unibind-database

Original：🇺🇸 English

Translated

1 scriptsChecked / no sensitive code detected

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.

2installs

Sourcegoogle-deepmind/science-skills

Added on2026-05-20

NPX Install

npx skill4agent add google-deepmind/science-skills unibind-database

SKILL.md Content

View Translation Comparison →

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

uv
: Read the
```
uv
```
skill and follow its Setup instructions to ensure
```
uv
```
is installed and on PATH.
User Notification: If LICENSE_NOTIFICATION.txt does not already exist in this skill 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.

bash

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

bash

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

2. List Collections

bash

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.

bash

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.

bash

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

bash

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

.

bash

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.

unibind-database

NPX Install

Tags

SKILL.md Content

UniBind Database Skill

Prerequisites

Quick Start

Core Rules

Utility Scripts

1. List Species

2. List Collections

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

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

5. Get Dataset Details

6. Download TFBS Files (BED / FASTA)

Anti-Patterns

unibind-database

NPX Install

Tags

SKILL.md Content

UniBind Database Skill

Prerequisites

Quick Start

Core Rules

Utility Scripts

1. List Species

2. List Collections

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

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

5. Get Dataset Details

6. Download TFBS Files (BED / FASTA)

Anti-Patterns

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