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GREIN: An Interactive Web Platform for Re-analyzing GEO RNA-seq Data
The vast amount of RNA-seq data deposited in Gene Expression Omnibus (GEO) and Sequence Read Archive (SRA) is still a grossly underutilized resource for biomedical research. To remove technical roadblocks for reusing these data, we have developed a web-application GREIN (GEO RNA-seq Experiments Inte...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6527554/ https://www.ncbi.nlm.nih.gov/pubmed/31110304 http://dx.doi.org/10.1038/s41598-019-43935-8 |
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author | Mahi, Naim Al Najafabadi, Mehdi Fazel Pilarczyk, Marcin Kouril, Michal Medvedovic, Mario |
author_facet | Mahi, Naim Al Najafabadi, Mehdi Fazel Pilarczyk, Marcin Kouril, Michal Medvedovic, Mario |
author_sort | Mahi, Naim Al |
collection | PubMed |
description | The vast amount of RNA-seq data deposited in Gene Expression Omnibus (GEO) and Sequence Read Archive (SRA) is still a grossly underutilized resource for biomedical research. To remove technical roadblocks for reusing these data, we have developed a web-application GREIN (GEO RNA-seq Experiments Interactive Navigator) which provides user-friendly interfaces to manipulate and analyze GEO RNA-seq data. GREIN is powered by the back-end computational pipeline for uniform processing of RNA-seq data and the large number (>6,500) of already processed datasets. The front-end user interfaces provide a wealth of user-analytics options including sub-setting and downloading processed data, interactive visualization, statistical power analyses, construction of differential gene expression signatures and their comprehensive functional characterization, and connectivity analysis with LINCS L1000 data. The combination of the massive amount of back-end data and front-end analytics options driven by user-friendly interfaces makes GREIN a unique open-source resource for re-using GEO RNA-seq data. GREIN is accessible at: https://shiny.ilincs.org/grein, the source code at: https://github.com/uc-bd2k/grein, and the Docker container at: https://hub.docker.com/r/ucbd2k/grein. |
format | Online Article Text |
id | pubmed-6527554 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-65275542019-05-30 GREIN: An Interactive Web Platform for Re-analyzing GEO RNA-seq Data Mahi, Naim Al Najafabadi, Mehdi Fazel Pilarczyk, Marcin Kouril, Michal Medvedovic, Mario Sci Rep Article The vast amount of RNA-seq data deposited in Gene Expression Omnibus (GEO) and Sequence Read Archive (SRA) is still a grossly underutilized resource for biomedical research. To remove technical roadblocks for reusing these data, we have developed a web-application GREIN (GEO RNA-seq Experiments Interactive Navigator) which provides user-friendly interfaces to manipulate and analyze GEO RNA-seq data. GREIN is powered by the back-end computational pipeline for uniform processing of RNA-seq data and the large number (>6,500) of already processed datasets. The front-end user interfaces provide a wealth of user-analytics options including sub-setting and downloading processed data, interactive visualization, statistical power analyses, construction of differential gene expression signatures and their comprehensive functional characterization, and connectivity analysis with LINCS L1000 data. The combination of the massive amount of back-end data and front-end analytics options driven by user-friendly interfaces makes GREIN a unique open-source resource for re-using GEO RNA-seq data. GREIN is accessible at: https://shiny.ilincs.org/grein, the source code at: https://github.com/uc-bd2k/grein, and the Docker container at: https://hub.docker.com/r/ucbd2k/grein. Nature Publishing Group UK 2019-05-20 /pmc/articles/PMC6527554/ /pubmed/31110304 http://dx.doi.org/10.1038/s41598-019-43935-8 Text en © The Author(s) 2019 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Mahi, Naim Al Najafabadi, Mehdi Fazel Pilarczyk, Marcin Kouril, Michal Medvedovic, Mario GREIN: An Interactive Web Platform for Re-analyzing GEO RNA-seq Data |
title | GREIN: An Interactive Web Platform for Re-analyzing GEO RNA-seq Data |
title_full | GREIN: An Interactive Web Platform for Re-analyzing GEO RNA-seq Data |
title_fullStr | GREIN: An Interactive Web Platform for Re-analyzing GEO RNA-seq Data |
title_full_unstemmed | GREIN: An Interactive Web Platform for Re-analyzing GEO RNA-seq Data |
title_short | GREIN: An Interactive Web Platform for Re-analyzing GEO RNA-seq Data |
title_sort | grein: an interactive web platform for re-analyzing geo rna-seq data |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6527554/ https://www.ncbi.nlm.nih.gov/pubmed/31110304 http://dx.doi.org/10.1038/s41598-019-43935-8 |
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