IFOM Computational Genomics

Computational genomics


My group is especially interested in studying the role of chromatin 3D organization in regulating genome functionality. Our expertise is particularly focused on the use of 3D chromatin architecture data, obtained by Hi-C and other high throughput techniques derived from chromosome conformation capture (3C). We also use functional genomics data, mainly derived from transcriptomics and epigenomics techniques based on next generation sequencing (NGS).

We adopt these omics data to gain mechanistic insights into transcription regulation at different levels.

On a large scale, we investigate mechanisms for the coordinated regulation of large chromatin domains in physiological and disease conditions. These involve, for example, the organization of the genome in distinct structural domains, such as Topological Associated Domains (TADs), or Lamina Associated Domains (LADs).

On a finer scale, instead, we study distal regulatory elements (enhancers) and their epigenetic or genetic alterations in genetics diseases and cancer. In this context, we leverage chromatin 3D organization data to refine the association of distal regulatory elements and their target genes, to characterize the functional role of enhancers in epigenetics and gene expression regulation, within the broader gene regulatory network.

Ongoing research projects

Ongoing projects include:

  1. Altered enhancer-genes regulatory network in cancer. We are working on the characterization of non-coding mutations in cancer altering the complex regulatory network of genes and their non-coding regulatory elements (promoters and enhancers).
  2. Heterochromatin alterations in aging and diseases. Together with a collaborator we are working on a novel experimental technique for characterizing chromatin accessibility in different normal and pathological conditions. We are applying it to study heterochromatin structure changes in aging and diseases.
  3. Chromatin architecture data analysis methods. We are working on novel computational biology methods for the analysis of functional genomics data, in particular for epigenetics marks (ChIP-seq data) and 3D chromatin architecture (Hi-C data).
  4. Single cells resolution definition of transcriptional circuits. Together with collaborators we are leveraging single cells genomics data to identify transcriptional and epigenetics regulatory modules activated in different processes with heterogenous or rare cell sub-populations. For example, this includes characterizing tumor infiltrating immune cells or, in other projects, the circuits governing cell identity definition or pluripotency characteristics of stem cells.

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