Understanding genome structure facilitates the use of wild lentil germplasm for breeding: A case study with shattering loci.

Plant breeders are generally reluctant to cross elite crop cultivars with their wild relatives to introgress novel desirable traits due to associated negative traits such as pod shattering. This results in a genetic bottleneck that could be reduced through better understanding of the genomic locations of the gene(s) controlling this trait. We integrated information on parental genomes, pod shattering data from multiple environments, and high-density genetic linkage maps to identify pod shattering quantitative trait loci (QTLs) in three lentil interspecific recombinant inbred line populations. The broad-sense heritability on a multi-environment basis varied from 0.46 (in LR-70, Lens culinaris × Lens odemensis) to 0.77 (in LR-68, Lens orientalis × L. culinaris). Genetic linkage maps of the interspecific populations revealed reciprocal translocations of chromosomal segments that differed among the populations, and which were associated with reduced recombination. LR-68 had a 2-5 translocation, LR-70 had 1-5, 2-6, and 2-7 translocations, and LR-86 had a 2-7 translocation in one parent relative to the other. Segregation distortion was also observed for clusters of single nucleotide polymorphisms on multiple chromosomes per population, further affecting introgression. Two major QTL, on chromosomes 4 and 7, were repeatedly detected in the three populations and contain several candidate genes. These findings will be of significant value for lentil breeders to strategically access novel superior alleles while minimizing the genetic impact of pod shattering from wild parents.

CRISPR-Cas9-based repeat depletion for high-throughput genotyping of complex plant genomes.

High-throughput genotyping enables the large-scale analysis of genetic diversity in population genomics and genome-wide association studies that combine the genotypic and phenotypic characterization of large collections of accessions. Sequencing-based approaches for genotyping are progressively replacing traditional genotyping methods because of the lower ascertainment bias. However, genome-wide genotyping based on sequencing becomes expensive in species with large genomes and a high proportion of repetitive DNA. Here we describe the use of CRISPR-Cas9 technology to deplete repetitive elements in the 3.76-Gb genome of lentil (Lens culinaris), 84% consisting of repeats, thus concentrating the sequencing data on coding and regulatory regions (single-copy regions). We designed a custom set of 566,766 gRNAs targeting 2.9 Gbp of repeats and excluding repetitive regions overlapping annotated genes and putative regulatory elements based on ATAC-seq data. The novel depletion method removed ∼40% of reads mapping to repeats, increasing those mapping to single-copy regions by ∼2.6-fold. When analyzing 25 million fragments, this repeat-to-single-copy shift in the sequencing data increased the number of genotyped bases of ∼10-fold compared to nondepleted libraries. In the same condition, we were also able to identify ∼12-fold more genetic variants in the single-copy regions and increased the genotyping accuracy by rescuing thousands of heterozygous variants that otherwise would be missed because of low coverage. The method performed similarly regardless of the multiplexing level, type of library or genotypes, including different cultivars and a closely related species (L. orientalis). Our results showed that CRISPR-Cas9-driven repeat depletion focuses sequencing data on single-copy regions, thus improving high-density and genome-wide genotyping in large and repetitive genomes.

Focusing the GWAS Lens on days to flower using latent variable phenotypes derived from global multienvironment trials.

Adaptation constraints within crop species have resulted in limited genetic diversity in some breeding programs and areas where new crops have been introduced, for example, for lentil (Lens culinaris Medik.) in North America. An improved understanding of the underlying genetics involved in phenology-related traits is valuable knowledge to aid breeders in overcoming limitations associated with unadapted germplasm and expanding their genetic diversity by introducing new, exotic material. We used a large, 18 site-year, multienvironment dataset phenotyped for phenology-related traits across nine locations and over 3 yr along with accompanying latent variable phenotypes derived from a photothermal model and principal component analysis (PCA) of days from sowing to flower (DTF) data for a lentil diversity panel (324 accessions), which has also been genotyped with an exome capture array. Genome-wide association studies (GWAS) on DTF across multiple environments helped confirm associations with known flowering-time genes and identify new quantitative trait loci (QTL), which may contain previously unknown flowering time genes. Additionally, the use of latent variable phenotypes, which can incorporate environmental data such as temperature and photoperiod as both GWAS traits and as covariates, strengthened associations, revealed additional hidden associations, and alluded to potential roles of the associated QTL. Our approach can be replicated with other crop species, and the results from our GWAS serve as a resource for further exploration into the complex nature of phenology-related traits across the major growing environments for cultivated lentil.

