bioservices

Name: bioservices
Author: K-Dense-AI

827installs3.1kforks

How to install bioservices?

k-dense-ai/scientific-agent-skills/bioservices

$npx -y skills add K-Dense-AI/scientific-agent-skills --skill bioservices

Installs into the current project.

Summary

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

Files · 1

View on GitHub

SKILL.md

1	# BioServices
2
3	## Overview
4
5	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.
6
7	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.
8
9	## When to Use This Skill
10
11	This skill should be used when:
12	- Retrieving protein sequences, annotations, or structures from UniProt, PDB, Pfam
13	- Analyzing metabolic pathways and gene functions via KEGG or Reactome
14	- Searching compound databases (ChEBI, ChEMBL, PubChem) for chemical information
15	- Converting identifiers between different biological databases (KEGG↔UniProt, compound IDs)
16	- Running sequence similarity searches (BLAST, MUSCLE alignment)
17	- Querying gene ontology terms (QuickGO, GO annotations)
18	- Accessing protein-protein interaction data (PSICQUIC, IntactComplex)
19	- Mining genomic data (BioMart, ArrayExpress, ENA)
20	- Integrating data from multiple bioinformatics resources in a single workflow
21
22	## Core Capabilities
23
24	### 1. Protein Analysis
25
26	Retrieve protein information, sequences, and functional annotations:
27
28	```python
29	from bioservices import UniProt
30
31	u = UniProt(verbose=False)
32
33	# Search for protein by name
34	results = u.search("ZAP70_HUMAN", frmt="tab", columns="id,genes,organism")
35
36	# Retrieve FASTA sequence
37	sequence = u.retrieve("P43403", "fasta")
38
39	# Map identifiers between databases
40	kegg_ids = u.mapping(fr="UniProtKB_AC-ID", to="KEGG", query="P43403")
41	```
42
43	Key methods:
44	- `search()`: Query UniProt with flexible search terms
45	- `retrieve()`: Get protein entries in various formats (FASTA, XML, tab)
46	- `mapping()`: Convert identifiers between databases
47
48	Reference: `references/services_reference.md` for complete UniProt API details.
49
50	### 2. Pathway Discovery and Analysis
51
52	Access KEGG pathway information for genes and organisms:
53
54	```python
55	from bioservices import KEGG
56
57	k = KEGG()
58	k.organism = "hsa" # Set to human
59
60	# Search for organisms
61	k.lookfor_organism("droso") # Find Drosophila species
62
63	# Find pathways by name
64	k.lookfor_pathway("B cell") # Returns matching pathway IDs
65
66	# Get pathways containing specific genes
67	pathways = k.get_pathway_by_gene("7535", "hsa") # ZAP70 gene
68
69	# Retrieve and parse pathway data
70	data = k.get("hsa04660")
71	parsed = k.parse(data)
72
73	# Extract pathway interactions
74	interactions = k.parse_kgml_pathway("hsa04660")
75	relations = interactions['relations'] # Protein-protein interactions
76
77	# Convert to Simple Interaction Format
78	sif_data = k.pathway2sif("hsa04660")
79	```
80
81	Key methods:
82	- `lookfor_organism()`, `lookfor_pathway()`: Search by name
83	- `get_pathway_by_gene()`: Find pathways containing genes
84	- `parse_kgml_pathway()`: Extract structured pathway data
85	- `pathway2sif()`: Get protein interaction networks
86
87	Reference: `references/workflow_patterns.md` for complete pathway analysis workflows.
88
89	### 3. Compound Database Searches
90
91	Search and cross-reference compounds across multiple databases:
92
93	```python
94	from bioservices import KEGG, UniChem
95
96	k = KEGG()
97
98	# Search compounds by name
99	results = k.find("compound", "Geldanamycin") # Returns cpd:C11222
100
101	# Get compound information with database links
102	compound_info = k.get("cpd:C11222") # Includes ChEBI links
103
104	# Cross-reference KEGG → ChEMBL using UniChem
105	u = UniChem()
106	chembl_id = u.get_compound_id_from_kegg("C11222") # Returns CHEMBL278315
107	```
108
109	Common workflow:
110	1. Search compound by name in KEGG
111	2. Extract KEGG compound ID
112	3. Use UniChem for KEGG → ChEMBL mapping
113	4. ChEBI IDs are often provided in KEGG entries
114
115	Reference: `references/identifier_mapping.md` for complete cross-dat

People who liked this also viewed

6 picks

Type

bioservices

827installs3.1kforks

View on GitHub

How to install bioservices?

k-dense-ai/scientific-agent-skills/bioservices

$npx -y skills add K-Dense-AI/scientific-agent-skills --skill bioservices

Installs into the current project.

