Cargando…
PhyloSim - Monte Carlo simulation of sequence evolution in the R statistical computing environment
BACKGROUND: The Monte Carlo simulation of sequence evolution is routinely used to assess the performance of phylogenetic inference methods and sequence alignment algorithms. Progress in the field of molecular evolution fuels the need for more realistic and hence more complex simulations, adapted to...
Autores principales: | , , , |
---|---|
Formato: | Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2011
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3102636/ https://www.ncbi.nlm.nih.gov/pubmed/21504561 http://dx.doi.org/10.1186/1471-2105-12-104 |
_version_ | 1782204399154626560 |
---|---|
author | Sipos, Botond Massingham, Tim Jordan, Gregory E Goldman, Nick |
author_facet | Sipos, Botond Massingham, Tim Jordan, Gregory E Goldman, Nick |
author_sort | Sipos, Botond |
collection | PubMed |
description | BACKGROUND: The Monte Carlo simulation of sequence evolution is routinely used to assess the performance of phylogenetic inference methods and sequence alignment algorithms. Progress in the field of molecular evolution fuels the need for more realistic and hence more complex simulations, adapted to particular situations, yet current software makes unreasonable assumptions such as homogeneous substitution dynamics or a uniform distribution of indels across the simulated sequences. This calls for an extensible simulation framework written in a high-level functional language, offering new functionality and making it easy to incorporate further complexity. RESULTS: PhyloSim is an extensible framework for the Monte Carlo simulation of sequence evolution, written in R, using the Gillespie algorithm to integrate the actions of many concurrent processes such as substitutions, insertions and deletions. Uniquely among sequence simulation tools, PhyloSim can simulate arbitrarily complex patterns of rate variation and multiple indel processes, and allows for the incorporation of selective constraints on indel events. User-defined complex patterns of mutation and selection can be easily integrated into simulations, allowing PhyloSim to be adapted to specific needs. CONCLUSIONS: Close integration with R and the wide range of features implemented offer unmatched flexibility, making it possible to simulate sequence evolution under a wide range of realistic settings. We believe that PhyloSim will be useful to future studies involving simulated alignments. |
format | Text |
id | pubmed-3102636 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2011 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-31026362011-05-27 PhyloSim - Monte Carlo simulation of sequence evolution in the R statistical computing environment Sipos, Botond Massingham, Tim Jordan, Gregory E Goldman, Nick BMC Bioinformatics Software BACKGROUND: The Monte Carlo simulation of sequence evolution is routinely used to assess the performance of phylogenetic inference methods and sequence alignment algorithms. Progress in the field of molecular evolution fuels the need for more realistic and hence more complex simulations, adapted to particular situations, yet current software makes unreasonable assumptions such as homogeneous substitution dynamics or a uniform distribution of indels across the simulated sequences. This calls for an extensible simulation framework written in a high-level functional language, offering new functionality and making it easy to incorporate further complexity. RESULTS: PhyloSim is an extensible framework for the Monte Carlo simulation of sequence evolution, written in R, using the Gillespie algorithm to integrate the actions of many concurrent processes such as substitutions, insertions and deletions. Uniquely among sequence simulation tools, PhyloSim can simulate arbitrarily complex patterns of rate variation and multiple indel processes, and allows for the incorporation of selective constraints on indel events. User-defined complex patterns of mutation and selection can be easily integrated into simulations, allowing PhyloSim to be adapted to specific needs. CONCLUSIONS: Close integration with R and the wide range of features implemented offer unmatched flexibility, making it possible to simulate sequence evolution under a wide range of realistic settings. We believe that PhyloSim will be useful to future studies involving simulated alignments. BioMed Central 2011-04-19 /pmc/articles/PMC3102636/ /pubmed/21504561 http://dx.doi.org/10.1186/1471-2105-12-104 Text en Copyright © 2011 Sipos et al; licensee BioMed Central Ltd. https://creativecommons.org/licenses/by/2.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0 (https://creativecommons.org/licenses/by/2.0/) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Software Sipos, Botond Massingham, Tim Jordan, Gregory E Goldman, Nick PhyloSim - Monte Carlo simulation of sequence evolution in the R statistical computing environment |
title | PhyloSim - Monte Carlo simulation of sequence evolution in the R statistical computing environment |
title_full | PhyloSim - Monte Carlo simulation of sequence evolution in the R statistical computing environment |
title_fullStr | PhyloSim - Monte Carlo simulation of sequence evolution in the R statistical computing environment |
title_full_unstemmed | PhyloSim - Monte Carlo simulation of sequence evolution in the R statistical computing environment |
title_short | PhyloSim - Monte Carlo simulation of sequence evolution in the R statistical computing environment |
title_sort | phylosim - monte carlo simulation of sequence evolution in the r statistical computing environment |
topic | Software |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3102636/ https://www.ncbi.nlm.nih.gov/pubmed/21504561 http://dx.doi.org/10.1186/1471-2105-12-104 |
work_keys_str_mv | AT siposbotond phylosimmontecarlosimulationofsequenceevolutionintherstatisticalcomputingenvironment AT massinghamtim phylosimmontecarlosimulationofsequenceevolutionintherstatisticalcomputingenvironment AT jordangregorye phylosimmontecarlosimulationofsequenceevolutionintherstatisticalcomputingenvironment AT goldmannick phylosimmontecarlosimulationofsequenceevolutionintherstatisticalcomputingenvironment |