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Related Experiment Video

Updated: Feb 5, 2026

Identification of Kinase-substrate Pairs Using High Throughput Screening

Identification of Kinase-substrate Pairs Using High Throughput Screening

Published on: August 29, 2015

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

Patrick V Holec^1,2, Joseph Berleant¹, Mark Bathe¹

¹Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.

Bioinformatics (Oxford, England)

|September 15, 2018

Summary

This summary is machine-generated.

We developed MAD-HYPE, a novel Bayesian inference algorithm to accurately pair T cell receptor (TCR) chains from sequencing data. This advance improves T cell repertoire analysis for immunology and immunotherapy.

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Published on: November 23, 2013

Area of Science:

Immunology
Bioinformatics
Computational Biology

Background:

T cell receptor (TCR) repertoire analysis provides insights into immune recognition.
Existing methods face technical limitations in robustly characterizing TCR populations and inferring heterodimeric chain pairings.

Purpose of the Study:

To introduce a novel analytical approach, MAD-HYPE, to enhance immune repertoire sequencing.
To improve the resolving power of low-cost immune repertoire sequencing technologies.
To enable more accurate characterization of T cell populations for immunological and immunotherapeutic applications.

Main Methods:

The method involves distributing T cells across a 96-well plate.
Barcoding and sequencing of relevant transcripts from individual T cells.
Utilizing Bayesian inference within the MAD-HYPE algorithm for TCR information extraction.

Main Results:

MAD-HYPE significantly improves the accuracy of inferring TCR heterodimeric chain pairings.
Enhanced resolving power for low-cost immune repertoire sequencing.
Facilitates more robust characterization of T cell populations.

Conclusions:

MAD-HYPE offers a powerful new tool for T cell repertoire analysis.
The algorithm advances capabilities in immunology and immunotherapy research.
Open-source availability promotes wider adoption and further development.