Computational approaches for omics data

A.A. 2020/2021
12
Crediti massimi
96
Ore totali
SSD
INF/01 ING-INF/05
Lingua
Inglese
Obiettivi formativi
Non definiti
Risultati apprendimento attesi
Non definiti
Programma e organizzazione didattica

Edizione unica

Responsabile
Teaching methods.
Lessons, webinars, seminars and Journal Clubs will be held in the classroom if possible, according to Government, Region and University rules. All lessons will be video-recorded and made available through the Ariel platform. If necessary, lessons will be held through Microsoft Teams (or other remote platform) and will be available both synchronously at the scheduled time, and asynchronously through Ariel. Laboratory practices will be held according to the rules of the hosting laboratories, if suitably equipped for the COVID emergency, otherwise they will be substituted with live or recorded video tutorials.

Program and reference material.
The program of the lectures will not be changed. All supporting material will be made available through the Ariel platform.

Methods of learning verification and evaluation criteria.
Exams will be held in preferentially in the classrooms, as explained in "Examination Modalities". Alternatively, the examination will take place using the Microsoft Teams or other remote platform.
Programma
Genomics
o Experimental design
o DNA/cDNA/RNA Sequencing including library preparation and QC
o Sequence assembly
o Sequence annotation (structural and functional) including GO and metabolic pathways annotations
o Reduced representation approaches
o Variant calling (including CNV and SV)
o Phenotype to genotype association methods (QTL, GWAS)
o Repeat annotation and analysis
o Reference gene annotations (RefSeq, GENCODE)
o Alternative splicing and alternative transcripts
o Mining and visualizing data: genome browsers

· Transcriptomics
o Experimental design
o De novo and genome-guided assembly
o Gene expression quantification, from qPCR to RNA-Seq
o Identification of differential expression
o Machine learning approaches to expression data analysis (clustering, dimensionality reduction, principal component analysis)
o Small and long non coding RNA identification and analysis
o Single cell RNA-Seq data analysis
Prerequisiti
Basic knowledge on genetics, molecular biology and biochemistry; basic knowledge of Python and R programming languages and statistics.
Metodi didattici
Class lectures and practices; during course practices, students will have the opportunity to use their laptop to develop and apply pipelines for the analysis of reference datasets.
Materiale di riferimento
Slides, notes and selected articles will be shared with students.
Modalità di verifica dell’apprendimento e criteri di valutazione
Students will be assigned projects, to be developed in small groups. At the exam, students will present and discuss with the teachers the results obtained.
INF/01 - INFORMATICA - CFU: 0
ING-INF/05 - SISTEMI DI ELABORAZIONE DELLE INFORMAZIONI - CFU: 0
Lezioni: 96 ore
Docente: Pavesi Giulio
Docente/i
Ricevimento:
Martedì o Venerdì, 15.00- 17.00
Via Celoria 26 (Dip. BioScienze) - 2B (Torre B - secondo piano)