AIAP: A Quality Control and Integrative Analysis Package to Improve ATAC-seq Data Analysis.

Area of Science:

• Genomics
• Molecular Biology
• Bioinformatics

Background:

• Assay for Transposase-Accessible Chromatin with high-throughput sequencing (ATAC-seq) is crucial for studying genome-wide chromatin accessibility.
• Existing ATAC-seq protocols like Omni-ATAC-seq enhance signal and reduce cell input, but standardized quality control (QC) metrics are lacking.
• High-quality ATAC-seq data is essential for reliable downstream analysis.

Purpose of the Study:

• To establish robust quality control (QC) metrics for ATAC-seq data.
• To develop an integrated analysis package (AIAP) for ATAC-seq data quality assurance, peak calling, and differential analysis.
• To enhance the sensitivity and accuracy of ATAC-seq data analysis.

Main Methods:

• Optimization of ATAC-seq analysis strategies.
• Definition and implementation of novel QC metrics: reads under peak ratio (RUPr), background (BG), promoter enrichment (ProEn), and subsampling enrichment (SubEn).
• Integration of QC metrics and analysis tools into the ATAC-seq Integrative Analysis Package (AIAP), available via Docker/Singularity.

Main Results:

• AIAP provides a comprehensive system for ATAC-seq quality assurance, peak calling, and differential analysis.
• Processing paired-end ATAC-seq data with AIAP significantly improved sensitivity (20%-60%) in peak calling and differential analysis.
• A user-friendly QC report generator and viewer (qATACViewer) were developed.

Conclusions:

• The developed QC metrics and AIAP provide a standardized and effective approach for assessing ATAC-seq data quality.
• AIAP enhances the reliability and sensitivity of ATAC-seq data analysis, leading to more accurate biological insights.
• The freely available AIAP software facilitates widespread adoption and improves the quality of ATAC-seq research.