bioservices · diff
v1.6 to v1.7
162 added, 336 removed. Audit A to A.
---
name: bioservices
- description: Unified Python interface to 40+ bioinformatics services. Use when querying multiple databases (UniProt, KEGG, ChEMBL, Reactome) in a single workflow with consistent API. Best for cross-database analysis, ID mapping across services. For quick single-database lookups use gget; for sequence/file manipulation use biopython.
+ description: Provides a Python interface to bioinformatics services including UniProt, KEGG, ChEMBL, Reactome, QuickGO, and UniChem. Used for cross-database protein annotation, pathway retrieval, chemical identifier mapping, and integrated biological data workflows with BioServices.
license: GPLv3 license
allowed-tools: Read Write Edit Bash
- compatibility: Requires Python 3.9–3.12 and internet access to 40+ bioinformatics web APIs. NCBI BLAST requires a contact email (`NCBI_EMAIL` env var or explicit parameter).
+ compatibility: Requires Python >=3.9,<4 with bioservices==1.16.0 and internet access. EMBL-EBI BLAST submission requires a real contact email; the bundled script reads NCBI_EMAIL or an explicit parameter.
metadata:
- version: "1.6"
+ version: "1.7"
+ last-reviewed: "2026-09-30"
skill-author: K-Dense Inc.
openclaw:
envVars:
- name: NCBI_EMAIL
required: false
- description: Email for NCBI service identification.
+ description: Contact email for the EMBL-EBI hosted NCBI BLAST service.
---
# BioServices
- ## Overview
-
- BioServices is a Python package providing programmatic access to approximately 40 bioinformatics web services and databases. Retrieve biological data, perform cross-database queries, map identifiers, analyze sequences, and integrate multiple biological resources in Python workflows. The package handles both REST and SOAP/WSDL protocols transparently.
+ ## When to use
- **Version note:** Examples target **bioservices 1.16.0** (PyPI, Mar 2026). Requires **Python 3.9–3.12**. UniProt REST changes in mid-2022 (bioservices ≥1.10) mainly affect tabular `columns` names — see upstream `_legacy_names` if parsing breaks. ChEMBL wrappers changed at 1.6.0 (2018 API); use `get_similarity`, `get_substructure`, `get_molecule` instead of pre-1.6 method names.
+ Use BioServices when combining protein annotation, gene/pathway membership,
+ compound cross-references, or genomic resources in Python. Its service clients
+ share transport helpers, but their request parameters and return types differ.
+ Use [the service reference](references/services_reference.md) before composing
+ clients; method names from PubChemPy, mygene, or older BioServices are not portable.
- ## When to Use This Skill
+ Targets **bioservices 1.16.0**, the current PyPI release at review. Package metadata
+ allows Python >=3.9,<4; the bundled tests were run in an isolated Python 3.13
+ environment. The previous 3.12 upper bound was not a package requirement.
- This skill should be used when:
- - Retrieving protein sequences, annotations, or structures from UniProt, PDB, Pfam
- - Analyzing metabolic pathways and gene functions via KEGG or Reactome
- - Searching compound databases (ChEBI, ChEMBL, PubChem) for chemical information
- - Converting identifiers between different biological databases (KEGG↔UniProt, compound IDs)
- - Running sequence similarity searches (BLAST, MUSCLE alignment)
- - Querying gene ontology terms (QuickGO, GO annotations)
- - Accessing protein-protein interaction data (PSICQUIC, IntactComplex)
- - Mining genomic data (BioMart, ArrayExpress, ENA)
- - Integrating data from multiple bioinformatics resources in a single workflow
+ ```bash
+ uv pip install "bioservices==1.16.0"
+ ```
- ## Core Capabilities
+ ## Workflow
- ### 1. Protein Analysis
+ 1. Resolve the requested organism and entity to stable accessions. Review search
+ hits before selecting one; gene symbols and compound names may be ambiguous.
+ 2. Inspect the service's actual response type, including pagination and errors.
+ BioServices can return an integer-like HTTP error or `None`, not only raise.
+ 3. Preserve one-to-many mappings, failed identifiers, taxonomy, database release,
+ query parameters, and retrieval date alongside derived tables.
+ 4. Distinguish annotations and inferred associations from experimental evidence.
