Publication
Title
On the viability of unsupervised T-cell receptor sequence clustering for epitope preference
Author
Abstract
Motivation The T-cell receptor (TCR) is responsible for recognizing epitopes presented on cell surfaces. Linking TCR sequences to their ability to target specific epitopes is currently an unsolved problem, yet one of great interest. Indeed, it is currently unknown how dissimilar TCR sequences can be before they no longer bind the same epitope. This question is confounded by the fact that there are many ways to define the similarity between two TCR sequences. Here we investigate both issues in the context of TCR sequence unsupervised clustering. Results We provide an overview of the performance of various distance metrics on two large independent data sets with 412 and 2835 TCR sequences respectively. Our results confirm the presence of structural distinct TCR groups that target identical epitopes. In addition, we put forward several recommendations to perform unsupervised T-cell receptor sequence clustering.
Language
English
Source (journal)
Bioinformatics. - Oxford, 1998, currens
Publication
Oxford : Oxford University Press , 2019
ISSN
1367-4803 [print]
1367-4811 [online]
DOI
10.1093/BIOINFORMATICS/BTY821
Volume/pages
35 :9 (2019) , p. 1461-1468
ISI
000469491000004
Pubmed ID
30247624
Full text (Publisher's DOI)
Full text (open access)
UAntwerpen
Faculty/Department
Research group
Project info
Predicting Immune responses by Modeling immunoSequencing data (PIMS).
An interdisciplinary study on the role of the HLA genes and T-cell diversity as risk factors for herpes zoster.
Efficient mining for unexpected patterns in complex biological data.
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Web of Science
Record
Identifier
Creation 24.09.2018
Last edited 09.10.2023
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