A Bayesian framework for high-throughput T cell receptor pairing
Author(s)
Holec, Patrick V
Berleant, Joseph
Bathe, Mark
Birnbaum, Michael E
Date Issued
September 2018
Journal
Bioinformatics
Publisher
Oxford University Press (OUP)
Citation
Holec, Patrick V et al. "A Bayesian framework for high-throughput T cell receptor pairing." Bioinformatics 35, 8 (September 2018): 1318–1325 © 2018 The Author(s)
Version
Author's final manuscript
Abstract
Motivation: The study of T cell receptor (TCR) 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 TCRs. 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 the relevant transcripts from each T cell. Multicell Analytical Deconvolution for High Yield Paired-chain Evaluation (MAD-HYPE) uses Bayesian inference to more accurately extract TCR information, improving our ability to study and characterize T cell populations for immunology and immunotherapy applications. Availability and implementation: 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.
MIT Department
Massachusetts Institute of Technology. Department of Biological Engineering
Koch Institute for Integrative Cancer Research at MIT
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1093/bioinformatics/bty801
