GENESIGNET

GENESIGNET constructs influence networks to uncover relationships between mutational signatures and biological processes by linking mutational patterns to molecular pathways.

Key Features:

• Influence Network Construction: Constructs influence networks among genes and mutational signatures using statistical techniques, including sparse partial correlation, to identify dominant influence relations.
• Application to Cancer Data Sets: Applies to cancer datasets to reveal relationships such as the impact of homologous recombination deficiency on clustered APOBEC mutations in breast cancer.
• Novel Interactions: Infers interactions including APOBEC hypermutation with regulatory T Cells (Tregs), APOBEC-associated DNA conformation changes, and a potential link between the SBS8 mutational signature and the Nucleotide Excision Repair (NER) pathway.
• Integration with Gene Expression: Integrates mutational signatures with gene expression to explore how mutation patterns relate to cellular functions and disease processes.
• Implementation: Implemented in Python.

Scientific Applications:

• Cancer research: Deciphers interplay between mutational processes and biological pathways in cancer, exemplified by links between homologous recombination deficiency and APOBEC mutations in breast cancer.
• Mechanistic hypothesis generation: Generates hypotheses about mutational mechanisms and pathway involvement, including APOBEC–Treg interactions and a proposed SBS8–NER association.

Methodology:

Constructs influence networks mapping relationships between genes and mutational signatures using statistical analyses to identify significant correlations and interactions, specifically employing sparse partial correlation.