The Advanced Data Analysis group is devoted to help Ri.MED researchers and collaborators to retrieve the most amount of information from their models and data, with a particular interest on projects focused on Cancer and Aging topics approached by using biological big data.
For instance, it supports the Drug Discovery Unit in the experimental design and data analysis of high-throughput screenings and it performs high-throughput data analysis, applied on a wide range of data source technologies, e.g., microarray, next generation sequencing, and other omic approaches.
Very often, the biological questions of interest and the outputs of the associated experimental designs cannot be analyzed by the commercial software available to the scientific community. In this case, we apply our expertise on computer programming and big data management for analyzing high-throughput data in customized ways.
The main scientific interest of the group is the study of biological complex systems, analyzed by integrating many sources of data describing the interactions among different “players”, such as RNA (e.g., mRNA, miRNA), genomic data, the activities of repetitive sequences, clinical data, and so on. 

Aims

• Provide scientific interpretation of biological big-data 
• Develop custom data analysis algorithms for omics data 
• Design pipelines to integrate the analysis of multiple sources of data 
• Unravel biological interaction networks 
• Design RNA-based therapeutics

Focus

• The role of DNA repetitive sequences in physiological and pathological conditions 
• The molecular aspects of aging and their role in cancer and degenerative diseases 
• microRNA, competitive endogenous RNA, long non coding RNA 
• Alternative splicing and its modulation

Expertise and resources

• Molecular biology, System biology, Statistics, Bioinformatics, Machine Learning 
• Long-read Next Generation Sequencing (Oxford Nanopore Technologies) 
• Genomics, Epigenomics, Transcriptomics, Metagenomics data analysis 
• R, Python, Matlab, Galaxy

WORKED WITH US

• Walter Arancio, PhD – Post Doc in Computational Biology funded by OBIND project (2020-2022). He is now Researcher at IRIB – CNR.
• Giorgio Bertolazzi, PhD – PhD Student in Economics and Statistics at UniPA, Dottorato Innovativo a Caratterizzazione Industriale (2019-2021), Project thesis: MicroRNA Interaction Network. He is now Researcher at DSEAS – UNIPA.