BackgroundAging is associated with functional decline of neurons and increased incidence of both neurodegenerative and ocular disease. Photoreceptor neurons in Drosophila melanogaster provide a powerful model for studying the molecular changes involved in functional senescence of neurons since decreased visual behavior precedes retinal degeneration. Here, we sought to identify gene expression changes and the genomic features of differentially regulated genes in photoreceptors that contribute to visual senescence.ResultsTo identify gene expression changes that could lead to visual senescence, we characterized the aging transcriptome of Drosophila sensory neurons highly enriched for photoreceptors. We profiled the nuclear transcriptome of genetically-labeled photoreceptors over a 40 day time course and identified increased expression of genes involved in stress and DNA damage response, and decreased expression of genes required for neuronal function. We further show that combinations of promoter motifs robustly identify age-regulated genes, suggesting that transcription factors are important in driving expression changes in aging photoreceptors. However, long, highly expressed and heavily spliced genes are also more likely to be downregulated with age, indicating that other mechanisms could contribute to expression changes at these genes. Lastly, we identify that circular RNAs (circRNAs) strongly increase during aging in photoreceptors.ConclusionsOverall, we identified changes in gene expression in aging Drosophila photoreceptors that could account for visual senescence. Further, we show that genomic features predict these age-related changes, suggesting potential mechanisms that could be targeted to slow the rate of age-associated visual decline.Electronic supplementary materialThe online version of this article (10.1186/s12864-017-4304-3) contains supplementary material, which is available to authorized users.

D&R is a new statistical approach to the analysis of large complex data. The data are divided into subsets. Computationally, each subset is a small dataset. Analytic methods are applied to each of the subsets, and the outputs of each method are recombined to form a result for the entire data. Computations can be run in parallel with no communication among them, making them embarrassingly parallel, the simplest possible parallel processing. Using D&R, a data analyst can apply almost any statistical or visualization method to large complex data. Direct application of most analytic methods to the entire data is either infeasible, or impractical. D&R enables deep analysis: comprehensive analysis, including visualization of the detailed data, that minimizes the risk of losing important information. One of our D&R research thrusts uses statistics to develop “best” division and recombination procedures for analytic methods. Another is a D&R computational environment that has two widely used components, R and Hadoop, and our RHIPE merger of them. Hadoop is a distributed database and parallel compute engine that executes the embarrassingly parallel D&R computations across a cluster. RHIPE allows analysis wholly from within R, making programming with the data very efficient. Copyright © 2012 John Wiley & Sons, Ltd.

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ObjectiveTo introduce Soda Pop, an R/Shiny application designed to be adisease agnostic time-series clustering, alarming, and forecastingtool to assist in disease surveillance “triage, analysis and reporting”workflows within the Biosurveillance Ecosystem (BSVE) [1]. In thisposter, we highlight the new capabilities that are brought to the BSVEby Soda Pop with an emphasis on the impact of metholodogicaldecisions.IntroductionThe Biosurveillance Ecosystem (BSVE) is a biological andchemical threat surveillance system sponsored by the Defense ThreatReduction Agency (DTRA). BSVE is intended to be user-friendly,multi-agency, cooperative, modular and threat agnostic platformfor biosurveillance [2]. In BSVE, a web-based workbench presentsthe analyst with applications (apps) developed by various DTRAfundedresearchers, which are deployed on-demand in the cloud(e.g., Amazon Web Services). These apps aim to address emergingneeds and refine capabilities to enable early warning of chemical andbiological threats for multiple users across local, state, and federalagencies.Soda Pop is an app developed by Pacific Northwest NationalLaboratory (PNNL) to meet the current needs of the BSVE forearly warning and detection of disease outbreaks. Aimed for use bya diverse set of analysts, the application is agnostic to data sourceand spatial scale enabling it to be generalizable across many diseasesand locations. To achieve this, we placed a particular emphasis onclustering and alerting of disease signals within Soda Pop withoutstrong prior assumptions on the nature of observed diseased counts.MethodsAlthough designed to be agnostic to the data source, Soda Pop wasinitially developed and tested on data summarizing Influenza-LikeIllness in military hospitals from collaboration with the Armed ForcesHealth Surveillance Branch. Currently, the data incorporated alsoincludes the CDC’s National Notifiable Diseases Surveillance System(NNDSS) tables [3] and the WHO’s Influenza A/B Influenza Data(Flunet) [4]. These data sources are now present in BSVE’s Postgresdata storage for direct access.Soda Pop is designed to automate time-series tasks of datasummarization, exploration, clustering, alarming and forecasting.Built as an R/Shiny application, Soda Pop is founded on the powerfulstatistical tool R [5]. Where applicable, Soda Pop facilitates nonparametricseasonal decomposition of time-series; hierarchicalagglomerative clustering across reporting areas and between diseaseswithin reporting areas; and a variety of alarming techniques includingExponential Weighted Moving Average alarms and Early AberrationDetection [6].Soda Pop embeds these techniques within a user-interface designedto enhance an analyst’s understanding of emerging trends in their dataand enables the inclusion of its graphical elements into their dossierfor further tracking and reporting. The ultimate goal of this softwareis to facilitate the discovery of unknown disease signals along withincreasing the speed of detection of unusual patterns within thesesignals.ConclusionsSoda Pop organizes common statistical disease surveillance tasksin a manner integrated with BSVE data source inputs and outputs.The app analyzes time-series disease data and supports a robust set ofclustering and alarming routines that avoid strong assumptions on thenature of observed disease counts. This attribute allows for flexibilityin the data source, spatial scale, and disease types making it useful toa wide range of analystsSoda Pop within the BSVE.KeywordsBSVE; Biosurveillance; R/Shiny; Clustering; AlarmingAcknowledgmentsThis work was supported by the Defense Threat Reduction Agency undercontract CB10082 with Pacific Northwest National LaboratoryReferences1. Dasey, Timothy, et al. “Biosurveillance Ecosystem (BSVE) WorkflowAnalysis.” Online journal of public health informatics 5.1 (2013).2. http://www.defense.gov/News/Article/Article/681832/dtra-scientistsdevelop-cloud-based-biosurveillance-ecosystem. Accessed 9/6/2016.3. Centers for Disease Control and Prevention. “National NotifiableDiseases Surveillance System (NNDSS).”4. World Health Organization. “FluNet.” Global Influenza Surveillanceand Response System (GISRS).5. R Core Team (2016). R: A language and environment for statisticalcomputing. R Foundation for Statistical Computing, Vienna, Austria.6. Salmon, Maëlle, et al. “Monitoring Count Time Series in R: AberrationDetection in Public Health Surveillance.” Journal of StatisticalSoftware [Online], 70.10 (2016): 1 - 35.