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tidytcells: standardizer for TR/MH nomenclature

T cell receptors (TR) underpin the diversity and specificity of T cell activity. As such, TR repertoire data is valuable both as an adaptive immune biomarker, and as a way to identify candidate therapeutic TR. Analysis of TR repertoires relies heavily on computational analysis, and therefore it is o...

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Detalles Bibliográficos
Autores principales: Nagano, Yuta, Chain, Benjamin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10634431/
https://www.ncbi.nlm.nih.gov/pubmed/37954585
http://dx.doi.org/10.3389/fimmu.2023.1276106
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author Nagano, Yuta
Chain, Benjamin
author_facet Nagano, Yuta
Chain, Benjamin
author_sort Nagano, Yuta
collection PubMed
description T cell receptors (TR) underpin the diversity and specificity of T cell activity. As such, TR repertoire data is valuable both as an adaptive immune biomarker, and as a way to identify candidate therapeutic TR. Analysis of TR repertoires relies heavily on computational analysis, and therefore it is of vital importance that the data is standardized and computer-readable. However in practice, the usage of different abbreviations and non-standard nomenclature in different datasets makes this data pre-processing non-trivial. tidytcells is a lightweight, platform-independent Python package that provides easy-to-use standardization tools specifically designed for TR nomenclature. The software is open-sourced under the MIT license and is available to install from the Python Package Index (PyPI). At the time of publishing, tidytcells is on version 2.0.0.
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spelling pubmed-106344312023-11-10 tidytcells: standardizer for TR/MH nomenclature Nagano, Yuta Chain, Benjamin Front Immunol Immunology T cell receptors (TR) underpin the diversity and specificity of T cell activity. As such, TR repertoire data is valuable both as an adaptive immune biomarker, and as a way to identify candidate therapeutic TR. Analysis of TR repertoires relies heavily on computational analysis, and therefore it is of vital importance that the data is standardized and computer-readable. However in practice, the usage of different abbreviations and non-standard nomenclature in different datasets makes this data pre-processing non-trivial. tidytcells is a lightweight, platform-independent Python package that provides easy-to-use standardization tools specifically designed for TR nomenclature. The software is open-sourced under the MIT license and is available to install from the Python Package Index (PyPI). At the time of publishing, tidytcells is on version 2.0.0. Frontiers Media S.A. 2023-10-25 /pmc/articles/PMC10634431/ /pubmed/37954585 http://dx.doi.org/10.3389/fimmu.2023.1276106 Text en Copyright © 2023 Nagano and Chain https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Immunology
Nagano, Yuta
Chain, Benjamin
tidytcells: standardizer for TR/MH nomenclature
title tidytcells: standardizer for TR/MH nomenclature
title_full tidytcells: standardizer for TR/MH nomenclature
title_fullStr tidytcells: standardizer for TR/MH nomenclature
title_full_unstemmed tidytcells: standardizer for TR/MH nomenclature
title_short tidytcells: standardizer for TR/MH nomenclature
title_sort tidytcells: standardizer for tr/mh nomenclature
topic Immunology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10634431/
https://www.ncbi.nlm.nih.gov/pubmed/37954585
http://dx.doi.org/10.3389/fimmu.2023.1276106
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