education

Below you will find descriptions of projects that bioinformatics students may do in fulfilment of project courses in their respective degrees. Please contact the person listed for further detail.

If you are a potential supervisor, click here.

Contact: Dr Sandie Degnan (s.degnan@uq.edu.au)

Aquaporins are pore-forming membrane channels that play critical roles in controlling the water contents of cells in all kingdoms of life. Their discovery in 1992 led to the 2003 Nobel Prize in Chemistry. Aquaporins are remarkable for permitting the transport of water, and sometimes other small solutes, across biological membranes, while at the same time being completely impermeable to charged species. Both their physiological functions and the physiological relevance of their selectivity to unconventional permeants are poorly understood, not least because of the paucity of data from non-model organisms living in diverse environments with respect to water availability or osmolality. This project will involve bioinformatic characterisation, and phylogenetic analysis, of aquaporins from marine animals representing phyletic lineages at the base of the animal tree, including sponges, ctenophores, cnidarians and placozoans. There will be opportunity to explore the possibility of acquisition by animals of aquaporins from bacteria, as has been demonstrated already in plants, and thus to help clarify fundamental issues about the evolutionary origin and diversification of animal aquaporins.

Available all year, for Master of Bioinformatics students; suitable for one semester, full-time.

Contact info: Nathan Palpant (n.palpant@uq.edu.au)

Project description: Analysing the transcriptional landscape of cardiovascular differentiation from stem cells at single cell resolution. Stem cells provide a mechanism for generating all cell types of the body. Understanding the mechanisms by which stem cells differentiation into these diverse cell types is central to utilizing them for understanding developmental biology and maximizing their translational potential for cell therapeutics and drug discovery. In this project, you will make use of and develop computational and statistical tools to study the transcriptional landscape of cardiovascular differentiation at single cell resolution. The project will include implementing protocols for quality control analysis, normalization, and clustering, analysing gene networks underlying cell subpopulations, identifying key genetic regulators of cell states, and helping develop novel strategies for studying and analysing single cell RNA-sequencing data to study biological questions.

Availability, requirements, etc: The project as available on an ongoing basis for honours or masters of Bioinformatics students, full time.

Contact: Dr Sally Mortlock (s.mortlock@imb.uq.edu.au), Professor Grant Montgomery (g.montgomery1@uq.edu.au).

The human endometrium plays a vital role in female fertility, embryo implantation, pregnancy and related diseases. Endometriosis is a disease occurring in 7-10% of women whereby tissue similar to that of the endometrium grows outside the uterus. Large scale genetic studies have identified 14 genomic regions associated with endometriosis some of which have been reported in other related diseases. This along with reports of comorbidity with other diseases has highlighted the potential of shared genetic risk loci and causal relationships between endometriosis and other diseases. Current studies integrate genetic, RNA-sequence and epigenetic data to understand how genetic variants control gene regulation and disease risk. The aim of this project is to integrate locally and externally accessible omic datasets to determine the genetic and epigenetic overlap between loci associated with endometrial gene regulation, endometriosis, other female reproductive disorders such as ovarian cancer, and related diseases including melanoma. Overlap in genomic risk loci will be tested using recently developed statistical and computational genomics tools (R, PLINK, Linux and GCTA). LD score regression will be used to estimate the genetic correlation between diseases and Generalised Summary data-based Mendelian randomisation will be used to test causal associations between these diseases, risk factors and endometriosis. Shared risk loci will be fine mapped to identify potential shared casual mechanisms. Ultimately by identifying shared genetic risk factors between diseases we hope to develop biomarkers to predict future disease susceptibility, aid in faster diagnosis and prioritise targets for functional follow-up studies and drug development. Students with a basic background in statistics and/or bioinformatics are encouraged to apply.

Available all year, for Master of Bioinformatics students; suitable for one or two semesters, full-time.

Contact: Dr Sally Mortlock (s.mortlock@imb.uq.edu.au), Professor Grant Montgomery (g.montgomery1@uq.edu.au).

Human endometrium is a highly specialised and complex tissue that plays vital role in female fertility, embryo implantation and pregnancy. Endometriosis is a disease occurring in 7-10% of women whereby tissue similar to that of the endometrium grows outside the uterus. Large scale genetic studies have identified 14 genomic regions associated with endometriosis. Recent investigations have also found associations between genetic variants and gene expression and methylation in the endometrium. The aim of this project is to integrate genetic, expression and methylation data with transcript level data from >300 endometrial samples to investigate genetic and epigenetic mechanisms regulating genes in endometriosis risk regions. Data from publically available databases such as ENCODE and Roadmap will also be downloaded to functionally annotate regions. Mapping out regulatory mechanisms in these risk regions will help prioritise target genes for functional analysis. Students with a basic background in statistics and/or bioinformatics are encouraged to apply.

