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wiSDOM: a visual and statistical analytics for interrogating microbiome.

Authors :
Su, Shih-Chi
Galvin, James E.
Yang, Shun-Fa
Chung, Wen-Hung
Chang, Lun-Ching
Source :
Bioinformatics. Sep2021, Vol. 37 Issue 17, p2795-2797. 3p.
Publication Year :
2021

Abstract

Motivation We proposed a wiSDOM (w eb-based i nclusionary analysis S uite for D isease- O riented M etagenomics) R Shiny application which comprises six functional modules: (i) initial visualization of sampling effort and distribution of dominant bacterial taxa among groups or individual samples at different taxonomic levels; (ii) statistical and visual analysis of α diversity; (iii) analysis of similarity (ANOSIM) of β diversity on UniFrac, Bray-Curtis, Horn-Morisita or Jaccard distance and visualizations; (iv) microbial biomarker discovery between two or more groups with various statistical and machine learning approaches; (v) assessment of the clinical validity of selected biomarkers by creating the interactive receiver operating characteristic (ROC) curves and calculating the area under the curve (AUC) for binary classifiers; and lastly (vi) functional prediction of metagenomes with PICRUSt or Tax4Fun. Results The performance of wiSDOM has been evaluated in several of our previous studies for exploring microbial biomarkers and their clinical validity as well as assessing the alterations in bacterial diversity and functionality. The wiSDOM can be customized and visualized as per users' needs and specifications, allowing researchers without programming background to conduct comprehensive data mining and illustration using an intuitive browser-based interface. Availability and implementation The browser-based R Shiny interface can be accessible via (https://lun-ching.shinyapps.io/wisdom/) and freely available at (https://github.com/lunching/wiSDOM). Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
13674803
Volume :
37
Issue :
17
Database :
Academic Search Index
Journal :
Bioinformatics
Publication Type :
Academic Journal
Accession number :
152405818
Full Text :
https://doi.org/10.1093/bioinformatics/btab057