DISEASENETMINER - A NOVEL TOOL FOR MINING INTEGRATED BIOLOGICAL NETWORKS OF HOST AND PATHOGEN INTERACTION

PROJECT SUMMARY

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. Only if 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 information 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.

DETAIL

This project focuses on the development of a software called DiseaseNetMiner. 

Project objectives are:

Use Ondex to integrate public multi-omics datasets, phenotype information, homology data and functional gene annotations for the key fungal model species Saccharomyces cerevisiae, Neurospora crassa and Aspergillus nidulans, and the pathogenic fungi Fusarium graminearum and Zymoseptoria tritici. This will deliver a semantically integrated knowledge network (graph data warehouse) of interlinked fungal species that we will provide access to through the KnetMiner web application. 

Create a novel combined pathogen-host network by integrating an existing wheat-arabidopsis-rice knowledge network and the new fungal network based on annotations to cross-species ontologies, curated pathogen-host interaction databases, text-mining using gene names and disease/phenotype ontologies, and a statistical correlation approach using data from dual, long time-series RNAseq experiments of infected plants. This plant disease knowledge network is estimated to have about 1 Million nodes and 5 Million edges, including all wheat, Arabidopsis and fungal genes. 

Develop the DiseaseNetMiner web application by extending the KnetMiner framework to fully support combined plant-pathogen networks and user-provided SNP/GWAS input data. This will require the development of new graph queries to explore combined networks and novel tools to visualise SNP-Disease relations within biological interaction networks and to include SNP-Disease information in the KnetMiner gene prioritisation algorithm. 

Evaluate and communicate DiseaseNetMiner with potential users that showed interest to test the developed tool. Prepare presentations and training material that highlight the potential and impact of DiseaseNetMiner for novel scientific discovery and insight.

KnetMiner and DiseaseNetMiner training at the European Bioinformatics Institute - March, 2017