Literature DB >> 29087435

Dynamic assessment of microbial ecology (DAME): a web app for interactive analysis and visualization of microbial sequencing data.

Brian D Piccolo1,2, Umesh D Wankhade1,2, Sree V Chintapalli1,2, Sudeepa Bhattacharyya1,2, Luo Chunqiao2, Kartik Shankar1,2.   

Abstract

Summary: 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 α-diversity and β-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. Availability and implementation: DAME was implemented in R but can be modified by Hypertext Markup Language (HTML), Cascading Style Sheets (CSS), and JavaScript. It is freely available on the web at https://acnc-shinyapps.shinyapps.io/DAME/. Local installation and source code are available through Github (https://github.com/bdpiccolo/ACNC-DAME). Any system with R can launch DAME locally provided the shiny package is installed. Contact: bdpiccolo@uams.edu.

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Year:  2018        PMID: 29087435      PMCID: PMC5860285          DOI: 10.1093/bioinformatics/btx686

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  11 in total

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3.  APE: Analyses of Phylogenetics and Evolution in R language.

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Journal:  Cell Host Microbe       Date:  2016-01-13       Impact factor: 21.023

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Journal:  Nat Methods       Date:  2010-04-11       Impact factor: 28.547

6.  Shiny-phyloseq: Web application for interactive microbiome analysis with provenance tracking.

Authors:  Paul J McMurdie; Susan Holmes
Journal:  Bioinformatics       Date:  2014-09-26       Impact factor: 6.937

7.  Seed: a user-friendly tool for exploring and visualizing microbial community data.

Authors:  Daniel Beck; Christopher Dennis; James A Foster
Journal:  Bioinformatics       Date:  2014-10-20       Impact factor: 6.937

8.  Calypso: a user-friendly web-server for mining and visualizing microbiome-environment interactions.

Authors:  Martha Zakrzewski; Carla Proietti; Jonathan J Ellis; Shihab Hasan; Marie-Jo Brion; Bernard Berger; Lutz Krause
Journal:  Bioinformatics       Date:  2017-03-01       Impact factor: 6.937

9.  phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data.

Authors:  Paul J McMurdie; Susan Holmes
Journal:  PLoS One       Date:  2013-04-22       Impact factor: 3.240

10.  Waste not, want not: why rarefying microbiome data is inadmissible.

Authors:  Paul J McMurdie; Susan Holmes
Journal:  PLoS Comput Biol       Date:  2014-04-03       Impact factor: 4.475

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  11 in total

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Authors:  Rachel J Sorensen; James S Drouillard; Teresa L Douthit; Qinghong Ran; Douglas G Marthaler; Qing Kang; Christopher I Vahl; James M Lattimer
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Review 2.  Microbiome data science.

Authors:  Sudarshan A Shetty; Leo Lahti
Journal:  J Biosci       Date:  2019-10       Impact factor: 1.826

3.  Neonatal Diet Impacts Bioregional Microbiota Composition in Piglets Fed Human Breast Milk or Infant Formula.

Authors:  Lauren R Brink; Katelin Matazel; Brian D Piccolo; Anne K Bowlin; Sree V Chintapalli; Kartik Shankar; Laxmi Yeruva
Journal:  J Nutr       Date:  2019-12-01       Impact factor: 4.798

4.  Human Breast-Milk Feeding Enhances the Humoral and Cell-Mediated Immune Response in Neonatal Piglets.

Authors:  John J Miklavcic; Thomas M Badger; Anne K Bowlin; Katelin S Matazel; Mario A Cleves; Tanya LeRoith; Manish K Saraf; Sree V Chintapalli; Brian D Piccolo; Kartik Shankar; Laxmi Yeruva
Journal:  J Nutr       Date:  2018-11-01       Impact factor: 4.798

5.  Effect of hay type on cecal and fecal microbiome and fermentation parameters in horses.

Authors:  Rachel J Sorensen; James S Drouillard; Teresa L Douthit; Qinghong Ran; Douglas G Marthaler; Qing Kang; Christopher I Vahl; James M Lattimer
Journal:  J Anim Sci       Date:  2021-01-01       Impact factor: 3.159

6.  Dietary supplementation with strawberry induces marked changes in the composition and functional potential of the gut microbiome in diabetic mice.

Authors:  Chrissa Petersen; Umesh D Wankhade; Divya Bharat; Kiana Wong; Jennifer Ellen Mueller; Sree V Chintapalli; Brian D Piccolo; Thunder Jalili; Zhenquan Jia; J David Symons; Kartik Shankar; Pon Velayutham Anandh Babu
Journal:  J Nutr Biochem       Date:  2019-01-18       Impact factor: 6.117

7.  Maternal High-Fat Diet Programs Offspring Liver Steatosis in a Sexually Dimorphic Manner in Association with Changes in Gut Microbial Ecology in Mice.

Authors:  Umesh D Wankhade; Ying Zhong; Ping Kang; Maria Alfaro; Sree V Chintapalli; Brian D Piccolo; Kelly E Mercer; Aline Andres; Keshari M Thakali; Kartik Shankar
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8.  Sex-Specific Changes in Gut Microbiome Composition following Blueberry Consumption in C57BL/6J Mice.

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9.  A comprehensive approach to stool donor screening for faecal microbiota transplantation in China.

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Journal:  Microb Cell Fact       Date:  2021-11-27       Impact factor: 5.328

10.  Predatory bacteria in the haemolymph of the cultured spiny lobster Panulirus ornatus.

Authors:  Mei C Ooi; Evan F Goulden; Gregory G Smith; Andrew R Bridle
Journal:  Microbiology (Reading)       Date:  2021-11       Impact factor: 2.777

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