Available all year, for Master of Bioinformatics students; suitable for one or two semesters, full-time.

Contact: Stuart Kellie (SCMB), s.kellie@uq.edu.au; Andy Barnes (SBS)

Contact: Rosalind Gilbert, ros.gilbert@daf.qld.gov.au, Department of Agriculture and Fisheries; Diane Ouwerkerk, diane.ouwerkerk@daf.qld.gov.au, Department of Agriculture and Fisheries.

Viruses infecting bacteria (bacteriophages or phages) are highly abundant in microbial ecosystems such as those found in the gut. This project will involve finding and annotating novel bacteriophages present as prophage elements within the genome sequences of gut-associated bacteria, for example, those infecting the genera often found in ruminant livestock (for example, Ruminococcus, Bacteroides and Butyrivibrio). Prophage elements will be annotated, characterised and compared to previously identified bacteriophages. The extent to which these novel prophages are found in gut microbial ecosystems will also be determined through comparison with metagenomic datasets. This computer-based project will use bioinformatics tools to build and interrogate sequence datasets, and combines interests in microbial genetics and viral ecology. Available all year, for Master of Bioinformatics or Honours students; suitable for 2 unit (one day per week, 1 semester), 4 unit (2 to 3 days per week, 1 semester) projects

Contact: Professor Naomi Wray (Naomi.wray@uq.edu.au), Dr. Sonia Shah (s.shah1@uq.edu.au)

We know that epigenetic processes such as DNA methylation, that are essential for the regulation of gene expression, are dynamic. They are influenced by genetics, lifestyle and environment. We can offer two Honours projects in the area of epigenetics of populations- one focused on alcohol consumption and the other on smoking. A number of genome-wide association studies (GWAS) have focused on identifying genetic variants that may influence susceptibility to alcohol or nicotine dependence. This project aims to perform a epigenome-wide association study (EWAS) to identify CpG sites in the genome whose methylation status is associated with alcohol consumption/smoking status. Using the EWAS-identified CpGs, we would like to develop a predictor for alcohol consumption/smoking from methylation data. In many research cohorts DNA and disease status is known, but environmental risk factors may not be available. Estimation of environmental risk factors from analysis of DNA methylation status will be a useful tool in research and forensics. This is a computer-based project suited to those with quantitative skills and some knowledge of statistics and bioinformatics.

Available all year, for Master of Bioinformatics students; suitable for one semester, full-time.

Contact: Dr Divya Mehta (d.mehta1@uq.edu.au), Dr Allan McRae(a.mcrae@uq.edu.au),

Background: By 2030, depression and anxiety are expected to be the number one health concern in the world. Epigenetics might help tie the complexities of how genes and environment interact in depression and anxiety disorders to bring about better understanding of the biological underpinnings of these disorders. DNA methylation is the addition of a methyl group by DNA methyltransferases at cytosine–phosphate-guanine dinucleotide (CpG) sites within the promoter regions of genes and is the most well studied epigenetic mechanism. Recent developments in microarrays allow measuring DNA methylation at 480,000 CpG sites in the genome in a fast, cost-effective and high throughput manner. CpG methylation in genes may be associated with the Hospital Anxiety and Depression (HAD) scale and could reflect the underlying genetic and/or non-genetic risk factors for anxiety and depression, having the potential to provide novel insight into the molecular pathways involved in the pathophysiology of disease. Aim of project: To perform an unbiased genome-wide analysis to identify changes in DNA methylation associated with anxiety and depression. Methods: The Lothian Birth Cohorts of 1921 and 1936 were combined of which 1366 participants have relevant methylation and phenotype data. Anxiety and Depression has been measured using the Hospital Anxiety and Depression Scale and methylation data was processed from peripheral blood samples using InfiniumHumanMethylation450 Array. All statistical analysis will be carried out in R, that is a free software providing language and environment for statistical computing and graphics. Expected outcomes: We expect to identify novel genes whose DNA methylation patterns are associated with depression or anxiety. The long term goal of all these studies is one day provide novel biomarkers to predict future susceptibility/onset of disease, improve diagnosis and aid in development of epigenetic based therapies. Requirements: This is a computer-based project suited for students with basic quantitative skills.

