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The REMBRANDT study – a large collection of genomic data from brain cancer patients

ABSTRACT: The REMBRANDT study – a large collection of genomic data from brain cancer patients

PROVIDER: PRJNA427347 | ENA |

REPOSITORIES: ENA

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The REMBRANDT study – a large collection of genomic data from brain cancer patients [SNP]

Project description:The REMBRANDT study – a large collection of genomic data from brain cancer patients [SNP]

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The REMBRANDT study – a large collection of genomic data from brain cancer patients [SNP]

Project description:The Rembrandt brain cancer dataset and includes clinical and biospecimen data from this study for 671 patients collected from 14 contributing institutions in 2005. Such combined datasets would provide researchers with a unique opportunity to conduct integrative analysis of gene expression and copy number changes alongside clinical outcomes this large brain cancer study. In 2015, the NCI retired the REMBRANDT data portal, and all molecular data including microarray gene expression, copy number, and clinical data, was migrated to the Georgetown Database of Cancer (G-DOC). The G-DOC platform can be accessed at: https://gdoc.georgetown.edu. G-DOC is a data integration platform that offers advanced computational tools to handle a variety of biomedical BIG DATA including gene expression arrays, NGS and medical images so that they can be analyzed in the full context of other omics and clinical information. After migration of the REMBRANDT dataset into G-DOC, we applied our novel algorithm for copy number data analysis called Chromosomal Instability Index (CINdex), published as an open source Bioconductor package . This submission includes the copy number data, processed on two Affymetrix 50K copy number arrays and gene expression data processed on the Affymetrix U133 Plus 2 array. It also includes the Chromsome and Cytoband CINdex matrices.

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Project description:The Rembrandt brain cancer dataset and includes clinical and biospecimen data from this study for 671 patients collected from 14 contributing institutions in 2005. Such combined datasets would provide researchers with a unique opportunity to conduct integrative analysis of gene expression and copy number changes alongside clinical outcomes (overall survival) in this large brain cancer study. In 2015, the NCI retired the REMBRANDT data portal, and all molecular data including microarray gene expression, copy number, and clinical data, was migrated to the Georgetown Database of Cancer (G-DOC). The G-DOC platform can be accessed at: https://gdoc.georgetown.edu. G-DOC is a data integration platform that offers advanced computational tools to handle a variety of biomedical BIG DATA including gene expression arrays, NGS and medical images so that they can be analyzed in the full context of other omics and clinical information. After migration of the REMBRANDT dataset into G-DOC, we applied our novel algorithm for copy number data analysis called Chromosomal Instability Index (CINdex), published as an open source Bioconductor package . This submission includes the copy number data, processed on two Affymetrix 50K copy number arrays and gene expression data processed on the Affymetrix U133 Plus2 array. It also includes the Chromosome and Cytoband CINdex matrices.

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The REMBRANDT study – a large collection of genomic data from brain cancer patients

Project description:This SuperSeries is composed of the SubSeries listed below.

2018-05-25 | GSE108476 | GEO

The REMBRANDT study, a large collection of genomic data from brain cancer patients.

Project description:The Rembrandt brain cancer dataset includes 671 patients collected from 14 contributing institutions from 2004-2006. It is accessible for conducting clinical translational research using the open access Georgetown Database of Cancer (G-DOC) platform. In addition, the raw and processed genomics and transcriptomics data have also been made available via the public NCBI GEO repository as a super series GSE108476. Such combined datasets would provide researchers with a unique opportunity to conduct integrative analysis of gene expression and copy number changes in patients alongside clinical outcomes (overall survival) using this large brain cancer study.

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