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RNA-seq

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...

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Updated: May 4, 2026

Chromatin Immunoprecipitation of Murine Brown Adipose Tissue

Chromatin Immunoprecipitation of Murine Brown Adipose Tissue

Published on: November 21, 2018

Large-scale quality analysis of published ChIP-seq data.

Georgi K Marinov¹, Anshul Kundaje, Peter J Park

¹Division of Biology, California Institute of Technology, Pasadena, California 91125.

G3 (Bethesda, Md.)

|December 19, 2013

Summary

This summary is machine-generated.

A systematic analysis of ChIP-seq data revealed that while most datasets are high-quality, a significant portion suffers from poor quality, impacting research. This study provides a framework for assessing ChIP-seq data quality to improve future genomic studies.

Keywords:

ChIP-seq chromatin immunoprecipitation cross-correlation quality assessment transcription factor

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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
Molecular Biology
Bioinformatics

Background:

Chromatin immunoprecipitation sequencing (ChIP-seq) is crucial for mapping protein-DNA interactions genome-wide.
Data quality in published ChIP-seq studies varies, hindering broader research community use.
The Encyclopedia of DNA Elements (ENCODE) project developed quality metrics for ChIP-seq data.

Purpose of the Study:

To conduct a uniform quality analysis of vertebrate transcription factor ChIP-seq datasets in the Gene Expression Omnibus (GEO) repository.
To assess the quality of publicly available ChIP-seq data and identify high-quality datasets for large-scale analysis.
To investigate the impact of data quality on the interpretation and usability of ChIP-seq profiles.

Main Methods:

Systematic analysis of vertebrate transcription factor ChIP-seq datasets from the GEO repository (as of April 1, 2012).
Application of quality assessment metrics, similar to those developed by ENCODE, to evaluate datasets.
Comparison of ChIP-seq data quality with control datasets (no immunoprecipitation and mock immunoprecipitation samples).

Main Results:

55% of analyzed ChIP-seq datasets were of high quality, while 20% were of poor quality and ~25% were of intermediate quality.
A significant subset of control datasets exhibited enrichment patterns similar to successful ChIP-seq data, potentially affecting peak calling and interpretation.
Identified high-quality published datasets suitable for large-scale integrated analysis.

Conclusions:

A substantial proportion of publicly available ChIP-seq data is of questionable quality, necessitating careful assessment.
The developed quality assessment framework can guide experimental design, publication review, and data stratification for diverse research applications.
Standardized quality control is essential for maximizing the utility of ChIP-seq data in genomics research.