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Convert TCR gene names

TCRconvertR converts V, D, J, and/or C gene names between the 10X Genomics, Adaptive Biotechnologies, and IMGT nomenclatures. It supports alpha-beta and gamma-delta T cell receptors (TCRs) for human, mouse, and rhesus macaque. Users can also define custom species, see: vignette("custom-species"). A Python version with command-line support is also available.

Background

TCR annotation tools use different gene naming conventions, making cross-dataset searches difficult (e.g., identifying 10X-annotated TCRs in Adaptive data). Manual conversion is complex and error-prone due to inconsistencies in naming rules.

TCRconvertR automates this process efficiently and accurately. Our approach is based on analyzing multiple 10X and Adaptive data sets to capture their naming variations.

Installation

Install the latest stable version from CRAN:

install.packages("TCRconvertR")

You can also install the development version from GitHub:

# install.packages("pak")
pak::pak("seshadrilab/tcrconvertr")

Usage

1. Load TCRs into a data frame

Examples of files you may want to load:

  • 10X: filtered_contig_annotations.csv
  • Adaptive: Sample_TCRB.tsv
  • IMGT: Output from MiXCR or other tools
library(TCRconvertR)

tcr_file <- get_example_path("tenx.csv") # Using built-in example file
tcrs <- read.csv(tcr_file)[c("barcode", "v_gene", "j_gene", "cdr3")]
tcrs
#>              barcode       v_gene  j_gene            cdr3
#> 1 AAACCTGAGACCACGA-1   TRAV29/DV5  TRAJ12    CAVMDSSYKLIF
#> 2 AAACCTGAGACCACGA-1 TRBV20/OR9-2 TRBJ2-1 CASSGLAGGYNEQFF
#> 3 AAACCTGAGGCTCTTA-1        TRDV2   TRDJ3 CASSGVAGGTDTQYF
#> 4 AAACCTGAGGCTCTTA-1        TRGV9   TRGJ1    CAVKDSNYQLIW

2. Convert 

new_tcrs <- convert_gene(tcrs, frm = "tenx", to = "adaptive")
#> Warning in convert_gene(tcrs, frm = "tenx", to = "adaptive"): Adaptive only
#> captures VDJ genes; C genes will be NA.
#> Converting from 10X. Using *01 as allele for all genes.
new_tcrs
#>              barcode             v_gene        j_gene            cdr3
#> 1 AAACCTGAGACCACGA-1      TCRAV29-01*01 TCRAJ12-01*01    CAVMDSSYKLIF
#> 2 AAACCTGAGACCACGA-1 TCRBV20-or09_02*01 TCRBJ02-01*01 CASSGLAGGYNEQFF
#> 3 AAACCTGAGGCTCTTA-1      TCRDV02-01*01 TCRDJ03-01*01 CASSGVAGGTDTQYF
#> 4 AAACCTGAGGCTCTTA-1      TCRGV09-01*01 TCRGJ01-01*01    CAVKDSNYQLIW

Contributing

Contributions are welcome! To contribute, submit a pull request. See the documentation for details.

Issues

To report a bug or request a feature please open an issue.

Contact

For other inquiries, contact Emma Bishop: emmab5 at uw dot edu.

Acknowledgments

This project was supported by the Fred Hutchinson Cancer Center Translational Data Science Integrated Research Center (TDS IRC) through the 2024 Data Scientist Collaboration Grant. Special thanks to Scott Chamberlain for development support and Shashidhar Ravishankar for gene name curation.