Su Shih-Chi, Galvin James E, Yang Shun-Fa, Chung Wen-Hung, Chang Lun-Ching
Whole-Genome Research Core Laboratory of Human Diseases, Chang Gung Memorial Hospital, Keelung 204, Taiwan.
Department of Dermatology, Drug Hypersensitivity Clinical and Research Center, Chang Gung Memorial Hospital, Linkou 333, Taiwan.
Bioinformatics. 2021 Sep 9;37(17):2795-2797. doi: 10.1093/bioinformatics/btab057.
We proposed a wiSDOM (web-based inclusionary analysis Suite for Disease-Oriented Metagenomics) 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.
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.
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 data are available at Bioinformatics online.
我们提出了一个wiSDOM（基于网络的疾病导向宏基因组学包容性分析套件）R Shiny应用程序，它包含六个功能模块：（i）在不同分类水平上对样本量以及优势细菌类群在组间或个体样本中的分布进行初步可视化；（ii）α多样性的统计和可视化分析；（iii）基于UniFrac、Bray-Curtis、Horn-Morisita或Jaccard距离对β多样性进行相似性分析（ANOSIM）及可视化；（iv）使用各种统计和机器学习方法在两个或更多组之间发现微生物生物标志物；（v）通过创建交互式接收器操作特征（ROC）曲线并计算二元分类器的曲线下面积（AUC）来评估所选生物标志物的临床有效性；最后（vi）使用PICRUSt或Tax4Fun对宏基因组进行功能预测。
在我们之前的几项研究中，已经对wiSDOM在探索微生物生物标志物及其临床有效性以及评估细菌多样性和功能变化方面的性能进行了评估。wiSDOM可以根据用户的需求和规格进行定制和可视化，使没有编程背景的研究人员能够使用直观的基于浏览器的界面进行全面的数据挖掘和说明。
基于浏览器的R Shiny界面可通过(https://lun-ching.shinyapps.io/wisdom/)访问，并可在(https://github.com/lunching/wiSDOM)上免费获取。
补充数据可在《生物信息学》在线获取。
