Rapid T cell receptor interaction grouping with ting.

Authors:
Felix Mölder, Ulrik Stervbo, Lucie Loyal, Petra Bacher, Nina Babel, Sven Rahmann
Year of publication:
2021
Volume:
-
Issue:
-
Issn:
1367-4803
Journal title abbreviated:
BIOINFORMATICS
Journal title long:
Bioinformatics
Impact factor:
5.610
Abstract:
<h4>Motivation</h4>Clustering T cell receptor repertoire (TCRR) sequences according to antigen specificity is challenging. The previously published tool GLIPH needs several days to weeks for clustering large repertoires, making its use impractical in larger studies. In addition, the methodology used in GLIPH suffers from shortcomings, including non-determinism, potential loss of significant antigen-specific sequences or inclusion of too many unspecific sequences.<h4>Results</h4>We present an algorithm for clustering TCRR sequences that scales efficiently to large repertoires. We clustered 36 real datasets with up to 62 000 unique CDR3β sequences using both an implementation of our method called ting, GLIPH and its successsor GLIPH2. While GLIPH required multiple weeks, ting only needed about one minute for the same task. GLIPH2 is comparably fast, but uses a different grouping paradigm. In addition, we found that in naïve repertoires, where no or very few antigen-specific CDR3 sequences or clusters should exist, our method indeed selects much fewer motifs and produces smaller clusters.<h4>Availability</h4>Our method has been implemented in Python as a tool called ting. It is available from GitHub (https://github.com/FelixMoelder/ting) or PyPI under the MIT license.