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antigen.garnish

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

Detailed flowchart.

Description

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.

Advantages

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
Speed and simplicity:
- 1000 variants are ranked in a single step in less than five minutes
- parallelized using parallel::mclapply and data.table::setDTthreads, see respective links for information on setting multicore usage
Integration with R/Bioconductor
- upstream/VCF processing
- exploratory data analysis, visualization

Installation

Requirements

Linux
R ≥ 3.4
- see documentation Imports for R, Biostrings package from Bioconductor
python-pip
sudo is required to install prediction tool dependencies

a Docker image is available, please see the wiki or contact us

Install all dependencies, prediction tools, and `antigen.garnish`

One-line installation script:

$ curl -fsSL http://get.rech.io/install_antigen.garnish.sh | sudo sh

if installing without using the above installation script, set $AG_DATA_DIR to the required data directory:

$ curl -fsSL "http://get.rech.io/antigen.garnish.tar.gz" | tar -xvz
$ export AG_DATA_DIR="$PWD/antigen.garnish"

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)

Workflow

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)
Add MHC alleles of interest - see examples below.
Run ensemble prediction method and perform antigen quality analysis including proteome-wide differential agretopicity, IEDB alignment score, and dissimilarity: garnish_affinity.
Summarize output by sample level with garnish_summary and garnish_plot, and prioritize the highest quality neoantigens per clone and sample with garnish_antigens.

Examples

Predict neoantigens from missense mutations, insertions, and deletions

library(magrittr)
library(data.table)
library(antigen.garnish)

  # load an example VCF
	dir <- system.file(package = "antigen.garnish") %>%
		file.path(., "extdata/testdata")

	dt <- "antigen.garnish_example.vcf" %>%
	file.path(dir, .) %>%

  # extract variants
    garnish_variants %>%

  # add space separated MHC types

  # see list_mhc() for nomenclature of supported alleles

	# MHC may also be set to "all_human" or "all_mouse" to use all supported alleles

      .[, MHC := c("HLA-A*01:47 HLA-A*02:01 HLA-DRB1*14:67")] %>%

  # predict neoantigens
    garnish_affinity

  # summarize predictions
    dt %>%
      garnish_summary %T>%
        print

  # generate summary graphs
    dt %>% garnish_plot

Predict neoantigens from gene fusions

library(magrittr)
library(data.table)
library(antigen.garnish)

  # load example jaffa output
	dir <- system.file(package = "antigen.garnish") %>%
		file.path(., "extdata/testdata")

	path <- "antigen.garnish_jaffa_results.csv" %>%
			file.path(dir, .)
	fasta_path <- "antigen.garnish_jaffa_results.fasta" %>%
			file.path(dir, .)

  # get predictions
    dt <- garnish_jaffa(path, db = "GRCm38", fasta_path) %>%

  # add MHC info with list_mhc() compatible names
    .[, MHC := "H-2-Kb"] %>%

  # get predictions
    garnish_affinity %>%

  # summarize predictions
    garnish_summary %T>%
    print

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

library(magrittr)
library(data.table)
library(antigen.garnish)

  # load example Microsoft Excel file
  dir <- system.file(package = "antigen.garnish") %>%
    file.path(., "extdata/testdata")

  path <- "antigen.garnish_test_input.xlsx" %>%
    file.path(dir, .)

  # predict neoantigens
    dt <- garnish_affinity(path = path) %T>%
      str

Directly calculate IEDB score and dissimilarity for a list of sequences

library(magrittr)
library(data.table)
library(antigen.garnish)

  # generate our character vector of sequences
  v <- c("SIINFEKL", "ILAKFLHWL", "GILGFVFTL")

  # calculate IEDB score
  v %>% iedb_score(db = "human") %>% print

	# calculate dissimilarity
	v %>% garnish_dissimilarity(db = "human") %>% print

Automated testing

From ./<Github repo>:

  devtools::test(reporter = "summary")

How are peptides generated?

  library(magrittr)
  library(data.table)
  library(antigen.garnish)

  # generate a fake peptide
    dt <- data.table::data.table(
       pep_base = "Y___*___THIS_IS_________*___A_CODE_TEST!______*__X",
       mutant_index = c(5, 25, 47, 50),
       pep_type = "test",
       var_uuid = c(
                    "front_truncate",
                    "middle",
                    "back_truncate",
                    "end")) %>%
  # create nmers
    make_nmers %T>% print

Plots and summary tables

garnish_plot output:

garnish_antigens output:

Citation

Richman LP, Vonderheide RH, and Rech AJ. "Neoantigen dissimilarity to the self-proteome predicts immunogenicity and response to immune checkpoint blockade." Cell Systems 9, 375-382.E4, (2019).

Contributing

We welcome contributions and feedback via Github or email.

Acknowledgments

We thank the follow individuals for contributions and helpful discussion:

License

Please see included license or contact us with questions.

mengchengyao / antigen.garnish Goto Github PK

antigen.garnish's Introduction