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ATAC-Seq Optimization for Cancer Epigenetics Research

ATAC-Seq Optimization for Cancer Epigenetics Research

Published on: June 30, 2022

CATCH-UP: A High-Throughput Upstream-Pipeline for Bulk ATAC-Seq and ChIP-Seq Data.

Simone G Riva¹, Emily Georgiades², E Ravza Gur²

¹MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford; MRC WIMM Centre for Computational Biology, MRC Weatherall Institute of Molecular Medicine, University of Oxford; simone.riva@imm.ox.ac.uk.

Journal of Visualized Experiments : Jove

|October 9, 2023

Summary

Reproducible analysis of gene regulation data is challenging due to diverse bioinformatics tools. CATCH-UP is a new Python pipeline for assay for transposase-accessible chromatin sequencing (ATAC-seq) and chromatin immunoprecipitation sequencing (ChIP-seq) data analysis.

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Published on: February 26, 2015

Area of Science:

Genomics and Bioinformatics
Molecular Biology
Epigenetics

Background:

Assay for transposase-accessible chromatin (ATAC) and chromatin immunoprecipitation (ChIP) sequencing coupled with next-generation sequencing (NGS) are powerful tools for studying gene regulation.
Lack of standardization in analyzing high-dimensional ATAC-seq and ChIP-seq data hinders reproducibility and leads to data discrepancies.
Diverse bioinformatics tools and varying computational skill requirements complicate the sequential processing of raw sequencing data into interpretable results.

Purpose of the Study:

To address the challenges in ATAC-seq and ChIP-seq data analysis by developing a standardized and user-friendly bioinformatics pipeline.
To provide a comprehensive solution for processing raw ATAC-seq and ChIP-seq data from fastq files to peak calls and bigwig tracks.
To enhance the reproducibility of gene regulation studies by simplifying data analysis and methodology reporting.

Main Methods:

Development of CATCH-UP, a Python-based upstream pipeline for analyzing bulk ATAC-seq and ChIP-seq datasets.
The pipeline integrates various bioinformatic tools for data processing, quality control, and peak calling.
Designed for ease of installation and use, requiring minimal computational expertise.

Main Results:

CATCH-UP successfully processes raw ATAC-seq and ChIP-seq data into visualizable bigwig tracks and peak calls.
The pipeline is modular, scalable, and parallelizable, adaptable to different computing infrastructures.
Facilitates standardized data analysis, improving the reporting of methodology for reproducible research.

Conclusions:

CATCH-UP offers a simplified, reproducible solution for analyzing ATAC-seq and ChIP-seq data.
The pipeline lowers the barrier to entry for researchers, enabling wider adoption of standardized analysis methods.
Enhances the reliability and comparability of gene regulation studies using sequencing-based epigenomic techniques.