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Ensemble tumor neoantigen prediction and multi-parameter quality analysis from direct input, SNVs, indels, or gene fusion variants.

Detailed flowchart.


An R package for neoantigen analysis that takes human or murine DNA missense mutations, insertions, deletions, or RNASeq-derived gene fusions and performs ensemble neoantigen prediction using 7 algorithms. Input is a VCF file, JAFFA output, or table of peptides or transcripts. Outputs are ranked and summarized by sample. Neoantigens are ranked by MHC I/II binding affinity, clonality, RNA expression, similarity to known immunogenic antigens, and dissimilarity to the normal peptidome.


  1. Thoroughness:
    • missense mutations, insertions, deletions, and gene fusions
    • human and mouse
    • ensemble MHC class I/II binding prediction using mhcflurry, mhcnuggets, netMHC, netMHCII, netMHCpan and netMHCIIpan
    • ranked by
      • MHC I/II binding affinity
      • clonality
      • RNA expression
      • similarity to known immunogenic antigens
      • dissimilarity to the normal peptidome
  2. Speed and simplicity:
  3. Integration with R/Bioconductor
    • upstream/VCF processing
    • exploratory data analysis, visualization



  • Linux
  • R ≥ 3.4
  • python-pip
  • sudo is required to install prediction tool dependencies


Install all dependencies, prediction tools, and antigen.garnish

One-line installation script:

  • detailed installation instructions for bootstrapping a fresh AWS instance can be found in the wiki
  • please note that netMHC, netMHCpan, netMHCII, and netMHCIIpan require academic-use only licenses

Package documentation (pdf)


  1. Prepare input for MHC affinity prediction and quality analysis:

    • VCF input - garnish_variants
    • Fusions from RNASeq via JAFFA- garnish_jaffa
    • Prepare table of direct transcript or peptide input - see manual page in R (?garnish_affinity)
  2. Add MHC alleles of interest - see examples below.
  3. Run ensemble prediction method and perform antigen quality analysis including proteome-wide differential agretopicity, IEDB alignment score, and dissimilarity: garnish_affinity.
  4. Summarize output by sample level with garnish_summary and garnish_plot, and prioritize the highest quality neoantigens per clone and sample with garnish_antigens.


Get full MHC affinity output from a Microsoft Excel file of variants

Directly calculate IEDB score and dissimilarity for a list of sequences

Automated testing

From ./<Github repo>:

  devtools::test(reporter = "summary")

Plots and summary tables

  • garnish_plot output:

  • garnish_antigens output:


Richman LP, Vonderheide RH, and Rech AJ. “Neoantigen dissimilarity to the self-proteome predicts immunogenicity and response to immune checkpoint blockade.” Cell Systems 2019 in press


We welcome contributions and feedback via Github or email.


We thank the follow individuals for contributions and helpful discussion:


Please see included license or contact us with questions.