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.

Study Plan

A.A. 2024/2025 A.A. 2023/2024 A.A. 2022/2023 A.A. 2021/2022 A.A. 2020/2021 A.A. 2019/2020 A.A. 2018/2019 A.A. 2017/2018 A.A. 2016/2017 A.A. 2015/2016

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 Molecular and Medical Biotechnology - Enrollment from 2025/2026

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 enrollment year.

1° Year

ModulesCreditsTAFSSD
6
B
BIO/10
6
B
MED/04
One course among the following
(a.a. 2024/2025: Clinical molecular biology not activated)
6
B
BIO/12
6
B
MED/03
6
B
MED/08
6
B
MED/03
Two courses among the following
6
B
BIO/10
6
B
CHIM/01
6
B
CHIM/06
6
B
INF/01
Three courses among the following
6
B
BIO/11
6
B
BIO/18
6
B
MED/07
6
B
BIO/18
6
B
MED/07
6
B
BIO/10
One course among the following
6
B
MED/46
6
B
MED/43
6
B
BIO/14
6
B
MED/15

2° Year  activated in the A.Y. 2025/2026

ModulesCreditsTAFSSD
12
D
-
2
F
-
Final exam
40
E
-
activated in the A.Y. 2025/2026
ModulesCreditsTAFSSD
12
D
-
2
F
-
Final exam
40
E
-
Modules Credits TAF SSD
Between the years: 1°- 2°
2 modules among the following (Biotechnology in neuroscience and Clinical proteomics 1st and 2nd year; the other modules only 2nd year)
A.A. 2025/26 BIOTECHNOLOGY IN NEUROSCIENCE not available
6
C
BIO/17
6
C
CHIM/01
6
C
BIO/15
6
C
FIS/01
6
C
MED/04
6
C
BIO/15

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.

A Basic activities
B Characterizing activities
C Related or complementary activities
D Activities to be chosen by the student
E Final examination
F Other training activities
S Placements in companies, public or private institutions and professional associations

Computational genomics  (2024/2025)

Teaching code

4S003667

Teacher

Alfonso Esposito

Coordinator

Alfonso Esposito

Credits

6

Language

English en

Scientific Disciplinary Sector (SSD)

BIO/18 - GENETICS

Period

Semester 2 dal Mar 3, 2025 al Jun 13, 2025.

Courses Single

Authorized

Lessons timetable MoodleMoodle Seminars 0

Learning objectives

The advent of the new sequencing technology (Next Generation Sequencing, NGS) had a great impact on the ability to study genome complexity at genomic, transcriptomic and epigenetic level and provided interesting opportunities for the development of bioinfomatic resources for data analyses and management. The course will provide a general overview of the main computational methods based in NGS data that can be applied in genomic studies (mainly focused on the human genome) as for example , sequence alignment, genome sequencing, genome resequencing for the identification of variants, transcriptomic analysis for the identification of differentially expressed genes. At the end of the course the student should be able to: Know the main data file formats Know the different algorithm used in genomic studies and their applications Setting up a pipeline for data managing and analysis

Prerequisites and basic notions

No specific prerequisites are required. Basic knowledge of bioinformatics (e.g. sequence alignment) may be helpful but is not essential to better understand the program.

Program

1. Introduction to next-generation sequencing (NGS) data a. Illumina sequencing bias and errors b. FastQ format c. Sequence quality verification d. Sequence preprocessing 2. Overview of genome assembly methods a. Overlap-layout-consensus b. Debrujin graph c. Assembly quality verification 3. Alignment of NGS data to a reference genome a. Dynamic programming b. Heuristic methods c. SAM/BAM format 4. Resequencing and variant calling a. Germline variant calling b. Somatic variant calling c. Bioinformatics methods for structural variant calling d. VCF and gVCF file formats 5. Computational methods for candidate gene prioritization 6. Analysis of transcriptomic and RNA-seq data a. RNA-seq sequence alignment b. Transcript reconstruction (genome-guided / denovo) c. Gene quantification d. Data normalization e. Identification of differentially expressed genes f. Enrichment analysis

Bibliography

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Didactic methods

The teaching methods adopted in the course are: - Frontal lessons - Computer laboratory exercises - Reading and presentation of scientific articles

Learning assessment procedures

The exam consists of a written test of the level of knowledge acquired on the topics covered in the course. The test consists of 6 open questions. The student must demonstrate that he/she has understood the functioning and application of the main bioinformatics programs and approaches explained in class.

Students with disabilities or specific learning disorders (SLD), who intend to request the adaptation of the exam, must follow the instructions given HERE

Evaluation criteria

The level of understanding of the topics and the command of language will be evaluated.

Criteria for the composition of the final grade

Each question will be assigned a score of 10, the final grade will be scaled to 30. The activity of presenting scientific articles is not mandatory, students who intend to present a scientific article will be able to have a bonus of up to two points to add to the exam grade.

Exam language

Inglese/Italiano