Studying at the University of Verona

Here you can find information on the organisational aspects of the Programme, lecture timetables, learning activities and useful contact details for your time at the University, from enrolment to graduation.

A.A. 2018/2019

Academic calendar

The academic calendar shows the deadlines and scheduled events that are relevant to students, teaching and technical-administrative staff of the University. Public holidays and University closures are also indicated. The academic year normally begins on 1 October each year and ends on 30 September of the following year.

Academic calendar

Course calendar

The Academic Calendar sets out the degree programme lecture and exam timetables, as well as the relevant university closure dates..

Definition of lesson periods
Period From To
I semestre Oct 1, 2018 Jan 31, 2019
II semestre Mar 4, 2019 Jun 14, 2019
Exam sessions
Session From To
Sessione invernale d'esame Feb 1, 2019 Feb 28, 2019
Sessione estiva d'esame Jun 17, 2019 Jul 31, 2019
Sessione autunnale d'esame Sep 2, 2019 Sep 30, 2019
Degree sessions
Session From To
Sessione Estiva Jul 18, 2019 Jul 18, 2019
Sessione Autunnale Oct 17, 2019 Oct 17, 2019
Sessione Invernale Mar 18, 2020 Mar 18, 2020
Holidays
Period From To
Festa di Ognissanti Nov 1, 2018 Nov 1, 2018
Sospensione dell'attività didattica Nov 2, 2018 Nov 3, 2018
Festa dell’Immacolata Dec 8, 2018 Dec 8, 2018
Vacanze di Natale Dec 24, 2018 Jan 6, 2019
Vacanze di Pasqua Apr 19, 2019 Apr 28, 2019
Festa della liberazione Apr 25, 2019 Apr 25, 2019
Festa del lavoro May 1, 2019 May 1, 2019
Festa del Santo Patrono May 21, 2019 May 21, 2019
Festa della Repubblica Jun 2, 2019 Jun 2, 2019
Vacanze estive Aug 5, 2019 Aug 18, 2019

Exam calendar

Exam dates and rounds are managed by the relevant Science and Engineering Teaching and Student Services Unit.
To view all the exam sessions available, please use the Exam dashboard on ESSE3.
If you forgot your login details or have problems logging in, please contact the relevant IT HelpDesk, or check the login details recovery web page.

Exam calendar

Should you have any doubts or questions, please check the Enrolment FAQs

Academic staff

A B C D G L M P S

Accordini Simone

simone.accordini@univr.it +39 045 8027657

Belussi Alberto

alberto.belussi@univr.it +39 045 802 7980

Bicego Manuele

manuele.bicego@univr.it +39 045 802 7072

Bombieri Cristina

cristina.bombieri@univr.it 045-8027209

Bombieri Nicola

nicola.bombieri@univr.it +39 045 802 7094

Boscaini Maurizio

maurizio.boscaini@univr.it

Busato Federico

federico.busato@univr.it

Calanca Andrea

andrea.calanca@univr.it +39 045 802 7847

Cicalese Ferdinando

ferdinando.cicalese@univr.it +39 045 802 7969

Combi Carlo

carlo.combi@univr.it 045 802 7985

Constantin Gabriela

gabriela.constantin@univr.it 045-8027102

Daducci Alessandro

alessandro.daducci@univr.it +39 045 8027025

Dall'Alba Diego

diego.dallalba@univr.it +39 045 802 7074

Delledonne Massimo

massimo.delledonne@univr.it 045 802 7962; Lab: 045 802 7058

Giugno Rosalba

rosalba.giugno@univr.it 0458027066

Laudanna Carlo

carlo.laudanna@univr.it 045-8027689

Liptak Zsuzsanna

zsuzsanna.liptak@univr.it +39 045 802 7032

Malerba Giovanni

giovanni.malerba@univr.it 045/8027685

Marcon Alessandro

alessandro.marcon@univr.it +39 045 802 7668

Maris Bogdan Mihai

bogdan.maris@univr.it +39 045 802 7074

Perduca Massimiliano

massimiliano.perduca@univr.it +39 045 802 7984

Sala Pietro

pietro.sala@univr.it 0458027850

Salvagno Gian Luca

gianluca.salvagno@univr.it 045 8124308-0456449264

Study Plan

The Study Plan includes all modules, teaching and learning activities that each student will need to undertake during their time at the University. Please select your Study Plan based on your enrolment year.