UAV Image-Based Crop Growth Analysis of 3D-Reconstructed Crop Canopies.

Plant growth rate is an essential phenotypic parameter for quantifying potential crop productivity. Under field conditions, manual measurement of plant growth rate is less accurate in most cases. Image-based high-throughput platforms offer great potential for rapid, non-destructive, and objective estimation of plant growth parameters. The aim of this study was to assess the potential for quantifying plant growth rate using UAV-based (unoccupied aerial vehicle) imagery collected multiple times throughout the growing season. In this study, six diverse lines of lentils were grown in three replicates of 1 m2 microplots with six biomass collection time-points throughout the growing season over five site-years. Aerial imagery was collected simultaneously with each manual measurement of the above-ground biomass time-point and was used to produce two-dimensional orthomosaics and three-dimensional point clouds. Non-linear logistic models were fit to multiple data collection points throughout the growing season. Overall, remotely detected vegetation area and crop volume were found to produce trends comparable to the accumulation of dry weight biomass throughout the growing season. The growth rate and G50 (days to 50% of maximum growth) parameters of the model effectively quantified lentil growth rate indicating significant potential for image-based tools to be used in plant breeding programs. Comparing image-based groundcover and vegetation volume estimates with manually measured above-ground biomass suggested strong correlations. Vegetation area measured from a UAV has utility in quantifying lentil biomass and is indicative of leaf area early in the growing season. For mid- to late-season biomass estimation, plot volume was determined to be a better estimator. Apart from traditional traits, the estimation and analysis of plant parameters not typically collected in traditional breeding programs are possible with image-based methods, and this can create new opportunities to improve breeding efficiency mainly by offering new phenotypes and affecting selection intensity.

An evolutionary look into the history of lentil reveals unexpected diversity.

The characterization and preservation of genetic variation in crops is critical to meeting the challenges of breeding in the face of changing climates and markets. In recent years, the use of single nucleotide polymorphisms (SNPs) has become routine, allowing us to understand the population structure, find divergent lines for crosses, and illuminate the origin of crops. However, the focus on SNPs overlooks other forms of variation, such as copy number variation (CNVs). Lentil is the third most important cold-season legume and was domesticated in the Fertile Crescent. We genotyped 324 accessions that represent its global diversity, and using both SNPs and CNVs, we dissected the population structure and genetic variation, and identified candidate genes. Eight clusters were detected, most of them located in or near the Fertile Crescent, even though different clusters were present in distinct regions. The cluster from South Asia was particularly differentiated and presented low diversity, contrasting with the clusters from the Mediterranean and the northern temperate. Accessions from North America were mainly assigned to one cluster and were highly diverse, reflecting the efforts of breeding programs to integrate variation. Thirty-three genes were identified as candidates under selection and among their functions were sporopollenin synthesis in pollen, a component of chlorophyll B reductase that partially determines the antenna size, and two genes related to the import system of chloroplasts. Eleven percent of all lentil genes and 21% of lentil disease resistance genes were affected by CNVs. The gene categories overrepresented in these genes were "enzymes," "Cell Wall Organization," and "external stimuli response." All the genes found in the latter were associated with pathogen response. CNVs provided information about population structure and might have played a role in adaptation. The incorporation of CNVs in diversity studies is needed for a broader understanding of how they evolve and contribute to domestication.

RNA-Seq and Gene Ontology Analysis Reveal Differences Associated With Low R/FR-Induced Shade Responses in Cultivated Lentil and a Wild Relative.

