Journalartikel

Jahr der Veröffentlichung: 2012

Zeitschrift: PLoS Computational Biology

Bandnummer: 8

Heftnummer: 1

eISSN: 1553-7358

DOI Link: https://doi.org/10.1371/journal.pcbi.1002286

Verlag: Public Library of Science

Abstract: 
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).

Harvard-Zitierstil: Seifert, M., Gohr, A., Strickert, M. and Grosse, I. (2012) Parsimonious Higher-Order Hidden Markov Models for Improved Array-CGH Analysis with Applications to Arabidopsis thaliana, PLoS Computational Biology, 8(1), Article e1002286. https://doi.org/10.1371/journal.pcbi.1002286

APA-Zitierstil: Seifert, M., Gohr, A., Strickert, M., & Grosse, I. (2012). Parsimonious Higher-Order Hidden Markov Models for Improved Array-CGH Analysis with Applications to Arabidopsis thaliana. PLoS Computational Biology. 8(1), Article e1002286. https://doi.org/10.1371/journal.pcbi.1002286