Decombinator V4: an improved AIRR compliant-software package for T-cell receptor sequence annotation?
Journal
Bioinformatics (Oxford, England)
ISSN: 1367-4811
Titre abrégé: Bioinformatics
Pays: England
ID NLM: 9808944
Informations de publication
Date de publication:
05 05 2021
05 05 2021
Historique:
received:
21
05
2020
revised:
15
07
2020
accepted:
20
08
2020
pubmed:
28
8
2020
medline:
4
6
2021
entrez:
28
8
2020
Statut:
ppublish
Résumé
Analysis of the T-cell receptor repertoire is rapidly entering the general toolbox used by researchers interested in cellular immunity. The annotation of T-cell receptors (TCRs) from raw sequence data poses specific challenges, which arise from the fact that TCRs are not germline encoded, and because of the stochastic nature of the generating process. In this study, we report the release of Decombinator V4, a tool for the accurate and fast annotation of large sets of TCR sequences. Decombinator was one of the early Python software packages released to analyse the rapidly increasing flow of T-cell receptor repertoire sequence data. The Decombinator package now provides Python 3 compatibility, incorporates improved sequencing error and PCR bias correction algorithms, and provides output which conforms to the international standards proposed by the Adaptive Immune Receptor Repertoire Community. The entire Decombinator suite is freely available at: https://github.com/innate2adaptive/Decombinator. Supplementary data are available at Bioinformatics online.
Identifiants
pubmed: 32853330
pii: 5898187
doi: 10.1093/bioinformatics/btaa758
pmc: PMC8098023
doi:
Substances chimiques
Receptors, Antigen, T-Cell
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
876-878Commentaires et corrections
Type : ErratumIn
Informations de copyright
© The Author(s) 2020. Published by Oxford University Press.
Références
Nat Commun. 2018 Feb 8;9(1):561
pubmed: 29422654
Front Immunol. 2017 Oct 12;8:1267
pubmed: 29075258
Nat Med. 2019 Oct;25(10):1549-1559
pubmed: 31591606
Front Immunol. 2014 Feb 05;5:22
pubmed: 24600447
Front Immunol. 2018 Sep 28;9:2206
pubmed: 30323809
Methods Enzymol. 2019;629:465-492
pubmed: 31727254
J Exp Med. 2018 Nov 5;215(11):2748-2759
pubmed: 30257862
Front Immunol. 2018 Nov 05;9:2547
pubmed: 30455696
Bioinformatics. 2013 Mar 1;29(5):542-50
pubmed: 23303508
Front Immunol. 2016 Jan 11;6:644
pubmed: 26793190