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 LICENSE_NOTIFICATION.txt does not already exist in this skill 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.

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.

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.

# 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.

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.

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):

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):

uv run scripts/list_tracks.py --search "jaspar" --output /tmp/jaspar_tracks.json

You can also filter by functional group:

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.