DiseaseNetMiner - A novel tool for mining integrated biological networks of host and pathogen interaction

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Modern society is increasingly under threat from a plethora of microscopic fungal pathogens, which cause diseases in agricultural and horticultural crops, and in farmed animals. Many of these diseases cause a significant, detrimental impact on global and local food security. Furthermore, there is a worrying tendency for pathogens to become more aggressive towards their hosts, to cause more disease (increased virulence) and for the anti-fungal chemicals (called fungicides) that are often used to control fungal pathogen outbreaks to become less effective. In the recent past, (during the genomics era) scientists have developed technologies to sequence and assemble all the chromosomes of an organisms and predict the gene content (i.e. obtain their complete genome blueprints). Now, in the post-genomic era, next generation sequencing technologies have been developed and this has led to an explosion of more genomic data alongside a wealth of gene expression, protein expression, genetic and biological data, which are used by scientists to describe pathogen-host interaction phenotypes and disease outcomes. However, for many scientists with expertise in biology, biochemistry or genetics, this 'omics' data explosion is often seen as a burden, 'an infinite data soup of varying qualities' that only those with specialist computing-based interpretation skills (called bioinformatics), but often only minimal specialist biological knowledge, can penetrate. Therefore, new computer based tools urgently need to be developed to allow researchers to connect, explore and compare all the large and small-scale datasets available for pathogenic species that cause diseases. Once we fully understand how fungal pathogens cause disease, and how the host species try to defend themselves, will it be possible to manipulate these processes and mechanisms and go on to devise new ways to reduce disease levels and thereby improve global food security. In this project, we will develop a novel software tool, called DiseaseNetMiner, which will be user-friendly and can be used by many different types of scientists to explore integrated biological networks that can predict processes controlling the disease-causing abilities of fungal pathogens. DiseaseNetMiner will deliver understandable outputs from diverse and complex large-scale data inputs. DiseaseNetMiner will allow researchers without specialist bioinformatics skill to explore and compare this wealth of existing data from multiple species with their own latest cutting-edge results to permit rapid progress and new discoveries. This fundamental tool will effectively connect different data types and then return the results in an accessible, explorable, as well as scalable, format that can be easily manipulated, displayed and interrogated. DiseaseNetMiner will create a novel research environment from which new scientific insights and biological discoveries can be made.The UK research community has been at the very forefront of research and discovery in this field. The initiative we propose will be an exceptionally useful and cost-effective way of ensuring that the leadership shown by the UK research community will continue in the decade ahead. We expect this to yield outcomes with huge impact in our field and beyond, to meet the grand challenges of our age.