The HTPmod Shiny application enables modeling and visualization of large-scale biological data

Area of Science:

• Genomics
• Phenomics
• Bioinformatics

Background:

• High-throughput technologies generate unprecedented volumes of genomics and phenomics data.
• Analyzing these large-scale datasets requires efficient bioinformatics tools for biological insight discovery.

Purpose of the Study:

• To introduce HTPmod, an interactive, open-source web application for high-throughput biological data modeling and visualization.
• To provide a tool for efficient exploration of large-scale, high-dimensional biological data.

Main Methods:

• HTPmod is implemented using the R Shiny framework.
• Integrates R's computational power and visualization capabilities.
• Incorporates various machine-learning approaches for data analysis.

Main Results:

• HTPmod effectively models and visualizes large-scale, high-dimensional datasets, including multiple omics data.
• The application reproduces results from original studies efficiently.
• Facilitates the discovery of novel biological insights through rapid data reinvestigation.

Conclusions:

• HTPmod is a versatile tool for modeling and visualizing high-throughput biological data.
• Enables straightforward and timely analysis of complex omics datasets.
• Supports the generation of new biological discoveries from existing data.