Available all year, for Master of Bioinformatics students; suitable for one semester, full-time.

Contact: Professor Naomi Wray (naomi.wray@uq.edu.au), Dr Divya Mehta (d.mehta1@uq.edu.au)

Telomeres are DNA-protein complexes located at chromosome ends. Telomere shortening is a marker of oxidative stress and ageing. Telomeres play a key role in diseases since they prevent chromosome fusion and maintain genome stability. Psychiatric disorders including Schizophrenia, Bipolar disorder and Major depressive disorders are associated with telomere shortening. This study will test the genetic link between telomere length and Schizophrenia. Polygenetic risk score is a measure depicting an individual’s genetic risk of disease. Polygenic risk scores have been increasingly used to understand the polygenic architecture of psychiatric diseases and investigate the overlap between psychiatric diseases and other phenotypes. This study will use polygenic scores from genetic variants associated with telomere length to test if these predict risk for Schizophrenia in an independent sample. We will use the most recently data from by the Psychiatric Genomics Consortium (over 60,000 people) and telomere length Genome-Wide Association Studies results. This study will make use of genetic data from genome-wide association studies and interrogate the genetic overlap between telomere length and schizophrenia using statistical pipelines in PLINK, R and Linux. Depending on initial results, we will extend these analyses to include other psychiatric disorders such as Bipolar disorder and Major depressive disorders. These results will provide evidence that the molecular underpinnings of telomere length also contribute towards the genetic susceptibility of psychiatric disorders. This is a computer-based project suited for students with basic quantitative skills.

Available all year, for Master of Bioinformatics students; suitable for one semester, full-time.

Contact: Professor Peter Visscher (Peter.Visscher@uq.edu.au), Dr Jake Gratten (j.gratten1@uq.edu.au), Dr Joseph Powell (joseph.powell@uq.edu.au)

Recent genome-wide association studies (GWAS) have identified >100 genomic regions containing common genetic variants underlying risk of schizophrenia, but the biological and molecular mechanisms of these associations are not yet understood. The prevailing theory is that many will be explained by mutations influencing gene regulation, so-called expression quantitative trait loci or eQTLs. eQTLs can be classified as cis or trans, according to whether they influence the RNA transcription levels of nearby or distant genes (respectively). Most attention to date has focused on cis-eQTLs but trans-eQTLs are likely to be extremely important.The starting hypothesis for this proposal is that trans-eQTLs explain a proportion of identified schizophrenia associations. A genome-wide scan will be performed using RNA transcription data from postmortem human brain to identify genes whose regulation is controlled in trans by eQTLs in schizophrenia associated genome regions. Common genetic variants (termed single nucleotide polymorphisms or SNPs) in the vicinity of these genes will then be assessed for enrichment of association signal in Psychiatric Genomics Consortium (PGC) GWAS datasets. Additional analyses are possible and we encourage the student to develop and explore their own ideas in consultation with the supervisors. This is a computer-based project suited to those with quantitative skills and some knowledge of statistics and bioinformatics and a desire to improve programming skills.

Available all year, for Master of Bioinformatics students; suitable for one semester, full-time.

Contact: Dr Marie Mangelsdorf, (m.mangelsdorf@uq.edu.au)

Motor neuron disease (MND) is a late onset neurodegenerative disease in which the motor neurons that control muscle movement die, leading to paralysis and death usually within 3 years of diagnosis. There is no treatment. MND may be both familial or sporadic. More than 20 genes for MND have been identified largely through analysis of familial cases and for most genes, sporadic cases have also been shown to harbour mutations in the same genes. Currently mutations in these genes account for ~60% of familial cases, and 10% of sporadic cases. We are undertaking multi-faceted genomics approach of sporadic cases in order to uncover the genetic causes of sporadic MND. One aspect of this study is whole exome sequencing (WES) of sporadic cases. The Honours project will validate and investigate sequence variants in candidate genes identified by WES. Techniques used to determine pathogenicity of the variants will include polymerase chain reaction, Sanger sequencing, molecular cloning, tissue culture and microscopy.

Available all year, for Master of Bioinformatics students; suitable for one semester, full-time.

