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 HMM-Fisher: identifying differential methylation using a hidden Markov model and Fisher’s exact test

2016 ◽  
Vol 15 (1) ◽  
Author(s):  
Shuying Sun ◽  
Xiaoqing Yu
Keyword(s):  
Dna Methylation ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Differential Methylation ◽  
Sequencing Data ◽  
Exact Test ◽  
Fisher's Exact Test ◽  
Fisher’S Exact Test ◽  
Methylation Patterns

AbstractDNA methylation is an epigenetic event that plays an important role in regulating gene expression. It is important to study DNA methylation, especially differential methylation patterns between two groups of samples (e.g. patients vs. normal individuals). With next generation sequencing technologies, it is now possible to identify differential methylation patterns by considering methylation at the single CG site level in an entire genome. However, it is challenging to analyze large and complex NGS data. In order to address this difficult question, we have developed a new statistical method using a hidden Markov model and Fisher’s exact test (HMM-Fisher) to identify differentially methylated cytosines and regions. We first use a hidden Markov chain to model the methylation signals to infer the methylation state as Not methylated (N), Partly methylated (P), and Fully methylated (F) for each individual sample. We then use Fisher’s exact test to identify differentially methylated CG sites. We show the HMM-Fisher method and compare it with commonly cited methods using both simulated data and real sequencing data. The results show that HMM-Fisher outperforms the current available methods to which we have compared. HMM-Fisher is efficient and robust in identifying heterogeneous DM regions.

scholarly journals RaptGen: A variational autoencoder with profile hidden Markov model for generative aptamer discovery

2021 ◽  
Author(s):  
Natsuki Iwano ◽  
Tatsuo Adachi ◽  
Kazuteru Aoki ◽  
Yoshikazu Nakamura ◽  
Michiaki Hamada
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
High Throughput Sequencing ◽  
Sequence Data ◽  
Hidden Markov ◽  
Bayesian Optimization ◽  
Sequencing Data ◽  
Latent Space ◽  
Variational Autoencoder ◽  
Profile Hidden Markov Model

AbstractNucleic acid aptamers are generated by an in vitro molecular evolution method known as systematic evolution of ligands by exponential enrichment (SELEX). A variety of candidates is limited by actual sequencing data from an experiment. Here, we developed RaptGen, which is a variational autoencoder for in silico aptamer generation. RaptGen exploits a profile hidden Markov model decoder to represent motif sequences effectively. We showed that RaptGen embedded simulation sequence data into low-dimension latent space dependent on motif information. We also performed sequence embedding using two independent SELEX datasets. RaptGen successfully generated aptamers from the latent space even though they were not included in high-throughput sequencing. RaptGen could also generate a truncated aptamer with a short learning model. We demonstrated that RaptGen could be applied to activity-guided aptamer generation according to Bayesian optimization. We concluded that a generative method by RaptGen and latent representation are useful for aptamer discovery. Codes are available at https://github.com/hmdlab/raptgen.

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

2021 ◽  
Author(s):  
Gökalp Çelik ◽  
TIMUR TUNCALI
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Clinical Sample ◽  
Simulated Data ◽  
Population Data ◽  
Next Generation Sequencing Data ◽  
Pseudoautosomal Region ◽  
Sequencing Data ◽  
Model Framework

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.

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