The analysis of data arising in Bioinformatics and Biostatistics requires the use of sophisticated statistical techniques and computing packages.  This subject introduces the basic elements of statistical modelling, computation and data analysis. Students will develop the ability to fit statistical models to data, estimate parameters of interest and test hypotheses.  Both classical and Bayesian approaches will be covered.  The importance of the underlying mathematical theory of statistics and the use of modern statistical software will be emphasised.

Concepts covered include: descriptive statistics, random sample, statistical inference, point estimation, interval estimation, properties of estimators, maximum likelihood, confidence intervals, hypothesis testing, Bayesian inference.  Applications covered include: exploratory data analysis, inference for samples from univariate distributions, simple linear regression, correlation, goodness-of-fit tests, analysis of variance.

The lectures in this subject are co-taught with MAST20005 Statistics; the practice classes are separate. 

Randomness is inherent in biological data and the analysis of data arising in both Bioinformatics and Biostatistics requires knowledge of sophisticated probability models and statistical techniques. This subject develops the underlying probability theory that is necessary to understand these models and techniques. Computer packages are used for numerical and theoretical calculations but no programming skills are required. Elements of Probability will be co-taught with MAST20006 Probability for Statistics.

AIMS

The aim of this subject is for students to develop familiarity and competence in assessing and designing computer programs for computational efficiency. Although computers manipulate data very quickly, to solve large-scale problems, we must design strategies so that the calculations combine effectively. Over the latter half of the 20th century, an elegant theory of computational efficiency developed. This subject introduces students to the fundamentals of this theory and to many of the classical algorithms and data structures that solve key computational questions. These questions include distance computations in networks, searching items in large collections, and sorting them in order.

INDICATIVE CONTENT

Topics covered include complexity classes and asymptotic notation; empirical analysis of algorithms; abstract data types including queues, trees, priority queues and graphs; algorithmic techniques including brute force, divide-and-conquer, dynamic programming and greedy approaches; space and time trade-offs; and the theoretical limits of algorithm power.

Bioinformatics is a key research tool in modern agriculture, medicine, and the life sciences in general. It forms a bridge between complex experimental and clinical data and the elucidation of biological knowledge. This subject presents bioinformatics in the context of its role in science, using examples from a variety of fields to illustrate the history, current status, and future directions of bioinformatics research and practice.

AIMS

This subject introduces the fundamental concepts of computing programming, and how to solve simple problems using high-level procedural language, with a specific emphasis on data manipulation, transformation, and visualisation of data.

INDICATIVE CONTENT

Fundamental programming constructs; fundamental data structures; abstraction; basic program structures; algorithmic problem solving; use of modules.

The subject assumes no prior knowledge of computer programming.

The subject introduces students to the molecular and cellular aspects of biological systems with particular emphasis on human biology. The course is arranged for students to generate an understanding of the molecular aspects of biology at the biomolecular, sub-cellular and cellular level. The genetic inheritance of traits is considered at the level of the individual and populations. This multi-disciplinary subject is co-taught by staff in the departments of Biochemistry and Molecular Biology and Genetics. There is particular emphasis on integration of these disciplines with students receiving both theoretical and practical knowledge of fundamental and frontier research and development in these areas. Students in the course will be extended through their participation in problem classes. They will write a major essay integrating the learnings with contemporary literature in the fields of genetics, molecular and cellular biology. Students will be mentored in this task by the course coordinator.

Bioinformatics is a key research tool in modern agriculture, medicine, and the life sciences in general. It forms a bridge between complex experimental and clinical data and the elucidation of biological knowledge. This subject presents bioinformatics in the context of its role in science, using examples from a variety of fields to illustrate the history, current status, and future directions of bioinformatics research and practice.

AIMS

The aim of this subject is for students to develop familiarity and competence in assessing and designing computer programs for computational efficiency. Although computers manipulate data very quickly, to solve large-scale problems, we must design strategies so that the calculations combine effectively. Over the latter half of the 20th century, an elegant theory of computational efficiency developed. This subject introduces students to the fundamentals of this theory and to many of the classical algorithms and data structures that solve key computational questions. These questions include distance computations in networks, searching items in large collections, and sorting them in order.

INDICATIVE CONTENT

Topics covered include complexity classes and asymptotic notation; empirical analysis of algorithms; abstract data types including queues, trees, priority queues and graphs; algorithmic techniques including brute force, divide-and-conquer, dynamic programming and greedy approaches; space and time trade-offs; and the theoretical limits of algorithm power.

Physiology is an integrative study of the control of normal body function. The specialised organ systems to be studied include the nervous, cardiovascular, muscular, respiratory, kidney and digestive systems. During this subject students will learn that physiology is an experimental science with many key concepts arising from qualitative and quantitative observation and analysis of living organisms. The lectures will incorporate active interaction between students and lecturers using live polling software to answer questions during lectures.

AIMS

This subject introduces the fundamental concepts of computing programming, and how to solve simple problems using high-level procedural language, with a specific emphasis on data manipulation, transformation, and visualisation of data.

