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Background The sharp increase of plant genome and transcriptome data provide valuable resources to investigate evolutionary consequences of gene duplication in a range of taxa, and unravel common principles underlying duplicate gene retention. Results We survey 141 sequenced plant genomes to elucidate consequences of gene and genome duplication, processes central to the evolution of biodiversity. We develop a pipeline named DupGen_finder to identify different modes of gene duplication in plants. Genes derived from whole-genome, tandem, proximal, transposed, or dispersed duplication differ in abundance, selection pressure, expression divergence, and gene conversion rate among genomes. The number of WGD-derived duplicate genes decreases exponentially with increasing age of duplication events—transposed duplication- and dispersed duplication-derived genes declined in parallel. In contrast, the frequency of tandem and proximal duplications showed no significant decrease over time, providing a continuous supply of variants available for adaptation to continuously changing environments. Moreover, tandem and proximal duplicates experienced stronger selective pressure than genes formed by other modes and evolved toward biased functional roles involved in plant self-defense. The rate of gene conversion among WGD-derived gene pairs declined over time, peaking shortly after polyploidization. To provide a platform for accessing duplicated gene pairs in different plants, we constructed the Plant Duplicate Gene Database. Conclusions We identify a comprehensive landscape of different modes of gene duplication across the plant kingdom by comparing 141 genomes, which provides a solid foundation for further investigation of the dynamic evolution of duplicate genes.

This paper demonstrates the application of aerial multispectral images in cotton plant phenotyping. Four phenotypic traits (plant height, canopy cover, vegetation index, and flower) were measured from multispectral images captured by a multispectral camera on an unmanned aerial system. Data were collected on eight different days from two fields. Ortho-mosaic and digital elevation models (DEM) were constructed from the raw images using the structure from motion (SfM) algorithm. A data processing pipeline was developed to calculate plant height using the ortho-mosaic and DEM. Six ground calibration targets (GCTs) were used to correct the error of the calculated plant height caused by the georeferencing error of the DEM. Plant heights were measured manually to validate the heights predicted from the imaging method. The error in estimation of the maximum height of each plot ranged from -40.4 to 13.5 cm among six datasets, all of which showed strong linear relationships with the manual measurement (R2 > 0.89). Plot canopy was separated from the soil based on the DEM and normalized differential vegetation index (NDVI). Canopy cover and mean canopy NDVI were calculated to show canopy growth over time and the correlation between the two indices was investigated. The spectral responses of the ground, leaves, cotton flower, and ground shade were analyzed and detection of cotton flowers was satisfactory using a support vector machine (SVM). This study demonstrated the potential of using aerial multispectral images for high throughput phenotyping of important cotton phenotypic traits in the field.

A LiDAR-based high-throughput phenotyping (HTP) system was developed for cotton plant phenotyping in the field. The HTP system consists of a 2D LiDAR and an RTK-GPS mounted on a high clearance tractor. The LiDAR scanned three rows of cotton plots simultaneously from the top and the RTK-GPS was used to provide the spatial coordinates of the point cloud during data collection. Configuration parameters of the system were optimized to ensure the best data quality. A height profile for each plot was extracted from the dense three dimensional point clouds; then the maximum height and height distribution of each plot were derived. In lab tests, single plants were scanned by LiDAR using 0.5° angular resolution and results showed an R2 value of 1.00 (RMSE = 3.46 mm) in comparison to manual measurements. In field tests using the same angular resolution; the LiDAR-based HTP system achieved average R2 values of 0.98 (RMSE = 65 mm) for cotton plot height estimation; compared to manual measurements. This HTP system is particularly useful for large field application because it provides highly accurate measurements; and the efficiency is greatly improved compared to similar studies using the side view scan.

Background Phylogenetic trees are widely used for genetic and evolutionary studies in various organisms. Advanced sequencing technology has dramatically enriched data available for constructing phylogenetic trees based on single nucleotide polymorphisms (SNPs). However, massive SNP data makes it difficult to perform reliable analysis, and there has been no ready-to-use pipeline to generate phylogenetic trees from these data. Results We developed a new pipeline, SNPhylo, to construct phylogenetic trees based on large SNP datasets. The pipeline may enable users to construct a phylogenetic tree from three representative SNP data file formats. In addition, in order to increase reliability of a tree, the pipeline has steps such as removing low quality data and considering linkage disequilibrium. A maximum likelihood method for the inference of phylogeny is also adopted in generation of a tree in our pipeline. Conclusions Using SNPhylo, users can easily produce a reliable phylogenetic tree from a large SNP data file. Thus, this pipeline can help a researcher focus more on interpretation of the results of analysis of voluminous data sets, rather than manipulations necessary to accomplish the analysis.

The occurrence of polyploidy in land plant evolution has led to an acceleration of genome modifications relative to other crown eukaryotes and is correlated with key innovations in plant evolution. Extensive genome resources provide for relating genomic changes to the origins of novel morphological and physiological features of plants. Ancestral gene contents for key nodes of the plant family tree are inferred. Pervasive polyploidy in angiosperms appears likely to be the major factor generating novel angiosperm genes and expanding some gene families. However, most gene families lose most duplicated copies in a quasi-neutral process, and a few families are actively selected for single-copy status. One of the great challenges of evolutionary genomics is to link genome modifications to speciation, diversification and the morphological and/or physiological innovations that collectively compose biodiversity. Rapid accumulation of genomic data and its ongoing investigation may greatly improve the resolution at which evolutionary approaches can contribute to the identification of specific genes responsible for particular innovations. The resulting, more ‘particulate’ understanding of plant evolution, may elevate to a new level fundamental knowledge of botanical diversity, including economically important traits in the crop plants that sustain humanity.

