66569 - DNA/RNA DYNAMICS

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 with cluster analyses will be acquired by the student.

Course contents

Epigenetics of human genome: basic concepts; DNA methylation and impact on gene expression; long non coding and small RNAs: the role of microRNAs; Microarrays technologies versus next generation sequencing (RNA-Seq).  Basic concepts of data mining; differentially expressed genes (parametric and non-parametric analyses for two samples; ANOVA, MANOVA, General Linear Model) cluster analyses (unsupervised methods: Principal Component Analysis, Multi Dimensional Scaling, Hierarchical clustering; Consensus tree, K-means clustering, Self-Organising Map). Data set analysis on whole genome DNA methylation and Affy chip transcriptomics;  RNA-seq workflow, introduction to ChIP-seq data analysis with application. Specific web tools on gene pathways or gene function identifications.

Readings/Bibliography

The most advanced papers will be shown during the lessons.
A comprehensive text: BIOINFORMATICS FOR HIGH THROUGHPUT SEQUENCING Published by SPRINGER 2012, Editors: Naiara Rodriguez-Ezpeleta; Michael Hackenberg, Ana M. Aransay. Useful for the specific lessons: MICROARRAY BIOINFORMATICS- by DOV STEKEL- Cambridge University press- reprinted on 2005.

Teaching methods

At the end of each lesson the teacher will propose a general discussion on the developed topic eventually by means of recent published articles

Assessment methods

Final examination will be performed by the administration of 10 questions in writing version (English language is the official language of this course) related to the program. Each answer has a value up to 3 scores and in the case of complete answers, the student will achieve the full mark cum Laude. In particular most of the questions will be related to the data analysis in terms of data mining and cluster analysis, being one of most important achievement of this course.  Other questions of final examination will be related to the comprehension of the biological problems which students have to resolve by means of  high throughput technologies and right experimental designs. This last aspect is complementary to data analysis and should be achieved by the student at the end of the course.
The examination will take a time of 1 hour and 30 minutes.

Teaching tools

Video projector is available in the class room. Lesson slides and published papers will be up-loaded in the course web site to help students for the best comprehension of the topics. Further, web tools for data analysis will be suggested and one lesson devoted to a practical exercise will be performed during the course.

Links to further information

http://www.unibo.it/SitoWebDocente/default.htm?UPN=miriam.capri%40unibo.it

Office hours

See the website of Miriam Capri