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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 microarray-based...

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Related Experiment Video

Updated: May 23, 2026

Introductory Analysis and Validation of CUT&RUN Sequencing Data

Introductory Analysis and Validation of CUT&RUN Sequencing Data

Published on: December 13, 2024

Normalization, bias correction, and peak calling for ChIP-seq.

Aaron Diaz¹, Kiyoub Park, Daniel A Lim

¹University of California, San Francisco, USA.

Statistical Applications in Genetics and Molecular Biology

|April 14, 2012

Summary

This summary is machine-generated.

Computational methods are needed to address biases in sequencing techniques like ChIP-seq and MeDIP-seq. This study presents new statistical models to improve data accuracy and reduce errors in identifying epigenetic modifications and protein binding sites.

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Last Updated: May 23, 2026

Introductory Analysis and Validation of CUT&RUN Sequencing Data

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Published on: December 13, 2024

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

Published on: March 7, 2018

Area of Science:

Genomics and Epigenetics
Computational Biology
Bioinformatics

Background:

Next-generation sequencing technologies like ChIP-seq and MeDIP-seq are powerful tools for profiling cellular states.
These methods are susceptible to various biases that can complicate data interpretation.
Robust computational methods for bias detection, removal, and multi-sample normalization are currently lacking.

Purpose of the Study:

To systematically characterize biases and properties in ChIP-seq data.
To develop rigorous statistical methods for separating ChIP-seq signal from background noise.
To introduce improved methods for bias correction and cell type-specific null model determination.

Main Methods:

Analysis of 62 publicly available ChIP-seq datasets using statistical models and signal processing.
Development of methods to separate reads into signal and background components.
Creation of cell type-specific null models to account for inherent biases.

Main Results:

The proposed method effectively separates signal from background, enhancing the signal-to-noise ratio.
Statistical methods were developed to correct for sequence-dependent and sonication biases.
Cell type-specific null models demonstrated improved specificity and lower false discovery rates compared to generic models.

Conclusions:

The developed computational methods improve the accuracy and reliability of ChIP-seq data analysis.
Addressing biases and implementing cell type-specific null models are crucial for precise identification of epigenetic modifications and protein binding sites.
This work provides a foundation for more robust interpretation of sequencing-based epigenomic data.