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Algorithms for Functional Genomics (COMP90014)

Graduate courseworkPoints: 12.5On Campus (Parkville)

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Year of offer2017
Subject levelGraduate coursework
Subject codeCOMP90014
Semester 2
FeesSubject EFTSL, Level, Discipline & Census Date


Technological advances in obtaining high throughput data from functioning cells have stimulated the development of new computational approaches to functional genomics and systems biology. This subject covers the theory and practice of the computational techniques used in genomics analysis, with an emphasis on functional genomics.

This 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.


The subject covers key algorithms used in genomics analyses, and their application. Topics include: computational analysis of microarray data; classification and clustering, and their application to functional genomics analysis; detecting variants in genomic data; next generation sequencing for DNA; next generation sequencing for RNA.

Intended learning outcomes


On completion of this subject the student is expected to:

  1. Describe and apply key algorithms used in the analysis of genomics data
  2. Describe next generation DNA sequencing, and compare and contrast it with the application of next generation sequencing to RNA (RNA-seq)
  3. Describe the application of machine learning techniques to gene expression data, and their strengths and weaknesses
  4. Understand the uses and limitations of bioinformatics software tools which use these algorithms, and apply these tools to practical data analysis
  5. 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.

Eligibility and requirements





Non-allowed subjects

Recommended background knowledge

One semester of computer programming or equivalent experience.

Core participation requirements

The University of Melbourne is committed to providing students with reasonable adjustments to assessment and participation under the Disability Standards for Education (2005), and the Assessment and Results Policy (MPF1326). Students are expected to meet the core participation requirements for their course. These can be viewed under Entry and Participation Requirements for the course outlines in the Handbook.

Further details on how to seek academic adjustments can be found on the Student Equity and Disability Support website: http://services.unimelb.edu.au/student-equity/home


Additional details

  • Project work includes two research reports spread evenly over the semester (30% in total). The total expected time commitment for project assessments is approximately 35 - 40 hours of work
  • A 3-hour written examination at the end of the semester (70%).

Hurdle requirement: To pass the subject students must obtain at least:

  • 50% overall
  • 35/70 on the end-of-semester examination
  • 15/30 on the project work.

Dates & times

  • Semester 2
    Principal coordinatorJan Schroeder
    Mode of deliveryOn Campus — Parkville
    Contact hours36 hours, comprising of one 2-hour lecture and one 1-hour workshop per week
    Total time commitment200 hours
    Teaching period24 July 2017 to 22 October 2017
    Last self-enrol date 4 August 2017
    Census date31 August 2017
    Last date to withdraw without fail22 September 2017
    Assessment period ends17 November 2017

    Semester 2 contact information

    Dr Jan Schroeder

    email: jan.schroeder@unimelb.edu.au

Time commitment details

200 hours

Further information

Last updated: 23 January 2019