Loan Nguyen

Novel genomic assessments for finding true DNA variants contributing to the difference in beef cattle fertility

This project is for a domestic PhD candidate with a scholarship. Please visit the link for more details.

This project aims to uncover the genetic basis of fertility traits in cattle by using cutting-edge long-read sequencing technology (Oxford Nanopore Technologies, ONT). Traditional genome-wide association studies (GWAS) have identified key regions associated with fertility, but the causal genetic variants remain elusive, particularly structural variants (SVs)—large genomic alterations (>50 bp)—which are often missed by short-read sequencing.

For the first time, this study will analyze structural variants in fertility-related genes, leveraging ONT sequencing and advanced bioinformatics approaches. By integrating gene expression data, fertility phenotypes, and genotypic information from over 28,000 cattle, this project will improve genomic selection models and enable the development of low-cost, high-accuracy sequencing strategies.

Students involved in this project will gain hands-on experience in genomics, bioinformatics, and computational biology, including long-read sequencing, GWAS, structural variant analysis, and genomic prediction modeling. The research is highly applicable to agriculture, animal breeding, and biotechnology, offering opportunities to contribute to real-world improvements in cattle fertility and genetic selection.

Using Low-Coverage Sequencing to Predict Age and Methane Emissions in cattle

This project is ideal for an MPhil or Honours student. The student will extract and sequence up to 1,000 hair samples, generating low-coverage sequencing data to assess methylation patterns and predict the age of the animals. Additionally, since these animals have recorded methane emission phenotypes, the dataset may also be used to identify individuals with low methane emissions.

A Path to Affordable Genomic Selection for Sugarcane

This project is ideal for an MPhil or Honours student.

Sugarcane is a vital agro-based industrial crop cultivated in tropical and sub-tropical regions. It serves as raw material for the production of sugar, bioethanol and bioenergy. With the strong population growth of nearly 2.5% as June 30, 2023 (The Australian Bureau of Statistics), a rise in domestic sugar consumption is forecasted in Australia. Unfortunately, sugarcane genomic improvement has seen limited productivity gains compared to other major crops, with a plateauing rate of genetic gains due to long breeding cycles and the low heritability of key traits. Additionally, the high cost of genotyping in sugarcane breeding, primarily due to the reliance on single nucleotide polymorphisms (SNP) arrays, presents a significant barrier to the extensive application of genomic prediction. This project aims to reduce sugarcane genotyping costs in Australia, leveraging innovative sequencing-based allele genotyping for allele dosage and focusing on the most informative markers, making genomic selection more accessible and cost-effective for sugarcane breeding programs.

A comparison of Full-length transcript sequencings methods

Precise gene annotations are essential to understanding the complexity of a species transcriptome and to connect genomic sequence to gene function and ultimately phenotype. RNA sequencing has been widely applied to build a reference transcriptome using short-read sequencing, followed by the assembly or mapping reads to accessible reference genomes. However, short-read sequencing is facing the challenge of lengthy transcripts, repetitive regions, and transposable elements. Long read-sequencing technology, represented by Pacbio Sequencing and Oxford Nanopore sequencing, has overwhelmed this challenge by generating full-length transcripts. Another advantage of Oxford Nanopore sequencing is the potential to direct sequence RNA molecules to remove PCR bias and identify the base modifications.

In this study, students will perform direct cDNA sequencing and direct RNA sequencing from the liver samples from two cattle genomes using Pacbio isoform sequencing and Nanopore sequencing. The generated sequencing datasets will be compared between technologies and published sequencing data from the same tissues (RNAseq and CAGEseq). The ultimate aim of this project is to advance our understanding of emerging technologies and deeping our understanding of the cattle transcriptome.

This project will developed molecular techniques (DNA extraction, library preparation and sequencing) and bioinformatics skills. Students will learn how to work with RNA samples and manage extensive sequencing data sets.