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ExpressionDB: An open source platform for distributing genome-scale datasets
RNA-sequencing (RNA-seq) and microarrays are methods for measuring gene expression across the entire transcriptome. Recent advances have made these techniques practical and affordable for essentially any laboratory with experience in molecular biology. A variety of computational methods have been de...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5667849/ https://www.ncbi.nlm.nih.gov/pubmed/29095940 http://dx.doi.org/10.1371/journal.pone.0187457 |
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author | Hughes, Laura D. Lewis, Scott A. Hughes, Michael E. |
author_facet | Hughes, Laura D. Lewis, Scott A. Hughes, Michael E. |
author_sort | Hughes, Laura D. |
collection | PubMed |
description | RNA-sequencing (RNA-seq) and microarrays are methods for measuring gene expression across the entire transcriptome. Recent advances have made these techniques practical and affordable for essentially any laboratory with experience in molecular biology. A variety of computational methods have been developed to decrease the amount of bioinformatics expertise necessary to analyze these data. Nevertheless, many barriers persist which discourage new labs from using functional genomics approaches. Since high-quality gene expression studies have enduring value as resources to the entire research community, it is of particular importance that small labs have the capacity to share their analyzed datasets with the research community. Here we introduce ExpressionDB, an open source platform for visualizing RNA-seq and microarray data accommodating virtually any number of different samples. ExpressionDB is based on Shiny, a customizable web application which allows data sharing locally and online with customizable code written in R. ExpressionDB allows intuitive searches based on gene symbols, descriptions, or gene ontology terms, and it includes tools for dynamically filtering results based on expression level, fold change, and false-discovery rates. Built-in visualization tools include heatmaps, volcano plots, and principal component analysis, ensuring streamlined and consistent visualization to all users. All of the scripts for building an ExpressionDB with user-supplied data are freely available on GitHub, and the Creative Commons license allows fully open customization by end-users. We estimate that a demo database can be created in under one hour with minimal programming experience, and that a new database with user-supplied expression data can be completed and online in less than one day. |
format | Online Article Text |
id | pubmed-5667849 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-56678492017-11-17 ExpressionDB: An open source platform for distributing genome-scale datasets Hughes, Laura D. Lewis, Scott A. Hughes, Michael E. PLoS One Research Article RNA-sequencing (RNA-seq) and microarrays are methods for measuring gene expression across the entire transcriptome. Recent advances have made these techniques practical and affordable for essentially any laboratory with experience in molecular biology. A variety of computational methods have been developed to decrease the amount of bioinformatics expertise necessary to analyze these data. Nevertheless, many barriers persist which discourage new labs from using functional genomics approaches. Since high-quality gene expression studies have enduring value as resources to the entire research community, it is of particular importance that small labs have the capacity to share their analyzed datasets with the research community. Here we introduce ExpressionDB, an open source platform for visualizing RNA-seq and microarray data accommodating virtually any number of different samples. ExpressionDB is based on Shiny, a customizable web application which allows data sharing locally and online with customizable code written in R. ExpressionDB allows intuitive searches based on gene symbols, descriptions, or gene ontology terms, and it includes tools for dynamically filtering results based on expression level, fold change, and false-discovery rates. Built-in visualization tools include heatmaps, volcano plots, and principal component analysis, ensuring streamlined and consistent visualization to all users. All of the scripts for building an ExpressionDB with user-supplied data are freely available on GitHub, and the Creative Commons license allows fully open customization by end-users. We estimate that a demo database can be created in under one hour with minimal programming experience, and that a new database with user-supplied expression data can be completed and online in less than one day. Public Library of Science 2017-11-02 /pmc/articles/PMC5667849/ /pubmed/29095940 http://dx.doi.org/10.1371/journal.pone.0187457 Text en https://creativecommons.org/publicdomain/zero/1.0/ This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 (https://creativecommons.org/publicdomain/zero/1.0/) public domain dedication. |
spellingShingle | Research Article Hughes, Laura D. Lewis, Scott A. Hughes, Michael E. ExpressionDB: An open source platform for distributing genome-scale datasets |
title | ExpressionDB: An open source platform for distributing genome-scale datasets |
title_full | ExpressionDB: An open source platform for distributing genome-scale datasets |
title_fullStr | ExpressionDB: An open source platform for distributing genome-scale datasets |
title_full_unstemmed | ExpressionDB: An open source platform for distributing genome-scale datasets |
title_short | ExpressionDB: An open source platform for distributing genome-scale datasets |
title_sort | expressiondb: an open source platform for distributing genome-scale datasets |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5667849/ https://www.ncbi.nlm.nih.gov/pubmed/29095940 http://dx.doi.org/10.1371/journal.pone.0187457 |
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