DESIGN AND ANALYSIS OF NGS EXPERIMENTS
cod. 1010759

Academic year 2023/24
2° year of course - First semester
Professor
- Roberto FERRARI
Academic discipline
Biologia molecolare (BIO/11)
Field
A scelta dello studente
Type of training activity
Student's choice
32 hours
of face-to-face activities
3 credits
hub: PARMA
course unit
in ITALIAN

Learning objectives

Knowledge and understanding.
Acquisition by students of
knowledge of Next-Generation Sequencing data analysis.
Ability to apply knowledge and understanding.
Through guided analysis of experiments
for the computer scientist's understanding of the aspects
molecules that pertain to the use of a particular NGS experiment.
Students will also acquire
the basic skills necessary to face the experimental study
bioinformatics analysis related to the understanding of gene expression and mechanisms
molecules involved through NGS experiments.

Prerequisites

Basics of using a computer for accessing databases and downloading files. Desirable prerequisite: unix basics.

Course unit content

General notions of Next-Generation sequencing (NGS).
Choice of the appropriate NGS experiment.
Tools for the analysis of NGS experiments.
NGS data format and consequent tools for manipulation and display.
NGS analysis: Quality Control (QC), alignments, alignment analysis, alignment visualization.
Data analysis of ChIP-seq, RNA-seq, Hi-C experiments, exome-sequencing, DNA methylation and other more specific methods.
Formatting of NGS data for viewing through the genome browser.
Secondary analytical tools for the analysis of specific genomic regions as promoters or regulatory regions.
Integration of NGS data.
Generation of figures (paper-ready) through R language.

Full programme

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Bibliography

No

Teaching methods

The course consists of lectures on the main topics covered by the program, and targeted in-depth analysis of topics of particular relevance and interest, also with the help of recently published NGS datasets.

Assessment methods and criteria

The assessment of the expected learning outcomes is based on an oral exam and a practical exercise on the topics covered.
The student must be able to apply one of the methods taught in the course applied to certain datasets.
Both the theoretical knowledge,
and the ability to apply that knowledge to resolution
of experimental problems

Other information

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