For what concern deep sequencing data, we defined new algorithms and tools for various purposes including deconvolution for genome assembly, RNA-seq analysis for isoform detection and chimeric RNA discovery, and the analysis of CHiP-seq and histone modification data, with a focus on developing integrative approaches.

Our computational methodologies are applied in various research areas, including molecular oncology, where they are used to study clonal evolution, discriminate between normal and malignant states based on IG and TCR gene rearrangements, and identify cancer biomarkers in colorectal, bladder, and cervix cancer through microRNA expression and methylation profiling.

The study explores cancer clonality using mathematical and computational methods, focusing on understanding the dynamics of biochemical reactions within biological pathways, including substrate and product concentrations and reaction rates.

We construct models using differential equations and various computational approaches, emphasizing the development of mathematical models for understanding different aspects of cancer development and progression, including simulating treatment outcomes.