Bio Core Biological Databases

Biological Databases

When to Use

• Deciding which database (NCBI, UniProt, PDB, Ensembl, GEO, SRA) holds the data you actually need
• Writing Bio.Entrez esearch/efetch/elink code to pull sequences, GenBank annotations, or PubMed abstracts
• Resolving accession types (NM_/NP_/XM_/GSE/SRR) and curated-vs-predicted status
• Fetching protein records from UniProt or 3D structures from PDB by accession/ID
• Chaining a lookup across databases: gene name -> RefSeq mRNA -> protein -> structure -> orthologs

Version Compatibility

• biopython >= 1.81, Python >= 3.10
• UniProt REST API (rest.uniprot.org), current JSON schema (2024+)
• RCSB PDB Search API v2 and Data API v1 (search.rcsb.org, data.rcsb.org)
• Ensembl REST API (rest.ensembl.org), Ensembl release 110+

Prerequisites

• pip install biopython
• stdlib only otherwise: urllib.request, json, urllib.parse
• Set Entrez.email (required by NCBI); get a free NCBI API key to raise rate limits
• Helpful follow-on skills: bio-sequence-manipulation-seq-objects, bio-structural-biology-structure-io

Database Selector

NCBI Accession Prefixes

GEO accessions: GSE (series/experiment), GSM (sample), GPL (platform), GDS (curated dataset). SRA hierarchy: Study (SRP) > Sample (SRS) > Experiment (SRX) > Run (SRR).

Goal: search, fetch, and cross-link NCBI records without exhausting rate limits. Approach: use Entrez.esearch/efetch/elink; always batch IDs and close handles.

from Bio import Entrez, SeqIO

Entrez.email = "your.email@example.com"  # required by NCBI
# Entrez.api_key = "..."                 # optional, raises limit 3 -> 10 req/s

def fetch_refseq_mrna(gene, organism="Homo sapiens"):
    """Find and fetch the top RefSeq mRNA GenBank record for a gene symbol."""
    handle = Entrez.esearch(
        db="nucleotide",
        term=f"{gene}[Gene] AND {organism}[Organism] AND RefSeq[Filter] AND mRNA[Filter]",
        retmax=1,
    )
    ids = Entrez.read(handle)["IdList"]
    handle.close()
    if not ids:
        return None
    handle = Entrez.efetch(db="nucleotide", id=ids[0], rettype="gb", retmode="text")
    record = SeqIO.read(handle, "genbank")
    handle.close()
    return record

record = fetch_refseq_mrna("insulin")  # NM_000207.3

# Extract CDS and translate directly from GenBank features
for feat in record.features:
    if feat.type == "CDS":
        cds_seq = feat.location.extract(record.seq)
        protein = cds_seq.translate(to_stop=True)
        annotated = feat.qualifiers.get("translation", [""])[0]

# Cross-database link: nucleotide -> protein
handle = Entrez.elink(dbfrom="nucleotide", db="protein", id="NM_000207.3")
link_results = Entrez.read(handle)
handle.close()

# Batch fetch (comma-separated IDs — much faster than looping efetch calls)
handle = Entrez.efetch(db="nucleotide", id="NM_000207.3,NM_001301717.2",
                        rettype="fasta", retmode="text")

Entrez search syntax: insulin[Gene], Homo sapiens[Organism], mRNA[Filter] AND RefSeq[Filter], 2020:2024[PDAT] (date range), CRISPR AND (cancer OR tumor).

Goal: pull a protein record and its structural cross-references from UniProt. Approach: hit the REST API directly with urllib — no extra dependency needed.

import json
import urllib.parse
import urllib.request


def fetch_uniprot(accession, fmt="json"):
    """Fetch one UniProt entry by accession (e.g. 'P01308' = human insulin)."""
    url = f"https://rest.uniprot.org/uniprotkb/{accession}.{fmt}"
    with urllib.request.urlopen(url) as r:
        return json.loads(r.read()) if fmt == "json" else r.read().decode()


def search_uniprot(query, limit=5):
    """Search UniProt; e.g. query='hemoglobin AND organism_id:9606 AND reviewed:true'."""
    encoded = urllib.parse.quote(query)
    url = f"https://rest.uniprot.org/uniprotkb/search?query={encoded}&size={limit}&format=json"
    with urllib.request.urlopen(url) as r:
        return json.loads(r.read())


insulin = fetch_uniprot("P01308")
name = insulin["proteinDescription"]["recommendedName"]["fullName"]["value"]
seq = insulin["sequence"]["value"]

# Cross-references to PDB structures embedded in the UniProt record
pdb_refs = [r for r in insulin.get("uniProtKBCrossReferences", []) if r["database"] == "PDB"]

Goal: fetch and parse a 3D structure once you have a PDB ID (e.g. from the UniProt cross-refs above). Approach: RCSB search API for discovery, files.rcsb.org for the actual coordinate file.

def download_pdb(pdb_id, fmt="pdb"):
    """Download a structure file from RCSB PDB ('pdb' or 'cif' format)."""
    ext = "pdb" if fmt == "pdb" else "cif"
    url = f"https://files.rcsb.org/download/{pdb_id}.{ext}"
    filename = f"{pdb_id}.{ext}"
    urllib.request.urlretrieve(url, filename)
    return filename

path = download_pdb("4INS")  # classic insulin hexamer crystal structure

Pitfalls

• GenBank vs RefSeq: GenBank has all submitted sequences (redundant, unreviewed); RefSeq is curated. Prefer RefSeq (NM_/NP_) for analysis
• SwissProt vs TrEMBL: SwissProt (570K entries, manually curated) vs TrEMBL (250M, auto-annotated); filter with reviewed:true for reliable annotations
• Rate limits: NCBI allows 3 req/s without an API key, 10/s with one (Entrez.api_key = "..."); batch with comma-separated IDs, never loop individual efetch calls
• GI numbers deprecated: use accession.version (NM_000207.3), not numeric GI IDs
• efetch handle must be closed: always handle.close() (or use with) — unclosed handles exhaust NCBI connections
• id as a list breaks efetch: pass a comma-separated string, not a Python list — a common LLM-generated bug
• PDB resolution matters: structures worse than ~3.5 Å only give reliable overall fold, not side-chain/atomic detail

See Also

• bio-database-access-entrez-search, bio-database-access-entrez-fetch, bio-database-access-entrez-link
• bio-database-access-uniprot-access
• bio-structural-biology-structure-io
• bio-database-access-geo-data, bio-database-access-sra-data