bioservices

Name: bioservices
Author: LeonChaoX

42installs61forks

How to install bioservices?

leonchaox/qinyan-academic-skills/bioservices

$npx -y skills add LeonChaoX/qinyan-academic-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

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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	## When to Use This Skill
8
9	This skill should be used when:
10	- Retrieving protein sequences, annotations, or structures from UniProt, PDB, Pfam
11	- Analyzing metabolic pathways and gene functions via KEGG or Reactome
12	- Searching compound databases (ChEBI, ChEMBL, PubChem) for chemical information
13	- Converting identifiers between different biological databases (KEGG↔UniProt, compound IDs)
14	- Running sequence similarity searches (BLAST, MUSCLE alignment)
15	- Querying gene ontology terms (QuickGO, GO annotations)
16	- Accessing protein-protein interaction data (PSICQUIC, IntactComplex)
17	- Mining genomic data (BioMart, ArrayExpress, ENA)
18	- Integrating data from multiple bioinformatics resources in a single workflow
19
20	## Core Capabilities
21
22	### 1. Protein Analysis
23
24	Retrieve protein information, sequences, and functional annotations:
25
26	```python
27	from bioservices import UniProt
28
29	u = UniProt(verbose=False)
30
31	# Search for protein by name
32	results = u.search("ZAP70_HUMAN", frmt="tab", columns="id,genes,organism")
33
34	# Retrieve FASTA sequence
35	sequence = u.retrieve("P43403", "fasta")
36
37	# Map identifiers between databases
38	kegg_ids = u.mapping(fr="UniProtKB_AC-ID", to="KEGG", query="P43403")
39	```
40
41	Key methods:
42	- `search()`: Query UniProt with flexible search terms
43	- `retrieve()`: Get protein entries in various formats (FASTA, XML, tab)
44	- `mapping()`: Convert identifiers between databases
45
46	Reference: `references/services_reference.md` for complete UniProt API details.
47
48	### 2. Pathway Discovery and Analysis
49
50	Access KEGG pathway information for genes and organisms:
51
52	```python
53	from bioservices import KEGG
54
55	k = KEGG()
56	k.organism = "hsa" # Set to human
57
58	# Search for organisms
59	k.lookfor_organism("droso") # Find Drosophila species
60
61	# Find pathways by name
62	k.lookfor_pathway("B cell") # Returns matching pathway IDs
63
64	# Get pathways containing specific genes
65	pathways = k.get_pathway_by_gene("7535", "hsa") # ZAP70 gene
66
67	# Retrieve and parse pathway data
68	data = k.get("hsa04660")
69	parsed = k.parse(data)
70
71	# Extract pathway interactions
72	interactions = k.parse_kgml_pathway("hsa04660")
73	relations = interactions['relations'] # Protein-protein interactions
74
75	# Convert to Simple Interaction Format
76	sif_data = k.pathway2sif("hsa04660")
77	```
78
79	Key methods:
80	- `lookfor_organism()`, `lookfor_pathway()`: Search by name
81	- `get_pathway_by_gene()`: Find pathways containing genes
82	- `parse_kgml_pathway()`: Extract structured pathway data
83	- `pathway2sif()`: Get protein interaction networks
84
85	Reference: `references/workflow_patterns.md` for complete pathway analysis workflows.
86
87	### 3. Compound Database Searches
88
89	Search and cross-reference compounds across multiple databases:
90
91	```python
92	from bioservices import KEGG, UniChem
93
94	k = KEGG()
95
96	# Search compounds by name
97	results = k.find("compound", "Geldanamycin") # Returns cpd:C11222
98
99	# Get compound information with database links
100	compound_info = k.get("cpd:C11222") # Includes ChEBI links
101
102	# Cross-reference KEGG → ChEMBL using UniChem
103	u = UniChem()
104	chembl_id = u.get_compound_id_from_kegg("C11222") # Returns CHEMBL278315
105	```
106
107	Common workflow:
108	1. Search compound by name in KEGG
109	2. Extract KEGG compound ID
110	3. Use UniChem for KEGG → ChEMBL mapping
111	4. ChEBI IDs are often provided in KEGG entries
112
113	Reference: `references/identifier_mapping.md` for complete cross-database mapping guide.
114
115	### 4. Sequence Analysis
116
117	Run BLAST searches and sequence alignments:
118
119	```python
120	from bioservices import NCBIblast
121
122	s = NCBIblast(verbose=False)
123
124	# Run BLASTP against UniProtKB
125	jobid = s.run(
126	program="blastp",
127	sequence=protein_sequence,
128	stype="protein",
129	database="uniprotkb",
130	email="your.email@example.com" # Required by NCBI
131	)
132
133	# Check job status and retrieve results
134	s.getStatus(jobid)
135	results = s.getResult(jobid, "out")
136	```
137
138	Note: BLAST jobs are asynchronous. Check status before retrieving results.
139
140	### 5. Identifier Mapping
141
142	Convert identifiers between different biological databases:
143
144	```python
145	from bioservices import UniProt, KEGG
146
147	# UniProt mapping (many database pairs supported)
148	u = UniProt()
149	results = u.mapping(
150	fr="UniProtKB_AC-ID", # Source database
151	to="KEGG", # Target database
152	query="P43403"

