On the viability of unsupervised T-cell receptor sequence clustering for epitope preference | Zendy

Pieter Meysman | Zendy; Nicolas De Neuter | Zendy; Sofie Gielis | Zendy; Danh Bui Thi | Zendy; Benson Ogunjimi | Zendy; Kris Laukens | Zendy

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On the viability of unsupervised T-cell receptor sequence clustering for epitope preference

Author(s) -

Pieter Meysman,

Nicolas De Neuter,

Sofie Gielis,

Danh Bui Thi,

Benson Ogunjimi,

Kris Laukens

Publication year - 2018

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/bty821

Subject(s) - t cell receptor , epitope , cluster analysis , computational biology , computer science , sequence (biology) , python (programming language) , context (archaeology) , similarity (geometry) , biology , t cell , artificial intelligence , antigen , genetics , programming language , paleontology , immune system , image (mathematics)

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.

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