Gene expression and regulation data

Understanding complex traits and diseases requires knowledge of genes and how their expression is regulated. In a study by Cristobal Uauy (JIC) and Andrea Brautigam (University of Bielefeld), public RNA-seq data was used to predict 1 million relations between wheat transcription factor and their targets [REF].

Such gene regulatory networks (GRN) can provide an important source of evidence for hypothesis generation and candidate gene discovery.
However, the real value is added when GRNs integrated and visualised in conjunction with other biological knowledge, i.e. protein-protein interactions, phenotype data, homologies and other information types.

As part of the Designing Future Wheat ISPG, we worked together with Cristobal and his team to integrate the GRN data into the public wheat knowledge graph for KnetMiner. The data was integrated as directed edges between genes with the GENIE3 weight used as an edge property, where larger weights correspond to more strongly supported relationships.

The public KnetMiner for wheat enables scientists like Cristobal to search for transcription factors and view a highly visual and rich knowledge network that KnetMiner generates for each query and annotates such that it’s easy for researchers to identify connections between genes and hidden links to phenotypes and diseases. For example, a search for “NAM-A1” returns two hits, TRAESCS6A02G108300 and TRAESCS6D02G096300, which correspond to NAM-A1 and its D genome homoeolog NAM-D1, respectively. Using the KnetMiner network, we can visualise the relationships between NAM-A1 and NAM-D1, seeing, for example, that they target each other in the GRN network. Associated traits are also included in the network, linking NAM-A1 and NAM-D1 to “Grain Protein Content”. Links to orthologous genes in other species are also included in the same network, such as the Arabidopsis thaliana orthologue ANAC018.

Cristobal Uauy says: With the wealth of genomic resources now available in wheat, having one place where we can query relationships across datasets and species is hugely important. With KnetMiner we can now visualise and access these relationships in a consistent and quick manner, meaning that our work on important agronomic traits is accelerated.

Connecting bioinformatics resources for wheat

Brief description of resources and how they have been connected

• KnetMiner
• Ensembl Plants
• Wheat-Expression
• WheatIS
• T3 toolbox
• Grain Genes