gh-k-dense-ai-claude-scientific-skills-scientific-skills/skills/bioservices/references/services_reference.md

BioServices: Complete Services Reference

This document provides a comprehensive reference for all major services available in BioServices, including key methods, parameters, and use cases.

Protein & Gene Resources

UniProt

Protein sequence and functional information database.

Initialization:

from bioservices import UniProt
u = UniProt(verbose=False)

Key Methods:

• search(query, frmt="tab", columns=None, limit=None, sort=None, compress=False, include=False, **kwargs)

  • Search UniProt with flexible query syntax
  • frmt: "tab", "fasta", "xml", "rdf", "gff", "txt"
  • columns: Comma-separated list (e.g., "id,genes,organism,length")
  • Returns: String in requested format

• retrieve(uniprot_id, frmt="txt")

  • Retrieve specific UniProt entry
  • frmt: "txt", "fasta", "xml", "rdf", "gff"
  • Returns: Entry data in requested format

• mapping(fr="UniProtKB_AC-ID", to="KEGG", query="P43403")

  • Convert identifiers between databases
  • fr/to: Database identifiers (see identifier_mapping.md)
  • query: Single ID or comma-separated list
  • Returns: Dictionary mapping input to output IDs

• searchUniProtId(pattern, columns="entry name,length,organism", limit=100)

  • Convenience method for ID-based searches
  • Returns: Tab-separated values

Common columns: id, entry name, genes, organism, protein names, length, sequence, go-id, ec, pathway, interactor

Use cases:

• Protein sequence retrieval for BLAST
• Functional annotation lookup
• Cross-database identifier mapping
• Batch protein information retrieval

---

KEGG (Kyoto Encyclopedia of Genes and Genomes)

Metabolic pathways, genes, and organisms database.

Initialization:

from bioservices import KEGG
k = KEGG()
k.organism = "hsa"  # Set default organism

Key Methods:

• list(database)

  • List entries in KEGG database
  • database: "organism", "pathway", "module", "disease", "drug", "compound"
  • Returns: Multi-line string with entries

• find(database, query)

  • Search database by keywords
  • Returns: List of matching entries with IDs

• get(entry_id)

  • Retrieve entry by ID
  • Supports genes, pathways, compounds, etc.
  • Returns: Raw entry text

• parse(data)

  • Parse KEGG entry into dictionary
  • Returns: Dict with structured data

• lookfor_organism(name)

  • Search organisms by name pattern
  • Returns: List of matching organism codes

• lookfor_pathway(name)

  • Search pathways by name
  • Returns: List of pathway IDs

• get_pathway_by_gene(gene_id, organism)

  • Find pathways containing gene
  • Returns: List of pathway IDs

• parse_kgml_pathway(pathway_id)

  • Parse pathway KGML for interactions
  • Returns: Dict with "entries" and "relations"

• pathway2sif(pathway_id)

  • Extract Simple Interaction Format data
  • Filters for activation/inhibition
  • Returns: List of interaction tuples

Organism codes:

• hsa: Homo sapiens
• mmu: Mus musculus
• dme: Drosophila melanogaster
• sce: Saccharomyces cerevisiae
• eco: Escherichia coli

Use cases:

• Pathway analysis and visualization
• Gene function annotation
• Metabolic network reconstruction
• Protein-protein interaction extraction

---

HGNC (Human Gene Nomenclature Committee)

Official human gene naming authority.

Initialization:

from bioservices import HGNC
h = HGNC()

Key Methods:

• search(query): Search gene symbols/names
• fetch(format, query): Retrieve gene information

Use cases:

• Standardizing human gene names
• Looking up official gene symbols

---

MyGeneInfo

Gene annotation and query service.

Initialization:

from bioservices import MyGeneInfo
m = MyGeneInfo()

Key Methods:

• querymany(ids, scopes, fields, species): Batch gene queries
• getgene(geneid): Get gene annotation

Use cases:

• Batch gene annotation retrieval
• Gene ID conversion

---

Chemical Compound Resources

ChEBI (Chemical Entities of Biological Interest)

Dictionary of molecular entities.

