Bioinformatics is the research field born from the link between Computer Science and Biology in order to analyze, use and share the huge wealth of data and knowledge made available by the recent developments in molecular biology and genetics. Bioinformatics is essential to make available and organize both the experimental data from molecular biology and medicine and the information obtained from clinical practice through electronic health records and hospital information systems. Nowadays, it had become urgent, both for doctors and biologists, to be able to query various online databases, know what type of information can be obtained from each of these, and know the basic principles of state–of-the-art bioinformatics methodologies and techniques.
The goal of the course is to provide students with basic methods and tools to work with bioinformatics databases, as well as to learn about some important bioinformatics data analysis approaches concerning in particular Next Generation Sequencing and Network medicine.
Degree courses/Phd courses: the Course is offered to students enrolled in all degree courses of the Faculty of Medicine and Surgery (20 seats available) and students of PhD programs of biomedical disciplines (40 seats available).
Program: During the 10 lessons of the course the following topics will be presented:
Module 1: Biomedical data repositories and online resources.
– Introduction, Biomolecular data types, Databank interoperability, Databank types;
– Primary databanks (Genbank, EMBL , DDBJ, UNIProt);
– Specialized databanks (Selection from NCBI/EBI, focus on Gene, DBSNP, Omim, KEGG, Reactome, GEO, Genome browsers, other);
– Databank access types and information extraction, Entrez and integrated portals;
– Bio-terminologies and Bio-ontologies, OBO and Gene Ontology.
Module 2. Computational methods and integrative bioinformatics approaches
– The NGS pipeline, sequence analysis and annotation;
– Analysis of gene expression and proteomics data, basic concepts and data analysis methods (filtering and selection, clustering, prediction);
– Integrative Bioinformatics – joint analysis of data and knowledge; enrichment, text mining, network-biology methods.
Module 3. Applications
– Gene networks and reverse engineering algorithms;
– Drug networks for clinical drug repurposing;
– Next generation sequencing for mutational profiling of longitudinal tumor biopsies.
– Integrative biology for anticancer therapy
Teacher coordinator: prof. Riccardo Bellazzi PhD – Chair Centre for Health Technologies, University of Pavia, Italy, Director, BMI Labs “Mario Stefanelli”, Dip. Ingegneria Industriale e dell’Informazione, University of Pavia, Director, LISRC Lab, IRCCS Fondazione S. Maugeri, Pavia (firstname.lastname@example.org).
Prof. RICCARDO BELLAZZI (Coordinator)
Dr. LUCA BELTRAME, Mario Negri Institute for Pharmacological Research, Milano
Prof. DIEGO DI BERNARDO, University of Napoli and TIGEM
Ing. IVAN LIMONGELLI, University of Pavia
Prof. MARCO MASSEROLI, Polytechnic University of Milan
Prof. MASSIMILIANO PAGANI, National Institute of Molecular Genetics (INGM), Milano
Credits: Students are required to attend at least 8 lectures and overcome a written test – For students 2 CFU ; For PHD Students credits depend on PhD programs (maybe 3 credits).
Recommended or required reading: the slides of the lectures.
Planned learning activities and teaching methods: Lectures will be held by the teacher, together with the contributions of speakers specialized in the different topics analyzed by the course. Practical classes with the use of students own PCs are planned.
Assessment methods and criteria: written test.
Teaching language: English.
Classroom: Aula della Biblioteca della Sezione Femminile, Piazza Ghislieri 5, Pavia.
For information: email@example.com
Link online registration: collegio.ghislieri.it/ccr
Registration open until availability (maximum 60 participants).