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Identification of optimal pod maturity stage in Canola is key for maximizing seed yield, quality and also an important phenotypic trait in crop improvement programs. The conventional method is via visual inspection of seed color change. Alternatively, hyperspectral sensors have potential to determine physiological status of the crops. Therefore, the objective of the study is to estimate canola seed maturity using field-based hyperspectral imaging. For this study, five canola genotypes (NAM-0, NAM-13, NAM-17, NAM-48, and NAM-76) were selected from an experiment of 56 populations. The experimental field was imaged using a UAV-mounted hyperspectral camera (400–1,000 nm) at five growth stages starting from pod formation to near-harvest maturity (BBCH-79(S1) to 88(S5)). For each genotype; pod and seed moisture were estimated on the same day of imaging. First-order derivative was conducted on reflectance data to determine optimal spectral wavebands. As a part of this study, a new vegetation index denoted “Canola-Pod-Maturity Index (CPMI)” was developed. CPMI was evaluated in comparison with four existing vegetation indices (mNDRE, PSRI, MCARI, WBI). CPMI showed a stronger relationship ( 0.81–0.98 for pods and 0.66–0.85 for seeds) with pod and seed moisture for all the genotypes. Furthermore, the new index was able to find differences among genotypes with variable maturity times.

Abstract Genomic prediction is a promising technology for advancing both plant and animal breeding, with many different prediction models evaluated in the literature. It has been suggested that the ability of powerful nonlinear models, such as deep neural networks, to capture complex epistatic effects between markers offers advantages for genomic prediction. However, these methods tend not to outperform classical linear methods, leaving it an open question why this capacity to model nonlinear effects does not seem to result in better predictive capability. In this work, we propose the theory that, because of a previously described principle called shortcut learning, deep neural networks tend to base their predictions on overall genetic relatedness rather than on the effects of particular markers such as epistatic effects. Using several datasets of crop plants [lentil (Lens culinaris Medik.), wheat (Triticum aestivum L.), and Brassica carinata A. Braun], we demonstrate the network's indifference to the values of the markers by showing that the same network, provided with only the locations of matches between markers for two individuals, is able to perform prediction to the same level of accuracy.

Abstract The concept of U’s triangle, which revealed the importance of polyploidization in plant genome evolution, described natural allopolyploidization events in Brassica using three diploids [B. rapa (A genome), B. nigra (B), and B. oleracea (C)] and derived allotetraploids [B. juncea (AB genome), B. napus (AC), and B. carinata (BC)]. However, comprehensive understanding of Brassica genome evolution has not been fully achieved. Here, we performed low-coverage (2–6×) whole-genome sequencing of 28 accessions of Brassica as well as of Raphanus sativus [R genome] to explore the evolution of six Brassica species based on chloroplast genome and ribosomal DNA variations. Our phylogenomic analyses led to two main conclusions. (1) Intra-species-level chloroplast genome variations are low in the three allotetraploids (2~7 SNPs), but rich and variable in each diploid species (7~193 SNPs). (2) Three allotetraploids maintain two 45SnrDNA types derived from both ancestral species with maternal dominance. Furthermore, this study sheds light on the maternal origin of the AC chloroplast genome. Overall, this study clarifies the genetic relationships of U’s triangle species based on a comprehensive genomics approach and provides important genomic resources for correlative and evolutionary studies.

Association mapping studies have enabled researchers to identify candidate loci for many important environmental tolerance factors, including agronomically relevant tolerance traits in plants. However, traditional genome-by-environment studies such as these require a phenotyping pipeline which is capable of accurately measuring stress responses, typically in an automated high-throughput context using image processing. In this work, we present Latent Space Phenotyping (LSP), a novel phenotyping method which is able to automatically detect and quantify response-to-treatment directly from images. We demonstrate example applications using data from an interspecific cross of the model C4 grass Setaria, a diversity panel of sorghum (S. bicolor), and the founder panel for a nested association mapping population of canola (Brassica napus L.). Using two synthetically generated image datasets, we then show that LSP is able to successfully recover the simulated QTL in both simple and complex synthetic imagery. We propose LSP as an alternative to traditional image analysis methods for phenotyping, enabling the phenotyping of arbitrary and potentially complex response traits without the need for engineering-complicated image-processing pipelines.

