Study level

  • PhD
  • Master of Philosophy
  • Honours
  • Vacation research experience scheme

Faculty/School

Faculty of Science

School of Computer Science

Topic status

We're looking for students to study this topic.

Research centre

Centre for Data Science

Supervisors

Professor Chris Drovandi
Position
ARC Future Fellow
Division / Faculty
Faculty of Science
Associate Professor Jim Hogan
Position
Associate Professor
Division / Faculty
Faculty of Science

External supervisors

  • Prof Tim Read, Emory University (Atlanta)

Overview

Genomic sequencing has changed radically since the first public sequencing projects more than 25 years ago. The original human genome project cost more than two billion dollars; sequencing a human genome now costs as little as a thousand, and we may sequence whole viruses and bacteria as a matter of routine.

The challenge now lies in rapidly analysing these genomes as they appear, and understanding quickly whether there is anything interesting in the new  sequence to warrant further inquiry. This project will use Bayesian probability to construct a model which captures the structure and variations of a large collection of sequences. We will then process new sequences as they appear, and use our model to ask a simple question: How surprising is this genome?

This project is run in cooperation with the School of Mathematical of Sciences.

Research activities

The activities will as always vary depending on the level of the student, but there is scope for:

  • development of Bayesian sequence models
  • modelling and analysis of large sequence collections
  • Bayesian computation and development of approximations to aid rapid inference
  • extensions of models to incorporate new biological categories and applications.

Outcomes

We are looking to develop Bayesian models and software tools to enable rapid characterisation of genome novelty. The range of outcomes will depend significantly on the collection chosen and the background metadata we are able to work with. There is scope for involvement at all levels, from running experiments to sophisticated probabilistic modelling.

Skills and experience

Ideally we are looking for students with a strong background in Bayesian probability and good programming skills. We will expect you to be able to deal with large and complex datasets and to learn the biology needed to understand the domain, though we will help you with this. Students with a mathematical focus - especially those who have studied data science or a joint programme in mathematics and computer science - are well suited for this work, but we will welcome anyone who ticks some of these boxes or comes with some combination of mathematics, computer science and molecular biology.

It isn't necessary for you to be an extraordinary software developer but you need to be comfortable in python or C# or Java or F# or other modern languages. We will help you along, but we need you to be able to program.

If you are undertaking this project as an Honours or PhD student you may be eligible to apply for a scholarship.

Scholarships

You may be eligible to apply for a research scholarship.

Explore our research scholarships

Keywords

Contact

Contact the supervisor for more information.