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Histone Modification

Histone Modification

The histone proteins have a flexible N-terminal tail extending out from the nucleosome. These histone tails are often subjected to post-translational modifications such as acetylation, methylation, phosphorylation, and ubiquitination. Particular combinations of these modifications form “histone codes” that influence the chromatin folding and tissue-specific gene expression.
Acetylation
The enzyme histone acetyltransferase adds acetyl group to the histones. Another enzyme, histone...

Histone Modification

Histone Modification

Spreading of Chromatin Modifications

Spreading of Chromatin Modifications

The histone proteins in the nucleosomes are post-translationally modified (PTM) to increase or decrease access to DNA. The commonly observed PTMs are methylation, acetylation, phosphorylation, and ubiquitination of lysine amino acids in the histone H3 tail region. These histone modifications have specific meaning for the cell. Hence, they are called "histone code". The protein complex involved in histone modification is termed as "reader-writer" complex.
Writers
The writer...

Histone Variants at the Centromere

Histone Variants at the Centromere

Histone variants are the histone proteins with structural and sequence variations. These variants may be regarded as “mutant” forms that replace their canonical histone counterparts in the nucleosomes. Specific post-translational modifications on the histone variants enable further chromatin complexity and regulate tissue-specific gene expression. The most common histone variants are from histone H2A, H2B, and linker histone H1 families. However, several variants of histone H3...

Predicting Molecular Geometry

Predicting Molecular Geometry

VSEPR Theory for Determination of Electron Pair Geometries

Prediction Intervals

Prediction Intervals

The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.

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Related Experiment Video

Updated: Jan 26, 2026

Detection of Histone Modifications in Plant Leaves

Detection of Histone Modifications in Plant Leaves

Published on: September 23, 2011

DeepHistone: a deep learning approach to predicting histone modifications.

Qijin Yin¹, Mengmeng Wu¹, Qiao Liu¹

¹MOE Key Laboratory of Bioinformatics; Bioinformatics Division, Beijing National Laboratory for Information Science and Technology, Tsinghua University, Beijing, 100084, China.

|April 11, 2019

Summary

This summary is machine-generated.

DeepHistone, a deep learning framework, accurately predicts histone modification sites by integrating DNA sequence and chromatin accessibility data. This computational approach overcomes limitations of expensive experimental methods for large-scale epigenomic analysis.

Keywords:

Chromatin accessibility Deep learning Genetic variation Histone modification Sequence analysis

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Last Updated: Jan 26, 2026

Detection of Histone Modifications in Plant Leaves

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Published on: September 23, 2011

Chromatin Immunoprecipitation ChIP of Histone Modifications from Saccharomyces cerevisiae

Chromatin Immunoprecipitation ChIP of Histone Modifications from Saccharomyces cerevisiae

Published on: December 29, 2017

Complete Workflow for Analysis of Histone Post-translational Modifications Using Bottom-up Mass Spectrometry: From Histone Extraction to Data Analysis

Complete Workflow for Analysis of Histone Post-translational Modifications Using Bottom-up Mass Spectrometry: From Histone Extraction to Data Analysis

Published on: May 17, 2016

Area of Science:

Computational biology
Genomics
Epigenetics

Background:

Histone modifications are crucial for understanding gene regulation and DNA damage.
High-throughput methods like ChIP-seq are costly and time-consuming for large-scale analysis.
Computational methods are needed to complement experimental approaches for epigenomic studies.

Purpose of the Study:

To develop a deep learning framework for accurate prediction of histone modification sites.
To integrate DNA sequence and chromatin accessibility data for enhanced prediction accuracy.
To provide a scalable computational solution for mapping histone modification landscapes.

Main Methods:

Developed DeepHistone, a deep learning framework.
Integrated DNA sequence information and chromatin accessibility data.
Validated the method through comprehensive experiments within and across epigenomes.

Main Results:

DeepHistone achieved superior performance compared to baseline methods.
The framework accurately predicted histone modification sites.
Extracted sequence signatures provided insights into regulatory mechanisms and functional variants.

Conclusions:

DeepHistone demonstrates the efficacy of deep learning for predicting epigenomic signals.
The method offers a powerful tool for large-scale epigenomic studies and variant functional analysis.
DeepHistone is publicly available for research use.