LQN303 Computational Genomics
To view more information for this unit, select Unit Outline from the list below. Please note the teaching period for which the Unit Outline is relevant.
| Unit code: | LQN303 |
|---|---|
| Credit points: | 12 |
| Timetable | Details in HiQ, if available |
| Availabilities |
|
| Domestic tuition unit fee | $3,744 |
| International unit fee | $5,148 |
Unit Outline: Semester 1 2025, Online
| Unit code: | LQN303 |
|---|---|
| Credit points: | 12 |
| Coordinator: | Rodney Lea | rodney.lea@qut.edu.au |
Overview
This unit provides an overview of the various tools used across genomic diagnosis and provided the opportunity for students to practice using these tools. Genomic scientists and healthcare professionals need to be familiar with the computational principles behind big data analysis used in array technology and next-generation sequencing. 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:
- Examine the different methods of computational genomics data including statistical genetics, linkage and association analysis, array technology, next generation sequencing (NGS) and database querying.
- Analyse the strengths, limitations, sensitivities and specificities of array technologies, whole exome sequencing and genomic databases.
- Draw upon knowledge and understanding of bioinformatics approaches to create pipeline to process data from a genomics (NGS) experiment in diagnostics.
- Critically interpret research findings in the context of the published literature, appraise the strengths and weakness of study design, and present findings in a scientific report.
Content
- Computational biology, bioinformatics, array technology, next generation/massively parallel sequencing
- The processes involved in performing linkage and association analysis, e.g. file types, programs etc
- Key concepts in statistical genetics as they relate to population genetics, genetic epidemiology and/or quantitative genetics
- Programs used at the different stages of processing whole exome or whole genome data e.g. demultiplexing, genotyping algorithms, data alignment, variant calling annotation etc
- Concepts/principles of genotyping algorithms e.g. GATK
- The principles of alignment for whole exome and whole genome data and examples of programs which can be used
- The principles of CNV detection using array data, exome data and genome data and give examples of programs which could be used for each
- Discuss the strengths and weaknesses of each technology as they pertain to diagnostics
- The principles of detection and analysis of genome-wide epigenetic patterns (e.g. methylation arrays)
- Online databases and web browsers e.g. dbSNP, InterPro, Ensembl, ClinVar, UCSC, ExAC, NCBI gene, COSMIC etc
- Online genomics tools e.g. BLAT, BioMart, Variant Effect Predictor etc
- The process of assigning biological annotation to genomic variants e.g. within a gene, coding or non-coding, in silico predictions, conservation, previously reported in association with phenotype etc
- MPS data files (i.e. FASTQ, BAM, VCF etc.), size differences, data contained within them and the quality metrics derived from these files (e.g. number of mutations, coverage, mapping quality, Phred quality scores, TsTv ratios etc.)
- Software which can be used to visualize (e.g IGV) and/or manipulate FASTQ, BAM, VCF files
- How gene expression might be studied using microarrays or RNA sequencing
- Situations where it might be appropriate, or inappropriate, to visualise data using custom tracks on an online tool (e.g. UCSC) versus a desktop tool
- use GenAI knowledge to generate code
Learning Approaches
This unit is designed to introduce you to the core concepts of computational biology. The online delivery is through Canvas. As the unit is developed around the principles of adult learning, theory and practice and open learning guidelines, you will engage in self-directed learning and will also work collaboratively with your peers. You will work on a predominantly, asynchronous learning environment where you can access lectures, materials and activity-based exercises that you will undertake at your own pace.
The Canvas site will provide you with learning 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.
Theoretical content is backed up by problem-based learning activities, where you have the opportunity to apply the theory learnt into practice.
You are expected to engage in ongoing conversations with other students and with academic staff within Canvas. 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 as per your study requirements.
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 will be given throughout the semester on a set of problem-solving case studies. Feedback on assessment 1 will be given regarding your analytical skills, ability to identify resources, reasoning and ability to interpret and summarise your findings. Assessment 2 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 Canvas site.
