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
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 2025/2026The 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
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3 courses among the following2° Year activated in the A.Y. 2023/2024
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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.
Programming for bioinformatics (2022/2023)
Teaching code
4S009830
Credits
12
Language
English
Scientific Disciplinary Sector (SSD)
INF/01 - INFORMATICS
The teaching is organized as follows:
Programmazione per la bioinformatica
Laboratorio
Learning objectives
Knowledge and understanding The course aims to provide students with the knowledge and understanding of the paradigms and advanced programming tools for the management of biomedical / bioinformatic data and information. Applying knowledge and understanding The student will therefore be able to a) apply the paradigms and advanced programming tools for the analysis of genomic, transcriptomics and proteomics data; b) apply the code performance analysis and identify critical issues and their optimization. Making judgements Ability to independently propose effective and efficient solutions for the biomedical and bioinformatics application domain; ability to identify critical issues for the treatment of complex bioinformatics problems. Communication The student will also be able to interact with various interlocutors in a multidisciplinary biomedical and bioinformatics context, to interact with colleagues in the performance of group work, and to interact with the interlocutors in the working or research environment. Lifelong learning skills Ability to understand scientific literature in the process of interpreting the results or proposed solution, and to carry out individual and group in-depth studies aimed at tackling problems from the research and business world.
Prerequisites and basic notions
Notions and practice on programming languages
Program
R Programming
Overview and History of R
Workspace and Files
Objects and Data Structures
Missing Values
Sequence of Numbers
Subsetting
Split-Apply-Combine Functions
Simulation
Reading Tabular Data
Logic
Control Structures
I/O operations
Functions
Base Graphics
Advanced Graphics
R for Bioinformatics
Overview of BioConductor
Basic BioConductor Data Structures: IRanges and GenomicRanges
Classes and functions for representing biological strings: Biostrings
Classes and functions for representing genomes: BSgenome, GenomicRanges,
Annotation functions and overview of annotation web tools
RNA-SEQ Data Analysis using R/Python and web tools
Introduction to NGS technologies and experimental design
Data Pre-processing, from Fastq to BAM
Indexing Reference Genome
Mapping reads to a reference genome
Sorting and indexing alignment
Map quality control
Variant Discovery and Call set Refinement
Differential Analysis
Limma, Glimma, EdgeR
DESeq2
Practice on coding RNA and ncRNA detection and analysis
Advanced Analyses of biological data in R: methods for graphs and networks.
Networks in igraph
Create networks
Edge, vertex, and network attributes
Specific graphs and graph models
Reading network data from files
Turning networks into igraph objects
Plotting networks with igraph
Network and node descriptives
Distances and paths
Subgroups and communities
Assortativity and Homophily
Reconstruction and analysis of co-regulatory and co-espressed networks
The course includes lectures on advanced topics such as Computational methods for the analysis of personal genomes, graph mining, and multilayer networks. Topics are defined each year in base of the current trends in medical bioinformatics research.
Bibliography
Didactic methods
Students will follow theoretical lessons and exercises whose content will be provided via notebook or R Markdown. Students will install and use the software related to the chosen topics and will analyze real cases.
Learning assessment procedures
The exam consists of a written part (A) and the development of a project (B). (A) consists in developing during the test day exercises and theoretical questions on the course program. (B) is the development of a project agreed upon with the teacher to be developed i class and/or at home (this depends on project's typology) (the project is valid throughout the academic year).
Evaluation criteria
Voting for parts A and B is expressed in thirty.
Criteria for the composition of the final grade
The final vote is calculated as min (31, ((A + B) / 2) + C).
C is expressed in the interval [-4, + 4] and reflects the maturation and scientific autonomy acquired during the development of the tests and the project, in the exposure and in the interpretation of the scientific literature and the scientific context of the project.
Exam language
Inglese
