The mere quantity of epigenetic data arising from genome and epigenome projects will pose a major bioinformatics challenge. According to raw estimations, the total amount of DNA sequence currently contained in the GenBank could triplicate in just one year.

In addition to developing more efficient methods for data processing and storage, it will be also necessary to develop advanced computational methods that help bench researchers to interpret genome and epigenome datasets, and clinical researchers to identify molecular markers of diseases such as cancer.

We will launch a Computational Genomic and Epigenomic Program aimed at developing new analytical tools and data management systems, machine learning techniques and prediction/analysis algorithms for the acquisition, storage, processing, and analysis of high-throughput genome and epigenome mapping data.

Particular emphasis will be given to the development of computational tools for the integration of diverse high-throughput experimental data (genome sequence, chromatin structure, transcription factor-DNA binding, gene expression, proteomics, chemogenomics).  Indeed, data integration is a crucial step for reproducing and predicting interactions between biological systems and the surrounding environment. It requires the creation of computational tools including graphic-based methods for reproducing the complex network of both endogenous and exogenous molecules and reactions and their interference with biological systems.

This Section will support the activities of both the Screening Unit and the Genomic Unit, as well as carry out specific research activities.