ModulesCreditsTAFSSD
Final exam
24
E
-

2° Year

ModulesCreditsTAFSSD
Final exam
24
E
-
Modules Credits TAF SSD
Between the years: 1°- 2°
English B2 level
4
F
-
Between the years: 1°- 2°
Between the years: 1°- 2°
Other activities
2
F
-

Legend | Type of training activity (TTA)

TAF (Type of Educational Activity) All courses and activities are classified into different types of educational activities, indicated by a letter.




SPlacements in companies, public or private institutions and professional associations

Teaching code

4S004550

Credits

12

Coordinatore

Ferdinando Cicalese

Scientific Disciplinary Sector (SSD)

INF/01 - INFORMATICS

Language

English

The teaching is organized as follows:

Algorithm design

Credits

6

Period

I semestre

Academic staff

Ferdinando Cicalese

Bioinformatics algorithms

Credits

6

Period

II semestre

Academic staff

Zsuzsanna Liptak

Learning outcomes

Students will acquire a wealth of advanced analytic tools which constitute the foundational basis of the algorithmic solution of important problems in bioinformatics

Knowledge and understanding
The aim of the course is to provide the student with the necessary skills and know-how for the design and analysis of algorithmic solutions to fundamental bioinformatics problems.

Applying knowledge and understanding
The students will acquire the ability to design algorithmic solutions for typical problems in bioinformatics and computational biology, e.g., analysis of
“omics”-data.

Making judgements
The students will be able to identify the critical structural elements of a problem and the most appropriate approaches to tackle complex problems in bioinformatics.

Communication
The students will acquire the ability to describe with appropriate precision and clarity, to both experts and non-specialists: a bioinformatics problem, its mathematical model and the corresponding solution.

Lifelong learning skills
The students will be able to deepen their know-how in bioinformatics autonomously. Based on the topics studied and the knowledge acquired, they will be able to read, understand, and apply material from advanced text-books and scientific article.

Program

------------------------
MM: Algorithm design
------------------------
Fundamental notions of algorithmic analysis (brief recap): graph traversals; shortest paths in graphs; minimum spanning tree; dynamic programming. Elements of computational complexity and NP-completeness Models of Genome Rearrangement: (i) polynomial time algorithm for sorting signed permutations; (ii) approximation algorithms for sorting unsigned permutations; (iii) Synteny Distance Some Fundamental Graph Problems: (i) Graph tours: Hamiltonian Cycles and Eulerian Cycles; efficient algorithms for Eulerian path and Eulerian cycle; (ii) The Traveling Salesman Problem: relationships to the hamiltonian cycle problems; inapproximability of the symmetric TSP; 2 approximation algorithm for the metric TSP Models for Physical Map: (i) polynomial time algorithm for The Consecutive Ones Property (C1P); (ii) approximation algorithm for the gap minimisation based on the metric TSP Models for DNA assembly: The Shortest Common Superstring problem and the approximation of the the maximum compression via weighted matching. Network Flow: maximum flow and min cut problems; maximum matching; decomposition of flow into edge disjoint paths; polynomial time algorithm for the minimum/maximum weight perfect matching in bipartite graphs. Models for Motif Finding: (i) the Consensus String Problem; (ii) Polynomial Time Approximation Scheme. Models of Haplotyping: polynomial time algorithms for the haplotyping problem for single individual on gapless data; extensions and parameterisations in the presence of data with gaps.
------------------------
MM: Bioinformatics algorithms
------------------------
Here is an overview of the topics that will be covered. The topics in brackets may vary. * Introduction Part I: Pairwise Sequence Comparison * Pairwise sequence alignment * String distances * Pairwise alignment in practice: BLAST, Scoring matrices (* RNA secondary structure prediction) Part II: Multiple sequence alignment * exact DP algorithm (* Carillo-Lipman search space reduction) * approximation algorithm, heuristics Part III: Phyogenetic reconstruction * distance based data: UPGMA, NJ * character based data: Perfect phylogeny (PP) (* character based data: Small Parsimony, Large Parsimony) Part IV: Sequence assembly algorithms (* Shotgun sequencing: SCS) * Sequencing by Hybridization and NGS: de Bruijn graphs, Euler tours