Initialization:

from bioservices import ChEBI
c = ChEBI()

Key Methods:

• getCompleteEntity(chebi_id): Full compound information
• getLiteEntity(chebi_id): Basic information
• getCompleteEntityByList(chebi_ids): Batch retrieval

Use cases:

• Small molecule information
• Chemical structure data
• Compound property lookup

---

ChEMBL

Bioactive drug-like compound database.

Initialization:

from bioservices import ChEMBL
c = ChEMBL()

Key Methods:

• get_molecule_form(chembl_id): Compound details
• get_target(chembl_id): Target information
• get_similarity(chembl_id): Get similar compounds for given
• get_assays(): Bioassay data

Use cases:

• Drug discovery data
• Find similar compounds
• Bioactivity information
• Target-compound relationships

---

UniChem

Chemical identifier mapping service.

Initialization:

from bioservices import UniChem
u = UniChem()

Key Methods:

• get_compound_id_from_kegg(kegg_id): KEGG → ChEMBL
• get_all_compound_ids(src_compound_id, src_id): Get all IDs
• get_src_compound_ids(src_compound_id, from_src_id, to_src_id): Convert IDs

Source IDs:

• 1: ChEMBL
• 2: DrugBank
• 3: PDB
• 6: KEGG
• 7: ChEBI
• 22: PubChem

Use cases:

• Cross-database compound ID mapping
• Linking chemical databases

---

PubChem

Chemical compound database from NIH.

Initialization:

from bioservices import PubChem
p = PubChem()

Key Methods:

• get_compounds(identifier, namespace): Retrieve compounds
• get_properties(properties, identifier, namespace): Get properties

Use cases:

• Chemical structure retrieval
• Compound property information

---

Sequence Analysis Tools

NCBIblast

Sequence similarity searching.

Initialization:

from bioservices import NCBIblast
s = NCBIblast(verbose=False)

Key Methods:

• run(program, sequence, stype, database, email, **params)

  • Submit BLAST job
  • program: "blastp", "blastn", "blastx", "tblastn", "tblastx"
  • stype: "protein" or "dna"
  • database: "uniprotkb", "pdb", "refseq_protein", etc.
  • email: Required by NCBI
  • Returns: Job ID

• getStatus(jobid)

  • Check job status
  • Returns: "RUNNING", "FINISHED", "ERROR"

• getResult(jobid, result_type)

  • Retrieve results
  • result_type: "out" (default), "ids", "xml"

Important: BLAST jobs are asynchronous. Always check status before retrieving results.

Use cases:

• Protein homology searches
• Sequence similarity analysis
• Functional annotation by homology

---

Pathway & Interaction Resources

Reactome

Pathway database.

Initialization:

from bioservices import Reactome
r = Reactome()

Key Methods:

• get_pathway_by_id(pathway_id): Pathway details
• search_pathway(query): Search pathways

Use cases:

• Human pathway analysis
• Biological process annotation

---

PSICQUIC

Protein interaction query service (federates 30+ databases).

Initialization:

from bioservices import PSICQUIC
s = PSICQUIC()

Key Methods:

• query(database, query_string)

  • Query specific interaction database
  • Returns: PSI-MI TAB format

• activeDBs

  • Property listing available databases
  • Returns: List of database names

Available databases: MINT, IntAct, BioGRID, DIP, InnateDB, MatrixDB, MPIDB, UniProt, and 30+ more

Query syntax: Supports AND, OR, species filters

• Example: "ZAP70 AND species:9606"

Use cases:

• Protein-protein interaction discovery
• Network analysis
• Interactome mapping

---

IntactComplex

Protein complex database.

Initialization:

from bioservices import IntactComplex
i = IntactComplex()

Key Methods:

• search(query): Search complexes
• details(complex_ac): Complex details

Use cases:

• Protein complex composition
• Multi-protein assembly analysis

---

OmniPath

Integrated signaling pathway database.

