f-divergence cutoff index to simultaneously identify differential expression in the integrated transcriptome and...

Area of Science:

• Bioinformatics
• Systems Biology
• Genomics and Proteomics

Background:

• Integrating 'omics' data (transcriptomics, proteomics) is crucial for understanding biological regulatory mechanisms.
• Existing tools lack the capability to identify differentially expressed genes (DEGs) across diverse 'omics' data types or multi-dimensional data, including time-course experiments.

Purpose of the Study:

• To introduce fCI (f-divergence Cut-out Index), a novel computational model for simultaneous DEG identification.
• To demonstrate the model's capability in analyzing continuous and discrete transcriptomic, proteomic, and integrated proteogenomic data.

Main Methods:

• Development of the fCI model based on f-divergence.
• Application of fCI to various transcriptomic, proteomic, and proteogenomic datasets, including those with time-course information.

Main Results:

• fCI successfully identifies DEGs across multiple, diverse datasets.
• The model accurately detects genes exhibiting functional modulation, developmental changes, or misregulation.
• Application to proteogenomics datasets revealed important genes with distinctive regulation patterns.

Conclusions:

• fCI provides a robust method for identifying DEGs in integrated 'omics' data.
• The tool facilitates a deeper understanding of gene regulation and biological processes.
• fCI is available as an R Bioconductor package and via http://software.steenlab.org/fCI/.