MDCS42270 Bioinformatics analysis of high throughput data

Academic Year 2023/2024

*** Not available in the academic year indicated above ***

The aim of this module is to enable students to gain the knowledge and understanding required to critically interpret existing genomic and proteomic data, and develop the skills to formulate their own research questions using a range of statistical and bioinformatics techniques.
The course will address topics focused on the analysis and interpretation of OMICs data covering
- The importance of experimental design of functional genomics experiments
- Bioinformatic strategies for the evaluation and interpretation of genomics data
- Single cell genomics
- The contribution of data visualization strategies to the interpretability of bioinformatics analyses
- Communication of knowledge and conclusions
- The contribution of machine learning to bioinformatics analysis
- Application of the FAIR (findable, accessible, interoperable and reusable) principles for best practice in the management and stewardship of research data

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Curricular information is subject to change

Learning Outcomes:

Upon completion of the module students should be able to

- Apply good experimental design principles towards the design of functional genomics experiments
- Deploy some of the most commonly utilized approaches used in bioinformatic analysis
- Derive insights from genomics data in conjunction with other data e.g. phenotype/clinical data
- Communicate the findings of a bioinformatics analysis to differing audiences with different levels of domain expertise and reflect on their own practice
- Understand the potential for machine learning to contribute to bioinformatics analyses
- Implement data visualization strategies to enhance the interpretability of genomic data
- Utilize the FAIR (findable, accessible, interoperable and reusable) principles for best practice in the management and stewardship of research data

Student Effort Hours: 
Student Effort Type Hours
Lectures

18
Specified Learning Activities

48
Autonomous Student Learning

50
Online Learning

4
Total

120
Approaches to Teaching and Learning:
Active/task-based learning; peer and group work; lectures; critical writing; reflective learning; 
Requirements, Exclusions and Recommendations

Not applicable to this module.


Module Requisites and Incompatibles
Not applicable to this module.
 
Assessment Strategy  
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade
Portfolio: Each student will keep a log tracking their progress over the weeks on what they have learnt; including reflections, evidence of activity and/or other materials Throughout the Trimester n/a Graded Yes

50
Group Project: Group project involves the generation and submission of a bioinformatics report and presentation of a poster Coursework (End of Trimester) n/a Other Yes

50

Carry forward of passed components
Yes
 
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
• Group/class feedback, post-assessment

How will my Feedback be Delivered?

Not yet recorded.

Name Role
Dr Carolina Correia Tutor
Mr Thomas Hall Tutor
Mr Ciaran Kennedy Tutor
Dr Vadim Zhernovkov Tutor
Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
 
Spring
      
Lecture Offering 1 Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Thurs 10:00 - 11:50