Contact: Professor Naomi Wray, (Naomi.Wray@uq.edu.au) In conjunction with Professor Ian Hickie, University of Sydney

Young people presenting at adolescent mental health facilities display prodromal symptoms for which diagnosis and hence optimum treatment strategies may be unclear. Some may be on trajectories for long-term mental health issues, while others are struggling with normal teenage angst and many not have long-term mental health problems. We have longitudinal data collected on a cohort of ~200 individuals attending a youth mental health clinic in Sydney. These subjects also provided a DNA sample and have been genome-wide genotyped. The student will make predictors of genetic risk for a range of psychiatric disorders (and also non-psychiatric disorders as a negative control). He/she will interrogate the longitudinal phenotypic data together with the genetic risk prediction. The ultimate goal is to determine if genetic risk prediction at time of first presentation at a youth mental health clinic will have any clinical utility. This is a computer-based project that requires numerical competency and combines interests in genetics, psychiatry and epidemiology.

Available all year, for Master of Bioinformatics students; suitable for one semester, full-time.

Contact: Professor Peter Visscher, (peter.visscher@uq.edu.au), Dr. Matthew Robinson (m.robinson11@uq.edu.au)

A central goal in modern biology is to understand how the integration of gene functions across a genome leads to the individual’s specific phenotype. A key facet to this effort is to develop models that mathematically link genetic to phenotypic variation, an aim that is central to studies of human medical genetics. However, in most genomics studies, analysis is largely confined to the nuclear genome, with much less attention paid to the organellar genome (mitochondrial DNA). This is in contrast to the central role that the organellar genome plays in controlling organismal metabolism and function, and increasing evidence from other non-human organisms that organellar genomic variation can modulate the effects of nuclear genomic variation.Genomic variation in human mitochondria has been linked to several severe diseases, and more recently quantitative studies of common human diseases have suggested that genetic variation in organellar genomes may modify the effects of nuclear loci. This study aims to directly estimate the amount of variation in gene expression attributable to organellar DNA and to interrogate the interaction of the nuclear and cytoplasmic genomes. This is a computer-based project suited to those with quantitative skills and some knowledge of statistics and/or bioinformatics Available all year, for Master of Bioinformatics students; suitable for one semester, full-time.

Contact: Professor Peter Visscher (peter.visscher@uq.edu.au), Dr. Allan McRae (a.mcrae@uq.edu.au)

X-inactivation inactivation is a process in which one of two copies of the X chromosomes in a female is inactivated early in development. Usually this inactivation occurs randomly, but one chromosome can be preferentially inactivated to mask X-linked disorders. In addition, some genes can escape inactivation and have the “inactivated” copy expressed. The extent of the escape from X-inactivation at a gene can vary among individuals, with some people showing complete inactivation at a gene while others show both copies of the gene expressing fully.This project will use gene-expression and DNA methylation microarray data to identify genes on the X-chromosome that vary across individuals in their extent of escape from X-inactivation. The identified gene will be tested to see if any genetic variants can be found to explain the variation between individuals. This is a computer-based project suited to those who have some knowledge of statistics and a willingness to learn basis data manipulation skills.

Available all year, for Master of Bioinformatics students; suitable for one semester, full-time.

Contact: Professor Naomi Wray, (Naomi.wray@uq.edu.au); In conjunction with Professor Grant Montgomery at BQIMR (Grant.Montgomery@qimrberghofer.edu.au)

Gene expression studies are providing important insight into variation between individuals and the causes and consequences of disease. Currently gene expression studies require biological samples such as blood to be fixed using RNA stabilization products so that the gene expression measured represents the gene expression at the time of collection. At the Berghofer Queensland Institute of Medical Research we have collected large cohorts of twins and their family members. The participants have completed a broad range of interviews, questionnaires and other tests. In more recent years RNA has been collected using PAXgene tubes. All samples have blood collected in EDTA tubes and blood smeared onto special filter paper. Recent publications have shown that RNA can be measured from dried blood spots. Measurement of RNA from historical samples would be very valuable for the BQIMR cohorts. It would also save grant funds if collection using expensive PAXgene tubes were no longer needed.In this project the student would work up protocols for measurement of RNA from dried blood spots and would compare gene expression levels in the sample individuals using RNA from dried blood spots and PAXgene tubes. If the project progresses well then extensions to consider gene expression from different paper types and the impact of the age of the sample can be examined.

This project is a lab-based project followed by analysis of gene expression data. It is suited to those with interests in molecular genetics. It is a project shared across BQIMR (lab work) and QBI (analysis). Available all year, for Master of Bioinformatics students; suitable for one semester, full-time.

