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.esearch) to check signatures
• CLI: esearch -version then esearch -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 Search

"Find NCBI records matching a query" -> ESearch returns matching record UIDs (not full records) from one NCBI database; EGQuery returns counts across all databases; EInfo describes a database's searchable fields and update timestamp.

The single most important fact: ESearch returns UIDs (PMIDs, GI numbers, gene IDs, etc.), not records. To get content the agent must call EFetch or ESummary. Forgetting this is the most common Entrez mistake.

• Python: Entrez.esearch(db=..., term=...) (BioPython)
• CLI: esearch -db pubmed -query 'CRISPR[Title]' (Entrez Direct, NBK179288)
• R: entrez_search(db=..., term=...) (rentrez)

Required Setup

from Bio import Entrez
import time

Entrez.email = 'researcher@institution.edu'  # NCBI requires; sets User-Agent
Entrez.api_key = 'YOUR_KEY'                  # 3 -> 10 req/sec; get at ncbi.nlm.nih.gov/account/settings/
Entrez.tool = 'project-name'                 # appears in NCBI usage logs; helps if rate-throttled

What ESearch actually does

ESearch sends the query string through the Entrez Query Translator (EQT), which rewrites unqualified terms into the canonical term[field] form, then runs the rewritten query against the per-database index. The result is a list of UIDs plus a QueryTranslation string showing exactly what was searched. Reproducible work always inspects QueryTranslation and builds queries that are translation-stable from the start.

handle = Entrez.esearch(db='nucleotide', term='human BRCA1')
record = Entrez.read(handle)
handle.close()
print(record['QueryTranslation'])
# '("homo sapiens"[Organism] OR human[All Fields]) AND (BRCA1[Gene Name] OR BRCA1[All Fields])'

The translator may expand human to the full taxonomy subtree, or coerce a gene symbol to [All Fields] if the symbol isn't unambiguous. Use field-qualified terms (Homo sapiens[ORGN] AND BRCA1[Gene Name]) for any query that will be re-run later.

Decision table: which utility for which question

| Question | Utility | Returns | Cost | |---|---|---|---| | "How many records match X in PubMed?" | ESearch with retmax=0 | Count + WebEnv | 1 call | | "Give me 20 matching UIDs" | ESearch | UIDs | 1 call | | "Give me ALL matching UIDs (>10K)" | ESearch + usehistory='y' | WebEnv/QueryKey | 1 call (then EFetch chunks server-side) | | "Does record X exist in db Y?" | ESearch with term='X[Accn]' | UIDs | 1 call | | "Which NCBI databases mention X at all?" | EGQuery | Counts across every db | 1 call | | "What searchable fields does db Y have?" | EInfo with db=Y | FieldList | 1 call | | "Last update timestamp for db Y?" | EInfo with db=Y | LastUpdate | 1 call | | "Did the user misspell X?" | ESpell | Spelling suggestion | 1 call |

EGQuery has been semi-deprecated since the 2022 site refactor — it still works but counts can lag the per-database indexes by 1-2 days. For authoritative cross-database counts, loop ESearch over a curated db list instead.

retmax silent caps

| Endpoint behavior | Cap | Workaround | |---|---|---| | Default retmax | 20 | Set explicitly | | Legacy esearch.fcgi (no usehistory) | 9,999 silent cap | Use history server | | usehistory='y' + ESearch | 100,000 per page | Page with retstart against the WebEnv | | EPost (to push IDs server-side) | 200 IDs per call | Chunk to multiple EPost calls; union with QueryKey |

The 9,999 cap is the bug that has shipped in countless lab pipelines: query returns "Count: 78,432" but IdList has 9,999 entries and there is no error. Always set retmax explicitly and either page or move to usehistory='y' whenever Count > retmax.

History server (WebEnv/QueryKey) semantics

| Property | Value | |---|---| | TTL | 8 hours absolute (per NCBI E-utils help, 2024) | | Idle eviction | Empirically ~15 min under load; can be shorter | | Chaining | Run another ESearch against WebEnv with term='#1 AND #2' to intersect prior QueryKeys | | Persistence | Session is per WebEnv string; do NOT share across processes when isolation matters | | Failure mode | Expired session returns HTTP 200 with <ERROR>WebEnv not found</ERROR> — must parse body, not status |

Chaining example:

h1 = Entrez.esearch(db='pubmed', term='CRISPR[Title]', usehistory='y')
r1 = Entrez.read(h1); h1.close()
webenv = r1['WebEnv']

h2 = Entrez.esearch(db='pubmed', term='2024[PDAT]', usehistory='y', WebEnv=webenv)
r2 = Entrez.read(h2); h2.close()

# Intersect QueryKey #1 (CRISPR) AND #2 (2024) into a new key
h3 = Entrez.esearch(db='pubmed', term=f'#{r1["QueryKey"]} AND #{r2["QueryKey"]}',
                    usehistory='y', WebEnv=webenv)
r3 = Entrez.read(h3); h3.close()
print(f'CRISPR & 2024: {r3["Count"]}')

Index lag

NCBI's Entrez indexer runs nightly (US Eastern). Records submitted Monday morning typically appear in ESearch results Wednesday at earliest. PubMed has additional MEDLINE indexing lag (1-3 weeks for full MeSH terms). For freshly-deposited data the more reliable check is EFetch on the known accession or NCBI Datasets API for genomes.

