Affiliation:
1. CINBIO, Universidade de Vigo, 36310 Vigo, Spain
2. Universidade de Vigo, Departamento de Bioquimica, Xenetica e Inmunoloxia, 36310 Vigo, Spain
3. Galicia Sur Health Research Institute (IIS Galicia Sur), 36310 Vigo, Spain
Abstract
Abstract
Motivation
The evolutionary processes of mutation and recombination, upon which selection operates, are fundamental to understand the observed molecular diversity. Unlike nucleotide sequences, the estimation of the recombination rate in protein sequences has been little explored, neither implemented in evolutionary frameworks, despite protein sequencing methods are largely used.
Results
In order to accommodate this need, here I present a computational framework, called ProteinEvolverABC, to jointly estimate recombination and substitution rates from alignments of protein sequences. The framework implements the approximate Bayesian computation approach, with and without regression adjustments and includes a variety of substitution models of protein evolution, demographics and longitudinal sampling. It also implements several nuisance parameters such as heterogeneous amino acid frequencies and rate of change among sites and, proportion of invariable sites. The framework produces accurate coestimation of recombination and substitution rates under diverse evolutionary scenarios. As illustrative examples of usage, I applied it to several viral protein families, including coronaviruses, showing heterogeneous substitution and recombination rates.
Availability and implementation
ProteinEvolverABC is freely available from https://github.com/miguelarenas/proteinevolverabc, includes a graphical user interface for helping the specification of the input settings, extensive documentation and ready-to-use examples. Conveniently, the simulations can run in parallel on multicore machines.
Supplementary information
Supplementary data are available at Bioinformatics online.
Funder
Spanish Ministerio de Ciencia e Innovación through the Grants
Universidade de Vigo/CISUG
Publisher
Oxford University Press (OUP)
Subject
Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability
Cited by
1 articles.
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