BioBTree

Get full details for one identifier.

SYNTAX: biobtree_entry(identifier="ID", dataset="dataset_name")

USE FOR:

• See all attributes of an entry

• Discover filterable fields

• Get detailed info (sequences, scores, descriptions)

• DISCOVER CONNECTIONS: xrefs show what datasets link to this entry

WORKFLOW: Get entry → see xrefs → check EDGES for where they lead → follow relevant paths

RETURNS: All attributes + xref counts to connected datasets

Map identifiers between databases.

SYNTAX: biobtree_map(terms="ID", chain=">>source>>target")

• Chain MUST start with ">>"

• Source MUST match input ID type

ID TYPE → SOURCE:

• ENSG* → >>ensembl

• P*/Q*/O* → >>uniprot

• CHEMBL* → >>chembl_molecule

• GO:* → >>go

• MONDO:* → >>mondo

• HP:* → >>hpo

• HGNC:* or gene symbols → >>hgnc

SOME DRUG EXPLORATION PATHS:

• chembl_molecule>>chembl_target>>uniprot (drug targets)

• pubchem>>pubchem_activity>>uniprot (bioactivity)

• ensembl>>reactome>>chebi (pathway chemicals - when no direct targets)

• Discover more via entry xrefs + EDGES

WARNING - GO terms with high xref_count (>100):

• Don't map GO → proteins → drugs (too many results)

• Instead: search drug class for condition → verify targets this GO term

DISEASE GENE PATTERNS:

• mondo>>gencc>>hgnc (curated)

• mondo>>clinvar>>hgnc (variant-based)

DISEASE → DRUG PATTERNS:

• mesh>>chembl_molecule (MeSH disease/condition → drugs with indications)

• mondo>>clinical_trials>>chembl_molecule (disease → trial drugs)

DISCOVERY APPROACH:

• Use biobtree_entry to see xrefs (what's connected)

• Use EDGES above to see where each dataset leads

• Build chains based on what connections exist for YOUR entity

RETURNS: mapped identifiers with dataset and name

EDGES (what connects to what): ensembl: uniprot, go, transcript, exon, ortholog, paralog, hgnc, entrez, refseq, bgee, gwas, gencc, antibody, scxa hgnc: ensembl, uniprot, entrez, gencc, pharmgkb_gene, msigdb, clinvar, mim, refseq, alphafold, collectri, gwas, dbsnp, hpo, cellphonedb entrez: ensembl, uniprot, refseq, go, biogrid, pubchem_activity, ctd_gene_interaction refseq: ensembl, entrez, taxonomy, ccds, uniprot, mirdb mirdb: refseq transcript: ensembl, exon, ufeature uniprot: ensembl, alphafold, interpro, pdb, ufeature, intact, string, string_interaction, biogrid, biogrid_interaction, chembl_target, go, reactome, rhea, swisslipids, bindingdb, antibody, pubchem_activity, cellphonedb, jaspar, signor, diamond_similarity, esm2_similarity alphafold: uniprot interpro: uniprot, go, interproparent, interprochild chembl_molecule: mesh, chembl_activity, chembl_target, pubchem, chebi, clinical_trials chembl_activity: chembl_molecule, chembl_assay, bao chembl_assay: chembl_activity, chembl_target, chembl_document, bao chembl_target: chembl_assay, uniprot, chembl_molecule pubchem: chembl_molecule, chebi, hmdb, pubchem_activity, pubmed, patent_compound, bindingdb, ctd, pharmgkb pubchem_activity: pubchem, ensembl, uniprot chebi: pubchem, rhea, intact swisslipids: uniprot, go, chebi, uberon, cl lipidmaps: chebi, pubchem dbsnp: hgnc, clinvar, pharmgkb_variant, alphamissense, spliceai clinvar: hgnc, mondo, hpo, dbsnp, orphanet alphamissense: uniprot, transcript gwas: gwas_study, efo, dbsnp, hgnc gwas_study: gwas, efo mondo: gencc, clinvar, efo, mesh, hpo, clinical_trials, antibody, cellxgene, cellxgene_celltype, orphanet, mondoparent, mondochild gencc: mondo, hpo, hgnc, ensembl clinical_trials: mondo, chembl_molecule pharmgkb: hgnc, dbsnp, mesh, pharmgkb_gene, pharmgkb_variant, pharmgkb_clinical, pharmgkb_guideline, pharmgkb_pathway pharmgkb_variant: pharmgkb_clinical, hgnc, mesh, dbsnp pharmgkb_gene: hgnc, entrez, ensembl, pharmgkb pharmgkb_clinical: dbsnp, hgnc, mesh, pharmgkb_variant pharmgkb_guideline: hgnc, pharmgkb pharmgkb_pathway: hgnc, pharmgkb ctd: mesh, ctd_gene_interaction, ctd_disease_association, pubchem ctd_gene_interaction: ctd, entrez, taxonomy, pubmed ctd_disease_association: ctd, mesh, mim, pubmed intact: uniprot, chebi, rnacentral string: uniprot, string_interaction string_interaction: string, uniprot biogrid: entrez, uniprot, refseq, taxonomy bgee: ensembl, uberon, cl, taxonomy, bgee_evidence bgee_evidence: bgee, uberon, cl cellxgene: cl, uberon, mondo, efo, taxonomy cellxgene_celltype: cl, uberon, mondo scxa: cl, uberon, taxonomy, ensembl, scxa_gene_experiment scxa_expression: ensembl, scxa, scxa_gene_experiment scxa_gene_experiment: ensembl, scxa, scxa_expression, cl rnacentral: uniprot, ensembl, intact, hgnc, refseq, ena reactome: ensembl, uniprot, chebi, go, reactomeparent, reactomechild rhea: chebi, uniprot, go go: ensembl, uniprot, reactome, msigdb, swisslipids, bgee, interpro, goparent, gochild hpo: clinvar, gencc, mondo, msigdb, orphanet, mim, hmdb, hgnc, hpoparent, hpochild efo: gwas, mondo, cellxgene, efoparent, efochild uberon: bgee, cellxgene, cellxgene_celltype, swisslipids, uberonparent, uberonchild cl: bgee, cellxgene, cellxgene_celltype, scxa, scxa_gene_experiment, clparent, clchild taxonomy: ensembl, uniprot, bgee, biogrid, ctd_gene_interaction, taxparent, taxchild mesh: pharmgkb, ctd, ctd_disease_association, pubchem, mondo, chembl_molecule, meshparent, meshchild eco: ecoparent, ecochild antibody: ensembl, uniprot, mondo, pdb msigdb: hgnc, entrez, go, hpo orphanet: hpo, uniprot, mondo, hgnc, clinvar, mim, mesh mim: clinvar, hpo, mondo, uniprot, ctd_disease_association hmdb: pubchem, hpo, chebi, uniprot collectri: hgnc # transcription factor → target gene interactions esm2_similarity: uniprot # protein structural similarity diamond_similarity: uniprot # protein sequence similarity cellphonedb: uniprot, ensembl, hgnc, pubmed # ligand-receptor pairs for cell-cell communication spliceai: hgnc pdb: uniprot, go, interpro, pfam, taxonomy, pubmed fantom5_promoter: ensembl, hgnc, entrez, uniprot, uberon, cl fantom5_enhancer: ensembl, uberon, cl fantom5_gene: ensembl, hgnc, entrez jaspar: uniprot, pubmed, taxonomy encode_ccre: taxonomy bao: chembl_activity, chembl_assay, baoparent, baochild brenda: uniprot, pubmed, brenda_kinetics, brenda_inhibitor brenda_kinetics: brenda brenda_inhibitor: brenda

