42001 Bioinformatics

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Subject handbook information prior to 2024 is available in the Archives.

UTS: Engineering: Biomedical Engineering
Credit points: 6 cp

Subject level:

Postgraduate

Result type: Grade, no marks

Requisite(s): ( 42721 Introduction to Biomedical Engineering OR ((120 credit points of completed study in Bachelor's Honours Embedded Degree owned by FEIT OR 120 credit points of completed study in Bachelor's Combined Honours Degree owned by FEIT OR 120 credit points of completed study in Bachelor's Combined Honours Degree co-owned by FEIT) AND (31005 Machine Learning OR 41160 Introduction to Biomedical Engineering)))
These requisites may not apply to students in certain courses. See access conditions.
Anti-requisite(s): 91816 Bioinformatics

Recommended studies:

knowledge of cell biology and basic programming skills

Description

Bioinformatics is the science of turning large quantities of biological measurements into meaningful and/or actionable information. This interdisciplinary field requires knowledge in biology, engineering and computer science including understanding of:

• biological contexts to initiate, plan and interpret the results of bioinformatics projects
• biotechnologies to develop and evaluate data processing and data mining strategies
• computer science to efficiently organise, store and manipulate data
• statistical methods to identify patterns and make predictions from the data, and
• visualisation techniques to interpret and communicate findings made from the data.

This subject offers an introduction to the acquisition, analysis and interpretation of experimental data with a major focus on state-of-the-art genome-wide biotechnologies. The overall aim is to provide students with the necessary background and practical techniques to initiate, conduct and further develop bioinformatics projects, and efficiently communicate project outcomes to domain experts such as biologists, engineers and medical professionals.

Subject learning objectives (SLOs)

1.	Understand and explain the biological context and the biotechnologies applied in modern genomics projects. (D.1)
2.	Understand common data formats and efficiently store, process, analyse, interpret and critically evaluate genomic projects. (D.1)
3.	Organize, implement and communicate real-world bioinformatics project to domain experts. (E.1)

Course intended learning outcomes (CILOs)

This subject also contributes specifically to the development of the following Course Intended Learning Outcomes (CILOs):

• Technically Proficient: FEIT graduates apply theoretical, conceptual, software and physical tools and advanced discipline knowledge to research, evaluate and predict future performance of systems characterised by complexity. (D.1)
• Collaborative and Communicative: FEIT graduates work as an effective member or leader of diverse teams, communicating effectively and operating autonomously within cross-disciplinary and cross-cultural contexts in the workplace. (E.1)

Contribution to the development of graduate attributes

Engineers Australia Stage 1 Competencies

Students enrolled in the Master of Professional Engineering should note that this subject contributes to the development of the following Engineers Australia Stage 1 competencies:

• 1.3. In-depth understanding of specialist bodies of knowledge within the engineering discipline.
• 2.2. Fluent application of engineering techniques, tools and resources.
• 3.2. Effective oral and written communication in professional and lay domains.

Teaching and learning strategies

Subject presentation includes combined workshop/laboratory activities, and research and development work for the assignments. Students will need to prepare for face-to-face classes using material on Canvas. Online lectures will present the computational, biological and biotechnological background. The workshop/laboratory activities focus on hands-on experience in biological data processing, analysis and interpretation of the results. Practical assignments can be performed anywhere. The labs will provide the tools necessary to complete these assignments, and also include discussions giving students opportunities to present problems and solutions. Preparation will help students to participate in the in-class individual and group exercises. Guest lectures about case studies of real-world applications of data mining techniques will be face-to-face.

Content (topics)

The following topics will be covered:

• Introduction to the human genome: organisation; regulation; expression.
• Introduction to biotechnologies to measure the human genome: DNA-seq; ChIP-seq; RNA-seq.
• Efficient organisation and processing of genomic data: Unix.
• Introduction to sequence alignments: Computational considerations.
• Processing, analysis and visualisation of genomic data: DNA-seq; RNA-seq; ChIP-seq.
• Interpretation of genomic data: DNA sequence variation; RNA expression variation; DNA and chromatin binding variation.

Assessment

Assessment task 1: Weekly Quizzes

Intent:	Students take weekly online lectures introducing concepts from biology, biotechnology and computer science which are required to work efficiently in bioinformatics and cancer genomics. In this weekly assignment, students answer five multiple-choice questions, based on the content covered in the weekly online lecture, to demonstrate their knowledge in the field.
Objective(s):	This assessment task addresses the following subject learning objectives (SLOs): 1 and 2 This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs): D.1
Type:	Quiz/test
Groupwork:	Individual
Weight:	20%
Length:	A total of nine quizzes with eight quizzes being marked (i.e. Module 1 is not marked; Module 4 is a face to face class without quiz). The total weight of all quizzes is 20% (i.e. each quiz is 2.5%).

Assessment task 2: Genomic data exploration and preparation

Intent:	Students develop a genomics data analysis pipeline and process a single real-world dataset. This assessment includes a critical evaluation of the data analysis pipeline and an interpretation of results in the biological/medical context.
Objective(s):	This assessment task addresses the following subject learning objectives (SLOs): 1, 2 and 3 This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs): D.1 and E.1
Type:	Presentation
Groupwork:	Group, individually assessed
Weight:	30%

Assessment task 3: Integrative genomics in action

Intent:	Students develop multiple genomics data analysis pipelines to process and then integrate multiple real-world datasets. This assessment includes a critical evaluation of the data analysis pipelines, a systems analysis on the integrated datasets, and an interpretation of results in the biological/medical context.
Objective(s):	This assessment task addresses the following subject learning objectives (SLOs): 1, 2 and 3 This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs): D.1 and E.1
Type:	Report
Groupwork:	Individual
Weight:	50%