Lentil is an important pulse crop not only because of its high nutrient value but also because of its ecological advantage in a sustainable agricultural system. Our previous work showed that the cultivated lentil and wild lentil germplasm respond differently to light environments, especially to low R/FR-induced shade conditions. Little is known about how cultivated and wild lentils respond to shade at the level of gene expression and function. In this study, transcriptomic profiling of a cultivated lentil (Lupa, L. culinaris) and a wild lentil (BGE 016880, L. orientalis) at several growth stages is presented. De novo transcriptomes were assembled for both genotypes, and differential gene expression analysis and gene ontology enrichment analysis were performed. The transcriptomic resources generated in this study provide fundamental information regarding biological processes and genes associated with shade responses in lentils. BGE 016880 and Lupa shared a high similarity in their transcriptomes; however, differential gene expression profiles were not consistent between these two genotypes. The wild lentil BGE 016880 had more differentially expressed genes than the cultivated lentil Lupa. Upregulation of genes involved in gibberellin, brassinosteroid, and auxin synthesis and signaling pathways, as well as cell wall modification, in both genotypes explains their similarity in stem elongation response under the shade. Genes involved in jasmonic acid and flavonoid biosynthesis pathways were downregulated in BGE 016880 only, and biological processes involved in defense responses were significantly enriched in the wild lentil BGE 016880 only. Downregulation of WRKY and MYB transcription factors could contribute to the reduced defense response in BGE 016880 but not in Lupa under shade conditions. A better understanding of shade responses of pulse crop species and their wild relatives will play an important role in developing genetic strategies for crop improvement in response to changes in light environments.

Genetic analysis of early phenology in lentil identifies distinct loci controlling component traits.

Modern-day domesticated lentil germplasm is generally considered to form three broad adaptation groups: Mediterranean, South Asian, and northern temperate, which correspond to the major global production environments. Reproductive phenology plays a key role in lentil adaptation to this diverse ecogeographic variation. Here, we dissect the characteristic earliness of the pilosae ecotype, suited to the typically short cropping season of South Asian environments. We identified two loci, DTF6a and DTF6b, at which dominant alleles confer early flowering, and we show that DTF6a alone is sufficient to confer early flowering under extremely short photoperiods. Genomic synteny confirmed the presence of a conserved cluster of three florigen (FT) gene orthologues among potential candidate genes, and expression analysis in near-isogenic material showed that the early allele is associated with a strong derepression of the FTa1 gene in particular. Sequence analysis revealed a 7.4 kb deletion in the FTa1-FTa2 intergenic region in the pilosae parent, and a wide survey of >350 accessions with diverse origin showed that the dtf6a allele is predominant in South Asian material. Collectively, these results contribute to understanding the molecular basis of global adaptation in lentil, and further emphasize the importance of this conserved genomic region for adaptation in temperate legumes generally.

Genetic basis for lentil adaptation to summer cropping in northern temperate environments.

The continued success of lentil (Lens culinaris Medik.) genetic improvement relies on the availability of broad genetic diversity, and new alleles need to be identified and incorporated into the cultivated gene pool. Availability of robust and predictive markers greatly enhances the precise transfer of genomic regions from unadapted germplasm. Quantitative trait loci (QTL) for key phenological traits in lentil were located using a recombinant inbreed line (RIL) population derived from a cross between an Ethiopian landrace (ILL 1704) and a northern temperate cultivar (CDC Robin). Field experiments were conducted at Sutherland research farm in Saskatoon and at Rosthern, Saskatchewan, Canada during 2018 and 2019. A linkage map was constructed using 21,634 single nucleotide polymorphisms (SNPs) located on seven linkage groups (LGs), which correspond to the seven haploid chromosomes of lentil. Eight QTL were identified for six phenological traits. Flowering-related QTL were identified at two regions on LG6. FLOWERING LOCUS T (FT) genes were annotated within the flowering time QTL interval based on the lentil reference genome. Similarly, a major QTL for postflowering developmental processes was located on LG5 with several senescence-associated genes annotated within the QTL interval. The flowering time QTL was validated in a different genetic background indicating the potential use of the identified markers for marker-assisted selection to precisely transfer genomic regions from exotic germplasm into elite crop cultivars without disrupting adaptation.