+ Pathway membership alone is not an enrichment analysis or causal finding.
+ 5. Run a small lookup before batching; honor provider limits and retain failures
+ separately from confirmed empty results.
- Retrieve protein information, sequences, and functional annotations:
+ ## Protein search, sequence retrieval, and mapping
```python
from bioservices import UniProt
u = UniProt(verbose=False)
-
- # Search for protein by name
- results = u.search("ZAP70_HUMAN", frmt="tab", columns="id,genes,organism")
-
- # Retrieve FASTA sequence
- sequence = u.retrieve("P43403", "fasta")
+ u.services.TIMEOUT = 30
+ rows = u.search(
+ "gene_exact:ZAP70 AND organism_id:9606 AND reviewed:true",
+ frmt="tsv", columns="accession,gene_names,organism_name,length",
+ limit=5, size=5,
+ )
+ if not isinstance(rows, str):
+ raise RuntimeError("UniProt search failed")
+ print(rows)
+ fasta = u.retrieve("P43403", frmt="fasta")
+ record = u.retrieve("P43403", frmt="json")
- # Map identifiers between databases
- kegg_ids = u.mapping(fr="UniProtKB_AC-ID", to="KEGG", query="P43403")
+ mapping = u.mapping(fr="UniProtKB_AC-ID", to="KEGG", query="P43403")
+ if not isinstance(mapping, dict) or "results" not in mapping:
+ raise RuntimeError("Mapping incomplete or failed")
+ kegg_ids = [row["to"] for row in mapping["results"] if row["from"] == "P43403"]
+ print(kegg_ids, mapping.get("failedIds", []))
```
- **Key methods:**
- - `search()`: Query UniProt with flexible search terms
- - `retrieve()`: Get protein entries in various formats (FASTA, XML, tab)
- - `mapping()`: Convert identifiers between databases
-
- Reference: `references/services_reference.md` for complete UniProt API details.
-
- ### 2. Pathway Discovery and Analysis
+ Use `frmt="tsv"`, not `"tab"`; current field names include `accession`,
+ `gene_names`, `organism_name`, `protein_name`, `go_id`, and `xref_pdb`.
+ `mapping()` returns a `results`/`failedIds` envelope, **not** a source-to-list
+ dictionary. Mapping **to** UniProt uses `to="UniProtKB"` and returns full records
+ in `row["to"]`; extract `primaryAccession`. `UniProtKB_AC-ID` is a source code.
+ See [identifier mapping](references/identifier_mapping.md) for allowed pairs,
+ normalization, and limits. For bounded searches set `size=limit` (at most 500);
+ 1.16.0 mixes the two values in its pagination loop.
- Access KEGG pathway information for genes and organisms:
+ ## KEGG pathways and networks
```python
from bioservices import KEGG
- k = KEGG()
- k.organism = "hsa" # Set to human
-
- # Search for organisms
- k.lookfor_organism("droso") # Find Drosophila species
-
- # Find pathways by name
- k.lookfor_pathway("B cell") # Returns matching pathway IDs
-
- # Get pathways containing specific genes
- pathways = k.get_pathway_by_gene("7535", "hsa") # ZAP70 gene
-
- # Retrieve and parse pathway data
- data = k.get("hsa04660")
- parsed = k.parse(data)
-
- # Extract pathway interactions
- interactions = k.parse_kgml_pathway("hsa04660")
- relations = interactions['relations'] # Protein-protein interactions
-
- # Convert to Simple Interaction Format
- sif_data = k.pathway2sif("hsa04660")
- ```
-
- **Key methods:**
- - `lookfor_organism()`, `lookfor_pathway()`: Search by name
- - `get_pathway_by_gene()`: Find pathways containing genes
- - `parse_kgml_pathway()`: Extract structured pathway data
- - `pathway2sif()`: Get protein interaction networks
-
- Reference: `references/workflow_patterns.md` for complete pathway analysis workflows.
