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A large-scale database of T-cell receptor beta (TCRβ) sequences and binding associations from natural and synthetic exposure to SARS-CoV-2
We describe the establishment and current content of the ImmuneCODE™ database, which includes hundreds of millions of T-cell Receptor (TCR) sequences from over 1,400 subjects exposed to or infected with the SARS-CoV-2 virus, as well as over 135,000 high-confidence SARS-CoV-2-specific TCRs. This data...
| Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
American Journal Experts
2020
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7418738/ https://www.ncbi.nlm.nih.gov/pubmed/32793896 http://dx.doi.org/10.21203/rs.3.rs-51964/v1 |
| _version_ | 1783569748194230272 |
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| author | Nolan, Sean Vignali, Marissa Klinger, Mark Dines, Jennifer N. Kaplan, Ian M. Svejnoha, Emily Craft, Tracy Boland, Katie Pesesky, Mitch Gittelman, Rachel M. Snyder, Thomas M. Gooley, Christopher J. Semprini, Simona Cerchione, Claudio Mazza, Massimiliano Delmonte, Ottavia M. Dobbs, Kerry Carreño-Tarragona, Gonzalo Barrio, Santiago Sambri, Vittorio Martinelli, Giovanni Goldman, Jason D. Heath, James R. Notarangelo, Luigi D. Carlson, Jonathan M. Martinez-Lopez, Joaquin Robins, Harlan S. |
| author_facet | Nolan, Sean Vignali, Marissa Klinger, Mark Dines, Jennifer N. Kaplan, Ian M. Svejnoha, Emily Craft, Tracy Boland, Katie Pesesky, Mitch Gittelman, Rachel M. Snyder, Thomas M. Gooley, Christopher J. Semprini, Simona Cerchione, Claudio Mazza, Massimiliano Delmonte, Ottavia M. Dobbs, Kerry Carreño-Tarragona, Gonzalo Barrio, Santiago Sambri, Vittorio Martinelli, Giovanni Goldman, Jason D. Heath, James R. Notarangelo, Luigi D. Carlson, Jonathan M. Martinez-Lopez, Joaquin Robins, Harlan S. |
| author_sort | Nolan, Sean |
| collection | PubMed |
| description | We describe the establishment and current content of the ImmuneCODE™ database, which includes hundreds of millions of T-cell Receptor (TCR) sequences from over 1,400 subjects exposed to or infected with the SARS-CoV-2 virus, as well as over 135,000 high-confidence SARS-CoV-2-specific TCRs. This database is made freely available, and the data contained in it can be downloaded and analyzed online or offline to assist with the global efforts to understand the immune response to the SARS-CoV-2 virus and develop new interventions. |
| format | Online Article Text |
| id | pubmed-7418738 |
| institution | National Center for Biotechnology Information |
| language | English |
| publishDate | 2020 |
| publisher | American Journal Experts |
| record_format | MEDLINE/PubMed |
| spelling | pubmed-74187382020-08-13 A large-scale database of T-cell receptor beta (TCRβ) sequences and binding associations from natural and synthetic exposure to SARS-CoV-2 Nolan, Sean Vignali, Marissa Klinger, Mark Dines, Jennifer N. Kaplan, Ian M. Svejnoha, Emily Craft, Tracy Boland, Katie Pesesky, Mitch Gittelman, Rachel M. Snyder, Thomas M. Gooley, Christopher J. Semprini, Simona Cerchione, Claudio Mazza, Massimiliano Delmonte, Ottavia M. Dobbs, Kerry Carreño-Tarragona, Gonzalo Barrio, Santiago Sambri, Vittorio Martinelli, Giovanni Goldman, Jason D. Heath, James R. Notarangelo, Luigi D. Carlson, Jonathan M. Martinez-Lopez, Joaquin Robins, Harlan S. Res Sq Article We describe the establishment and current content of the ImmuneCODE™ database, which includes hundreds of millions of T-cell Receptor (TCR) sequences from over 1,400 subjects exposed to or infected with the SARS-CoV-2 virus, as well as over 135,000 high-confidence SARS-CoV-2-specific TCRs. This database is made freely available, and the data contained in it can be downloaded and analyzed online or offline to assist with the global efforts to understand the immune response to the SARS-CoV-2 virus and develop new interventions. American Journal Experts 2020-08-04 /pmc/articles/PMC7418738/ /pubmed/32793896 http://dx.doi.org/10.21203/rs.3.rs-51964/v1 Text en This work is licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/) , which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use. |
| spellingShingle | Article Nolan, Sean Vignali, Marissa Klinger, Mark Dines, Jennifer N. Kaplan, Ian M. Svejnoha, Emily Craft, Tracy Boland, Katie Pesesky, Mitch Gittelman, Rachel M. Snyder, Thomas M. Gooley, Christopher J. Semprini, Simona Cerchione, Claudio Mazza, Massimiliano Delmonte, Ottavia M. Dobbs, Kerry Carreño-Tarragona, Gonzalo Barrio, Santiago Sambri, Vittorio Martinelli, Giovanni Goldman, Jason D. Heath, James R. Notarangelo, Luigi D. Carlson, Jonathan M. Martinez-Lopez, Joaquin Robins, Harlan S. A large-scale database of T-cell receptor beta (TCRβ) sequences and binding associations from natural and synthetic exposure to SARS-CoV-2 |
| title | A large-scale database of T-cell receptor beta (TCRβ) sequences and binding associations from natural and synthetic exposure to SARS-CoV-2 |
| title_full | A large-scale database of T-cell receptor beta (TCRβ) sequences and binding associations from natural and synthetic exposure to SARS-CoV-2 |
| title_fullStr | A large-scale database of T-cell receptor beta (TCRβ) sequences and binding associations from natural and synthetic exposure to SARS-CoV-2 |
| title_full_unstemmed | A large-scale database of T-cell receptor beta (TCRβ) sequences and binding associations from natural and synthetic exposure to SARS-CoV-2 |
| title_short | A large-scale database of T-cell receptor beta (TCRβ) sequences and binding associations from natural and synthetic exposure to SARS-CoV-2 |
| title_sort | large-scale database of t-cell receptor beta (tcrβ) sequences and binding associations from natural and synthetic exposure to sars-cov-2 |
| topic | Article |
| url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7418738/ https://www.ncbi.nlm.nih.gov/pubmed/32793896 http://dx.doi.org/10.21203/rs.3.rs-51964/v1 |
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