JASPAR Database Skill - Complete Content

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Lets your agent look up transcription factor binding profiles in the JASPAR genomics database.

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Details

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JASPAR Database Skill - Complete ContentStart free
About this skill

Query JASPAR for transcription factor binding site (TFBS) profiles (PWMs/PFMs). Search by TF name, species, or class; scan DNA sequences for TF binding sites; compare matrices; essential for regulatory genomics, motif analysis, and GWAS regulatory variant interpretation.

What this skill tells your AI

The instructions your AI receives, as published by freedomintelligence/openclaw-medical-skills in skills/jaspar-database/SKILL.md and read by ahel’s review.

Name: jaspar-database

Description: "Query JASPAR for transcription factor binding site (TFBS) profiles (PWMs/PFMs). Search by TF name, species, or class; scan DNA sequences for TF binding sites; compare matrices; essential for regulatory genomics, motif analysis, and GWAS regulatory variant interpretation."

License: CC0-1.0

Skill Author: Kuan-lin Huang

Overview

JASPAR (https://jaspar.elixir.no/) serves as the authoritative open-access repository of curated transcription factor binding profiles represented as position frequency matrices. The 2024 version contains approximately 1,210 non-redundant profiles across 164 eukaryotic species, with each profile derived from experimental validation methods.

Core Capabilities

The skill provides REST API access and Python implementations for:

  1. Profile searching by transcription factor name, species, family, or classification
  2. Matrix retrieval with PFM/PWM conversion and scoring
  3. Sequence scanning across forward and reverse complement strands
  4. Variant impact assessment comparing reference versus alternative allele binding affinity
  5. Multi-TF workflow automation for promoter and regulatory element analysis

Key Workflows

  • Finding all binding sites in promoter regions
  • Assessing regulatory variant effects on transcription factor recognition
  • Motif enrichment analysis from ChIP-seq and ATAC-seq data

Best Practices

The resource recommends using the CORE collection for most analyses, setting thresholds at 80% of maximum score for general prediction, always scanning both DNA strands, and validating predictions against experimental ChIP-seq datasets.

Signals

GitHub stars
3k
Forks
412
Last commit
Jul 2026
Advanced
Item type
skill
Key
jaspar-database
Source
github.com/freedomintelligence/openclaw-medical-skills