Digital expression explorer 2: a repository of uniformly processed RNA sequencing data

Ziemann, Mark, Kaspi, Antony and El-Osta, Assam 2019, Digital expression explorer 2: a repository of uniformly processed RNA sequencing data, Gigascience, vol. 8, no. 4, doi: 10.1093/gigascience/giz022.

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Title Digital expression explorer 2: a repository of uniformly processed RNA sequencing data
Author(s) Ziemann, MarkORCID iD for Ziemann, Mark orcid.org/0000-0002-7688-6974
Kaspi, Antony
El-Osta, Assam
Journal name Gigascience
Volume number 8
Issue number 4
Total pages 13
Publisher Oxford University Press
Place of publication Oxford, Eng.
Publication date 2019-04
ISSN 2047-217X
Keyword(s) RNA-seq
data reuse
gene expression
transcriptome
Summary BACKGROUND: RNA sequencing (RNA-seq) is an indispensable tool in the study of gene regulation. While the technology has brought with it better transcript coverage and quantification, there remain considerable barriers to entry for the computational biologist to analyse large data sets. There is a real need for a repository of uniformly processed RNA-seq data that is easy to use. FINDINGS: To address these obstacles, we developed Digital Expression Explorer 2 (DEE2), a web-based repository of RNA-seq data in the form of gene-level and transcript-level expression counts. DEE2 contains >5.3 trillion assigned reads from 580,000 RNA-seq data sets including species Escherichia coli, yeast, Arabidopsis, worm, fruit fly, zebrafish, rat, mouse, and human. Base-space sequence data downloaded from the National Center for Biotechnology Information Sequence Read Archive underwent quality control prior to transcriptome and genome mapping using open-source tools. Uniform data processing methods ensure consistency across experiments, facilitating fast and reproducible meta-analyses. CONCLUSIONS: The web interface allows users to quickly identify data sets of interest using accession number and keyword searches. The data can also be accessed programmatically using a specifically designed R package. We demonstrate that DEE2 data are compatible with statistical packages such as edgeR or DESeq. Bulk data are also available for download. DEE2 can be found at http://dee2.io.
Language eng
DOI 10.1093/gigascience/giz022
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2019, The Authors
Persistent URL http://hdl.handle.net/10536/DRO/DU:30120763

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