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Parsimonious Higher-Order Hidden Markov Models for Improved Array-CGH Analysis with Applications to Arabidopsis thaliana

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  • Michael Seifert
  • André Gohr
  • Marc Strickert
  • Ivo Grosse

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). Author Summary: Array-based comparative genomics is a standard approach for the identification of DNA copy number polymorphisms between closely related genomes. The huge amounts of data produced by these experiments require efficient and accurate bioinformatics tools for the identification of copy number polymorphisms. Hidden Markov Models (HMMs) are frequently used for analyzing such data sets, but current models are based on first-order HMMs only having limited capabilities to model spatial dependencies between measurements of closely adjacent chromosomal regions. We develop parsimonious higher-order HMMs enabling the interpolation between a mixture model ignoring spatial dependencies and a higher-order HMM exhaustively modeling these dependencies to overcome this limitation. In an in-depth case study with Arabidopsis thaliana, we find that parsimonious higher-order HMMs clearly improve the identification of copy number polymorphisms in comparison to standard first-order HMMs and other frequently used methods. Functional analysis of identified polymorphisms revealed details of genomic differences between the accessions C24 and Col-0 of Arabidopsis thaliana. An additional study on human cell lines further indicates that parsimonious HMMs are well-suited for the analysis of Array-CGH data.

Suggested Citation

  • Michael Seifert & André Gohr & Marc Strickert & Ivo Grosse, 2012. "Parsimonious Higher-Order Hidden Markov Models for Improved Array-CGH Analysis with Applications to Arabidopsis thaliana," PLOS Computational Biology, Public Library of Science, vol. 8(1), pages 1-15, January.
    • Handle: RePEc:plo:pcbi00:1002286
    DOI: 10.1371/journal.pcbi.1002286
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    References listed on IDEAS

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    1. Ramón Díaz-Uriarte & Oscar M Rueda, 2007. "ADaCGH: A Parallelized Web-Based Application and R Package for the Analysis of aCGH Data," PLOS ONE, Public Library of Science, vol. 2(8), pages 1-10, August.
    2. Fridlyand, Jane & Snijders, Antoine M. & Pinkel, Dan & Albertson, Donna G. & Jain, A.N.Ajay N., 2004. "Hidden Markov models approach to the analysis of array CGH data," Journal of Multivariate Analysis, Elsevier, vol. 90(1), pages 132-153, July.
    3. Oscar M Rueda & Ramón Díaz-Uriarte, 2007. "Flexible and Accurate Detection of Genomic Copy-Number Changes from aCGH," PLOS Computational Biology, Public Library of Science, vol. 3(6), pages 1-8, June.
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    1. De Gregorio, Juan & Sánchez, David & Toral, Raúl, 2022. "An improved estimator of Shannon entropy with applications to systems with memory," Chaos, Solitons & Fractals, Elsevier, vol. 165(P1).

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