BIOT9009 - Bioinformatics

Module Details

Module Code: BIOT9009
Title: Bioinformatics
Long Title: Bioinformatics
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 a comprehensive overview of bioinformatics and the application of computational methods for the analysis and representation of DNA sequences and biological data. The students will gain and in-depth understanding of how application of bioinformatics methods can lead to biological discoveries in the areas of genomics, life sciences and pharmaceutical industry.
 
Learning Outcomes
On successful completion of this module the learner will be able to:
# Learning Outcome Description
LO1 Critically review advantages and application of bioinformatic strategies for the evaluation and interpretation of sequence data.
LO2 Critically assess the organisation and file structure of sequence data and apply suitable tools for file format interconversion.
LO3 Critically review available options for storage, retrieval and analysis of nucleotide and protein sequence data.
LO4 Critically evaluate strength and limitations of current gene prediction methods and recommend suitable options for different organisms.
LO5 Critically review principles and methods for sequence motif finding and their application in genomic datasets.
LO6 Critically review principles and methods of sequence alignment and their application for data mining and function prediction.
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
Bioinformatic methods and applications
An introduction to common bioinformatic methods and their application for sequence data analysis.
Sequence data format and databases
A detailed overview of typical sequence file formats and their file structure and organisation. Resources available for sequence visualisation, editing and file format conversions. Overview of public databases available for storage and distribution of genomic datasets. Available utilities for batch download of genomic data.
Gene prediction and motif finding
A comprehensive evaluation of the available methods and tools for gene mining in genomic datasets. Critical analysis and application of the available tools in various organisms, their strengths and weaknesses and selection criteria. Motifs and motif finding in genomic datasets, significance, prediction sensitivity and challenges in current methods.
Sequence alignments
A comprehensive overview of global and local, pairwise and multiple sequence alignment algorithms and their application in homology searches and sequence data analysis. Protein-profile and profile-profile HMM-based alignments for the detection of remote homologs and protein domains.
Functional prediction and analysis
A detailed review of the predictive methods for functional annotation of genomic sequence data. Commonly used sequence comparison tools and their utilisation for protein function assignment. Review of online resources for functional classification of enzymes and metabolic pathways.
Phylogenetic analysis
A comprehensive analysis of the current methods in phylogenetic inference. Construction of phylogenetic trees and evaluation of strengths and weaknesses in phylogenetic inference and statistical assessment. File format and available tools for inspection and representation of phylogenetic data.
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 Sem End Learning Outcomes 1,2,3,4,5,6
Assessment Description
Practical evaluation of a problem-based assessment with presentation of methods, results and discussion.
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
  • Arthur M. Lesk. (2019), Introduction to Bioinformatics, Oxford University Press, [ISBN: 9780198794141].
  • A. D. Baxevanis, G. D. Bader, D. S. Wishart. (2020), Bioinformatics, Wiley, [ISBN: 9781119335580].
Supplementary Book Resources
  • David W. Mount. (2004), Bioinformatics: Sequence and Genome Analysis, Cold Spring Harbor Press, [ISBN: 9780879697129].
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 2 Mandatory
CR_SCPBI_9 Postgraduate Diploma in Science in Computational Biology 2 Mandatory