Background Plant population density is an important factor for agricultural production systems due to its substantial influence on crop yield and quality. Traditionally, plant population density is estimated by using either field assessment or a germination-test-based approach. These approaches can be laborious and inaccurate. Recent advances in deep learning provide new tools to solve challenging computer vision tasks such as object detection, which can be used for detecting and counting plant seedlings in the field. The goal of this study was to develop a deep-learning-based approach to count plant seedlings in the field. Results Overall, the final detection model achieved F1 scores of 0.727 (at I O U all ) and 0.969 (at I O U 0.5 ) on the S e e d l i n g All testing set in which images had large variations, indicating the efficacy of the Faster RCNN model with the Inception ResNet v2 feature extractor for seedling detection. Ablation experiments showed that training data complexity substantially affected model generalizability, transfer learning efficiency, and detection performance improvements due to increased training sample size. Generally, the seedling counts by the developed method were highly correlated ( R 2 = 0.98) with that found through human field assessment for 75 test videos collected in multiple locations during multiple years, indicating the accuracy of the developed approach. Further experiments showed that the counting accuracy was largely affected by the detection accuracy: the developed approach provided good counting performance for unknown datasets as long as detection models were well generalized to those datasets. Conclusion The developed deep-learning-based approach can accurately count plant seedlings in the field. Seedling detection models trained in this study and the annotated images can be used by the research community and the cotton industry to further the development of solutions for seedling detection and counting.

There is an increasing awareness that as a result of structural variation, a reference sequence representing a genome of a single individual is unable to capture all of the gene repertoire found in the species. A large number of genes affected by presence/absence and copy number variation suggest that it may contribute to phenotypic and agronomic trait diversity. Here we show by analysis of the Brassica oleracea pangenome that nearly 20% of genes are affected by presence/absence variation. Several genes displaying presence/absence variation are annotated with functions related to major agronomic traits, including disease resistance, flowering time, glucosinolate metabolism and vitamin biosynthesis. Brassica oleracea is a single species that includes diverse crops such as cabbage, broccoli and Brussels sprouts. Here, the authors identify genes not captured in existing B. oleracea reference genomes by the assembly of a pangenome and show variations in gene content that may be related to important agronomic traits

Correlated gene arrangements among taxa provide a valuable framework for inference of shared ancestry of genes and for the utilization of findings from model organisms to study less-well-understood systems. In angiosperms, comparisons of gene arrangements are complicated by recurring polyploidy and extensive genome rearrangement. New genome sequences and improved analytical approaches are clarifying angiosperm evolution and revealing patterns of differential gene loss after genome duplication and differential gene retention associated with evolution of some morphological complexity. Because of variability in DNA substitution rates among taxa and genes, deviation from collinearity might be a more reliable phylogenetic character.

Brassica napus (2 n  = 4 x  = 38, AACC) is an important allopolyploid crop derived from interspecific crosses between Brassica rapa (2 n  = 2 x  = 20, AA) and Brassica oleracea (2 n  = 2 x  = 18, CC). However, no truly wild B. napus populations are known; its origin and improvement processes remain unclear. Here, we resequence 588 B. napus accessions. We uncover that the A subgenome may evolve from the ancestor of European turnip and the C subgenome may evolve from the common ancestor of kohlrabi, cauliflower, broccoli, and Chinese kale. Additionally, winter oilseed may be the original form of B. napus . Subgenome-specific selection of defense-response genes has contributed to environmental adaptation after formation of the species, whereas asymmetrical subgenomic selection has led to ecotype change. By integrating genome-wide association studies, selection signals, and transcriptome analyses, we identify genes associated with improved stress tolerance, oil content, seed quality, and ecotype improvement. They are candidates for further functional characterization and genetic improvement of B. napus . Brassica napus is a globally important oil crop, but the origin of the allotetraploid genome and its improvement process are largely unknown. Here, the authors take a population genetic approach to resolve its origin and evolutionary history, and identify candidate genes related to important agricultural traits.

Plant breeding programs and a wide range of plant science applications would greatly benefit from the development of in-field high throughput phenotyping technologies. In this study, a terrestrial LiDAR-based high throughput phenotyping system was developed. A 2D LiDAR was applied to scan plants from overhead in the field, and an RTK-GPS was used to provide spatial coordinates. Precise 3D models of scanned plants were reconstructed based on the LiDAR and RTK-GPS data. The ground plane of the 3D model was separated by RANSAC algorithm and a Euclidean clustering algorithm was applied to remove noise generated by weeds. After that, clean 3D surface models of cotton plants were obtained, from which three plot-level morphologic traits including canopy height, projected canopy area, and plant volume were derived. Canopy height ranging from 85th percentile to the maximum height were computed based on the histogram of the z coordinate for all measured points; projected canopy area was derived by projecting all points on a ground plane; and a Trapezoidal rule based algorithm was proposed to estimate plant volume. Results of validation experiments showed good agreement between LiDAR measurements and manual measurements for maximum canopy height, projected canopy area, and plant volume, with -values of 0.97, 0.97, and 0.98, respectively. The developed system was used to scan the whole field repeatedly over the period from 43 to 109 days after planting. Growth trends and growth rate curves for all three derived morphologic traits were established over the monitoring period for each cultivar. Overall, four different cultivars showed similar growth trends and growth rate patterns. Each cultivar continued to grow until ~88 days after planting, and from then on varied little. However, the actual values were cultivar specific. Correlation analysis between morphologic traits and final yield was conducted over the monitoring period. When considering each cultivar individually, the three traits showed the best correlations with final yield during the period between around 67 and 109 days after planting, with maximum -values of up to 0.84, 0.88, and 0.85, respectively. The developed system demonstrated relatively high throughput data collection and analysis.