AIAP

AIAP performs integrative quality control and analysis of ATAC-seq data to evaluate chromatin accessibility and improve peak calling and differential chromatin accessibility analyses.

Key Features:

• Quality Control Metrics: Implements ATAC-seq–specific QC metrics including Reads Under Peak Ratio (RUPr), Background (BG), Promoter Enrichment (ProEn), and Subsampling Enrichment (SubEn).
• Improved Analysis Strategy: Integrates QC tests into a comprehensive analysis strategy to ensure high-quality data processing and accurate downstream results.
• Enhanced Peak Calling and Differential Analysis: Improves sensitivity of peak calling and differential accessibility analysis by 20%–60% when applied to paired-end ATAC-seq datasets.
• Containerization: Distributed as Docker and Singularity containers to enable reproducible deployment of the analysis pipeline.
• Benchmarking and Recommendations: Benchmarked using ENCODE ATAC-seq datasets to establish QC recommendations.

Scientific Applications:

• Regulatory element identification: Detects accessible chromatin regions to identify regulatory elements associated with gene expression.
• Epigenetic and disease studies: Supports investigation of epigenetic modifications and their roles in disease mechanisms through chromatin accessibility profiling.
• Transcriptional regulation analysis: Enables comparison of chromatin accessibility across cell types and conditions to study transcriptional regulation.

Methodology:

Performs data quality assessment using defined QC metrics (RUPr, BG, ProEn, SubEn) and integrates these QC tests into the analysis workflow; applies peak calling optimization using improved algorithms for more sensitive peak detection; employs refined statistical methods for differential chromatin accessibility analysis.