Genome Graphs are a relatively recent but rapidly growing international bioinformatics process used to construct pangenomes (the entire gene set of all strains of a species) to better detect gene variation.
This Genomics Aotearoa project will implement and assess Genome Graph methods for improved variant detection across all organisms covering both large (human-scale) and small (microbial-scale) genomes to better understand biological functions in New Zealand populations.
The move towards pangenome-based analysis in New Zealand is a step-change in bioinformatics practice, allowing Genomics Aotearoa to establish this critical capability within our research community. Making genome graph methodology publicly accessible will contribute to understanding human health, as well as supporting primary industry, and research on the country’s environmental issues.
A range of genome graph-based methods will be benchmarked to explore optimal combinations of sequence data for constructing pangenomes. These methods will be applied across exemplar projects relevant to New Zealand, including detection of structural variants in sheep; detection of complex structural variants in admixed human populations; analysis of mobile genes in microbial genomes; strategies for pangenome construction for endemic bacterial populations; and sequencing strategies for pangenome construction for admixed populations. It’s hoped this will provide valuable insight into pangenomes for analysing complex structural variants in humans, including in Māori and Pacific populations.
The project will involve collaboration between the University of Otago, ESR, AgResearch, Massey University, University of Auckland, and Te Herenga Waka—Victoria University of Wellington, including two postdoctoral researchers.
Outcomes
This project will deliver:
- Implementation and assessment of Genome Graph methods for the analysis of complex structural variation and admixture
- Workflows and benchmarks for New Zealand-relevant applications
- Training workshops to build capability and increase the uptake of these methods within the New Zealand genomics research community.
Team
- Mik Black, University of Otago, co-leader
- Joep de Ligt, ESR, co-leader
- Rudiger Brauning, AgResearch
- Shannon Clarke, AgResearch
- Murray Cox, Massey University
- Kim Handley, University of Auckland
- Peter Ritchie, Te Herenga Waka—Victoria University of Wellington
- Phillip Wilcox, University of Otago