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.

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 sem. Oct 3, 2016 Jan 31, 2017
II sem. Mar 1, 2017 Jun 9, 2017
Exam sessions
Session From To
Sessione invernale Appelli d'esame Feb 1, 2017 Feb 28, 2017
Sessione estiva Appelli d'esame Jun 12, 2017 Jul 31, 2017
Sessione autunnale Appelli d'esame Sep 1, 2017 Sep 29, 2017
Degree sessions
Session From To
Sessione estiva Appelli di Laurea Jul 19, 2017 Jul 19, 2017
Sessione autunnale Appelli di laurea Oct 18, 2017 Oct 18, 2017
Sessione invernale Appelli di laurea Mar 21, 2018 Mar 21, 2018
Period From To
Festa di Ognissanti Nov 1, 2016 Nov 1, 2016
Festa dell'Immacolata Concezione Dec 8, 2016 Dec 8, 2016
Vacanze di Natale Dec 23, 2016 Jan 8, 2017
Vacanze di Pasqua Apr 14, 2017 Apr 18, 2017
Anniversario della Liberazione Apr 25, 2017 Apr 25, 2017
Festa del Lavoro May 1, 2017 May 1, 2017
Festa della Repubblica Jun 2, 2017 Jun 2, 2017
Vacanze estive Aug 8, 2017 Aug 20, 2017

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


Accordini Simone +39 045 8027657

Belussi Alberto +39 045 802 7980

Bicego Manuele +39 045 802 7072

Bombieri Cristina 045-8027284

Bombieri Nicola +39 045 802 7094

Cicalese Ferdinando +39 045 802 7969

Combi Carlo 045 802 7985

Constantin Gabriela 045-8027102

Cristani Marco +39 045 802 7841

Daducci Alessandro +39 045 8027025

Delledonne Massimo 045 802 7962; Lab: 045 802 7058

Franco Giuditta +39 045 802 7045

Giugno Rosalba 0458027066

Laudanna Carlo 045-8027689

Liptak Zsuzsanna +39 045 802 7032

Malerba Giovanni 045/8027685

Manca Vincenzo 045 802 7981

Marcon Alessandro +39 045 802 7668

Menegaz Gloria +39 045 802 7024

Perduca Massimiliano +39 045 802 7984

Sala Pietro 0458027850

Salvagno Gian Luca 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.

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





English en

Scientific Disciplinary Sector (SSD)


The teaching is organized as follows:

Algorithm design




I sem.

Academic staff

Ferdinando Cicalese

Bioinformatics algorithms




II sem.

Academic staff

Zsuzsanna Liptak

Learning outcomes

MM: Algorithm design
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. This module focuses on general principles of advanced algorithm design, using examples taken from classical solutions of real-life bioinformatics problems. Within the overall goals of the Masters Course, the module Algorithm Design will provide the students with: a wealth of advanced techniques for tackling nontrivial problems in bioinformatics; the skill to design algorithmic solutions for typical problems in genome analysis; the ability to identify the structural elements that make a problem difficult or a solution inefficient; and the capability to propose appropriate approaches to the solution of hard problems in bioinformatics
MM: Bioinformatics algorithms
To learn about some of the basic algorithmic problems and solutions behind common bioinformatics applications (sequence alignment, sequence similarity, sequence assembly, RNA folding).


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. * Introduction Part I: Pairwise Sequence Comparison * Pairwise sequence alignment * String distances * Pairwise alignment in practice: BLAST, Scoring matrices Part II: Multiple sequence alignment * exact DP algorithm * Carillo-Lipman search space reduction * approximation algorithm, heuristics Part III: RNA folding * Nussinov and Zuker algorithms, * approximation algorithm Part IV: Sequence assembly algorithms * Shotgun sequencing: SCS and other models * Sequencing by Hybridization and NGS: de Bruijn graphs, Euler tours


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 J.C. Setubal, J. Meidanis Introduction to Computational Biology Pws Pub Co 1997
Bioinformatics algorithms H.J. Böckenhauer, D. Bongartz Algorithmic Aspects of Bioinformatics Springer 2007
Bioinformatics algorithms Enno Ohlebusch Bioinformatics Algorithms 2013 978-3-00-041316-2
Bioinformatics algorithms Joao Setubal and Joao Meidanis Introduction to Computational Biology 1997

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 exams.

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.

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.



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.

Career management

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