corto

corto infers context-specific gene regulatory networks and performs master regulator analysis to identify transcription factors driving gene expression perturbations from RNA-Seq and ATAC-Seq signatures.

Key Features:

• Gene network inference: uses Spearman correlation and Data Processing Inequality (DPI) to infer networks between "centroid" and "target" variables in gene expression datasets.
• Master regulator analysis: identifies transcription factors and regulators that drive observed gene expression signatures.
• Copy number variation correction: provides optional correction for copy number variations (CNVs) to account for genomic effects on expression levels.
• Input data compatibility: processes signatures derived from RNA-Seq and ATAC-Seq data.
• Benchmarking: benchmarked across 39 human tumor and 27 normal tissue datasets for context-specific network inference.
• Parallel processing: supports multi-threading to improve computational performance.

Scientific Applications:

• Cancer research: infers context-specific regulatory networks and master regulators relevant to tumor biology.
• Biomarker discovery: identifies candidate diagnostic or prognostic regulator biomarkers from expression signatures.
• Therapeutic target identification: prioritizes transcription factors or regulators as potential therapeutic targets.
• Regulatory mechanism analysis: elucidates transcriptional regulatory mechanisms from RNA-Seq and ATAC-Seq signatures.

Methodology:

Network inference is computed by Spearman correlation followed by application of the Data Processing Inequality (DPI) to centroid–target pairs, with an optional correction for copy number variations (CNVs).