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Abstract The number of plant species with genomic and transcriptomic data has been increasing rapidly. The grasses—Poaceae—have been well represented among species with published reference genomes. However, as a result the genomes of wild grasses are less frequently targeted by sequencing efforts. Sequence data from wild relatives of crop species in the grasses can aid the study of domestication, gene discovery for breeding and crop improvement, and improve our understanding of the evolution of C₄photosynthesis. Here, we used long‐read sequencing technology to characterize the transcriptomes of three C₃panicoid grass species:Dichanthelium oligosanthes,Chasmanthium laxum, andHymenachne amplexicaulis. Based on alignments to the sorghum genome, we estimate that assembled consensus transcripts from each species capture between 54.2% and 65.7% of the conserved syntenic gene space in grasses. Genes co‐opted into C₄were also well represented in this dataset, despite concerns that because these genes might play roles unrelated to photosynthesis in the target species, they would be expressed at low levels and missed by transcript‐based sequencing. A combined analysis using syntenic orthologous genes from grasses with published reference genomes and consensus long‐read sequences from these wild species was consistent with previously published phylogenies. It is hoped that these data, targeting underrepresented classes of species within the PACMAD grasses—wild species and species utilizing C₃photosynthesis—will aid in future studies of domestication and C₄evolution by decreasing the evolutionary distance between C₄and C₃species within this clade, enabling more accurate comparisons associated with evolution of the C₄pathway. 

Abstract Advances in genome sequencing and annotation have eased the difficulty of identifying new gene sequences. Predicting the functions of these newly identified genes remains challenging. Genes descended from a common ancestral sequence are likely to have common functions. As a result, homology is widely used for gene function prediction. This means functional annotation errors also propagate from one species to another. Several approaches based on machine learning classification algorithms were evaluated for their ability to accurately predict gene function from non‐homology gene features. Among the eight supervised classification algorithms evaluated, random‐forest‐based prediction consistently provided the most accurate gene function prediction. Non‐homology‐based functional annotation provides complementary strengths to homology‐based annotation, with higher average performance in Biological Process GO terms, the domain where homology‐based functional annotation performs the worst, and weaker performance in Molecular Function GO terms, the domain where the accuracy of homology‐based functional annotation is highest. GO prediction models trained with homology‐based annotations were able to successfully predict annotations from a manually curated “gold standard” GO annotation set. Non‐homology‐based functional annotation based on machine learning may ultimately prove useful both as a method to assign predicted functions to orphan genes which lack functionally characterized homologs, and to identify and correct functional annotation errors which were propagated through homology‐based functional annotations.