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Subject: BIOINFORMATION TECHNOLOGY (A.A. 2020/2021)

master degree course in COMPUTER SCIENCE

Course year 1
CFU 6
Teaching units Unit Bioinformatica
Related or Additional Studies (lesson)
  • TAF: Supplementary compulsory subjects SSD: ING-IND/34 CFU: 6
Teachers: Silvio BICCIATO
Exam type oral
Evaluation final vote
Teaching language Italiano
Contents download pdf download

Teachers

Silvio BICCIATO

Overview

The goal of the Bioinformatics module in the Functional Genomics class is illustrating the application of information theory and computational methods to the analysis of biochemical sequence data. Specifically, the class will illustrate theoretical and applied computational methods for the analysis of genomic data.

Admission requirements

The class is intended to students with basic notions of mathematics, informatics and statistics, organic chemistry, cell biology, genetics, biochemistry, and molecular biology.

Course contents

Introduction to Bioinformatics: General overview of bioinformatics and bioinformatics applications in genomics. Introduction to biological data repositories. Primary sequence databases (EMBL, GenBank, DDBJ). Meta- and specialized databases (Ensembl/UCSC Genome Browser, LocusLink, RefSeq, Gene Ontology, Pfam, Kegg). Protein sequence databases. Structure of primary sequence databases: EMBL and GenBank flat-files. Genome browsers. Retrieval systems: Entrez and SRS.
Sequence alignment in nucleic acids and proteins: Sequence alignment principles and definitions. Homology and similarity. Definition of an alignment score. PAM and BLOSUM substitution matrices. The dot matrix method for alignment visualization. Methods of exact alignment. Needleman-Wunsch global alignment algorithm. Dynamic programming. Smith-Waterman local alignment algorithm. Heuristic methods: FASTA, BLAST. Multiple sequence alignment: ClustalW. Visualization methods of multiple sequence alignments: sequence logo representation.

Teaching methods

The teaching method is composed of class lessons and practical training on bioinformatics topics. Computational resources are utilized for the practical training sessions

Assessment methods

The learning level is verified in an oral exam session. A total of 6 exam sessions are planned for each academic year. During the oral exam session, students are required to illustrate due bioinformatics topics related to biological databases, identification of patterns in genomic sequences, sequence alignment and database search. During the oral exam session, it will be also verified the student’s knowledge and understanding of bioinformatics algorithms for the analysis of genomic data and her/his ability to apply computational methods for the study of genomic sequences. The exam final grade will be quantified based on the narrative of the proposed exam topics.

Learning outcomes

Knowledge and understanding.
Through class lessons, suggested readings and class discussions, students acquire knowledge on principles and methods for genomic and post-genomic analysis, comprising the bioinformatics methods for genomic data management and analysis

Ability to apply knowledge and understanding.
Through computer training sessions, students apply bioinformatics methods for genome analysis

Making judgements.
Class discussion allow students to improve their ability to gather and interpret relevant genomic data and to critically evaluate computational and experimental methods to address biological problems

Communication skills.
Through class discussions, students learn how to communicate information, data analyses, problems and solutions to both specialist and non-specialist audiences

Learning skills.
The course activities allow students to developed those learning skills that are necessary for them to continue to undertake further study with a high degree of autonomy