Professor Jenkins’ research focuses on pancreatic cancer, an inflammation-associated cancer and the fourth most common cause of cancer death worldwide, with an extremely low 5% five-year survival rate. Typically studies look at gene expression patterns between normal pancreas and cancerous pancreas in order to identify unique signatures, which can be indicative of sensitivity or resistance to specific chemotherapeutic treatments.
“Using next generation gene sequencing, involving big instruments, big data and big computing – allows near-term disruptive change in the clinical treatment of pancreatic cancer.” Prof. Jenkins, Monash Health..
To date, gene expression studies have largely focused on samples taken from open surgical biopsy; a procedure known to be very invasive and only possible in 20% of pancreatic cancers. Prof Jenkins’ group, in collaboration with Dr Daniel Croagh from the Department of Upper Gastrointestinal and Hepatobiliary Surgery at Monash Medical Centre, recently trialled an alternative less invasive process available to nearly all pancreatic cancer patients known as endoscopic ultrasound-guided fine-needle aspirate (EUS-FNA) which uses a thin, hollow needle to collect the samples of cells from which genetic material can be extracted and analysed. The challenge then becomes to ensure gene sequencing from EUS-FNA samples is comparable to open surgical biopsy such that established analysis and treatment can be used.
Twenty-four EUS-FNA-derived genetic samples from normal and cancerous pancreas were sequenced at the MHTP Medical Genomics Facility producing a total amount of 40Gb of raw data. Those data were securely transferred onto R@CMon by the Monash Bioinformatics Platform for processing, statistical analysis and computational exploration using state-of-the-art Bioinformatics methods.
Results thus far from this study show that data from EUS-FNA-derived samples were of high quality and also allowed the identification of gene expression signatures between normal and cancerous pancreas. Professor Jenkins’ group is now confident that EUS-FNA-derived material not only has the potential to capture nearly all of pancreatic cancer patients (compared to ~20% by surgery), but to also improve patient management and their treatment in the clinic.
“The current clinical genomics research space requires specialized high performance computational and storage infrastructure to support the processing and long term storage of those so-called “big data”. Thus R@CMon plays a major role in the discovery and development of new therapies and the improvement of Human health care in general.” Roxane Legaie, Senior Bioinformatician, Monash Bioinformatics Platform
The Ramialison Group at the Australian Regenerative Medicine Institute (ARMI) located in the biomedical research precinct of Monash University, Clayton specialises in systems biology both on the bench and through computational analysis. Their work is driven by the in vivo and in silico dissection of regulatory mechanisms involved in heart development, where deregulation of such mechanisms cause congenital heart disease, which results in 1 out of every 100 babies to be born with heart defects in Australia.
Heatmap generated from transcriptomic data from heart samples (Nathalia Tan)
Their research focuses on identifying DNA elements that play a crucial role in the development of the heart and, that could be impaired in disease. To identify these sequences, several genome-wide interrogation technologies (genomics and transcriptomics) are employed on different model organisms such as mouse or zebrafish. Downstream analysis of the data generated from these experiments involves high performance computing and requires large storage, which can be up to hundreds of gigabytes in size for a single project.
To optimise their investigation into heart development, the R@CMon team has deployed a dedicated Decoding Heart Development and Disease (DHDD) server on the Monash node of the NeCTAR Research Cloud infrastructure, which has now been running for over a year. This has not only provided the group with faster processing speeds in comparison to running jobs on a local desktop, but also an appropriate file storage infrastructure with persistent storage for files that are regularly accessed during analysis. Through VicNode, the group has been given vault storage for archiving completed results for their various research projects. With the assistance R@CMon, the group has been able to easily add users to the server as it continues to grow with new members and local collaborators.
Web interface for the Trawler web service.
In addition to the DHDD server, the R@CMon team also assisted the Ramialison Group in deploying a dedicated cloud server that has been used to develop the Trawler motif discovery tool web service. The implementation of this tool allows the group to quickly and easily analyse next-generation sequencing data and identify overrepresented motifs, which has led to a manuscript that is currently in preparation. The Ramialison Group envisage future developments of similar simple and easy to use bioinformatics analysis tools through R@CMon.
The Epigenetics and Chromatin (EpiC) Lab at Monash University is working on understanding how mutations in certain chromatin factors promote the formation of brain tumours. This project involves the generation and analysis of high-throughput sequencing data of chromatin modifications and remodellers in normal and mutated cells. The sequencing is carried out at the MHTP Medical Genomics Facility and the resulting datasets are then imported into the analysis workflow running on the Monash node (R@CMon) of the NeCTAR Research Cloud. The sequencing reads are first aligned to the repetitive fraction of the genome using a script developed by Day et al. (Genome Biology 2010) to determine enrichment at repeats. Sequencing reads are then aligned to the genome using Bowtie. The resulting files are filtered for quality, poor matches and PCR duplicates using customised Perl scripts. The filtered files are then imported into SeqMonk for further analysis.
