myAriel

Over the last few years, research has faced an impressive advancement in experimental methods. This course intends to expose the students to a large number of techniques in order to build a solid knowledge on the wide variety of available experimental tools. The course aims to equip students with a detailed understanding and knowledge of the molecular principles underlying the advanced techniques currently used in research laboratories. Students will acquire a deep knowledge on the molecular strategies adopted to purify and sequence nucleic acids. The course will provide the theoretical principles on which the techniques discussed in class are based, and the practical and technical details on the main biochemical techniques such as electrophoresis, chromatography and in vitro techniques that use antibodies. Students will also develop a better understanding of the experimental strategies applied to genome manipulation.

Assays in experimental biology generate large amounts of data that must be critially assessed and processed appropriately to extract meaningful biological knowledge and generate testable hypotheses. Proficiency in data wrangling and data visualisation, the ability to unravel complex relationships in biological data and the ability to create transparent and reproducible workflows constitute crucial skills for the modern biologist. In addition, a good understanding of principles of experimental design are central to the critical assessment of experimental data. With data as the focus and R/RStudio as the tool, students are exposed and trained in a unified view of experimental design and data analysis. Students will develop expertise in data organisation, visualisation, analysis and interpretation using both conventional biological data and complex large scale (BIG) biological data.

The aims of this course are to enable students to (i) analyse data from a well-designed biological experiment, (ii) create a transparent reproducible analysis workflow using Rmarkdown in R/Rstudio that includes exploratory analyses, statistical modelling, model assessment and parameter estimation, (iv) understand the power and pitfalls of statistical analyses, (v) implement methods for the analysis of gene expression (RNA-Seq) data and the interpretation of the final results.

Throughout the course, we will use R programming language and the R/Studio software environment.