B8307 - COMPUTATIONAL GENOMICS

Learning outcomes

The student knows the computational methods that are currently used to extract relevant biological and medical information from sequencing data. At the end of the course, the student is able to use i) bioinformatic pipelines for the analyses of sequencing experiments; ii) statistical methods for the identification of genetic variants associated to phenotypic traits; and iii) machine learning methods for the analysis of population structure, and genotype-phenotype relations. At the end of the course,the student is able to critically analyse the scientific literature and the state of the art regarding computational genomics, and to dialogue with genetic experts for the design and implementation of computational strategies for research and clinical applications.

Course contents

Sequencing technologies. Sanger’s sequencing. Next generation sequencing. Single-molecule sequencing. Biological databanks. Databases of biological sequences and structures. Databases of biological networks. Reference genomes and annotations. Overview of datasets from cancer research. Large-scale repository of biomedical information. Alignment of biological sequences. Algorithms for local, global, and multiple alignments. Bioinformatic pipelines for mapping short/long reads. Identification of variants. Algorithms for germline and somatic variant calling. Algorithms for the identification of Copy Number Variations. Genome wide association studies. Methods to identify Single Nucleotide Polymorphisms associated with phenotypic traits. Definition and clinical applications of Polygenic Risk Scores. Machine Learning algorithms for omics data. Dimensionality reduction algorithms for omics data. Machine Learning algorithms to combine multi-omics data.

Readings/Bibliography

Slides from classes will be available. Current literature will be circulated in class.

Teaching methods

Traditional lectures in class.

Assessment methods

The exam consists of a written test in which the achievement of the educational objectives will be assessed. It will be required to define a bioinformatic pipeline for the analysis of sequencing experiments, and for extracting clinical or biological relevant information from sequencing data. Students with Specific Learning Disorders (SLD) or temporary/permanent disabilities are advised to contact the University Office in advance, at the following address: https://site.unibo.it/studenti-con-disabilita-e-dsa/en. The office will be responsible for proposing any necessary accommodations, which must in any case be submitted at least 15 days in advance for approval by the course instructor, who will assess their appropriateness in relation to the learning objectives of the course.

Office hours

See the website of Simone Furini