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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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ChIP-AP: an integrated analysis pipeline for unbiased ChIP-seq analysis.

Jeremiah Suryatenggara¹, Kol Jia Yong^1,2, Danielle E Tenen³

¹Cancer Science Institute of Singapore, National University of Singapore, Singapore, 117599, Singapore.

Briefings in Bioinformatics

|December 29, 2021

Summary

ChIP-AP integrates four peak callers for chromatin immunoprecipitation sequencing (ChIP-seq) analysis. This approach improves confidence and coverage of protein-DNA interactions, providing a more comprehensive view of the binding landscape.

Keywords:

ChIP-seq automated analysis pipeline histone mark integrated analysis pipeline multiple peak callers transcription factor binding

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Area of Science:

Genomics
Molecular Biology
Bioinformatics

Background:

Chromatin immunoprecipitation coupled with sequencing (ChIP-seq) is vital for identifying protein-DNA interactions.
Peak calling is the most critical analytical step in ChIP-seq data analysis.
Existing peak callers have varying selectivity and specificity, often with limited overlap.

Purpose of the Study:

To develop an integrated ChIP-seq analysis pipeline to improve peak detection confidence and coverage.
To create a unified approach that combines results from multiple peak callers.
To provide a more comprehensive survey of the protein-DNA binding landscape.

Main Methods:

Development of ChIP-AP, an integrated analysis pipeline for ChIP-seq data.
Utilizing four independent peak callers within the pipeline.
Seamless processing of raw sequencing files to final integrated results.

Main Results:

ChIP-AP enables better gauging of peak confidence through multi-algorithm detection.
The pipeline captures peaks missed by individual callers, enhancing binding landscape coverage.
Integrated results are presented in a single table for flexible data exploration.

Conclusions:

ChIP-AP offers a more comprehensive coverage of the ChIP-seq binding landscape.
The integrated approach enhances peak detection confidence and sensitivity.
Investigators can explore data with custom thresholds without additional wet-lab work.