ROHMM -- A Flexible Hidden Markov Model Framework To Detect Runs of Homozygosity From Genotyping Data

Pseudoautosomal Region ◽

Sequencing Data ◽

Runs of long homozygous stretches (ROH) are considered to be the result of consanguinity and usually contain recessive deleterious disease causing mutations (Szpiech et al., 2013). Several algorithms have been developed to detect ROHs. Here, we developed a simple, alternative strategy by examining X chromosome non-pseudoautosomal region to detect the ROHs from next generation sequencing data utilizing the genotype probabilities and the Hidden Markov Model algorithm as a tool, namely ROHMM. It is implemented purely in java and contains both command-line and a graphical user interface. We tested ROHMM on simulated data as well as real population data from 1000G Project and a clinical sample. Our results have shown that ROHMM can perform robustly producing highly accurate homozygosity estimations under all conditions thereby meeting and even exceeding the performance of its natural competitors.

BCFtools/RoH: a hidden Markov model approach for detecting autozygosity from next-generation sequencing data

Bioinformatics ◽

10.1093/bioinformatics/btw044 ◽

2016 ◽

Vol 32 (11) ◽

pp. 1749-1751 ◽

Cited By ~ 184

Author(s):

Vagheesh Narasimhan ◽

Petr Danecek ◽

Aylwyn Scally ◽

Yali Xue ◽

Chris Tyler-Smith ◽

...

Keyword(s):

Next Generation Sequencing ◽

Markov Model ◽

Hidden Markov Model ◽

Hidden Markov ◽

Next Generation ◽

Sequencing Data ◽

Generation Sequencing ◽

Model Approach

Spatially Enhanced Differential RNA Methylation Analysis from Affinity-Based Sequencing Data with Hidden Markov Model

BioMed Research International ◽

10.1155/2015/852070 ◽

2015 ◽

Vol 2015 ◽

pp. 1-12 ◽

Cited By ~ 2

Author(s):

Yu-Chen Zhang ◽

Shao-Wu Zhang ◽

Lian Liu ◽

Hui Liu ◽

Lin Zhang ◽

...

Keyword(s):

Markov Model ◽

Hidden Markov Model ◽

Hidden Markov ◽

Statistical Significance ◽

Simulated Data ◽

Real Data ◽

Differential Methylation ◽

Methylation Site ◽

Sequencing Data ◽

Rna Methylation

With the development of new sequencing technology, the entire N6-methyl-adenosine (m6A) RNA methylome can now be unbiased profiled with methylated RNA immune-precipitation sequencing technique (MeRIP-Seq), making it possible to detect differential methylation states of RNA between two conditions, for example, between normal and cancerous tissue. However, as an affinity-based method, MeRIP-Seq has yet provided base-pair resolution; that is, a single methylation site determined from MeRIP-Seq data can in practice contain multiple RNA methylation residuals, some of which can be regulated by different enzymes and thus differentially methylated between two conditions. Since existing peak-based methods could not effectively differentiate multiple methylation residuals located within a single methylation site, we propose a hidden Markov model (HMM) based approach to address this issue. Specifically, the detected RNA methylation site is further divided into multiple adjacent small bins and then scanned with higher resolution using a hidden Markov model to model the dependency between spatially adjacent bins for improved accuracy. We tested the proposed algorithm on both simulated data and real data. Result suggests that the proposed algorithm clearly outperforms existing peak-based approach on simulated systems and detects differential methylation regions with higher statistical significance on real dataset.

Challenges in detecting genomic copy number aberrations using next-generation sequencing data and the eXome Hidden Markov Model: a clinical exome-first diagnostic approach

Human Genome Variation ◽

10.1038/hgv.2016.25 ◽

2016 ◽

Vol 3 (1) ◽

Cited By ~ 13

Author(s):

Toshiyuki Yamamoto ◽

Keiko Shimojima ◽

Yumiko Ondo ◽

Katsumi Imai ◽

Pin Fee Chong ◽

...

