Laboratory of Bioinformatics I E (Two semesters) Rita Casadio

Course aims: The aim of the course is to provide students with basic knowledge of the tools for sequences analysis.  At the end of the I part, the student will be able to: - work with different biological file formats and read them - write programs in python to implement useful functions for biological sequence analysis - use and implement programs for sequence alignments, and protein structure predictions. In the II part the student will be trained on possible computational solutions to hot problems in the omic era. At the end of the course students will be acquire skills concerning: - Genome annotation. - Genome comparison. – Analysis of protein folding and protein stability with computational approaches. - Structural Bioinformatics with PC tools. - Molecular recognition at a cellular level. - Protein-protein and protein-DNA/RNA interactions. - Protein and gene networks.
Course contents:   Algorithms for searching sequence motifs. Algorithms for pair wise sequence alignment: global; semi global; local. Handling of available implementations, such as Align and Lalign. Algorithms for data base searching: BLAST and FASTA. Multiple sequence alignments: basics and progressive. Sequence-Profile and Profile-Profile alignments. Basics of Machine Learning Methods: Neural Networks and Hidden Markov Models and their application to sequence and structure analysis. Bioinformatics and Biomolecules: Structural Biochemistry with PC tools. A new perspective for DNA, RNA and proteins and their interactions in a Systems Biology perspective. Supermolecular complexes. Molecular recognition at a cellular level. Protein-protein and protein-DNA/RNA interaction. Structural System Biology.

Readings/Bibliography: on line, selected papers and books.
-Introduction to Bioinformatics, Arthur M. Lesk, 2008, Oxford University Press.- Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids, Richard Durbin, S. Eddy, A. Krogh, G. Mitchison, 1998, Cambridge University Press.- Bioinformatics, 2nd Edition. The Machine Learning Approach. Pierre Baldi and Søren Brunak. MIT press, August 2001.

Teaching methods: Lectures, practicum, seminars, lab activity
Assessment methods: For each semester, the test of assessment will be written based on a series of questions to test the knowledge of the student, followed by an oral session. The student during the course is requested to present a program to demonstrate her/his capabilities in programming to solve problems of sequence analysis in bioinformatics.
Teaching tools: Video beam, PC, overhead projector, laboratory activity