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Visualising gene-trait networks for plant breeding

A new software tool, known as KnetMiner, that makes it easier for plant breeders to mine genomics data and find novel ways of improving the performance of all kinds of crops, has been developed by Rothamsted Research under the leadership of Dr Keywan Hassani-Pak, Head of Bioinformatics.

KnetMiner enables biologists to take their own high-throughput experimental data and to interpret them in the context of all publicly available knowledge. This can help users to understand their own data faster and more effectively.

For a particular target species, such as a crop plant, KnetMiner integrates all the relevant genomics and omics information that is present in more than 25 sources bringing it together in the form of a heterogeneous knowledge network. The data is integrated so that new relationships are created, based, for example, on co-occurrences of genes and phenotypes in the scientific literature.

However, the process of collecting data for a particular organism, cleaning it and converting it into a format that is usable in KnetMiner is time-consuming. So, with support from Innovate UK, Rothamsted has collaborated with Cambridge-based Genestack to migrate KnetMiner onto the Genestack platform[1]. This allows users to simply ‘point and click’ on data that is in the public domain to create a network and then overlay new data, using KnetMiner to visualise it. Networks can be built up with collaborators in a secure environment. Genestack now hosts over 40 plant and crop networks, as well as a prototype human disease network. Although it originated in agri-research, network mining for gene discovery is generic and Genestack provides an environment for building and distributing these large-scale knowledge networks.

Knowledge networks are a way of showing visually the connection between phenotypes with the genotype of a given species. The nodes are different shapes to represent various biological entities (such as genes, publications, or pathways), which are connected by relevant relationships (such as encodes, published in, interacts with). They are very effective at showing complex and highly interconnected biological data.

One of the unique features of KnetMiner is that it allows users to see how and why a prediction was made. They can understand the results because the process is transparent and the provenance is visualised.



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