BIOT9008 - Applied Genomics

Module Details

Module Code: BIOT9008
Title: Applied Genomics
Long Title: Applied Genomics
NFQ Level: Expert
Valid From: Semester 1 - 2021/22 ( September 2021 )
Duration: 1 Semester
Credits: 5
Field of Study: 4218 - Biotechnology
Module Delivered in: 2 programme(s)
Module Description: This module provides practical and molecular knowledge to implement computational tools for the analysis of genomic datasets. This module focuses on equipping the students with theoretical and practical skills for evaluating and applying current tools available for comparative analysis and representation of genomic data.
 
Learning Outcomes
On successful completion of this module the learner will be able to:
# Learning Outcome Description
LO1 Critically evaluate methods and tools available for the analysis of genomic datasets
LO2 Critically review and apply genome assembly and annotation pipeline for genomic and metagenomic dataset.
LO3 Critically review and apply comparative genomic methods for genome analysis and representation of genomic datasets.
LO4 Critically review and apply genomic analysis methods for taxonomical and functional profiling of microbiome datasets
LO5 Critically evaluate and apply analysis methods for the evaluation of differential gene expression in genomic data.
LO6 Demonstrate ability to apply available tools for high-throughput sequencing reads mapping and analysis
Dependencies
Module Recommendations

This is prior learning (or a practical skill) that is strongly recommended before enrolment in this module. You may enrol in this module if you have not acquired the recommended learning but you will have considerable difficulty in passing (i.e. achieving the learning outcomes of) the module. While the prior learning is expressed as named MTU module(s) it also allows for learning (in another module or modules) which is equivalent to the learning specified in the named module(s).
Incompatible Modules
These are modules which have learning outcomes that are too similar to the learning outcomes of this module. You may not earn additional credit for the same learning and therefore you may not enrol in this module if you have successfully completed any modules in the incompatible list.
No incompatible modules listed
Co-requisite Modules
No Co-requisite modules listed
Requirements

This is prior learning (or a practical skill) that is mandatory before enrolment in this module is allowed. You may not enrol on this module if you have not acquired the learning specified in this section.
No requirements listed
 
Indicative Content
Genomic data analysis for shotgun sequencing data
An introduction to common genomic methods for the analysis of (meta)genomic and (meta)transcriptomic datasets.
Assembly and annotation of genomic datasets
A detailed overview of de novo, reference-based and hybrid assembly methods for the assembly of sequencing raw reads obtained from different NGS platforms. Available tools for the automatic gene prediction and functional annotation of genomic and metagenomic datasets. Computational methods and binning algorithms for the obtainment and validation of metagenome reconstructed genomes.
Comparative genomics and pangenome analysis
A critical evaluation of comparative genomic analysis methods. Available tools for the implementation of comparative genomic workflows for data analysis and data visualisation. Comparison, evaluation and application of available pipelines for pangenome analysis and representation. Phylogenomic inference based on computed orthologous core genes.
Taxonomical profiling of microbiomes
A comprehensive evaluation of current strategies and best procedures for the analysis of metagenomic sequencing datasets. Current methods in taxonomical and functional profiling of microbiome communities. Evaluation of OTU classification performance and accuracy. Gene mining an marker gene classification in metagenomic datasets.
Differential gene expression
An overview of current methods for the evaluation of differential gene expression in transcriptomic datasets. Evaluation of available pipelines for reads alignment, read counting, differential expression analysis and data representation.
SNP genotyping
Analysis of current methods for the detection of single nucleotide polymorphisms in genomic datasets. Reads mapping and extraction of nucleotide variants. Data format for storage of reads alignment data and alignment visualisation strategies.
Module Content & Assessment
Assessment Breakdown%
Coursework100.00%

Assessments

Coursework
Assessment Type Multiple Choice Questions % of Total Mark 20
Timing Week 6 Learning Outcomes 1,2,3
Assessment Description
20 MCQ questions with negative marking
Assessment Type Multiple Choice Questions % of Total Mark 20
Timing Week 12 Learning Outcomes 4,5,6
Assessment Description
20 MCQ questions with negative marking
Assessment Type Practical/Skills Evaluation % of Total Mark 60
Timing Every Second Week Learning Outcomes 1,2,3,4,5,6
Assessment Description
Practical evaluation of a problem-based
assessment with detailed description of methods and results, to be uploaded online
No End of Module Formal Examination
Reassessment Requirement
Repeat examination
Reassessment of this module will consist of a repeat examination. It is possible that there will also be a requirement to be reassessed in a coursework element.

The University reserves the right to alter the nature and timings of assessment

 

Module Workload

Workload: Full Time
Workload Type Contact Type Workload Description Frequency Average Weekly Learner Workload Hours
Lecture Contact Online learning Every Week 2.00 2
Lab Contact Online learning Every Week 2.00 2
Independent & Directed Learning (Non-contact) Non Contact Individual study Every Week 3.00 3
Total Hours 7.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 4.00
Workload: Part Time
Workload Type Contact Type Workload Description Frequency Average Weekly Learner Workload Hours
Lecture Contact Online learning Every Week 2.00 2
Lab Contact Online learning Every Week 2.00 2
Independent Learning Non Contact Individual study Every Week 3.00 3
Total Hours 7.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 4.00
 
Module Resources
Recommended Book Resources
  • T. A. Brown. (2018), Genomes 4, Garland Science, [ISBN: 9780815345084].
  • J. Izard. (2015), Metagenomics for Microbiology, Elsevier Academic Press, [ISBN: 9780124104723].
Supplementary Book Resources
  • Arthur M. Lesk. (2017), Introduction to Genomics, Oxford University Press, [ISBN: 9780198754831].
This module does not have any article/paper resources
Other Resources
 
Module Delivered in
Programme Code Programme Semester Delivery
CR_SCOBI_9 Master of Science in Computational Biology 3 Mandatory
CR_SCPBI_9 Postgraduate Diploma in Science in Computational Biology 3 Mandatory