INDICATIVE CONTENT

Fundamental programming constructs; fundamental data structures; abstraction; basic program structures; algorithmic problem solving; use of modules.

The subject assumes no prior knowledge of computer programming.

The subject introduces students to the molecular and cellular aspects of biological systems with particular emphasis on human biology. The course is arranged for students to generate an understanding of the molecular aspects of biology at the biomolecular, sub-cellular and cellular level. The genetic inheritance of traits is considered at the level of the individual and populations. This multi-disciplinary subject is co-taught by staff in the departments of Biochemistry and Molecular Biology and Genetics. There is particular emphasis on integration of these disciplines with students receiving both theoretical and practical knowledge of fundamental and frontier research and development in these areas. Students in the course will be extended through their participation in problem classes. They will write a major essay integrating the learnings with contemporary literature in the fields of genetics, molecular and cellular biology. Students will be mentored in this task by the course coordinator.

Randomness is inherent in biological data and the analysis of data arising in both Bioinformatics and Biostatistics requires knowledge of sophisticated probability models and statistical techniques. This subject develops the underlying probability theory that is necessary to understand these models and techniques. Computer packages are used for numerical and theoretical calculations but no programming skills are required. Elements of Probability will be co-taught with MAST20006 Probability for Statistics.

Bioinformatics is a key research tool in modern agriculture, medicine, and the life sciences in general. It forms a bridge between complex experimental and clinical data and the elucidation of biological knowledge. This subject presents bioinformatics in the context of its role in science, using examples from a variety of fields to illustrate the history, current status, and future directions of bioinformatics research and practice.

The analysis of data arising in Bioinformatics and Biostatistics requires the use of sophisticated statistical techniques and computing packages.  This subject introduces the basic elements of statistical modelling, computation and data analysis. Students will develop the ability to fit statistical models to data, estimate parameters of interest and test hypotheses.  Both classical and Bayesian approaches will be covered.  The importance of the underlying mathematical theory of statistics and the use of modern statistical software will be emphasised.

Concepts covered include: descriptive statistics, random sample, statistical inference, point estimation, interval estimation, properties of estimators, maximum likelihood, confidence intervals, hypothesis testing, Bayesian inference.  Applications covered include: exploratory data analysis, inference for samples from univariate distributions, simple linear regression, correlation, goodness-of-fit tests, analysis of variance.

The lectures in this subject are co-taught with MAST20005 Statistics; the practice classes are separate. 

Physiology is an integrative study of the control of normal body function. The specialised organ systems to be studied include the nervous, cardiovascular, muscular, respiratory, kidney and digestive systems. During this subject students will learn that physiology is an experimental science with many key concepts arising from qualitative and quantitative observation and analysis of living organisms. The lectures will incorporate active interaction between students and lecturers using live polling software to answer questions during lectures.

Bioinformatics involves the analysis of biological data and randomness is inherent in both the biological processes themselves and the sampling mechanisms by which they are observed. This subject first introduces stochastic processes and their applications in Bioinformatics, including evolutionary models. It then considers the application of classical statistical methods including estimation, hypothesis testing, model selection, multiple comparisons, and multivariate statistical techniques in Bioinformatics.

Bioinformatics is a diverse discipline that draws on a range of technical areas and is applied to a range of biological problems. In this subject a series of case studies is used to illustrate the application of bioinformatics to biological,agricultural, and medical problems. These case studies will be directly based on current practical research and taught by the researchers.

AIMS

Technological advances in obtaining high throughput data have stimulated the development of new computational approaches to bioinformatics. This subject will cover core computational challenges in analysing bioinformatics data. We cover important algorithmic approaches and data structures used in solving these problems, and the challenges that arise as these problems increase in scale.

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

The subject covers key algorithms used in bioinformatics, with a focus on genomics. Indicative topics are: sequence alignment (dynamic algorithms and seed-and-extend), genome assembly, variant detection, phylogenetic construction, genomic intervals, complexity and correctness of algorithms, clustering and classification of genomics data, data reduction and visualisation. The subject assumes you have experience in programming and familiarity with the foundations of genomics.

As a scientist, it is not only important to be able to experiment, research and discover, it is also vital that you can communicate your research effectively in a variety of ways. Even the most brilliant research is wasted if no one knows it has been done or if your target audience is unable to understand it.

In this subject you will develop your written and oral communication skills to ensure that you communicate your science as effectively as possible. We will cover effective science writing and oral presentations across a number of formats: writing a thesis; preparing, submitting and publishing journal papers; searching for, evaluating and citing appropriate references; peer review, making the most of conferences; applying for grants and jobs; and using social media to publicise your research.

You will have multiple opportunities to practice, receive feedback and improve both your oral and written communication skills.

Please note: students must be undertaking their own research in order to enrol in this subject.

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.

