ucsc-conservation-and-tfbs — Claude Code skill: conservation and TFBS
Pulls evolutionary conservation scores (phyloP, phastCons) and transcription factor binding sites out of the UCSC Genome Browser. Handy when you are reading non-coding variants.
npx skills add google-deepmind/science-skills --skill ucsc-conservation-and-tfbs -a claude-code
npx skills add google-deepmind/science-skills --skill ucsc_conservation_and_tfbs -a claude-codeWhat ucsc-conservation-and-tfbs does
The skill gives your agent access to evolutionary constraint scores and conserved elements from the UCSC Genome Browser. It does not compute them itself — it reads the PHAST tracks: phastCons for functional blocks, phyloP for constraint at a single base. The hg38 assembly is used by default unless you say otherwise.
Three scripts ship with it: scripts/get_conservation.py for conservation scores, scripts/get_tfbs.py for binding sites, scripts/list_tracks.py for discovering tracks. Phylogenetic depth is set with --collection — vertebrate (100-vertebrate Multiz), mammal (Hiller 470-way) or primate (30-primate). For accelerated evolution there is --analyze, which returns mean, min and max phyloP plus a heuristic is_accelerated boolean.
You need uv on PATH before the first run. The skill also asks you to notify the user about UCSC terms and drop .licenses/ucsc_conservation_and_tfbs_LICENSE.txt in the workspace root with the notification text and a timestamp. Queries go through the wrapper scripts only, since they enforce the rate limit. Always redirect output with --output to a file and parse it separately, or a large JSON blob lands straight in your context. Tracks without a year in the name (jaspar, ReMap) are containers and fail with a 400 error, so pass a specific subtrack such as jaspar2026.
What it helps with
- check whether a non-coding variant lands on a conserved base
- find conservation peaks across a regulatory element
- compare constraint at one locus across primates, mammals and vertebrates
- test whether a locus is evolving faster than neutral drift
- locate TF binding sites in an interval via ENCODE, JASPAR or ReMap tracks
Example prompts for the agent
- get phyloP for chr1:215867804 and chr1:215867823, write the result to a file
- find conserved elements in chr8:11748914-11749085 using phastCons
- show TP53 sites in chr6:36670000-36690000 from the jaspar2024 track
How to install ucsc-conservation-and-tfbs
1Skills CLI (Vercel)
npx skills add google-deepmind/science-skills --skill ucsc-conservation-and-tfbs -a claude-codenpx skills add google-deepmind/science-skills --skill ucsc_conservation_and_tfbs -a claude-code2Manually: put the files in place
~/.claude/skills/ucsc_conservation_and_tfbs/SKILL.mdCopy the whole skill folder; for one project use .claude/skills/ucsc_conservation_and_tfbs/
1Skills CLI (Vercel)
npx skills add google-deepmind/science-skills --skill ucsc_conservation_and_tfbs -a codex2Manually: put the files in place
.agents/skills/ucsc_conservation_and_tfbs/SKILL.mdFor all projects use ~/.agents/skills/
1Skills CLI (Vercel)
npx skills add google-deepmind/science-skills --skill ucsc_conservation_and_tfbs -a cursor2Manually: put the files in place
.cursor/skills/ucsc_conservation_and_tfbs/SKILL.mdCursor also reads .agents/skills/ and .claude/skills/
1GitHub CLI
gh skill install google-deepmind/science-skills ucsc_conservation_and_tfbs2Manually: put the files in place
.github/skills/ucsc_conservation_and_tfbs/SKILL.md1Skills CLI (Vercel)
npx skills add google-deepmind/science-skills --skill ucsc_conservation_and_tfbs -a gemini-cli2Platform CLI
gemini skills install https://github.com/google-deepmind/science-skills --path skills/ucsc_conservation_and_tfbsManually: put the files in place
.devin/skills/ucsc_conservation_and_tfbs/SKILL.mdThe legacy .windsurf/skills/ path still works
Skills CLI (Vercel)
npx skills add google-deepmind/science-skills --skill ucsc_conservation_and_tfbs -a opencodeClaude.ai
Zip the folder and upload it in Settings → Capabilities → Skills
Step by step, with commands for each platform: How to install skills in Claude Code: five ways
Questions about ucsc-conservation-and-tfbs
How do I install ucsc-conservation-and-tfbs in Claude?
