High-throughput comparison, functional annotation, and metabolic modeling of plant genomes using the PlantSEED resource

Significance Genes must be annotated with their correct functions if genome data are to support hypothesis building and metabolic engineering. PlantSEED was developed to streamline the process of annotating plant genome sequences, to construct metabolic models based on genome annotations automatically, and to use models to test the annotation of these sequences, allowing the detection of gaps and errors in gene annotations and the prediction of new functions. PlantSEED is designed to grow in an iterative manner by including new plant genome sequences, new annotations harvested from the literature, and improved biochemical data, all of which are integrated in a consistent manner into the PlantSEED genomes and metabolic models. The increasing number of sequenced plant genomes is placing new demands on the methods applied to analyze, annotate, and model these genomes. Today’s annotation pipelines result in inconsistent gene assignments that complicate comparative analyses and prevent efficient construction of metabolic models. To overcome these problems, we have developed the PlantSEED, an integrated, metabolism-centric database to support subsystems-based annotation and metabolic model reconstruction for plant genomes. PlantSEED combines SEED subsystems technology, first developed for microbial genomes, with refined protein families and biochemical data to assign fully consistent functional annotations to orthologous genes, particularly those encoding primary metabolic pathways. Seamless integration with its parent, the prokaryotic SEED database, makes PlantSEED a unique environment for cross-kingdom comparative analysis of plant and bacterial genomes. The consistent annotations imposed by PlantSEED permit rapid reconstruction and modeling of primary metabolism for all plant genomes in the database. This feature opens the unique possibility of model-based assessment of the completeness and accuracy of gene annotation and thus allows computational identification of genes and pathways that are restricted to certain genomes or need better curation. We demonstrate the PlantSEED system by producing consistent annotations for 10 reference genomes. We also produce a functioning metabolic model for each genome, gapfilling to identify missing annotations and proposing gene candidates for missing annotations. Models are built around an extended biomass composition representing the most comprehensive published to date. To our knowledge, our models are the first to be published for seven of the genomes analyzed.