Computational Biology


Research Interests

High-throughput bioinformatics, statistical machine learning, applications in molecular biology, personalised medicine, health, regulation of gene transcription.

Description of Research

Bioinformatics is an interdisciplinary research field combining computer science methods and statistical/mathematical modelling to analyse biological measurement data. New ways of conducting research in biology are now available with direct applications in cancer research and other biomedical. We develop and apply these methods for the integrative analysis of large-scale biological and clinical data. We work on a diverse set of biological problems, including genomics and medical genomics, network inference, and molecular oncology. The ultimate goal is the understanding of the mechanistic basis of human diseases, using a combination of computational and experimental data.

We collaborate with national and international research groups in molecular biology and personalized medicine applications.

 a) High-throughput bioinformatics. High-throughput sequencing data analysis for bulk and single cell data using probabilistic / machine learning methods. The projects based on High-throughput bioinformatics used the capability of monitoring the genes activity in order to describe a better picture of the underlying biology.

b) Statistical machine learning. Gaussian processes, non-parametric methods. Deep generative models, deep learning. The projects based on Statistical machine learning want to overcome the difficulties of interpretation of big data by the use innovative deep learning methods.

c) Applications in molecular biology / personalized medicine / health. Cancer research, cancer-immunology. Biobank and health data analysis. Clinical data is a special kind of data. Our interests are in the integration of clinical information with High-throughput molecular profiles.

d) Regulation of gene transcription. In the projects related to the regulation of gene transcription, we want to explore the still hidden code that is present in DNA sequence responsible for cell-specific gene activation.

In 2021 the Group collaborated with several groups in the analysis of NGS data sets including ICGEB groups (Chakraborty et al., Blood 2021; Ramos et al Cell Rep 2021; Fantinatti et al BMC Genomics, 2021; Fryk et al EBiomedicine 2021; Bez et al Env Microbiol 2021). The group is also taking part in two ICGEB Collaborative Research Programme (CRP) grants which started in 2022 and has initiated research collaboration with a private company on the analysis and interpretation of microbiome data sets. The research group has been very productive: examples include studies of SARS Cov-2 spike protein structure and dynamics (ACS Chem Neurosci 2021; 3 Biotech, 2022; J Biomol Struct Dyn 2022; Front genet 2022) as well as structural and inhibitory insights into other proteins such as human methyltransferase (J Mol Struct Dyn 2021), of the Tau tubulin kinase involved in neurodegeneration (J Cell Biochem 2021) and of a Plasmodium falciparum serine protease (Molecules 2021).

In modern computational biology, several approaches such as high-throughput gene expression analysis, network protein analysis, and integration of clinical data must be integrated unitedly

Recent Publications

Ius T, Ciani Y, Ruaro ME, Isola M, Sorrentino M, Bulfoni M, Candotti V, Correcig  C, Bourkoula E, Manini I, Pegolo E, Mangoni D, Marzinotto S, Radovic S, Toffoletto B, Caponnetto F, Zanello A, Mariuzzi L, Di Loreto C, Beltrami AP, Piazza S, Skrap M, Cesselli D. An NF-κB signature predicts low-grade glioma prognosis: a precision medicine approach based on patient-derived stem cells. Neuro-oncology. 2018; 20(6):776-787. PubMed [journal] PMID: 29228370, PMCID: PMC5961156

Verardo R, Piazza S, Klaric E, Ciani Y, Bussadori G, Marzinotto S, Mariuzzi L, Cesselli D, Beltrami AP, Mano M, Itoh M, Kawaji H, Lassmann T, Carninci P, Hayashizaki Y, Forrest AR, Beltrami CA, Schneider C. Specific mesothelial signature marks the heterogeneity of mesenchymal stem cells from high-grade serous ovarian cancer. Stem cells (Dayton, Ohio). 2014; 32(11):2998-3011. PubMed [journal] PMID: 25069783

Gomes S, Bosco B, Loureiro JB, Ramos H, Raimundo L, Soares J, Nazareth N,Barcherini V, Domingues L, Oliveira C, Bisio A, Piazza S, Bauer MR, Brás JP,Almeida MI, Gomes C, Reis F, Fersht AR, Inga A, Santos MMM, Saraiva L. SLMP53-2Restores Wild-Type-Like Function to Mutant p53 through Hsp70: Promising Activity in Hepatocellular Carcinoma. Cancers (Basel). 2019 Aug 10;11(8). pii: E1151. doi:10.3390/cancers11081151. PubMed PMID: 31405179; PubMed Central PMCID: PMC6721528

Walerych D, Lisek K, Sommaggio R, Piazza S, Ciani Y, Dalla E, Rajkowska K, Gaweda-Walerych K, Ingallina E, Tonelli C, Morelli MJ, Amato A, Eterno V, Zambelli A, Rosato A, Amati B, Wiśniewski JR, Del Sal G. Proteasome machinery is  instrumental in a common gain-of-function program of the p53 missense mutants in  cancer. Nature cell biology. 2016; 18(8):897-909. PubMed [journal] PMID: 27347849

Forrest AR, Kawaji H,.., Piazza S,  Carninci P, Hayashizaki Y. A promoter-level mammalian expression atlas. Nature. 2014; 507(7493):462-70. NIHMSID: NIHMS607910 PubMed [journal] PMID: 24670764, PMCID: PMC4529748

Carninci P, Kasukawa T,.., Piazza S, Suzuki H, Kawai J, Hayashizaki Y. The transcriptional landscape of the mammalian genome. Science (New York, N.Y.). 2005; 309(5740):1559-63. PubMed [journal] PMID: 16141072