VET40470 Bioinformatics

VET40470 Bioinformatics

VET40470 Bioinformatics 150 150 Peter

VET40470 Bioinformatics

Academic Year 2021/2022

This 10-day course is designed for graduate students and researchers in veterinary and animal sciences. It will be delivered by Prof Gavin Conant (North Carolina State University), Prof Chris Elsik (University of Missouri), Dr Mazdak Salavati (University of Edinburgh) and Prof Denis Shields (University College Dublin). 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. An assessment examination (MCQ) will take place on 21 August.

Course objectives include:
• Knowledge and understanding of
• Scripting, 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

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.

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