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jaspar-database

Query the JASPAR database for Transcription Factor (TF) binding profiles. Use when retrieving Position Frequency Matrices (PFMs) or Position Weight Matrices (PWMs) for specific TFs, resolving gene symbols to JASPAR Matrix IDs, or getting TF metadata. Supports multiple output formats (MEME, TRANSFAC, PFM, JASPAR, YAML).

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포함된 파일(3)

  • SKILL.md4.4 KB
  • references/citation.bib2.6 KB
  • scripts/jaspar_api.py8.6 KB

SKILL.md(원문)

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JASPAR Skill

JASPAR is the definitive open-access database for Transcription Factor (TF) binding profiles, stored as Position Frequency Matrices (PFMs).

Use this skill to map abstract sequence motifs or genomic regions to specific biological regulators (e.g., "what TFs bind here?" or "what is the motif for CTCF?").

Prerequisites

  1. uv: Read the uv skill and follow its Setup instructions to ensure uv is installed and on PATH.
  2. User Notification: If .licenses/jaspar_database_LICENSE.txt does not already exist in the workspace root directory then (1) prominently notify the user to check the terms at https://jaspar.elixir.no/ and https://jaspar.elixir.no/api/, then (2) create the file recording the notification text and timestamp.

Core Rules

CRITICAL: You MUST respect the JASPAR API Terms of Use by adhering to the following:

  • Use the Wrapper: ALWAYS execute the provided helper scripts to query the database rather than accessing the database directly. The scripts automatically enforce the required rate limit gracefully.
  • Maximum API Window Size: The genomic window for a single API query MUST NOT exceed 100,000 bp (100kb). The jaspar_api.py script automatically chunks larger requests for you to bypass this limitation when querying larger regions.
  • Valid Matrix IDs: get_tf_motif, get_tf_metadata, and get_tf_pwm require a stable JASPAR Matrix ID (e.g., MA0488.2). If a user provides a gene symbol (e.g., JUN), you must resolve it first using resolve_tf_id.
  • Taxonomy Required: Resolving IDs requires a tax_id to ensure targeted searches. Common IDs: Human=9606, Mouse=10090.
  • Notification: If this skill is used, ensure this is mentioned in the output.

Utility Scripts

Run all commands using the bundled Python script:

1. Resolve TF to Matrix ID

Maps a transcription factor name to a stable Matrix ID. Required step before fetching motifs if only a gene name is provided.

uv run scripts/jaspar_api.py resolve_tf_id --name "JUN" --tax-id 9606

2. Get TF Motif (PFM)

Retrieves the raw Position Frequency Matrix for a specific TF. Supports --format flag.

uv run scripts/jaspar_api.py get_tf_motif --matrix-id "MA0488.2"
uv run scripts/jaspar_api.py get_tf_motif --matrix-id "MA0488.2" --format meme

3. Get TF Metadata

Retrieves TF class, family, and links to external databases (e.g., UniProt). Supports --format flag.

uv run scripts/jaspar_api.py get_tf_metadata --matrix-id "MA0488.2"
uv run scripts/jaspar_api.py get_tf_metadata --matrix-id "MA0488.2" --format yaml

4. Compute PWM (Position Weight Matrix)

Fetches the PFM for a matrix and converts it to log-odds scores (PWM).

uv run scripts/jaspar_api.py get_tf_pwm --matrix-id "MA0488.2"
uv run scripts/jaspar_api.py get_tf_pwm --matrix-id "MA0488.2" --pseudocount 0.1

5. Infer Matrix from Protein Sequence

Infers potential JASPAR matrix profiles from a raw transcription factor protein sequence.

uv run scripts/jaspar_api.py infer_from_sequence --sequence "QAQLLPSHHVG"

6. Get TF Flexible Model (TFFM)

Retrieves metadata for a JASPAR TF Flexible Model. (Note: The JASPAR TFFM endpoints occasionally experience 500 Internal Server errors).

uv run scripts/jaspar_api.py get_tffm --tffm-id "TFFM0001.1"

Output Formats

The get_tf_motif and get_tf_metadata commands accept an optional --format flag. Supported formats: json (default), jsonp, jaspar, meme, transfac, pfm, yaml.

Anti-Patterns

  • DON'T pass gene symbols (e.g., JUN) to get_tf_motif. You must pass the MA... Matrix ID.
  • DON'T forget the --tax-id when resolving a TF name.
  • DON'T use this skill for determining tissue-specific epigenetic availability (JASPAR shows potential binding, not actual tissue expression context).
  • DON'T use this skill to model how a specific protein mutation affects binding.

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