QUADrATiC: scalable gene expression connectivity mapping for repurposing FDA-approved therapeutics

Background: Gene expression connectivity mapping has proven to be a powerful and flexible tool for research. Its application has been shown in a broad range of research topics, most commonly as a means of identifying potential small molecule compounds, which may be further investigated as candidates for repurposing to treat diseases. The public release of voluminous data from the Library of Integrated Cellular Signatures (LINCS) programme further enhanced the utilities and potentials of gene expression connectivity mapping in biomedicine. Results: We describe QUADrATiC (http://go.qub.ac.uk/QUADrATiC), a user-friendly tool for the exploration of gene expression connectivity on the subset of the LINCS data set corresponding to FDA-approved small molecule compounds. It enables the identification of compounds for repurposing therapeutic potentials. The software is designed to cope with the increased volume of data over existing tools, by taking advantage of multicore computing architectures to provide a scalable solution, which may be installed and operated on a range of computers, from laptops to servers. This scalability is provided by the use of the modern concurrent programming paradigm provided by the Akka framework. The QUADrATiC Graphical User Interface (GUI) has been developed using advanced Javascript frameworks, providing novel visualization capabilities for further analysis of connections. There is also a web services interface, allowing integration with other programs or scripts.Conclusions: QUADrATiC has been shown to provide an improvement over existing connectivity map software, in terms of scope (based on the LINCS data set), applicability (using FDA-approved compounds), usability and speed. It offers potential to biological researchers to analyze transcriptional data and generate potential therapeutics for focussed study in the lab. QUADrATiC represents a step change in the process of investigating gene expression connectivity and provides more biologically-relevant results than previous alternative solutions.

Embracing an integromic approach to tissue biomarker research in cancer: Perspectives and lessons learned

Modern approaches to biomedical research and diagnostics targeted towards precision medicine are generating ‘big data’ across a range of high-throughput experimental and analytical platforms. Integrative analysis of this rich clinical, pathological, molecular and imaging data represents one of the greatest bottlenecks in biomarker discovery research in cancer and other diseases. Following on from the publication of our successful framework for multimodal data amalgamation and integrative analysis, Pathology Integromics in Cancer (PICan), this article will explore the essential elements of assembling an integromics framework from a more detailed perspective. PICan, built around a relational database storing curated multimodal data, is the research tool sitting at the heart of our interdisciplinary efforts to streamline biomarker discovery and validation. While recognizing that every institution has a unique set of priorities and challenges, we will use our experiences with PICan as a case study and starting point, rationalizing the design choices we made within the context of our local infrastructure and specific needs, but also highlighting alternative approaches that may better suit other programmes of research and discovery. Along the way, we stress that integromics is not just a set of tools, but rather a cohesive paradigm for how modern bioinformatics can be enhanced. Successful implementation of an integromics framework is a collaborative team effort that is built with an eye to the future and greatly accelerates the processes of biomarker discovery, validation and translation into clinical practice.

Leveraging Stratification in Twitter Sampling

With Tweet volumes reaching 500 million a day, sampling is inevitable for any application using Twitter data. Realizing this, data providers such as Twitter, Gnip and Boardreader license sampled data streams priced in accordance with the sample size. Big Data applications working with sampled data would be interested in working with a large enough sample that is representative of the universal dataset. Previous work focusing on the representativeness issue has considered ensuring the global occurrence rates of key terms, be reliably estimated from the sample. Present technology allows sample size estimation in accordance with probabilistic bounds on occurrence rates for the case of uniform random sampling. In this paper, we consider the problem of further improving sample size estimates by leveraging stratification in Twitter data. We analyze our estimates through an extensive study using simulations and real-world data, establishing the superiority of our method over uniform random sampling. Our work provides the technical know-how for data providers to expand their portfolio to include stratified sampled datasets, whereas applications are benefited by being able to monitor more topics/events at the same data and computing cost.

On the Virtualization of CUDA Based GPU Remoting on ARM and X86 Machines in the GVirtuS Framework

The astonishing development of diverse and different hardware platforms is twofold: on one side, the challenge for the exascale performance for big data processing and management; on the other side, the mobile and embedded devices for data collection and human machine interaction. This drove to a highly hierarchical evolution of programming models. GVirtuS is the general virtualization system developed in 2009 and firstly introduced in 2010 enabling a completely transparent layer among GPUs and VMs. This paper shows the latest achievements and developments of GVirtuS, now supporting CUDA 6.5, memory management and scheduling. Thanks to the new and improved remoting capabilities, GVirtus now enables GPU sharing among physical and virtual machines based on x86 and ARM CPUs on local workstations,computing clusters and distributed cloud appliances.

Deep Corals, Deep Learning: Moving the Deep Net Towards Real-Time Image Annotation

The mismatch between human capacity and the acquisition of Big Data such as Earth imagery undermines commitments to Convention on Biological Diversity (CBD) and Aichi targets. Artificial intelligence (AI) solutions to Big Data issues are urgently needed as these could prove to be faster, more accurate, and cheaper. Reducing costs of managing protected areas in remote deep waters and in the High Seas is of great importance, and this is a realm where autonomous technology will be transformative.

Is big best? Queen size, usurpation and nest closure in a primitively eusocial sweat bee (Lasioglossum malachurum).

For primitively eusocial insects in which a single foundress establishes a nest at the start of the colony cycle, the solitary provisioning phase before first worker emergence represents a risky period when other, nestless foundresses may attempt to usurp the nest. In the primitively eusocial sweat bee Lasioglossum malachurum (Hymenoptera, Halictidae), spring foundresses compete for nests which are dug into hard soil. Nest-searching foundresses (‘floaters’) frequently inspected nests during this solitary phase and thereby exerted a usurpation pressure on resident queens. Usurpation has been hypothesised to increase across the solitary provisioning phase and favour closure of nests at an aggregation, marking the termination of the solitary provisioning phase by foundresses, before worker emergence. However, our experimental and observational data suggest that usurpation pressure may remain constant or even decrease across the solitary provisioning phase and therefore cannot explain nest closure before first worker emergence. Levels of aggression during encounters between residents and floaters were surprisingly low (9% of encounters across 2 years), and the outcome of confrontations was in favour of residents (resident maintains residency in 94% of encounters across 2 years). Residents were significantly larger than floaters. However, the relationship between queen size and offspring production, though positive, was not statistically significant. Size therefore seems to confer a considerable advantage to a queen during the solitary provisioning phase in terms of nest residency, but its importance in terms of worker production appears marginal. Factors other than intraspecific usurpation need to be invoked to explain the break in provisioning activity of a foundress before first worker emergence.

'Big, strong and healthy'. Young children's identification of food and drink that contribute to healthy growth

Growing awareness of the importance of healthy diet in early childhood makes it important to chart the development of children's understanding of food and drink. This study aimed to document young children's evaluation of food and drink as healthy, and to explore relationships with socioeconomic status, family eating habits, and children's television viewing. Data were gathered from children aged 3-5. years (. n=. 172) in diverse socioeconomic settings in Ireland, and from their parents. Results demonstrated that children had very high levels of ability to identify healthy foods as important for growth and health, but considerably less ability to reject unhealthy items, although knowledge of these increased significantly between ages 3 and 5. Awareness of which foods were healthy, and which foods were not, was not related to family socioeconomic status, parent or child home eating habits, or children's television viewing. Results highlighted the importance of examining young children's response patterns, as many of the youngest showed a consistent 'yes bias'; however, after excluding these responses, the significant findings remained. Findings suggest it is important to teach children about less healthy foods in the preschool years. © 2013 Elsevier Ltd.