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BC5203 - Advanced Bioinformatics

Credit points: 3
Year: 2023
Student Contribution Band: Band 2
Administered by: College of Public Health, Medical and Vet Sciences

This subject provides an overview of widely used computational methods in molecular biology. Lectures describe core concepts and techniques required to analyse, visualise and interpret datasets involving many thousands of genes, genomic variants or microbial taxa. Practicals work through concrete examples of methods covered in lectures, and introduce general purpose tools such as the R language and the unix shell to efficiently work with large biological datasets. Topics covered include an introduction to R and the unix shell, differential gene expression, finding and interpreting genomic variants and metagenomics. In this subject, students will critically evaluate and plan research involving large sequencing data and bioinformatics. Students will independently prepare a grant proposal and oral presentation, demonstrating their ability apply bioinformatics tools to questions of disease or ecological processes.

Learning Outcomes

  • to critically evaluate results from widely used bioinformatic tools based on an understanding of their core concepts and assumptions;
  • to solve questions in molecular biology using appropriate statistical and bioinformatic techniques to analyse genomic sequencing data;
  • to apply the results from bioinformatics tools to make inferences on the evolutionary and cellular processes involved in disease or ecological events;
  • to write and debug short computer programs for the purposes of data reformatting, analysis and visualisation;
  • To critically analyse preliminary data forming the justification of an individually prepared grant proposal and oral presentation.

Subject Assessment

  • Written > Examination (centrally administered) - (30%) - Individual
  • Written > Problem task - (20%) - Individual
  • Written > Research report - (50%) - Individual.

Special Assessment Requirements

Achieve an overall percentage of 50% or more; Complete all assignments and achieve an average assignment mark of at least 50%: Complete at least 6 coding assignments (delivered during tutorials) and achieve an average coding assignment mark of 50%: Pass the final exam

Assumed
Knowledge:
Students must have a basic understanding of STATISTICS which includes knowledge of basic probability and ability to use R for data analysis (or have done the JCU R Bootcamp). SC5202 or SC2202 or will have acquired equivalent knowledge through industry experience.

Availabilities

Townsville, Study Period 2, Internal
Census Date 24-Aug-2023
Coordinator: Dr Ira Cooke
Lecturers: Dr Roger Huerlimann, Assoc. Professor Matt Field, Dr Craig McFarlane, Assoc. Professor Ulf Schmitz, Dr Ira Cooke.
Workload expectations:

The student workload for this 3 credit point subject is approximately 130 hours.

  • 24 hours lectures
  • 12 hours tutorials
  • 24 hours specialised
  • assessment and self-directed study

Study Period 2, External
Census Date 24-Aug-2023
Coordinator: Dr Ira Cooke
Lecturers: Assoc. Professor Matt Field, Assoc. Professor Ulf Schmitz, Dr Ira Cooke.
Workload expectations:

The student workload for this 3 credit point subject is approximately 130 hours.

  • 24 hours lectures
  • 12 hours tutorials
  • 24 hours specialised
  • assessment and self-directed study
Method of Delivery: WWW - LearnJCU

Note: Minor variations might occur due to the continuous Subject quality improvement process, and in case of minor variation(s) in assessment details, the Subject Outline represents the latest official information.