Field-qualified query patterns (per database)

| Database | Common fields | Notes | |---|---|---| | pubmed | [Title], [TIAB] (title+abstract), [MeSH], [Author], [Journal], [PDAT], [DCOM], [PMC] | [TIAB] is more permissive than [Title]; [MeSH] requires the term to be indexed (lags) | | nucleotide | [Organism], [Gene Name], [Accn], [SLEN], [Filter], [PROP] | srcdb_refseq[PROP] restricts to RefSeq; biomol_genomic[PROP] filters molecule type | | protein | [Organism], [Gene Name], [Accn], [MOLWT], [PROP] | swissprot[Filter] restricts to reviewed | | gene | [Gene/Locus], [Organism], [Chromosome], [Gene Type] | [Gene Type] includes protein-coding, pseudo, ncRNA | | sra | [Organism], [Platform], [Strategy], [Library Source], [BioProject] | [Strategy] accepts RNA-Seq, WGS, ChIP-Seq, etc. | | gds (GEO) | [Organism], [Entry Type], [GDS Type], [Platform] | gse[Entry Type] for Series, gds[Entry Type] for curated DataSets | | taxonomy | [Scientific Name], [Common Name], [Rank], [TXID] | TXID is the numeric taxonomy ID | | clinvar | [Gene Name], [Clinical Significance], [Variation Type] | pathogenic[CLIN] for pathogenic only |

Filter properties that newcomers miss

# Curated RefSeq mRNA only, human, between 500 and 5000 nt
term = 'Homo sapiens[ORGN] AND srcdb_refseq[PROP] AND biomol_mrna[PROP] AND 500:5000[SLEN]'

# Reviewed SwissProt human kinases
term = 'Homo sapiens[ORGN] AND swissprot[Filter] AND kinase[Protein Name]'

# PubMed: human studies in last 30 days, full-text in PMC
term = 'CRISPR[Title] AND humans[MeSH Terms] AND last 30 days[EDAT] AND pubmed pmc[sb]'

Organism field gotcha

[Organism] (and the alias [ORGN]) is taxonomy-walked: searching mammalia[ORGN] returns records from every species in Mammalia. To get records tagged at exactly that node use [Organism:exp] (no taxonomic expansion). Most workflows want the default walk, but multi-species queries that "blow up" by 100x are almost always a missing :exp.

Code patterns

Single search with explicit retmax

Goal: Get matching UIDs for a focused query without hitting silent caps.

Approach: Set retmax explicitly to the maximum the caller wants; if Count > retmax either page or switch to history server.

Reference (BioPython 1.83+):

def search_ncbi(db, term, max_results=100):
    handle = Entrez.esearch(db=db, term=term, retmax=max_results)
    record = Entrez.read(handle); handle.close()
    count = int(record['Count'])
    if count > max_results:
        print(f'WARNING: {count} matched, returning first {max_results}; use history server for full set')
    return record['IdList'], count, record['QueryTranslation']

Paged retrieval (only when enumeration without fetching is required)

Goal: Stream all matching UIDs to a file when downstream work can't use the history server.

Approach: Page through retstart increments; respect rate limit; stop at total.

def stream_all_ids(db, term, batch_size=10000):
    h = Entrez.esearch(db=db, term=term, retmax=0)
    total = int(Entrez.read(h)['Count']); h.close()
    delay = 0.1 if Entrez.api_key else 0.34
    for start in range(0, total, batch_size):
        h = Entrez.esearch(db=db, term=term, retstart=start, retmax=batch_size)
        r = Entrez.read(h); h.close()
        for uid in r['IdList']:
            yield uid
        time.sleep(delay)

For any download workflow, history-server retrieval is strictly better — see batch-downloads skill.

History server for downstream EFetch

Goal: Push a large result set to NCBI servers so EFetch can pull it in batches without re-sending IDs.

Approach: ESearch with usehistory='y'; capture WebEnv and QueryKey; pass to EFetch.

Reference (BioPython 1.83+):

h = Entrez.esearch(db='nucleotide',
                   term='Homo sapiens[ORGN] AND srcdb_refseq[PROP] AND biomol_mrna[PROP]',
                   usehistory='y', retmax=0)
r = Entrez.read(h); h.close()
webenv, query_key, count = r['WebEnv'], r['QueryKey'], int(r['Count'])
print(f'{count} mRNAs queued on history server; use webenv/query_key with efetch')

Inspect the translation before trusting a query

Goal: Catch translator misinterpretation before producing publication results.

