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Abstract Poa pratensis, commonly known as Kentucky bluegrass, is a popular cool-season grass species used as turf in lawns and recreation areas globally. Despite its substantial economic value, a reference genome had not previously been assembled due to the genome’s relatively large size and biological complexity that includes apomixis, polyploidy, and interspecific hybridization. We report here a fortuitous de novo assembly and annotation of a P. pratensis genome. Instead of sequencing the genome of a C4 grass, we accidentally sampled and sequenced tissue from a weedy P. pratensis whose stolon was intertwined with that of the C4 grass. The draft assembly consists of 6.09 Gbp with an N50 scaffold length of 65.1 Mbp, and a total of 118 scaffolds, generated using PacBio long reads and Bionano optical map technology. We annotated 256K gene models and found 58% of the genome to be composed of transposable elements. To demonstrate the applicability of the reference genome, we evaluated population structure and estimated genetic diversity in P. pratensis collected from three North American prairies, two in Manitoba, Canada and one in Colorado, USA. Our results support previous studies that found high genetic diversity and population structure within the species. The reference genome and annotation will be an important resource for turfgrass breeding and study of bluegrasses. 

Abstract Crop genomics has seen dramatic advances in recent years due to improvements in sequencing technology, assembly methods, and computational resources. These advances have led to the development of new tools to facilitate crop improvement. The study of structural variation within species and the characterization of the pan-genome has revealed extensive genome content variation among individuals within a species that is paradigm shifting to crop genomics and improvement. Here, we review advances in crop genomics and how utilization of these tools is shifting in light of pan-genomes that are becoming available for many crop species. 

In this work, we sequenced and annotated the genome of Streptochaeta angustifolia , one of two genera in the grass subfamily Anomochlooideae, a lineage sister to all other grasses. The final assembly size is over 99% of the estimated genome size. We find good collinearity with the rice genome and have captured most of the gene space. Streptochaeta is similar to other grasses in the structure of its fruit (a caryopsis or grain) but has peculiar flowers and inflorescences that are distinct from those in the outgroups and in other grasses. To provide tools for investigations of floral structure, we analyzed two large families of transcription factors, AP2-like and R2R3 MYBs, that are known to control floral and spikelet development in rice and maize among other grasses. Many of these are also regulated by small RNAs. Structure of the gene trees showed that the well documented whole genome duplication at the origin of the grasses (ρ) occurred before the divergence of the Anomochlooideae lineage from the lineage leading to the rest of the grasses (the spikelet clade) and thus that the common ancestor of all grasses probably had two copies of the developmental genes. However, Streptochaeta (and by inference other members of Anomochlooideae) has lost one copy of many genes. The peculiar floral morphology of Streptochaeta may thus have derived from an ancestral plant that was morphologically similar to the spikelet-bearing grasses. We further identify 114 loci producing microRNAs and 89 loci generating phased, secondary siRNAs, classes of small RNAs known to be influential in transcriptional and post-transcriptional regulation of several plant functions. 

Abstract Background PacBio sequencing is an incredibly valuable third-generation DNA sequencing method due to very long read lengths, ability to detect methylated bases, and its real-time sequencing methodology. Yet, hitherto no tool was available for analyzing the quality of, subsampling, and filtering PacBio data. Results Here we present SequelTools , a command-line program containing three tools: Quality Control, Read Subsampling, and Read Filtering. The Quality Control tool quickly processes PacBio Sequel raw sequence data from multiple SMRTcells producing multiple statistics and publication-quality plots describing the quality of the data including N50, read length and count statistics, PSR, and ZOR. The Read Subsampling tool allows the user to subsample reads by one or more of the following criteria: longest subreads per CLR or random CLR selection. The Read Filtering tool provides options for normalizing data by filtering out certain low-quality scraps reads and/or by minimum CLR length. SequelTools is implemented in bash, R, and Python using only standard libraries and packages and is platform independent. Conclusions SequelTools is a program that provides the only free, fast, and easy-to-use quality control tool, and the only program providing this kind of read subsampling and read filtering for PacBio Sequel raw sequence data, and is available at https://github.com/ISUgenomics/SequelTools . 

Native Americans domesticated maize ( Zea mays ssp. mays ) from lowland teosinte parviglumis ( Zea mays ssp. parviglumis) in the warm Mexican southwest and brought it to the highlands of Mexico and South America where it was exposed to lower temperatures that imposed strong selection on flowering time. Phospholipids are important metabolites in plant responses to low-temperature and phosphorus availability and have been suggested to influence flowering time. Here, we combined linkage mapping with genome scans to identify High PhosphatidylCholine 1 ( HPC1 ), a gene that encodes a phospholipase A1 enzyme, as a major driver of phospholipid variation in highland maize. Common garden experiments demonstrated strong genotype-by-environment interactions associated with variation at HPC1, with the highland HPC1 allele leading to higher fitness in highlands, possibly by hastening flowering. The highland maize HPC1 variant resulted in impaired function of the encoded protein due to a polymorphism in a highly conserved sequence. A meta-analysis across HPC1 orthologs indicated a strong association between the identity of the amino acid at this position and optimal growth in prokaryotes. Mutagenesis of HPC1 via genome editing validated its role in regulating phospholipid metabolism. Finally, we showed that the highland HPC1 allele entered cultivated maize by introgression from the wild highland teosinte Zea mays ssp. mexicana and has been maintained in maize breeding lines from the Northern United States, Canada, and Europe. Thus, HPC1 introgressed from teosinte mexicana underlies a large metabolic QTL that modulates phosphatidylcholine levels and has an adaptive effect at least in part via induction of early flowering time.