Contact: Professor Naomi Wray (Naomi.wray@uq.edu.au), Dr Qiongyi Zhao (q.zhao@uq.edu.au)

Recent advances in sequencing technologies provide unprecedented opportunities to characterize individual genomic landscapes and identify mutations relevant for diagnosis and therapy. Specifically, whole-exome sequencing (WES) using next-generation sequencing (NGS) technologies is gaining popularity in the human genetics community due to the moderate costs, manageable data amounts and straightforward interpretation of analysis results.Although WES is getting more and more popular, there is still no consensus on what is an optimal sequencing depth per individual sample for the performance of calling variants in terms of accuracy, sensitivity and specificity. In this project, we are going to investigate the effect of sequencing depths on variants discovery using GATK pipeline based on our in-house WES data, calling variants at both individual level and joint genotyping level. If time allowed, this study can be extended to test different variants callers in GATK or even include other publicly available variants calling methods (eg. Illumina ELAND and CASAVA software tools). Results of this study will be useful to guide the study design for future WES projects. The student will learn some basic skill of programming (ideally PERL) and gain experience in using some popular NGS analysis tools. This is a computer-based project suited to a student with basic quantitative skills.

Available all year, for Master of Bioinformatics students; suitable for one semester, full-time.

Contact: Dr Marie Mangelsdorf (m.mangelsdorf@uq.edu.au), Dr Divya Mehta (d.mehta1@uq.edu.au)

Pregnancy, a psychosocial stressor, interestingly has a long-term beneficial effect for development of multiple sclerosis. The precise mechanisms involved in this protective effect are not clear. This project aims to evaluate longitudinal gene expression profiles in women during pregnancy. Whole-genome gene expression data will be analyzed to identify genes significantly differentially expressed across pregnancy. Top candidates will be selected for further analysis in a rat model of multiple sclerosis. RNA will be extracted from brain and spinal cord from rats and gene expression tested by qPCR. This project encompasses statistics, bioinformatics and wet-laboratory procedures, providing a holistic research experience. Do a subset of genes confer a protective effect against multiple sclerosis via altered gene expression profiles in pregnancy? Available all year, for Master of Bioinformatics students; suitable for one semester, full-time.

Contact: Jess Morgan, Jessica.morgan@uq.edu.au, The University of Queensland. I am looking for a motivated student to help me investigate applying bioinformatics techniques to characterise species of gnathiid isopods that parasitise fish. Specifically, we aim to capture the complete mtDNA of the parasites (plus any other markers would be a downstream bonus). I will need a student capable of constructing contigs from a next gen library (either ion torrent or illumina as determined by their lit review) then mining the contigs to scaffold against published crustacean mt genomes via Blast searches. This is a bioinformatics project for semester 2, 2014 and depending on funding may have the option to extend into semester 1, 2015. If you are interested in applying please forward a short resume and your academic record to Jess Morgan (Jessica.morgan@uq.edu.au). Available for Master of Bioinformatics students; suitable for one or two semesters, full-time

Contact: Joseph Rothnagel, j.rothnagel@uq.edu.au, School of Chemistry and Molecular Biosciences, The University of Queensland.

Short peptides (sPEPs) that are encoded by short Open Reading Frames (sORFs) are surprisingly common in eukaryote genomes. Recent bioinformatic and ribosomal footprinting studies have identified several thousand sORFs with coding potential and several sPEPs have been identified by mass spectrometry. However, their role in cellular functions remains to be determined. In this project you will identify and characterize sPEPs using bioinformatic tools to interrogate large data sets from genomic, transcriptomic and proteomic experiments. You will help to determine the contribution of sPEPs to the human proteome, and provide insights into their roles. Available all year, for Master of Bioinformatics students; suitable for one semester, full-time.

Contact: Elizabeth Gillam (e.gillam@uq.edu.au), Yosephine Gumulya (y.gumulya@uq.edu.au), or Mikael Boden (m.boden@uq.edu.au). We are looking for a student interested in sequence analysis for purposes of reconstructing ancestral proteins, potentially to have existed many millions years ago. This work can be focused on the application of current methodologies to generate candidate ancestral sequences, and/or the development of computational methodologies to support the process of generating candidate ancestors or ancestral components. The former angle is short term (a #2 course or part of a longer project course) and is suited to candidates with an interest in sequence analysis for understanding evolution of protein function and protein engineering. The latter angle is longer term (semester long) and suits candidates with strong computational skills.

