A workflow for simplified analysis of ATAC-cap-seq data in R.

Area of Science:

• Genomics
• Bioinformatics
• Molecular Biology

Background:

• Assay for Transposase-Accessible Chromatin (ATAC)-cap-seq combines ATAC-seq with targeted nucleic acid enrichment.
• Analytical challenges arise from small, biologically dependent regions of interest in ATAC-cap-seq data.
• Standard RNA sequencing pipelines can yield misleading results for ATAC-cap-seq analysis.

Purpose of the Study:

• To develop a user-friendly tool, atacR, for analyzing ATAC-cap-seq enrichment experiments.
• To provide accessible normalization and differential abundance analysis for ATAC-cap-seq data.
• To enable nonspecialist users to perform reproducible ATAC-cap-seq data analysis.

Main Methods:

• Developed the atacR package in R for ATAC-cap-seq data analysis.
• Implemented comprehensive summary functions and diagnostic plots.
• Provided multiple normalization strategies (control regions, library size, least variable regions) and differential abundance detection methods (bootstrap t, Bayes factor, edgeR).

Main Results:

• atacR offers straightforward between-sample normalization for ATAC-cap-seq data.
• Compared three differential abundance detection methods, finding the Bayes factor method offered the greatest overall detection power.
• The edgeR method showed slightly stronger performance in simulations with fewer changed genes.

Conclusions:

• The atacR package empowers nonspecialist users to effectively analyze ATAC-cap-seq data.
• The tool ensures reproducible analysis through appropriate statistical methods.
• Implemented in pure R, atacR integrates seamlessly with Bioconductor workflows.