Improving Gene Models in Barley Using an Enterprise Knowledge Management Platform

Genome sequence data in the public domain, combined with the decreasing cost of sequencing individual lines, has created exciting opportunities for both plant breeders and engineering scientists.  Researchers have more information than ever to base their hypotheses and downstream decisions.  But these opportunities also bring significant challenges: large sample sizes, complex genomes, and incomplete genome assemblies.   Moreover, important information may not be organized or managed in a way that makes the data easily accessible or comparable.  To get a handle on this data and enable interrogations based on sequences, genes, and phenotypes, we developed a platform for genome management and analysis with agribusiness goals in mind.  Genedata Selector is an enterprise genome management system which is scalable, includes interactive visualization tools, and can be used to guide the discovery of genotype-phenotype relationships.  In this study, we apply the platform to investigate barley, a highly repetitive 5.1Gb genome, and discover genes that could be targeted for beer making.  As a first step, RNA-seq data was used to refine gene model predictions of the recently published barley genome.  We then used the nucleotide sequence data to identify amylase homologs which could have a role in the malting process.  Information for these genes is then annotated in the system, so that the genomic ranges can be quantified for downstream gene expression experiments.  Next, we compared the amylase sequences to those in rice, bacteria, and fungi, to learn about conserved protein domains that could be targeted to alter malting.  At each step in the analysis, data are stored, tracked, and annotated through a central database, and all information is accessible and searchable by users’ laptops via a friendly and feature-rich interface.  Through this example we demonstrate the scalability of the system for large data volumes and the capability to integrate public and proprietary data for sequence and pathway-based analyses.