数据与复现生物信息与基因组学FreedomIntelligence/OpenClaw-Medical-Skills数据与复现

bio-workflows-neoantigen-pipeline

维护者 FreedomIntelligence · 最近更新 2026年4月1日

Neoantigen prediction: somatic variants → MHC binding → immunogenicity.

OpenClawNanoClaw分析处理复现实验bio-workflows-neoantigen-pipeline🧠 bioos extended suitebioos extended bioinformatics suiteneoantigen

原始来源

FreedomIntelligence/OpenClaw-Medical-Skills

https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-workflows-neoantigen-pipeline

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

2 min

核心说明

Complete workflow ，面向 somatic variants to ranked neoantigen vaccine candidates ，用于 personalized cancer immunotherapy。
arcasHLA extract tumor.bam -t 8 -o hla_output/ arcasHLA genotype hla_output/tumor.extracted.1.fq.gz hla_output/tumor.extracted.2.fq.gz \ -g，B，C，DRB1，DQB1，DPB1 -t 8 -o hla_output/。

原始文档

SKILL.md 摘录

Step 1: HLA Typing (if not provided)

HLA types are critical for MHC binding prediction. If not already known from clinical testing:


## Parse results

cat hla_output/tumor.genotype.json
python
import json

with open('hla_output/tumor.genotype.json') as f:
    hla_data = json.load(f)

hla_alleles = []
for gene, alleles in hla_data.items():
    for allele in alleles:
        hla_alleles.append(f'HLA-{allele}')

## Format for pVACseq: HLA-A*02:01,HLA-A*24:02,HLA-B*07:02,...

hla_string = ','.join(hla_alleles)
print(f'HLA alleles: {hla_string}')

适用场景

Use bio-workflows-neoantigen-pipeline ，用于 genomics 、 bioinformatics workflows。
Apply bio-workflows-neoantigen-pipeline to sequencing，variant，或 omics analysis tasks。