Computational Approaches for Omics Data

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.

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.