Gaurav D. Moghe

We have optimized and extended the widely used annotation engine MAKER in order to better support plant genome annotation efforts. New features include better parallelization for large repeat-rich plant genomes, noncoding RNA annotation capabilities, and support for pseudogene identification. We have benchmarked the resulting software tool kit, MAKER-P, using the Arabidopsis (Arabidopsis thaliana) and maize (Zea mays) genomes. Here, we demonstrate the ability of the MAKER-P tool kit to automatically update, extend, and revise the Arabidopsis annotations in light of newly available data and to annotate pseudogenes and noncoding RNAs absent from The Arabidopsis Informatics Resource 10 build. Our results demonstrate that MAKER-P can be used to manage and improve the annotations of even Arabidopsis, perhaps the best-annotated plant genome. We have also installed and benchmarked MAKER-P on the Texas Advanced Computing Center. We show that this public resource can de novo annotate the entire Arabidopsis and maize genomes in less than 3 h and produce annotations of comparable quality to those of the current The Arabidopsis Information Resource 10 and maize V2 annotation builds.

The Poaceae family, also known as the grasses, includes agronomically important cereal crops such as rice, maize, sorghum, and wheat. Previous comparative studies have shown that much of the gene content is shared among the grasses; however, functional conservation of orthologous genes has yet to be explored. To gain an understanding of the genome-wide patterns of evolution of gene expression across reproductive tissues, we employed a sequence-based approach to compare analogous transcriptomes in species representing three Poaceae subgroups including the Pooideae (Brachypodium distachyon), the Panicoideae (sorghum), and the Ehrhartoideae (rice). Our transcriptome analyses reveal that only a fraction of orthologous genes exhibit conserved expression patterns. A high proportion of conserved orthologs include genes that are upregulated in physiologically similar tissues such as leaves, anther, pistil, and embryo, while orthologs that are highly expressed in seeds show the most diverged expression patterns. More generally, we show that evolution of gene expression profiles and coding sequences in the grasses may be linked. Genes that are highly and broadly expressed tend to be conserved at the coding sequence level while genes with narrow expression patterns show accelerated rates of sequence evolution. We further show that orthologs in syntenic genomic blocks are more likely to share correlated expression patterns compared with non-syntenic orthologs. These findings are important for agricultural improvement because sequence information is transferred from model species, such as Brachypodium, rice, and sorghum to crop plants without sequenced genomes.

Plants produce hundreds of thousands of small molecules known as specialized metabolites, many of which are of economic and ecological importance. This remarkable variety is a consequence of the diversity and rapid evolution of specialized metabolic pathways. These novel biosynthetic pathways originate via gene duplication or by functional divergence of existing genes, and they subsequently evolve through selection and/or drift. Studies over the past two decades revealed that diverse specialized metabolic pathways have resulted from the incorporation of primary metabolic enzymes. We discuss examples of enzyme recruitment from primary metabolism and the variety of paths taken by duplicated primary metabolic enzymes toward integration into specialized metabolism. These examples provide insight into processes by which plant specialized metabolic pathways evolve and suggest approaches to discover enzymes of previously uncharacterized metabolic networks.

Recent advances in sequencing and informatic technologies have led to a deluge of publicly available genomic data. While it is now relatively easy to sequence, assemble, and identify genic regions in diploid plant genomes, functional annotation of these genes is still a challenge. Over the past decade, there has been a steady increase in studies utilizing machine learning algorithms for various aspects of functional prediction, because these algorithms are able to integrate large amounts of heterogeneous data and detect patterns inconspicuous through rule-based approaches. The goal of this review is to introduce experimental plant biologists to machine learning, by describing how it is currently being used in gene function prediction to gain novel biological insights. In this review, we discuss specific applications of machine learning in identifying structural features in sequenced genomes, predicting interactions between different cellular components, and predicting gene function and organismal phenotypes. Finally, we also propose strategies for stimulating functional discovery using machine learning-based approaches in plants.

Polyploidization events are frequent among flowering plants, and the duplicate genes produced via such events contribute significantly to plant evolution. We sequenced the genome of wild radish (Raphanus raphanistrum), a Brassicaceae species that experienced a whole-genome triplication event prior to diverging from Brassica rapa. Despite substantial gene gains in these two species compared with Arabidopsis thaliana and Arabidopsis lyrata, ∼70% of the orthologous groups experienced gene losses in R. raphanistrum and B. rapa, with most of the losses occurring prior to their divergence. The retained duplicates show substantial divergence in sequence and expression. Based on comparison of A. thaliana and R. raphanistrum ortholog floral expression levels, retained radish duplicates diverged primarily via maintenance of ancestral expression level in one copy and reduction of expression level in others. In addition, retained duplicates differed significantly from genes that reverted to singleton state in function, sequence composition, expression patterns, network connectivity, and rates of evolution. Using these properties, we established a statistical learning model for predicting whether a duplicate would be retained postpolyploidization. Overall, our study provides new insights into the processes of plant duplicate loss, retention, and functional divergence and highlights the need for further understanding factors controlling duplicate gene fate.