Advanced Genomics and Epigenomics

EPIGENOMICS (6 CFU)

Introduction to epigenomics
· Chromatin structure
· DNA modifications
· Histone variants and post-translational modifications
· Polycomb (PcG) and Trithorax group (TrxG) protein complexes
· Chromatin remodeling
· Chromatin organization in the cell nucleus
· RNA-based mechanisms of gene silencing
· Dosage Compensation Systems
· Genomic imprinting


Genome-wide assays and bioinformatic analysis protocols for the characterization of genetic and epigenetic regulation of gene transcription:
Nucleosome occupancy and DNA accessibility (e.g. ATAC-Seq)
DNA Methylation (e.g. BS-Seq, third generation sequencing)
Chromatin conformation capture assays (e.g. Hi-C, ChIA-Pet)
ChiP-Seq for Transcription Factors
ChiP-Seq for histone modifications and definition of chromatin states
Single cell assays in epigenomics

ADVANCED GENOMICS (6 CFU)

Transcription factor binding site analysis: Information Theory, Position weight matrices;

Network theory: network formalization of biological systems, topological analysis for the identification of important nodes;

Gene regulatory network reconstruction using gene expression compendia (CLR and similar algorithms);

Transposon insertion mutagenesis - theoretical basis of fitness measurements in competition experiments, techniques (Tn-Seq, Tn-seq circle, TraDIS, Rb-Tn-seq...) and analysis;

The small RNA (sRNA) world:
-Non coding RNAs in Bacteria: theoretical aspects, analysis. Case studies: Quorum sensing regulation in Vibrio fischeri and in Vibrio cholerae
-sRNAs in Plants. Experimental approaches, sequencing and the quest for targets aided by PARE (degradome sequencing) in plants.

Meta-taxonomics, meta-genomics, meta-transcriptomics for the characterization and the engineering of microbial communities

More or less half of the CFUs concern practical lessons (PL) by using R. The topics of PLs are decided on the basis of recent papers and dataset and are intended to provide an idea about setting up a research work on biologically sound questions. Some PLs are based on code prepared by the teacher; in other cases, the teacher will present a paper and the associated data, and students will be encouraged to explore specific aspects by their own, under the supervision of the teacher. In these cases, lessons require a more active participation of students to define questions and the way to answer them with the data at hand. Use of AI to generate code is allowed, but students must demonstrate they understand their own code when discussing their code and results. Exam projects will be started in class at the end of the course giving the possibility to students attending classes to set up their analysis.