Keyword(s):

Next Generation Sequencing ◽

Markov Model ◽

Hidden Markov Model ◽

Copy Number ◽

Hidden Markov ◽

Sequencing Data ◽

Copy Number Aberrations ◽

Genomic Copy Number ◽

Generation Sequencing

A Nonhomogeneous Hidden Markov Model for Gene Mapping Based on Next-Generation Sequencing Data

Journal of Computational Biology ◽

10.1089/cmb.2014.0258 ◽

2015 ◽

Vol 22 (2) ◽

pp. 178-188 ◽

Cited By ~ 7

Author(s):

Fatemeh Zamanzad Ghavidel ◽

Jürgen Claesen ◽

Tomasz Burzykowski

Keyword(s):

Next Generation Sequencing ◽

Markov Model ◽

Gene Mapping ◽

Hidden Markov Model ◽

Hidden Markov ◽

Next Generation ◽

Sequencing Data ◽

Generation Sequencing

A whole-genome-based approach for estimation and characterization of individual inbreeding

10.1101/106765 ◽

2017 ◽

Author(s):

T. Druet ◽

M. Gautier

Keyword(s):

Markov Model ◽

Hidden Markov Model ◽

Hidden Markov ◽

Real Data ◽

Genetic Distances ◽

Error Rates ◽

Base Population ◽

Sequencing Data ◽

Model Framework ◽

Marker Allele

AbstractInbreeding results from the mating of related individuals and has negative consequence because it brings together deleterious variants in one individual. Inbreeding is associated with recessive diseases and reduced production or fitness. In general, inbreeding is estimated with respect to a base population that needs to be defined. Ancestors in generations anterior to the base population are considered unrelated. We herein propose a model that estimates inbreeding relative to multiple age-based classes. Each inbreeding distribution is associated to a different time in the past: recent inbreeding generating longer homozygous stretches than more ancient. Our model is a mixture of exponential distribution implemented in a hidden Markov model framework that uses marker allele frequencies, genetic distances, genotyping error rates and the sequences of observed genotypes. Based on simulations studies, we show that the inbreeding coefficients and the age of inbreeding are correctly estimated. Mean absolute errors of estimators are low, the efficiency depending on the available information. When several inbreeding classes are simulated, the model captures them if their ages are sufficiently different. Genotyping errors or low-fold sequencing data are easily accommodated in the hidden Markov model framework. Application to real data sets illustrate that the method can reveal recent different demographic histories among populations, some of them presenting very recent bottlenecks or founder effects. The method also clearly identifies individuals resulting from extreme consanguineous matings.

A hidden markov model framework for traffic event detection using video features

2004 International Conference on Image Processing, 2004. ICIP '04. ◽

10.1109/icip.2004.1421719 ◽

2005 ◽

Cited By ~ 11

Author(s):

Xiaokun Li ◽

F.M. Porikli

Keyword(s):

Markov Model ◽

Hidden Markov Model ◽

Event Detection ◽

Hidden Markov ◽

2006 IEEE International Conference on Acoustics Speed and Signal Processing Proceedings ◽

Hidden Markov Model Framework Using Independent Component Analysis Mixture Model

10.1109/icassp.2006.1661335 ◽

2006 ◽

Author(s):

Jian Zhou ◽

Xiao-Ping Zhang

Keyword(s):

Independent Component Analysis ◽

Markov Model ◽

Hidden Markov Model ◽

Mixture Model ◽

Hidden Markov ◽

Component Analysis ◽

Independent Component ◽

Bayesian hidden Markov models for delineating the pathology of Alzheimer’s disease

Statistical Methods in Medical Research ◽

10.1177/0962280217748675 ◽

2017 ◽

Vol 28 (7) ◽

pp. 2112-2124 ◽

Cited By ~ 3

Author(s):

Kai Kang ◽

Jingheng Cai ◽

Xinyuan Song ◽

Hongtu Zhu

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Markov Model ◽

Cognitive Decline ◽

Hidden Markov Model ◽

Hidden Markov ◽

Semiparametric Regression ◽

Alzheimer’s disease is a firmly incurable and progressive disease. The pathology of Alzheimer’s disease usually evolves from cognitive normal, to mild cognitive impairment, to Alzheimer’s disease. The aim of this paper is to develop a Bayesian hidden Markov model to characterize disease pathology, identify hidden states corresponding to the diagnosed stages of cognitive decline, and examine the dynamic changes of potential risk factors associated with the cognitive normal–mild cognitive impairment–Alzheimer’s disease transition. The hidden Markov model framework consists of two major components. The first one is a state-dependent semiparametric regression for delineating the complex associations between clinical outcomes of interest and a set of prognostic biomarkers across neurodegenerative states. The second one is a parametric transition model, while accounting for potential covariate effects on the cross-state transition. The inter-individual and inter-process differences are taken into account via correlated random effects in both components. Based on the Alzheimer’s Disease Neuroimaging Initiative data set, we are able to identify four states of Alzheimer’s disease pathology, corresponding to common diagnosed cognitive decline stages, including cognitive normal, early mild cognitive impairment, late mild cognitive impairment, and Alzheimer’s disease and examine the effects of hippocampus, age, gender, and APOE-[Formula: see text] on degeneration of cognitive function across the four cognitive states.