PhytoOracle: Scalable, modular phenomics data processing pipelines.

As phenomics data volume and dimensionality increase due to advancements in sensor technology, there is an urgent need to develop and implement scalable data processing pipelines. Current phenomics data processing pipelines lack modularity, extensibility, and processing distribution across sensor modalities and phenotyping platforms. To address these challenges, we developed PhytoOracle (PO), a suite of modular, scalable pipelines for processing large volumes of field phenomics RGB, thermal, PSII chlorophyll fluorescence 2D images, and 3D point clouds. PhytoOracle aims to (i) improve data processing efficiency; (ii) provide an extensible, reproducible computing framework; and (iii) enable data fusion of multi-modal phenomics data. PhytoOracle integrates open-source distributed computing frameworks for parallel processing on high-performance computing, cloud, and local computing environments. Each pipeline component is available as a standalone container, providing transferability, extensibility, and reproducibility. The PO pipeline extracts and associates individual plant traits across sensor modalities and collection time points, representing a unique multi-system approach to addressing the genotype-phenotype gap. To date, PO supports lettuce and sorghum phenotypic trait extraction, with a goal of widening the range of supported species in the future. At the maximum number of cores tested in this study (1,024 cores), PO processing times were: 235 minutes for 9,270 RGB images (140.7 GB), 235 minutes for 9,270 thermal images (5.4 GB), and 13 minutes for 39,678 PSII images (86.2 GB). These processing times represent end-to-end processing, from raw data to fully processed numerical phenotypic trait data. Repeatability values of 0.39-0.95 (bounding area), 0.81-0.95 (axis-aligned bounding volume), 0.79-0.94 (oriented bounding volume), 0.83-0.95 (plant height), and 0.81-0.95 (number of points) were observed in Field Scanalyzer data. We also show the ability of PO to process drone data with a repeatability of 0.55-0.95 (bounding area).

A Composite Analysis of Flowering Time Regulation in Lettuce.

Plants undergo profound physiological changes when transitioning from vegetative to reproductive growth. These changes affect crop production, as in the case of leafy vegetables. Lettuce is one of the most valuable leafy vegetable crops in the world. Past genetic studies have identified multiple quantitative trait loci (QTLs) that affect the timing of the floral transition in lettuce. Extensive functional molecular studies in the model organism Arabidopsis provide the opportunity to transfer knowledge to lettuce to explore the mechanisms through which genetic variations translate into changes in flowering time. In this review, we integrated results from past genetic and molecular studies for flowering time in lettuce with orthology and functional inference from Arabidopsis. This summarizes the basis for all known genetic variation underlying the phenotypic diversity of flowering time in lettuce and how the genetics of flowering time in lettuce projects onto the established pathways controlling flowering time in plants. This comprehensive overview reveals patterns across experiments as well as areas in need of further study. Our review also represents a resource for developing cultivars with delayed flowering time.

Drone phenotyping and machine learning enable discovery of loci regulating daily floral opening in lettuce.

Flower opening and closure are traits of reproductive importance in all angiosperms because they determine the success of self- and cross-pollination. The temporal nature of this phenotype rendered it a difficult target for genetic studies. Cultivated and wild lettuce, Lactuca spp., have composite inflorescences that open only once. An L. serriola×L. sativa F6 recombinant inbred line (RIL) population differed markedly for daily floral opening time. This population was used to map the genetic determinants of this trait; the floral opening time of 236 RILs was scored using time-course image series obtained by drone-based phenotyping on two occasions. Floral pixels were identified from the images using a support vector machine with an accuracy >99%. A Bayesian inference method was developed to extract the peak floral opening time for individual genotypes from the time-stamped image data. Two independent quantitative trait loci (QTLs; Daily Floral Opening 2.1 and qDFO8.1) explaining >30% of the phenotypic variation in floral opening time were discovered. Candidate genes with non-synonymous polymorphisms in coding sequences were identified within the QTLs. This study demonstrates the power of combining remote sensing, machine learning, Bayesian statistics, and genome-wide marker data for studying the genetics of recalcitrant phenotypes.

Genome assembly with in vitro proximity ligation data and whole-genome triplication in lettuce.

Lettuce (Lactuca sativa) is a major crop and a member of the large, highly successful Compositae family of flowering plants. Here we present a reference assembly for the species and family. This was generated using whole-genome shotgun Illumina reads plus in vitro proximity ligation data to create large superscaffolds; it was validated genetically and superscaffolds were oriented in genetic bins ordered along nine chromosomal pseudomolecules. We identify several genomic features that may have contributed to the success of the family, including genes encoding Cycloidea-like transcription factors, kinases, enzymes involved in rubber biosynthesis and disease resistance proteins that are expanded in the genome. We characterize 21 novel microRNAs, one of which may trigger phasiRNAs from numerous kinase transcripts. We provide evidence for a whole-genome triplication event specific but basal to the Compositae. We detect 26% of the genome in triplicated regions containing 30% of all genes that are enriched for regulatory sequences and depleted for genes involved in defence.

Dm3 is one member of a large constitutively expressed family of nucleotide binding site-leucine-rich repeat encoding genes.

The major cluster of resistance genes in lettuce cv. Diana contains approximately 32 nucleotide binding site-leucine-rich repeat encoding genes. Previous molecular dissection of this complex region had identified a large gene, RGC2B, as a candidate for encoding the downy mildew resistance gene, Dm3. This article describes genetic and transgenic complementation data that demonstrated RGC2B is necessary and sufficient to confer resistance with Dm3 specificity. Ethylmethanesulphonate was used to induce mutations to downy mildew susceptibility in cv. Diana (Dm1, Dm3, Dm7, and Dm8). Nineteen families were identified with a complete loss of resistance in one of the four resistance specificities. Sequencing revealed a variety of point mutations in RGC2B in the six dm3 mutants. Losses of resistance were due to single changes in amino acid sequence or a change in an intron splice site. These mutations did not cluster in any particular region of RGC2B. A full-length genomic copy of RGC2B was isolated from a lambdaphage library and introduced into two genotypes of lettuce. Transgenics expressing RGC2B exhibited resistance to all isolates expressing Avr3 from a wide range of geographical origins. In a wildtype Dm3-expressing genotype, many of the RGC2 family members are expressed at low levels throughout the plant.