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Get Started Free →Access nucleotide sequence data from the European Nucleotide Archive
.claude/skills/brycewang-stanford-ena-sequence-api/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-03 | ✗→✓ | ▲ Improved | -1% | 0% |
| case-06 | ✗→✓ | ▲ Improved | 69% | 0% |
| case-13 | ✗→✓ | ▲ Improved | 110% | 0% |
| case-01 | ✓→✓ | = Same ✓ | 28% | 0% |
| case-02 | ✓→✓ | = Same ✓ | 18% | 0% |
The European Nucleotide Archive (ENA) at EMBL-EBI is one of the three global nucleotide sequence databases (with NCBI GenBank and DDBJ). It provides access to raw sequencing reads, assembled sequences, and functional annotations from all organisms. The API supports accession lookup, text search, and bulk data retrieval. Free, no authentication required.
bash# Search for studies curl "https://www.ebi.ac.uk/ena/portal/api/search?query=CRISPR+cas9&result=study&limit=20&format=json" # Search for samples curl "https://www.ebi.ac.uk/ena/portal/api/search?query=human+gut+microbiome&result=sample&limit=20&format=json" # Search for runs (sequencing data) curl "https://www.ebi.ac.uk/ena/portal/api/search?query=RNA-seq+cancer&result=read_run&limit=20&format=json"
bash# Get record by accession curl "https://www.ebi.ac.uk/ena/browser/api/xml/PRJEB12345" # Get in JSON format curl "https://www.ebi.ac.uk/ena/browser/api/summary/PRJEB12345" # Get sequence in FASTA curl "https://www.ebi.ac.uk/ena/browser/api/fasta/AF123456" # Get in EMBL flat file format curl "https://www.ebi.ac.uk/ena/browser/api/embl/AF123456"
bash# Search by organism curl "https://www.ebi.ac.uk/ena/portal/api/search?query=tax_tree(9606)&result=study&limit=20&format=json" # Get taxonomy details curl "https://www.ebi.ac.uk/ena/taxonomy/rest/tax-id/9606"
| Type | Description | Example accession | |------|-------------|-------------------| | study | Research project | PRJEB12345 | | sample | Biological sample | SAMEA12345 | | experiment | Library/protocol | ERX12345 | | read_run | Sequencing run | ERR12345 | | analysis | Computed analysis | ERZ12345 | | sequence | Assembled sequence | AF123456 | | wgs_set | Whole genome shotgun | AABR00000000 |
| Parameter | Description | Example | |-----------|-------------|---------| | query | Search text or taxonomy | query=SARS-CoV-2 | | result | Result type | result=study | | limit | Max results (default 100K) | limit=50 | | offset | Pagination offset | offset=100 | | format | Response format | json, tsv, xml | | fields | Specific fields | fields=accession,description |
pythonimport requests PORTAL_URL = "https://www.ebi.ac.uk/ena/portal/api" BROWSER_URL = "https://www.ebi.ac.uk/ena/browser/api" def search_studies(query: str, limit: int = 20) -> list: """Search ENA for research studies.""" params = { "query": query, "result": "study", "limit": limit, "format": "json", "fields": "study_accession,study_title,study_description," "tax_id,scientific_name,center_name", } resp = requests.get(f"{PORTAL_URL}/search", params=params) resp.raise_for_status() return resp.json() def search_runs(query: str, limit: int = 20) -> list: """Search for sequencing runs.""" params = { "query": query, "result": "read_run", "limit": limit, "format": "json", "fields": "run_accession,experiment_title,instrument_platform," "library_strategy,read_count,base_count", } resp = requests.get(f"{PORTAL_URL}/search", params=params) resp.raise_for_status() return resp.json() def get_fasta(accession: str) -> str: """Retrieve sequence in FASTA format.""" resp = requests.get(f"{BROWSER_URL}/fasta/{accession}") resp.raise_for_status() return resp.text def get_study_runs(study_accession: str) -> list: """Get all sequencing runs for a study.""" params = { "query": f'study_accession="{study_accession}"', "result": "read_run", "format": "json", "fields": "run_accession,fastq_ftp,read_count,base_count", "limit": 1000, } resp = requests.get(f"{PORTAL_URL}/search", params=params) resp.raise_for_status() return resp.json() # Example: find COVID-19 sequencing studies studies = search_studies("SARS-CoV-2 whole genome", limit=5) for s in studies: print(f"{s['study_accession']}: {s['study_title']}") print(f" Organism: {s.get('scientific_name')}") # Example: find RNA-seq runs runs = search_runs("RNA-seq breast cancer", limit=5) for r in runs: reads = int(r.get("read_count", 0)) print(f"{r['run_accession']}: {r.get('experiment_title', '')}") print(f" Platform: {r.get('instrument_platform')} | " f"Reads: {reads:,}")
bash# Download FASTQ files (from run metadata) # The fastq_ftp field provides FTP paths: wget ftp://ftp.sra.ebi.ac.uk/vol1/fastq/ERR123/ERR123456/ERR123456_1.fastq.gz # Bulk download via Aspera (faster) ascp -QT -l 300m -P33001 \ era-fasp@fasp.sra.ebi.ac.uk:/vol1/fastq/ERR123/ERR123456/ ./
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | pass→pass | 10,580 | 8,371 | -21% | 1 | 1 | 0% | 2,246 | 2,884 | +28% | 0 | 0 | — |
case-02 | pass→pass | 10,144 | 2,538 | -75% | 1 | 1 | 0% | 1,898 | 2,249 | +18% | 0 | 0 | — |
case-03 | fail→pass | 11,854 | 2,592 | -78% | 1 | 1 | 0% | 2,230 | 2,214 | -1% | 0 | 0 | — |
case-04 | pass→pass | 9,817 | 4,676 | -52% | 1 | 1 | 0% | 1,873 | 2,626 | +40% | 0 | 0 | — |
case-05 | pass→pass | 9,272 | 3,729 | -60% | 1 | 1 | 0% | 1,712 | 2,405 | +40% | 0 | 0 | — |
case-06 | fail→pass | 8,573 | 1,947 | -77% | 1 | 1 | 0% | 1,238 | 2,093 | +69% | 0 | 0 | — |
case-07 | pass→pass | 6,450 | 2,301 | -64% | 1 | 1 | 0% | 1,155 | 2,117 | +83% | 0 | 0 | — |
case-08 | pass→pass | 7,562 | 6,501 | -14% | 1 | 1 | 0% | 1,510 | 2,711 | +80% | 0 | 0 | — |
case-09 | pass→pass | 7,354 | 3,919 | -47% | 1 | 1 | 0% | 1,346 | 2,483 | +84% | 0 | 0 | — |
case-10 | pass→pass | 11,124 | 3,629 | -67% | 1 | 1 | 0% | 2,155 | 2,371 | +10% | 0 | 0 | — |
case-11 | pass→pass | 9,151 | 3,777 | -59% | 1 | 1 | 0% | 1,502 | 2,445 | +63% | 0 | 0 | — |
case-12 | pass→pass | 5,310 | 2,789 | -47% | 1 | 1 | 0% | 842 | 2,208 | +162% | 0 | 0 | — |
case-13 | fail→pass | 20,570 | 2,853 | -86% | 1 | 1 | 0% | 1,066 | 2,237 | +110% | 0 | 0 | — |
case-14 | pass→pass | 13,788 | 8,622 | -37% | 1 | 1 | 0% | 2,673 | 3,629 | +36% | 0 | 0 | — |
case-15 | pass→pass | 13,317 | 6,955 | -48% | 1 | 1 | 0% | 2,539 | 3,155 | +24% | 0 | 0 | — |
case-16 | pass→pass | 7,362 | 3,817 | -48% | 1 | 1 | 0% | 1,478 | 2,452 | +66% | 0 | 0 | — |
case-17 | pass→pass | 11,346 | 9,528 | -16% | 1 | 1 | 0% | 2,057 | 3,449 | +68% | 0 | 0 | — |
case-18 | pass→pass | 15,725 | 8,515 | -46% | 1 | 1 | 0% | 2,710 | 3,311 | +22% | 0 | 0 | — |
case-19 | pass→pass | 8,491 | 7,077 | -17% | 1 | 1 | 0% | 1,534 | 3,141 | +105% | 0 | 0 | — |
case-20 | pass→pass | 10,281 | 2,895 | -72% | 1 | 1 | 0% | 1,865 | 2,256 | +21% | 0 | 0 | — |
case-21 | pass→pass | 9,525 | 4,602 | -52% | 1 | 1 | 0% | 1,708 | 2,645 | +55% | 0 | 0 | — |
case-22 | pass→pass | 5,154 | 3,146 | -39% | 1 | 1 | 0% | 1,004 | 2,261 | +125% | 0 | 0 | — |
case-23 | pass→pass | 7,861 | 6,703 | -15% | 1 | 1 | 0% | 1,468 | 3,015 | +105% | 0 | 0 | — |
case-24 | pass→pass | 4,875 | 4,177 | -14% | 1 | 1 | 0% | 870 | 2,568 | +195% | 0 | 0 | — |
case-25 | pass→pass | 6,444 | 3,271 | -49% | 1 | 1 | 0% | 1,063 | 2,298 | +116% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 25 cases were attempted, and 24 counted toward the lift figure. The other 1 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of +12 percentage points is the difference between those two pass rates over the 24 comparable cases.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.