FILTER SYNTAX: >>dataset[field operator value]

OPERATORS: == equals >>dataset[field=="value"] != not equals >>dataset[field!="value"]

   greater than     >>dataset[field>value]

< less than >>dataset[field<value]

= greater or equal >>dataset[field>=value] <= less or equal >>dataset[field<=value] contains string match >>dataset[field.contains("value")]

LOGICAL OPERATORS: && AND >>dataset[field1>5 && field2<10] || OR >>dataset[field=="A" || field=="B"] ! NOT >>dataset[!field] or >>dataset[!(field=="value")]

TYPE RULES:

• FLOAT: use decimal point (70.0 not 70)

• INT: no decimal (2 not 2.0)

• STRING: quote values ("Pathogenic", "PHASE3")

• BOOL: true/false (no quotes)

EXAMPLES:

chembl_molecule[highestDevelopmentPhase==4] # approved drugs chembl_molecule[highestDevelopmentPhase>=3] # Phase 3+ clinical_trials[phase=="PHASE3"] go[type=="biological_process"] clinvar[germline_classification=="Pathogenic"] reactome[name.contains("signaling")]

Search 70+ biological databases.

SYNTAX: biobtree_search(terms="entity")

BEFORE SEARCHING - Use your training knowledge to plan:

1. What type of entity is this? (disease, process, drug, gene, protein)

2. What is the query asking for? (drugs, genes, function, etc.)

3. What equivalent terms might give better results? (e.g., "temperature homeostasis" is a process → related condition is "fever")

4. Choose best entry point for query type (disease terms for drug queries)

WORKFLOW:

1. Search WITHOUT dataset filter first (discover where entity exists)

2. Use IDs from results with biobtree_map

QUERY PATTERNS (choose based on question):

"DRUG FOR DISEASE/CONDITION X":

• Prefer disease terms (mesh/mondo/efo) over GO terms for drug queries

• If search only returns GO term, search for the related CONDITION instead (e.g., "temperature homeostasis" → search "fever" instead)

• Search disease → mondo → clinical_trials → chembl_molecule

• OR search drug class directly (e.g., "antipyretic", "NSAID", "antibiotic")

• Verify mechanism for top 2-3 drugs only (don't enumerate all proteins!)

"DRUG TARGETS" (use BOTH paths for complete picture):

• chembl: >>chembl_molecule>>chembl_target>>uniprot (mechanism-level)

• pubchem: >>pubchem>>pubchem_activity>>uniprot (protein-level, often 50+ targets)

• Filter approved: >>chembl_molecule[highestDevelopmentPhase==4]

"DISEASE GENES":

• Search disease → mondo/hpo → gencc/clinvar/orphanet → hgnc

"PROTEIN FUNCTION":

• Search protein → uniprot → go/reactome

"MECHANISM QUERIES" (drug-disease):

• Use biobtree_entry to see what's connected (xrefs)

• Check EDGES to see where each xref leads

• Follow connections relevant to your question

• Build chain: Drug → Target → [connections] → Disease

RETURNS: id | dataset | name | xref_count