BackgroundSacred lotus is a basal eudicot with agricultural, medicinal, cultural and religious importance. It was domesticated in Asia about 7,000 years ago, and cultivated for its rhizomes and seeds as a food crop. It is particularly noted for its 1,300-year seed longevity and exceptional water repellency, known as the lotus effect. The latter property is due to the nanoscopic closely packed protuberances of its self-cleaning leaf surface, which have been adapted for the manufacture of a self-cleaning industrial paint, Lotusan.ResultsThe genome of the China Antique variety of the sacred lotus was sequenced with Illumina and 454 technologies, at respective depths of 101× and 5.2×. The final assembly has a contig N50 of 38.8 kbp and a scaffold N50 of 3.4 Mbp, and covers 86.5% of the estimated 929 Mbp total genome size. The genome notably lacks the paleo-triplication observed in other eudicots, but reveals a lineage-specific duplication. The genome has evidence of slow evolution, with a 30% slower nucleotide mutation rate than observed in grape. Comparisons of the available sequenced genomes suggest a minimum gene set for vascular plants of 4,223 genes. Strikingly, the sacred lotus has 16 COG2132 multi-copper oxidase family proteins with root-specific expression; these are involved in root meristem phosphate starvation, reflecting adaptation to limited nutrient availability in an aquatic environment.ConclusionsThe slow nucleotide substitution rate makes the sacred lotus a better resource than the current standard, grape, for reconstructing the pan-eudicot genome, and should therefore accelerate comparative analysis between eudicots and monocots.

The soybean aphid (Aphis glycines Matsumura) is an important soybean [Glycine max (L.) Merr.] pest in North America. The dominant aphid resistance gene Rag1 was previously mapped from the cultivar 'Dowling' to a 12 cM marker interval on soybean chromosome 7 (formerly linkage group M). The development of additional genetic markers mapping closer to Rag1 was needed to accurately position the gene to improve the eVectiveness of markerassisted selection (MAS) and to eventually clone it. The objectives of this study were to identify single nucleotide polymorphisms (SNPs) near Rag1 and to position these SNPs relative to Rag1. To generate a Wne map of the Rag1 interval, 824 BC 4 F 2 and 1,000 BC 4 F 3 plants segregating for the gene were screened with markers Xanking Rag1. Plants with recombination events close to the gene were tested with SNPs identiWed in previous studies along with new SNPs identiWed from the preliminary Williams 82 draft soybean genome shotgun sequence using direct re-sequencing and gene-scanning melt-curve analysis. Progeny of these recombinant plants were evaluated for aphid resistance. These eVorts resulted in the mapping of Rag1 between the two SNP markers 46169.7 and 21A, which corresponds to a physical distance on the Williams 82 8£ draft assembly (Glyma1.01) of 115 kilobase pair (kb). Several candidate genes for Rag1 are present within the 115-kb interval. The markers identiWed in this study that are closely linked to Rag1 will be a useful resource in MAS for this important aphid resistance gene.

Soybean [Glycine max (L.) Merr.] seeds contain high levels of mineral nutrients essential for human and animal nutrition. High throughput elemental profiling (ionomics) has identified mutants in model plant species grown in controlled environments. Here, we describe a method for identifying potential soybean ionomics mutants grown in a field setting and apply it to 975 N-nitroso-Nmethylurea (NMU) mutagenized lines. After performing a spatial correction, we identified mutants using either visual scoring of standard score (z-score) plots or computational ranking of putative mutants followed by visual confirmation. Although there was a large degree of overlap between the methods, each method identified unique lines. The visual scoring approach identified 22 out of 427 (5%) potential mutants, 70% (16 out of 22) of which were confirmed when seeds from the same parent plant were regrown in the field. We also performed simulations to determine an optimal strategy for screening large populations.

The complete genome sequence of soybean allows an unprecedented opportunity for the discovery of the genes controlling important traits. In particular, the potential functions of regulatory genes are a priority for analysis. The basic helix-loop-helix (bHLH) family of transcription factors is known to be involved in controlling a wide range of systems critical for crop adaptation and quality, including photosynthesis, light signalling, pigment biosynthesis, and seed pod development. Using a hidden Markov model search algorithm, 319 genes with basic helix-loop-helix transcription factor domains were identified within the soybean genome sequence. These were classified with respect to their predicted DNA binding potential, intron/exon structure, and the phylogeny of the bHLH domain. Evidence is presented that the vast majority (281) of these 319 soybean bHLH genes are expressed at the mRNA level. Of these soybean bHLH genes, 67% were found to exist in two or more homeologous copies. This dataset provides a framework for future studies on bHLH gene function in soybean. The challenge for future research remains to define functions for the bHLH factors encoded in the soybean genome, which may allow greater flexibility for genetic selection of growth and environmental adaptation in this widely grown crop.

BackgroundGenotyping-by-sequencing (GBS), a method to identify genetic variants and quickly genotype samples, reduces genome complexity by using restriction enzymes to divide the genome into fragments whose ends are sequenced on short-read sequencing platforms. While cost-effective, this method produces extensive missing data and requires complex bioinformatics analysis. GBS is most commonly used on crop plant genomes, and because crop plants have highly variable ploidy and repeat content, the performance of GBS analysis software can vary by target organism. Here we focus our analysis on soybean, a polyploid crop with a highly duplicated genome, relatively little public GBS data and few dedicated tools.ResultsWe compared the performance of five GBS pipelines using low-coverage Illumina sequence data from three soybean populations. To address issues identified with existing methods, we developed GB-eaSy, a GBS bioinformatics workflow that incorporates widely used genomics tools, parallelization and automation to increase the accuracy and accessibility of GBS data analysis. Compared to other GBS pipelines, GB-eaSy rapidly and accurately identified the greatest number of SNPs, with SNP calls closely concordant with whole-genome sequencing of selected lines. Across all five GBS analysis platforms, SNP calls showed unexpectedly low convergence but generally high accuracy, indicating that the workflows arrived at largely complementary sets of valid SNP calls on the low-coverage data analyzed.ConclusionsWe show that GB-eaSy is approximately as good as, or better than, other leading software solutions in the accuracy, yield and missing data fraction of variant calling, as tested on low-coverage genomic data from soybean. It also performs well relative to other solutions in terms of the run time and disk space required. In addition, GB-eaSy is built from existing open-source, modular software packages that are regularly updated and commonly used, making it straightforward to install and maintain. While GB-eaSy outperformed other individual methods on the datasets analyzed, our findings suggest that a comprehensive approach integrating the results from multiple GBS bioinformatics pipelines may be the optimal strategy to obtain the largest, most highly accurate SNP yield possible from low-coverage polyploid sequence data.