Biography and expertise
Biography
Before joining SCU as senior research fellow in bioinformatics/computational biology, I was a bioinformatics scientist at the International Rice Research Institute engaging in institute-specific and global projects (e.g. the 3,000 Rice Genomes Project, characterizing the genomic diversity and population structure of the largest, sequenced representative collection of a crop species to-date; the International Rice Informatics Consortium; Genomic Open-source Breeding Informatics Initiative; Excellence in Breeding: Galaxy; Rice Galaxy) and continue to actively engage in global initiatives for the development of data and interoperability standards for agricultural data (Crop Ontology, Rice Data Interoperability Working Group).
My work contributes to the following UN Sustainable Development Goals
Research
My bioinformatics research interests include adoption, creation, and implementation of integrative analyses methods for high-density omics-type and genetic data in crop plants leading to candidate gene discovery and marker development for breeding applications. My computing technology interests are in the development and implementation of re-usable analysis software workflows for high throughput datasets with high computational resource demand, and exploring/ implementing technologies for embedding into data and information systems (biological databases, web applications) to be FAIR (Findable, Accessible, interoperable, Reusable) compliant with focus on data systems infrastructure creation, ontologies/controlled vocabulary development, and Application Programming Interface high-level design. I am also currently exploring digital methodologies for phenotype measurements such as computer vision and machine learning, with the aim of establishing a low-cost phenotyping platform for Southern Cross Plant Science focus crops.
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Organisational affiliations
Past affiliations
Highlights - Output
Journal article
Rice Galaxy: an open resource for plant science
Published 2019
Gigascience, 8, 5
BACKGROUND: Rice molecular genetics, breeding, genetic diversity, and allied research (such as rice-pathogen interaction) have adopted sequencing technologies and high-density genotyping platforms for genome variation analysis and gene discovery. Germplasm collections representing rice diversity, improved varieties, and elite breeding materials are accessible through rice gene banks for use in research and breeding, with many having genome sequences and high-density genotype data available. Combining phenotypic and genotypic information on these accessions enables genome-wide association analysis, which is driving quantitative trait loci discovery and molecular marker development. Comparative sequence analyses across quantitative trait loci regions facilitate the discovery of novel alleles. Analyses involving DNA sequences and large genotyping matrices for thousands of samples, however, pose a challenge to non-computer savvy rice researchers.
FINDINGS: The Rice Galaxy resource has shared datasets that include high-density genotypes from the 3,000 Rice Genomes project and sequences with corresponding annotations from 9 published rice genomes. The Rice Galaxy web server and deployment installer includes tools for designing single-nucleotide polymorphism assays, analyzing genome-wide association studies, population diversity, rice-bacterial pathogen diagnostics, and a suite of published genomic prediction methods. A prototype Rice Galaxy compliant to Open Access, Open Data, and Findable, Accessible, Interoperable, and Reproducible principles is also presented.
CONCLUSIONS: Rice Galaxy is a freely available resource that empowers the plant research community to perform state-of-the-art analyses and utilize publicly available big datasets for both fundamental and applied science.
Education
RFLP mapping of genes conferring resistance to blast in the rice cultivars IAC165 and C039 across environments (Dr Rebecca J Nelson - MSc supervisor)
Identifying novel candidate defense genes against rice blast by disease-resistance transcriptome analysis (Dr. Hei Leung , PhD supervisor)