DIGGIT: a Bioconductor package to infer genetic variants driving cellular phenotypes

Area of Science:

• Genomics
• Systems Biology
• Bioinformatics

Background:

• Identifying driver mutations in human diseases is challenging due to limited cohort sizes and statistical model constraints.
• Understanding the genetic basis of diseases requires methods that can systematically discover causal alterations.

Purpose of the Study:

• To present a novel method for the systematic discovery of genetic alterations that causally determine human diseases.
• To implement this method as an R-system package called Driver-gene Inference by Genetical-Genomic Information Theory (diggit).

Main Methods:

• Prioritizing genes upstream of functional disease drivers within regulatory networks.
• Inferring regulatory networks de novo from experimental data.
• Utilizing genetical-genomic information theory for driver gene discovery.

Main Results:

• The development and implementation of the diggit R package for driver gene discovery.
• A systematic approach to identify causal genetic determinants of disease.

Conclusions:

• The diggit package offers a new tool for researchers to identify driver mutations and understand disease etiology.
• This method enhances the discovery of genetic alterations by focusing on upstream regulatory elements.