Computational analysis of biological sequences. Algorithms for sequence evolutionary analyses. Phylogenetic techniques. Processing of massive sequencing data. Genomics. Comparative genomics. Metagenomics. Gene expression data analysis. Mathematical models in biology. Metabolic models: reconstruction and analysis. Basic concepts in systems biology.
Stefano Pascarella, Alessandro Paiardini, Bioinformatica
Dalla sequenza alla struttura delle proteine, Zanichelli.2011
Learning Objectives
Educational objectives: Knowledge acquired: The course will provides students with knowledge on the theoretical and technical concepts of bioinformatics.
Competence and skill acquired: students will acquire the competences on the most advanced methodologies in bioinformatics with potential applications in different research areas, such as microbiology, medicine and environmental sciences.
Prerequisites
Recommended Courses: Genetics; Molecular Biology
Teaching Methods
Teaching Methods: CFU: 6
The course includes both lectures and practical classes. During laboratory classes, the same topics encountered during lectures will be analysed by means of real study cases and ad hoc developed examples.
Contact hours for lectures and laboratory activities: 56
Further information
Frequency of lectures, practice and lab:
The attendance of the lessons is strongly recommended.
Teaching tools: slides of the lessons; scientific publications.
Office hours: to be set according to the student needs.
Type of Assessment
Exam modality: written exam
Course program
Computational analysis of biological sequences. Main file formats in bioinformatics. Main algorithm for the analysis of biological sequences ( introduction to dynamic programming, Needleman-Wunsch , Smith-Waterman, BLAST). Phylogenetic reconstruction techniques: ditance-based methods (UPGMA, Neighbor joining). Main massive sequencing technologies. Processing of massive sequencing data: quality assessment and reads trimming. Reads mapping. Genome assembly and gene prediction. Comparative genomics. Metagenomics and statistical models in metagenomics. Genomic variants detection: SNPs andindels. RNAseq expression data analysis. Mathematical models in biology: formalization and simulation approaches. Metabolic models: reconstruction and simulation. Basic concepts in systems biology. –omics data integration.