A new method to improve the efficiency of flanking sequence identification by genome walking was developed based on an expanded, sequential list of criteria for selecting candidate enzymes, plus several other optimization steps. These criteria include: step (1) initially choosing the most appropriate restriction enzyme according to the average fragment size produced by each enzyme determined using in silico digestion of genomic DNA, step (2) evaluating the in silico frequency of fragment size distribution between individual chromosomes, step (3) selecting those enzymes that generate fragments with the majority between 100 bp and 3,000 bp, step (4) weighing the advantages and disadvantages of blunt-end sites vs. cohesive-end sites, step (5) elimination of methylation sensitive enzymes with methylation-insensitive isoschizomers, and step (6) elimination of enzymes with recognition sites within the binary vector sequence (T-DNA and plasmid backbone). Step (7) includes the selection of a second restriction enzyme with highest number of recognition sites within regions not covered by the first restriction enzyme. Step (8) considers primer and adapter sequence optimization, selecting the best adapter-primer pairs according to their hairpin/dimers and secondary structure. In step (9), the efficiency of genomic library development was improved by column-filtration of digested DNA to remove restriction enzyme and phosphatase enzyme, and most important, to remove small genomic fragments (

Abstract  The weedy crucifer species Thlaspi arvense has the ability to acclimate to lower temperatures than Arabidopsis thaliana and the related crop species, Brassica napus. As a step towards understanding the genetic basis for this enhanced low temperature response, we isolated and sequenced 8.7 kb of genomic DNA encompassing the T. arvense CBF locus. CBF is a transcription factor believed to play a pivotal role in the development of plant freezing tolerance. Sequence analysis revealed that T. arvense contains a single copy of CBF, whereas the co-linear, homologous region in A. thaliana contains three tandem copies. Genes that flank CBF in A. thaliana are also present in a co-linear arrangement in T. arvense. Comparative sequence alignment also revealed the presence of conserved sequence blocks between T. arvense and A. thaliana promoter regions. The expression of T. arvense CBF responds rapidly to low temperature but not demonstrably to ABA, dehydration or high salt, which is comparable to that of the A. thaliana CBF genes. Over-expression of Ta-CBF in transgenic A. thaliana resulted in the development of constitutive freezing tolerance, comparable to that of cold acclimated A. thaliana. [ABSTRACT FROM AUTHOR]

Abstract??Thlaspi arvense, a wild species from the Brassicaceae family, was shown to have a higher level of freezing tolerance than either of its close relatives, the model plantArabidopsis thalianaor the cropBrassica napus(canola). Over 600 clones were sequenced from a subtractive cDNA library generated from cold treatedT. arvensetissue, establishing thatT. arvenseshared significant sequence identity with bothA. thalianaandB. napus(90?92%). In light of the strong sequence similarity betweenT. arvenseandA. thalianaand to exploit the available genomics resources forArabidopsis, the efficacy of using long 70?mer oligonucleotide whole genomeArabidopsismicroarrays was tested forT. arvense. Gene expression inT. arvenseleaf tissue during the very early stages of cold acclimation (or cold stress) was assayed at three time points and compared to an untreated control. This analysis highlights some of the difficulties and benefits of using cross-species microarray analysis. The data suggested thatT. arvenseresponds in a similar fashion to cold stress as the model plantA. thaliana. However, for a number of genes quantitative differences in the level and timing of expression were identified. One of the most notable differences suggested that sulphur assimilation leading to the increased production of the methyl donor S-adenosyl-methionine was playing a role in the response ofT. arvenseto cold stress. [ABSTRACT FROM AUTHOR]