scholarly journals Detect differentially methylated regions using non-homogeneous hidden Markov model for bisulfite sequencing data

Methods ◽  
2020 ◽  
Author(s):  
Yingyu Chen ◽  
Chin Kiu Kwok ◽  
Hangjin Jiang ◽  
Xiaodan Fan
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Bisulfite Sequencing ◽  
Hidden Markov ◽  
Sequencing Data ◽  
Differentially Methylated Regions ◽  
Bisulfite Sequencing Data

A hidden markov model for identifying differentially methylated sites in bisulfite sequencing data

Biometrics ◽  
2018 ◽  
Vol 75 (1) ◽  
pp. 210-221 ◽  
Author(s):  
Farhad Shokoohi ◽  
David A. Stephens ◽  
Guillaume Bourque ◽  
Tomi Pastinen ◽  
Celia M. T. Greenwood ◽  
...  
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Bisulfite Sequencing ◽  
Hidden Markov ◽  
Sequencing Data ◽  
Bisulfite Sequencing Data

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

2016 ◽  
Vol 32 (11) ◽  
pp. 1749-1751 ◽  
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 ◽  
Next Generation ◽  
Sequencing Data ◽  
Generation Sequencing ◽  
Model Approach

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

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
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.

scholarly journals metilene: fast and sensitive calling of differentially methylated regions from bisulfite sequencing data

2015 ◽  
Vol 26 (2) ◽  
pp. 256-262 ◽  
Author(s):  
Frank Jühling ◽  
Helene Kretzmer ◽  
Stephan H. Bernhart ◽  
Christian Otto ◽  
Peter F. Stadler ◽  
...  
Keyword(s):  
Bisulfite Sequencing ◽  
Sequencing Data ◽  
Differentially Methylated Regions ◽  
Bisulfite Sequencing Data
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