BIOL3003K Big Data Bioinformatics

Academic Year 2024/2025

Research in biology is increasingly dependent on large datasets such as those generated by DNA or RNA sequencing technologies. This is also true for medical diagnosis and prognosis, and the emerging field of precision medicine. Interpretation of these large datasets requires a range of skills and knowledge drawn from computer science, biology, mathematics and statistics. The purpose of this evolving interdisciplinary scientific field, known as bioinformatics, is to understand and interpret biological data. The main objective of this unique module is to help equip the next generation of biological scientists with a sufficient working knowledge of bioinformatics methods and concepts such that they can understand and be capable of interpreting biological relevance of big datasets generated by cutting-edge 'omics' technologies. Where possible and appropriate, the application of these bioinformatics methods will be illustrated with biological or biomedical examples.

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Curricular information is subject to change

Learning Outcomes:

On successfully completing the module you will:

1) understand the relevance and applications of various omics data and technologies
2) be able to identify and analyse several file formats of biological data
3) understand genome assembly, genome annotation, variation and comparative genomics
4) be familiar with the concept of reference genomes and associated concepts and terminology
5) know how to use the command line interface
6) be familiar with shell scripting and basic programming
7) be able to organise and manipulate large-scale biological data using various tools
8) know how to navigate and explore several databases that house biological data
9) be able to apply bioinformatic methods to real genomic/transcriptomic datasets
10) be able to apply basic coding to automate analyses

Indicative Module Content:

1. Omics fields, their importance and applications

2. Past and current sequencing technologies and large scale sequencing projects

3. Databases and data formats

4. Genomics in food safety and agriculture

5. Reference genomes and genome browsers

6. Genome assembly and annotation

7. Molecular evolution, mutations and genetic variation

8. Comparative genomics and phylogenetics

9. Web-based bioinformatics tool kits

10. Gene regulation and expression

11. Command line interface - basic commands and shell scripting

12. Basic programming using Python and R

13. Accessing and querying online databases using APIs

14. Using genomics for human disease diagnosis

Student Effort Hours: 
Student Effort Type Hours
Specified Learning Activities

20
Autonomous Student Learning

85
Lectures

20
Total

125
Approaches to Teaching and Learning:
This module will be delivered through lectures and workshops. During workshops the students will implement and practice the bioinformatic methods introduced in the lectures using real biological datasets. Task-based learning will include problem sheets and practical tasks pertaining to data analysis. Students will also be required to carry out some group work. Lectures will be interactive, providing core learning materials covered in the module.
 
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

Not yet recorded.

Carry forward of passed components
No
 
Resit In Terminal Exam
Summer No
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
Dr Guerrino Macori Lecturer / Co-Lecturer