Available all year, for Master of Bioinformatics students; suitable for one semester (#2 – #8).

Contact: Richard J. Lewis (r.lewis@uq.edu.au) The one-year project would analyse the transcriptome from the venom duct of a cone snail species using advanced bioinformatic tools and prepare table and figures to visualise the data. If the study goes well the work by the student should end up as part of a publication. The following articles provide background to the project.

Dutertre S, Jin A-H, Vetter I, Hamilton B, Sunagar K, Lavergne V, Dutertre V, Fry BG, Antunes A, Venter DJ, Alewood PF, Lewis RJ (2014) Evolution of separate predation- and defence-evoked venoms in carnivorous cone snails. Nature Communications 5:3521.

Dutertre S, Jin AH, Kaas Q, Jones A, Alewood PF, Lewis RJ (2013) Deep venomics reveals the mechanism for expanded peptide diversity in cone snail venom. Mol Cell Proteomics 12:312-329.

Contact: Mikael Bodén (m.boden@uq.edu.au)

Biological data at greater scale give statistical power to distinguish meaningful signals from mere noise or artefacts, i.e. to identify "drivers" and "determinants" of function and structure. To enable the integration of uncertain and incomplete data, and of biological expertise, my group is developing probabilistic modelling tools and machine learning approaches to go hand-in-hand. These can provide interpretations of "whole system" data, aimed at understanding of basis of disease and other scientifically relevant phenotypes. Projects are available for students with interests of developing algorithms or applications of them to understand regulatory events, involving the use of genome-wide data, including ChIP-seq, ChIA-PET, chromatin state assays, etc.

Availability all year, for bioinformatics students with analytical skills, Honours or Masters.

Contact: Dr Stefano Meliga (s.meliga@uq.edu.au)

The Nanopatch is a high density microprojection array for epidermal and dermal delivery of vaccines. Application to mouse skin has resulted into immunogenicity comparable with intramuscular injection using less than 1/100 of the dose. However, the mechanisms underlying this low-dose potent response have not yet been fully understood. Experimental evidence suggests that enhanced immunogenicity is triggered by precise targeting of vaccine to skin antigen-presenting cells in conjunction with generation of controlled levels of cell damage. We offer student projects aiming at the numerical investigation of Nanopatch-mediated vaccine targeting, generation of inflammation and triggering of signalling pathways leading to immune response. The successful candidate will be developing and applying mathematical / statistical models to verify the mode-of-action hypotheses, and have the chance to drive the design of the next-generation delivery device. This project is computer-centred and will be taking place at AIBN. Requirements: Applied mathematical and statistical skills, programming ability (e.g. MATLAB, R), interest in working in a highly multidisciplinary team is a plus, knowledge of immunology is not mandatory.

Available all year.

Contact: Dr Cheong Xin Chan (c.chan1@uq.edu.au)

Symbiodinium are a specialised group of dinoflagellate algae that grow symbiotically with diverse coral reef animals including corals and sponges. A modest, episodic increase in ocean water temperature can break down the coral-dinoflagellate symbiotic association (thus cause coral bleaching); unless this symbiosis is soon re-established, corals are at risk for starvation, disease and death. Our group is interested in the genome evolution of Symbiodinium and their closely related polar species Polarella, specifically related to their evolutionary transition from free-living to symbiotic lifestyles, and its functional implications for the coral host and the health of coral reefs in light of global climate change. This project aims to discover genes and functions in Symbiodinium and Polarella that are specific and/or relevant to environmental adaptation using a comparative genomic approach, using newly sequenced and existing data (Illumina, PacBio and Nanopore).

This project is strictly computational based. The researcher will acquire skills in genomics and bioinformatics of non-model systems, specifically in the analysis of high-throughput sequencing data, comparative analysis of large-genome-scale data, and functional annotation of genome sequences. The researcher will work as part of a team, and is expected to produce a report or oral presentation at the end of their project. Research outcomes may be included in a scholarly publication.

This project is available on an on-going basis, and is suitable for Masters students or advanced undergraduate students (year 3+) with a strong background in life sciences (i.e. biology and related subject areas), mathematics, and/or computer sciences. A background in genomics and/or bioinformatics is desirable but not essential. This project will require scripting (e.g. Python), high-performance computing in the UNIX environment, and/or R.