-
- ### 3. Compound Database Searches
-
- Search and cross-reference compounds across multiple databases:
-
- ```python
- from bioservices import KEGG, UniChem
-
- k = KEGG()
-
- # Search compounds by name
- results = k.find("compound", "Geldanamycin") # Returns cpd:C11222
-
- # Get compound information with database links
- compound_info = k.get("cpd:C11222") # Includes ChEBI links
-
- # Query a supported source or a verified InChIKey (aspirin shown)
- u = UniChem()
- response = u.get_compounds("BSYNRYMUTXBXSQ-UHFFFAOYSA-N", "inchikey")
- chembl_ids = sorted({
- source["compoundId"]
- for match in response.get("compounds", [])
- for source in match.get("sources", [])
- if source.get("shortName") == "chembl"
- })
- print(chembl_ids) # ['CHEMBL25'] in the live smoke test
- ```
-
- **UniChem 2:** bioservices 1.16.0 uses `get_compounds`; KEGG is not a supported
- source. Use the KEGG entry's ChEBI cross-reference (`"CHEBI:…"`, `"chebi"`) or a
- verified InChIKey, preserve all mappings, and inspect ambiguous structures.
- An empty response or service failure does not establish that a mapping is absent.
-
- **Common workflow:**
- 1. Search compound by name in KEGG
- 2. Extract KEGG compound ID
- 3. Extract and verify a ChEBI ID from the selected KEGG entry
- 4. Query UniChem using ChEBI; report missing or ambiguous mappings
-
- Reference: `references/identifier_mapping.md` for complete cross-database mapping guide.
-
- ### 4. Sequence Analysis
-
- Run BLAST searches and sequence alignments. NCBI requires a contact email — prefer the `NCBI_EMAIL` environment variable (same convention as BioPython Entrez and other repo skills):
-
- ```python
- import os
- from bioservices import NCBIblast
-
- s = NCBIblast(verbose=False)
- email = os.environ["NCBI_EMAIL"] # set before running: export NCBI_EMAIL=you@lab.org
-
- # Run BLASTP against UniProtKB
- jobid = s.run(
- program="blastp",
- sequence=protein_sequence,
- stype="protein",
- database="uniprotkb",
- email=email,
- )
-
- # Check job status and retrieve results
- s.getStatus(jobid)
- results = s.getResult(jobid, "out")
+ k = KEGG(verbose=False)
+ k.services.url = "https://rest.kegg.jp"
+ pathway_names = k.get_pathway_by_gene("7535", "hsa") # dict: pathway ID -> name
+ print(pathway_names)
+ kgml = k.parse_kgml_pathway("hsa04660")
+ entries = {entry["id"]: entry for entry in kgml["entries"]}
+ for relation in kgml["relations"][:5]:
+ print(entries[relation["entry1"]]["name"], relation["name"],
+ entries[relation["entry2"]]["name"])
```
- **Note:** BLAST jobs are asynchronous. Check status before retrieving results.
+ The reviewed `/list/organism` endpoint returned HTTP 400 despite remaining in
+ the manual. SDK methods that validate against that catalogue can fail. The
+ bundled compound and pathway-list scripts use the documented scoped endpoints
+ through `k.services.http_get` to avoid that unrelated catalogue dependency.
- ### 5. Identifier Mapping
+ KEGG `get` permits at most ten entries; KGML permits one pathway per request.
+ Keep requests at or below three per second. `list`/`find` return TSV strings;
+ `get` returns a flat-file string unless an option changes the representation.
+ KGML entries include genes, compounds, groups, and maps. A relation can produce
+ several subtype records; those counts are neither unique genes nor independent
+ physical interactions. Entry IDs are local to each pathway. The bundled SIF
+ export namespaces them as `pathway#entry` so combining pathways cannot merge
+ unrelated nodes. `pathway2sif(..., uniprot=False)` is an optional lossy projection
+ of gene-to-gene activation/inhibition, not a complete pathway network.
