First cycle
degree courses
Second cycle
degree courses
Single cycle
degree courses
School of Science
Course unit
SC05100858, A.A. 2018/19

Information concerning the students who enrolled in A.Y. 2016/17

Information on the course unit
Degree course First cycle degree in
SC1166, Degree course structure A.Y. 2015/16, A.Y. 2018/19
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Number of ECTS credits allocated 5.0
Type of assessment Evaluation
Course unit English denomination BIOINFORMATIC 2
Website of the academic structure
Department of reference Department of Biology
E-Learning website
Mandatory attendance
Language of instruction Italian
Single Course unit The Course unit can be attended under the option Single Course unit attendance
Optional Course unit The Course unit can be chosen as Optional Course unit

Teacher in charge CHIARA ROMUALDI BIO/11

ECTS: details
Type Scientific-Disciplinary Sector Credits allocated
Other -- -- 5.0

Course unit organization
Period First semester
Year 3rd Year
Teaching method frontal

Type of hours Credits Teaching
Hours of
Individual study
Laboratory 1.0 16 9.0 No turn
Lecture 4.0 32 68.0 No turn

Start of activities 01/10/2018
End of activities 18/01/2019

Examination board
Board From To Members of the board
7 BIOINFORMATICA 2 2018-2019 01/10/2018 30/11/2019 ROMUALDI CHIARA (Presidente)
SALES GABRIELE (Membro Effettivo)
6 BIOINFORMATICA 2 2017/2018 01/10/2017 25/11/2018 ROMUALDI CHIARA (Presidente)
SALES GABRIELE (Membro Effettivo)

Prerequisites: Basic knowledge of Bioinformatics (database and hints of programming) and notions of molecular biology, genetics and biochemistry.
Target skills and knowledge: The course introduce the main algorithms for sequence analysis both nucleic acids and protein sequences. Moreover, the course gives an overview of the main applications of bioinformatic analysis in modern biomedical research. The course aims to educate students to a critical evaluation of the methods proposed and to scientific independence in the use of the basic bioinformatics algorithms for data and sequence analysis.
Examination methods: Written examination with open questions and practical exercises. The exam will evaluate the knowledge acquired, the ability to summarise and discuss appropriately the acquired concepts.
Assessment criteria: 1) the knowledge acquired during the course
2) the ability of generalize and apply to case studies the methods proposed
3) the summarization skills and language
4) critical interpretation of the results in specific case studies
Course unit contents: 1. Sequence Analysis
1.1 Sequence comparison, global and local alignment algorithms, statistical significance of an alignment
1.2. substitution matrices (PAM and BLOSUM), database sequence query, BLAST
1.3. Multiple alignments, ClustalW, T-Coffie and other methods; database query with multiple alignments methods
1.4 Motif discovery, profile matrix and Markov models

2. Phylogenetic tree reconstruction and inference
2.1. UPGMA
2.2. Neighbour-joining
2.3 Maximum likelihood tree

3. Protein structure prediction
3.1. secondary structure prediction
3.2 3D structure prediction

4. Introduction to system biology
4.1. Introduction to NGS data analysis
4.2 Introduction to Network Analysis
Planned learning activities and teaching methods: The course is organized in lectures and exercises in computer lab. The topics are presented with electronic slides.
The teaching is interactive, with questions and presentation of case studies, to promote discussion and critical reflection in the classroom.
Moreover during the exercises in the computer lab the student faces specific problems, presenting at the end of each exercise a report on the activity carried out and on the results obtained.
Additional notes about suggested reading: All materials presented in lectures and exercise are available to students in e-learning platform and teachers web site.
Textbooks (and optional supplementary readings)
  • Stefano Pascarella e Alessandro Paiardini, Bioinformatica, dalla sequenza alla struttura delle proteine. Bologna: Zanichelli Editore, 2011. Cerca nel catalogo

Innovative teaching methods: Teaching and learning strategies
  • Laboratory
  • Case study
  • Interactive lecturing
  • Working in group
  • Auto correcting quizzes or tests for periodic feedback or exams
  • Use of online videos
  • Loading of files and pages (web pages, Moodle, ...)

Innovative teaching methods: Software or applications used
  • Moodle (files, quizzes, workshops, ...)

Sustainable Development Goals (SDGs)
Good Health and Well-Being Life on Land