OmicsView: Omics data analysis through interactive visual analytics

Area of Science:

• Bioinformatics
• Genomics
• Computational Biology

Background:

• Next-generation sequencing (NGS) generates vast amounts of human tissue transcriptional data.
• Bench scientists require accessible tools to analyze this data for disease research.
• Public repositories house petabytes of expression data, necessitating user-friendly mining platforms.

Purpose of the Study:

• To introduce OmicsView, an open-source analytics and visualization platform for gene expression data.
• To enable bench scientists with limited computational expertise to access and analyze large omics datasets.
• To facilitate the identification of disease biomarkers and the effects of interventions.

Main Methods:

• Development of the OmicsView platform, an open-source tool for expression data analysis.
• Preloading OmicsView with thousands of samples from diverse disease areas and normal tissues, including the GTEx database.
• Processing all included data with a harmonized bioinformatics pipeline.
• Demonstrating platform utility through a Crohn's disease data mining case study.

Main Results:

• OmicsView provides a user-friendly interface for accessing and analyzing large-scale transcriptional data.
• The platform successfully facilitated a data mining exercise for Crohn's disease.
• Key disease pathologies and significant biomarkers for disease and treatment response were rapidly identified.

Conclusions:

• OmicsView empowers researchers, particularly those with limited computational backgrounds, to effectively mine public expression datasets.
• The platform streamlines the discovery of disease mechanisms and therapeutic targets.
• OmicsView represents a valuable resource for advancing translational research in various disease areas.