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A recent book edited by Neal Stewart summarized the salient information about the genomics of weeds. It is a relatively slim volume, in part because so little is known of weed genomics. The rapidly evolving weeds of modern agriculture cry out for understanding their evolution at the genomic level. Still, granting agencies have been reluctant to fund weed genomics in the past, stating that it was enough to support the sequencing of economically important plants (crops), and forgetting that there may be use to sequencing plants that have extremely negative economicimpactsonthose crops. This attitude hasbeenchanging, and the costs have come down. The following two articles make significant additions to the sparse literature on weed genomics. These studies are among the first papers to provide large-scale genomic data sets for major weed species. The authors make good arguments for why the two species chosen are good models to investigate weeds and herbicide resistance.
The first of the two papers is on Amaranthus tuberculatus = A. rudis (waterhemp), a species that has evolved resistance to herbicides of four different mode of action herbicide classes in recent years. For example, this species evolved resistance to inhibitors of protoporphyrinogen oxidase (PPO) by a complete codon deletion in the PPO gene,4,5 rendering PPO resistant to an array of herbicidal PPO inhibitors, while maintaining sufficient catalytic activity for normal porphyrin synthesis. The authors took advantage of next-generation pyrosequencing technology to produce over 100 million nucleotides of gene sequence. Such data provide a wealth of information that should benefit future research efforts. This paper not only provides a valuable resource on the genome of Amaranthus tuberculatus, but demonstrates the utility of this information in analysis of genes encoding herbicide target molecules. Eleven herbicide target site genes have now been identified in this species; however, one of the two potential PPO target sites was not found in the BLAST search of the transcriptome data set from this paper, showing the need to perform transcriptome analyses on material at different stages of physiological development.