VET40470 Bioinformatics

Academic Year 2024/2025

This two-week course is designed for graduate students and researchers in veterinary and animal sciences. It will be delivered by a team of internationally renowned bioinformaticians. Entry requirements for the course include an undergraduate degree in Agriculture, Life Sciences or Veterinary Medicine. It will cover basic scripting, an introduction to next-generation sequencing, protein structure modelling and analysis, molecular evolution and phylogenetics, and network biology.

Course objectives include:
Knowledge and understanding of
• Scripting, annotation, next generation sequencing, protein structure modelling, molecular evolution and genetics, SNP data and use in GWAS
• Application of knowledge to genomic data sets
• Critical analysis and evaluation of data
• Communication of conclusions and knowledge
• Implementation of experimental design to future projects

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Curricular information is subject to change

Learning Outcomes:

LEARNING OUTCOMES
Knowledge and understanding: This certificate enabled attendees to learn and demonstrate specialist knowledge and understanding in advanced bioinformatics techniques including scripting, next generation sequencing, protein structure modelling and analysis, molecular evolution and phylogenetics, network biology, SNP data and use in Genome Wide Association Studies (GWAS).

Applying knowledge and understanding: This certificate course enabled attendees to apply the knowledge and understanding to manipulate large genomic data sets from animal studies to generate meaningful results.

Making judgement: The course allowed attendees to critically analyse and evaluate large data sets from animal studies and develop new hypotheses within the field of animal and veterinary sciences.

Communications and working skills: Attendees improved their ability to communicate their conclusions and knowledge obtained from the large datasets and rationale underpinning these to specialist and non-specialist audiences.

Learning skills: The course allowed attendees to manipulate and critically analyse data sets and implement successful experimental design in the future with a high degree of autonomy.

Indicative Module Content:

Topics to be covered include:
- Genome structure, sequencing, assembly and annotation
- Next-generation sequencing LINUX/UNIX
- Biostatistics and exploratory data analysis in R
- Intro to evolutionary selection Population genomics
- Genome Wide Association Studies (GWAS)
- Scripting for evolutionary simulation
- Population genetics, drift and selection
- Refining evolution Sims
- Tree-thinking/Darwin’s big idea Phylogenetics
- Network biology—Theory and Cytoscape
- Metabolic modelling/FBA Protein structure prediction

Student Effort Hours: 
Student Effort Type Hours
Specified Learning Activities

20

Autonomous Student Learning

20

Lectures

15

Computer Aided Lab

45

Total

100

Approaches to Teaching and Learning:
Lectures,
Computer based active/task-based learning; peer and group work. 
Requirements, Exclusions and Recommendations

Not applicable to this module.


Module Requisites and Incompatibles
Not applicable to this module.
 
Assessment Strategy  
Description Timing Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
Exam (In-person): Part 1, Short answer questions.
Alternative Semi-Linear Conversion Grade Scale 50% Pass (80% = A-)
Week 2 Other No

50

Yes
Exam (In-person): Part 2, Multiple Choice questions
Alternative Semi-Linear Conversion Grade Scale 50% Pass (80% = A-)
Week 2 Other No

50

Yes

Carry forward of passed components
No
 
Remediation Type Remediation Timing
In-Module Resit Prior to relevant Programme Exam Board
Please see Student Jargon Buster for more information about remediation types and timing. 
Feedback Strategy/Strategies

• Feedback individually to students, post-assessment

How will my Feedback be Delivered?

Not yet recorded.

Name Role
Gavin Conant Lecturer / Co-Lecturer
Dr Nicola Fletcher Lecturer / Co-Lecturer
Rafael Guerrero Lecturer / Co-Lecturer
Professor Denis Shields Lecturer / Co-Lecturer