Run npx skills add google-deepmind/science-skills --skill ucsc-conservation-and-tfbs -a claude-code, then npx skills add google-deepmind/science-skills --skill ucsc_conservation_and_tfbs -a claude-code. The skill file ends up at ~/.claude/skills/ucsc_conservation_and_tfbs/SKILL.md.
What does ucsc-conservation-and-tfbs do?
It fetches phyloP and phastCons conservation scores and transcription factor binding sites from the UCSC Genome Browser. That tells you whether a variant sits somewhere functionally important.
What do I need before running it?
uv on PATH and a .licenses/ucsc_conservation_and_tfbs_LICENSE.txt file in the workspace root, which the skill creates after notifying you about UCSC terms. Queries run through the wrapper scripts, which handle the rate limit.
Source text
SKILL.md · Apache-2.0
---
name: ucsc-conservation-and-tfbs
description: >
Fetch Evolutionary Conservation scores (phyloP, phastCons) and Transcription
Factor Binding Sites (TFBS) from the UCSC Genome Browser. Use when analyzing
whether genomic variants or regions are evolutionarily conserved, functionally
important, or bounded by TF regulators across major projects (ENCODE, JASPAR,
ReMap).
---
# Conservation Scores & TFBS Lookup (UCSC)
This skill provides access to evolutionary constraint scores and conserved
elements from the UCSC Genome Browser. It retrieves scores from the PHAST
package — specifically `phastCons` (identifying functional blocks) and `phyloP`
(measuring individual site constraint) — calculated from multiple alignments.
Use this skill to determine if a non-coding variant hits a site that hasn't
changed since a common ancestor (which is a strong signal for pathogenicity) or
to find conservation peaks across a regulatory element.
## 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/ucsc_conservation_and_tfbs_LICENSE.txt
does not already exist in the workspace root directory then (1) prominently
notify the user to check the terms at
https://genome.ucsc.edu/conditions.html and
https://genome.ucsc.edu/goldenPath/help/api.html, then (2) create the file
recording the notification text and timestamp.
## 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.
- **Large Output Handling**: Always pass --output to redirect output to a
file. Parse it separately (using jq or your own code).
- **Notification**: If this skill is used, ensure this is mentioned in the
output.
## Utility Scripts
This skill includes scripts to query different types of genomic data from UCSC:
1. **`scripts/get_conservation.py`**: For Evolutionary Conservation scores
(phyloP, phastCons).
2. **`scripts/get_tfbs.py`**: For Transcription Factor Binding Sites (TFBS).
3. **`scripts/list_tracks.py`**: For listing available tracks based on search
or group constraints.
Always use the `hg38` genome assembly by default, unless the user has specified
otherwise.
### Fetching Conservation for Specific Variants
To get the evolutionary constraint at a single base, or a list of specific
bases. This is optimal for single nucleotide variants (SNVs). `phyloP` is the
best metric for individual bases.
```bash
uv run scripts/get_conservation.py --coordinates "chr1:215867804" "chr1:215867823" --output /tmp/cons_output.json
```
### Fetching Regions and Conserved Elements
To identify "conservation peaks" across a non-coding regulatory element (like an
enhancer) to see if an ISM-predicted importance peak aligns with evolutionary
history. `phastCons` is best for functional windows due to HMM smoothing. The
`--conserved-elements` flag will also retrieve predefined blocks under extreme
constraint.
```bash
uv run scripts/get_conservation.py --coordinates "chr8:11748914-11749085" --conserved-elements --output /tmp/region_cons.json
```
### Lineage-Specific Constraints
You can control the evolutionary depth using the `--collection` flag. The
default (`vertebrate`) uses the **100-vertebrate Multiz alignment** for both
hg38 and hg19, matching the UCSC Genome Browser's default comparative genomics
tracks.
#### hg38 Collections
- **`vertebrate`** (default): UCSC 100-vertebrate Multiz alignment. phyloP:
`phyloP100way`, phastCons: `phastCons100way`.
- **`mammal`**: Hiller Lab 470-way mammalian alignment. phyloP:
`phyloP470wayBW`, phastCons: `phastCons470way`.
