New Big Data Era Pushes Training Need For Bioinformatics In Life Sciences
News Apr 28, 2015
In the advent of big data, the requirement for bioinformatics training as an integral part in life science research is becoming increasingly apparent. For the first time, an international consortium of bioinformatics educators and trainers across the globe have come together to transcend institutional and international boundaries to share bioinformatics training expertise, experience, and resources.
The Global Organisation for Bioinformatics Learning, Education & Training (GOBLET), which includes The Genome Analysis Centre (TGAC), is focusing on developing a training portal into a global, community-centred resource and supporting activities to aid the next generation of bioinformaticians. GOBLET aims to develop high-quality, comprehensive, branded training materials and courses, and is currently exploring accreditation mechanisms.
The recent advancement of high-throughput technologies and big data in the life sciences has left in its wake a high demand for bioinformatics training. Universities are finding it hard to keep up with this demand, where many students and early-stage researchers are feeling the impact across continents, not having the relevant skills to manage, analyse, and interpret data with confidence.
Formed in 2012, GOBLET is a not-for-profit foundation working on the development of an open and sustainable support structure for global bioinformatics communities. Through building and sustaining a collaborative community that shares resources, best practices and expertise, GOBLET aims to improve the standard of bioinformatics training provided worldwide.
Previous provision for bioinformatics training for life scientists struggled to meet the needs of this diverse group and keep pace with the rapidly evolving field of bioinformatics. The global standard of training also suffered owing to a lack of co-ordination between research sectors, leading to duplication and waste of resources.
However, as technological advances have continued to increase the quantity of data available for analysis, it has become ever more crucial that life scientists receive effective bioinformatics training to allow them to conduct successful research. By ensuring that life scientists understand how to best apply bioinformatics tools, this training facilitates both better use of data and easier communication between life and computational scientists, resulting in a higher standard of research.
Since its formation, GOBLET has quadrupled its membership and successfully built upon the earlier work of the Bioinformatics Training Network. Over recent years, GOBLET’s achievements have included: co-launching with the ISCB Education committee a Community of Special Interest (CoSI) for Computational Biology Education (CoBE) at the Intelligent Systems for Molecular Biology conference 2014; running various community-building events and workshops for the discussion of the design and content of bioinformatics training, and launching a training portal that acts as both a central repository of training materials and courses, and a catalogue of trainers and course organisers.
Vicky Schneider, a member of the GOBLET executive and Head of 361° Division at TGAC, said: “We are really proud to be part of GOBLET and the great work that has already taken place in preparing the current and next generation of bioinformaticians. It’s fantastic that the training portal is being developed as an accessible resource to raise the standard of data research within the global scientific community.”
Emily Angiolini, Scientific Training and Education Team Manager in 361o Division at TGAC, said “It is vital that we begin to bridge this gap in skills and the open sharing of relevant training materials coupled with the community engagement offered by GOBLET is leap in the right direction.”
The paper, titled: “GOBLET: The Global Organisation for Bioinformatics Learning, Education and Training” is published in PLOS Computational Biology.
An artificial intelligence (AI) approach based on deep learning convolutional neural network (CNN) could identify nuanced mammographic imaging features specific for recalled but benign (false-positive) mammograms and distinguish such mammograms from those identified as malignant or negative.