This module aims to provide and equip students with the knowledge of statistics and computational tools needed to independently complete genomic medicine research. This module complements the knowledge from the wider course to ensure all students are competent in planning research, and can perform appropriate statistical analysis in R. The module is designed to prepare students for research projects and presents material in this context. This module serves as an informatics foundation for students learning bioinformatics and statistical languages. In particular, the module introduces students to the Genomics England Research Environment, and provides self-directed, and supervised learning of the Unix command line environment and the R statistics language.

In GMO7 you will learn about:

• How to write a research grant proposal

• Up to date computational and experimental tools for genomic medicine research projects

• The Genomics England research environment and how to perform research on the 100,000 genomes dataset.

• How to use the Unix command line and Galaxy to perform basic genomic analysis

• Fundamental concepts in statistics offering students a foundation and framework for understanding more complex methods.

• Implementation of the statistical methods illustrated during the course using the R environment.

• Basic concepts in using R

• Basic statistical and mathematical concepts including correlation, regression, model selection, and generalized linear models

• Exploration of bayesian statistics and tools applied for haplotype estimation and Machine Learning tools for non-linear regression will also be explored

By the end of this module students will be able to:

• Demonstrate a rounded knowledge of the classic and modern tools and technologies used in research projects. Emphasis will be placed on the overlap between modern and traditional approaches and the type of data they might be presented within a research project

• Understand the conceptual basis for statistical tests, and be aware of the various tools that they can implement when exploring either genomic or expression data

• Choose the correct statistical test based on the data source and experimental design and implement the test in R

• Have a thorough understanding of the types of computational tool available for use in research projects, and the best platform to use them on (Galaxy, command line, HPC etc.)

• Have a good understanding of how to access the central University Linux cluster from an external computer, in preparation for accessing remote High Performance Computing services.