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T-Cell Receptor Repertoire Analysis with Computational Tools—An Immunologist’s Perspective
Over the last few years, there has been a rapid expansion in the application of information technology to biological data. Particularly the field of immunology has seen great strides in recent years. The development of next-generation sequencing (NGS) and single-cell technologies also brought forth...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8700004/ https://www.ncbi.nlm.nih.gov/pubmed/34944090 http://dx.doi.org/10.3390/cells10123582 |
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author | Arunkumar, Mahima Zielinski, Christina E. |
author_facet | Arunkumar, Mahima Zielinski, Christina E. |
author_sort | Arunkumar, Mahima |
collection | PubMed |
description | Over the last few years, there has been a rapid expansion in the application of information technology to biological data. Particularly the field of immunology has seen great strides in recent years. The development of next-generation sequencing (NGS) and single-cell technologies also brought forth a revolution in the characterization of immune repertoires. T-cell receptor (TCR) repertoires carry comprehensive information on the history of an individual’s antigen exposure. They serve as correlates of host protection and tolerance, as well as biomarkers of immunological perturbation by natural infections, vaccines or immunotherapies. Their interrogation yields large amounts of data. This requires a suite of highly sophisticated bioinformatics tools to leverage the meaning and complexity of the large datasets. Many different tools and methods, specifically designed for various aspects of immunological research, have recently emerged. Thus, researchers are now confronted with the issue of having to choose the right kind of approach to analyze, visualize and ultimately solve their task at hand. In order to help immunologists to choose from the vastness of available tools for their data analysis, this review addresses and compares commonly used bioinformatics tools for TCR repertoire analysis and illustrates the advantages and limitations of these tools from an immunologist’s perspective. |
format | Online Article Text |
id | pubmed-8700004 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-87000042021-12-24 T-Cell Receptor Repertoire Analysis with Computational Tools—An Immunologist’s Perspective Arunkumar, Mahima Zielinski, Christina E. Cells Review Over the last few years, there has been a rapid expansion in the application of information technology to biological data. Particularly the field of immunology has seen great strides in recent years. The development of next-generation sequencing (NGS) and single-cell technologies also brought forth a revolution in the characterization of immune repertoires. T-cell receptor (TCR) repertoires carry comprehensive information on the history of an individual’s antigen exposure. They serve as correlates of host protection and tolerance, as well as biomarkers of immunological perturbation by natural infections, vaccines or immunotherapies. Their interrogation yields large amounts of data. This requires a suite of highly sophisticated bioinformatics tools to leverage the meaning and complexity of the large datasets. Many different tools and methods, specifically designed for various aspects of immunological research, have recently emerged. Thus, researchers are now confronted with the issue of having to choose the right kind of approach to analyze, visualize and ultimately solve their task at hand. In order to help immunologists to choose from the vastness of available tools for their data analysis, this review addresses and compares commonly used bioinformatics tools for TCR repertoire analysis and illustrates the advantages and limitations of these tools from an immunologist’s perspective. MDPI 2021-12-18 /pmc/articles/PMC8700004/ /pubmed/34944090 http://dx.doi.org/10.3390/cells10123582 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Review Arunkumar, Mahima Zielinski, Christina E. T-Cell Receptor Repertoire Analysis with Computational Tools—An Immunologist’s Perspective |
title | T-Cell Receptor Repertoire Analysis with Computational Tools—An Immunologist’s Perspective |
title_full | T-Cell Receptor Repertoire Analysis with Computational Tools—An Immunologist’s Perspective |
title_fullStr | T-Cell Receptor Repertoire Analysis with Computational Tools—An Immunologist’s Perspective |
title_full_unstemmed | T-Cell Receptor Repertoire Analysis with Computational Tools—An Immunologist’s Perspective |
title_short | T-Cell Receptor Repertoire Analysis with Computational Tools—An Immunologist’s Perspective |
title_sort | t-cell receptor repertoire analysis with computational tools—an immunologist’s perspective |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8700004/ https://www.ncbi.nlm.nih.gov/pubmed/34944090 http://dx.doi.org/10.3390/cells10123582 |
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