A Bayesian framework for high-throughput T cell receptor pairing.

Motivation: The study of T cell receptor repertoires has generated new insights into immune system recognition. However, the ability to robustly characterize these populations has been limited by technical barriers and an inability to reliably infer heterodimeric chain pairings for T cell receptors.

Results: Here, we describe a novel analytical approach to an emerging immune repertoire sequencing method, improving the resolving power of this low-cost technology. This method relies upon the distribution of a T cell population across a 96-well plate, followed by barcoding and sequencing of relevant portions of each T cell genome. Multicell Analytical Deconvolution for High Yield Paired-chain Evaluation (MADHYPE) uses Bayesian inference to more accurately extract T cell receptor information, improving our ability to study and characterize T cell populations for immunology and immunotherapy applications.

Availability: The MAD-HYPE algorithm is released as an open-source project under the Apache License and is available from https://github.com/birnbaumlab/MAD-HYPE.

Supplementary Information: Supplementary data are available at Bioinformatics online.