Module Details

Module Code: BIOT8010
Title: Bioinformatics for Biomedical
Long Title: Bioinformatics for Biomedical
NFQ Level: Advanced
Valid From: Semester 1 - 2017/18 ( September 2017 )
Duration: 1 Semester
Credits: 5
Field of Study: 4218 - Biotechnology
Module Delivered in: 1 programme(s)
Module Description: This module provides an introduction to bioinformatics for Biomedical scientists – specifically the use of computational models and databases to manage sequence data generated in a modern clinical diagnostics setting.
 
Learning Outcomes
On successful completion of this module the learner will be able to:
# Learning Outcome Description
LO1 Interpret information from a range of database sources and apply strategies for the analysis and management of sequence data.
LO2 Evaluate quality, redundancy and annotation in sequence databases.
LO3 Recommend suitable options for efficient database searches and explain the significance of the results in terms of E-value, bit score, and % identity.
LO4 Critically assess the strengths and weaknesses of bioinformatics tools available for sequence analysis.
LO5 Identify molecular markers using content and signal sensors for full gene sequences or motifs/patterns.
LO6 Predict evolutionary relatedness using the three major classes of phylogenetic inference and evaluate the statistical tests available to assess the reliability of a tree.
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
Sequence databases
An analysis of the most common primary and secondary sequence and structure databases (both DNA and protein) used in modern molecular diagnostics.
Information retrieval systems
An overview of systems (Entrez and SRS -Sequence Retrieval System) available to biomedical scientists for simple, intuitive access to the data in sequence databases (both NCBI and EMBL and their associated annotation keys).
Gene prediction
An overview of the available gene (single, dual and multiple gene predictors eg Genome scan) and motif/pattern prediction algorithms
Sequence alignment and databases searching
Sequence alignment attempts to align two or more sequences (DNA or protein) such that regions of structural or functional similarity between the molecules are highlighted - facilitating molecular probe design. A detailed overview of scoring matrices (Blosum and PAM) and gap penalties will be used to assess alignment quality.
Phylogenetics
The study of the evolutionary history of species, organisms, genes or protein sequences through the construction and analysis of mathematical entities known as trees or phylogenies. The three main tree building algorithms (Neighbour Joining, Max Parsimony and Max Likelihood) will be assessed and their relative strengths and weaknesses analysed, along with the statistical tests available to assess the reliability of a tree.
Application of Bioinformatics in Biomedical Science
A case study on the application of in silico analysis in Biomedical Science; requiring students to apply their acquired knowledge of sequence databases, SRS, gene prediction, multiple sequence alignment and Phylogenetics.
Module Content & Assessment
Assessment Breakdown%
Coursework100.00%

Assessments

Coursework
Assessment Type Multiple Choice Questions % of Total Mark 60
Timing Sem End Learning Outcomes 1,2,3,4,5,6
Assessment Description
50 Question MCQ with negative marking
Assessment Type Practical/Skills Evaluation % of Total Mark 40
Timing Every Week Learning Outcomes 1,2,3,4,5,6
Assessment Description
in silico problem based assessments with detailed write-ups to be submitted 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 Face to face and blended learning Every Week 2.00 2
Lab Contact in silico problem based learning Every Week 2.00 2
Independent & Directed Learning (Non-contact) Non Contact eportfolio Every Week 3.00 3
Total Hours 7.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 4.00
This module has no Part Time workload.
 
Module Resources
Recommended Book Resources
  • Supratim Choudhuri. (2014), Bioinformatics for Beginners Genes, Genomes, Molecular Evolution, Databases and Analytical Tools, [ISBN: 978-0-12-4104].
This module does not have any article/paper resources
This module does not have any other resources
 
Module Delivered in
Programme Code Programme Semester Delivery
CR_SBISC_8 Bachelor of Science (Honours) in Biomedical Science 7 Mandatory