DNA/RNA Dynamics. Miriam Capri

Course aim: 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. At the end of the course, 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: Human genome characteristics and epigenetic modifications. Impact of methylation changes on gene expression and DNA dynamics. New advances on RNA pool and RNA molecules dynamics. Transcriptome analysis: microarrays vs deep sequencing technologies (high throughput sequencing tools, RNA-seq). Data output and filtering. Data mining for the identification of statistically significant changes. Cluster analysis and pathway analysis. New tools for the RNA-seq data analysis.
Readings/Bibliography: Ozsolak F. et al. Direct RNA sequencing. Nature, 461:814-818, 2009; Ozsolak F, Milos PM. RNA sequencing: advances, challenges and opportunities. Nat Rev Genet. 2011 Feb;12(2):87-98. Book: Next-Generation Genome Sequencing: Towards Personalised Medicine by Michal Janitz-2008 -WILEY-VCH
Teaching methods: Lectures, seminars and discussions on new published papers, practical demonstration on data output.
Assessment methods: At the end of the course a test of assessment will be written based on a new published paper to determine the knowledge of the student on the most fundamental statistic approaches, followed by an oral section to complete the examination.
Teaching tools: Video beam, PC, overhead projector