Computational Genomics (COMP90016)

Graduate courseworkPoints: 12.5On Campus (Parkville)

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Overview

Availability
Semester 1
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AIM

The study of genomics is on the forefront of biology. Current laboratory technologies generate huge amounts of data. Computational analysis is necessary to make sense of these data. This subject covers a broad range of approaches to the computational analysis of genomic data. Students learn the theory behind the different approaches to genomic analysis, preparing them to use existing methods appropriately and positioning them to develop new ways to analyse genomic data.

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

This subject covers computational analysis of genomic data, from the perspective of information theory. Topics include information theoretic analysis of genomic sequences; sequence comparison, including heuristic approaches and multiple sequence alignment; and approaches to motif finding and genome annotation, including probabilistic modelling and visualization, computational detection of RNA families, and current challenges in protein structure determination. Practical work includes writing bioinformatics applications programs and preparing a research report that uses existing bioinformatics web resources.

Intended learning outcomes

INTENDED LEARNING OUTCOMES (ILO)


On completion of this subject the student is expected to:





  • Describe and analyse critically the most commonly used computational approaches to processing genomic data and their theoretical underpinnings
  • Describe current research issues in bioinformatics
  • Outline a variety of algorithms used for processing genomic data and describe in some detail their operation and strengths and limitations
  • Select algorithms appropriate to a given bioinformatics application
  • Write simple bioinformatics computer programs and use bioinformatics programming libraries
  • Describe the role of information theory in analysis of biological data.

Generic skills

On completion of this subject students should have the following skills:

  • Ability to undertake problem identification, formulation and solution
  • Ability to utilise a systems approach to complex problems and to design an operational performance
  • Ability to manage information and documentation
  • Capacity for creativity and innovation
  • Ability to communicate effectively with both the engineering team and the community at large.

Last updated: 3 November 2022

Eligibility and requirements

Prerequisites

One of the following:
COMP90059 COMP10001 COMP10002 COMP20005 COMP90041

Or entry into one of the following courses:

  • Master of Engineering (Biomedical)
  • Master of Engineering (Biomedical with Business)
  • Master of Engineering (Software)
  • Master of Engineering (Software with Business)
  • Master of Science (Computer Science)
     

Corequisites

None

Non-allowed subjects

None

Recommended background knowledge

One semester of computer programming or equivalent experience.

Inherent requirements (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

Last updated: 3 November 2022

Assessment

Additional details

  • Programming Assignment 1, requiring approximately 13 - 15 hours of work, due in week 4 (10%), addresses Intended Learning Outcomes (ILOs) 5 and 6
  • Programming Assignment 2, requiring approximately 13 - 15 hours of work, due in week 6 (10%), addresses Intended Learning Outcomes (ILOs) 5 and 6
  • Programming Assignment 3, requiring approximately 13 - 15 hours of work, due in week 9 (10%), addresses ILOs 5 and 6
  • Programming Assignment 4, requiring approximately 13 - 15 hours of work, due in week 11 (10%) addresses Intended Learning Outcomes (ILOs) 5 and 6.
  • A 2.5-hour written examination at the end of the semester (60%) addresses ILOs 1-4 & 6.

Hurdle requirement: To pass the subject students must obtain at least: 50% overall 30/60 on the end-of-semester examination 20/40 on the project work.

Last updated: 3 November 2022

Dates & times

  • Semester 1
    Mode of deliveryOn Campus (Parkville)
    Contact hours36 hours, comprised of one 2-hour lecture and one 1-hour workshop per week.
    Total time commitment200 hours
    Teaching period 4 March 2019 to 2 June 2019
    Last self-enrol date15 March 2019
    Census date31 March 2019
    Last date to withdraw without fail10 May 2019
    Assessment period ends28 June 2019

    Semester 1 contact information

    Dieter Bulach

    email: dieter.bulach@unimelb.edu.au

Time commitment details

200 hours

Last updated: 3 November 2022

Further information

  • Texts

    Prescribed texts

    None

  • Subject notes

    LEARNING AND TEACHING METHODS

    The subject will be delivered through a combination of lectures, group discussion, and supervised laboratory. The assigned project work is also a key feature in the learning process.

    INDICATIVE KEY LEARNING RESOURCES

    Students will have access to lecture notes and audio recording of the lectures. Papers drawn from the current literature are posted on the LMS for each topic.

     

    CAREERS / INDUSTRY LINKS

    The subject provides an overview of computational genomics, and as such is a foundation for applied and research careers in bioinformatics. Guest lectures are given by practitioners in the field.

  • Related Handbook entries

    This subject contributes to the following:

    TypeName
    CourseMaster of Science (Bioinformatics)
    CourseMaster of Data Science
    CoursePh.D.- Engineering
    CourseMaster of Philosophy - Engineering
    CourseDoctor of Philosophy - Engineering
    CourseMaster of Science (Computer Science)
    Specialisation (formal)Biomedical
    Specialisation (formal)Software
    MajorComputer Science
    Specialisation (formal)Biomedical with Business
  • Available through the Community Access Program

    About the Community Access Program (CAP)

    This subject is available through the Community Access Program (also called Single Subject Studies) which allows you to enrol in single subjects offered by the University of Melbourne, without the commitment required to complete a whole degree.

    Please note Single Subject Studies via Community Access Program is not available to student visa holders or applicants

    Entry requirements including prerequisites may apply. Please refer to the CAP applications page for further information.

    Additional information for this subject

    Subject coordinator approval required

  • Available to Study Abroad and/or Study Exchange Students

    This subject is available to students studying at the University from eligible overseas institutions on exchange and study abroad. Students are required to satisfy any listed requirements, such as pre- and co-requisites, for enrolment in the subject.

Last updated: 3 November 2022