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The Immune Signatures data resource, a compendium of systems vaccinology datasets

Vaccines are among the most cost-effective public health interventions for preventing infection-induced morbidity and mortality, yet much remains to be learned regarding the mechanisms by which vaccines protect. Systems immunology combines traditional immunology with modern ‘omic profiling technique...

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Autores principales: Diray-Arce, Joann, Miller, Helen E. R., Henrich, Evan, Gerritsen, Bram, Mulè, Matthew P., Fourati, Slim, Gygi, Jeremy, Hagan, Thomas, Tomalin, Lewis, Rychkov, Dmitry, Kazmin, Dmitri, Chawla, Daniel G., Meng, Hailong, Dunn, Patrick, Campbell, John, Sarwal, Minnie, Tsang, John S., Levy, Ofer, Pulendran, Bali, Sekaly, Rafick, Floratos, Aris, Gottardo, Raphael, Kleinstein, Steven H., Suárez-Fariñas, Mayte
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9584267/
https://www.ncbi.nlm.nih.gov/pubmed/36266291
http://dx.doi.org/10.1038/s41597-022-01714-7
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author Diray-Arce, Joann
Miller, Helen E. R.
Henrich, Evan
Gerritsen, Bram
Mulè, Matthew P.
Fourati, Slim
Gygi, Jeremy
Hagan, Thomas
Tomalin, Lewis
Rychkov, Dmitry
Kazmin, Dmitri
Chawla, Daniel G.
Meng, Hailong
Dunn, Patrick
Campbell, John
Sarwal, Minnie
Tsang, John S.
Levy, Ofer
Pulendran, Bali
Sekaly, Rafick
Floratos, Aris
Gottardo, Raphael
Kleinstein, Steven H.
Suárez-Fariñas, Mayte
author_facet Diray-Arce, Joann
Miller, Helen E. R.
Henrich, Evan
Gerritsen, Bram
Mulè, Matthew P.
Fourati, Slim
Gygi, Jeremy
Hagan, Thomas
Tomalin, Lewis
Rychkov, Dmitry
Kazmin, Dmitri
Chawla, Daniel G.
Meng, Hailong
Dunn, Patrick
Campbell, John
Sarwal, Minnie
Tsang, John S.
Levy, Ofer
Pulendran, Bali
Sekaly, Rafick
Floratos, Aris
Gottardo, Raphael
Kleinstein, Steven H.
Suárez-Fariñas, Mayte
author_sort Diray-Arce, Joann
collection PubMed
description Vaccines are among the most cost-effective public health interventions for preventing infection-induced morbidity and mortality, yet much remains to be learned regarding the mechanisms by which vaccines protect. Systems immunology combines traditional immunology with modern ‘omic profiling techniques and computational modeling to promote rapid and transformative advances in vaccinology and vaccine discovery. The NIH/NIAID Human Immunology Project Consortium (HIPC) has leveraged systems immunology approaches to identify molecular signatures associated with the immunogenicity of many vaccines. However, comparative analyses have been limited by the distributed nature of some data, potential batch effects across studies, and the absence of multiple relevant studies from non-HIPC groups in ImmPort. To support comparative analyses across different vaccines, we have created the Immune Signatures Data Resource, a compendium of standardized systems vaccinology datasets. This data resource is available through ImmuneSpace, along with code to reproduce the processing and batch normalization starting from the underlying study data in ImmPort and the Gene Expression Omnibus (GEO). The current release comprises 1405 participants from 53 cohorts profiling the response to 24 different vaccines. This novel systems vaccinology data release represents a valuable resource for comparative and meta-analyses that will accelerate our understanding of mechanisms underlying vaccine responses.
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spelling pubmed-95842672022-10-21 The Immune Signatures data resource, a compendium of systems vaccinology datasets Diray-Arce, Joann Miller, Helen E. R. Henrich, Evan Gerritsen, Bram Mulè, Matthew P. Fourati, Slim Gygi, Jeremy Hagan, Thomas Tomalin, Lewis Rychkov, Dmitry Kazmin, Dmitri Chawla, Daniel G. Meng, Hailong Dunn, Patrick Campbell, John Sarwal, Minnie Tsang, John S. Levy, Ofer Pulendran, Bali Sekaly, Rafick Floratos, Aris Gottardo, Raphael Kleinstein, Steven H. Suárez-Fariñas, Mayte Sci Data Data Descriptor Vaccines are among the most cost-effective public health interventions for preventing infection-induced morbidity and mortality, yet much remains to be learned regarding the mechanisms by which vaccines protect. Systems immunology combines traditional immunology with modern ‘omic profiling techniques and computational modeling to promote rapid and transformative advances in vaccinology and vaccine discovery. The NIH/NIAID Human Immunology Project Consortium (HIPC) has leveraged systems immunology approaches to identify molecular signatures associated with the immunogenicity of many vaccines. However, comparative analyses have been limited by the distributed nature of some data, potential batch effects across studies, and the absence of multiple relevant studies from non-HIPC groups in ImmPort. To support comparative analyses across different vaccines, we have created the Immune Signatures Data Resource, a compendium of standardized systems vaccinology datasets. This data resource is available through ImmuneSpace, along with code to reproduce the processing and batch normalization starting from the underlying study data in ImmPort and the Gene Expression Omnibus (GEO). The current release comprises 1405 participants from 53 cohorts profiling the response to 24 different vaccines. This novel systems vaccinology data release represents a valuable resource for comparative and meta-analyses that will accelerate our understanding of mechanisms underlying vaccine responses. Nature Publishing Group UK 2022-10-20 /pmc/articles/PMC9584267/ /pubmed/36266291 http://dx.doi.org/10.1038/s41597-022-01714-7 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Data Descriptor
Diray-Arce, Joann
Miller, Helen E. R.
Henrich, Evan
Gerritsen, Bram
Mulè, Matthew P.
Fourati, Slim
Gygi, Jeremy
Hagan, Thomas
Tomalin, Lewis
Rychkov, Dmitry
Kazmin, Dmitri
Chawla, Daniel G.
Meng, Hailong
Dunn, Patrick
Campbell, John
Sarwal, Minnie
Tsang, John S.
Levy, Ofer
Pulendran, Bali
Sekaly, Rafick
Floratos, Aris
Gottardo, Raphael
Kleinstein, Steven H.
Suárez-Fariñas, Mayte
The Immune Signatures data resource, a compendium of systems vaccinology datasets
title The Immune Signatures data resource, a compendium of systems vaccinology datasets
title_full The Immune Signatures data resource, a compendium of systems vaccinology datasets
title_fullStr The Immune Signatures data resource, a compendium of systems vaccinology datasets
title_full_unstemmed The Immune Signatures data resource, a compendium of systems vaccinology datasets
title_short The Immune Signatures data resource, a compendium of systems vaccinology datasets
title_sort immune signatures data resource, a compendium of systems vaccinology datasets
topic Data Descriptor
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9584267/
https://www.ncbi.nlm.nih.gov/pubmed/36266291
http://dx.doi.org/10.1038/s41597-022-01714-7
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