Experiences in the development of a data management system for genomics

GMQL is a high-level query language for genomics, which operates on datasets described through GDM, a unifying data model for processed data formats. They are ingredients for the integration of processed genomic datasets, i.e. of signals produced by the genome after sequencing and long data extraction pipelines. While most of the processing load of today’s genomic platforms is due to data extraction pipelines, we anticipate soon a shift of attention towards processed datasets, as such data are being collected by large consortia and are becoming increasingly available. In our view, biology and personalized medicine will increasingly rely on data extraction and analysis methods for inferring new knowledge from existing heterogeneous repositories of processed datasets, typically augmented with the results of experimental data targeting individuals or small populations. While today’s big data are raw reads of the sequencing machines, tomorrow’s big data will also include billions or trillions of genomic regions, each featuring specific values depending on the processing conditions. Coherently, GMQL is a high-level, declarative language inspired by big data management, and its execution engines include classic cloud-based systems, from Pig to Flink to SciDB to Spark. In this paper, we discuss how the GMQL execution environment has been developed, by going through a major version change that marked a complete system redesign; we also discuss our experiences in comparatively evaluating the four platforms

Grid infrastructures for secure access to and use of bioinformatics data: experiences from the BRIDGES project

The BRIDGES project was funded by the UK Department of Trade and Industry (DTI) to address the needs of cardiovascular research scientists investigating the genetic causes of hypertension as part of the Wellcome Trust funded (£4.34M) cardiovascular functional genomics (CFG) project. Security was at the heart of the BRIDGES project and an advanced data and compute grid infrastructure incorporating latest grid authorisation technologies was developed and delivered to the scientists. We outline these grid infrastructures and describe the perceived security requirements at the project start including data classifications and how these evolved throughout the lifetime of the project. The uptake and adoption of the project results are also presented along with the challenges that must be overcome to support the secure exchange of life science data sets. We also present how we will use the BRIDGES experiences in future projects at the National e-Science Centre

Educating Future Nursing Scientists: Recommendations for Integrating Omics Content in PhD Programs

Preparing the next generation of nursing scientists to conduct high-impact, competitive, sustainable, innovative, and interdisciplinary programs of research requires that the curricula for PhD programs keep pace with emerging areas of knowledge and health care/biomedical science. A field of inquiry that holds great potential to influence our understanding of the underlying biology and mechanisms of health and disease is omics. For the purpose of this article, omics refers to genomics, transcriptomics, proteomics, epigenomics, exposomics, microbiomics, and metabolomics. Traditionally, most PhD programs in schools of nursing do not incorporate this content into their core curricula. As part of the Council for the Advancement of Nursing Science\u27s Idea Festival for Nursing Science Education, a work group charged with addressing omics preparation for the next generation of nursing scientists was convened. The purpose of this article is to describe key findings and recommendations from the work group that unanimously and enthusiastically support the incorporation of omics content into the curricula of PhD programs in nursing. The work group also calls to action faculty in schools of nursing to develop strategies to enable students needing immersion in omics science and methods to execute their research goals

Joint meeting of CORE Organic Pilot Research Projects and CORE Organic Funding Body Network (8 June 2009 - Rome, Italy)

The ERA-NET CORE Organic, conducted in 2004-2007, launched 8 transnational pilot projects, funded by the participants’ funding bodies. These projects are running for the period 2007-2010 and reached their mid-term at the end of 2008. The meeting was an opportunity to present the projects and their preliminary results at mid-term, including new research needs and how research may benefit the organic sector, and to have a discussion between project coordinators, CORE Organic Funding Body Network and other participants on experiences with transnational research arising from such projects

nsroot: Minimalist Process Isolation Tool Implemented With Linux Namespaces

Data analyses in the life sciences are moving from tools run on a personal computer to services run on large computing platforms. This creates a need to package tools and dependencies for easy installation, configuration and deployment on distributed platforms. In addition, for secure execution there is a need for process isolation on a shared platform. Existing virtual machine and container technologies are often more complex than traditional Unix utilities, like chroot, and often require root privileges in order to set up or use. This is especially challenging on HPC systems where users typically do not have root access. We therefore present nsroot, a lightweight Linux namespaces based process isolation tool. It allows restricting the runtime environment of data analysis tools that may not have been designed with security as a top priority, in order to reduce the risk and consequences of security breaches, without requiring any special privileges. The codebase of nsroot is small, and it provides a command line interface similar to chroot. It can be used on all Linux kernels that implement user namespaces. In addition, we propose combining nsroot with the AppImage format for secure execution of packaged applications. nsroot is open sourced and available at: https://github.com/uit-no/nsroo

BUSM Dean's report

Reports from the Office of the Dean, Boston University School of Medicine

Towards data grids for microarray expression profiles

The UK DTI funded Biomedical Research Informatics Delivered by Grid Enabled Services (BRIDGES) project developed a Grid infrastructure through which research into the genetic causes of hypertension could be supported by scientists within the large Wellcome Trust funded Cardiovascular Functional Genomics project. The BRIDGES project had a focus on developing a compute Grid and a data Grid infrastructure with security at its heart. Building on the work within BRIDGES, the BBSRC funded Grid enabled Microarray Expression Profile Search (GEMEPS) project plans to provide an enhanced data Grid infrastructure to support richer queries needed for the discovery and analysis of microarray data sets, also based upon a fine-grained security infrastructure. This paper outlines the experiences gained within BRIDGES and outlines the status of the GEMEPS project, the open challenges that remain and plans for the future

Developing an intervention to facilitate family communication about inherited genetic conditions, and training genetic counsellors in its delivery.

Many families experience difficulty in talking about an inherited genetic condition that affects one or more of them. There have now been a number of studies identifying the issues in detail, however few have developed interventions to assist families. The SPRinG collaborative have used the UK Medical Research Council's guidance on Developing and Evaluating Complex Interventions, to work with families and genetic counsellors (GCs) to co-design a psycho-educational intervention to facilitate family communication and promote better coping and adaptation to living with an inherited genetic condition for parents and their children (<18 years). The intervention is modelled on multi-family discussion groups (MFDGs) used in psychiatric settings. The MFDG was developed and tested over three phases. First focus groups with parents, young people, children and health professionals discussed whether MFDG was acceptable and proposed a suitable design. Using evidence and focus group data, the intervention and a training manual were developed and three GCs were trained in its delivery. Finally, a prototype MFDG was led by a family therapist and co-facilitated by the three GCs. Data analysis showed that families attending the focus groups and intervention thought MFDG highly beneficial, and the pilot sessions had a significant impact on their family' functioning. We also demonstrated that it is possible to train GCs to deliver the MFDG intervention. Further studies are now required to test the feasibility of undertaking a definitive randomised controlled trial to evaluate its effectiveness in improving family outcomes before implementing into genetic counselling practice.The National Institute of Health Research funded the study but any views expressed do not necessarily reflect those of the Authority. Funded by NIHR reference number: RP-DG-1211-10015