The OncoFinder algorithm for minimizing the errors introduced by the high-throughput methods of transcriptome...

Area of Science:

• Bioinformatics
• Genomics
• Systems Biology

Background:

• Gene expression data from diverse platforms (sequencing, microarrays) are difficult to compare due to inherent variability.
• Traditional analytical methods struggle with high-error, multi-platform gene expression data, limiting downstream applications.

Purpose of the Study:

• To introduce OncoFinder, a bioinformatic tool for quantitative estimation of signaling pathway activation (SPA).
• To demonstrate OncoFinder's ability to enable platform-agnostic comparison of gene expression data, minimizing errors.
• To validate OncoFinder's effectiveness in analyzing intracellular pathway functional changes.

Main Methods:

• Development of OncoFinder, a bioinformatic tool for quantitative SPA estimation.
• Mapping gene expression data onto known and custom signaling pathways to minimize errors.
• Comparative analysis of gene expression datasets from Next-Generation Sequencing (NGS) and microarray platforms for the same biological samples.

Main Results:

• Direct comparison of NGS and microarray datasets showed minimal correlation (R² < 0.1).
• Application of the OncoFinder algorithm revealed strong correlations between NGS and microarray data.
• OncoFinder generated nearly identical SPA profiles for the same samples across different platforms, enabling platform-agnostic analysis.

Conclusions:

• OncoFinder significantly reduces errors in transcriptome-wide experimental techniques.
• The platform-agnostic nature of OncoFinder allows for more accurate characterization of transcriptome and interactome functional states.
• OncoFinder is a valuable tool for genetics, physiology, biomedicine, and molecular diagnostics.