/bio-batch-downloads

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--- name: bio-batch-downloads description: Download large datasets from NCBI efficiently using history server, batching, and rate limiting. Use when performing bulk sequence downloads, handling large query results, or production-scale data retrieval. tool_type: python primary_tool: Bio.Entrez measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools:

• read_file
• run_shell_command

---

Batch Downloads

Download large numbers of records from NCBI efficiently using the history server, batching, and proper rate limiting.

Required Setup

from Bio import Entrez
import time

Entrez.email = 'your.email@example.com'  # Required by NCBI
Entrez.api_key = 'your_api_key'          # Recommended for large downloads

Rate Limits

| Authentication | Requests/Second | Delay Between | |---------------|-----------------|---------------| | Email only | 3 | 0.34 seconds | | Email + API key | 10 | 0.1 seconds |

Get an API key at: https://www.ncbi.nlm.nih.gov/account/settings/

History Server

The history server stores search results on NCBI servers, enabling efficient batch retrieval without re-sending large ID lists.

How It Works

1. Search with `usehistory='y'` 2. Get `WebEnv` (session ID) and `query_key` (result set ID) 3. Fetch results in batches using these identifiers 4. Results stay available for ~15 minutes

# Search with history
handle = Entrez.esearch(db='nucleotide', term='human[orgn] AND mRNA[fkey]', usehistory='y')
search = Entrez.read(handle)
handle.close()

webenv = search['WebEnv']
query_key = search['QueryKey']
total = int(search['Count'])

print(f"Found {total} records, stored in history")

Core Pattern: Batch Download

from Bio import Entrez, SeqIO
import time

Entrez.email = 'your.email@example.com'

def batch_download(db, term, output_file, rettype='fasta', batch_size=500):
    # Search with history
    handle = Entrez.esearch(db=db, term=term, usehistory='y')
    search = Entrez.read(handle)
    handle.close()

    webenv = search['WebEnv']
    query_key = search['QueryKey']
    total = int(search['Count'])

    print(f"Downloading {total} records...")

    with open(output_file, 'w') as out:
        for start in range(0, total, batch_size):
            print(f"  Fetching {start+1}-{min(start+batch_size, total)}...")

            handle = Entrez.efetch(
                db=db,
                rettype=rettype,
                retmode='text',
                retstart=start,
                retmax=batch_size,
                webenv=webenv,
                query_key=query_key
            )
            out.write(handle.read())
            handle.close()

            time.sleep(0.34)  # Rate limiting (no API key)

    print(f"Saved to {output_file}")

Code Patterns

Download All Search Results

from Bio import Entrez
import time

Entrez.email = 'your.email@example.com'
Entrez.api_key = 'your_api_key'  # Optional

def download_search_results(db, term, output_file, rettype='fasta', batch_size=500):
    # Search with history server
    handle = Entrez.esearch(db=db, term=term, usehistory='y', retmax=0)
    search = Entrez.read(handle)
    handle.close()

    webenv = search['WebEnv']
    query_key = search['QueryKey']
    total = int(search['Count'])

    if total == 0:
        print("No records found")
        return

    delay = 0.1 if Entrez.api_key else 0.34

    with open(output_file, 'w') as out:
        for start in range(0, total, batch_size):
            end = min(start + batch_size, total)
            print(f"Downloading {start+1}-{end} of {total}")

            attempts = 3
            for attempt in range(attempts):
                try:
                    handle = Entrez.efetch(db=db, rettype=rettype, retmode='text',
                                           retstart=start, retmax=batch_size,
                                           webenv=webenv, query_key=query_key)
                    out.write(handle.read())
                    handle.close()
                    break
                except Exception as e:
                    if attempt < attempts - 1:
                        print(f"  Retry {attempt+1}: {e}")
                        time.sleep(5)
                    else:
                        raise

            time.sleep(delay)

    print(f"Downloaded {total} records to {output_file}")

download_search_results('nucleotide', 'human[orgn] AND insulin[gene] AND mRNA[fkey]', 'insulin_mrna.fasta')

Download by ID List

def download_by_ids(db, ids, output_file, rettype='fasta', batch_size=200):
    total = len(ids)
    delay = 0.1 if Entrez.api_key else 0.34

    with open(output_file, 'w') as out:
        for start in range(0, total, batch_size):
            batch = ids[start:start+batch_size]
            print(f"Downloading {start+1}-{start+len(batch)} of {total}")

            handle = Entrez.efetch(db=db, id=','.join(batch), rettype=rettype, retmode='text')
            out.write(handle.read())
            handle.close()

            time.sleep(delay)

    print(f"Downloaded {total} records to {output_file}")

# Example with list of IDs
ids = ['NM_007294', 'NM_000059', 'NM_000546', 'NM_001126112', 'NM_004985']
download_by_ids('nucleotide', ids, 'genes.fasta')

Post IDs to History (EPost)

For very large ID lists, post them to the history server first:

def post_and_download(db, ids, output_file, rettype='fasta', batch_size=500):
    # Post IDs to history server
    handle = Entrez.epost(db=db, id=','.join(ids))
    result = Entrez.read(handle)
    handle.close()