atac-chipseq-downstream-analysis

To annotate ATAC-seq peaks, you run a local Python script on your peak files to map genomic contexts like promoter or exonic regions and identify nearest genes. It processes BED, narrowPeak, or broadPeak formats to produce annotated results.

Yes, ChIP-seq peak quality control is supported by generating QC metrics and plots such as peak width distribution, score distribution, and TSS enrichment. The script processes your peak files to output ready-to-publish visualizations.

Differential peak comparison across multiple conditions is performed by running the analysis script on your peak sets. It calculates summary statistics and generates visual outputs like Venn diagrams and heatmaps to highlight condition-specific differences.

Yes, this downstream analysis works directly with MACS2 or SEACR-like peak files in narrowPeak, broadPeak, or standard BED formats. It applies genome-aware annotation to process these inputs and deliver annotated genomic context.

Running ATAC/ChIP-seq peak annotation requires the Python dependencies pandas, numpy, matplotlib, and requests. These libraries support data manipulation, visualization generation, and genome-aware annotation fetching within the local script.

Peak annotation requires genome-aware context mapping to accurately classify peak locations relative to genomic features like promoters or exons. This ensures the biological interpretation of your BED or narrowPeak inputs correctly matches the reference genome's coordinate system.