- Convert identifiers between different biological databases:
+ ## Compound cross-references
```python
- from bioservices import UniProt, KEGG
-
- # UniProt mapping (many database pairs supported)
- u = UniProt()
- results = u.mapping(
- fr="UniProtKB_AC-ID", # Source database
- to="KEGG", # Target database
- query="P43403" # Identifier(s) to convert
- )
-
- # KEGG gene ID → UniProt
- kegg_to_uniprot = u.mapping(fr="KEGG", to="UniProtKB_AC-ID", query="hsa:7535")
-
- # For compounds, use UniChem
from bioservices import UniChem
- u = UniChem()
- compound_mappings = u.get_compounds("CHEBI:15365", "chebi")
- ```
- **Supported mappings (UniProt):**
- - UniProtKB ↔ KEGG
- - UniProtKB ↔ Ensembl
- - UniProtKB ↔ PDB
- - UniProtKB ↔ RefSeq
- - And many more (see `references/identifier_mapping.md`)
+ uc = UniChem(verbose=False)
+ response = uc.get_compounds("CHEBI:15365", "chebi") # aspirin
+ if not isinstance(response, dict) or "compounds" not in response:
+ raise RuntimeError("UniChem request failed")
+ chembl_ids = sorted({source["compoundId"]
+ for match in response["compounds"] for source in match.get("sources", [])
+ if source.get("shortName") == "chembl"})
+ print(chembl_ids)
+ ```
- ### 6. Gene Ontology Queries
+ UniChem 2 uses `POST /api/v1/compounds` with a JSON body; BioServices assembles it.
+ Discover source names through `uc.source_ids`; KEGG is absent at review. From a
+ KEGG compound, preserve every ChEBI cross-reference and use only a uniquely
+ resolved, structurally reviewed candidate. Check charge, stereochemistry, salts,
+ and parent forms before merging data. Multiple unresolved name hits or mappings remain
+ unresolved in the bundled compound script. Empty results do not prove absence.
- Access GO terms and annotations:
+ ## QuickGO annotations
```python
from bioservices import QuickGO
g = QuickGO(verbose=False)
-
- # Retrieve GO term information
- term_info = g.Term("GO:0003824", frmt="obo")
-
- # Search annotations
- annotations = g.Annotation(protein="P43403", format="tsv")
- ```
-
- ### 7. Protein-Protein Interactions
-
- Query interaction databases via PSICQUIC. **PSICQUIC is not shipped by every
- release — it is absent from 1.16.0** — so import it defensively and fall back to
- `IntactComplex`, `OmniPath`, or `STRING` when it is missing:
-
- ```python
- from bioservices import PSICQUIC
-
- s = PSICQUIC(verbose=False)
-
- # Query specific database (e.g., MINT)
- interactions = s.query("mint", "ZAP70 AND species:9606")
-
- # List available interaction databases
- databases = s.activeDBs
+ terms = g.get_go_terms("GO:0003824") # list of term dictionaries
+ page = g.Annotation(geneProductId="UniProtKB:P43403", includeFields="goName",
+ limit=100, page=1)
+ if not isinstance(page, dict) or "results" not in page:
+ raise RuntimeError("QuickGO request failed")
+ for annotation in page["results"][:5]:
+ print(annotation["goId"], annotation["goName"], annotation["goAspect"])
+ print(page["pageInfo"]) # current, total, resultsPerPage
```
- **Available databases:** MINT, IntAct, BioGRID, DIP, and 30+ others.
-
- ## Multi-Service Integration Workflows
+ `Term`, `Annotation(protein=..., format="tsv")`, and fixed TSV column offsets
+ belong to the old API. Fetch pages 1 through `pageInfo.total`; the SDK permits
+ 1–100 rows per page. Preserve qualifiers, evidence codes, references and taxon.
+ The protein script summarizes distinct positive terms and excludes `NOT`
+ assertions; its summary is not a raw annotation export or an enrichment test.
- BioServices excels at combining multiple services for comprehensive analysis. Common integration patterns:
+ ## Sequence similarity and associations
- ### Complete Protein Analysis Pipeline
+ `NCBIblast` wraps **EMBL-EBI Job Dispatcher**, not NCBI's BLAST URL API.
+ The SDK's current methods are `get_status`, `get_result`, `get_result_types`,
+ and `get_parameter_details`. Contact email is required by EMBL-EBI; this skill's
+ `NCBI_EMAIL` variable is a local convention, not automatically read by the SDK.
+ The submission example is illustrative; no live BLAST job was submitted in review.
- Execute a full protein characterization workflow:
+ ```python
+ import os
+ from bioservices import NCBIblast
- ```bash
- export NCBI_EMAIL=your.email@example.com
- python scripts/protein_analysis_workflow.py ZAP70_HUMAN
- # Or pass email as optional second argument if NCBI_EMAIL is unset
- python scripts/protein_analysis_workflow.py ZAP70_HUMAN your.email@example.com
+ blast = NCBIblast(verbose=False)
+ blast.services.url = "https://www.ebi.ac.uk/Tools/services/rest/ncbiblast"
+ # protein_sequence must contain the actual query sequence.
