Jagadesan Sankarasubramanian, Guda Chittibabu
Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center, Omaha, Nebraska, United States of America.
Center for Biomedical Informatics Research and Innovation, University of Nebraska Medical Center, Omaha, Nebraska, United States of America.
PLoS One. 2025 Apr 7;20(4):e0319949. doi: 10.1371/journal.pone.0319949. eCollection 2025.
The human microbiome exerts tremendous influence on maintaining a balance between human health and disease. High-throughput sequencing has enabled the study of microbial communities at an unprecedented resolution. Generation of massive amounts of sequencing data has also presented novel challenges to analyzing and visualizing data to make biologically relevant interpretations. We have developed an interactive Metagenome Data Analysis and Visualization (MetaDAVis) tool for 16S rRNA as well as the whole genome sequencing data analysis and visualization to address these challenges using an R Shiny application. MetaDAVis can perform six different types of analyses that include: i) Taxonomic abundance distribution; ii) Alpha and beta diversity analyses; iii) Dimension reduction tasks using PCA, t-SNE, and UMAP; iv) Correlation analysis using taxa- or sample-based data; v) Heatmap generation; and vi) Differential abundance analysis. MetaDAVis creates interactive and dynamic figures and tables from multiple methods enabling users to easily understand their data using different variables. Our program is user-friendly and easily customizable allowing those without any programming background to perform comprehensive data analyses using a standalone or web-based interface.
人类微生物组对维持人类健康与疾病之间的平衡具有巨大影响。高通量测序使人们能够以前所未有的分辨率研究微生物群落。大量测序数据的产生也给分析和可视化数据以做出生物学相关解释带来了新挑战。我们开发了一种交互式宏基因组数据分析与可视化（MetaDAVis）工具，用于16S rRNA以及全基因组测序数据分析和可视化，以使用R Shiny应用程序应对这些挑战。MetaDAVis可以执行六种不同类型的分析，包括：i）分类丰度分布；ii）α和β多样性分析；iii）使用主成分分析（PCA）、t-分布随机邻域嵌入（t-SNE）和均匀流形近似与投影（UMAP）进行降维任务；iv）使用基于分类群或样本的数据进行相关性分析；v）生成热图；以及vi）差异丰度分析。MetaDAVis通过多种方法创建交互式和动态的图表，使用户能够使用不同变量轻松理解他们的数据。我们的程序用户友好且易于定制，使那些没有任何编程背景的人能够使用独立或基于网络的界面进行全面的数据分析。
