Teaching code

4S003667

Teacher

Nicola Vitulo

Coordinator

Nicola Vitulo

Credits

6

Language

English

Scientific Disciplinary Sector (SSD)

BIO/18 - GENETICS

Period

Semester 2 dal Mar 4, 2024 al Jun 14, 2024.

Courses Single

Authorized

Lessons timetable Moodle Seminars 0

Learning objectives

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

Prerequisites and basic notions

No specific prerequisites are required. Basic knowledge of bioinformatics (eg sequence alignment) may be useful but not essential to better understand the program.

Program

1. Introduction to Next Generation Sequencing (NGS) data
a. Illumination bias and sequencing errors
b. FastQ format
c. Checking the quality of the sequences d. Sequence preprocessing
2. Overview of genome assembly methods
a. Overlap-layout-consensus
b. Debrujin graph
c. Assembly quality verification
3. Alignment of NGS data on a reference genome
a. Dynamic programming
b. Heuristic methods
c. SAM / BAM format
4. Re-sequencing and calling variants
a. Identification of germline variants
b. Identification of somatic variants
c. Bioinformatics methods for the identification of structural variants d. VCF and gVCF file format
5. Computational methods for the prioritization of candidate genes
6. Analysis of transcriptomics and RNA-seq data
a. Alignment of RNA-seq sequences
b. Reconstruction of transcripts (genome-guided / denovo)
c. Gene quantification
d. Data normalization
e. Identification of differentially expressed genes
f. Enrichment analysis

Laboratory

Some lessons will be devoted to exercises carried out with the computer. In particular, the following topics will be addressed:
- Use of the main bash commands
- Execution of some programs studied in class
- Development of simple pipelines for data analysis

Didactic methods

The teaching methods adopted in the course are:
- Frontal lessons
- Computer laboratory exercises
- Reading and presentation of scientific articles

Learning assessment procedures

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.

Evaluation criteria

The level of understanding of the topics and the language properties will be evaluated

Criteria for the composition of the final grade

Each question will be assigned a score of 10, the final grade will be scaled to 30.
The presentation of scientific articles is not compulsory, students who intend to submit a scientific article can have a bonus of up to two points to be added to the grade of the exam

Exam language

Inglese/Italiano