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RNA-seq03:21

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Related Experiment Video

Updated: Jun 2, 2026

Rare Event Detection Using Error-corrected DNA and RNA Sequencing
10:36

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

Comparing multiple ChIP-sequencing experiments.

Hatice Gulcin Ozer1, Yi-Wen Huang, Jiejun Wu

  • 1Department of Biomedical Informatics, The Ohio State University , Columbus, OH, 43210, USA. gulcin.ozer@osumc.edu

Journal of Bioinformatics and Computational Biology
|April 28, 2011
PubMed
Summary
This summary is machine-generated.

Comparing multiple ChIP-sequencing experiments is challenging. This study introduces a novel method to identify differential genomic regions by analyzing protein-binding profiles across various experiments without normalization, aiding breast cancer research.

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Last Updated: Jun 2, 2026

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High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture (4C-seq)
09:06

High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture (4C-seq)

Published on: October 5, 2018

Area of Science:

  • Genomics
  • Epigenetics
  • Bioinformatics

Background:

  • High-throughput sequencing generates vast datasets, complicating multi-experiment comparisons.
  • ChIP-sequencing (Chromatin immunoprecipitation sequencing) is crucial for studying protein-DNA interactions and epigenetic regulation.

Purpose of the Study:

  • To develop and evaluate a method for comparing multiple ChIP-sequencing experiments.
  • To investigate epigenetic regulation in breast cancer and the impact of estrogen using ChIP-sequencing data.

Main Methods:

  • Utilized 50 ChIP-sequencing datasets from Illumina Genome Analyzer II.
  • Evaluated experiment correlation based on read counts in transcribed and promoter regions.
  • Adapted a gene stability method from RT-PCR to identify variable genomic regions and background signals.

Main Results:

  • Identified distinct sets of highly variable genes in transcribed versus promoter regions.
  • Gene ontology and function enrichment analysis confirmed the biological relevance of identified variable genes.
  • The proposed method effectively selects differential genomic regions without sample normalization.

Conclusions:

  • The developed method enables robust comparison of multiple ChIP-sequencing experiments.
  • This approach facilitates the identification of key genomic regions involved in epigenetic regulation, particularly in breast cancer.
  • The findings highlight distinct variability patterns between transcribed and promoter regions.