Non syndromic Hereditary Hearing Loss Workbench

This research stream focuses on human non syndromic Hereditary Hearing Loss (HHL), a largely genetically heterogeneous disorder which represents more than 70% of all hereditary hearing loss cases. it is associated with abnormalities of the middle ear and/or inner ear and has no associated visible abnormalities of the external ear or any related medical problems. For further details click here.

The causes of HHL are complex and not completely known on the genetic front. About 50 genes have been already recognized to be associated with the disorder, however, a large number of susceptibility loci (chromosomal regions) are still to be analysed in order to identify the specific genes responsible of the phenotype. Their dimensions are tremendously heterogeneous, often containing tens or hundreds of genes, whose systematic experimental characterization is extremely difficult and time consuming. In this case a bioinformatic approach is an essential requirement in order to prioritize all the candidate genes contained in the susceptibility loci and to suggest an order of priority for the experimental validations.

To support such activities we are designing, building and experimenting a specialized bioinformatic "workbench" with the aim to systematically collect knowledge about the intra-species molecular biodiversity of HHL (from both structured biological databases and unstructured textual sources), and provide a bioinformatic analysis pipeline to perform gene selection and scoring on HHL candidate genes.

An important revenue of this activity will be to lay the groundwork to study the molecular biodiversity of other complex diseases following the HHL Workbench design model.

Development of bioinformatic tools in order to classify cell-systems

The aim of this research line is to create a predictive system able to select cells characterized by high quality and performance by considering high resolution images.

The selection of the “best” elements inside a cell population, can be based on molecular and/or expression patterns. However, in some case, become useful or needed, systems based on the analysis of morpho-structural characteristics. One of the possible applications under the proposed project concerns the choice of oocytes to support the techniques of assisted fertilization. Indeed, in recent years, increasingly high number of couples who take the road of assisted fertilization. The causes are to be found not only in genetic and environmental factors including socio-economic research that lead to a pregnancy in more advanced age. Whereas the current protocol for assisted reproduction and the limitations in force in Italy legislation (Law 40/2004), is of crucial importance to the choice of eggs, that are capable and mature to deal with the subsequent embryo development. In this scenario fits perfectly the development of a system whose purpose is to develop and "validate" an algorithm to "self-learning", able to classify the eggs based on morphological and structural features, observed by images, in order to support the biologist in the choice.