ProfileDB: a resource for proteomics and cross-omics biomarker discovery

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics and Proteomics

Background:

• High-throughput omics data are increasing in size and complexity, challenging researchers.
• Current analysis pipelines for different omics techniques hinder unified biomarker discovery and cross-experiment comparisons.
• Integrating diverse omics data with clinical and pathway information is difficult.

Purpose of the Study:

• To present ProfileDB, a comprehensive web-based application for integrating and analyzing high-throughput omics data.
• To provide tools for unified biomarker discovery across different omics data types.
• To facilitate the comparison of different experiments and gain insights into complex biological systems.

Main Methods:

• Developed ProfileDB, a web application integrating Transcriptomics, Proteomics, and Metabolomics data.
• Integrated clinical parameters and prior knowledge on pathways and ontologies.
• Implemented dedicated tools for study inspection and data visualization.

Main Results:

• ProfileDB enables the integration of cross-omics data with clinical parameters and biological knowledge.
• The application provides tools for intuitive study inspection and data visualization.
• Demonstrated use cases for proteomics biomarker identification using ProfileDB.

Conclusions:

• ProfileDB offers a unified strategy for biomarker discovery from complex, cross-omics datasets.
• The application simplifies the analysis and comparison of high-throughput experiments.
• ProfileDB is publicly available and includes sample datasets for reproducible research.