Steve Quenette

Next-generation sequencing: a challenge to meet the increasing demand for training workshops in Australia

The widespread adoption of high-throughput next-generation sequencing (NGS) technology among the Australian life science research community is highlighting an urgent need to up-skill biologists in tools required for handling and analysing their NGS data. There is currently a shortage of cutting-edge bioinformatics training courses in Australia as a consequence of a scarcity of skilled trainers with time and funding to develop and deliver training courses. To address this, a consortium of Australian research organizations, including Bioplatforms Australia, the Commonwealth Scientific and Industrial Research Organisation and the Australian Bioinformatics Network, have been collaborating with EMBL-EBI training team. A group of Australian bioinformaticians attended the train-the-trainer workshop to improve training skills in developing and delivering bioinformatics workshop curriculum. A 2-day NGS workshop was jointly developed to provide hands-on knowledge and understanding of typical NGS data analysis workflows. The road show–style workshop was successfully delivered at five geographically distant venues in Australia using the newly established Australian NeCTAR Research Cloud. We highlight the challenges we had to overcome at different stages from design to delivery, including the establishment of an Australian bioinformatics training network and the computing infrastructure and resource development. A virtual machine image, workshop materials and scripts for configuring a machine with workshop contents have all been made available under a Creative Commons Attribution 3.0 Unported License. This means participants continue to have convenient access to an environment they had become familiar and bioinformatics trainers are able to access and reuse these resources.

Explaining StGermain: An aspect oriented environment for building extensible computational mechanics modeling software

HPC scientific computational models are notoriously difficult to develop, debug, and maintain. The reasons for this are multifaceted - including difficulty of parallel programming, the lack of standard frameworks, and the lack of software engineering skills in scientific software developers. In this paper we discuss the drivers, design and deployment of StGermain, a software framework that significantly simplifies the development of a spectrum of HPC computational mechanics models. The key distinction between StGermain and conventional approaches to developing computational models is that StGermain decomposes parallel scientific applications into a hierarchical architecture, supporting applications collectively built by a diverse community of scientists, modelers, computational scientists, and software engineers.

Thermal insulation and geothermal targeting, with specific reference to coal-bearing basins

Coal-bearing basins have increased economic potential for enhanced geothermal systems owing to thermal insulation provided by the coal and associated organic-rich sediments. In such insulation-dominated prospects, heat refraction effects associated with buried insulators can produce negative surface heat-flow anomalies in the most prospective areas. The Latrobe Valley in Victoria, Australia, is an archetypal coal basin. Using numerical simulations incorporating the coal geometries, we show that the Latrobe Valley coals might elevate the temperature of the rocks at 4 km depth by some 30–35°C relative to a ‘base’ condition with no coal. This effectively boosts the average geothermal gradient by about 30%, with a corresponding improvement in the economic case for geothermal energy in the Latrobe Valley.

Underworld-GT Applied to Guangdong, a Tool to Explore the Geothermal Potential of the Crust

Geothermal energy potential is usually discussed in the context of conventional or engineered systems and at the scale of an individual reservoir. Whereas exploration for conventional reservoirs has been relatively easy, with expressions of resource found close to or even at the surface, exploration for non-conventional systems relies on temperature inherently increasing with depth and searching for favourable geological environments that maximise this increase. To utilitise the information we do have, we often assimilate available exploration data with models that capture the physics of the dominant underlying processes. Here, we discuss computational modelling approaches to exploration at a regional or crust scale, with application to geothermal reservoirs within basins or systems of basins. Target reservoirs have (at least) appropriate temperature, permeability and are at accessible depths. We discuss the software development approach that leads to effective use of the tool Underworld. We explore its role in the process of modelling, understanding computational error, importing and exporting geological knowledge as applied to the geological system underpinning the Guangdong Province, China.

Development of a cloud-based Bioinformatics Training Platform

Abstract The Bioinformatics Training Platform (BTP) has been developed to provide access to the computational infrastructure required to deliver sophisticated hands-on bioinformatics training courses. The BTP is a cloud-based solution that is in active use for delivering next-generation sequencing training to Australian researchers at geographically dispersed locations. The BTP was built to provide an easy, accessible, consistent and cost-effective approach to delivering workshops at host universities and organizations with a high demand for bioinformatics training but lacking the dedicated bioinformatics training suites required. To support broad uptake of the BTP, the platform has been made compatible with multiple cloud infrastructures. The BTP is an open-source and open-access resource. To date, 20 training workshops have been delivered to over 700 trainees at over 10 venues across Australia using the BTP.

Scientific Software Frameworks and Grid Computing

Scientific research applications, or codes, are notoriously difficult to develop, use, and maintain. This is often because scientific software is written from scratch in traditional programming languages such as C and Fortran, by scientists rather than expert programmers. By contrast, modern commercial applications software is generally written using toolkits and software frameworks that allow new applications to be rapidly assembled from existing component libraries. In recent years, scientific software frameworks have started to appear, both for grid-enabling existing applications and for developing applications from scratch. This paper compares and contrasts existing scientific frameworks and extrapolates existing trends.