(Soybean-SCN PIPE): A Cross Computational Approach in Soybean Functional Genomics

Soybean is one of the major Canadian grain crops and its production is expanding in Canada mainly Western Canada and northern regions. The list of novel factors affecting these pathways in soybean, and in model plants like Arabidopsis, continues to grow suggesting the presence of other novel players which are yet to be discovered.
The soybean Protein-protein Interaction Prediction Engine (Soybean-PIPE) is a computational tool used to predict protein-protein interactions in soybean. Protein-Protein Interactions (PPIs) are essential molecular interactions that define the biology of a cell, its development and its responses to various stimuli. Theoretically, if a gene interacts with groups of genes involved in one specific pathway, that gene might also be involved in that specific pathway (“guilt by association”).
Briefly, PIPE searches for re-occurring short polypeptide sequences between known interacting protein pairs and novel proteins and predicts interactions based on protein sequence information and a database of known interacting protein pairs (to achieve a specificity of 99.95%).
In an independent study (Samanfar et al., 2016), we have used three different approaches; bioinformatics (Soybean-PIPE), classical plant breeding, and molecular biology (analysis of SSR and SNP haplotypes) to identify a novel gene involved in time of flowering and maturity in soybean. This strategy successfully identified a new maturity locus tentatively called “E10” and the underlying candidate gene (FT4).
Identification of molecular markers tagging the PIPE-identified genes controlling flowering and maturity in soybean will allow soybean breeders to efficiently develop varieties using molecular marker assisted breeding. Allele specific markers will allow stacking of early maturity alleles to develop even earlier maturing cultivars. This bioinformatics approach (soybean-PIPE) will also help to bridge the gap in knowledge of the flowering and maturity pathway in soybean and can be applied to other important traits such as seed protein content, oil quality and host-pathogen interactions.