Workflow for read alignment, sorting, indexing, mapping statistics, and downstream-ready alignment artifacts.
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Query Ensembl for genomic data. Use when user asks about gene coordinates, genomic sequences, variants, gene structure, exons, transcripts, or species comparison. Triggers on "ensembl", "gene coordinates", "genomic location", "exon", "transcript", "variant location", "rsid", "rs number".
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
Query the Ensembl REST API for genomic annotations, sequences, and variants.
import requests
import json
BASE_URL = "https://rest.ensembl.org"
HEADERS = {"Content-Type": "application/json", "Accept": "application/json"}
# 1. Gene lookup by symbol
def lookup_gene(symbol, species="homo_sapiens"):
url = f"{BASE_URL}/lookup/symbol/{species}/{symbol}"
r = requests.get(url, headers=HEADERS, params={"expand": 1})
r.raise_for_status()
return r.json()
# 2. Get sequence
def get_sequence(ensembl_id, seq_type="genomic"):
url = f"{BASE_URL}/sequence/id/{ensembl_id}"
r = requests.get(url, headers=HEADERS, params={"type": seq_type})
r.raise_for_status()
return r.json()
# 3. Variant lookup by rsID
def lookup_variant(rsid, species="homo_sapiens"):
url = f"{BASE_URL}/variation/{species}/{rsid}"
r = requests.get(url, headers=HEADERS)
r.raise_for_status()
return r.json()
# 4. Get overlapping features in a region
def overlap_region(species, chrom, start, end, feature="gene"):
url = f"{BASE_URL}/overlap/region/{species}/{chrom}:{start}-{end}"
r = requests.get(url, headers=HEADERS, params={"feature": feature})
r.raise_for_status()
return r.json()
# 5. Cross-species homologs
def get_homologs(ensembl_id, target_species=None):
url = f"{BASE_URL}/homology/id/{ensembl_id}"
params = {}
if target_species:
params["target_species"] = target_species
r = requests.get(url, headers=HEADERS, params=params)
r.raise_for_status()
return r.json()
# Example: look up BRCA2
gene = lookup_gene("BRCA2")
print(f"Gene: {gene['display_name']}")
print(f"Ensembl ID: {gene['id']}")
print(f"Location: chr{gene['seq_region_name']}:{gene['start']}-{gene['end']}")
print(f"Strand: {'+' if gene['strand'] == 1 else '-'}")
print(f"Biotype: {gene['biotype']}")
print(f"Description: {gene.get('description', 'N/A')}")
| Endpoint | Use |
|---|---|
/lookup/symbol/{species}/{symbol} | Gene info by symbol |
/lookup/id/{id} | Info by Ensembl ID |
/sequence/id/{id}?type=genomic | Get sequence |
/variation/{species}/{rsid} | Variant info |
/overlap/region/{species}/{chr}:{start}-{end} | Features in region |
/homology/id/{id} | Orthologs/paralogs |
/vep/{species}/hgvs/{hgvs} | Variant effect prediction |
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Workflow for read alignment, sorting, indexing, mapping statistics, and downstream-ready alignment artifacts.
中文简介正在准备中。当前显示原文说明。
Validate protein designs using AlphaFold2 structure prediction. Use this skill when: (1) Validating designed sequences fold correctly, (2) Predicting binder-target complex structures, (3) Calculating confidence metrics (pLDDT, pTM, ipTM), (4) Self-consistency validation of designs, (5) Multi-chain complex prediction with AlphaFold-Multimer. For faster single-chain prediction, use esm. For QC thresholds, use protein-qc.
中文简介正在准备中。当前显示原文说明。
AlphaFold2 / AlphaFold-Multimer structure prediction for validation and confidence scoring. Use this skill when: (1) Validating designed sequences fold correctly, (2) Predicting binder-target complex structures, (3) Calculating confidence metrics (pLDDT, pTM, ipTM), (4) Self-consistency validation of designs, (5) Multi-chain complex prediction with AlphaFold-Multimer. For faster single-chain prediction, use esm2-sequence-scoring. For QC thresholds, use protein-design-qc.
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Workflow for event-level and isoform-level splicing analysis with sashimi-ready outputs and splice QC.
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ATAC-seq processing with assay QC, MACS3 peak calling, consensus peak matrices, differential accessibility, and motif or footprint follow-up.
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End-to-end binder design using BindCraft hallucination. Use this skill when: (1) Designing protein binders with built-in AF2 validation, (2) Running production-quality binder campaigns, (3) Using different design protocols (fast, default, slow), (4) Need joint backbone and sequence optimization, (5) Want high experimental success rate. For backbone-only generation, use rfdiffusion. For QC thresholds, use protein-qc. For tool selection guidance, use binder-design.
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