RESUMO
The U.S. Environmental Protection Agency (EPA) is actively involved in supporting citizen science projects and providing communities with information and assistance for conducting their own air pollution monitoring. As part of a Regional Applied Research Effort (RARE) project, EPA's Office of Research and Development (ORD) worked collaboratively with EPA Region 2 and the Ironbound Community Corporation (ICC) in Newark, New Jersey, to develop and test the "Air Sensor Toolbox for Citizen Scientists." In this collaboration, citizen scientists measured local gaseous and particulate air pollution levels by using a customized low-cost sensor pod designed and fabricated by EPA. This citizen science air quality measurement project provided an excellent opportunity for EPA to evaluate and improve the Toolbox resources available to communities. The Air Sensor Toolbox, developed in coordination with the ICC, can serve as a template for communities across the country to use in developing their own air pollution monitoring programs in areas where air pollution is a concern. This pilot project provided an opportunity for a highly motivated citizen science organization and the EPA to work together directly to address environmental concerns within the community. Useful lessons were learned about how to improve coordination between the government and communities and the types of tools and technologies needed for conducting an effective citizen science project that can be applied to future efforts.
RESUMO
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 and Saccharomyces 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 the p 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 for E. coli and S. cerevisiae, respectively, and applied a regression model within the iTAP database to accurately predict over 93 and 73 % of the growth rates of E. coli and S. 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 at https://sites.google.com/a/vt.edu/biomolecular-engineering-lab/software .