ITAP: integrated transcriptomics and phenotype database for stress response of Escherichia coli and Saccharomyces cerevisiae. (December 2015)

• Record Type: Journal Article
• Authors: Sundararaman, Niveda; Ash, Christine; Guo, Weihua; Button, Rebecca; Singh, Jugroop; Feng, Xueyang
• Is Part Of: BMC research notes. Volume 8:Number 1(2015)
• Page Start: 1
• Page End: 7
• Publication Date: 2015-12
• Subjects: Open source -- Transcriptomics–phenotype correlation -- Yeast -- Escherichia coli; Medicine -- Periodicals; Biology -- Periodicals; 610.5
• DOI: 10.1186/s13104-015-1759-7
• Languages: English
• ISSNs: 1756-0500

Abstract

Abstract Background Organisms are subject to various stress conditions, which affect both the organism's gene expression and phenotype. It is critical to understand microbial responses to stress conditions and uncover the underlying molecular mechanisms. To this end, it is necessary to build a database that collects transcriptomics and phenotypic data of microbes growing under various stress factors for in-depth systems biology analysis. Despite of numerous databases that collect gene expression profiles, to our best knowledge, there are few, if any, databases that collect both transcriptomics and phenotype data simultaneously. In light of this, we have developed an open source, web-based database, namely integrated transcriptomics and phenotype (iTAP) database, that records and links the transcriptomics and phenotype data for two model microorganisms, Escherichia coli andSaccharomyces cerevisiae in response to exposure of various stress conditions. Results To collect the data, we chose relevant research papers from the PubMed database containing all the necessary information for data curation including experimental conditions, transcriptomics data, and phenotype data. The transcriptomics data, including thep value and fold change, were obtained through the comparison of test strains against control strains using Gene Expression Omnibus's GEO2R analyzer. The phenotype data, including the cell growth rate and the productivity, volumetric rate, and mass-based yield of byproducts, were calculated independently from charts or graphs within the reference papers. Since the phenotype data was never reported in a standardized format, the curation of correlated transcriptomics–phenotype datasets became extremely tedious and time-consuming. Despite the challenges, till now, we successfully correlated 57 and 143 datasets of transcriptomics and phenotype forE. coli andS. cerevisiae, respectively, and applied a regression model within the iTAP database to accurately predict over 93 and 73 % of the growth rates ofE. coli andS. cerevisiae, respectively, directly from the transcriptomics data. Conclusion This is the first time that transcriptomics and phenotype data are categorized and correlated in an open-source database. This allows biologists to access the database and utilize it to predict the phenotype of microorganisms from their transcriptomics data. The iTAP database is freely available athttps://sites.google.com/a/vt.edu/biomolecular-engineering-lab/software .