Computational Approaches for Omics Data

A.Y. 2025/2026
12
Max ECTS
96
Overall hours
SSD
INF/01 ING-INF/05
Language
English
Learning objectives
Students will be introduced to various computational approaches applied to second- and third-generation omics (e.g., DNA-seq, RNA-seq, ChIP-seq, ATAC-seq, CLIP-seq) that have been recently developed for the molecular study of biological systems, with a particular focus on translational oncology. Students will acquire the fundamental tools for omics big data science, starting from sample management to basic statistics useful for the quantitative description of signals. The course covers descriptive analytics techniques tailored to large-scale biological datasets, including summary statistics, distribution analysis, and data visualization methods suitable for high-dimensional omics data. The course will delve into algorithms developed to identify genetic, genomic, and epitranscriptional alterations in oncological models. The student will also be introduced to machine learning and deep learning techniques and their application in oncology.
Expected learning outcomes
By the end of the course, the student will understand the principles of computational biology necessary to analyze omics data related to genetic and epitranscriptional information in tumors using second- and third-generation sequencing techniques. The student will learn the fundamental concepts of the bioinformatics method, including principles of statistics and artificial intelligence, and their application in the analysis of genomic and epitranscriptomic alterations. The student will directly apply these concepts in specific tutorials.
Single course

This course cannot be attended as a single course. Please check our list of single courses to find the ones available for enrolment.

Course syllabus and organization

Single session

Course syllabus
· Introduction to omics data.
· Genomics and Epi-Transcriptomics second- and third-generation sequencing, including at single-cell and spatial approaches.
· Introduction to sample and data management techniques and computational pipelines (e.g., nf-core).
· Exploratory data analysis using descriptive statistics.
· Statistical approaches for large-scale data.
· Algorithms for genomics and epitranscriptomic data analysis.
· Foundations of artificial intelligence
· Use of machine learning techniques, including deep learning, for the analysis and interpretation of omics datasets.

The course is centered on the analysis of datasets and key research questions in translational and cancer biology.
Prerequisites for admission
Courses of either of the "Alignment plans" of the 1st semester.
Teaching methods
The course includes lectures and hands-on sessions. During practical sessions, students will use their laptops to develop and apply analyses—primarily in the R programming language—on reference datasets, based on the methods covered in the course.
Teaching Resources
Slides, notes and selected articles will be shared with students.
Assessment methods and Criteria
Students will be assigned projects during the course. At the exam, they will present and discuss their results with the teacher, which will serve as a starting point for the oral evaluation of the course topics.
INF/01 - INFORMATICS - University credits: 6
ING-INF/05 - INFORMATION PROCESSING SYSTEMS - University credits: 6
Lessons: 96 hours
Professor: Cereda Matteo
Professor(s)