Tooluniverse Sequence Retrieval

Biological Sequence Retrieval

Retrieve DNA, RNA, and protein sequences with proper disambiguation and cross-database handling.

IMPORTANT: Always use English terms in tool calls. Only try original-language terms as fallback. Respond in the user's language.

LOOK UP DON'T GUESS: Never assume accession numbers or sequence versions. Always retrieve and verify from NCBI or ENA.

Domain Reasoning

Sequence quality hierarchy: RefSeq (NM_/NP_ = curated) > RefSeq predicted (XM_/XP_) > GenBank (submitted). Prefer the MANE Select transcript for human canonical isoforms. Check version numbers -- annotations improve across versions.

Workflow

Phase 0: Clarify (if needed) → Phase 1: Disambiguate Gene/Organism → Phase 2: Search & Retrieve → Phase 3: Report

Phase 0: Clarification (When Needed)

Ask ONLY if: gene exists in multiple organisms, sequence type unclear, or strain matters. Skip for: specific accessions, clear organism+gene combos, complete genome requests with organism.

Phase 1: Gene/Organism Disambiguation

Accession Type Decision Tree

CRITICAL: Never try ENA tools with RefSeq accessions -- they return 404.

Identity Checklist

• Organism confirmed (scientific name)
• Gene symbol/name identified
• Sequence type determined (genomic/mRNA/protein)
• Accession prefix identified for tool selection

Phase 2: Data Retrieval (Internal)

Retrieve silently. Do NOT narrate the search process.

# Search NCBI Nucleotideresult = tu.tools.NCBI_search_nucleotide(    operation="search", organism=organism, gene=gene,    strain=strain, keywords=keywords, seq_type=seq_type, limit=10)
# Get accessions from UIDsaccessions = tu.tools.NCBI_fetch_accessions(operation="fetch_accession", uids=result["data"]["uids"])
# Retrieve sequence (FASTA or GenBank format)sequence = tu.tools.NCBI_get_sequence(operation="fetch_sequence", accession=accession, format="fasta")
# ENA alternative (non-RefSeq accessions only)entry = tu.tools.ena_get_entry(accession=accession)fasta = tu.tools.ena_get_sequence_fasta(accession=accession)

Fallback Chains

Phase 3: Report Sequence Profile

Present as a Sequence Profile Report. Hide search process. Include:

1. Search Summary: query, database, result count
2. Primary Sequence: accession, type (RefSeq/GenBank), organism, strain, length, molecule, topology, curation level
3. Sequence Preview: first lines of FASTA (truncated)
4. Annotations Summary: CDS/tRNA/rRNA/regulatory feature counts (from GenBank format)
5. Alternative Sequences: ranked by relevance and curation, with ENA compatibility
6. Cross-Database References: RefSeq, GenBank, ENA/EMBL, BioProject, BioSample
7. Download Options: FASTA (for BLAST/alignment), GenBank (for annotation)

Curation Level Tiers

Reasoning Framework

Sequence quality: Prefer RefSeq over GenBank. Check version numbers. Sequences with "PREDICTED" in definition are not experimentally validated.

Accession guidance: RefSeq = NCBI-only. GenBank = mirrored in ENA/EMBL. Default to RefSeq mRNA (NM_) for human/model organisms; most complete genome assembly for microbial queries.

Cross-database reconciliation: Same sequence may have different accessions (e.g., GenBank U00096 = RefSeq NC_000913 for E. coli K-12). Always report both when available. Discrepancies between GenBank/RefSeq typically indicate RefSeq curation corrected submission errors.

Synthesis Questions

1. What is the highest-quality accession available?
2. Are there alternative accessions in other databases?
3. What is the annotation completeness?
4. Is the sequence from the expected organism/strain?
5. What download format suits the user's downstream analysis?

Error Handling

Tool Reference

NCBI Tools: NCBI_search_nucleotide (search), NCBI_fetch_accessions (UID→accession), NCBI_get_sequence (retrieve) ENA Tools (GenBank/EMBL only): ena_get_entry (metadata), ena_get_sequence_fasta (FASTA), ena_get_entry_summary (summary)

Search Parameters Reference

NCBI_search_nucleotide: operation="search", organism (scientific name), gene (symbol), strain, keywords, seq_type (complete_genome/mrna/refseq), limit

NCBI_get_sequence: operation="fetch_sequence", accession, format (fasta/genbank)

Whole-Organism Reference Proteomes (UniProt)

A different granularity from Phases 1-3 above: src/tooluniverse/data/uniprot_ref_tools.json's UniProtRef_search_proteomes/UniProtRef_get_proteome (no API key required) return organism-LEVEL proteome metadata — total protein count, chromosome/component breakdown, genome accession — not one sequence. Reach for these when the question is "what is species X's reference proteome and how big is it" (e.g. before a proteome-wide analysis, or to find the right genome-assembly accession to hand to Phase 2 above), not for fetching an individual gene/transcript sequence.

