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Algorithms for Functional Genomics (COMP90014)

Graduate courseworkPoints: 12.5On Campus (Parkville)

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Overview

Availability
Semester 2
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AIMS

Technological advances in obtaining high throughput data have stimulated the development of new computational approaches to bioinformatics. This subject will cover core computational challenges in analysing bioinformatics data. We cover important algorithmic approaches and data structures used in solving these problems, and the challenges that arise as these problems increase in scale.

The subject is a core subject in the MSc (Bioinformatics), and is an elective in the Master of Information Technology and the Master of Engineering. It can also be taken by PhD students and by undergraduate students, subject to the approval of the lecturer.

INDICATIVE CONTENT

The subject covers key algorithms used in bioinformatics, with a focus on genomics. Indicative topics are: sequence alignment (dynamic algorithms and seed-and-extend), genome assembly, variant detection, phylogenetic construction, genomic intervals, complexity and correctness of algorithms, clustering and classification of genomics data, data reduction and visualisation. The subject assumes you have experience in programming and familiarity with the foundations of genomics.

Intended learning outcomes

On completion of this subject the student is expected to be able to:
 
1. Make use of data from standard bioinformatics data representations.
2. Describe the computational challenges posed by common bioinformatics analyses.
3. Describe the feasibility constraints imposed by computational complexity of algorithms, and make appropriate
    decisions about algorithm choices.
4. Describe and understand important algorithms and data structures used to address key questions in       bioinformatics.
5. Implement simple but functional versions of these algorithms.
6. Make appropriate decisions about the application of bioinformatics tools and tool parameters.
7. Extract information from data and visualise it, using bioinformaticstechniques and plots, and simple
    machine learning approaches.

INTENDED LEARNING OUTCOMES (ILO)


On completion of this subject the student is expected to:



  • Describe and apply key algorithms used in the analysis of genomics data
  • Describe next generation DNA sequencing, and compare and contrast it with the application of next generation sequencing to RNA (RNA-seq)
  • Describe the application of machine learning techniques to gene expression data, and their strengths and weaknesses
  • Understand the uses and limitations of bioinformatics software tools which use these algorithms, and apply these tools to practical data analysis
  • Describe the limitations of current methods in functional genomics.

Generic skills

Having completed this unit the student is expected to have the following skills:

  • Read the current literature in functional genome analysis
  • Describe current research issues in computational analysis of functional genomics data
  • Investigate current genomics software tools, understand their principles and limitations, and apply them appropriately.

Last updated: 3 November 2022