Examination Methods

------------------------
MM: Algorithm design
------------------------
The exam verifies that the students can master the fundamental tools and techniques for the analysis and design of algorithms and that they understand how these techniques are employed in the solution of some classical computational problems arising in bioinformatics. The exam consists of a written test with open questions. The test includes some mandatory exercises and a set of exercises among which the student can choose what to work on. The mandatory exercises are meant to evaluate the student's knowledge of classical algorithms and analysis tools as seen during the course. "Free-choice" exercises test the ability of students to model "new" toy problems and design and analyse algorithmic solutions for it. The grade for the module Algorithm Design is determined by the result of the written test and the result of homework to be solved periodically during the semester. The overall grade for "Fundamental Algorithms for Bioinformatics" is computed by averaging the grades awarded for the two modules.
------------------------
MM: Bioinformatics algorithms
------------------------
Written exam, followed by oral exam. You are only admitted to the oral if you have passed the written exam. The written exam consists of theoretical questions (problems studied, analysis of algorithms studied, mathematical properties, which algorithms exist for a problem etc.), as well as applications of algorithms to concrete examples (computing a pairwise alignment with the DP algorithm etc.) In the oral exam, the student will explain in detail their solutions to the written exam, and show to what extent they have mastered the topics. Students of the Masters in Molecular and medical biotechnology will have separate questions.

Bibliografia

Reference texts
Activity Author Title Publishing house Year ISBN Notes
Algorithm design J. Kleinberg, É. Tardos Algorithm Design (Edizione 1) Addison Wesley 2006 978-0321295354
Algorithm design H.J. Böckenhauer, D. Bongartz Algorithmic Aspects of Bioinformatics Springer 2007
Algorithm design Neil C. Jones, Pavel A. Pevzner An introduction to bioinformatics algorithms (Edizione 1) MIT Press 2004 0-262-10106-8
Algorithm design V. Mäkinen, D. Belazzougui, F. Cunial, and A.I. Tomescu Genome Scale Algorithm Design (Edizione 1) Cambridge University Press 2015 ISBN 978-1-107-07853-6
Algorithm design J.C. Setubal, J. Meidanis Introduction to Computational Biology Pws Pub Co 1997
Bioinformatics algorithms Dan Gusfield Algorithms on Strings, Trees, and Sequences Cambridge University Press 1997 0 521 58519 8
Bioinformatics algorithms Enno Ohlebusch Bioinformatics Algorithms 2013 978-3-00-041316-2
Bioinformatics algorithms Joao Setubal and Joao Meidanis Introduction to Computational Biology 1997

Type D and Type F activities

Modules not yet included

Career prospects


Module/Programme news

News for students

There you will find information, resources and services useful during your time at the University (Student’s exam record, your study plan on ESSE3, Distance Learning courses, university email account, office forms, administrative procedures, etc.). You can log into MyUnivr with your GIA login details.

Attendance

As stated in point 25 of the Teaching Regulations for the A.Y. 2021/2022, attendance at the course of study is not mandatory.
Please refer to the Crisis Unit's latest updates for the mode of teaching.

Gestione carriere


Graduation


Further services

I servizi e le attività di orientamento sono pensati per fornire alle future matricole gli strumenti e le informazioni che consentano loro di compiere una scelta consapevole del corso di studi universitario.