name: ena-database description: "ENA REST API for sequences, reads, assemblies, and annotations. Portal API search, Browser API retrieval (XML/FASTA/EMBL), file reports for FASTQ/BAM URLs, taxonomy, cross-refs. For multi-DB Python use bioservices; for NCBI-only use pubmed-database or Biopython Entrez." license: Unknown

ENA Database — European Nucleotide Archive Programmatic Access

Overview

The European Nucleotide Archive (ENA) is EMBL-EBI's comprehensive nucleotide sequence database, encompassing raw sequencing reads, genome assemblies, annotated sequences, and associated metadata. It mirrors and extends INSDC data (GenBank, DDBJ). All access is via REST APIs with no authentication required.

When to Use

• Searching for sequencing studies, samples, or experiments by organism, project, or keyword
• Downloading raw FASTQ/BAM files for reanalysis of public sequencing datasets
• Retrieving genome assemblies with quality statistics (N50, contig count, genome size)
• Fetching nucleotide sequences in FASTA or EMBL flat-file format by accession
• Exploring taxonomic lineage and finding organisms by partial name
• Cross-referencing ENA records with external databases (ArrayExpress, UniProt, PDB)
• Building bulk download lists for large-scale sequencing projects
• For multi-database Python queries (ENA + UniProt + KEGG), prefer bioservices instead
• For NCBI-specific queries (PubMed literature, GenBank records), use pubmed-database or Biopython Entrez

Prerequisites

pip install requests

API constraints:

• Rate limit: 50 requests per second across all ENA APIs
• No authentication required
• Large result sets: use pagination (limit + offset) or streaming (limit=0 for TSV download)
• Portal API base: https://www.ebi.ac.uk/ena/portal/api
• Browser API base: https://www.ebi.ac.uk/ena/browser/api
• Taxonomy API base: https://www.ebi.ac.uk/ena/taxonomy/rest
• Cross-ref API base: https://www.ebi.ac.uk/ena/xref/rest

Quick Start

import requests
import time

BASE_PORTAL = "https://www.ebi.ac.uk/ena/portal/api"
BASE_BROWSER = "https://www.ebi.ac.uk/ena/browser/api"
BASE_TAXONOMY = "https://www.ebi.ac.uk/ena/taxonomy/rest"
BASE_XREF = "https://www.ebi.ac.uk/ena/xref/rest"

def ena_query(endpoint, params=None, base=BASE_PORTAL):
    """Reusable ENA API caller with rate-limit compliance."""
    resp = requests.get(f"{base}/{endpoint}", params=params)
    resp.raise_for_status()
    time.sleep(0.02)  # 50 req/sec limit
    return resp

# Search for human RNA-seq studies
resp = ena_query("search", params={
    "result": "study",
    "query": 'tax_tree(9606) AND library_strategy="RNA-Seq"',
    "fields": "study_accession,study_title",
    "format": "json",
    "limit": 3,
})
studies = resp.json()
for s in studies:
    print(f"{s['study_accession']}: {s['study_title'][:60]}")
# PRJEB12345: Transcriptome analysis of human liver tissue...

Core API

Module 1: Portal API Search

The Portal API provides advanced metadata search across all ENA data types with boolean query syntax, field selection, and pagination.

# Search read runs for a specific study
resp = ena_query("search", params={
    "result": "read_run",
    "query": 'study_accession="PRJEB1787"',
    "fields": "run_accession,sample_accession,instrument_model,read_count,base_count",
    "format": "json",
    "limit": 5,
})
runs = resp.json()
for r in runs:
    print(f"{r['run_accession']} — {r.get('instrument_model', 'N/A')}, "
          f"{int(r.get('read_count', 0)):,} reads")
# ERR123456 — Illumina HiSeq 2000, 45,231,890 reads

# Count total results without fetching data
count_resp = ena_query("count", params={
    "result": "read_run",
    "query": 'study_accession="PRJEB1787"',
})
print(f"Total runs: {count_resp.text.strip()}")
# Total runs: 142

Module 2: Browser API Retrieval

Fetch individual records by accession in multiple formats: XML, FASTA, EMBL flat-file, or plain text.

# Retrieve XML metadata for a study
resp = ena_query("xml/PRJEB1787", base=BASE_BROWSER)
print(resp.text[:300])
# <?xml version="1.0" encoding="UTF-8"?><PROJECT_SET>...

