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.
Type D and Type F activities
This information is intended exclusively for students already enrolled in this course.If you are a new student interested in enrolling, you can find information about the course of study on the course page:
Laurea magistrale in Medical bioinformatics - Enrollment from 2025/2026Type D learning activities are the student's choice, type F activities are additional knowledge useful for job placement (internships, transversal skills, project works, etc.). According to the Teaching Regulations of the Course, some activities can be chosen and entered independently in the booklet, others must be approved by a special committee to verify their consistency with the study plan. Type D or F learning activities can be covered by the following activities.
1. Modules taught at the University of Verona
Include the modules listed below and/or in the Course Catalogue (which can also be filtered by language of delivery via Advanced Search).
Booklet entry mode: if the teaching is included among those listed below, the student can enter it independently during the period in which the curriculum is open; otherwise, the student must make a request to the Secretariat, sending the form to carriere.scienze@ateneo.univr.it during the period indicated.
2. CLA certificate or language equivalency
In addition to those required by the curriculum/study plan, the following are recognized for those matriculated from A.Y. 2021/2022:
- English language: 3 CFUs are recognized for each level of proficiency above that required by the course of study (if not already recognized in the previous course of study).
- Other languages and Italian for foreigners: 3 CFUs are recognized for each proficiency level starting from A2 (if not already recognized in the previous study cycle).
These CFUs will be recognized, up to a maximum of 6 CFUs in total, of type F if the study plan allows it, or of type D. Additional elective credits for language knowledge may be recognized only if consistent with the student's educational project and if adequately justified.
Those enrolled until A.Y. 2020/2021 should consult the information found here.
Method of inclusion in the booklet: request the certificate or equivalency from CLA and send it to the Student Secretariat - Careers for the inclusion of the exam in the career, by email: carriere.scienze@ateneo.univr.it
3. Transversal skills
Discover the training paths promoted by the University's TALC - Teaching and learning center intended for students regularly enrolled in the academic year of course delivery https://talc.univr.it/it/competenze-trasversali
Mode of inclusion in the booklet: the teaching is not expected to be included in the curriculum. Only upon obtaining the Open Badge will the booklet CFUs be automatically validated. The registration of CFUs in career is not instantaneous, but there will be some technical time to wait.
4. Contamination lab
The Contamination Lab Verona (CLab Verona) is an experiential course with modules on innovation and enterprise culture that offers the opportunity to work in teams with students from all areas to solve challenges set by companies and organisations.
Upon completion of a CLab, students will be entitled to receive 6 CFU (D- or F-type credits).
Find out more: https://www.univr.it/clabverona
PLEASE NOTE: In order to be admitted to any teaching activities, including those of your choice, you must be enrolled in the academic year in which the activities in question are offered. Students who are about to graduate in the December and April sessions are therefore advised NOT to undertake extracurricular activities in the new academic year in which they are not enrolled, as these graduation sessions are valid for students enrolled in the previous academic year. Therefore, students who undertake an activity in an academic year in which they are not enrolled will not be granted CFU credits.
5. Internship/internship period
In addition to the CFUs stipulated in the curriculum/study plan (check carefully what is indicated on the Teaching Regulations) here you can find information on how to activate the internship.
Check in the regulations which activities can be Type D and which can be Type F.
Please also note that for traineeships activated after 1 October 2024, it will be possible to recognise excess hours in terms of type D credits, limited only to traineeship experiences carried out at host organisations outside the University.
| years | Modules | TAF | Teacher |
|---|---|---|---|
| 1° 2° | Attention Laboratory | D |
Pietro Sala
(Coordinator)
|
| 1° 2° | Elements of Cosmology and General Relativity | D |
Claudia Daffara
(Coordinator)
|
| 1° 2° | Introduction to quantum mechanics for quantum computing | D |
Claudia Daffara
(Coordinator)
|
| 1° 2° | Introduction to smart contract programming for ethereum | D |
Sara Migliorini
(Coordinator)
|
| 1° 2° | Python programming language [English edition] | D |
Carlo Combi
(Coordinator)
|
| 1° 2° | Mini-course on Deep Learning & Medical Imaging | D |
Vittorio Murino
(Coordinator)
|
| 1° 2° | BEYOND ARDUINO: FROM PROTOTYPE TO PRODUCT WITH STM MICROCONTROLLER | D |
Franco Fummi
(Coordinator)
|
| 1° 2° | APP REACT PLANNING | D |
Graziano Pravadelli
(Coordinator)
|
| 1° 2° | HW components design on FPGA | D |
Franco Fummi
(Coordinator)
|
| years | Modules | TAF | Teacher |
|---|---|---|---|
| 1° 2° | Attention Laboratory | D |
Pietro Sala
(Coordinator)
|
| 1° 2° | LaTeX Language | D |
Enrico Gregorio
(Coordinator)
|
| 1° 2° | Python programming language [Edizione in italiano] | D |
Carlo Combi
(Coordinator)
|
| 1° 2° | Rapid prototyping on Arduino | D |
Franco Fummi
(Coordinator)
|
| 1° 2° | Programming Challanges | D |
Romeo Rizzi
(Coordinator)
|
| 1° 2° | Protection of intangible assets (SW and invention)between industrial law and copyright | D |
Mila Dalla Preda
(Coordinator)
|
Computational Analysis of Genome-Scale Sequences (2024/2025)
Teaching code
4S009834
Teacher
Coordinator
Credits
6
Language
English
Scientific Disciplinary Sector (SSD)
INF/01 - INFORMATICS
Period
Semester 1 dal Oct 1, 2024 al Jan 31, 2025.
