atac-seq-peak-annotation

atac-seq-peak-annotation

Summary

Annotate differentially accessible peaks from single-cell ATAC-seq data with transcription factor motifs and regulatory elements using SnapATAC2's motif enrichment workflow. This skill identifies which regulatory motifs are overrepresented in peak regions and computes statistical significance, enabling inference of transcriptional regulators driving chromatin accessibility differences.

When to use

Apply this skill after differential peak analysis (tl.diff_test) has identified peaks that differ in accessibility across cell types or conditions. Use it to gain mechanistic insight into which transcription factors likely regulate the differentially accessible chromatin regions, particularly when you need to map peaks to specific regulatory proteins rather than just genes.

When NOT to use

• Input peak set is from peak calling (tl.macs3) rather than differential analysis — use for differential peaks specifically, not all peaks in a dataset
• You need to map peaks to target genes or regulatory elements by proximity — use gene annotation tools (e.g. tl.marker_regions) instead
• The motif database is organism-specific (e.g. CIS-BP is curated for specific genomes) and your species/context is not well-covered

Inputs

• Differentially accessible peak set (GRanges or BED-like object from tl.diff_test)
• Peak genomic sequences (FASTA or in-memory)
• CIS-BP motif database (position-weight matrices and motif metadata from datasets.cis_bp)

Outputs

• Motif enrichment table (columns: motif_id, tf_name, enrichment_score, p_value, q_value)
• Motif occurrence coordinates within peak regions (optional detailed output)
• Background model statistics used for enrichment computation

How to apply

Load differentially accessible peaks (output from tl.diff_test) as a feature set into SnapATAC2. Load the CIS-BP motif database using datasets.cis_bp to obtain motif definitions and position-weight matrices. Invoke tl.motif_enrichment on the peak set, which scans for motif occurrences within the differential regions and computes enrichment statistics against a background model. The function returns a motif enrichment table containing motif IDs, TF names, enrichment scores, and p-values. Validate that all required columns are non-null, that p-values reflect statistical significance of motif overrepresentation (typically p < 0.05), and that enrichment scores indicate direction and magnitude of motif overrepresentation relative to background.