bio-entrez-link

name	bio-entrez-link
description	Find cross-database references between NCBI databases using Biopython Bio.Entrez (ELink). Use when navigating gene to protein/structure, sequence to publication, PubMed to GEO, BioProject to SRA runs, or discovering all link relationships for a record. Covers linkname semantics, cmd= variants, asymmetric link warnings, neighbor_history for >200 input IDs, and per-database link tables.
origin	openai4s
category	bioskills/database-access
metadata	{"tool_type":"python","primary_tool":"Bio.Entrez","third_party":{"name":"GPTomics/bioSkills","repository":"https://github.com/GPTomics/bioSkills","commit":"d91ed3d563019e649dc854c56ccd62551359488a","license":"MIT"}}

Version Compatibility

Reference examples tested with: BioPython 1.83+, Entrez Direct 21.0+

Before using code patterns, verify installed versions match. If versions differ:

• Python: pip show biopython then help(Bio.Entrez.elink) to check signatures
• CLI: elink -version then elink -help to confirm flags

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Entrez Link

"Find records linked to this record in another NCBI database" -> ELink walks the curated, weekly-maintained link tables between Entrez databases. A link is an asserted relationship (e.g. "this PubMed article describes this nucleotide sequence"), not a similarity hit.

ELink is the navigation layer of Entrez. The decision that matters most is which linkname to use — not which databases. A single (dbfrom, db) pair can have a dozen linkname variants distinguishing curation level, evidence type, and direction. Picking the wrong one is the difference between 5 high-confidence matches and 500 noisy automated assertions.

• Python: Entrez.elink(dbfrom=..., db=..., id=..., linkname=...) (BioPython)
• CLI: elink -db pubmed -target gene -name pubmed_gene_rif (Entrez Direct)
• R: entrez_link(dbfrom=..., db=..., id=...) (rentrez)

Required Setup

from Bio import Entrez
Entrez.email = '[e-mail]'
Entrez.api_key = 'optional_api_key'  # raises rate to 10 req/sec

The linkname decision (most important)

For most (dbfrom, db) pairs NCBI exposes multiple link tables. The qualifiers in the name encode the curation level and the evidence source. Choose deliberately.

gene -> protein (representative example)

linkname	Returns	When to use
gene_protein	All linked proteins (curated + automated)	Exploration; expect 10-1000x more hits
gene_protein_refseq	RefSeq proteins only	Reference-quality analyses; orthology
gene_protein_swissprot	Reviewed UniProt entries with NCBI cross-ref	Functional annotation; literature support

pubmed -> gene

linkname	Returns
pubmed_gene	Genes mentioned in this paper (text-mined + curated)
pubmed_gene_rif	Genes with a Reference Into Function (curated, high-quality)
pubmed_gene_pubmed	Other PubMed records sharing gene linkage (rare use)

nucleotide -> protein

linkname	Returns
nuccore_protein	All proteins encoded by this nucleotide record (CDS-linked)
nuccore_protein_refseq	RefSeq proteins only

Discover what link names exist for a pair

h = Entrez.elink(dbfrom='gene', db='protein', id='672', cmd='acheck')
record = Entrez.read(h); h.close()
for ls in record[0]['IdCheckList']['IdLinkSet'][0]['LinkInfo']:
    print(f'{ls["Name"]}  -> {ls["DbTo"]} | {ls["MenuTag"]} ({ls["HtmlTag"]})')

cmd='acheck' is the only authoritative way to enumerate available linknames — they change with each NCBI release.

Decision table: which cmd for which goal

Goal	cmd	Returns
Get linked records	neighbor (default)	Linked IDs in target db
Get linked + relevance scores	neighbor_score	IDs with similarity scores (mostly pubmed_pubmed)
Get >200 source IDs in one go	neighbor_history	WebEnv + QueryKey for downstream EFetch
Enumerate available links	acheck	List of all linknames for source IDs
Check if any link exists	ncheck	Boolean per source ID
Check specific link exists	lcheck	Boolean per source ID + linkname
Get NCBI HTML link URLs	llinks	URLs to Entrez record pages
Get external provider links	prlinks	URLs to journal sites, etc.

