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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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Updated: Jun 4, 2026

Semiconductor Sequencing for Preimplantation Genetic Testing for Aneuploidy

Semiconductor Sequencing for Preimplantation Genetic Testing for Aneuploidy

Published on: August 25, 2019

ChIPseqR: analysis of ChIP-seq experiments.

Peter Humburg¹, Chris A Helliwell, David Bulger

¹Department of Statistics, Macquarie University, North Ryde, NSW 2109, Australia. peter.humburg@well.ox.ac.uk

BMC Bioinformatics

|February 2, 2011

Summary

This summary is machine-generated.

ChIPseqR is a new algorithm for analyzing ChIP-seq data, specifically for nucleosome positioning and histone modifications. It offers improved sensitivity and resolution for genomic studies.

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Comparative Lesions Analysis Through a Targeted Sequencing Approach

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Area of Science:

Genomics
Molecular Biology
Bioinformatics

Background:

High-throughput sequencing combined with chromatin immunoprecipitation (ChIP-seq) allows high-resolution, genome-wide protein binding studies.
The increasing volume of ChIP-seq data necessitates advanced analytical methods.
Existing ChIP-seq analysis algorithms primarily target transcription factors and are unsuitable for other experimental types.

Purpose of the Study:

To introduce ChIPseqR, a novel algorithm designed for analyzing ChIP-seq data.
To specifically address the analysis of nucleosome positioning and histone modification ChIP-seq experiments.

Main Methods:

Development of the ChIPseqR algorithm.
Performance evaluation using short-read sequencing data of Arabidopsis thaliana mononucleosomes.
Validation with simulated ChIP-seq data.

Main Results:

ChIPseqR demonstrates enhanced sensitivity and spatial resolution compared to existing methods.
The algorithm maintains high specificity in its analyses.
Analysis of predicted nucleosomes identified distinct patterns in their sequences and placement.

Conclusions:

ChIPseqR provides a significant improvement for analyzing nucleosome positioning and histone modification ChIP-seq data.
The tool enhances the understanding of nucleosome organization and its sequence-specific characteristics.