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The 2017 Galaxy Community Conference (GCC2017) is being held in Montpellier, France, 26-30 June.  GCC2017 will include keynotes and accepted talks, poster sessions, demos, birds-of-a-feather meetups, exhibitors, and plenty of networking opportunities. There will also be three days of pre-conference activities, including hackathons and training. If you work in data-intensive biomedical research, there is no better place than GCC2017 to present your work and to learn from others.
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Friday, June 30 • 15:20 - 16:35
D04: KnetMiner: an application suite to integrate, search and interactively explore large knowledge networks

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Ajit Singh, Monika Mistry, Marco Brandizi, Chris Rawlings, Keywan Hassani-Pak

Rothamsted Research, Harpenden, AL5 2JQ – United Kingdom


The process of evaluating the candidacy of potential candidate genes involves numerous challenges in terms of data acquisition, integration, mining and visualisation. The KnetMiner suite of tools aim to facilitate gene discovery and enable biologists and breeders to quickly identify genes, biological processes and pathways influencing complex, polygenic traits. KnetMiner features a data integration platform (www.ondex.org) to integrate and unify information from varied data sources, be it structured or unstructured data, such as gene function annotations, protein-protein interaction data, biochemical pathways, gene expression data, citations in scientific literature and homology information from related organisms, to develop heterogeneous genome-scale knowledge networks.

The KnetMiner web application enables users to interrogate these GSKNs with gene lists, QTL information and trait-related keywords and quickly identify potential candidate genes and networks of associated entities to aid candidate gene discovery and hypothesis generation. This demo will showcase the KnetMiner instance for Arabidopsis. We will query the Arabidopsis knowledge network, which contains several datasets including public GWAS and protein-protein interaction data, with trait-related keywords and explore the ranked candidate genes in Gene View. We will then explore and identify overlapping gene, QTL, SNP and GWAS data in Genomaps and generate gene knowledge networks that can be interactively explore in KnetMaps with a view to identify candidate genes involved in plausible pathways.

KnetMiner is used by different labs at Rothamsted Research and elsewhere to accelerate gene discovery pipelines for crop breeding and crop improvement. While we have so far mostly concentrated on crop species, the approaches we have taken are generic and GSKNs and KnetMiner servers can readily be built for other species as well. KnetMiner is open source and available at http://knetminer.rothamsted.ac.uk.


Ajit Singh

Rothamsted Research, Harpenden, AL5 2JQ – United Kingdom

Friday June 30, 2017 15:20 - 16:35
Le Corum Le Corum

Attendees (3)