Master's degree in Molecular and Medical Biotechnology

Computational genomics

Course code
Name of lecturer
Nicola Vitulo
Nicola Vitulo
Number of ECTS credits allocated
Academic sector
Language of instruction
II semestre dal Mar 1, 2021 al Jun 11, 2021.

Lesson timetable

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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


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. 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

Assessment methods and criteria

The exam consists of a written verification of the level of knowledge regarding the argument of the course. The exam consist of six open questions. The student need to demonstrate the understanding of the method and application of the major bioinformatic programs and approaches learned during the course.

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