# Retrieve FASTA sequence for a coding sequence
resp = ena_query("fasta/M10051.1", base=BASE_BROWSER)
print(resp.text[:200])
# >ENA|M10051|M10051.1 Human insulin mRNA, complete cds.
# AGCCCTCCAGGACAGGCTGCAT...

# Retrieve EMBL flat-file format
resp = ena_query("embl/M10051.1", base=BASE_BROWSER)
print(resp.text[:300])
# ID   M10051; SV 1; linear; mRNA; STD; HUM; 786 BP.
# ...

Module 3: File Reports and Downloads

Get download URLs for FASTQ, submitted, and analysis files. File reports return FTP and Aspera paths.

# Get FASTQ file URLs for specific runs
resp = ena_query("filereport", params={
    "accession": "ERR000589",
    "result": "read_run",
    "fields": "run_accession,fastq_ftp,fastq_bytes,fastq_md5",
    "format": "json",
})
files = resp.json()
for f in files:
    ftp_urls = f.get("fastq_ftp", "").split(";")
    sizes = f.get("fastq_bytes", "").split(";")
    for url, size in zip(ftp_urls, sizes):
        if url:
            print(f"ftp://{url}  ({int(size)/1e6:.1f} MB)")
# ftp://ftp.sra.ebi.ac.uk/vol1/fastq/ERR000/ERR000589/ERR000589_1.fastq.gz  (234.5 MB)
# ftp://ftp.sra.ebi.ac.uk/vol1/fastq/ERR000/ERR000589/ERR000589_2.fastq.gz  (241.2 MB)

Module 4: Taxonomy Queries

Look up organisms by taxonomy ID, scientific name, or partial name match.

# Lookup by taxonomy ID
resp = ena_query("tax-id/9606", base=BASE_TAXONOMY)
tax = resp.json()
print(f"{tax['scientificName']} (taxId: {tax['taxId']}, rank: {tax['rank']})")
# Homo sapiens (taxId: 9606, rank: species)
print(f"Lineage: {tax['lineage'][:80]}...")

# Search by scientific name
resp = ena_query("scientific-name/Arabidopsis thaliana", base=BASE_TAXONOMY)
result = resp.json()
print(f"Tax ID: {result['taxId']}, Common: {result.get('commonName', 'N/A')}")
# Tax ID: 3702, Common: thale cress

# Suggest organisms by partial name
resp = ena_query("suggest-for-search/salmo", base=BASE_TAXONOMY)
suggestions = resp.json()
for s in suggestions[:3]:
    print(f"  {s['scientificName']} (taxId: {s['taxId']})")
# Salmo salar (taxId: 8030)
# Salmo trutta (taxId: 8032)
# Salmonella enterica (taxId: 28901)

Module 5: Cross-Reference Service

Find links between ENA records and external databases (ArrayExpress, UniProt, PDB, etc.).

# Find cross-references for an ENA accession
resp = ena_query("json/search", base=BASE_XREF, params={
    "accession": "M10051",
})
xrefs = resp.json()
for x in xrefs[:5]:
    print(f"  {x['Source']} → {x['Source Primary Accession']} "
          f"({x.get('Source Description', '')[:50]})")
# UniProt → P01308 (Insulin precursor)
# PDB → 1A7F (Crystal structure of human insulin)

# Search cross-references by external database
resp = ena_query("json/search", base=BASE_XREF, params={
    "source": "UniProt",
    "accession": "P01308",
})
xrefs = resp.json()
for x in xrefs[:3]:
    print(f"  ENA: {x['Target Primary Accession']} — {x.get('Target Description', '')[:60]}")

Module 6: CRAM Reference Registry

Retrieve reference sequences used in CRAM files by MD5 or SHA1 checksum. Essential for CRAM decompression.

# Look up reference by MD5 checksum
md5 = "aef131c3b4b05d8e2b3f907faba5af9b"  # example
try:
    resp = ena_query(
        f"cram/md5/{md5}",
        base="https://www.ebi.ac.uk/ena/cram"
    )
    print(f"Reference found: {len(resp.content)} bytes")
except requests.HTTPError as e:
    if e.response.status_code == 404:
        print("Reference not found — check MD5 checksum")
    else:
        raise

Key Concepts

ENA Data Hierarchy

Query Syntax Operators

Result Types

Discoverable Fields

Use the returnFields endpoint to discover available fields for any result type:

resp = ena_query("returnFields", params={"result": "read_run"})
fields = resp.text.strip().split("\n")
print(f"Available fields for read_run: {len(fields)}")
print(fields[:10])
# ['accession', 'altitude', 'assembly_quality', 'assembly_software', ...]

