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Beyond similarity assessment: selecting the optimal model for sequence alignment via the Factorized Asymptotic Bayesian algorithm
MOTIVATION: Pair Hidden Markov Models (PHMMs) are probabilistic models used for pairwise sequence alignment, a quintessential problem in bioinformatics. PHMMs include three types of hidden states: match, insertion and deletion. Most previous studies have used one or two hidden states for each PHMM s...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5860613/ https://www.ncbi.nlm.nih.gov/pubmed/29040374 http://dx.doi.org/10.1093/bioinformatics/btx643 |
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author | Takeda, Taikai Hamada, Michiaki |
author_facet | Takeda, Taikai Hamada, Michiaki |
author_sort | Takeda, Taikai |
collection | PubMed |
description | MOTIVATION: Pair Hidden Markov Models (PHMMs) are probabilistic models used for pairwise sequence alignment, a quintessential problem in bioinformatics. PHMMs include three types of hidden states: match, insertion and deletion. Most previous studies have used one or two hidden states for each PHMM state type. However, few studies have examined the number of states suitable for representing sequence data or improving alignment accuracy. RESULTS: We developed a novel method to select superior models (including the number of hidden states) for PHMM. Our method selects models with the highest posterior probability using Factorized Information Criterion, which is widely utilized in model selection for probabilistic models with hidden variables. Our simulations indicated that this method has excellent model selection capabilities with slightly improved alignment accuracy. We applied our method to DNA datasets from 5 and 28 species, ultimately selecting more complex models than those used in previous studies. AVAILABILITY AND IMPLEMENTATION: The software is available at https://github.com/bigsea-t/fab-phmm. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. |
format | Online Article Text |
id | pubmed-5860613 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-58606132018-03-28 Beyond similarity assessment: selecting the optimal model for sequence alignment via the Factorized Asymptotic Bayesian algorithm Takeda, Taikai Hamada, Michiaki Bioinformatics Original Papers MOTIVATION: Pair Hidden Markov Models (PHMMs) are probabilistic models used for pairwise sequence alignment, a quintessential problem in bioinformatics. PHMMs include three types of hidden states: match, insertion and deletion. Most previous studies have used one or two hidden states for each PHMM state type. However, few studies have examined the number of states suitable for representing sequence data or improving alignment accuracy. RESULTS: We developed a novel method to select superior models (including the number of hidden states) for PHMM. Our method selects models with the highest posterior probability using Factorized Information Criterion, which is widely utilized in model selection for probabilistic models with hidden variables. Our simulations indicated that this method has excellent model selection capabilities with slightly improved alignment accuracy. We applied our method to DNA datasets from 5 and 28 species, ultimately selecting more complex models than those used in previous studies. AVAILABILITY AND IMPLEMENTATION: The software is available at https://github.com/bigsea-t/fab-phmm. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Oxford University Press 2018-02-15 2017-10-12 /pmc/articles/PMC5860613/ /pubmed/29040374 http://dx.doi.org/10.1093/bioinformatics/btx643 Text en © The Author 2017. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Original Papers Takeda, Taikai Hamada, Michiaki Beyond similarity assessment: selecting the optimal model for sequence alignment via the Factorized Asymptotic Bayesian algorithm |
title | Beyond similarity assessment: selecting the optimal model for sequence alignment via the Factorized Asymptotic Bayesian algorithm |
title_full | Beyond similarity assessment: selecting the optimal model for sequence alignment via the Factorized Asymptotic Bayesian algorithm |
title_fullStr | Beyond similarity assessment: selecting the optimal model for sequence alignment via the Factorized Asymptotic Bayesian algorithm |
title_full_unstemmed | Beyond similarity assessment: selecting the optimal model for sequence alignment via the Factorized Asymptotic Bayesian algorithm |
title_short | Beyond similarity assessment: selecting the optimal model for sequence alignment via the Factorized Asymptotic Bayesian algorithm |
title_sort | beyond similarity assessment: selecting the optimal model for sequence alignment via the factorized asymptotic bayesian algorithm |
topic | Original Papers |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5860613/ https://www.ncbi.nlm.nih.gov/pubmed/29040374 http://dx.doi.org/10.1093/bioinformatics/btx643 |
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