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2023/24 Undergraduate Module Catalogue

BIOL2113 Introduction to Omics Biology

10 creditsClass Size: 260

Module manager: Dr Matt Bawn
Email: m.bawn@leeds.ac.uk

Taught: Semester 1 (Sep to Jan) View Timetable

Year running 2023/24

This module is mutually exclusive with

BIOL2114Omics and Big Data Biology

Module replaces

BIOL2111 Genetic Engineering, 10c, S1

This module is not approved as a discovery module

Module summary

Biology is at the heart of many of the Global Challenges facing society, e.g. the rise of antimicrobial resistance, disease and food production. Modern high-throughput and Omics analytical techniques are at the forefront of equipping biologists with the necessary tools and capabilities to face these challenges. Dealing with this increasing scale and complexity of data generated through these technologies has only been possible through synergy with computational and data science, creating a new frontier of discovery and interdisciplinary research. This module will introduce these themes and their implications. Students will develop practical omics and data-science skills throughout the module before consolidating their learning in group-based and individual research projects.

Objectives

The objective of the module is to provide students with an overview of modern omics and big data approaches to understand how these concepts are driving discovery in modern biology. Students will consolidate these concepts by applying them to real-world data to evaluate data-driven research.

Learning outcomes
By the end of this module, students will be able to:
1. Describe ways in which ‘omics-based methods have advanced understanding of and eukaryotic biology and evolution;
2. . Analyse and interpret omics data within an experimental context and draw evidence based conclusions;
3. Evaluate the most appropriate approaches to experimental design and apply to experimental problems;
4. Critically evaluate the scientific literature from journal articles and incorporate relevant material into written assignments.

Skills outcomes
Computer and bioinformatic skills - Data interpretation - Data and research paper evaluation - Managing knowledge - Problem solving - Recording practical data - lab book management - Report writing – Group working – Project Management – Data management – analysis of Omics data.


Syllabus

The module will be comprised of two sections, each dealing with a different aspect of omics technology, applications, and analysis. There will be an emphasis on the inclusion of reading the primary literature in these sections.

There are two units on prokaryotic systems and two units on eukaryotics.
- in the first section, students will study how modern genomics has been applied to bacterial systems.
-In the second, they will expand on this to learn how genomics is informing public health surveillance of bacterial diseases.
- In the third, they will study genomics, transcriptomics and translatomics in eukaryotic systems.
- The fourth section will deal with the theory outcomes and analysis of proteomics.

Each section will have associated computational exercises set as formative assessments to develop computational skills and see practical application of methods demonstrated in taught lectures. Each section will also contain a workshop where students may seek help and guidance to complete computational tasks.

Teaching methods

Delivery typeNumberLength hoursStudent hours
Lectures61.006.00
Practicals42.008.00
Private study hours86.00
Total Contact hours14.00
Total hours (100hr per 10 credits)100.00

Opportunities for Formative Feedback

Formative self-directed bioinformatics exercises (4x 2hrs)
Formative bioinformatics workshops in computer cluster (6x 2hrs)

Methods of assessment


Coursework
Assessment typeNotes% of formal assessment
ReportIndividual report (1750 words)100.00
Total percentage (Assessment Coursework)100.00

Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated

Reading list

There is no reading list for this module

Last updated: 10/10/2023 09:41:10

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