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.
Type D and Type F activities
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 Molecular and Medical Biotechnology - Enrollment from 2025/2026Modules not yet included
Computational genomics (2017/2018)
Teaching code
4S003667
Teacher
Coordinator
Credits
6
Language
English
Scientific Disciplinary Sector (SSD)
BIO/18 - GENETICS
Period
II sem. dal Mar 1, 2018 al Jun 15, 2018.
Learning outcomes
The advent of the new sequencing technology (Next Generation Sequencing, NGS) had a great impact on the ability to study genome complexity at genomic, transcriptomic and epigenetic level and provided interesting opportunities for the development of bioinfomatic resources for data analyses and management.
The course will provide a general overview of the main computational methods based in NGS data that can be applied in genomic studies (mainly focused on the human genome) as for example , sequence alignment, genome sequencing, genome resequencing for the identification of variants, transcriptomic analysis for the identification of differentially expressed genes.
At the end of the course the student should be able to:
Know the main data file formats
Know the different algorithm used in genomic studies and their applications
Setting up a pipeline for data managing and analysis
Program
1. Introduction to Next Generation Sequencing (NGS) data
• Biases and sequencing errors of Illumina technology
• FastQ file format
• Quality reads assessment (FastQC software)
• Reads preprocessing
2. Overview of bioinformatics methods for genome assembly
• Overlap-layout-consensus
• Debrujin graph
• Genome assembly assessment
3. Sequence alignment of NGS data
• Dynamic programming
• Heuristic methods
• SAM/BAM format
4. Resequencing and variant calling
• Identification of germline variants
• Identification of somatic variants
• Bioinformatics methods for the identification of structural variations (Insertion and Deletion, Translocation,Copy number variation)
• Variant Calling File (VCF) format and Genomic VCF format
5. Computational tools for prioritizing candidate genes
6. Analyse epigenetic data using bioinformatics tools
7. Transcriptomic analysis and RNA-seq
• RNA-seq genome alignment (TopHat, STAR)
• Transcripts reconstruction
• Gene quantification
• Data normalization
• Identification of differentially expressed genes
• Gene enrichment and gene set analysis
Bioinformatics laboratory
• Introduction to bash and linux operative system
• Usage of FastQC software for sequence quality assessment
• Setting up of a pre-processing sequence pipeline
• Sequence alignment with bowtie2
• BAM/SAM file manipulation
Examination Methods
The exam consists of a written verification of the level of knowledge regarding the argument of the course. The student must demonstrate understanding of the method and application of the major bioinformatic programs and approaches learned during the course. He will also demonstrate the ability to setup a bioinformatic pipeline within the contest of different arguments.
