Module Information
Course Delivery
| Delivery Type | Delivery length / details |
|---|---|
| Lecture | 20 x 1 Hour Lectures |
| Practical | 10 x 1 Hour Practicals |
Assessment
| Assessment Type | Assessment length / details | Proportion |
|---|---|---|
| Semester Assessment | Essay with accompanying source code/data | 100% |
| Supplementary Assessment | Essay with accompanying source code/data Students should resit failed components | 100% |
Learning Outcomes
On successful completion of this module students should be able to:
1. Describe the current algorithms and data structures used in bioinformatic sequence analysis.
2. Perform an analysis of a set of DNA sequences and interpret the results.
3. Demonstrate an understanding of the potential sources of error in this type of data and subsequent analyses.
Brief description
This module introduces students to computational bioinformatics. The module will cover the basics of DNA sequence analysis, including sequencing, assembly, similarity/searching, and annotation. The emphasis will be on the computational algorithms that allow us to discover information from biological sequence. This is a fast-moving field, and the content of the course will cover classic algorithms and newer developments.
Aims
Computational Bioinformatics is a subject in demand in industry and in academia. Graduates would be highly employable. This module would also reflect the expertise of one of our 4 research groups, which is currently not represented at all in our teaching.
Content
Sequencing. Short read and long read sequencing.
Sequence quality. Inspecting the results of sequencing. Analysis of the types of error.
Assembly. Reference genomes.
Sequence similarity, alignment and k-mer algorithms.
Burrows Wheeler transformations and short read alignment.
Genome annotation. ORF-finding. Codons, frames, introns/exons, non-coding regions.
Ethics and security when working with genomic data.
Module Skills
| Skills Type | Skills details |
|---|---|
| Application of Number | Inherent in subject. |
| Communication | Documenting code, essay writing. |
| Improving own Learning and Performance | Automatic feedback from the process of using and coding the computational tools. |
| Information Technology | Inherent in subject. |
| Personal Development and Career planning | No, though the skills acquired during this module are in high demand. |
| Problem solving | Inherent in subject matter. |
| Research skills | Using a computer and online tools. Readings from current scientific literature. |
| Subject Specific Skills | Data analysis skills, algorithm and data structure skills. |
| Team work | Will be used for some workshops/pracs. |
Notes
This module is at CQFW Level 6