SwarmTCR: a computational approach to predict the specificity of T cell receptors.
BMC Bioinformatics
; 22(1): 422, 2021 Sep 07.
Article
em En
| MEDLINE
| ID: mdl-34493215
ABSTRACT
BACKGROUND:
With more T cell receptor sequence data becoming available, the need for bioinformatics approaches to predict T cell receptor specificity is even more pressing. Here we present SwarmTCR, a method that uses labeled sequence data to predict the specificity of T cell receptors using a nearest-neighbor approach. SwarmTCR works by optimizing the weights of the individual CDR regions to maximize classification performance.RESULTS:
We compared the performance of SwarmTCR against another nearest-neighbor method and showed that SwarmTCR performs well both with bulk sequencing data and with single cell data. In addition, we show that the weights returned by SwarmTCR are biologically interpretable.CONCLUSIONS:
Computationally predicting the specificity of T cell receptors can be a powerful tool to shed light on the immune response against infectious diseases and cancers, autoimmunity, cancer immunotherapy, and immunopathology. SwarmTCR is distributed freely under the terms of the GPL-3 license. The source code and all sequencing data are available at GitHub ( https//github.com/thecodingdoc/SwarmTCR ).Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Software
/
Receptores de Antígenos de Linfócitos T
Tipo de estudo:
Prognostic_studies
/
Risk_factors_studies
Idioma:
En
Revista:
BMC Bioinformatics
Assunto da revista:
INFORMATICA MEDICA
Ano de publicação:
2021
Tipo de documento:
Article
País de afiliação:
Estados Unidos