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数据与复现
BI
bio-immunoinformatics-neoantigen-prediction
维护者 FreedomIntelligence · 最近更新 2026年4月1日
Identify tumor neoantigens from somatic mutations using pVACtools for personalized cancer immunotherapy. Predict mutant peptides that bind patient HLA and may elicit T-cell responses. Use when identifying vaccine targets or checkpoint inhibitor response biomarkers from tumor sequencing data.
原始来源
FreedomIntelligence/OpenClaw-Medical-Skills
https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-immunoinformatics-neoantigen-prediction
- 维护者
- FreedomIntelligence
- 许可
- MIT
- 最近更新
- 2026年4月1日
技能摘要
来自 SKILL.md 的关键信息
核心说明
- CLI：pvacseq run ，支持 VEP-annotated VCF 、 patient HLA types (pVACtools)。
- Identify neoantigens ，面向 my tumor mutations" → Predict mutant peptides ，面向 somatic variants that bind patient HLA alleles 、 may elicit T-cell responses ，用于 personalized cancer immunotherapy. CLI：pvacseq run ，支持 VEP-annotated VCF 、 patient HLA types (pVACtools)。
- conda create -n pvactools python=3.8 conda activate pvactools pip install pvactools。
原始文档
SKILL.md 摘录
pVACtools Pipeline (Ensembl VEP 111+)
Goal: Install pVACtools and its IEDB prediction engine dependencies.
Approach: Install via pip (optionally in a dedicated conda environment) and download IEDB tools for binding prediction.
## Download IEDB tools
pvactools download_iedb_tools
pVACseq Workflow (Ensembl VEP 111+)
Goal: Run the full pVACseq neoantigen prediction pipeline on a VEP-annotated VCF.
Approach: Provide annotated VCF with patient HLA alleles and select binding prediction algorithms; pVACseq generates mutant peptides and predicts MHC binding.
适用场景
- 适合在identifying vaccine targets 或 checkpoint inhibitor response biomarkers ，面向 tumor sequencing data时使用。
不适用场景
- Do not rely on this catalog entry alone ，用于 installation 或 maintenance details。
上游相关技能
- immunoinformatics/mhc-binding-prediction - MHC binding details
- immunoinformatics/immunogenicity-scoring - Prioritization
- variant-calling/variant-calling - Input somatic mutations
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