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Massive mining of publicly available RNA-seq data from human and mouse
RNA sequencing (RNA-seq) is the leading technology for genome-wide transcript quantification. However, publicly available RNA-seq data is currently provided mostly in raw form, a significant barrier for global and integrative retrospective analyses. ARCHS4 is a web resource that makes the majority o...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5893633/ https://www.ncbi.nlm.nih.gov/pubmed/29636450 http://dx.doi.org/10.1038/s41467-018-03751-6 |
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author | Lachmann, Alexander Torre, Denis Keenan, Alexandra B. Jagodnik, Kathleen M. Lee, Hoyjin J. Wang, Lily Silverstein, Moshe C. Ma’ayan, Avi |
author_facet | Lachmann, Alexander Torre, Denis Keenan, Alexandra B. Jagodnik, Kathleen M. Lee, Hoyjin J. Wang, Lily Silverstein, Moshe C. Ma’ayan, Avi |
author_sort | Lachmann, Alexander |
collection | PubMed |
description | RNA sequencing (RNA-seq) is the leading technology for genome-wide transcript quantification. However, publicly available RNA-seq data is currently provided mostly in raw form, a significant barrier for global and integrative retrospective analyses. ARCHS4 is a web resource that makes the majority of published RNA-seq data from human and mouse available at the gene and transcript levels. For developing ARCHS4, available FASTQ files from RNA-seq experiments from the Gene Expression Omnibus (GEO) were aligned using a cloud-based infrastructure. In total 187,946 samples are accessible through ARCHS4 with 103,083 mouse and 84,863 human. Additionally, the ARCHS4 web interface provides intuitive exploration of the processed data through querying tools, interactive visualization, and gene pages that provide average expression across cell lines and tissues, top co-expressed genes for each gene, and predicted biological functions and protein–protein interactions for each gene based on prior knowledge combined with co-expression. |
format | Online Article Text |
id | pubmed-5893633 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-58936332018-04-13 Massive mining of publicly available RNA-seq data from human and mouse Lachmann, Alexander Torre, Denis Keenan, Alexandra B. Jagodnik, Kathleen M. Lee, Hoyjin J. Wang, Lily Silverstein, Moshe C. Ma’ayan, Avi Nat Commun Article RNA sequencing (RNA-seq) is the leading technology for genome-wide transcript quantification. However, publicly available RNA-seq data is currently provided mostly in raw form, a significant barrier for global and integrative retrospective analyses. ARCHS4 is a web resource that makes the majority of published RNA-seq data from human and mouse available at the gene and transcript levels. For developing ARCHS4, available FASTQ files from RNA-seq experiments from the Gene Expression Omnibus (GEO) were aligned using a cloud-based infrastructure. In total 187,946 samples are accessible through ARCHS4 with 103,083 mouse and 84,863 human. Additionally, the ARCHS4 web interface provides intuitive exploration of the processed data through querying tools, interactive visualization, and gene pages that provide average expression across cell lines and tissues, top co-expressed genes for each gene, and predicted biological functions and protein–protein interactions for each gene based on prior knowledge combined with co-expression. Nature Publishing Group UK 2018-04-10 /pmc/articles/PMC5893633/ /pubmed/29636450 http://dx.doi.org/10.1038/s41467-018-03751-6 Text en © The Author(s) 2018 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 Lachmann, Alexander Torre, Denis Keenan, Alexandra B. Jagodnik, Kathleen M. Lee, Hoyjin J. Wang, Lily Silverstein, Moshe C. Ma’ayan, Avi Massive mining of publicly available RNA-seq data from human and mouse |
title | Massive mining of publicly available RNA-seq data from human and mouse |
title_full | Massive mining of publicly available RNA-seq data from human and mouse |
title_fullStr | Massive mining of publicly available RNA-seq data from human and mouse |
title_full_unstemmed | Massive mining of publicly available RNA-seq data from human and mouse |
title_short | Massive mining of publicly available RNA-seq data from human and mouse |
title_sort | massive mining of publicly available rna-seq data from human and mouse |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5893633/ https://www.ncbi.nlm.nih.gov/pubmed/29636450 http://dx.doi.org/10.1038/s41467-018-03751-6 |
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