pycistarget-motif-enrichment

Run motif enrichment on scATAC-seq region sets with pycistarget to derive TF cistromes.

1|Updated Nov 20, 2025

Search Tags:#transcription-factors #scatac-seq #scenic-plus #pycistarget #motif-enrichment #cistrome

Ready to run

npx skills add https://github.com/tony-zhelonkin/SciAgent-toolkit --skill pycistarget-motif-enrichment

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: pycistarget-motif-enrichment
Source: https://github.com/tony-zhelonkin/SciAgent-toolkit/tree/main/skills/pycistarget-motif-enrichment
Command: npx skills add https://github.com/tony-zhelonkin/SciAgent-toolkit --skill pycistarget-motif-enrichment

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Motif enrichment analysis on scATAC-seq region sets to identify transcription factor binding sites and derive cistromes for downstream SCENIC+ GRN inference.

Core Features & Use Cases

Two approaches: cisTarget (NES-based) and DEM (Wilcoxon-based) to compute motif enrichments from region sets derived from pycisTopic.
Cistrome extraction: Generates TF-bound region sets (cistromes) for downstream regulatory network inference with SCENIC+.
SCENIC+ integration ready: Structured results suitable for standalone SCENIC+ workflows or Snakemake pipelines with explicit database and annotation guidelines.

Quick Start

Run pycistarget motif enrichment on your ATAC region sets to produce cistromes for downstream SCENIC+ analysis.

Frequently Asked Questions about pycistarget-motif-enrichment

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I run motif enrichment on scATAC-seq region sets to derive cistromes?▼

Motif enrichment on scATAC-seq region sets is performed using pycistarget to identify transcription factor binding sites and derive cistromes. You apply cisTarget and DEM methods with precomputed databases to compute enrichments from region sets derived from pycisTopic.

What is the difference between cisTarget and DEM methods for motif enrichment?▼

The cisTarget method computes motif enrichment using NES-based scoring, while DEM uses a Wilcoxon-based approach. Both methods are available in pycistarget to process scATAC-seq region sets and generate distinct per-TF region associations.

Do I need precomputed databases to run motif enrichment for SCENIC+ analysis?▼

Yes, precomputed databases are required to run motif enrichment for SCENIC+ analysis. The pycistarget workflow requires these databases along with configured species and annotations to ensure reproducible cistrome extraction results.

How do I extract per-TF region associations from scATAC-seq topic or metacell sets?▼

Per-TF region associations are extracted from scATAC-seq topic or metacell sets by running pycistarget motif enrichment. This process generates TF-bound region sets, known as cistromes, which are structured for downstream SCENIC+ GRN inference.

Can I use pycistarget motif enrichment results in a Snakemake pipeline?▼

Yes, pycistarget motif enrichment results are structured for standalone SCENIC+ workflows and Snakemake pipelines. The outputs provide explicit database and annotation guidelines suitable for seamless downstream regulatory network integration.

What inputs are needed to define scATAC cistromes using pycistarget?▼

To define scATAC cistromes using pycistarget, you need scATAC-seq region sets derived from pycisTopic, precomputed motif databases, and configured species annotations. These inputs ensure accurate transcription factor cistrome extraction.