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Enhanced Bayesian modelling in BAPS software for learning genetic structures of populations
BACKGROUND: During the most recent decade many Bayesian statistical models and software for answering questions related to the genetic structure underlying population samples have appeared in the scientific literature. Most of these methods utilize molecular markers for the inferences, while some ar...
Autores principales: | , , , |
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Formato: | Texto |
Lenguaje: | English |
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BioMed Central
2008
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2629778/ https://www.ncbi.nlm.nih.gov/pubmed/19087322 http://dx.doi.org/10.1186/1471-2105-9-539 |
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author | Corander, Jukka Marttinen, Pekka Sirén, Jukka Tang, Jing |
author_facet | Corander, Jukka Marttinen, Pekka Sirén, Jukka Tang, Jing |
author_sort | Corander, Jukka |
collection | PubMed |
description | BACKGROUND: During the most recent decade many Bayesian statistical models and software for answering questions related to the genetic structure underlying population samples have appeared in the scientific literature. Most of these methods utilize molecular markers for the inferences, while some are also capable of handling DNA sequence data. In a number of earlier works, we have introduced an array of statistical methods for population genetic inference that are implemented in the software BAPS. However, the complexity of biological problems related to genetic structure analysis keeps increasing such that in many cases the current methods may provide either inappropriate or insufficient solutions. RESULTS: We discuss the necessity of enhancing the statistical approaches to face the challenges posed by the ever-increasing amounts of molecular data generated by scientists over a wide range of research areas and introduce an array of new statistical tools implemented in the most recent version of BAPS. With these methods it is possible, e.g., to fit genetic mixture models using user-specified numbers of clusters and to estimate levels of admixture under a genetic linkage model. Also, alleles representing a different ancestry compared to the average observed genomic positions can be tracked for the sampled individuals, and a priori specified hypotheses about genetic population structure can be directly compared using Bayes' theorem. In general, we have improved further the computational characteristics of the algorithms behind the methods implemented in BAPS facilitating the analyses of large and complex datasets. In particular, analysis of a single dataset can now be spread over multiple computers using a script interface to the software. CONCLUSION: The Bayesian modelling methods introduced in this article represent an array of enhanced tools for learning the genetic structure of populations. Their implementations in the BAPS software are designed to meet the increasing need for analyzing large-scale population genetics data. The software is freely downloadable for Windows, Linux and Mac OS X systems at . |
format | Text |
id | pubmed-2629778 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2008 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-26297782009-01-28 Enhanced Bayesian modelling in BAPS software for learning genetic structures of populations Corander, Jukka Marttinen, Pekka Sirén, Jukka Tang, Jing BMC Bioinformatics Methodology Article BACKGROUND: During the most recent decade many Bayesian statistical models and software for answering questions related to the genetic structure underlying population samples have appeared in the scientific literature. Most of these methods utilize molecular markers for the inferences, while some are also capable of handling DNA sequence data. In a number of earlier works, we have introduced an array of statistical methods for population genetic inference that are implemented in the software BAPS. However, the complexity of biological problems related to genetic structure analysis keeps increasing such that in many cases the current methods may provide either inappropriate or insufficient solutions. RESULTS: We discuss the necessity of enhancing the statistical approaches to face the challenges posed by the ever-increasing amounts of molecular data generated by scientists over a wide range of research areas and introduce an array of new statistical tools implemented in the most recent version of BAPS. With these methods it is possible, e.g., to fit genetic mixture models using user-specified numbers of clusters and to estimate levels of admixture under a genetic linkage model. Also, alleles representing a different ancestry compared to the average observed genomic positions can be tracked for the sampled individuals, and a priori specified hypotheses about genetic population structure can be directly compared using Bayes' theorem. In general, we have improved further the computational characteristics of the algorithms behind the methods implemented in BAPS facilitating the analyses of large and complex datasets. In particular, analysis of a single dataset can now be spread over multiple computers using a script interface to the software. CONCLUSION: The Bayesian modelling methods introduced in this article represent an array of enhanced tools for learning the genetic structure of populations. Their implementations in the BAPS software are designed to meet the increasing need for analyzing large-scale population genetics data. The software is freely downloadable for Windows, Linux and Mac OS X systems at . BioMed Central 2008-12-16 /pmc/articles/PMC2629778/ /pubmed/19087322 http://dx.doi.org/10.1186/1471-2105-9-539 Text en Copyright © 2008 Corander et al; licensee BioMed Central Ltd. http://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) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Methodology Article Corander, Jukka Marttinen, Pekka Sirén, Jukka Tang, Jing Enhanced Bayesian modelling in BAPS software for learning genetic structures of populations |
title | Enhanced Bayesian modelling in BAPS software for learning genetic structures of populations |
title_full | Enhanced Bayesian modelling in BAPS software for learning genetic structures of populations |
title_fullStr | Enhanced Bayesian modelling in BAPS software for learning genetic structures of populations |
title_full_unstemmed | Enhanced Bayesian modelling in BAPS software for learning genetic structures of populations |
title_short | Enhanced Bayesian modelling in BAPS software for learning genetic structures of populations |
title_sort | enhanced bayesian modelling in baps software for learning genetic structures of populations |
topic | Methodology Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2629778/ https://www.ncbi.nlm.nih.gov/pubmed/19087322 http://dx.doi.org/10.1186/1471-2105-9-539 |
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