Parsimonious higher-order hidden Markov models for improved array-CGH analysis with applications to Arabidopsis thaliana.

Array-based comparative genomic hybridization (Array-CGH) is an important technology in molecular biology for the detection of DNA copy number polymorphisms between closely related genomes. Hidden Markov Models (HMMs) are popular tools for the analysis of Array-CGH data, but current methods are only...

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Autores principales:	Michael Seifert, André Gohr, Marc Strickert, Ivo Grosse
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2012
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/d4228748ac3244cc99c7c36767d30014
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spelling	oai:doaj.org-article:d4228748ac3244cc99c7c36767d300142021-11-18T05:51:39ZParsimonious higher-order hidden Markov models for improved array-CGH analysis with applications to Arabidopsis thaliana.1553-734X1553-735810.1371/journal.pcbi.1002286https://doaj.org/article/d4228748ac3244cc99c7c36767d300142012-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22253580/pdf/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Array-based comparative genomic hybridization (Array-CGH) is an important technology in molecular biology for the detection of DNA copy number polymorphisms between closely related genomes. Hidden Markov Models (HMMs) are popular tools for the analysis of Array-CGH data, but current methods are only based on first-order HMMs having constrained abilities to model spatial dependencies between measurements of closely adjacent chromosomal regions. Here, we develop parsimonious higher-order HMMs enabling the interpolation between a mixture model ignoring spatial dependencies and a higher-order HMM exhaustively modeling spatial dependencies. We apply parsimonious higher-order HMMs to the analysis of Array-CGH data of the accessions C24 and Col-0 of the model plant Arabidopsis thaliana. We compare these models against first-order HMMs and other existing methods using a reference of known deletions and sequence deviations. We find that parsimonious higher-order HMMs clearly improve the identification of these polymorphisms. Moreover, we perform a functional analysis of identified polymorphisms revealing novel details of genomic differences between C24 and Col-0. Additional model evaluations are done on widely considered Array-CGH data of human cell lines indicating that parsimonious HMMs are also well-suited for the analysis of non-plant specific data. All these results indicate that parsimonious higher-order HMMs are useful for Array-CGH analyses. An implementation of parsimonious higher-order HMMs is available as part of the open source Java library Jstacs (www.jstacs.de/index.php/PHHMM).Michael SeifertAndré GohrMarc StrickertIvo GrossePublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 8, Iss 1, p e1002286 (2012)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Michael Seifert André Gohr Marc Strickert Ivo Grosse Parsimonious higher-order hidden Markov models for improved array-CGH analysis with applications to Arabidopsis thaliana.
description	Array-based comparative genomic hybridization (Array-CGH) is an important technology in molecular biology for the detection of DNA copy number polymorphisms between closely related genomes. Hidden Markov Models (HMMs) are popular tools for the analysis of Array-CGH data, but current methods are only based on first-order HMMs having constrained abilities to model spatial dependencies between measurements of closely adjacent chromosomal regions. Here, we develop parsimonious higher-order HMMs enabling the interpolation between a mixture model ignoring spatial dependencies and a higher-order HMM exhaustively modeling spatial dependencies. We apply parsimonious higher-order HMMs to the analysis of Array-CGH data of the accessions C24 and Col-0 of the model plant Arabidopsis thaliana. We compare these models against first-order HMMs and other existing methods using a reference of known deletions and sequence deviations. We find that parsimonious higher-order HMMs clearly improve the identification of these polymorphisms. Moreover, we perform a functional analysis of identified polymorphisms revealing novel details of genomic differences between C24 and Col-0. Additional model evaluations are done on widely considered Array-CGH data of human cell lines indicating that parsimonious HMMs are also well-suited for the analysis of non-plant specific data. All these results indicate that parsimonious higher-order HMMs are useful for Array-CGH analyses. An implementation of parsimonious higher-order HMMs is available as part of the open source Java library Jstacs (www.jstacs.de/index.php/PHHMM).
format	article
author	Michael Seifert André Gohr Marc Strickert Ivo Grosse
author_facet	Michael Seifert André Gohr Marc Strickert Ivo Grosse
author_sort	Michael Seifert
title	Parsimonious higher-order hidden Markov models for improved array-CGH analysis with applications to Arabidopsis thaliana.
title_short	Parsimonious higher-order hidden Markov models for improved array-CGH analysis with applications to Arabidopsis thaliana.
title_full	Parsimonious higher-order hidden Markov models for improved array-CGH analysis with applications to Arabidopsis thaliana.
title_fullStr	Parsimonious higher-order hidden Markov models for improved array-CGH analysis with applications to Arabidopsis thaliana.
title_full_unstemmed	Parsimonious higher-order hidden Markov models for improved array-CGH analysis with applications to Arabidopsis thaliana.
title_sort	parsimonious higher-order hidden markov models for improved array-cgh analysis with applications to arabidopsis thaliana.
publisher	Public Library of Science (PLoS)
publishDate	2012
url	https://doaj.org/article/d4228748ac3244cc99c7c36767d30014
work_keys_str_mv	AT michaelseifert parsimonioushigherorderhiddenmarkovmodelsforimprovedarraycghanalysiswithapplicationstoarabidopsisthaliana AT andregohr parsimonioushigherorderhiddenmarkovmodelsforimprovedarraycghanalysiswithapplicationstoarabidopsisthaliana AT marcstrickert parsimonioushigherorderhiddenmarkovmodelsforimprovedarraycghanalysiswithapplicationstoarabidopsisthaliana AT ivogrosse parsimonioushigherorderhiddenmarkovmodelsforimprovedarraycghanalysiswithapplicationstoarabidopsisthaliana
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Parsimonious higher-order hidden Markov models for improved array-CGH analysis with applications to Arabidopsis thaliana.

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