Common Workflows

Workflow 1: Study Exploration Pipeline

Search for a study, list its samples, then retrieve run metadata.

import json

# Step 1: Find studies by keyword
resp = ena_query("search", params={
    "result": "study",
    "query": 'study_title="*SARS-CoV-2*" AND first_public>=2023-01-01',
    "fields": "study_accession,study_title,center_name",
    "format": "json",
    "limit": 3,
})
studies = resp.json()
study_acc = studies[0]["study_accession"]
print(f"Selected: {study_acc} — {studies[0]['study_title'][:60]}")

# Step 2: List samples in the study
resp = ena_query("search", params={
    "result": "sample",
    "query": f'study_accession="{study_acc}"',
    "fields": "sample_accession,scientific_name,collection_date",
    "format": "json",
    "limit": 5,
})
samples = resp.json()
print(f"Found {len(samples)} samples (showing first 5)")
for s in samples:
    print(f"  {s['sample_accession']} — {s.get('scientific_name', 'N/A')}")

# Step 3: Get run metadata for each sample
for s in samples[:2]:
    resp = ena_query("search", params={
        "result": "read_run",
        "query": f'sample_accession="{s["sample_accession"]}"',
        "fields": "run_accession,instrument_model,read_count,library_strategy",
        "format": "json",
    })
    runs = resp.json()
    for r in runs:
        print(f"  {r['run_accession']}: {r.get('library_strategy','N/A')}, "
              f"{int(r.get('read_count',0)):,} reads")
    time.sleep(0.02)

Workflow 2: Bulk FASTQ Download URL Collection

Search for runs matching criteria and collect download URLs.

# Step 1: Search for Illumina RNA-Seq runs from a specific organism
resp = ena_query("search", params={
    "result": "read_run",
    "query": ('tax_tree(10090) AND library_strategy="RNA-Seq" '
              'AND instrument_platform="ILLUMINA" AND read_count>=10000000'),
    "fields": "run_accession,study_accession,read_count",
    "format": "json",
    "limit": 10,
})
runs = resp.json()
print(f"Found {len(runs)} runs meeting criteria")

# Step 2: Get file reports with download URLs
download_list = []
for run in runs[:5]:
    acc = run["run_accession"]
    resp = ena_query("filereport", params={
        "accession": acc,
        "result": "read_run",
        "fields": "run_accession,fastq_ftp,fastq_bytes,fastq_md5",
        "format": "json",
    })
    for f in resp.json():
        urls = f.get("fastq_ftp", "").split(";")
        md5s = f.get("fastq_md5", "").split(";")
        for url, md5 in zip(urls, md5s):
            if url:
                download_list.append({"url": f"ftp://{url}", "md5": md5, "run": acc})
    time.sleep(0.02)

print(f"\nDownload list: {len(download_list)} files")
for d in download_list[:4]:
    print(f"  {d['run']}: {d['url'].split('/')[-1]}")

Workflow 3: Taxonomic Assembly Exploration

Find organisms, search their assemblies, and check quality statistics.

# Step 1: Find organism by partial name
resp = ena_query("suggest-for-search/drosophila", base=BASE_TAXONOMY)
taxa = resp.json()
target_tax = None
for t in taxa[:5]:
    print(f"  {t['scientificName']} (taxId: {t['taxId']})")
    if t["scientificName"] == "Drosophila melanogaster":
        target_tax = t["taxId"]
print(f"\nSelected: taxId={target_tax}")

# Step 2: Search assemblies for this organism
resp = ena_query("search", params={
    "result": "assembly",
    "query": f'tax_eq({target_tax}) AND assembly_level="chromosome"',
    "fields": ("assembly_accession,assembly_name,assembly_level,"
               "genome_representation,n50,total_length"),
    "format": "json",
    "limit": 5,
})
assemblies = resp.json()
for a in assemblies:
    n50 = int(a.get("n50", 0))
    size = int(a.get("total_length", 0))
    print(f"  {a['assembly_accession']}: {a.get('assembly_name','N/A')}, "
          f"N50={n50:,}, Size={size/1e6:.1f} Mb")
# GCA_000001215.4: Release 6 plus ISO1 MT, N50=25,286,936, Size=143.7 Mb