Identification of anthracnose race 1 resistance loci in lentil by integrating linkage mapping and genome-wide association study.

Anthracnose, caused byColletotrichum lentis, is a devastating disease of lentil (Lens culinaris Medik.) in western Canada. Growing resistant lentil cultivars is the most cost-effective and environmentally friendly approach to prevent seed yield losses that can exceed 70%. To identify loci conferring resistance to anthracnose race 1 in lentil, biparental quantitative trait loci (QTL) mapping of two recombinant inbred line (RIL) populations was integrated with a genome-wide association study (GWAS) using 200 diverse lentil accessions from a lentil diversity panel. A major-effect QTL (qAnt1.Lc-3) conferring resistance to race 1 was mapped to lentil chromosome 3 and colocated on the lentil physical map for both RIL populations. Clusters of candidate nucleotide-binding leucine-rich repeat (NB-LRR) and other defense-related genes were uncovered within the QTL region. A GWAS detected 14 significant single nucleotide polymorphism (SNP) markers associated with race 1 resistance on chromosomes 3, 4, 5, and 6. The most significant GWAS SNPs on chromosome 3 supported qAnt1.Lc-3 and delineated a region of 1.6 Mb containing candidate resistance genes. The identified SNP markers can be directly applied in marker-assisted selection (MAS) to accelerate the introgression of race 1 resistance in lentil breeding.

A PostgreSQL Tripal solution for large-scale genotypic and phenotypic data.

Researchers are seeking cost-effective solutions for management and analysis of large-scale genotypic and phenotypic data. Open-source software is uniquely positioned to fill this need through user-focused, crowd-sourced development. Tripal, an open-source toolkit for developing biological data web portals, uses the GMOD Chado database schema to achieve flexible, ontology-driven storage in PostgreSQL. Tripal also aids research-focused web portals in providing data according to findable, accessible, interoperable, reusable (FAIR) principles. We describe here a fully relational PostgreSQL solution to handle large-scale genotypic and phenotypic data that is implemented as a collection of freely available, open-source modules. These Tripal extension modules provide a holistic approach for importing, storage, display and analysis within a relational database schema. Furthermore, they embody the Tripal approach to FAIR data by providing multiple search tools and ensuring metadata is fully described and interoperable. Our solution focuses on data integrity, as well as optimizing performance to provide a fully functional system that is currently being used in the production of Tripal portals for crop species. We fully describe the implementation of our solution and discuss why a PostgreSQL-powered web portal provides an efficient environment for researcher-driven genotypic and phenotypic data analysis.

The INCREASE project: Intelligent Collections of food-legume genetic resources for European agrofood systems.

Food legumes are crucial for all agriculture-related societal challenges, including climate change mitigation, agrobiodiversity conservation, sustainable agriculture, food security and human health. The transition to plant-based diets, largely based on food legumes, could present major opportunities for adaptation and mitigation, generating significant co-benefits for human health. The characterization, maintenance and exploitation of food-legume genetic resources, to date largely unexploited, form the core development of both sustainable agriculture and a healthy food system. INCREASE will implement, on chickpea (Cicer arietinum), common bean (Phaseolus vulgaris), lentil (Lens culinaris) and lupin (Lupinus albus and L. mutabilis), a new approach to conserve, manage and characterize genetic resources. Intelligent Collections, consisting of nested core collections composed of single-seed descent-purified accessions (i.e., inbred lines), will be developed, exploiting germplasm available both from genebanks and on-farm and subjected to different levels of genotypic and phenotypic characterization. Phenotyping and gene discovery activities will meet, via a participatory approach, the needs of various actors, including breeders, scientists, farmers and agri-food and non-food industries, exploiting also the power of massive metabolomics and transcriptomics and of artificial intelligence and smart tools. Moreover, INCREASE will test, with a citizen science experiment, an innovative system of conservation and use of genetic resources based on a decentralized approach for data management and dynamic conservation. By promoting the use of food legumes, improving their quality, adaptation and yield and boosting the competitiveness of the agriculture and food sector, the INCREASE strategy will have a major impact on economy and society and represents a case study of integrative and participatory approaches towards conservation and exploitation of crop genetic resources.

