Background: Sacred 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. Results: The 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. Conclusions: The 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.

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‐N‐methylurea (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. All data from the screen is available at the Ionomics Hub File Transfer Page ( http://www.ionomicshub.org/home/PiiMS/dataexchange ).

Genotyping-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. We 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. We 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.

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.

Two maize phytochrome-interacting factor (PIF) basic helix-loop-helix (bHLH) family members, ZmPIF3.1 and ZmPIF3.2, were identified, cloned and expressed in vitro to investigate light-signaling interactions. A phylogenetic analysis of sequences of the maize bHLH transcription factor gene family revealed the extent of the PIF family, and a total of seven predicted PIF-encoding genes were identified from genes encoding bHLH family VIIa/b proteins in the maize genome. To investigate the role of maize PIFs in phytochrome signaling, full-length cDNAs for phytochromes PhyA2, PhyB1, PhyB2 and PhyC1 from maize were cloned and expressed in vitro as chromophorylated holophytochromes. We showed that ZmPIF3.1 and ZmPIF3.2 interact specifically with the Pfr form of maize holophytochrome B1 (ZmphyB1), showing no detectable affinity for the Pr form. Maize holophytochrome B2 (ZmphyB2) showed no detectable binding affinity for PIFs in either Pr or Pfr forms, but phyB Pfr from Arabidopsis interacted with ZmPIF3.1 similarly to ZmphyB1 Pfr. We conclude that subfunctionalization at the protein-protein interaction level has altered the role of phyB2 relative to that of phyB1 in maize. Since the phyB2 mutant shows photomorphogenic defects, we conclude that maize phyB2 is an active photoreceptor, without the binding of PIF3 seen in other phyB family proteins.