Approach: Always print QueryTranslation for new queries and lock the rewritten string into the codebase as the canonical query.

h = Entrez.esearch(db='pubmed', term='covid vaccine efficacy 2024', retmax=0)
r = Entrez.read(h); h.close()
print(r['QueryTranslation'])
# '("covid 19 vaccines"[MeSH Terms] OR ("covid 19"[All Fields] AND ...
# Now use this rewritten string explicitly to guarantee reproducibility.

Discover fields for a database

def list_fields(db):
    h = Entrez.einfo(db=db); r = Entrez.read(h); h.close()
    return [(f['Name'], f['FullName'], f['Description']) for f in r['DbInfo']['FieldList']]

Spell-check before searching (catches typo-driven empty results)

h = Entrez.espell(db='pubmed', term='breast canser')
r = Entrez.read(h); h.close()
print(r['CorrectedQuery'])  # 'breast cancer'

Failure modes

Silent retmax cap

• Trigger: Count > 9999 with no usehistory='y'; IdList capped at 9999.
• Mechanism: Legacy esearch.fcgi enforces a 9999 cap for non-history responses.
• Symptom: Pipeline returns "the first 9999" with no error; downstream stats are wrong.
• Fix: Always check int(record['Count']) <= len(record['IdList']); switch to history server above ~5000.

Query translation mismatch

• Trigger: Unqualified ambiguous term (e.g. MARCH1 — Excel-renamed gene vs month abbreviation).
• Mechanism: EQT falls back to [All Fields] when no unambiguous mapping is found.
• Symptom: Either zero hits (gene symbol not in [All Fields]) or huge non-specific hits.
• Fix: Use field-qualified terms; for gene symbols, use HGNC ID via gene db lookup first.

WebEnv expiration mid-pipeline

• Trigger: Long-running batch job; session > 8 hours or idle > 15 min.
• Mechanism: Server evicts WebEnv; subsequent EFetch returns <ERROR> body with HTTP 200.
• Symptom: Silent empty results halfway through a download.
• Fix: Parse error bodies (not just status codes); re-run ESearch and resume from retstart.

Index lag for fresh deposits

• Trigger: Querying a record submitted < 48h ago.
• Mechanism: Indexer is batch (Tue/Fri primary); record exists but not searchable.
• Symptom: ESearch by accession returns empty; direct EFetch by accession succeeds.
• Fix: If the accession is known, use EFetch directly; only use ESearch for content-based discovery.

Organism over-expansion

• Trigger: [ORGN] query on a higher taxon (e.g. Vertebrata[ORGN]).
• Mechanism: Default behavior walks the entire taxonomy subtree.
• Symptom: 1000x more hits than intended.
• Fix: Use [Organism:exp] to disable the walk, or constrain to a specific species/genus.

Empty IdList with no error

• Trigger: Misspelled field name ([gene] works; [gene_name] returns nothing).
• Mechanism: Unknown field is silently coerced to [All Fields] — but combined with AND of a real field, the AND prunes everything.
• Symptom: Query that "should" match gets 0 results.
• Fix: Run EInfo on the db first to confirm field names; check QueryTranslation.

Rate-limit math

| Auth | req/sec allowed | Sleep between calls | Bulk-friendly? | |---|---|---|---| | Email only | 3 | 0.34 s | Use history server, not parallel calls | | Email + API key | 10 | 0.10 s | Modest parallelism (4 workers) is safe | | Institutional bulk | Email eutilities@ncbi.nlm.nih.gov | Negotiated | For >100K queries; courtesy expected |

NCBI's terms of use ask that heavy automated queries run outside US weekday business hours (9 AM-5 PM ET). For genuinely bulk work, prefer the history server over parallel API calls — chunking against one session is faster and friendlier than scaling out.

Common errors

| Error / symptom | Cause | Solution | |---|---|---| | HTTPError 429 | Rate limit exceeded | Add time.sleep(0.34) or use API key | | HTTPError 400 | Field name or bracket malformed | Inspect EInfo field list; check brackets | | RuntimeError: ... email | Missing Entrez.email | Set globally before any call | | Empty IdList, large Count | Hit retmax cap | Set retmax explicitly or use history | | <ERROR>WebEnv not found</ERROR> (HTTP 200) | Session expired | Re-run ESearch; parse XML body for errors | | Query gives wildly wrong count | EQT misinterpretation | Print QueryTranslation; use field-qualified terms |

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. E-utilities In-Depth: Parameters, Syntax and More. NBK25499 (online manual; check current revision).
• Cock PJ et al. (2009) Biopython: freely available Python tools for computational molecular biology and bioinformatics. Bioinformatics 25:1422-1423.

Related Skills

• entrez-fetch - Retrieve actual records once UIDs are in hand
• entrez-link - Cross-database navigation via ELink
• batch-downloads - History-server batch retrieval pipelines
• ncbi-datasets-cli - Modern alternative for genome/gene metadata (Datasets v2 CLI)
• geo-data - Specialized search semantics for the gds database
• sra-data - SRA metadata search before downloading runs
• ensembl-rest - Ensembl REST as alternative for Ensembl-native queries