Assessment
Overview
There are two summative assessment items in LQN303. Assessment 1 is a portfolio based on problem solving tasks and case studies. Workshops during the semester will assist you in completing the portfolio. Assessment 2 is a a problem-solving case study.
Unit Grading Scheme
7- point scale
Assessment Tasks
Assessment: Portfolio
Assessment 1 is a portfolio based on problem solving tasks and case studies. Workshops during the semester will assist you in completing the portfolio. Throughout the semester, formative case studies and problem-solving tasks incorporated into the workshops will help you confidently complete the final portfolio. Completing the portfolio will ensure that you acquire coherent knowledge of the essential elements of computational genomics, applicable in both laboratory and clinical settings.
Assessment task will be summative, graded on 1-7 scale.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Assessment: Problem solving case study
In this authentic assessment, you'll step into the role of a clinical bioinformatician. You will be required to perform an end-to-end analysis of whole exome sequence data set derived from a specific patient(s) undergoing molecular genetic diagnosis for a severe disease. As part of this process, you will establish a detailed workflow of appropriate bioinformatics methodologies using AI. You will analyse raw NGS sequence data using appropriate bioinformatics tools. You will then finally produce a detailed report of your work to the laboratory head (who the clinical bioinformatician reports to).
Assessment task will be summative, graded on 1-7 scale.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Academic Integrity
Academic integrity is a commitment to undertaking academic work and assessment in a manner that is ethical, fair, honest, respectful and accountable.
The Academic Integrity Policy sets out the range of conduct that can be a failure to maintain the standards of academic integrity. This includes, cheating in exams, plagiarism, self-plagiarism, collusion and contract cheating. It also includes providing fraudulent or altered documentation in support of an academic concession application, for example an assignment extension or a deferred exam.
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.
Breaching QUT’s Academic Integrity Policy or engaging in conduct that may defeat or compromise the purpose of assessment can lead to a finding of student misconduct (Code of Conduct – Student) and result in the imposition of penalties under the Management of Student Misconduct Policy, ranging from a grade reduction to exclusion from QUT.
Resources
In addition to online lecture notes, a selection of online textbooks, journal articles, and internet resources will be made available each week through the QUT Library.
Risk Assessment Statement
There are no out of the ordinary risks associated with this unit.
Course Learning Outcomes
This unit is designed to support your development of the following course/study area learning outcomes.LS72 Graduate Diploma in Diagnostic Genomics
- Apply scientific knowledge and skills, focused on current genomic trends in practice and research, utilising digital capabilities.
Relates to: ULO1, ULO3, ULO4 - Critically evaluate scientific findings and locate solutions to solve complex genomics problems, employing high order cognitive skills, clinical reasoning, and reflective practice.
Relates to: ULO2, ULO3 - Practise within a framework of personal accountability, collegiality and ethical judgment, while valuing cultural safety and sensitivity in professional practice, clinical decision-making and research.
Relates to: ULO2, ULO4
LS81 Master of Diagnostic Genomics
- Apply scientific knowledge and skills, focused on current genomic trends in practice and research, utilising digital capabilities.
Relates to: ULO1, ULO3, ULO4, Portfolio, Problem solving case study - Critically evaluate scientific findings and locate solutions to solve complex genomics problems, employing high order cognitive skills, clinical reasoning, and reflective practice.
Relates to: ULO2, ULO3, Problem solving case study - Develop and apply professional oral and written communication skills that inform effective collaboration and digital interactions with colleagues and other stakeholders across the medical and scientific contexts.
Relates to: Problem solving case study - Practise within a framework of personal accountability, collegiality and ethical judgement, drawing upon Indigenous perspectives, cultural safety and sensitivity in professional practice, clinical decision-making and research.
Relates to: ULO2, ULO4, Problem solving case study - Plan and execute a substantial academic activity in the field of diagnostic genomics to address a specific research question.
Relates to: ULO4