- **`primate`**: UCSC 30-primate Multiz alignment. phyloP: `phyloP30way`,
phastCons: `phastCons30way`.
#### hg19 Collections
- **`vertebrate`** (default): UCSC 100-vertebrate Multiz alignment. phyloP:
`phyloP100way`, phastCons: `phastCons100way`.
- **`vertebrate46`**: UCSC 46-vertebrate Multiz alignment (legacy). phyloP:
`phyloP46wayAll`, phastCons: `phastCons46way`.
- **`mammal`**: 46-way placental mammal subset. phyloP:
`phyloP46wayPlacental`, phastCons: `phastCons46wayPlacental`.
- **`primate`**: 46-way primate subset. phyloP: `phyloP46wayPrimates`,
phastCons: `phastCons46wayPrimates`.
```bash
# hg38 mammal (Hiller 470-way)
uv run scripts/get_conservation.py --coordinates "chr5:1045330-1046172" --collection mammal --output /tmp/mammal_cons.json
# hg19 with legacy 46-vertebrate alignment
uv run scripts/get_conservation.py --coordinates "chr5:1045330-1046172" --genome hg19 --collection vertebrate46 --output /tmp/vert46_cons.json
```
### Analyzing Evolutionary Acceleration
To analyze whether a specific locus is undergoing evolutionary acceleration
(i.e. evolving more rapidly than the neutral drift baseline), use `--analyze`.
This will compute scalar statistics (mean, min, max) for `phyloP` scores and
provide a heuristic boolean `is_accelerated` to simplify your evaluation.
```bash
uv run scripts/get_conservation.py --coordinates "chr5:1045330-1046172" --analyze --output /tmp/accelerated_cons.json
```
### Fetching Transcription Factor Binding Sites (TFBS)
To identify transcription factor binding sites for a given genomic interval.
This is useful for interpreting non-coding variants that might disrupt TF
binding.
Run `scripts/get_tfbs.py` with `--coordinates` and `--tracks`. You can query
multiple tracks at once.
```bash
uv run scripts/get_tfbs.py --coordinates "chr11:1001000-1010000" --tracks encRegTfbsClustered --output /tmp/tfbs_encode.json
```
JASPAR tracks may return very large result sets. Use `--tf-filter` to keep only
items whose `TFName` field contains the given substring (case-insensitive):
```bash
uv run scripts/get_tfbs.py --coordinates "chr6:36670000-36690000" --tracks jaspar2024 --tf-filter TP53 --output /tmp/tp53_sites.json
```
#### Common Verified Tracks (hg38)
- **ENCODE**: `encRegTfbsClustered` (TF Clusters)
- **JASPAR**: `jaspar2026`, `jaspar2024` (Predicted TFBS)
- **ReMap**: `ReMapTFs` (ChIP-seq Atlas)
> [!CAUTION] Tracks like `jaspar` or `ReMap` without years are often "container"
> tracks and will fail with a 400 error. Always use the specific subtrack name
> (e.g., `jaspar2026`).
### Listing Available Tracks
To list available tracks (such as different versions of JASPAR, or purely to
discover what tracks exist for a particular genome assembly):
```bash
uv run scripts/list_tracks.py --search "jaspar" --output /tmp/jaspar_tracks.json
```
You can also filter by functional group:
```bash
uv run scripts/list_tracks.py --group "regulation" --output /tmp/regulation_tracks.json
```
## Anti-Patterns
* **DON'T** query mammalian (`--collection mammal`) constraint if you are
explicitly looking for deep evolutionary roots across all vertebrates. Use
the default `vertebrate` collection.
* **DON'T** use this skill for determining the ancestral state reconstruction
of a nucleotide (this skill provides measures of *how much* sites have
changed, not *what* the ancestral nucleotide was).
* **DON'T** assume low conservation strictly means neutral/useless sequence;
it could also reflect a high local mutation rate which conservation scores
alone cannot distinguish.
* **DON'T** print output on standard out, or run cat on output to files. The
output is too large. Use jq or write your own code to parse the output
files.
* **DON'T** use hg19 unless the user has explicitly asked for it. The default
should be to always use hg38.
© google-deepmind · Apache-2.0 · google-deepmind/science-skills