Genetic and gene expression analysis of flowering time regulation by light quality in lentil.

BACKGROUND AND AIMS: Flowering time is important due to its roles in plant adaptation to different environments and subsequent formation of crop yield. Changes in light quality affect a range of developmental processes including flowering time, but little is known about light quality-induced flowering time control in lentil. This study aims to investigate the genetic basis for differences in flowering response to light quality in lentil. METHODS: We explored variation in flowering time caused by changes in red/far-red-related light quality environments of a lentil interspecific recombinant inbred line (RIL) population developed from a cross between Lens culinaris cv. Lupa and L. orientalis accession BGE 016880. A genetic linkage map was constructed and then used for identifying quantitative trait loci (QTLs) associated with flowering time regulation under different light quality environments. Differential gene expression analysis through transcriptomic study and RT-qPCR were used to identify potential candidate genes. KEY RESULTS: QTL mapping located 13 QTLs controlling flower time under different light quality environments, with phenotypic variance explained ranging from 1.7 to 62.9 %. Transcriptomic profiling and gene expression analysis for both parents of this interspecific RIL population identified flowering-related genes showing environment-specific differential expression (flowering DEGs). One of these, a member of the florigen gene family FTa1 (LcFTa1), was located close to three major QTLs. Furthermore, gene expression results suggested that two other florigen genes (LcFTb1 and LcFTb2), MADS-box transcription factors such as LcAGL6/13d, LcSVPb, LcSOC1b and LcFULb, as well as bHLH transcription factor LcPIF6 and Gibberellin 20 oxidase LcGA20oxC,G may also be involved in the light quality response. CONCLUSIONS: Our results show that a major component of flowering time sensitivity to light quality is tightly linked to LcFTa1 and associated with changes in its expression. This work provides a foundation for crop improvement of lentil with better adaptation to variable light environments.

The tepary bean genome provides insight into evolution and domestication under heat stress.

Tepary bean (Phaseolus acutifolis A. Gray), native to the Sonoran Desert, is highly adapted to heat and drought. It is a sister species of common bean (Phaseolus vulgaris L.), the most important legume protein source for direct human consumption, and whose production is threatened by climate change. Here, we report on the tepary genome including exploration of possible mechanisms for resilience to moderate heat stress and a reduced disease resistance gene repertoire, consistent with adaptation to arid and hot environments. Extensive collinearity and shared gene content among these Phaseolus species will facilitate engineering climate adaptation in common bean, a key food security crop, and accelerate tepary bean improvement.

Intelligent Characterization of Lentil Genetic Resources: Evolutionary History, Genetic Diversity of Germplasm, and the Need for Well-Represented Collections.

The genetic and phenotypic characterization of crops allows us to elucidate their evolutionary and domestication history, the genetic basis of important traits, and the use of variation present in landraces and wild relatives to enhance resilience. In this context, we aim to provide an overview of the main genetic resources developed for lentil and their main outcomes, and to suggest protocols for continued work on this important crop. Lens culinaris is the third-most-important cool-season grain and its use is increasing as a quick-cooking, nutritious, plant-based source of protein. L. culinaris was domesticated in the Fertile Crescent, and six additional wild taxa (L. orientalis, L. tomentosus, L. odemensis, L. lamottei, L. ervoides, and L. nigricans) are recognized. Numerous genetic diversity studies have shown that wild relatives present high levels of genetic variation and provide a reservoir of alleles that can be used for breeding programs. Furthermore, the integration of genetics/genomics and breeding techniques has resulted in identification of quantitative trait loci and genes related to attributes of interest. Genetic maps, massive genotyping, marker-assisted selection, and genomic selection are some of the genetic resources generated and applied in lentil. In addition, despite its size (∼4 Gbp) and complexity, the L. culinaris genome has been assembled, allowing a deeper understanding of its architecture. Still, major knowledge gaps exist in lentil, and a deeper understanding and characterization of germplasm resources, including wild relatives, is critical to lentil breeding and improvement. © 2021 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Recording of lentil seed descriptors Basic Protocol 2: Lentil seed imaging Basic Protocol 3: Lentil seed increase Basic Protocol 4: Recording of primary lentil seed INCREASE descriptors.

