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Chromatin Immunoprecipitation- ChIP

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Chromatin immunoprecipitation, or ChIP, is an antibody-based technique used to identify sites on DNA that bind to transcription factors of interest or histone proteins. It also helps determine the type of histone modifications such as acetylation, phosphorylation, or methylation.
Types of ChIP
ChIP can be divided into two types - X-ChIP and N-ChIP. X-ChIP involves in vivo cross-linking of histones and regulatory proteins to DNA, fragmenting the DNA by sonication, and isolating the protein-DNA...

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Updated: Nov 9, 2025

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

A Bayesian hierarchical model for analyzing methylated RNA immunoprecipitation sequencing data.

Minzhe Zhang¹, Qiwei Li¹, Yang Xie^1,2,3

¹Quantitative Biomedical Research Center, Department of Clinical Sciences, U.T. Southwestern Medical Center, Dallas, TX 75390, USA.

Quantitative Biology (Beijing, China)

|April 9, 2021

Summary

We developed a Bayesian model to detect mRNA methylation sites from MeRIP-seq data. This robust method improves accuracy and spatial resolution for RNA epigenetics research.

Keywords:

Bayesian inference MeRIP-seq data RNA epigenomics hidden Markov model zero-inflated negative binomial

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Methylated DNA Immunoprecipitation

Methylated DNA Immunoprecipitation

Published on: January 2, 2009

Area of Science:

Bioinformatics
RNA epigenetics
Computational biology

Background:

Methylated RNA immunoprecipitation sequencing (MeRIP-seq) is an emerging technology for studying RNA epigenetics.
Detecting mRNA methylation sites from MeRIP-seq data requires effective peak calling algorithms.
Existing methods may face challenges with data characteristics like zero-inflation and limited replicates.

Purpose of the Study:

To develop a robust Bayesian hierarchical model for accurate detection of mRNA methylation sites from MeRIP-seq data.
To address limitations of existing peak calling algorithms in handling MeRIP-seq data complexities.
To improve the accuracy, robustness, and spatial resolution of methylation site identification.

Main Methods:

A Bayesian hierarchical model incorporating a zero-inflated negative binomial model for count data.
Utilized a hidden Markov model (HMM) to capture spatial dependencies in read enrichment.
Employed Markov chain Monte Carlo (MCMC) algorithms for parameter inference and Bayesian model averaging for stability with few replicates.
Developed an R Shiny application for interactive demonstration and provided open-source code.

Main Results:

The proposed Bayesian model outperformed existing methods (exomePeak, MeTPeak) in simulation studies, particularly with zero-inflated data.
Real MeRIP-seq data analysis demonstrated superior identification of biologically relevant methylation sites.
The method achieved better spatial resolution in pinpointing methylation sites compared to competing approaches.

Conclusions:

A novel Bayesian hierarchical model effectively identifies methylation peaks in MeRIP-seq data.
The developed method offers significant advantages in accuracy, robustness, and spatial resolution over current techniques.
This work provides a valuable tool for advancing RNA epigenetics research through improved methylation site detection.