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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...

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Autores principales: Lavery, Jessica A, Brown, Samantha, Curry, Michael A, Martin, Axel, Sjoberg, Daniel D, Whiting, Karissa
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2022
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.
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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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