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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A scelta un insegnamento traA scelta due insegnamenti tra2° Year activated in the A.Y. 2017/2018
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A scelta tre insegnamenti tra| Modules | Credits | TAF | SSD |
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A scelta un insegnamento traA scelta due insegnamenti tra| Modules | Credits | TAF | SSD |
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A scelta tre insegnamenti traLegend | 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 laboratory for bioinformatics (2016/2017)
Teaching code
4S004548
Teacher
Coordinator
Credits
12
Language
English
Scientific Disciplinary Sector (SSD)
INF/01 - INFORMATICS
Period
II sem., I sem.
Learning outcomes
The course aims to provide the programming and interpretation tools necessary for the analysis of genomic, transcriptional and proteomic data from the latest generation technologies. For each subject, theoretical lessons are given followed by practices in laboratory.
At the completion of the course the students will be able to program, according to the data to be analyzed and the biomedical question to solve, the appropriate analysis pipeline. They will also be able to interpret the obtained results.
Program
Programming in R. Introduction. Data Structures: Vectors, Matrices, Lines, Data Frame. Data Frame. Functions. In / Out. Visualization, the grammar of graphics and ggplot2.
Statistics: median, MAD, rank test, Spearman, robust linear mode, multiple testing, linear models,
Program with Bioconductor. Structure, principles and function. Sequence alignment and aligners, Experimental design, batch effects and confounding, RNA-Seq data analysis and differential expression, Methylation analysis, CNV analysis, Microarray analysis. Annotation resources, Gene set enrichment analysis.
Introduction and Basics of Programming in Python and Bash.
Advanced analysis algorithms: Clustering and classification, resampling: cross-validation, bootstrap, and permutation tests, biological network analysis.
Didactic material (Mainly based on continuously updated scientific articles and online programming guides) is available in the course e-learning platform of the University.
Examination Methods
The exam consists of a written part (A) and the development of a project (B). A consists in developing a R program for solving a given problem using genomic, transcriptomic or proteomic data. B is the development of a project agreed upon with the teacher after request by email and appointment for the elaboration of the specifications (the project is valid throughout the academic year). The projects have different levels of difficulty. Every difficulty corresponds to a maximum evaluation value. Students will hold an interview to comment the A and B parts.
Concerning point A, attendants at the course have the right to participate in two intermediate trials scheduled during the year. The tests consist of the development of an R program for biomedical data analysis. The two tests will have a cumulative vote expressed in thirty and it will be communicated to the students at the end of the course.
Concerning point B, attendees at the course will be able to expose to the class their project, the research context in which the project is located, and the state of progress of the course.
Voting for parts A and B is expressed in thirty.
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.
