jaspar-database
google-deepmind/science-skills
Query JASPAR database for transcription factor binding profiles and position weight matrices.
What is jaspar-database?
Access the JASPAR open-access database to retrieve Position Frequency Matrices (PFMs) and Position Weight Matrices (PWMs) for transcription factors. Use this skill to map sequence motifs to biological regulators, resolve gene symbols to matrix IDs, and retrieve TF metadata in multiple formats.
- Resolve transcription factor gene symbols to stable JASPAR Matrix IDs with taxonomy filtering
- Retrieve Position Frequency Matrices (PFMs) and Position Weight Matrices (PWMs) for specific TFs
- Fetch transcription factor metadata including class, family, and external database links
- Convert PFM to log-odds PWM scores with configurable pseudocount
- Infer potential JASPAR matrix profiles from raw protein sequences
- Support multiple output formats: JSON, MEME, TRANSFAC, PFM, JASPAR, and YAML
How to install jaspar-database
npx skills add https://github.com/google-deepmind/science-skills --skill jaspar-database- uv package manager installed and on PATH
- JASPAR API Terms of Use acknowledgment (user will be prompted to review https://jaspar.elixir.no/)
- Taxonomy ID for the organism of interest (e.g., 9606 for human, 10090 for mouse)
How to use jaspar-database
- 1.If using JASPAR for the first time, review the license terms at https://jaspar.elixir.no/ when prompted
- 2.Resolve a transcription factor name to its Matrix ID using: uv run scripts/jaspar_api.py resolve_tf_id --name "GENE_SYMBOL" --tax-id TAXONOMY_ID
- 3.Retrieve the PFM for the matrix using: uv run scripts/jaspar_api.py get_tf_motif --matrix-id "MA0000.0"
- 4.Optionally convert to PWM format using: uv run scripts/jaspar_api.py get_tf_pwm --matrix-id "MA0000.0"
- 5.Specify output format with --format flag (json, meme, transfac, pfm, jaspar, yaml) as needed for downstream tools
Use cases
- Identify what transcription factors bind to a genomic region by querying their motifs
- Retrieve the binding profile for a known TF (e.g., CTCF) to use in downstream analysis
- Convert a gene symbol like JUN to its JASPAR Matrix ID for motif scanning
- Export transcription factor binding profiles in MEME format for use with motif-finding tools
- Obtain TF metadata and cross-references to UniProt and other databases for annotation
- Bioinformaticians analyzing transcription factor binding sites
- Computational biologists studying gene regulation
- Researchers working with genomic sequence analysis
- Scientists building motif-scanning or ChIP-seq analysis pipelines
jaspar-database FAQ
PFM (Position Frequency Matrix) shows raw nucleotide counts at each position; PWM (Position Weight Matrix) converts these to log-odds scores for binding affinity prediction.
You must resolve gene symbols to Matrix IDs first using resolve_tf_id. Passing gene symbols directly to motif queries will fail.
Common IDs include 9606 for human, 10090 for mouse, and 6239 for C. elegans. Specify the correct organism to get accurate TF matches.
The maximum window is 100,000 bp (100kb) per API query. The jaspar_api.py script automatically chunks larger requests.
No. JASPAR shows potential binding profiles based on consensus motifs, not actual tissue-specific expression or epigenetic availability.
Full instructions (SKILL.md)
Source of truth, from google-deepmind/science-skills.
name: jaspar-database description: > 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).
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
uv: Read theuvskill and follow its Setup instructions to ensureuvis installed and on PATH.- 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.pyscript automatically chunks larger requests for you to bypass this limitation when querying larger regions. - Valid Matrix IDs:
get_tf_motif,get_tf_metadata, andget_tf_pwmrequire a stable JASPAR Matrix ID (e.g.,MA0488.2). If a user provides a gene symbol (e.g.,JUN), you must resolve it first usingresolve_tf_id. - Taxonomy Required: Resolving IDs requires a
tax_idto 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) toget_tf_motif. You must pass theMA...Matrix ID. - DON'T forget the
--tax-idwhen 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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