Analytical Standard Uncertainty Evaluation on R@CMon

Leave a Reply

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.”

Arvind Rajan
Monash University Scholar
Electrical and Computer Systems Engineering

MaxQuant Proteomic Searches on R@CMon

Leave a Reply

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!

VISIONET on R@CMon (Update)

Leave a Reply

Back in early 2014, the R@CMon team assisted SBI Australia to deploy the VISIONET (Visualizing Transcriptomic Profiles Integrated with Overlapping Transcription Factor Networks) visualisation web service on the Monash node of the NeCTAR Research Cloud. Since then, VISIONET has been further enhanced to support more complex transcription factor network topologies. To date, VISIONET has been published in two papers.

Nim, H.T., Boyd, S.E., and Rosenthal, N.A. (2014). Systems approaches in integrative cardiac biology: Illustrations from cardiac heterocellular signalling studies. Progress in Biophysics and Molecular Biology 117, 69-77.

Nim, H.T., Furtado, M.E., Costa, M.W., Rosenthal, N.A, Kitano, H., and Boyd, S.E.. (2015). VISIONET: intuitive visualisation of overlapping transcription factor networks, with applications in cardiogenic gene discovery. BMC Bioinformatics.

The R@CMon team will continue supporting SBI Australia with its plan to further develop the VISIONET web service this year.

Stock Price Impact Models Study on R@CMon Phase 2 (Update)

Leave a Reply

A mere six months ago Paul Lajbcygier and his research group used R@CMon Phase 2 “specialist kit” for processing and analysing higher frequency stock data, as part of their stock price impact models study. Since then, they’ve been running extraction queries continuously and recently published a paper highlighting their latest findings while acknowledging the NeCTAR Research Cloud infrastructure.

Lajbcygier, P., Sojka, J. (2015). The Viability of Alternative Indexation when including all Costs”, International Review of Financial Analysis

The group will continue to use the high-memory instance on R@CMon Phase 2 as they progress their research pipeline and the R@CMon team will continue to support them on their journey.

“I expect that over the coming months we will fully utilise the generous resources on the Monash node of the NeCTAR Research Cloud as we extend our research into this cutting edge and exciting data intensive topic.”

Associate Professor Paul Lajbcygier
Faculty of Business and Economics
Department of Accounting and Finance
Department of Banking and Finance
Monash University

Rail Network Catastrophe Analysis on R@CMon

Leave a Reply

Monash University, through the Institute of Railway Technology (IRT), has been working on a research project with Vale S.A., a Brazilian multinational metals and mining corporation and one of the largest logistical operators in Brazil, to continuously monitor and assess the health of the Carajás Railroad Passenger Train (EFC) mixed-use rail network in Northern Brazil. This project will identify locations that produce “significant dynamic responses” with the aim for proactive maintenance to prevent catastrophic rail failure. As a part of this project, IRT researchers have been involved in (a) the analysis of the collected data and (b) the establishment of a database with visualisation capabilities that allows for the interrogation of the analysed data.

GPU-powered DataMap analysis and visualisation on R@CMon.

Researchers use the DataMap analysis software for data interrogation and visualisation. DataMap is a Windows-based client-server tool that integrates data from various measurements and recording systems into a geographical map. Traditionally they have the software running on a commodity laptop with a dedicated GPU connecting to their database server. To scale to larger models, conduct more rigorous analysis and visualisation, as well as support remote collaboration, the system of tools needed to go beyond the laptop.

The R@CMon team supported IRT in deploying the software on the NeCTAR Research Cloud. The deployed instance runs on the Monash-licensed Windows flavours with GPU-passthrough to support DataMap’s DirectX requirements.

GPU-powered DataMap analysis and visualisation on R@CMon.

Through the Research Cloud IRT researchers and Vale S.A. counterparts are able to collaborate for modelling, analysis and results using remote access to the GPU-enabled virtual machines.

“The assistance of R@CMon in providing virtual machines that have GPU support, has been instrumental in facilitating global collaboration between staff located at Vale S.A. (Brazil) and Monash University (Australia).”

Dr. Paul Reichl

Senior Research Engineer and Data Scientist

Institute of Railway Technology