A Generalized Hidden Markov Model for Determining Sequence-based Predictors of Nucleosome Positioning
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Carlee Moser
Chromatin structure, in terms of positioning of nucleosomes and nucleosome-free regions in the DNA, has been found to have an immense impact on various cell functions and processes, ranging from transcriptional regulation to growth and development. In spite of numerous experimental and computational approaches being developed in the past few years to determine the intrinsic relationship between chromatin structure (nucleosome positioning) and DNA sequence features, there is yet no universally accurate approach to predict nucleosome positioning from the underlying DNA sequence alone. We here propose an alternative approach to predicting nucleosome positioning from sequence, making use of characteristic sequence differences, and inherent dependencies in overlapping sequence features. Our nucleosomal positioning prediction algorithm, based on the idea of generalized hierarchical hidden Markov models (HGHMMs), was used to predict nucleosomal state based on the DNA sequence in yeast chromosome III, and compared with two other existing methods. The HGHMM method performed favorably among the three models in terms of specificity and sensitivity, and provided estimates that were largely consistent with predictions from the method of Yuan and Liu (2008). However, all the methods still give higher than desirable misclassification rates, indicating that sequence-based features may provide only limited information towards understanding positioning of nucleosomes. The method is implemented in the open-source statistical software R, and is freely available from the authors’ website.
©2012 Walter de Gruyter GmbH & Co. KG, Berlin/Boston
Artikel in diesem Heft
- Editorial Introduction
- Special Issue on Computational Statistical Methods for Genomics and Systems Biology
- Article
- A Generalized Hidden Markov Model for Determining Sequence-based Predictors of Nucleosome Positioning
- Gene Filtering in the Analysis of Illumina Microarray Experiments
- Principal Components of Heritability for High Dimension Quantitative Traits and General Pedigrees
- Bayesian Sparsity-Path-Analysis of Genetic Association Signal using Generalized t Priors
- A Family-Based Probabilistic Method for Capturing De Novo Mutations from High-Throughput Short-Read Sequencing Data
- Adjusting for Spurious Gene-by-Environment Interaction Using Case-Parent Triads
- Querying Genomic Databases: Refining the Connectivity Map
- A Model-Based Analysis to Infer the Functional Content of a Gene List
- Candidate Pathway Based Analysis for Cleft Lip with or without Cleft Palate
- Improving Pedigree-based Linkage Analysis by Estimating Coancestry Among Families
Artikel in diesem Heft
- Editorial Introduction
- Special Issue on Computational Statistical Methods for Genomics and Systems Biology
- Article
- A Generalized Hidden Markov Model for Determining Sequence-based Predictors of Nucleosome Positioning
- Gene Filtering in the Analysis of Illumina Microarray Experiments
- Principal Components of Heritability for High Dimension Quantitative Traits and General Pedigrees
- Bayesian Sparsity-Path-Analysis of Genetic Association Signal using Generalized t Priors
- A Family-Based Probabilistic Method for Capturing De Novo Mutations from High-Throughput Short-Read Sequencing Data
- Adjusting for Spurious Gene-by-Environment Interaction Using Case-Parent Triads
- Querying Genomic Databases: Refining the Connectivity Map
- A Model-Based Analysis to Infer the Functional Content of a Gene List
- Candidate Pathway Based Analysis for Cleft Lip with or without Cleft Palate
- Improving Pedigree-based Linkage Analysis by Estimating Coancestry Among Families