Courses Single
Authorized
Learning objectives
In this course we study advanced data structures for the analysis of genomic sequences, and in general, of textual data. Knowledge and understanding The course provides an understanding of the basic challenges and fundamental issues in processing textual data such as genomic sequences; knowledge of some of the most common computational problems on strings and sequences in genomic data analysis and other applications; familiarity with the most important text indices and their use in solving these problems, including complexity analysis (time and space). Applying knowledge and understanding At the end of the course the student will be able to translate typical problems of genomic sequence analysis in operations and algorithms on textual data and evaluate the computational cost incurred. Making judgements At the end of the course the student will be able to judge whether a given algorithm or data structure is appropriate for the problem at hand, including the evaluation of the computational cost incurred. Communication At the end of the course the student will be able to correctly formalize algorithms on sequences with or without the use of advanced text data structures. Lifelong learning skills At the end of the course the student will be able to read and understand independently scientific articles and specialized texts which use advanced string data structures for the analysis of genomic sequences or other textual data.
Prerequisites and basic notions
Algorithms and data structures. No knowledge of biology is necessary for following the course.
Program
In recent years, we have seen vast progress of research in computational biology, in which the use of dedicated data structures for genomic sequences, and other types of biological sequence data, has been decisive. At the same time, these methods can be, and are being, applied to all other kinds of textual data.
The explosion of the amounts of data available ("big data") is one of the major challenges for computer science today. Much of this data is in form of text (or can be easily rendered in textual form): genomic sequences and other biological sequences, webpages, emails, scanned books, musical data, and many others. In order to be able to efficiently store, process, and extract information from this data, we need dedicated data structures and algorithms, i.e. data structures specifically developed for strings, also referred to as text indexes.
Program of the course:
1) introduction to strings (sequences), their basic properties and fundamental issues: alphabet size, character comparison, string sorting
2) classical exact pattern matching algorithms (non index-based): Knuth-Morris-Pratt, Boyer-Moore, Karp-Rabin, possibly Aho-Corasick
3) Text indexes I:
- tries
- suffix trees
4) Text indexes II:
- suffix arrays and related data structures
- Burrows-Wheeler Transform (BWT), FM-index
For each of these text indexes, we will study their properties, efficient construction, and applications to specific string problems.
No previous knowledge of biology is necessary for following the course.
Bibliography
Didactic methods
Lectures, exercise sessions, homework with discussion in class. In special cases it is possible to access the online streaming.
Learning assessment procedures
Final exam: written and oral. In the written exam, both theoretical questions will be asked (running times and storage space of algorithms, properties of the data structures studied), and concrete examples will have to be solved (compute the suffix tree, suffix array, BWT etc. of a given string, apply certain algorithms). In the oral exam, the student will have the opportunity to explain in detail his/her solution and show to what extent he/she has understood the topics studied.
The exam is the same for all students (whether or not they followed the lectures).
Evaluation criteria
The exam will show that the student
- has acquired sufficient understanding of the most important issues with respect to handling large textual data (alphabet type, comparison of strings, string sorting, size of textual data)
- can apply, explain, and analyze the algorithms studied for non-index based pattern matching
- can apply, explain, and analyze the data structures studied, in particular construction algorithms for and storage space required by these data structures (inverted index, trie, suffix tree, suffix array, BWT)
- can apply, explain, and analyze some applications of these data structures to problems on strings, such as pattern matching, matching statistics, palindromes, LZ-compression, etc.
Criteria for the composition of the final grade
complete grade based on the written and the oral exam
Exam language
English
