scholarly journals Fast MCMC sampling for hidden markov models to determine copy number variations

2011 ◽  
Vol 12 (1) ◽  
Author(s):  
Md Pavel Mahmud ◽  
Alexander Schliep
Keyword(s):  
Hidden Markov Models ◽  
Copy Number ◽  
Markov Models ◽  
Hidden Markov ◽  
Copy Number Variations ◽  
Mcmc Sampling

scholarly journals Variational Inference for Coupled Hidden Markov Models Applied to the Joint Detection of Copy Number Variations

2019 ◽  
Vol 15 (1) ◽  
Author(s):  
Xiaoqiang Wang ◽  
Emilie Lebarbier ◽  
Julie Aubert ◽  
Stéphane Robin
Keyword(s):  
Hidden Markov Models ◽  
Copy Number ◽  
Markov Models ◽  
Hidden Markov ◽  
R Package ◽  
Copy Number Variations ◽  
Structural Variations ◽  
Statistical Framework ◽  
Coupled Hidden Markov Models ◽  
Hidden Markov Process

Abstract Hidden Markov models provide a natural statistical framework for the detection of the copy number variations (CNV) in genomics. In this context, we define a hidden Markov process that underlies all individuals jointly in order to detect and to classify genomics regions in different states (typically, deletion, normal or amplification). Structural variations from different individuals may be dependent. It is the case in agronomy where varietal selection program exists and species share a common phylogenetic past. We propose to take into account these dependencies inthe HMM model. When dealing with a large number of series, maximum likelihood inference (performed classically using the EM algorithm) becomes intractable. We thus propose an approximate inference algorithm based on a variational approach (VEM), implemented in the CHMM R package. A simulation study is performed to assess the performance of the proposed method and an application to the detection of structural variations in plant genomes is presented.

scholarly journals Fast Bayesian Inference of Copy Number Variants using Hidden Markov Models with Wavelet Compression

2015 ◽  
Author(s):  
John Wiedenhoeft ◽  
Eric Brugel ◽  
Alexander Schliep
Keyword(s):  
Hidden Markov Models ◽  
Copy Number ◽  
Markov Models ◽  
State Of The Art ◽  
Hidden Markov ◽  
Copy Number Variants ◽  
Computational Effort ◽  
The State ◽  
Haar Wavelets ◽  
Link Type

AbstractBy combining Haar wavelets with Bayesian Hidden Markov Models, we improve detection of genomic copy number variants (CNV) in array CGH experiments compared to the state-of-the-art, including standard Gibbs sampling. At the same time, we achieve drastically reduced running times, as the method concentrates computational effort on chromosomal segments which are difficult to call, by dynamically and adaptively recomputing consecutive blocks of observations likely to share a copy number. This makes routine diagnostic use and re-analysis of legacy data collections feasible; to this end, we also propose an effective automatic prior. An open source software implementation of our method is available at http://bioinformatics.rutgers.edu/Software/HaMMLET/. The web supplement is at http://bioinformatics.rutgers.edu/Supplements/HaMMLET/.Author SummaryIdentifying large-scale genome deletions and duplications, or copy number variants (CNV), accurately in populations or individual patients is a crucial step in indicating disease factors or diagnosing an individual patient's disease type. Hidden Markov Models (HMM) are a type of statistical model widely used for CNV detection, as well as other biological applications such as the analysis of gene expression time course data or the analysis of discrete-valued DNA and protein sequences.As with many statistical models, there are two fundamentally different inference approaches. In the frequentist framework, a single estimate of the model parameters would be used as a basis for subsequent inference, making the identification of CNV dependent on the quality of that estimate. This is an acute problem for HMM as methods for finding globally optimal parameters are not known. Alternatively, one can use a Bayesian approach and integrate over all possible parameter choices. While the latter is known to lead to significantly better results, the much—up to hundreds of times—larger computational effort prevents wide adaptation so far.Our proposed method addresses this by combining Haar wavelets and HMM. We greatly accelerate fully Bayesian HMMs, while simultaneously increasing convergence and thus the accuracy of the Gibbs sampler used for Bayesian computations, leading to substantial improvements over the state-of-the-art.

Estimating Personality Impression from Speech Record Using Hidden Markov Models

2015 ◽  
Vol 135 (12) ◽  
pp. 1517-1523 ◽  
Author(s):  
Yicheng Jin ◽  
Takuto Sakuma ◽  
Shohei Kato ◽  
Tsutomu Kunitachi
Keyword(s):  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov

Hidden Markov Processes

2014 ◽  
Author(s):  
M. Vidyasagar
Keyword(s):  
Hidden Markov Models ◽  
Markov Processes ◽  
Viterbi Algorithm ◽  
Markov Models ◽  
Hidden Markov ◽  
Local Alignment ◽  
Biological Applications ◽  
Standard Material ◽  
Hidden Markov Processes ◽  
Genomics And Proteomics

This book explores important aspects of Markov and hidden Markov processes and the applications of these ideas to various problems in computational biology. It starts from first principles, so that no previous knowledge of probability is necessary. However, the work is rigorous and mathematical, making it useful to engineers and mathematicians, even those not interested in biological applications. A range of exercises is provided, including drills to familiarize the reader with concepts and more advanced problems that require deep thinking about the theory. Biological applications are taken from post-genomic biology, especially genomics and proteomics. The topics examined include standard material such as the Perron–Frobenius theorem, transient and recurrent states, hitting probabilities and hitting times, maximum likelihood estimation, the Viterbi algorithm, and the Baum–Welch algorithm. The book contains discussions of extremely useful topics not usually seen at the basic level, such as ergodicity of Markov processes, Markov Chain Monte Carlo (MCMC), information theory, and large deviation theory for both i.i.d and Markov processes. It also presents state-of-the-art realization theory for hidden Markov models. Among biological applications, it offers an in-depth look at the BLAST (Basic Local Alignment Search Technique) algorithm, including a comprehensive explanation of the underlying theory. Other applications such as profile hidden Markov models are also explored.

Sign in / Sign up

Export Citation Format

Share Document