Course Unit Page

Academic Year 2018/2019

Learning outcomes

At the end of the course, the student has the basic knowledge that gene transcription is intrinsically a dynamic process based on chromatin remodeling and a complex RNAs pool mediating the transcript regulation. In particular, the student will be acquainted with the most up-dated high throughput technologies (microarrays and deep sequencing) from two points of view such as biological and statistics. Data mining and cluster analyses will be acquired by the student.

Course contents



•DNA and RNA dynamics: the meaning •Microarrays: origin and history and next generation sequencing •Stanford University Method - Competitive method •Affymetrix Method - Non Competitive method •Illumina Method- Non Competitive •Next Generation Sequencing: basic concepts •Analysis and discussion of published articles

Elements of basic statistics in Module I:

•Normalization methods; •description of the main parametric and non-parametric statistical tests such as t Student test, Wilcoxon signed-rank test, ANOVA, Mann-Whitney test; •main methods for multiple test correction (FDR, Bonferroni, Benjamini-Hochberg). •Unsupervised data analyses •pathways reconstruction and mapping of expression values onto known pathways and ontologies embedded in databases (GeneOntology).

Practical application of data mining in R environment in Module II. In particular, an overview of the bioinformatic tools currently available to explore and analyse microarray data sets, with a particular focus on DNA methylation microarrays. Through the use of example-oriented exercises, the student will learn how to use R environment and Bioconductor packages to manage genomic data and answer biological questions • Practical application of ChIP-Sequencing



Somna Datt, Dan Nettleton Editors- Springer 2014


press- reprinted in 2005

Statistics (The Easier Way) with R: an informal text on applied statistics by Nicole M. Radziwil, 2015

Teaching methods

Lessons will be both frontal in module I and applicative in module II. Take home messages will be highlighted and discussed during the lessons. Published papers will be shown and discussed during the lessons.

At the end of course, a verification test will be proposed to evaluate the level of acquired knowledge and the effectiveness of lessons.




Assessment methods

Examination will be divided in two parts: 1. home-made report on data processing and analysis (10 scores); 2. written test based on 5 programme-related questions (20 scores).

Laude will be added in the case of excellent performance

Teaching tools

The teacher will use personal laptop, projector and slides.

Students will be provided with slides related to each lessons and papers/reviews obtained by up-dated scientific literature.



Office hours

See the website of Miriam Capri

See the website of Maria Giulia Bacalini