Bioinformatics and Functional Genomics

Wedi cwblhau'r modiwl dylai'r myfyrwyr fedru:

1. Use genomics and related databases to analyse gene and genome inter-relationships.
2. Independently demonstrate retained knowledge of the basic technological principles of transcriptome, proteome and metabolome analyses.
3. Show familiarity with a range of computational methods being used to interpret functional genomics data.
4. Appreciate current research strategies for gene discovery and determining gene function (functional genomics) and biological data analysis and interpretation (bioinformatics).
5. Demonstrate skills and knowledge of the concepts underlying database access, sequence analysis, protein classification and functional assignment.

This module is an integrated series of lectures, seminars and workshops covering the modern discipline of bioinformatics with a particular focus on Functional Genomics. Functional Genomics has been defined as the development and application of global (genome-wide or system-wide) experimental approaches to assess gene function by making use of the information and reagents provided by genome sequencing and mapping. High throughput experimental technologies based on measuring gene expression, protein structure and metabolomics are being used to generate large datasets to aid understanding of gene function. Any understanding of function genomic experiments must employ "big data" analyses approaches. This module will consider functional genomic technologies; especially as applied to the human genome. Via an extensive computer-based practical programme it will use R-based statistical analyses. This practical programme will be supported by tutorials in computer rooms which will consider

1) what are bioinformatics databases;
2) guide student on how to make effective database queries and retrieve data;
3) using databases to inform proteomic and metabolomic analyses.
4) introduction to coding; focusing on R statistical language.

A considerable component of self-learning will be expected; guided by internet resource such as R studio https://www.rstudio.com/home/ and Software Carpentry Foundation http://swcarpentry.github.io/r-novice-inflammation/ and

The module will begin by considering the different “knowledge discovery” approach represented by big data analyses. It will compare and contrast, deductive and inductive reasoning approaches and how to validate hypotheses generated by both approaches (1 lecture).
This will be followed by sections considering different types of big data being generated which may be classified as genomics and functional genomes.
Genomic data sets will focus on the human genome and consider how this is being exploited to understand key traits, most especially disease. A particular focus will be on the importance of epigenetic changes in influencing cellular function and disease (3 lectures).
Functional genomic approaches arose out of the need to identify the function of previously uncharacterized DNA sequence detected in genomes. These approaches used gene and protein expression and metabolite on a global scale involving high-throughput methods to provide detailed knowledge of how cells and organisms function. In this module, different functional genomic levels will be separately considered by members of staff who are experts in each.
Thus, transcriptomics approaches to measure gene expression, often using next generation sequencing approaches will be discussed. Other sections will consider the large-scale analyses of proteins (proteomics) and metabolites (metabolomics). Both sections will involve students becoming familiar with Mass Spectroscopy, Nuclear Magnetic Resonance (NMR). (3 lectures for each topic). An additional three lectures will feature case studies from researchers at Aberyswyth University.

A central core of the module will be to introduce students to coding-based computational approaches to analyze genomic and functional genomic data. A crucial component will be to improve student’s knowledge of database organization, effective database queries and data retrieval.
These aspects will be developed in eight tutorial exercises in computer rooms and tested in three computer-based workshops. The first will extract and visualize transcriptomic data but the following will introduce / develop the students coding skills.
R is a programming language for statistical analyses and graphics. It is widely used by biologists and the students will further analyze their transcriptomic data using R in a practical workshop. They will become proficient in the use of R in univariate and multivariate analyses and the appropriate visualization of the results.

A considerable component of self-learning will be expected; guided by internet resource such as R studio https://www.rstudio.com/home/ and Software Carpentry Foundation http://swcarpentry.github.io/r-novice-inflammation/

Mae'r modiwl hwn yn cydymffurfio a FfCChC Lefel 6