LQN303 Computational Genomics

Unit Outline: Semester 1 2024, Online

Overview

Genomic scientists and clinicians working in genomics need to be familiar with the computational principles behind big data analysis used in array technology and next-generation sequencing as these platforms become mainstream. Genomic scientists need to develop a working knowledge of common computer programs and databases used to interpret such data. Clinicians should be familiar with how genomic variants are interpreted and the strengths and limitations of different approaches.

Learning Outcomes

On successful completion of this unit you will be able to:

1. Describe the different methods of computational genomics data including statistical genetics, linkage and association analysis, array technology and NGS.
2. Analyse the strengths, limitations, sensitivities and specificities of array technologies, whole exome and whole genome sequencing.
3. Apply knowledge of bioinformatics to create a pipeline to process data from a NGS experiment.
4. Critically interpret research findings in the context of the published literature, address strengths and weakness of study design, and clearly identify all ethical aspects of the research.

Content

Learning Approaches

This unit is designed to introduce you to the core concepts of computational biology. The online delivery is through Canvas. The unit is developed around the principles of adult learning, theory and practice and open learning guidelines. This predominantly, asynchronous learning environment allows you to go through lectures, materials and exercises at your own pace.

The Canvas site will provide you with resources including pre-recorded lectures, research papers, media articles and videos. You will also be able to access online meetings, interactive exercises and online message boards. There will be at least one webinar or video-conferencing in which a concept is explained and students will be expected to solve a problem or discuss approaches to a case during the virtual class.

Canvas will facilitate your ongoing conversations with other students and with academic staff. Guidance will be provided, through regular announcements in the Canvas site for you in terms of appropriate self-pacing of your study during the semester. You will be expected to post questions on message boards or email the unit co-ordinator.

You will be encouraged to read widely and to think critically about the nature and scope of how computational biology relates to the field of diagnostic genomics.

Feedback on Learning and Assessment

The online webinars and discussion boards are the key places you can ask for and receive feedback on your understanding of course materials. Feedback on assessment 1 and assessment 2 will be given regarding your analytical skills, ability to identify resources, reasoning and ability to interpret and summarise your findings. Assessment 3 feedback will be by way of a mark which reflects your theoretical knowledge. Each assessment item will include individual feedback on your progress as stated above and feedback will be offered to the group through the Announcements page on the Backboard site.

Assessment

Overview

There are three summative assessment items in LQN303. Assessment 1 is a workbook of dataset analyses from basic bioinformatic workshops held during the first half of the unit. Assessment 2 is a workbook based on genome-wide association bioinformatics and advanced computational genomic workshops held in the second half of the semester. Assessment 3 is a test consisting of multiple choice, short answer and long answer exam questions covering the semester's content.

Unit Grading Scheme

7- point scale

Assessment Tasks

Assessment: Workbook 1

You will be required to perform a series of four bioinformatic analyses as applied to 'omics' datasets based on the content covered in lectures and workshops.
Assessment task will be summative, graded on 1-7 scale.

This is an assignment for the purposes of an extension.

Relates to learning outcomes
2, 3 and 4.

Assessment: Workbook 2

You will be required to perform a series of four computational genomic analyses, as applied to 'omics' datasets, based on the content covered in lectures and workshops.
Assessment task will be summative, graded on 1-7 scale.

This is an assignment for the purposes of an extension.

Relates to learning outcomes
2, 3 and 4.

Assessment: Examination

This online assessment piece will consist of multiple choice, short answer and long answer questions. It is aimed to ensure that you have a coherent knowledge of the essential elements of computational genomics which are required in both laboratory and clinical settings.

Relates to learning outcomes
1.

Academic Integrity

Students are expected to engage in learning and assessment at QUT with honesty, transparency and fairness. Maintaining academic integrity means upholding these principles and demonstrating valuable professional capabilities based on ethical foundations.

Failure to maintain academic integrity can take many forms. It includes cheating in examinations, plagiarism, self-plagiarism, collusion, and submitting an assessment item completed by another person (e.g. contract cheating). It can also include providing your assessment to another entity, such as to a person or website.

You are encouraged to make use of QUT’s learning support services, resources and tools to assure the academic integrity of your assessment. This includes the use of text matching software that may be available to assist with self-assessing your academic integrity as part of the assessment submission process.

Further details of QUT’s approach to academic integrity are outlined in the Academic integrity policy and the Student Code of Conduct. Breaching QUT’s Academic integrity policy is regarded as student misconduct and can lead to the imposition of penalties ranging from a grade reduction to exclusion from QUT.

Resources

Risk Assessment Statement

There are no out of the ordinary risks associated with this unit.