PT  - JOURNAL ARTICLE
AU  - Felix Mölder
AU  - Ulrik Stervbo
AU  - Lucie Loyal
AU  - Petra Bacher
AU  - Nina Babel
AU  - Sven Rahmann
TI  - Rapid T cell receptor interaction grouping with ting
AID  - 10.1101/2020.05.04.069914
DP  - 2020 Jan 01
TA  - bioRxiv
PG  - 2020.05.04.069914
4099  - http://biorxiv.org/content/early/2020/05/05/2020.05.04.069914.short
4100  - http://biorxiv.org/content/early/2020/05/05/2020.05.04.069914.full
AB  - Motivation Clustering of antigen-specific T cell receptor repertoire (TCRR) sequences is challenging. The recently published tool GLIPH aims to solve this problem. However, clustering large repertoires takes several days to weeks, making its use impractical in larger studies. In addition, the methodology used in GLIPH suffers from several shortcomings, including non-determinism, potential loss of significant antigen-specific sequences or inclusion of too many unspecific sequences.Results We present an algorithm for clustering TCRR sequences that scales efficiently to large repertoires. We clustered 26 real datasets with up to 62 000 unique CDR3β sequences using both GLIPH and an implementation of our method called ting. While GLIPH required multiple weeks, ting only needed about one hour for the same task. 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 fewer sequences.Availability Our method has been implemented in Python as a tool called ting, using numpy and NetworkX. It is available on GitHub (https://github.com/FelixMoelder/ting) and on PyPI under the MIT license.Contact felix.moelder{at}uni-due.de or sven.rahmann{at}uni-due.deCompeting Interest StatementThe authors have declared no competing interest.