Real example chain (verified live): UniProtRef_search_proteomes {"query": "SARS-CoV-2"} -> 3 results, including UP000464024 (proteome_type: "Reference proteome", 17 proteins) alongside two "Non Reference proteome" entries for the same organism (5 and 10 proteins) — always check proteome_type and prefer "Reference proteome" rather than assuming the first hit is canonical. Feeding UP000464024 into UniProtRef_get_proteome confirms components: [{"name": "Segment", "protein_count": 17, "genome_accession": "MN908947"}] (the real SARS-CoV-2 reference genome accession) and total_protein_count: 17. For human (UP000005640) and E. coli K-12 (UP000000625), get_proteome returns per-chromosome/plasmid component breakdowns the same way.

Registry duplication, verified live: UniProt_search_proteomes (in src/tooluniverse/data/uniprot_proteomes_tools.json, hitting rest.uniprot.org/proteomes/search directly) does the SAME search as UniProtRef_search_proteomes above, but is a separate, older tool implementation. Its advantage: one call returns the full rich record (BUSCO completeness, genome assembly, per-component protein counts, full citation list) that UniProtRef_search_proteomes requires a second UniProtRef_get_proteome call to obtain — verified live on organism_name: human, organism_name:escherichia coli, organism_name:mouse, all 3 returning the full nested object in one shot. If you only need the ID or proteomeType for triage, either tool works; if you need full detail immediately, prefer UniProt_search_proteomes and skip the follow-up call.

Sequence Archive and Clustering (UniParc, UniRef)

A different question again from Phases 1-3 and the proteome tools above: "has this exact sequence been seen before, and under how many accessions," and "what other sequences are near-identical to this one."

Real example (verified live, EGFR/human): UniParc_search {"query": "gene:EGFR AND organism_id:9606", "size": 3} -> 3 distinct UPI entries, each with a different set of UniProt accessions (Q8NDU8.1, Q9BZS2.1, Q9UMG5.1) and different oldest_created dates — UniParc treats each unique amino-acid string as one entry regardless of which UniProt accession(s) submitted it, so isoforms/fragments with distinct sequences get distinct UPIs even though they share a gene name. UniRef_get_cluster {"cluster_id": "UniRef90_P00533"} -> a real 119-member cluster spanning Boreoeutheria (the common ancestor taxon), with a representative_member (EGFR_HUMAN, 1210 aa) that carries 9+ merged UniProtKB accessions (P00533, O00688, ...) — note the seed_id (UPI0005F3CCCC, a UniParc ID) and representative_member are not always the same sequence.

Use UniParc when the question is "which UniProt accessions actually share this exact sequence" (deduplication); use UniRef when the question is "what else looks like this sequence at X% identity" (finding related proteins across species for MSA/phylogenetics input, or reducing redundancy before a proteome-wide analysis).

Subcellular Location Vocabulary (UniProt Locations)

UniProtLocations_search/UniProtLocations_get_location resolve UniProt's controlled subcellular-location vocabulary (organelles, membranes, topology, orientation) by ID (SL-XXXX) or free-text search.

Real gotcha, verified live: the common term "plasma membrane" does NOT surface UniProt's actual term for it — UniProt's controlled vocabulary calls this location "Cell membrane" (SL-0039), not "Plasma membrane." Searching "plasma membrane" or "Plasma membrane" returns only tangentially related hits (e.g. SL-0552 "Rhabdomere membrane," whose definition happens to mention "plasma membrane," not the term itself). Search a broader, UniProt-native term ("membrane", "cell") and scan the results rather than assuming your everyday biology term matches UniProt's naming — this is a real vocabulary mismatch, not a search bug.

Sample Metadata (EBI BioSamples)

BioSamples_search/BioSamples_search_by_filter/BioSamples_get_sample/ BioSamples_get_relationships/BioSamples_get_facets query EBI's 60M+ sample-metadata archive (organism, tissue, disease, experimental context) for samples that back sequencing/omics submissions — a different layer from the sequence itself, useful for "what samples exist for condition X" before going to ENA/SRA for the actual data.

Real gotcha, verified live: BioSamples_search with a multi-word phrase ("EGFR lung adenocarcinoma") returned zero results, while the single term "EGFR" returned real hits immediately — the search does not implicitly AND multiple free-text words the way a search engine would. Use one keyword at a time, or use BioSamples_search_by_filter (structured attribute/value pairs, e.g. attribute="organism", value="Homo sapiens") when you need to combine multiple real constraints. get_facets is useful up front to discover which attribute values actually exist before filtering on one (verified live: returns real facet counts, e.g. 432,302 samples for a broad text query).

