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TIRTLtools R package

Package is in active development and may change frequently.

News

Dec. 8th 2025

Nov. 24th 2025

Installation

Run the following code in R or Rstudio to install TIRTLtools:

if (!requireNamespace("remotes", quietly = TRUE)) {
  install.packages("remotes") ## Run if you do not have the 'remotes' package installed
}
remotes::install_github("NicholasClark/TIRTLtools") ## install via the GitHub Repo

Load the package in R with:

library(TIRTLtools)

Documentation and usage instructions

For more information, see our website: https://nicholasclark.github.io/TIRTLtools/

Overview

TIRTLtools is a software package for analyzing T-cell receptor (TCR) repertoires created using TIRTL-seq (Throughput-Intensive Rapid TCR Library sequencing) (Pogorelyy and Kirk et al., Nature Methods 2025). We provide functions for analysis of paired TCR repertoires as well as single-chain bulk TCR-sequencing data.

In addition to various analysis and plotting functions, we provide an efficient batched GPU implementation of TCRdist (Dash et al., Nature 2017) that works with both NVIDIA and Apple Silicon GPUs. In testing, we were able to calculate pairwise TCRdist for a repertoire of ~1 million TCRs in a few hours using a MacBook Pro (16-core GPU, M4 Pro).

The TIRTL-seq assay

The T-cell receptor (TCR), which allows for adaptive immune response, is made up of two separate protein chains, TCR-alpha and TCR-beta. Bulk single-chain TCR-sequencing allows for cost-effective in-depth TCR-repertoire profiling, but does not provide chain pairings, which are essential for determining T-cell specificity. Single-cell TCR sequencing can produce paired chain data, but is much more expensive and thus limited to thousands of cells in total and cost-prohibitive for cohort-scale studies.

TIRTL-seq is a novel assay (published Nov. 2025) that can rapidly and economically sequence single-chain TCRs from millions of human T-cells and produce paired TCR-sequencing information for tens of thousands of T cell clones per sample at a fraction of the cost of single-cell sequencing. Cells are split across wells in 384-well or 96-well plates with between 2,500 and 25,000 cells per well and alpha/beta TCR chains are sequenced simultaneously in each well. Computational algorithms based on chain co-occurrence and correlation of chain frequency are used to identify alpha/beta chain pairs that derive from the same T-cell clone.

We developed a new algorithm (T-SHELL) allows for effective pairing of the most abundant clones, which had frustrated previous algorithms. Along with paired TCR information, single-chain read frequency can be used to identify expanded clones in one sample as well as to track clone expansions and contractions longitudinally across samples.

TIRTL-seq assay protocols

See protocols.io for the 384-well and 96-well TIRTL-seq protocols.

Bug reports

For any bug reports or new feature requests, please create a GitHub Issue:

References

For details on our pairing pipeline, see the TIRTL-seq paper (Pogorelyy and Kirk et al., Nature Methods 2025) and our github repository.

For a summary of the TIRTL-seq method and its importance, see the Immunology News & Views article in Nature Methods highlighting TIRTL-seq: TIRTL-seq: heroes with T-SHELL.

For details on the MAD-HYPE algorithm, see Holec and Berleant et al., 2019.

For details on MiXCR, see their website and publications in Nature Methods (2015) and Nature Biotechnology (2017).

For details on TCRdist, see Dash et al., Nature 2017

Citing TIRTL-seq

Please cite the TIRTL-seq paper:

If using the 384-well or 96-well TIRTL-seq protocol, please cite the protocols.io protocols: