motif-database-query-and-matching

motif-database-query-and-matching

Summary

Query a curated motif database (e.g., CIS-BP) and match motif position-weight matrices (PWMs) against a set of DNA sequences (typically differentially accessible peaks from single-cell ATAC-seq). This skill produces motif occurrence calls and enrichment statistics that identify transcription factor binding sites overrepresented in the input peak set.

When to use

You have a set of differentially accessible peaks (output from differential accessibility testing, e.g., tl.diff_test) from single-cell ATAC-seq data and want to identify which transcription factor motifs are overrepresented in those peaks relative to a background model, to infer regulatory drivers of chromatin accessibility changes.

When NOT to use

• Input peaks have not been validated for reproducibility or consistency; enrichment results on noisy peak sets will be unreliable.
• Background model or motif database is not appropriate for the organism or cell type (e.g., using a vertebrate motif database on plant ATAC-seq data).
• You are interested only in de novo motif discovery rather than matching against known databases.

Inputs

• Differentially accessible peak set (BED-like format or genomic interval set)
• Motif position-weight matrix (PWM) library from CIS-BP or equivalent curated database

Outputs

• Motif enrichment table (columns: motif ID, TF name, enrichment score, p-value, q-value)
• Peak-motif match annotation (which motifs occur in which peaks)

How to apply

Load differentially accessible peaks as a feature set into SnapATAC2. Retrieve motif position-weight matrices from a curated motif database (datasets.cis_bp provides the CIS-BP dataset). Invoke tl.motif_enrichment on the peak set; the function scans for motif occurrences within the differential regions and computes enrichment statistics (typically log-odds ratios or similar) against a background model. Validate the output motif enrichment table for required columns (motif IDs, enrichment scores, p-values), non-null values, and statistical significance thresholds (typically p < 0.05). The resulting table ranks motifs by overrepresentation, allowing prioritization of candidate regulatory transcription factors.