Ucsc Conservation And Tfbs
Google DeepMindOfficialPublished by Google DeepMind
- Skill
- Tool integration
- Science and research
- Apache-2.0
- Low risk
- Has scripts
AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
This skill provides a programmatic interface to retrieve genomic data from the UCSC Genome Browser, specifically focusing on evolutionary constraint scores and regulatory element binding sites. It enables researchers to analyze genomic variants by fetching phyloP and phastCons scores to assess functional importance, or by querying TFBS tracks from major databases like ENCODE, JASPAR, and ReMap to investigate regulatory disruption. The skill includes helper scripts to manage data retrieval, handle large outputs, and support various genome assemblies and evolutionary collections.
What it does
- Retrieves phyloP scores for individual base-level evolutionary constraint analysis.
- Fetches phastCons scores and conserved elements for regional functional analysis.
- Queries transcription factor binding sites (TFBS) from ENCODE, JASPAR, and ReMap tracks.
- Supports multiple evolutionary depths including vertebrate, mammal, and primate alignments.
- Provides statistical analysis of conservation scores to identify potentially accelerated regions.
- Allows discovery of available genomic tracks through search and group filtering.
When to use it
- When evaluating the functional impact or pathogenicity of non-coding genomic variants.
- When determining if a specific genomic region is evolutionarily conserved across species.
- When investigating whether a variant disrupts known transcription factor binding sites.
- When comparing regulatory elements against established ChIP-seq or predicted binding data.
Advantages
- Automates complex queries to the UCSC Genome Browser database.
- Provides standardized metrics for evolutionary constraint and regulatory binding.
- Supports flexible evolutionary depth settings for comparative genomics.
- Includes built-in rate limiting and large-output management for stable performance.
Usage examples
- “Get the phyloP conservation scores for the variant at chr1:215867804.”
- The agent executes the conservation script to return the evolutionary constraint score for the specified base.
- “Find all TP53 binding sites in the region chr6:36670000-36690000 using the JASPAR 2024 track.”
- The agent filters the JASPAR 2024 track for TP53 and returns the identified binding sites within the requested coordinates.
- “Analyze the evolutionary acceleration of the region chr5:1045330-1046172.”
- The agent computes mean, min, and max phyloP scores and provides a boolean indicator of evolutionary acceleration.
- “List all available tracks related to regulation for the hg38 assembly.”
- The agent returns a list of regulatory tracks available in the UCSC Genome Browser for hg38.
Install
Download the skill as a ZIP:
https://autogineer.com/agent-skills/google-deepmind/ucsc-conservation-and-tfbs/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip ucsc-conservation-and-tfbs.zip -d .agents/skills
Documentation by Google DeepMind
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
uv: Read theuvskill and follow its Setup instructions to ensureuvis installed and on PATH.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:
scripts/get_conservation.py: For Evolutionary Conservation scores (phyloP, phastCons).scripts/get_tfbs.py: For Transcription Factor Binding Sites (TFBS).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.
uv run scripts/get_conservation.py --coordinates "chr1:215867804" "chr1:215867823" --output /tmp/cons_output.jsonFetching 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.
uv run scripts/get_conservation.py --coordinates "chr8:11748914-11749085" --conserved-elements --output /tmp/region_cons.jsonLineage-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.
# 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.jsonAnalyzing 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.
uv run scripts/get_conservation.py --coordinates "chr5:1045330-1046172" --analyze --output /tmp/accelerated_cons.jsonFetching 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.
uv run scripts/get_tfbs.py --coordinates "chr11:1001000-1010000" --tracks encRegTfbsClustered --output /tmp/tfbs_encode.jsonJASPAR tracks may return very large result sets. Use --tf-filter to keep only
items whose TFName field contains the given substring (case-insensitive):
uv run scripts/get_tfbs.py --coordinates "chr6:36670000-36690000" --tracks jaspar2024 --tf-filter TP53 --output /tmp/tp53_sites.jsonCommon Verified Tracks (hg38)
ENCODE:
encRegTfbsClustered(TF Clusters)JASPAR:
jaspar2026,jaspar2024(Predicted TFBS)ReMap:
ReMapTFs(ChIP-seq Atlas)
[!CAUTION] Tracks like
jasparorReMapwithout 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):
uv run scripts/list_tracks.py --search "jaspar" --output /tmp/jaspar_tracks.jsonYou can also filter by functional group:
uv run scripts/list_tracks.py --group "regulation" --output /tmp/regulation_tracks.jsonAnti-Patterns
DON'T query mammalian (
--collection mammal) constraint if you are explicitly looking for deep evolutionary roots across all vertebrates. Use the defaultvertebratecollection.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.
Files
- ATTRIBUTION.mdAdded by AutoGineer934 B
- LICENSELicence file added by AutoGineer11.1 KiB
- SKILL.md7.5 KiB
- references/citation.bib810 B
- scripts/get_conservation.pyScript7.5 KiB
- scripts/get_tfbs.pyScript4.9 KiB
- scripts/list_tracks.pyScript4.2 KiB
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Requirements and compatibility
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
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Needs: Needs Python
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Attribution and licence
- Published by
- Google DeepMind
- Publisher
- Google DeepMind (Official)
- Source
- google-deepmind/science-skills · skills/ucsc_conservation_and_tfbs · commit 4de6786
- Licence
- Apache License 2.0 (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
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