FDR made easy in differential feature discovery and correlation analyses

Area of Science:

• Genomics
• Proteomics
• Bioinformatics

Background:

• High-throughput biological analyses generate vast datasets, leading to multiple comparison problems.
• Statistical methods like Global False Discovery Rate (gFDR) and Local FDR (lFDR) are crucial for controlling errors in these analyses.
• Traditional FDR computation is complex and computationally intensive, posing a barrier for bench-side biologists.

Purpose of the Study:

• To develop an accessible web portal for FDR analysis in high-throughput biological data.
• To provide computational capabilities for analyzing differential gene/protein expression and molecular-clinical correlations.
• To bridge the digital divide and empower biologists with advanced statistical tools.

Main Methods:

• Development of a user-friendly web portal.
• Implementation of server-side computing for FDR, differential expression, and correlation analyses.
• Integration of statistical algorithms for gFDR and lFDR.

Main Results:

• The web portal provides easy access to complex FDR computations.
• Enables analysis of differential gene/protein expression and molecular-clinical correlations.
• Facilitates interpretation of large-scale biological data for bench-side researchers.

Conclusions:

• The developed web portal democratizes access to advanced FDR analysis for biologists.
• It overcomes computational and expertise barriers in genomics and proteomics research.
• Facilitates more robust interpretation of high-throughput data and clinical correlations.