The neighbor_history cmd is essential when source id count exceeds ~200 — past that, the URL-length limit makes the comma-joined form fail. With neighbor_history ELink puts results on the history server and returns WebEnv/QueryKey for downstream pickup.

Asymmetric link warning

ELink relationships are not guaranteed symmetric. pubmed_gene and gene_pubmed may return different sets because:

• Direction-dependent curation: gene-to-PubMed is curated by NCBI staff (GeneRIF); PubMed-to-gene includes text-mining.
• Cutoffs: some link tables truncate at N best links in one direction but not the other.
• Index lag asymmetry: when one db updates faster than the other.

If round-trip consistency matters (e.g. "every gene mentioned in this paper, then every paper mentioning each gene"), expect the round-trip set to be larger than the input — and never assume A -> B -> A returns the original ID alone.

Per-database link catalog (curated subset)

gene

Target	Common linknames	Notes
protein	gene_protein, gene_protein_refseq, gene_protein_swissprot	RefSeq is the safe default
nuccore	gene_nuccore, gene_nuccore_refseqrna, gene_nuccore_refseqgene	refseqrna for mRNA, refseqgene for the curated gene region
pubmed	gene_pubmed, gene_pubmed_rif	RIF is curated and high-quality
homologene	gene_homologene	Deprecated 2014 but data still queryable
snp	gene_snp	dbSNP entries in gene region
clinvar	gene_clinvar	Clinical variants
omim	gene_omim	Disease associations

nuccore / nucleotide

Target	Common linknames
protein	nuccore_protein, nuccore_protein_refseq
gene	nuccore_gene
taxonomy	nuccore_taxonomy
biosample	nuccore_biosample
sra	nuccore_sra
pubmed	nuccore_pubmed, nuccore_pubmed_refseq

protein

Target	Common linknames
nuccore	protein_nuccore, protein_nuccore_cds, protein_nuccore_mrna
gene	protein_gene
structure	protein_structure
cdd	protein_cdd (conserved domains)
pubmed	protein_pubmed

pubmed

Target	Common linknames
pubmed	pubmed_pubmed, pubmed_pubmed_citedin, pubmed_pubmed_refs
gene	pubmed_gene, pubmed_gene_rif
protein	pubmed_protein
nuccore	pubmed_nuccore
gds	pubmed_gds (GEO datasets cited in paper)
sra	pubmed_sra

bioproject

Target	Common linknames
biosample	bioproject_biosample
sra	bioproject_sra
pubmed	bioproject_pubmed

Code patterns

Single source -> single target

Goal: Get RefSeq proteins for a single gene.

Approach: ELink with explicit linkname to restrict to curated set.

Reference (BioPython 1.83+):

def gene_to_refseq_proteins(gene_id):
    h = Entrez.elink(dbfrom='gene', db='protein', id=gene_id, linkname='gene_protein_refseq')
    r = Entrez.read(h); h.close()
    if not r[0]['LinkSetDb']:
        return []
    return [link['Id'] for link in r[0]['LinkSetDb'][0]['Link']]

print(gene_to_refseq_proteins('672'))  # BRCA1

Batch source -> target (small batch)

Goal: Get linked proteins for a list of <200 gene IDs in one call.

Approach: Comma-join IDs; one linkset per input in the response.

Reference (BioPython 1.83+):

def batch_gene_protein(gene_ids):
    h = Entrez.elink(dbfrom='gene', db='protein', id=','.join(gene_ids), linkname='gene_protein_refseq')
    r = Entrez.read(h); h.close()
    out = {}
    for linkset in r:
        src = linkset['IdList'][0]
        out[src] = [link['Id'] for link in linkset['LinkSetDb'][0]['Link']] if linkset['LinkSetDb'] else []
    return out

Large batch via history server

Goal: Link 5,000 gene IDs to proteins without hitting URL-length limits.

