corto: a lightweight R package for gene network inference and master regulator analysis.

Area of Science:

• Bioinformatics
• Computational Biology
• Systems Biology

Background:

• Gene network inference and master regulator analysis (MRA) are crucial for understanding transcriptional perturbations from gene expression data.
• Existing tools for these analyses often require significant computational resources, limiting accessibility.

Purpose of the Study:

• To develop a fast, lightweight R package named 'corto' for gene network inference and MRA.
• To provide a user-friendly tool that can run on standard hardware, accommodating various gene expression data types.

Main Methods:

• Developed 'corto' as an R package available on CRAN and GitHub.
• Implemented optional corrections for copy-number variations.
• Designed the package to process gene expression signatures from RNA-Seq and ATAC-Seq data.
• Benchmarked 'corto' on 39 human tumor and 27 normal tissue datasets for context-specific gene network inference.

Main Results:

• 'corto' demonstrated efficient performance in inferring context-specific gene networks.
• The package successfully handled gene expression data from different sequencing platforms (RNA-Seq, ATAC-Seq).
• Benchmarking confirmed the reliability and speed of 'corto' across diverse human tissue datasets.

Conclusions:

• 'corto' provides a computationally efficient and accessible solution for gene network inference and MRA.
• The package facilitates the analysis of transcriptional perturbations, supporting research in various biological contexts.
• Its cross-platform and multi-threaded nature enhances its utility for researchers worldwide.