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Friday, July 7 • 11:54am - 12:12pm
Dynamic Assessment of Microbial Ecology (*DAME*): A Shiny App for Analysis and Visualization of Microbial Sequencing Data

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Keywords: Shiny, microbiome, sequencing, ecology, 16S rRNA
Webpages: https://acnc-shinyapps.shinyapps.io/DAME/, https://github.com/bdpiccolo/ACNC-DAME
A new renaissance in knowledge about the role of commensal microbiota in health and disease is well underway facilitated by culture-independent sequencing technologies; however, microbial sequencing data poses new challenges (e.g., taxonomic hierarchy, overdispersion) not generally seen in more traditional sequencing outputs. Additionally, complex study paradigms from clinical or basic research studies necessitate a multilayered analysis pipeline that can seamlessly integrate both primary bioinformatics and secondary statistical analysis combined with data visualization.
In order to address this need, we created a web-based Shiny app, titled DAME, which allows users not familiar with R programming to import, filter, and analyze microbial sequencing data from experimental studies. DAME only requires two files (a BIOM file with sequencing reads combined with taxonomy details, and a csv file containing experimental metadata), which upon upload will trigger the app to render a linear work-flow controlled by the user. Currently, DAME supports group comparisons of several ecological estimates of α-diversity (ANOVA) and β-diversity indices (ordinations and PERMANOVA). Additionally, pairwise differential comparisons of operational taxonomic units (OTUs) using Negative Binomial Regression at all taxonomic levels can be performed. All analyses are accompanied by dynamic graphics and tables for complete user interactivity. DAME leverages functions derived from phyloseq, vegan, and DESeq2 packages for microbial data organization and analysis and DT, highcharter* and scatterD3 for table and plot visualizations. Downloadable options for α-diversity measurements and DESeq2 table outputs are also provided.
The current release (v0.1) is available online (https://acnc-shinyapps.shinyapps.io/DAME/) and in the Github repository (https://github.com/bdpiccolo/ACNC-DAME). *This app uses Highsoft software with non-commercial packages. Highsoft software product is not free for commercial use. Funding supported by United States Department of Agriculture-Agricultural Research Service Project: 6026-51000-010-05S.

Speakers
avatar for Federico Marini

Federico Marini

IMBEI - Mainz
Just got my PhD in Biostatistics/Bioinformatics at the IMBEI @University Medical Center in Mainz, Germany



Friday July 7, 2017 11:54am - 12:12pm
3.02 Wild Gallery