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Auckland University of Technology PhD student Zoe King is building an analysis pipeline for assessing soil ecosystem health of a kauri forest.
Kauri (Agathis australis) are ancient coniferous trees that are only located in the northern third of New Zealand (Ecroyd 1982). Since 1972, these trees have been under threat from a disease caused by the oomycete Phytophthora agathidicida. This pathogen infects the roots of kauri, starving them of nutrition and causing them to die. The sharp increase in kauri deaths across most kauri forests has called for a greater understanding of this pathogen, how it affects, and is affected by, its environment, and how it can be controlled. Interactions between soil microbial species are complex and not well understood. In particular, the interactions between harmful microorganisms and other plant-associated microorganisms in soil are in urgent need of greater research efforts to enhance the management of important diseases, such as kauri dieback. To date, no research has been undertaken into the soil microbial communities surrounding symptomatic and asymptomatic kauri in the Auckland region.
Auckland University of Technology PhD student Zoe King is hoping to begin to address this research gap by seeking patterns in soil microbial communities (including bacteria, fungi, and archaea) and their functional potential from soil samples surrounding 96 kauri trees across three different locations throughout Auckland. Extracted environmental DNA from these samples has been sequenced (amplicon (16S, 18S, ITS), and shotgun metagenome), to provide a snapshot of the composition and potential functionality of soil microbial communities surrounding symptomatic and asymptomatic kauri.
Variation in community composition and functionality will be quantified to test for differences around individual trees and at different locations. The variation will also be assessed to determine associations to abiotic factors. This research aims to create an analysis pipeline for assessing soil ecosystem health of a kauri forest and to provide insight into kauri soil microbial communities in the Auckland region.
Tāne Mahuta is the largest known living kauri tree, located in the Waipoua Forest in Northland, New Zealand. Photo by Szilas, Wikimedia Commons.
Processing the amplicon and metagenomic data requires a significant amount of computational power, which NeSI’s HPC platform offered
The processing workflows are complex, requiring stitching together multiple software packages and ensuring they are configured to run well on the HPC platform
There is not currently a publicly available reference genome for Kauri, workarounds for which add even more complexity into the processing pipeline Large amounts of data need to be transferred to and from the HPC platform
Dinindu Senanayake and Chris Scott, Research Software Engineers working at New Zealand eScience Infrastructure at the time, worked with Zoe to build the analysis pipelines for her soil sample data.
Rclone, a command-line program to manage files on cloud storage, was used to transfer data directly between the HPC Platform and Onedrive/Sharepoint
Analysis pipelines have been designed, configured and implemented. Zoe can now leverage the pipeline to the remaining data.
When embarking on this journey to process large amounts of sequence data I had generated, I quickly realised that this was a mammoth task that would require large computer resources and insight into how to create a suitable pipeline for analysis. The support and help I received from both Dini and Chris has been an invaluable experience for my PhD project. I was taught how to use GitHub as a means of version control and project management, how to transfer and validate large amounts of data to and from OneDrive and NeSI, how to create a pipeline for data analysis, and how to implement this on NeSI’s HPC platform. Dini and Chris have helped me not only process my data, but I have also learnt so much more about dealing with large datasets, as well as, increasing my BASH knowledge and learning how to write SLURM scripts. The knowledge and skills I have learnt from Dini and Chris will help me efficiently process large datasets and troubleshoot issues that may arise to complete the data processing required for my PhD project. Without the help received from Dini and Chris, processing this large dataset would have been infinitely more challenging.
When embarking on this journey to process large amounts of sequence data I had generated, I quickly realised that this was a mammoth task that would require large computer resources and insight into how to create a suitable pipeline for analysis.
The support and help I received from both Dini and Chris has been an invaluable experience for my PhD project. I was taught how to use GitHub as a means of version control and project management, how to transfer and validate large amounts of data to and from OneDrive and NeSI, how to create a pipeline for data analysis, and how to implement this on NeSI’s HPC platform.
Dini and Chris have helped me not only process my data, but I have also learnt so much more about dealing with large datasets, as well as, increasing my BASH knowledge and learning how to write SLURM scripts.
The knowledge and skills I have learnt from Dini and Chris will help me efficiently process large datasets and troubleshoot issues that may arise to complete the data processing required for my PhD project. Without the help received from Dini and Chris, processing this large dataset would have been infinitely more challenging.
Zoe King, School of Science, Auckland University of Technology
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