MS203-15 Bioinformatics

Department: Warwick Medical School
Level: Undergraduate Level 2
Module leader: Laura Baxter
Credit value: 15
Module duration: 10 weeks
Assessment: 100% coursework
Study location: University of Warwick main campus, Coventry

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

Indicative reading list

Reading lists can be found in Talis

Specific reading list for the module

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.
Ability to apply key bioinformatics methods for sequence data processing, including genome assembly and transcriptomic analysis.
Competence in handling, analysing, and interpreting high-throughput nucleotide sequencing data to extract quantitative biological information.

Transferable skills

Analytical reasoning using large-scale quantitative datasets
Interpreting statistical outputs to support biological conclusions
Designing reproducible computational workflows
Critically evaluating methodological approaches
Communicating complex quantitative analyses clearly to scientific audiences

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 A2

	Weighting	Study time	Eligible for self-certification
Assessment component
Group Presentation	50%	15 hours	No
GROUP PRESENTATION 3-5 students per group (group sizes made as equal as possible given student numbers). Each group member must be responsible for a clearly defined analytical component. All analytical components are designed to require equivalent depth of technical understanding and critical evaluation, ensuring parity of cognitive demand across group members. All members should contribute to both analysis and interpretation. The presentation is group-based, but marking is individual, based on: Quality of analytical contribution Clarity and depth of explanation of their component Ability to answer questions on their own and related components Each required to speak for approximately 4 minutes. 1 question per student. Questions may relate to: Their own analysis The wider pipeline Interpretation or limitations
Reassessment component
Group Presentation Reassessment: Slides + Written Transcript			No
REASSESSMENT COMPONENT FOR GROUP PRESENTATION: Reassessment applies only to the individual student(s) requiring reassessment and not to the full group. The reassessment will consist of an individual 700-word written submission in the format of presentation slides accompanied by a transcript. The submission must focus solely on the bioinformatic analytical component for which the student was responsible in the original group assessment. The student should: Explain the analytical method applied Justify parameter and statistical choices Interpret the resulting data Critically evaluate methodological strengths and limitations Contextualise findings using relevant literature The reassessment must be entirely the student’s own work.
Assessment component
Bioinformatics Report	50%	15 hours	Yes (extension)
Students complete data analysis, evaluation and interpretation of data, based on techniques taught in the block, and present it as a 2000 word report.
Reassessment component is the same

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)