The main focus of my research is the analysis of whole-genome and whole-exome sequencing data to understand how genetic variations affect human health.
After years being applied mainly for research purposes, now WGS and WES are quickly spreading also on clinical settings and in everyday life thanks to direct-to-costumer private services. New sequencing machines can now produce Tb of data in few days posing a great challenge for downstream data analysis.
As a bioinformatician, I try to address the constant need for new computational methods to extract useful information from the big amount of genomic data and identify trait-related variants. My interest is especially on addressing the huge gap in the interpretation of non-coding variants and other kinds of usually disregarded variants to improve the diagnostic yield of WES and WGS analysis.
Coming from a genetic/biological background, I want my results to be biologically meaningful as well as easily understandable.
Characterization of three sialidases from Danio rerio.
Forcella M. et al, (2021), Biochimie, 187, 57 - 66
Using data from the 100,000 Genomes Project to resolve conflicting interpretations of a recurrent TUBB2A mutation
Ragoussis V. et al, (2021), Journal of Medical Genetics
Alterations observed in the interferon α and β signaling pathway in MDD patients are marginally influenced by cis-acting alleles.
Magri C. et al, (2021), Scientific reports, 11
GREEN-DB: a framework for the annotation and prioritization of non-coding regulatory variants in whole-genome sequencing
Giacopuzzi E. et al, (2020)
Investigating an in silico approach for prioritizing antidepressant drug prescription based on drug-induced expression profiles and predicted gene expression.
Shoaib M. et al, (2020), The pharmacogenomics journal