/unibind_database

name: unibind-database
description: >-
  Queries the UniBind database for experimentally validated transcription factor
  (TF) binding sites. Use when retrieving direct TF-DNA interaction datasets,
  downloading binding site coordinates (BED/FASTA) for local analysis, or
  listing available datasets by species, cell line, or TF name. Don't use to
  query specific intervals, locations, genes, motif models or expression
  data.

UniBind Database Skill

UniBind is a database of direct TF–DNA interactions across 9 species, integrating ChIP-seq peaks with JASPAR TF binding profiles via the DAMO framework.

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/unibind_database_LICENSE.txt does not already exist in the workspace root directory then (1) prominently notify the user to check the terms at https://unibind.uio.no/ and https://unibind.uio.no/api/overview, then (2) create the file recording the notification text and timestamp.

Quick Start

Query commands print JSON to stdout by default. Most outputs are small enough to read directly. For large outputs (`list_cell_lines`, `list_tfs`), pipe through `jq` to extract only the fields you need.

uv run <SKILL DIR>/scripts/unibind_api.py list_species

The `download_tfbs` command writes BED/FASTA files to `--output-dir` instead. You may optionally use `--output <path>` on any query command to save results to a file if needed.

Core Rules

• **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.

• **Output**: Query commands print JSON to stdout. Most responses are compact

and can be read directly.

• **Large Results**: `list_cell_lines` and `list_tfs` produce large output.

Pipe these through `jq` to extract specific fields rather than reading the full output into context.

• **Saving to File**: Use `--output <path>` when you need to reference the

data later or when processing very large results with `jq`.

• **Pagination**: Use `--page` and `--page-size` (max 1000) to chunk large

result sets.

• **Ordering**: Use `--order field_name` (prefix with `-` for descending) on

any list command.

• **Notification**: If this skill is used, ensure this is mentioned in the

output.

Utility Scripts

*Replace `<SKILL DIR>` with the absolute path to this skill's directory.*

1. List Species

uv run <SKILL DIR>/scripts/unibind_api.py list_species

2. List Collections

uv run <SKILL DIR>/scripts/unibind_api.py list_collections

3. List Cell Lines & TFs (large output — use `jp`)

These commands return large datasets. Use `uvx --from jmespath jp` to extract only the fields you need.

uv run <SKILL DIR>/scripts/unibind_api.py list_cell_lines | uvx --from jmespath jp "results[].name"
uv run <SKILL DIR>/scripts/unibind_api.py list_tfs | uvx --from jmespath jp "results[].tf_name"

4. List and Filter Datasets (and Profile-Specific Datasets)

Filter datasets using the following arguments:

• `--species` (e.g., "Homo sapiens")
• `--tf-name` (e.g., "CTCF")
• `--cell-line` (e.g., "mESC")
• `--collection` (e.g., Permissive, Robust)
• `--search` (a search term)
• `--biological-condition` (biological condition or source)
• `--data-source` (source of data, e.g., "ENCODE")
• `--has-pvalue` ("true" or "false")
• `--identifier` (e.g., "GSE60130")
• `--jaspar-id` (JASPAR database profile matrix ID)
• `--model` (prediction model)
• `--summary` (summary filter)
• `--threshold-pvalue` (p-value threshold)

Use `list_datasets` for standard datasets, or `list_specific_datasets` for profile-specific queries.

uv run <SKILL DIR>/scripts/unibind_api.py list_datasets --species "Homo sapiens" --tf-name "CTCF" --data-source "ENCODE"
uv run <SKILL DIR>/scripts/unibind_api.py list_specific_datasets --species "Mus musculus" --cell-line "mESC"

5. Get Dataset Details

uv run <SKILL DIR>/scripts/unibind_api.py get_dataset "EXP047889.HMLE-Twist-ER_breast_cancer.SMAD3"

6. Download TFBS Files (BED / FASTA)

Downloads all TFBS files for a dataset to a local directory. Use `--format bed` (default) or `--format fasta`.

uv run <SKILL DIR>/scripts/unibind_api.py download_tfbs "EXP047889.HMLE-Twist-ER_breast_cancer.SMAD3" --output-dir /tmp/tfbs --format bed

Anti-Patterns

• **DON'T** attempt to use the UniBind API to query specific genomic

intervals, locations, or genes.

• **DON'T** guess or hallucinate genome coordinates. Always use

`ensembl-database` as an external check if you're pulling local BED tracks for offline bedtools intersection.

• **DON'T** use for motif models (PFMs). Use the **jaspar-database** skill

instead.

• **DON'T** use for gene expression data. UniBind only stores binding events.
• **DON'T** assume tissue-specific expression from dataset lists alone.
• **DON'T** use `cat` to read large JSON output files into context. The output

is too large. Use `jq` or write your own code to parse the output files.