Key Parameters

Best Practices

1. Use tax_tree() over tax_eq() for organism queries — it includes subspecies and strains automatically
2. Request only needed fields — reduces response size and server load significantly
3. Prefer JSON format for programmatic access; TSV for large bulk exports (lower overhead)
4. Use limit=0 for streaming large result sets directly to file, avoiding memory issues
5. Check fastq_ftp and submitted_ftp — some runs have submitted files but no processed FASTQ
6. Verify downloads with MD5 — file reports include fastq_md5 for integrity checking
7. Anti-pattern: Do not fetch all fields then filter client-side — use query syntax server-side

Common Recipes

Recipe: Assembly Quality Filtering

# Find high-quality chromosome-level assemblies
resp = ena_query("search", params={
    "result": "assembly",
    "query": ('tax_tree(7742) AND assembly_level="chromosome" '
              'AND genome_representation="full"'),
    "fields": "assembly_accession,scientific_name,n50,total_length,assembly_level",
    "format": "json",
    "limit": 10,
})
for a in resp.json():
    n50 = int(a.get("n50", 0))
    if n50 > 1_000_000:  # N50 > 1 Mb
        print(f"{a['assembly_accession']}: {a['scientific_name']}, N50={n50:,}")

Recipe: Cross-Database Linking

# Find UniProt/PDB cross-references for an ENA sequence
resp = ena_query("json/search", base=BASE_XREF, params={
    "accession": "M10051",
})
xrefs = resp.json()
by_source = {}
for x in xrefs:
    src = x.get("Source", "unknown")
    by_source.setdefault(src, []).append(x["Source Primary Accession"])
for src, accs in by_source.items():
    print(f"  {src}: {', '.join(accs[:5])}")
# UniProt: P01308
# PDB: 1A7F, 1AI0, 1BEN

Recipe: Retry Session with Exponential Backoff

from requests.adapters import HTTPAdapter
from urllib3.util.retry import Retry

def ena_session():
    """Create a requests session with retry logic for ENA APIs."""
    session = requests.Session()
    retry = Retry(
        total=3, backoff_factor=1.0,
        status_forcelist=[429, 500, 502, 503, 504],
    )
    session.mount("https://", HTTPAdapter(max_retries=retry))
    return session

session = ena_session()
resp = session.get(f"{BASE_PORTAL}/search", params={
    "result": "study",
    "query": 'tax_tree(9606)',
    "fields": "study_accession",
    "format": "json",
    "limit": 5,
})
print(f"Status: {resp.status_code}, results: {len(resp.json())}")

Troubleshooting

Bundled Resources

This skill is self-contained. The original entry had a separate references/api_reference.md (490 lines) covering all 6 API endpoints in detail. That content has been fully consolidated inline:

• Portal API (search, count, returnFields, filereport) — Core API Modules 1 and 3, Key Parameters table
• Browser API (XML, FASTA, EMBL retrieval) — Core API Module 2
• Taxonomy REST API (tax-id, scientific-name, suggest-for-search) — Core API Module 4
• Cross-Reference Service (json/search) — Core API Module 5
• CRAM Reference Registry (md5/sha1 lookup) — Core API Module 6
• Rate limiting and error handling — Prerequisites, Troubleshooting, Recipe 3 (retry session)
• Query syntax and result types — Key Concepts section (3 tables)
• Pagination and bulk download — Key Parameters, Best Practices, Workflow 2
• Omitted: detailed EMBL format field-by-field breakdown (rarely needed programmatically); Aspera download command examples (tool-specific, not requests-based)

Related Skills

• bioservices-multi-database — unified Python interface covering ENA via bioservices; prefer for multi-database workflows
• pubmed-database — PubMed literature search via NCBI E-utilities
• pysam-genomic-files — downstream processing of FASTQ/BAM/CRAM files retrieved from ENA
• biopython-molecular-biology — NCBI Entrez access and sequence parsing (GenBank/FASTA)
• ncbi-blast (planned) — BLAST sequence similarity search

References

• ENA Portal API docs: https://www.ebi.ac.uk/ena/portal/api/doc
• ENA Browser API docs: https://www.ebi.ac.uk/ena/browser/api/doc
• ENA Taxonomy REST API: https://www.ebi.ac.uk/ena/taxonomy/rest/
• ENA Cross-Reference Service: https://www.ebi.ac.uk/ena/xref/rest/
• ENA data model guide: https://ena-docs.readthedocs.io/en/latest/submit/general-guide/data-model.html
• INSDC standards: https://www.insdc.org/