+ job_id = blast.run(program="blastp", sequence=protein_sequence, stype="protein",
+ database="uniprotkb", email=os.environ["NCBI_EMAIL"])
+ status = blast.get_status(job_id)
+ if status == "FINISHED":
+ result_types = blast.get_result_types(job_id)
+ report = blast.get_result(job_id, "out")
```
- This script demonstrates:
- 1. UniProt search for protein entry
- 2. FASTA sequence retrieval
- 3. BLAST similarity search
- 4. KEGG pathway discovery
- 5. PSICQUIC interaction mapping
-
- ### Pathway Network Analysis
-
- Analyze all pathways for an organism:
-
- ```bash
- python scripts/pathway_analysis.py hsa output_directory/
- ```
+ Poll with a delay and deadline. Stop on `FAILURE`, `ERROR`, or `NOT_FOUND`;
+ only retrieve completed jobs. Retain the job ID when a local wait times out.
+ Use the bundled script's bounded polling rather than an unbounded loop.
- Extracts and analyzes:
- - All pathway IDs for organism
- - Protein-protein interactions per pathway
- - Interaction type distributions
- - Exports to CSV/SIF formats
+ `PSICQUIC` is absent from 1.16.0. Use `STRING.get_interaction_partners` for
+ scored associations and provide the verified taxonomy ID; this is a different
+ evidence source, not an equivalent PSICQUIC replacement. STRING's default
+ functional edges can be indirect and do not establish physical binding.
- ### Cross-Database Compound Search
+ ## Bundled workflows
- Map compound identifiers across databases:
+ Run from this skill directory after installation:
```bash
+ python scripts/protein_analysis_workflow.py P43403 --skip-blast
+ python scripts/pathway_analysis.py hsa output_directory/ --limit 2
python scripts/compound_cross_reference.py Geldanamycin
- ```
-
- Retrieves:
- - KEGG compound ID
- - ChEBI identifier
- - ChEMBL identifier
- - Basic compound properties
-
- ### Batch Identifier Conversion
-
- Convert multiple identifiers at once:
-
- ```bash
python scripts/batch_id_converter.py input_ids.txt --from UniProtKB_AC-ID --to KEGG
- ```
-
- ## Best Practices
-
- ### Output Format Handling
-
- Different services return data in various formats:
- - **XML**: Parse using BeautifulSoup (most SOAP services)
- - **Tab-separated (TSV)**: Pandas DataFrames for tabular data
- - **Dictionary/JSON**: Direct Python manipulation
- - **FASTA**: BioPython integration for sequence analysis
-
- ### Rate Limiting and Verbosity
-
- Control API request behavior:
-
- ```python
- from bioservices import KEGG
-
- k = KEGG(verbose=False) # Suppress HTTP request details
- k.TIMEOUT = 30 # Adjust timeout for slow connections
- ```
-
- ### Error Handling
-
- Wrap service calls in try-except blocks:
-
- ```python
- try:
- results = u.search("ambiguous_query")
- if results:
- # Process results
- pass
- except Exception as e:
- print(f"Search failed: {e}")
- ```
-
- ### Organism Codes
-
- Use standard organism abbreviations:
- - `hsa`: Homo sapiens (human)
- - `mmu`: Mus musculus (mouse)
- - `dme`: Drosophila melanogaster
- - `sce`: Saccharomyces cerevisiae (yeast)
-
- List all organisms: `k.list("organism")` or `k.organismIds`
-
- ### Integration with Other Tools
-
- BioServices works well with:
- - **BioPython**: Sequence analysis on retrieved FASTA data
- - **Pandas**: Tabular data manipulation
- - **PyMOL**: 3D structure visualization (retrieve PDB IDs)
- - **NetworkX**: Network analysis of pathway interactions
- - **Galaxy**: Custom tool wrappers for workflow platforms
-
- ## Resources
-
- ### scripts/
-
- Executable Python scripts demonstrating complete workflows:
-
- - `protein_analysis_workflow.py`: End-to-end protein characterization
- - `pathway_analysis.py`: KEGG pathway discovery and network extraction
- - `compound_cross_reference.py`: Multi-database compound searching
- - `batch_id_converter.py`: Bulk identifier mapping utility
-
- Scripts can be executed directly or adapted for specific use cases.
