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Related Concept Videos

Spreading of Chromatin Modifications02:25

Spreading of Chromatin Modifications

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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...
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Chromatin Immunoprecipitation- ChIP02:36

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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.
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Histone Modification02:32

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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.
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Euchromatin01:01

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The extent of chromatin compaction can be studied by staining chromatin using specific DNA binding dyes. Under the microscope, the dense-compacted regions take up more dye, appearing darker, while the less-compact areas take up less dye and appear lighter. Based on the compaction level, chromatins are classified into two primary forms – euchromatin and heterochromatin.
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Heterochromatin02:38

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The extent of chromatin compaction can be studied by staining chromatin using specific DNA binding dyes. Under the microscope, the dense-compacted regions that take up more dye are called heterochromatin. Heterochromatin is further classified into two forms – constitutive heterochromatin and facultative heterochromatin.
Constitutive heterochromatin: It is a highly compact region of chromatin that is mostly concentrated in the centromere and telomere. Unlike euchromatin, the amino acid at...
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Learning chromatin states with factorized information criteria.

Michiaki Hamada1, Yukiteru Ono2, Ryohei Fujimaki3

  • 1Department of Electrical Engineering and Bioscience, Faculty of Science and Engineering, Waseda University, 55N-06-10, 3-4-1, Okubo Shinjuku-ku, Tokyo 169-8555, Japan, Computational Biology Research Center, National Institute of Advanced Industrial Science and Technology (AIST), 2-41-6, Aomi, Koto-ku, Tokyo 135-0064, Japan.

Bioinformatics (Oxford, England)
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Summary

This study introduces a new method using hidden Markov models (HMM) to automatically identify and characterize genome-wide chromatin states from epigenetic data, improving upon existing techniques.

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Area of Science:

  • Genomics
  • Epigenetics
  • Computational Biology

Background:

  • The genome and epigenome are crucial for biological functions like transcription and DNA repair.
  • Specific histone modifications define chromatin states linked to cellular functions.
  • Next-generation sequencing (NGS) allows high-resolution genome-wide epigenetic analysis, but chromatin states remain incompletely understood.

Purpose of the Study:

  • To develop a novel computational method for estimating genome-wide chromatin states from NGS data.
  • To automatically determine the number of chromatin states and characterize them.
  • To provide an unbiased approach for chromatin state analysis.

Main Methods:

  • Utilized a hidden Markov model (HMM) for chromatin state estimation.
  • Incorporated a factorized information criteria model selection technique.
  • Employed computational experiments with simulated and real biological datasets.

Main Results:

  • The proposed method automatically estimates the correct number of chromatin states.
  • It successfully characterizes individual chromatin states using HMM.
  • Demonstrated superior performance compared to ChromHMM, identifying more states in real datasets.

Conclusions:

  • The developed method offers an unbiased and automated approach to chromatin state analysis.
  • It effectively leverages HMM and factorized information criteria for accurate epigenetic profiling.
  • This advancement aids in a more comprehensive understanding of genome function and regulation.