Approach: EPost the IDs first (chunked at 200), then ELink with cmd='neighbor_history' referencing the WebEnv. Downstream EFetch picks up linked IDs from the history server.

Reference (BioPython 1.83+):

def post_then_link(gene_ids, target='protein', linkname='gene_protein_refseq'):
    # EPost in chunks of 200
    webenv = None
    for i in range(0, len(gene_ids), 200):
        chunk = gene_ids[i:i+200]
        kwargs = {'db': 'gene', 'id': ','.join(chunk)}
        if webenv:
            kwargs['WebEnv'] = webenv
        h = Entrez.epost(**kwargs)
        r = Entrez.read(h); h.close()
        webenv = r['WebEnv']
        query_key = r['QueryKey']
        time.sleep(0.1 if Entrez.api_key else 0.34)

    # Link with neighbor_history
    h = Entrez.elink(dbfrom='gene', db=target, linkname=linkname,
                     cmd='neighbor_history', WebEnv=webenv, query_key=query_key)
    r = Entrez.read(h); h.close()
    # WebEnv is at the top level of the response; QueryKey is per-LinkSetDbHistory entry.
    return r[0]['WebEnv'], r[0]['LinkSetDbHistory'][0]['QueryKey']

we, qk = post_then_link(['672', '675', '7157'] * 1000)
# Downstream: Entrez.efetch(db='protein', WebEnv=we, query_key=qk, retstart=..., retmax=500)

Discover all available links

Goal: Before writing a pipeline, enumerate what link tables NCBI exposes for a (dbfrom, source-id) pair.

Approach: cmd='acheck' returns the full LinkInfo list per source.

Reference (BioPython 1.83+):

def list_link_names(dbfrom, id):
    h = Entrez.elink(dbfrom=dbfrom, id=id, cmd='acheck')
    r = Entrez.read(h); h.close()
    info = r[0]['IdCheckList']['IdLinkSet'][0]['LinkInfo']
    return [(i['Name'], i['DbTo'], i['MenuTag']) for i in info]

for name, target, label in list_link_names('gene', '672'):
    print(f'{name:<40} -> {target:<15} ({label})')

Chain links (gene -> protein -> structure)

def gene_to_structures(gene_id):
    h = Entrez.elink(dbfrom='gene', db='protein', id=gene_id, linkname='gene_protein_refseq')
    r = Entrez.read(h); h.close()
    if not r[0]['LinkSetDb']:
        return []
    prot_ids = [l['Id'] for l in r[0]['LinkSetDb'][0]['Link'][:10]]
    time.sleep(0.1 if Entrez.api_key else 0.34)
    h = Entrez.elink(dbfrom='protein', db='structure', id=','.join(prot_ids))
    r = Entrez.read(h); h.close()
    out = []
    for ls in r:
        if ls['LinkSetDb']:
            out.extend(l['Id'] for l in ls['LinkSetDb'][0]['Link'])
    return out

Get neighbor_score for related PubMed articles

def related_pubmed(pmid, top=10):
    h = Entrez.elink(dbfrom='pubmed', db='pubmed', id=pmid,
                     linkname='pubmed_pubmed', cmd='neighbor_score')
    r = Entrez.read(h); h.close()
    if not r[0]['LinkSetDb']:
        return []
    return [(l['Id'], int(l['Score'])) for l in r[0]['LinkSetDb'][0]['Link'][:top]]

BioProject -> SRA runs

For SRA discovery, pysradb.SRAweb().sra_metadata(prjna, detailed=True) (see sra-data) is the higher-fidelity path — returns SRR accessions directly with run-level metadata in one call. Use ELink only when staying inside Bio.Entrez:

def bioproject_to_sra(prjna):
    # Convert PRJNA to UID first
    h = Entrez.esearch(db='bioproject', term=f'{prjna}[BioProject]')
    r = Entrez.read(h); h.close()
    if not r['IdList']:
        return []
    bp_uid = r['IdList'][0]
    time.sleep(0.1 if Entrez.api_key else 0.34)
    # Link to SRA
    h = Entrez.elink(dbfrom='bioproject', db='sra', id=bp_uid)
    r = Entrez.read(h); h.close()
    return [l['Id'] for l in r[0]['LinkSetDb'][0]['Link']] if r[0]['LinkSetDb'] else []

