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A data processing pipeline for the AACR project GENIE biopharma collaborative data with the {genieBPC} R package
MOTIVATION: Data from the American Association for Cancer Research Project Genomics Evidence Neoplasia Information Exchange Biopharma Collaborative (GENIE BPC) represent comprehensive clinical data linked to high-throughput sequencing data, providing a multi-institution, pan-cancer, publicly availab...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9822536/ https://www.ncbi.nlm.nih.gov/pubmed/36519837 http://dx.doi.org/10.1093/bioinformatics/btac796 |
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author | Lavery, Jessica A Brown, Samantha Curry, Michael A Martin, Axel Sjoberg, Daniel D Whiting, Karissa |
author_facet | Lavery, Jessica A Brown, Samantha Curry, Michael A Martin, Axel Sjoberg, Daniel D Whiting, Karissa |
author_sort | Lavery, Jessica A |
collection | PubMed |
description | MOTIVATION: Data from the American Association for Cancer Research Project Genomics Evidence Neoplasia Information Exchange Biopharma Collaborative (GENIE BPC) represent comprehensive clinical data linked to high-throughput sequencing data, providing a multi-institution, pan-cancer, publicly available data repository. GENIE BPC data provide detailed demographic, clinical, treatment, genomic and outcome data for patients with cancer. These data result in a unique observational database of molecularly characterized tumors with comprehensive clinical annotation that can be used for health outcomes and precision medicine research in oncology. Due to the inherently complex structure of the multiple phenomic and genomic datasets, the use of these data requires a robust process for data integration and preparation in order to build analytic models. RESULTS: We present the {genieBPC} package, a user-friendly data processing pipeline to facilitate the creation of analytic cohorts from the GENIE BPC data that are ready for clinico-genomic modeling and analyses. AVAILABILITY AND IMPLEMENTATION: {genieBPC} is available on CRAN and GitHub. |
format | Online Article Text |
id | pubmed-9822536 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-98225362023-01-09 A data processing pipeline for the AACR project GENIE biopharma collaborative data with the {genieBPC} R package Lavery, Jessica A Brown, Samantha Curry, Michael A Martin, Axel Sjoberg, Daniel D Whiting, Karissa Bioinformatics Applications Note MOTIVATION: Data from the American Association for Cancer Research Project Genomics Evidence Neoplasia Information Exchange Biopharma Collaborative (GENIE BPC) represent comprehensive clinical data linked to high-throughput sequencing data, providing a multi-institution, pan-cancer, publicly available data repository. GENIE BPC data provide detailed demographic, clinical, treatment, genomic and outcome data for patients with cancer. These data result in a unique observational database of molecularly characterized tumors with comprehensive clinical annotation that can be used for health outcomes and precision medicine research in oncology. Due to the inherently complex structure of the multiple phenomic and genomic datasets, the use of these data requires a robust process for data integration and preparation in order to build analytic models. RESULTS: We present the {genieBPC} package, a user-friendly data processing pipeline to facilitate the creation of analytic cohorts from the GENIE BPC data that are ready for clinico-genomic modeling and analyses. AVAILABILITY AND IMPLEMENTATION: {genieBPC} is available on CRAN and GitHub. Oxford University Press 2022-12-15 /pmc/articles/PMC9822536/ /pubmed/36519837 http://dx.doi.org/10.1093/bioinformatics/btac796 Text en © The Author(s) 2022. Published by Oxford University Press. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Applications Note Lavery, Jessica A Brown, Samantha Curry, Michael A Martin, Axel Sjoberg, Daniel D Whiting, Karissa A data processing pipeline for the AACR project GENIE biopharma collaborative data with the {genieBPC} R package |
title | A data processing pipeline for the AACR project GENIE biopharma collaborative data with the {genieBPC} R package |
title_full | A data processing pipeline for the AACR project GENIE biopharma collaborative data with the {genieBPC} R package |
title_fullStr | A data processing pipeline for the AACR project GENIE biopharma collaborative data with the {genieBPC} R package |
title_full_unstemmed | A data processing pipeline for the AACR project GENIE biopharma collaborative data with the {genieBPC} R package |
title_short | A data processing pipeline for the AACR project GENIE biopharma collaborative data with the {genieBPC} R package |
title_sort | data processing pipeline for the aacr project genie biopharma collaborative data with the {geniebpc} r package |
topic | Applications Note |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9822536/ https://www.ncbi.nlm.nih.gov/pubmed/36519837 http://dx.doi.org/10.1093/bioinformatics/btac796 |
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