Organism Taxonomy (EBI Taxonomy)

EBITaxonomy_get_by_id/get_by_scientific_name/search_by_name/suggest resolve organism names/IDs (NCBI Taxonomy, mirrored by EBI) — lineage, rank, division, genetic code. Use search_by_name for common names ("mouse," "fruit fly") and suggest for type-ahead partial matching; get_by_scientific_name/get_by_id for an exact, already-known name/ID. Verified live: Homo sapiens -> tax_id: "9606", full lineage string "Eukaryota; Metazoa; Chordata; ... Hominidae; Homo;".

Cross-Domain Quick Lookup (BioThings Gateway)

BioThings_list_apis/BioThings_query/BioThings_get_entity/ BioThings_get_metadata reach ~50 BioThings-hosted APIs (drug-drug interactions, gene-disease text-mining, PubMed-mined predications, and more) through one Elasticsearch-style query interface — useful as a fast first check for whether ToolUniverse has DEDICATED coverage of a resource before reaching for this generic gateway (BioThings_list_apis's preferred_tooluniverse_tool field tells you when a richer dedicated tool already exists; prefer that instead). Call BioThings_get_metadata before writing a fielded BioThings_query so field names are correct. Verified live: BioThings_get_entity {"api": "mondo", "entity_id": "MONDO:0010329"} returns a real MONDO disease-ontology record with ancestor terms; querying the ddinter API returns real drug-drug-interaction pairs with severity. This complements tooluniverse-data-integration-analysis's multi-database evidence-gathering workflow as a fast single-call option when a dedicated tool doesn't already exist for the resource you need.

Cross-Database ID Resolution (Bioregistry, Identifiers.org, TogoID)

Three overlapping registries for "I have an ID in system A, what's the equivalent in system B" — verified live with a shared example (human TP53): NCBI Gene 7157 -> TogoID_convert {"ids": "7157", "source": "ncbigene", "target": "ensembl_gene"} -> real ENSG00000141510; that Ensembl gene's HGNC ID 11998 resolves via both Bioregistry_resolve_reference {"prefix": "hgnc", "identifier": "11998"} and IdentifiersOrg_resolve {"namespace": "hgnc", "local_id": "11998"} to the same genenames.org record, confirming the two registries agree.

• Bioregistry (Bioregistry_resolve_reference/get_registry/get_prefix_mappings/search_registries) — 2600+ prefixes, returns multiple provider URLs per identifier (bioregistry.io, identifiers.org, n2t.net, bio2rdf, and resource-specific links like GenCC/INDRA when relevant) plus prefix metadata (ID regex pattern, homepage). Use search_registries first when you don't know the exact prefix for a database.
• Identifiers.org (IdentifiersOrg_resolve/get_namespace/search_namespaces/list_namespaces) — the original MIRIAM registry; resolve returns full institutional/provider metadata (which organization hosts the resolver, recommended vs. alternate providers) that Bioregistry's default link doesn't surface. Prefer this when you need to know WHO operates the canonical resolver for a namespace, not just a URL.
• TogoID (TogoID_convert/list_datasets) — the only one of the three that actually CONVERTS an ID between 117 dataset types (not just gives you a URL for the ID you already have) — e.g. NCBI Gene -> Ensembl Gene -> UniProt -> PDB in one call each. Call list_datasets first to get valid source/target dataset-type strings.

Reach for this cluster when a workflow needs to hop between ID systems that this skill's dedicated tools (UniProt/UniParc/UniRef/EBI Taxonomy above) don't directly connect — e.g. converting a variant-analysis skill's Ensembl gene ID into the NCBI Gene ID a different tool expects.

EBI dbfetch (One Interface, Many Databases)

dbfetch_fetch_entry (db, id, format) / dbfetch_fetch_batch (db, ids comma-separated, format) / dbfetch_list_databases / dbfetch_list_formats — fetches raw records by accession from any of 16 EBI-hosted databases (uniprotkb, pdb, embl, ena_sequence, refseqp, refseqn, ensemblgene, ensembltranscript, interpro, medline, taxonomy, uniprot, chembl, afdb, imgtligm, hgnc) through one uniform call, and can fetch several accessions from the same database in a single request via fetch_batch. No API key required. Verified live: dbfetch_fetch_entry {"db": "uniprotkb", "id": "P04637", "format": "fasta"} returns the real human p53 (P53_HUMAN) FASTA sequence; dbfetch_fetch_batch {"db": "uniprotkb", "ids": "P04637,P01308", "format": "fasta"} returns both p53 and insulin in one response.

When to use this instead of the dedicated tools above: reach for dbfetch when you need one-off or batch fetches across DIFFERENT database types without juggling per-database tools/clients (e.g. grabbing a UniProt entry and a PDB entry and an EMBL entry in a short session), or specifically need fetch_batch's multi-accession-in-one-call convenience. Prefer the dedicated tools above (UniProt/UniParc/ENA/NCBI) when you need their richer structured fields (cross-references, feature annotations, curation-level metadata) — dbfetch returns the database's native flat/FASTA/XML text, not a parsed structured object.