Initialization:

from bioservices import OmniPath
o = OmniPath()

Key Methods:

• interactions(datasets, organisms): Get interactions
• ptms(datasets, organisms): Post-translational modifications

Use cases:

• Cell signaling analysis
• Regulatory network mapping

---

Gene Ontology

QuickGO

Gene Ontology annotation service.

Initialization:

from bioservices import QuickGO
g = QuickGO()

Key Methods:

• Term(go_id, frmt="obo")

  • Retrieve GO term information
  • Returns: Term definition and metadata

• Annotation(protein=None, goid=None, format="tsv")

  • Get GO annotations
  • Returns: Annotations in requested format

GO categories:

• Biological Process (BP)
• Molecular Function (MF)
• Cellular Component (CC)

Use cases:

• Functional annotation
• Enrichment analysis
• GO term lookup

---

Genomic Resources

BioMart

Data mining tool for genomic data.

Initialization:

from bioservices import BioMart
b = BioMart()

Key Methods:

• datasets(dataset): List available datasets
• attributes(dataset): List attributes
• query(query_xml): Execute BioMart query

Use cases:

• Bulk genomic data retrieval
• Custom genome annotations
• SNP information

---

ArrayExpress

Gene expression database.

Initialization:

from bioservices import ArrayExpress
a = ArrayExpress()

Key Methods:

• queryExperiments(keywords): Search experiments
• retrieveExperiment(accession): Get experiment data

Use cases:

• Gene expression data
• Microarray analysis
• RNA-seq data retrieval

---

ENA (European Nucleotide Archive)

Nucleotide sequence database.

Initialization:

from bioservices import ENA
e = ENA()

Key Methods:

• search_data(query): Search sequences
• retrieve_data(accession): Retrieve sequences

Use cases:

• Nucleotide sequence retrieval
• Genome assembly access

---

Structural Biology

PDB (Protein Data Bank)

3D protein structure database.

Initialization:

from bioservices import PDB
p = PDB()

Key Methods:

• get_file(pdb_id, file_format): Download structure files
• search(query): Search structures

File formats: pdb, cif, xml

Use cases:

• 3D structure retrieval
• Structure-based analysis
• PyMOL visualization

---

Pfam

Protein family database.

Initialization:

from bioservices import Pfam
p = Pfam()

Key Methods:

• searchSequence(sequence): Find domains in sequence
• getPfamEntry(pfam_id): Domain information

Use cases:

• Protein domain identification
• Family classification
• Functional motif discovery

---

Specialized Resources

BioModels

Systems biology model repository.

Initialization:

from bioservices import BioModels
b = BioModels()

Key Methods:

• get_model_by_id(model_id): Retrieve SBML model

Use cases:

• Systems biology modeling
• SBML model retrieval

---

COG (Clusters of Orthologous Genes)

Orthologous gene classification.

Initialization:

from bioservices import COG
c = COG()

Use cases:

• Orthology analysis
• Functional classification

---

BiGG Models

Metabolic network models.

Initialization:

from bioservices import BiGG
b = BiGG()

Key Methods:

• list_models(): Available models
• get_model(model_id): Model details

Use cases:

• Metabolic network analysis
• Flux balance analysis

---

General Patterns

Error Handling

All services may throw exceptions. Wrap calls in try-except:

try:
    result = service.method(params)
    if result:
        # Process result
        pass
except Exception as e:
    print(f"Error: {e}")

Verbosity Control

Most services support verbose parameter:

service = Service(verbose=False)  # Suppress HTTP logs

Rate Limiting

Services have timeouts and rate limits:

service.TIMEOUT = 30  # Adjust timeout
service.DELAY = 1     # Delay between requests (if supported)

Output Formats

Common format parameters:

• frmt: "xml", "json", "tab", "txt", "fasta"
• format: Service-specific variants

Caching

Some services cache results:

service.CACHE = True  # Enable caching
service.clear_cache()  # Clear cache

Additional Resources

For detailed API documentation:

• Official docs: https://bioservices.readthedocs.io/
• Individual service docs linked from main page
• Source code: https://github.com/cokelaer/bioservices