This subject describes current technologies used to sequence genomes - the starting point for comparative analyses of genes and proteins. The field of informatics has evolved to analyse and interpret large amounts of data generated by the new biotechnologies. Advanced topics will include transcriptome technologies, genome evolution and sequence similarity analysis techniques to identify protein orthologues and paralogues. The subject will cover bioinformatic analysis of protein structure and motifs at the secondary and tertiary levels, and modelling studies aimed at drug design. This subject will explore the latest developments in bioinformatics and detail how systems biology is helping to model complex biological processes.

This subject involves the development and application of the tools of bioinformatics to address a significant research problem. The subject also provides students with skills and knowledge for understanding original research and enhanced written and oral communication skills.

The process of matching students with supervisors and research projects will occur in the first semester of enrolment of the Bioinformatics stream of the Master of Science. Apart from the help and guidance from their supervisor(s) each student also has a committee that regularly meets with them and provides additional help and expertise. This committee is responsible for assessment of the research project subject.

Students need to ensure they have completed a total of 50points Research Project by the end of their course.

This subject involves the development and application of the tools of bioinformatics to address a significant research problem. The subject also provides students with skills and knowledge for understanding original research and enhanced written and oral communication skills.

The process of matching students with supervisors and research projects will occur in the first semester of enrolment of the Bioinformatics stream of the Master of Science. Apart from the help and guidance from their supervisor(s) each student also has a committee that regularly meets with them and provides additional help and expertise. This committee is responsible for assessment of the research project subject.

Students need to ensure they have completed a total of 50points Research Project by the end of their course.

This subject involves the development and application of the tools of bioinformatics to address a significant research problem. The subject also provides students with skills and knowledge for understanding original research and enhanced written and oral communication skills.

The process of matching students with supervisors and research projects will occur in the first semester of enrolment of the Bioinformatics stream of the Master of Science. Apart from the help and guidance from their supervisor(s) each student also has a committee that regularly meets with them and provides additional help and expertise. This committee is responsible for assessment of the research project subject.

Students need to ensure they have completed a total of 50points Research Project by the end of their course.

This subject involves the development and application of the tools of bioinformatics to address a significant research problem. The subject also provides students with skills and knowledge for understanding original research and enhanced written and oral communication skills.

The process of matching students with supervisors and research projects will occur in the first semester of enrolment of the Bioinformatics stream of the Master of Science. Apart from the help and guidance from their supervisor(s) each student also has a committee that regularly meets with them and provides additional help and expertise. This committee is responsible for assessment of the research project subject.

Students need to ensure they have completed a total of 50points Research Project by the end of their course.

This subject involves the development and application of the tools of bioinformatics to address a significant research problem. The subject also provides students with skills and knowledge for understanding original research and enhanced written and oral communication skills.

The process of matching students with supervisors and research projects will occur in the first semester of enrolment of the Bioinformatics stream of the Master of Science. Apart from the help and guidance from their supervisor(s) each student also has a committee that regularly meets with them and provides additional help and expertise. This committee is responsible for assessment of the research project subject.

Students need to ensure they have completed a total of 50points Research Project by the end of their course.

This subject involves the development and application of the tools of bioinformatics to address a significant research problem. The subject also provides students with skills and knowledge for understanding original research and enhanced written and oral communication skills.

The process of matching students with supervisors and research projects will occur in the first semester of enrolment of the Bioinformatics stream of the Master of Science. Apart from the help and guidance from their supervisor(s) each student also has a committee that regularly meets with them and provides additional help and expertise. This committee is responsible for assessment of the research project subject.

Students need to ensure they have completed a total of 50points Research Project by the end of their course.

This subject involves the development and application of the tools of bioinformatics to address a significant research problem. The subject also provides students with skills and knowledge for understanding original research and enhanced written and oral communication skills.

The process of matching students with supervisors and research projects will occur in the first semester of enrolment of the Bioinformatics stream of the Master of Science. Apart from the help and guidance from their supervisor(s) each student also has a committee that regularly meets with them and provides additional help and expertise. This committee is responsible for assessment of the research project subject.

Students need to ensure they have completed a total of 50points Research Project by the end of their course.

This subject involves the development and application of the tools of bioinformatics to address a significant research problem. The subject also provides students with skills and knowledge for understanding original research and enhanced written and oral communication skills.

The process of matching students with supervisors and research projects will occur in the first semester of enrolment of the Bioinformatics stream of the Master of Science. Apart from the help and guidance from their supervisor(s) each student also has a committee that regularly meets with them and provides additional help and expertise. This committee is responsible for assessment of the research project subject.

Students need to ensure they have completed a total of 50points Research Project by the end of their course.

This subject involves the development and application of the tools of bioinformatics to address a significant research problem. The subject also provides students with skills and knowledge for understanding original research and enhanced written and oral communication skills.

The process of matching students with supervisors and research projects will occur in the first semester of enrolment of the Bioinformatics stream of the Master of Science. Apart from the help and guidance from their supervisor(s) each student also has a committee that regularly meets with them and provides additional help and expertise. This committee is responsible for assessment of the research project subject.

Students need to ensure they have completed a total of 50points Research Project by the end of their course.