Large-scale atlas of microarray data reveals the distinct expression landscape of different tissues in Arabidopsis

He, F., Yoo, S., Wang, D., Kumari, S., Gerstein, M., Ware, D., Maslov, S. (2016) Large-scale atlas of microarray data reveals the distinct expression landscape of different tissues in Arabidopsis. Plant J, 86 (6). pp. 472-80. ISSN 1365-313X (Electronic)0960-7412 (Linking)

URL: http://www.ncbi.nlm.nih.gov/pubmed/27015116
DOI: 10.1111/tpj.13175

Abstract

Transcriptome datasets from thousands of samples of the model plant Arabidopsis thaliana have been collectively generated by multiple individual labs. Although integration and meta-analysis of these samples has become routine in the plant research community, it is often hampered by the lack of metadata or differences in annotation styles by different labs. In this study, we carefully selected and integrated 6,057 Arabidopsis microarray expression samples from 304 experiments deposited to NCBI GEO. Metadata such as tissue type, growth condition, and developmental stage were manually curated for each sample. We then studied global expression landscape of the integrated dataset and found that samples of the same tissue tend to be more similar to each other than to samples of other tissues, even in different growth conditions or developmental stages. Root has the most distinct transcriptome compared to aerial tissues, but the transcriptome of cultured root is more similar to those of aerial tissues as the former samples lost their cellular identity. Using a simple computational classification method, we showed that the tissue type of a sample can be successfully predicted based on its expression profile, opening the door for automatic metadata extraction and facilitating re-use of plant transcriptome data. As a proof of principle we applied our automated annotation pipeline to 708 RNA-seq samples from public repositories and verified accuracy of our predictions with samples' metadata provided by authors. This article is protected by copyright. All rights reserved.

Item Type: Paper
Uncontrolled Keywords: Arabidopsis automatic reconstruction of missing metadata expression data integration global transcriptional landscape metadata annotation re-use of public expression data
Subjects: organism description > plant > Arabidopsis
bioinformatics
bioinformatics > genomics and proteomics
bioinformatics > genomics and proteomics > analysis and processing > microarray gene expression processing
CSHL Authors:
Communities: CSHL labs > Ware lab
Depositing User: Matt Covey
Date Deposited: 31 Mar 2016 19:30
Last Modified: 04 Aug 2016 20:17
Related URLs:
URI: http://repository.cshl.edu/id/eprint/32466

Actions (login required)

Administrator's edit/view item Administrator's edit/view item
CSHL HomeAbout CSHLResearchEducationNews & FeaturesCampus & Public EventsCareersGiving