Summary

Files · 1

View on GitHub

SKILL.md

1	# BioServices
2
3	## Overview
4
5	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.
6
7	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.
8
9	## When to Use This Skill
10
11	This skill should be used when:
12	- Retrieving protein sequences, annotations, or structures from UniProt, PDB, Pfam
13	- Analyzing metabolic pathways and gene functions via KEGG or Reactome
14	- Searching compound databases (ChEBI, ChEMBL, PubChem) for chemical information
15	- Converting identifiers between different biological databases (KEGG↔UniProt, compound IDs)
16	- Running sequence similarity searches (BLAST, MUSCLE alignment)
17	- Querying gene ontology terms (QuickGO, GO annotations)
18	- Accessing protein-protein interaction data (PSICQUIC, IntactComplex)
19	- Mining genomic data (BioMart, ArrayExpress, ENA)
20	- Integrating data from multiple bioinformatics resources in a single workflow
21
22	## Core Capabilities
23
24	### 1. Protein Analysis
25
26	Retrieve protein information, sequences, and functional annotations:
27
28	```python
29	from bioservices import UniProt
30
31	u = UniProt(verbose=False)
32
33	# Search for protein by name
34	results = u.search("ZAP70_HUMAN", frmt="tab", columns="id,genes,organism")
35
36	# Retrieve FASTA sequence
37	sequence = u.retrieve("P43403", "fasta")
38
39	# Map identifiers between databases
40	kegg_ids = u.mapping(fr="UniProtKB_AC-ID", to="KEGG", query="P43403")
41	```
42
43	Key methods:
44	- `search()`: Query UniProt with flexible search terms
45	- `retrieve()`: Get protein entries in various formats (FASTA, XML, tab)
46	- `mapping()`: Convert identifiers between databases
47
48	Reference: `references/services_reference.md` for complete UniProt API details.
49
50	### 2. Pathway Discovery and Analysis
51
52	Access KEGG pathway information for genes and organisms:
53
54	```python
55	from bioservices import KEGG
56
57	k = KEGG()
58	k.organism = "hsa" # Set to human
59
60	# Search for organisms
61	k.lookfor_organism("droso") # Find Drosophila species
62
63	# Find pathways by name
64	k.lookfor_pathway("B cell") # Returns matching pathway IDs
65
66	# Get pathways containing specific genes
67	pathways = k.get_pathway_by_gene("7535", "hsa") # ZAP70 gene
68
69	# Retrieve and parse pathway data
70	data = k.get("hsa04660")
71	parsed = k.parse(data)
72
73	# Extract pathway interactions
74	interactions = k.parse_kgml_pathway("hsa04660")
75	relations = interactions['relations'] # Protein-protein interactions
76
77	# Convert to Simple Interaction Format
78	sif_data = k.pathway2sif("hsa04660")
79	```
80
81	Key methods:
82	- `lookfor_organism()`, `lookfor_pathway()`: Search by name
83	- `get_pathway_by_gene()`: Find pathways containing genes
84	- `parse_kgml_pathway()`: Extract structured pathway data
85	- `pathway2sif()`: Get protein interaction networks
86
87	Reference: `references/workflow_patterns.md` for complete pathway analysis workflows.
88
89	### 3. Compound Database Searches
90
91	Search and cross-reference compounds across multiple databases:
92
93	```python
94	from bioservices import KEGG, UniChem
95
96	k = KEGG()
97
98	# Search compounds by name
99	results = k.find("compound", "Geldanamycin") # Returns cpd:C11222
100
101	# Get compound information with database links
102	compound_info = k.get("cpd:C11222") # Includes ChEBI links
103
104	# Cross-reference KEGG → ChEMBL using UniChem
105	u = UniChem()
106	chembl_id = u.get_compound_id_from_kegg("C11222") # Returns CHEMBL278315
107	```
108
109	Common workflow:
110	1. Search compound by name in KEGG
111	2. Extract KEGG compound ID
112	3. Use UniChem for KEGG → ChEMBL mapping
113	4. ChEBI IDs are often provided in KEGG entries
114
115	Reference: `references/identifier_mapping.md` for complete cross-dat

People who liked this also viewed

6 picks

Type