GeneTrail: A Framework for the Analysis of High-Throughput Profiles

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• High-throughput techniques like next-generation sequencing generate complex, high-dimensional, and noisy biological data.
• Analyzing these datasets requires sophisticated bioinformatics tools to extract meaningful biological insights.
• Existing tools may not offer comprehensive functionality for diverse data types and analysis needs.

Purpose of the Study:

• To present GeneTrail, a versatile tool suite for analyzing and visualizing multi-omics data.
• To provide a framework for identifying deregulated biological processes and their driving factors.
• To demonstrate the utility of GeneTrail with a real-world single-cell COVID-19 dataset.

Main Methods:

• GeneTrail integrates state-of-the-art methods for analyzing (epi-)genomic, transcriptomic, miRNomic, and proteomic profiles.
• The framework supports standard bulk, time-series, and single-cell measurements.
• Includes visualization capabilities for enhanced data interpretation.

Main Results:

• GeneTrail successfully analyzed a single-cell COVID-19 dataset, uncovering complex molecular mechanisms.
• The tool suite demonstrated its capability in identifying deregulated biological processes.
• It effectively detected driving factors within these deregulated processes.

Conclusions:

• GeneTrail offers a powerful and comprehensive solution for the analysis of high-throughput biological data.
• Its ability to handle diverse data types and identify key biological drivers makes it valuable for research.
• The freely accessible web service facilitates broader adoption and application in biological research.