Failure modes

Wrong linkname gives wrong order of magnitude

• Trigger: Using gene_protein when gene_protein_refseq was intended.
• Mechanism: gene_protein includes all automated and predicted entries (XP_* RefSeq plus all GenBank submissions).
• Symptom: 500 proteins returned per gene instead of the expected 1-5 canonical isoforms.
• Fix: Pick the curated linkname; verify counts on a known gene.

Empty LinkSetDb on valid input

• Trigger: Gene with no linked records in the requested target.
• Mechanism: record[0]['LinkSetDb'] is an empty list, not raising an error.
• Symptom: KeyError if code assumes record[0]['LinkSetDb'][0] always exists.
• Fix: Always guard if not record[0]['LinkSetDb']: return [].

Asymmetric round-trip

• Trigger: Pipeline does genes_for_paper(pmid) -> papers_for_each_gene -> set of PMIDs.
• Mechanism: pubmed_gene (text-mined + curated) is larger than gene_pubmed (curated only); the round-trip set is not closed.
• Symptom: Original PMID may not appear in the round-trip set; new PMIDs do.
• Fix: Document the directional asymmetry; use the more-curated linkname (*_rif variants) when fidelity matters.

URL length limit on large batches

• Trigger: Comma-joined id= with 200+ IDs.
• Mechanism: HTTP GET URL exceeds NCBI's parsing limit (~2000 chars).
• Symptom: HTTP 414 URI Too Long, or silent truncation.
• Fix: EPost the IDs first, then ELink with cmd='neighbor_history'.

One linkset per input ID, indexing confusion

• Trigger: Sending 5 IDs, then accessing record[0]['LinkSetDb'][0]['Link'] expecting the union.
• Mechanism: ELink returns one LinkSet per input UID, indexed by position.
• Symptom: Only the first input's links are processed; rest are dropped.
• Fix: Iterate for linkset in record: and map by linkset['IdList'][0].

Mismatched dbfrom and id namespace

• Trigger: Passing a PMID into dbfrom='nucleotide'.
• Mechanism: ELink returns no error — it just looks up the PMID as a nucleotide UID, finds nothing.
• Symptom: Empty LinkSetDb on a "valid" ID.
• Fix: Validate that the ID matches the source db namespace (PMIDs are db=pubmed, GeneIDs are db=gene).

Common errors

Error / symptom	Cause	Solution
KeyError: 'LinkSetDb'	Empty result not guarded	if not record[0]['LinkSetDb']: return []
HTTPError 414	Comma-joined id too long	Use EPost + neighbor_history
HTTPError 400	Invalid linkname or wrong db namespace	Use cmd='acheck' to enumerate valid links
500 hits instead of 5	Wrong linkname (e.g. gene_protein vs _refseq)	Pick curated variant
Round-trip set differs from input	Asymmetric link tables	Document; use curated variants

References

• Sayers EW et al. (2024) Database resources of the National Center for Biotechnology Information in 2024. Nucleic Acids Res 52:D33-D43.
• Kans J. (2024) Entrez Direct: E-utilities on the Unix Command Line. NCBI Bookshelf NBK179288.
• NCBI. ELink help. NBK25499.

Related Skills

• entrez-search - Resolve UIDs before linking
• entrez-fetch - Retrieve linked records' content
• batch-downloads - History-server retrieval after ELink with neighbor_history
• geo-data - Specialized gds <-> pubmed/bioproject links (gds->sra ELink unreliable; use pysradb)
• ncbi-datasets-cli - Modern alternative for gene/genome cross-reference queries