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bioservices

42installs61forks

View on GitHub

How to install bioservices?

leonchaox/qinyan-academic-skills/bioservices

$npx -y skills add LeonChaoX/qinyan-academic-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	## When to Use This Skill
8
9	This skill should be used when:
10	- Retrieving protein sequences, annotations, or structures from UniProt, PDB, Pfam
11	- Analyzing metabolic pathways and gene functions via KEGG or Reactome
12	- Searching compound databases (ChEBI, ChEMBL, PubChem) for chemical information
13	- Converting identifiers between different biological databases (KEGG↔UniProt, compound IDs)
14	- Running sequence similarity searches (BLAST, MUSCLE alignment)
15	- Querying gene ontology terms (QuickGO, GO annotations)
16	- Accessing protein-protein interaction data (PSICQUIC, IntactComplex)
17	- Mining genomic data (BioMart, ArrayExpress, ENA)
18	- Integrating data from multiple bioinformatics resources in a single workflow
19
20	## Core Capabilities
21
22	### 1. Protein Analysis
23
24	Retrieve protein information, sequences, and functional annotations:
25
26	```python
27	from bioservices import UniProt
28
29	u = UniProt(verbose=False)
30
31	# Search for protein by name
32	results = u.search("ZAP70_HUMAN", frmt="tab", columns="id,genes,organism")
33
34	# Retrieve FASTA sequence
35	sequence = u.retrieve("P43403", "fasta")
36
37	# Map identifiers between databases
38	kegg_ids = u.mapping(fr="UniProtKB_AC-ID", to="KEGG", query="P43403")
39	```
40
41	Key methods:
42	- `search()`: Query UniProt with flexible search terms
43	- `retrieve()`: Get protein entries in various formats (FASTA, XML, tab)
44	- `mapping()`: Convert identifiers between databases
45
46	Reference: `references/services_reference.md` for complete UniProt API details.
47
48	### 2. Pathway Discovery and Analysis
49
50	Access KEGG pathway information for genes and organisms:
51
52	```python
53	from bioservices import KEGG
54
55	k = KEGG()
56	k.organism = "hsa" # Set to human
57
58	# Search for organisms
59	k.lookfor_organism("droso") # Find Drosophila species
60
61	# Find pathways by name
62	k.lookfor_pathway("B cell") # Returns matching pathway IDs
63
64	# Get pathways containing specific genes
65	pathways = k.get_pathway_by_gene("7535", "hsa") # ZAP70 gene
66
67	# Retrieve and parse pathway data
68	data = k.get("hsa04660")
69	parsed = k.parse(data)
70
71	# Extract pathway interactions
72	interactions = k.parse_kgml_pathway("hsa04660")
73	relations = interactions['relations'] # Protein-protein interactions
74
75	# Convert to Simple Interaction Format
76	sif_data = k.pathway2sif("hsa04660")
77	```
78
79	Key methods:
80	- `lookfor_organism()`, `lookfor_pathway()`: Search by name
81	- `get_pathway_by_gene()`: Find pathways containing genes
82	- `parse_kgml_pathway()`: Extract structured pathway data
83	- `pathway2sif()`: Get protein interaction networks
84
85	Reference: `references/workflow_patterns.md` for complete pathway analysis workflows.
86
87	### 3. Compound Database Searches
88
89	Search and cross-reference compounds across multiple databases:
90
91	```python
92	from bioservices import KEGG, UniChem
93
94	k = KEGG()
95
96	# Search compounds by name
97	results = k.find("compound", "Geldanamycin") # Returns cpd:C11222
98
99	# Get compound information with database links
100	compound_info = k.get("cpd:C11222") # Includes ChEBI links
101
102	# Cross-reference KEGG → ChEMBL using UniChem
103	u = UniChem()
104	chembl_id = u.get_compound_id_from_kegg("C11222") # Returns CHEMBL278315
105	```
106
107	Common workflow:
108	1. Search compound by name in KEGG
109	2. Extract KEGG compound ID
110	3. Use UniChem for KEGG → ChEMBL mapping
111	4. ChEBI IDs are often provided in KEGG entries
112
113	Reference: `references/identifier_mapping.md` for complete cross-database mapping guide.
114
115	### 4. Sequence Analysis
116
117	Run BLAST searches and sequence alignments:
118
119	```python
120	from bioservices import NCBIblast
121
122	s = NCBIblast(verbose=False)
123
124	# Run BLASTP against UniProtKB
125	jobid = s.run(
126	program="blastp",
127	sequence=protein_sequence,
128	stype="protein",
129	database="uniprotkb",
130	email="your.email@example.com" # Required by NCBI
131	)
132
133	# Check job status and retrieve results
134	s.getStatus(jobid)
135	results = s.getResult(jobid, "out")
136	```
137
138	Note: BLAST jobs are asynchronous. Check status before retrieving results.
139
140	### 5. Identifier Mapping
141
142	Convert identifiers between different biological databases:
143
144	```python
145	from bioservices import UniProt, KEGG
146
147	# UniProt mapping (many database pairs supported)
148	u = UniProt()
149	results = u.mapping(
150	fr="UniProtKB_AC-ID", # Source database
151	to="KEGG", # Target database
152	query="P43403"

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