Module Information
Module Identifier
BR37120
Module Title
Bioinformatics and Functional Genomics
Academic Year
2022/2023
Co-ordinator
Semester
Semester 1
Pre-Requisite
Other Staff
Course Delivery
Assessment
| Assessment Type | Assessment length / details | Proportion |
|---|---|---|
| Semester Assessment | Transcriptomic analyses (virtual poster based on R analyses) | 15% |
| Semester Assessment | Bioinformatic assessment (Written description of analysis pipeline) | 15% |
| Semester Assessment | Tutorial Exercise (Short answer questions) | 10% |
| Semester Exam | 3 Hours 3 x 500-1000 word essays | 60% |
| Supplementary Assessment | (3 x 2000 word essays). Students must take elements of assessment equivalent to those that led to failure of the module. | 40% |
| Supplementary Assessment | 3 Hours (3 x 500-1000 word essays). Students must take elements of assessment equivalent to those that led to failure of the module. | 60% |
Learning Outcomes
On successful completion of this module students should be able to:
1. Use genomics and related databases to analyse gene and genome inter-relationships.
2. Demonstrate an understanding of the basic technological principles of transcriptome, proteome and metabolome analyses.
3. Evaluate the value and constraints associated with the study of model systems for functional genomics.
4. Show familiarity with a range of computational methods being used to interpret functional genomics data.
5. Appreciate current research strategies for gene discovery and determining gene function (functional genomics) and biological data analysis and interpretation (bioinformatics).
6. Demonstrate skills and knowledge of the concepts underlying database access, sequence analysis, protein classification and functional assignment.
Brief description
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 dat" 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 Software Carpentry Foundation http://swcarpentry.github.io/r-novice-inflammation/ and R studio https://www.rstudio.com/home/
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 Software Carpentry Foundation http://swcarpentry.github.io/r-novice-inflammation/ and R studio https://www.rstudio.com/home/
Content
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 Software Carpentry Foundation http://swcarpentry.github.io/r-novice-inflammation/ and R studio https://www.rstudio.com/home/
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 Software Carpentry Foundation http://swcarpentry.github.io/r-novice-inflammation/ and R studio https://www.rstudio.com/home/
Module Skills
| Skills Type | Skills details |
|---|---|
| Application of Number | Students will have opportunity to collect and interpret data in practical classes with respect to quality and quantity. This will include the Application of statistically-based web tools for analysis of ‘omic data to interpret, derive hypotheses and criticize. Feedback on this will be provided with the returned assignment. |
| Communication | Students will develop effective listening skills for the lectures. Students will develop effective written communication skills in practical class write-ups. Feedback on this will be provided with returned assignment. |
| Improving own Learning and Performance | Although not formally assessed, the student's ability to devise and monitor time management, learning and performance skills will be developed throughout module via attending lectures and practical classes. |
| Information Technology | Students will develop skills in accessing the web for information sources and free software for functional genomic analyses and data display. |
| Personal Development and Career planning | |
| Problem solving | Students will develop skills in lectures. Practicals will be designed to allow students to gain experience in extracting and interpreting data. Feedback will be provided with the returned assignment. |
| Research skills | Practical classes will develop skills in the extractions and analysis of data from web-accessible databases and the critical evaluation of data. Feedback will be provided with returned assignments. |
| Subject Specific Skills | Accessing, assimilating and storing information via remote computer servers. |
| Team work |
Notes
This module is at CQFW Level 6