-
- ### references/
-
- Detailed documentation loaded as needed:
-
- - `services_reference.md`: Comprehensive list of all 40+ services with methods
- - `workflow_patterns.md`: Detailed multi-step analysis workflows
- - `identifier_mapping.md`: Complete guide to cross-database ID conversion
-
- Load references when working with specific services or complex integration tasks.
-
- ## Installation
-
- ```bash
- uv pip install "bioservices==1.16.0"
+ python scripts/batch_id_converter.py --list-databases
```
- Dependencies are installed automatically. Upstream CI tests Python 3.9–3.12 ([PyPI](https://pypi.org/project/bioservices/), [docs](https://bioservices.readthedocs.io/)).
-
- ## Credentials
-
- Most services need no API key. Exceptions:
-
- | Service | Requirement |
- |---------|-------------|
- | NCBI BLAST | Contact email via `NCBI_EMAIL` or `email=` in `NCBIblast.run()` |
- | Some EBI services | Optional; check service docs if rate-limited |
-
- Set once per shell session:
-
- ```bash
- export NCBI_EMAIL=your.email@example.com
- ```
+ - [Protein analysis](scripts/protein_analysis_workflow.py): UniProt, optional
+ BLAST, all mapped KEGG genes, STRING associations, paginated QuickGO terms.
+ Prefer a stable accession; free-text searches display and use the first hit.
+ - [Pathway analysis](scripts/pathway_analysis.py): KGML entry/subtype counts and
+ CSV/SIF exports. Missing KGML is reported and skipped.
+ - [Compound lookup](scripts/compound_cross_reference.py): unique exact KEGG name match (or sole hit),
+ all ChEBI candidates, guarded UniChem mapping, ChEBI/ChEMBL properties.
+ - [Batch converter](scripts/batch_id_converter.py): preserves multiple targets;
+ CSV distinguishes `Success`, explicit `Unmapped`, and request/incomplete `Failed`.
- Use a real institutional or lab address — NCBI may contact you about heavy BLAST usage.
+ See [workflow patterns](references/workflow_patterns.md) for integration examples.
+ Network smoke tests covered the public core lookups; unit tests use current
+ response fixtures. Genome-scale downloads, paid/authenticated resources, and
+ live BLAST submissions were not tested. Service availability is not guaranteed.
- ## Additional Information
+ ## Sources and service limits
- For detailed API documentation and advanced features, refer to:
- - Official documentation: https://bioservices.readthedocs.io/
- - Source code: https://github.com/cokelaer/bioservices
- - Service-specific references in `references/services_reference.md`
+ Current signatures were checked against the [1.16.0 SDK documentation](https://bioservices.readthedocs.io/en/main/references.html)
+ and installed source. Provider contracts: [UniProt mapping fields](https://rest.uniprot.org/configure/idmapping/fields),
+ [KEGG API](https://www.kegg.jp/kegg/rest/keggapi.html),
+ [QuickGO API](https://www.ebi.ac.uk/QuickGO/api/index.html),
+ [Job Dispatcher](https://www.ebi.ac.uk/jdispatcher/docs/webservices/),
+ [STRING API](https://string-db.org/help/api/).
+ Configure timeouts on the actual transport, e.g. `k.services.TIMEOUT = 30`;
+ `k.TIMEOUT = 30` merely creates an unused attribute on many wrapper classes.
+ Use `cache=True` in supported constructors; `CACHE`/`DELAY` are not uniform
+ BioServices controls. STRING 1.16.0 issues direct requests without the transport's
+ timeout or rate limiter; bound large workflows externally and use provider
+ bulk downloads when appropriate.
## Citing Scientific Agent Skills
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> Kassis, T., Agarwal, V., He, Y., Patel, D., & Brueckner, A. M. (2026). Scientific Agent
> Skills: A Library of Procedural Knowledge for Research Agents. arXiv:2609.00065.
> https://doi.org/10.48550/arXiv.2609.00065
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