MS203-15 Patterns and Populations
Introductory description
This module focuses on how complex biological systems can be described and analysed using high-throughput sequencing data and bioinformatics tools. By its nature, bioinformatics is an interdisciplinary subject at the intersection between biology, computer science and maths/statistics, and helps us to understand and interpret biological data, especially when the data sets are large and complex. The module establishes both knowledge and practical skills. Alongside this, students will develop their skills in critical thinking, reasoning and good scientific practice.
Module aims
The overarching aim is to train students to become comfortable with using bioinformatics tools to analyse high-throughput sequencing data.
The module will focus primarily on computational methods to evaluate and analyse data. Specifically, students will be equipped to extract quantitative data from raw sequencing reads and link changes at the transcriptional level to biological outcomes. But at a fundamental level, the foundational skills and techniques learnt paves the way for the students to apply any bioinformatic analysis to any data set in the future, such is their generality.
We want to instil confidence, build problem solving skills, promote teamwork, encourage scientific discussion, and deepen critical thinking and logical reasoning.
Outline syllabus
This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.
This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ:
Genes and gene network analysis of high-throughput sequencing data. How to handle data, detect and correct biases, and assure data quality. How to extract differential expression and gene network information.
Learning outcomes
By the end of the module, students should be able to:
- Utilise software packages for analysing sequencing data
- Comprehend concepts underlying transcriptomics data and their representation
- Interpret and apply data to understand biological systems
- Apply statistical knowledge to estimate uncertainties in analysis
- Present concisely, appropriately and effectively on topics discussed in class
- Synthesise biological concepts related to network interaction
- Understand how to use the literature to deepen understanding
- Evaluate biological data using different techniques and able to critique the appropriateness of each methodology
Interdisciplinary
The students will apply computational and mathematical methods to understand and describe complex biological phenomena.
Subject specific skills
Knowledge of key methods in bioinformatics, data handling and analysis. Knowledge how to apply analysis tools to extract quantitative data from nucleotide reads of biological samples.
Transferable skills
Students will be able to demonstrate integrated thinking across the Sciences. Oral presentations on quantitative methods applied to biological problems.
Study time
| Type | Required |
|---|---|
| Lectures | 8 sessions of 1 hour (5%) |
| Practical classes | 7 sessions of 2 hours (9%) |
| Private study | 98 hours (65%) |
| Assessment | 30 hours (20%) |
| Total | 150 hours |
Private study description
Reading around topics and learning tools to perform analyses. Also includes deepening understanding of statistical approaches.
Costs
No further costs have been identified for this module.
You do not need to pass all assessment components to pass the module.
Assessment group A1
| Weighting | Study time | Eligible for self-certification | |
|---|---|---|---|
Assessment component |
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| Group Presentation | 50% | 15 hours | No |
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Presentation related to bioinformatic analysis |
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Reassessment component |
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| Group Presentation reassessment | No | ||
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Reassessment of Group Presentation to be submitted as a written report, consisting of slides accompanied by up to 750 words explaining the analysis in lieu of an oral presentation. |
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Assessment component |
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| Bioinformatic data analysis | 50% | 15 hours | Yes (extension) |
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Students complete data analysis, evaluation and interpretation of data, based on techniques taught in the block. |
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Reassessment component is the same |
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Feedback on assessment
Written feedback against a marking rubric for group presentations.
Written feedback against a marking rubric for the written report.
Pre-requisites
This module combines computational skills and biological knowledge. Therefore, prior experience of at least one computer programming language (preferably R, Unix and/or Python) and at least A level biology would be beneficial.
If you are interested in taking this module, but are unsure if your level of experience is sufficient please contact the module leader.
Courses
This module is Core for:
- Year 2 of UMDA-CF10 Undergraduate Integrated Natural Sciences (MSci)