Bayesian Hidden Markov Models in DNA Sequence Segmentation using R: The case of Simian Vacuolating virus (SV40)

James Totterdell, Darfiana Nur, Kerrie Mengersen

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    1 Citation (Scopus)

    Abstract

    Segmentation models aim to partition compositionally heterogeneous domains into homogeneous segments which may be reflective of biological function. Due to the latent nature of the segments a natural approach to segmentation that has gained favour recently uses Bayesian hidden Markov models (HMMs). Concomitantly in the last few decades, the free R programming language has become a dominant tool for computational statistics, visualization and data science. Therefore, this paper aims to fully exploit R to fit a Bayesian HMM for DNA segmentation. The joint posterior distribution of parameters in the model to be considered is derived followed by the algorithms that can be used for estimation. Functions following these algorithms (Gibbs Sampling, Data Augmentation and Label Switching) are then fully implemented in R. The methodology is assessed through extensive simulation studies and then being applied to analyse Simian Vacuolating virus (SV40). It is concluded that: (1) the algorithms and functions in R can correctly estimate sequence segmentation if the HMM structure is assumed; (2) the performance of the model improves with sequence length; (3) R is reasonably fast for short to medium sequence lengths and number of segments and (4) the segmentation of SV40 appears to correspond with the two major transcripts, early and late, that regulate the expression of SV40 genes.

    Original languageEnglish
    Pages (from-to)2799-2827
    Number of pages29
    JournalJOURNAL OF STATISTICAL COMPUTATION AND SIMULATION
    Volume87
    Issue number14
    Early online date2017
    DOIs
    Publication statusPublished - 22 Sept 2017

    Keywords

    • Bayesian modelling
    • data augmentation
    • DNA sequence
    • Gibbs sampler algorithm
    • hidden Markov models
    • label switching algorithm
    • R statistical software
    • segmentation modelling
    • Simian Vacuolating virus (SV40)

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