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ARS Home » Southeast Area » Little Rock, Arkansas » Arkansas Children's Nutrition Center » Research » Publications at this Location » Publication #344445

Research Project: Impact of Early Dietary Factors on Child Development and Health

Location: Arkansas Children's Nutrition Center

Title: Dynamic Assessment of Microbial Ecology (DAME): A web app for interactive analysis and visualization of microbial sequencing data

Author
item Piccolo, Brian - Arkansas Children's Nutrition Research Center (ACNC)
item Wankhade, Umesh - Arkansas Children's Nutrition Research Center (ACNC)
item Chintapalli, Sree - Arkansas Children's Nutrition Research Center (ACNC)
item Bhattacharyya, Sudeepa - Arkansas Children's Nutrition Research Center (ACNC)
item Luo, Chunqiao - University Of Arkansas
item Shankar, Kartik - Arkansas Children's Nutrition Research Center (ACNC)

Submitted to: Bioinformatics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/23/2017
Publication Date: 3/15/2018
Citation: Piccolo, B.D., Wankhade, U.D., Chintapalli, S.V., Bhattacharyya, S., Luo, C., Shankar, K. 2018. Dynamic Assessment of Microbial Ecology (DAME): A web app for interactive analysis and visualization of microbial sequencing data. Bioinformatics. 34(6):1050-1052. https://doi.org/10.1093/bioinformatics/btx686.

Interpretive Summary: There is great interest in understanding the role of the gut bacteria ("microbiome") in health and disease, since the population and activities of these naturally-occurring microbes are readily altered by diet, during pregnancy and child development, and by one's metabolic health status. Many scientists are now using DNA-based methods to capture the microbial diversity across several locations of the human body, including regions of the small and large intestines. However, the massive data generated by these methods pose unique challenges for analysis that may not be understood by many clinical and basic scientists. To facilitate quicker and more user-friendly approaches to understand microbiome data, we have developed an interactive web based app, called Dynamic Assessment of Microbial Ecology (DAME), for exploration and visualization of microbial DNA based measurements. DAME is different from other web-based apps for several reasons: 1) DAME allows the user to select/deselect groups and samples used in downstream analyses without reloading new files, 2) DAME provides the ability to run one or more taxonomic levels at the same time, and 3) DAME provides fully interactive tables and graphics that update in real-time, based on user selections. DAME was built in the R statistical language and uses the most up-to-date ecological based packages for analysis of alpha-diversity, beta-diversity, and taxa based group comparisons. It is freely available online at https://acnc-shinyapps.shinyapps.io/DAME/ and is available for local installation at https://github.com/bdpiccolo/ACNC-DAME provided that R is installed with the shiny package. Development of such tools in the era of "Big Data" will greatly facilitate interpretation of complicated biological results, even for novice users, and will broaden the audience for the knowledge so gained. This approach supports a more rapid and dynamic ability to unravel unique links between diet and microbiome, in turn associating these with health outcomes.

Technical Abstract: Dynamic Assessment of Microbial Ecology (DAME) is a shiny-based web application for interactive analysis and visualization of microbial sequencing data. DAME provides researchers not familiar with R programming the ability to access the most current R functions utilized for ecology and gene sequencing data analyses. Currently, DAME supports group comparisons of several ecological estimates of a-diversity and B-diversity, along with differential abundance analysis of individual taxa. Using the Shiny framework, the user has complete control of all aspects of the data analysis, including sample/experimental group selection and filtering, estimate selection, statistical methods, and visualization parameters. Furthermore, graphical and tabular outputs are supported by R packages using D3.js and are fully interactive.