CausalR: extracting mechanistic sense from genome scale data.

Area of Science:

• Genomics
• Systems Biology
• Bioinformatics

Background:

• Genome-scale data analysis often yields descriptive rather than mechanistic interpretations.
• Causal interaction data can enhance the understanding of biological systems.

Purpose of the Study:

• To present CausalR, the first open-source platform for causal network analysis.
• To enable mechanistic interpretation of genome-scale data.

Main Methods:

• CausalR is implemented in the R programming language.
• The platform is parallelized for efficient computation.
• It includes functions for regulator prediction and network reconstruction.

Main Results:

• CausalR generates network and annotation files compatible with Cytoscape for visualization.
• The Sequential Causal Analysis of Networks approach is introduced to limit false positives.

Conclusions:

• CausalR provides a robust tool for causal network analysis.
• The platform facilitates a shift from descriptive to mechanistic understanding of genome-scale data.
• It supports the prediction of regulatory elements within biological networks.