approach for inferring relative site-wise evolutionary rates, primarily from protein data. We extend the original method for more general use in several key ways: (i) we increase the support for nucleotide data with additional models, (ii) we allow for datasets of arbitrary size, (iii) we support analysis of site-partitioned datasets to correct for the presence of recombination breakpoints, (iv) we produce rate estimates at all sites rather than at just a subset of sites, and (v) we implemented LEISR as MPI-enabled to support rapid, high-throughput analysis. LEISR is available in HyPhy starting with version 2.3.8, and it is accessible as an option in the HyPhy analysis menu (“Relative evolutionary rate inference”), which calls the HyPhy batchfile LEISR.bf.

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