Course Description

Course Name

Genomes

Session: VDNS3121

Hours & Credits

18 Credit Points

Prerequisites & Language Level

Taught In English

  • There is no language prerequisite for courses at this language level.

Overview

Genome content and genetic elements within genomes. Genome variation and its effects within and between species. How genomes influence phenotype. Bioinformatic methods used for analysis of genomes.

GENE 315 explores eukaryote genomes and genomic variation and is, thus, highly relevant for all students interested in modern genetics. The central theme of genomic variation links the various modules within the lecture course, which sit alongside laboratory streams designed to reinforce the concepts being taught in class. This includes coverage of both laboratory-based experimental genetics techniques, along with computational methods for the analysis of genetic data.

Teaching Arrangements
There are twelve weeks of laboratory classes, run across the five teaching modules, and students are assigned to one of two lab streams (Wednesday and Thursday afternoons). Laboratory classes run from 2.00 pm to 5.50 pm (room G09, Biochemistry building).

Course Structure
The lecture course is divided into 5 blocks:
- Exploring the genome
- Genome content
- Genome variation and its effects within species
- From genome to phenotype
- Implications of the genomic revolution

The lecture course is complemented by a laboratory course, which provides training in data analysis and relevant genetic methods, including genome assembly and annotation; identification and assessment of genomic variation; quantitative genetics and genomic selection; and the impact of genetic variants on phenotype.

Learning Outcomes
Knowledge and understanding of the basic principles of eukaryote genomics:
- How genomic sequence is obtained and analysed
- Genome annotation
- Genome content and complexity
- Cross-species genomic comparisons
- Methods for the identification of genomic variants
- Quantitative genetics and genomic selection
- Interpretation of phenotype variation in the context of large-scale genomic data sets
- Computer technology skills relating to the analysis of genomic data

*Course content subject to change