This is an archived handbook for a previous year. The current undergraduate and postgraduate handbooks can be viewed using the links above.
Postgraduate Handbook 2022-2023 (archived)
Module BIOL40715: Bioinformatics and Data Science
Department: Biosciences
BIOL40715: Bioinformatics and Data Science
| Type | Level | Credits | Availability | Module Cap | Location |
|---|---|---|---|---|---|
| Tied | 4 | 15 | Available in 2022/2023 | None. | Durham |
Tied To
Pre-requisites
- None
Corequisites
- None
Excluded Combinations
- None
Aims
- To provide students with a broad understanding of bioinformatics.
- To provide students with the knowledge and skills of R environment for data analysis and visualization.
- To provide students with the knowledge and skills to analyse genomic and transcriptomic data using open source software.
- To provide students with the knowledge and skills to analyse DNA and protein sequence data.
- To provide students with the knowledge and skills to analyse Next Generation Sequencing data.
- To provide students with the knowledge and skills to use public bioinformatics databases.
Content
- Introduction of bioinformatics.
- R environment for data analysis and visualization.
- Linux and high-performance computing
- Analysis of RNA-seq data using open source software.
- Analysis of small-scale mutations in genome sequencing data using open source software.
- Analysis of DNA and protein sequence data.
- Public bioinformatics databases.
Learning Outcomes
Subject-specific Knowledge
- Essential knowledge of the R environment for data analysis and visualization.
- Essential knowledge of Linux and high-performance computing.
- Essential knowledge of RNAseq data analysis.
- Essential knowledge of genome sequencing data analysis.
- Essential knowledge of DNA and protein sequence data analysis.
- Familiar with major public bioinformatics databases.
Subject-specific Skills
- Ability to use R environment for data analysis and visualization.
- Ability to use Linux and high-performance computing.
- Ability to analyse RNAseq data using open source software.
- Ability to analyse small-scale mutations in genome sequencing data using open source software.
- Ability to analyse DNA and protein sequence data.
- Ability to use major public bioinformatics databases.
Key Skills
- Data analysis, visualization and interpretation
- Linux and high-performance computing
- Hypothesis building
- Problem solving
Modes of Teaching, Learning and Assessment and how these contribute to the learning outcomes of the module
- Sixteen 3 hour workshops over spring term will be delivered.
Teaching Methods and Learning Hours
| Activity | Number | Frequency | Duration | Total Hours | Attendance Monitored |
|---|---|---|---|---|---|
| Workshops | 16 | Twice per week in one term | 3 hours | 48 | |
| Preparation and reading | 102 | ||||
| Total | 150 | ||||
Summative Assessment
| Component | Component Weighting | Element | Length/Duration | Element Weighting | Resit Opportunity |
|---|---|---|---|---|---|
| Coursework | 100% | Basic Bioinformatics Report | 40% | ||
| Mini project report | 60% |
Formative Assessment
Formative verbal feedback on submitted work report
Students who do not attend monitored activities shown under Teaching Methods
and Learning Hours, or who fail to complete the summative or formative
assessment(s) specified above, may be subject to the Academic Progress
procedures defined in the University's General Regulation V, and may be
required to leave the University.