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Visualizing genomic characteristics across an RNA-Seq based reference landscape of normal and neoplastic brain
In order to better understand the relationship between normal and neoplastic brain, we combined five publicly available large-scale datasets, correcting for batch effects and applying Uniform Manifold Approximation and Projection (UMAP) to RNA-Seq data. We assembled a reference Brain-UMAP including...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10014937/ https://www.ncbi.nlm.nih.gov/pubmed/36918656 http://dx.doi.org/10.1038/s41598-023-31180-z |
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author | Arora, Sonali Szulzewsky, Frank Jensen, Matt Nuechterlein, Nicholas Pattwell, Siobhan S. Holland, Eric C. |
author_facet | Arora, Sonali Szulzewsky, Frank Jensen, Matt Nuechterlein, Nicholas Pattwell, Siobhan S. Holland, Eric C. |
author_sort | Arora, Sonali |
collection | PubMed |
description | In order to better understand the relationship between normal and neoplastic brain, we combined five publicly available large-scale datasets, correcting for batch effects and applying Uniform Manifold Approximation and Projection (UMAP) to RNA-Seq data. We assembled a reference Brain-UMAP including 702 adult gliomas, 802 pediatric tumors and 1409 healthy normal brain samples, which can be utilized to investigate the wealth of information obtained from combining several publicly available datasets to study a single organ site. Normal brain regions and tumor types create distinct clusters and because the landscape is generated by RNA-Seq, comparative gene expression profiles and gene ontology patterns are readily evident. To our knowledge, this is the first meta-analysis that allows for comparison of gene expression and pathways of interest across adult gliomas, pediatric brain tumors, and normal brain regions. We provide access to this resource via the open source, interactive online tool Oncoscape, where the scientific community can readily visualize clinical metadata, gene expression patterns, gene fusions, mutations, and copy number patterns for individual genes and pathway over this reference landscape. |
format | Online Article Text |
id | pubmed-10014937 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-100149372023-03-16 Visualizing genomic characteristics across an RNA-Seq based reference landscape of normal and neoplastic brain Arora, Sonali Szulzewsky, Frank Jensen, Matt Nuechterlein, Nicholas Pattwell, Siobhan S. Holland, Eric C. Sci Rep Article In order to better understand the relationship between normal and neoplastic brain, we combined five publicly available large-scale datasets, correcting for batch effects and applying Uniform Manifold Approximation and Projection (UMAP) to RNA-Seq data. We assembled a reference Brain-UMAP including 702 adult gliomas, 802 pediatric tumors and 1409 healthy normal brain samples, which can be utilized to investigate the wealth of information obtained from combining several publicly available datasets to study a single organ site. Normal brain regions and tumor types create distinct clusters and because the landscape is generated by RNA-Seq, comparative gene expression profiles and gene ontology patterns are readily evident. To our knowledge, this is the first meta-analysis that allows for comparison of gene expression and pathways of interest across adult gliomas, pediatric brain tumors, and normal brain regions. We provide access to this resource via the open source, interactive online tool Oncoscape, where the scientific community can readily visualize clinical metadata, gene expression patterns, gene fusions, mutations, and copy number patterns for individual genes and pathway over this reference landscape. Nature Publishing Group UK 2023-03-14 /pmc/articles/PMC10014937/ /pubmed/36918656 http://dx.doi.org/10.1038/s41598-023-31180-z Text en © The Author(s) 2023, corrected publication 2023 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Arora, Sonali Szulzewsky, Frank Jensen, Matt Nuechterlein, Nicholas Pattwell, Siobhan S. Holland, Eric C. Visualizing genomic characteristics across an RNA-Seq based reference landscape of normal and neoplastic brain |
title | Visualizing genomic characteristics across an RNA-Seq based reference landscape of normal and neoplastic brain |
title_full | Visualizing genomic characteristics across an RNA-Seq based reference landscape of normal and neoplastic brain |
title_fullStr | Visualizing genomic characteristics across an RNA-Seq based reference landscape of normal and neoplastic brain |
title_full_unstemmed | Visualizing genomic characteristics across an RNA-Seq based reference landscape of normal and neoplastic brain |
title_short | Visualizing genomic characteristics across an RNA-Seq based reference landscape of normal and neoplastic brain |
title_sort | visualizing genomic characteristics across an rna-seq based reference landscape of normal and neoplastic brain |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10014937/ https://www.ncbi.nlm.nih.gov/pubmed/36918656 http://dx.doi.org/10.1038/s41598-023-31180-z |
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