bio-workflows-neoantigen-pipeline
End-to-end neoantigen discovery from somatic variants to ranked vaccine candidates. Integrates HLA typing, MHC binding prediction, pVACtools neoantigen calling, and immunogenicity scoring. Use when identifying tumor neoantigens for personalized vaccine design or checkpoint biomarkers.
npx skills add BioTender-max/awesome-bio-agent-skills --skill neoantigen-pipeline --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
## Version Compatibility Reference examples tested with: Ensembl VEP 111+, MHCflurry 2.1+, OptiType 1.3+, matplotlib 3.8+, numpy 1.26+, pVACtools 4.1+, pandas 2.2+, seaborn 0.13+ Before using code patterns, verify installed versions match. If versions differ: - Python: `pip show <package>` then `help(module.function)` to check signatures - CLI: `<tool> --version` then `<tool> --help` to confirm flags If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying. # Neoantigen Pipeline **"Predict neoantigens from my tumor sequencing data"** → Orchestrate HLA typing (OptiType), somatic variant calling, pVACtools neoantigen prediction, MHC binding scoring, and immunogenicity-based candidate ranking for personalized cancer immunotherapy. Complete workflow from somatic variants to ranked neoantigen vaccine candidates for personalized cancer immunotherapy. ## Workflow Overview ``` Somatic VCF (annotated) + Tumor RNA-seq (optional) | v [1. HLA Typing] --> arcasHLA / OptiType (if types not provided) | v [2. MHC Binding Prediction] --> MHCflurry / NetMHCpan | v [3. Neoantigen Calling] --> pVACseq
- Version Compatibility
- Workflow Overview
- Prerequisites (Ensembl VEP 111+)
- Primary Path: pVACseq Pipeline
- Step 1: HLA Typing (if not provided)
- Step 2: VCF Annotation with VEP
- Step 3: Run pVACseq (Ensembl VEP 111+)
- Step 4: Filter and Rank Candidates
- Step 5: MHC Class II Neoantigens (CD4+ T cell help)
- Alternative: Standalone MHCflurry
- Visualization
- Parameter Recommendations
- Troubleshooting
- Output Files
pip install pvactools mhcflurry vatools mhcflurry-downloads fetch conda install -c bioconda vep arcashla optitype From tumor RNA-seq BAM 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 \ Parse results cat hla_output/tumor.genotype.json Annotate somatic VCF vep --input_file somatic.vcf \
What does the bio-workflows-neoantigen-pipeline skill do?
End-to-end neoantigen discovery from somatic variants to ranked vaccine candidates. Integrates HLA typing, MHC binding prediction, pVACtools neoantigen calling, and immunogenicity scoring. Use when identifying tumor neoantigens for personalized vaccine design or checkpoint biomarkers.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill neoantigen-pipeline --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.