Overlap analysis using SeqMonk
This allows for rapid visualisation of individual aligned reads across the entire genome. The inbuilt MACs peak caller is used for first pass peak calling. A selection of peaks is then validated in the lab by ChIP-qPCR experiments and peak-calling parameters can be adjusted based on these results. Overlap analysis with regions of interest can be performed in SeqMonk. Aligned sequence files are converted to BigWig format using customised Perl scripts and uploaded onto the NeCTAR Object Storage (Swift), which can then be loaded seamlessly on the UCSC Genome Browser for visualisation and further investigation. Once the sequence files are uploaded to the object storage, it can then be easily compared against public ENCODE datasets and UCSC genomic annotations to identify any potentially interesting correlations.
Aligned sequence visualisation using the UCSC Genome Browser.
The R@CMon team and the Monash Bioinformatics Platform supported the EpiC Lab by deploying a dedicated analysis instance on the NeCTAR Research Cloud based on the training environment first developed for the BPA-CSIRO Bioinformatics Training Platform. The open access and reusability of the training platform means it can be easily readapted to various analysis workflows. The R@CMon team and the Monash Bioinformatics Platform will continue to engage with the EpiC Lab as they grow and scale their analysis workflow on the NeCTAR Research Cloud.
Arvind Rajan is a scholar from the School of Engineering at the Monash University Sunway Malaysia campus. Arvind’s project, “Analytical Uncertainty Evaluation of Multivariate Polynomial”, supported by Monash University Malaysia (HDR scholarship) and the Malaysia Fundamental Research Grant Scheme, extends analytical method of “Guide to the Expression of Uncertainty in Measurement (GUM)” by the development of a systematic framework – the Analytical Standard Uncertainty Evaluation (ASUE) for the analytical standard measurement uncertainty evaluation of non-linear systems. The framework is the first step towards the simplification and standardisation of the GUM analytical method for non-linear systems.
The ASUE Toolbox
The R@CMon team supported the ASUE team at Sunway in deploying the framework on the NeCTAR Research Cloud. The project has been given access to the Monash-licensed Windows Server 2012 image and Windows-optimised instance flavour for configuration of the Internet Information Services (IIS) and ASP.NET stack. The ASUE team developed and deployed the framework on NeCTAR using remote desktop access (yes once again – even from overseas!). Mathematica, specifically webMathematica is then used on the NeCTAR instance to power the web-based dynamic ASUE Toolbox. The ASUE toolbox has been published in Measurement, a journal by International Measurement Confederation (IMEKO) and IEEE Access, an open access journal:
Y. C. Kuang, A. Rajan, M. P.-L. Ooi, and T. C. Ong, “Standard uncertainty evaluation of multivariate polynomial,” Measurement, vol. 58, pp. 483-494, Dec. 2014
A. Rajan, M. P. Ooi, Y. C. Kuang, and S. N. Demidenko, “Analytical Standard Uncertainty Evaluation Using Mellin Transform,” Access, IEEE, vol. 3, pp. 209-222, 2015
“The NeCTAR Research Cloud is a great service for researchers to host their own website and share the outcome of their research with engineers, practitioners and other professional community. Honestly, if it is not for the NeCTAR Research Cloud, I doubt our team could have made it this far. The support has been incredible so far. I will continue to publish my work using this service.”
Monash University Scholar
Electrical and Computer Systems Engineering
David Stroud, NHMRC Doherty Fellow and member of the Ryan Lab from the Department of Biochemistry and Molecular Biology, Monash University does proteomics research and uses the MaxQuant quantitative proteomics software as part of his analysis workflows. MaxQuant is designed for processing high-resolution Mass Spectrometry data and is freely available on the Microsoft Windows platform. Step one in the workflow is to do sample analyses using Liquid chromatography-mass spectrometry (LC-MS) on a Thermo Orbitrap Mass-spectrometer. This step produces raw files containing spectra that represent thousands of peptides. The resulting raw files are then loaded into MaxQuant to perform searches where spectra are compared against known list of peptides. A quantification step is then performed enabling peptide abundance to be compared across samples. Once this process is completed, the resulting tab delimited files are captured for downstream analysis.
Inspection of results using the MaxQuant software.
MaxQuant searches are both CPU and IO intensive tasks. A typical search takes 24 to 48 hours, and in some cases up to a week, depending on the size of the raw files being processed. David has been running his workflow on his own machine with 8 cores, 16 gigabytes of memory (RAM) and a solid state drive (SSD) for storage where a standard search takes 2 to 3 weeks to complete. Performing large MaxQuant searches on the local machine became a struggle, and David needed a bigger machine with a desktop environment to scale up his analysis workflow. The R@CMon team assisted David in deploying the MaxQuant software on the Monash node of the NeCTAR Research Cloud with an m1.xxlarge instance, spawned using the Monash-licensed Windows Server 2012 image. MaxQuant searches on the NeCTAR instance shows a 3-4x speed-up compared to the local machine, what takes several weeks on the local machine now just takes several days on the NeCTAR instance.
Maxquant search of Thermo RAW files.
The R@CMon team are currently working with David to explore further scaling options. The high-memory and PCIe SSD-enabled specialist kit on R@CMon Phase 2 can be exploited by MaxQuant for bursting IO intensive activities during searches. More on this coming soon!