/bio-entrez-fetch

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--- name: bio-entrez-fetch description: Retrieve records from NCBI databases using Biopython Bio.Entrez. Use when downloading sequences, fetching GenBank records, getting document summaries, or parsing NCBI data into Biopython objects. 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

---

Entrez Fetch

Retrieve records from NCBI databases using Biopython's Entrez module (EFetch, ESummary utilities).

Required Setup

from Bio import Entrez

Entrez.email = 'your.email@example.com'  # Required by NCBI
Entrez.api_key = 'your_api_key'          # Optional, raises rate limit 3->10 req/sec

Core Functions

Entrez.efetch() - Retrieve Full Records

Fetch complete records in various formats from any NCBI database.

# Fetch GenBank record by ID
handle = Entrez.efetch(db='nucleotide', id='NM_007294', rettype='gb', retmode='text')
genbank_text = handle.read()
handle.close()

# Fetch FASTA sequence
handle = Entrez.efetch(db='nucleotide', id='NM_007294', rettype='fasta', retmode='text')
fasta_text = handle.read()
handle.close()

# Fetch multiple records
handle = Entrez.efetch(db='nucleotide', id='NM_007294,NM_000059', rettype='fasta', retmode='text')

**Key Parameters:** | Parameter | Description | Example | |-----------|-------------|---------| | `db` | Database name | `'nucleotide'`, `'protein'`, `'pubmed'` | | `id` | Record ID(s) | `'NM_007294'` or `'123,456,789'` | | `rettype` | Return type | `'fasta'`, `'gb'`, `'abstract'` | | `retmode` | Return mode | `'text'`, `'xml'` | | `retstart` | Start index | `0` | | `retmax` | Max records | `20` | | `WebEnv` | History server session | From esearch | | `query_key` | History server query | From esearch |

Common Return Types by Database

**Nucleotide/Protein:** | rettype | retmode | Description | |---------|---------|-------------| | `'fasta'` | `'text'` | FASTA sequence | | `'gb'` | `'text'` | GenBank flat file | | `'gp'` | `'text'` | GenPept flat file (protein) | | `'gbwithparts'` | `'text'` | GenBank with contig sequences | | `'seqid'` | `'text'` | Seq-id only | | `'acc'` | `'text'` | Accession only |

**PubMed:** | rettype | retmode | Description | |---------|---------|-------------| | `'abstract'` | `'text'` | Abstract text | | `'medline'` | `'text'` | MEDLINE format | | `'xml'` | `'xml'` | Full PubMed XML |

**Gene:** | rettype | retmode | Description | |---------|---------|-------------| | `'gene_table'` | `'text'` | Gene table format | | `'xml'` | `'xml'` | Full gene XML |

Entrez.esummary() - Document Summaries

Get brief summaries without downloading full records. Faster than efetch.

# Get summary for nucleotide record
handle = Entrez.esummary(db='nucleotide', id='NM_007294')
record = Entrez.read(handle)
handle.close()

summary = record[0]  # First (only) record
print(f"Title: {summary['Title']}")
print(f"Length: {summary['Length']}")
print(f"Organism: {summary['Organism']}")

**Common Summary Fields:**

# Nucleotide/Protein
summary['Title']          # Record title/description
summary['Caption']        # Short identifier
summary['Length']         # Sequence length
summary['Organism']       # Source organism
summary['TaxId']          # Taxonomy ID
summary['AccessionVersion']  # Full accession.version

# PubMed
summary['Title']          # Article title
summary['AuthorList']     # Authors
summary['Source']         # Journal
summary['PubDate']        # Publication date
summary['DOI']            # Digital Object Identifier

Parsing with Biopython

Parse into SeqRecord Objects

from Bio import Entrez, SeqIO

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

# Parse GenBank into SeqRecord
handle = Entrez.efetch(db='nucleotide', id='NM_007294', rettype='gb', retmode='text')
record = SeqIO.read(handle, 'genbank')
handle.close()

print(f"ID: {record.id}")
print(f"Length: {len(record.seq)}")
print(f"Features: {len(record.features)}")

# Parse FASTA into SeqRecord
handle = Entrez.efetch(db='nucleotide', id='NM_007294', rettype='fasta', retmode='text')
record = SeqIO.read(handle, 'fasta')
handle.close()

Parse Multiple Records

# Fetch multiple as FASTA
handle = Entrez.efetch(db='nucleotide', id='NM_007294,NM_000059,NM_000546', rettype='fasta', retmode='text')
records = list(SeqIO.parse(handle, 'fasta'))
handle.close()

for record in records:
    print(f"{record.id}: {len(record.seq)} bp")

Parse XML with Entrez.read()

# For structured data, use XML mode
handle = Entrez.efetch(db='gene', id='672', retmode='xml')
records = Entrez.read(handle)
handle.close()

# Navigate nested structure
gene = records[0]
print(f"Gene: {gene['Entrezgene_gene']['Gene-ref']['Gene-ref_locus']}")

Code Patterns

Fetch Sequence by Accession

from Bio import Entrez, SeqIO

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

def fetch_sequence(accession, db='nucleotide'):
    handle = Entrez.efetch(db=db, id=accession, rettype='fasta', retmode='text')
    record = SeqIO.read(handle, 'fasta')
    handle.close()
    return record

seq = fetch_sequence('NM_007294')
print(f"{seq.id}: {seq.seq[:50]}...")

Fetch GenBank with Features

def fetch_genbank(accession):
    handle = Entrez.efetch(db='nucleotide', id=accession, rettype='gb', retmode='text')
    record = SeqIO.read(handle, 'genbank')
    handle.close()
    return record

gb = fetch_genbank('NM_007294')
for feature in gb.features:
    if feature.type == 'CDS':
        print(f"CDS: {feature.location}")
        pri