Genomic selection for lentil breeding: Empirical evidence.

Genomic selection (GS) is a marker-based selection initially suggested for livestock breeding and is being encouraged for crop breeding. Several statistical models are used to implement GS; however, none have been tested for use in lentil (Lens culinaris Medik.) breeding. This study was conducted to compare the accuracy of different GS models and prediction scenarios based on empirical data and to make recommendations for designing genomic selection strategies for lentil breeding. We evaluated nine single-trait (ST) models, two multiple-trait (MT) models, and a model that incorporates genotype × environment interaction (GEI) using populations from a lentil diversity panel and two recombinant inbred lines (RILs). The lines in all populations were phenotyped for five phenological traits and genotyped using a custom exome capture assay. Within-population, across-population, and across-environment genomic predictions were made. Prediction accuracy varied among the evaluated models, populations, prediction scenarios, and traits. Single-trait models showed similar accuracy in the absence of large effect quantitative trait loci (QTL) but BayesB outperformed all models when there were QTL with relatively large effects. Models that accounted for GEI and MT-GS models increased prediction accuracy for a low heritability trait by up to 66 and 14%, respectively. Moderate to high accuracies were obtained for within-population (range of .36-.85) and across-environment (range of .19-.89) predictions but across-population prediction accuracy was very low. Results suggest that GS can be implemented in lentil breeding to make predictions within populations and across environments, but across-population prediction should not be considered when the population size is small.

The BELT and phenoSEED platforms: shape and colour phenotyping of seed samples.

BACKGROUND: Quantitative and qualitative assessment of visual and morphological traits of seed is slow and imprecise with potential for bias to be introduced when gathered with handheld tools. Colour, size and shape traits can be acquired from properly calibrated seed images. New automated tools were requested to improve data acquisition efficacy with an emphasis on developing research workflows. RESULTS: A portable imaging system (BELT) supported by image acquisition and analysis software (phenoSEED) was created for small-seed optical analysis. Lentil (Lens culinaris L.) phenotyping was used as the primary test case. Seeds were loaded into the system and all seeds in a sample were automatically individually imaged to acquire top and side views as they passed through an imaging chamber. A Python analysis script applied a colour calibration and extracted quantifiable traits of seed colour, size and shape. Extraction of lentil seed coat patterning was implemented to further describe the seed coat. The use of this device was forecasted to eliminate operator biases, increase the rate of acquisition of traits, and capture qualitative information about traits that have been historically analyzed by eye. CONCLUSIONS: Increased precision and higher rates of data acquisition compared to traditional techniques will help to extract larger datasets and explore more research questions. The system presented is available as an open-source project for academic and non-commercial use.

Postharvest seed coat darkening in pinto bean (Phaseolus vulgaris) is regulated by Psd , an allele of the basic helix-loop-helix transcription factor P.

Pinto bean (Phaseolus vulgaris) is one of the leading market classes of dry beans that is most affected by postharvest seed coat darkening. The process of seed darkening poses a challenge for bean producers and vendors as they encounter significant losses in crop value due to decreased consumer preference for darker beans. Here, we identified a novel allele of the P gene, Psd , responsible for the slow darkening seed coat in pintos, and identified trait-specific sequence polymorphisms which are utilized for the development of new gene-specific molecular markers for breeding. These tools can be deployed to help tackle this economically important issue for bean producers. SUMMARY: Postharvest seed coat darkening in pinto bean is an undesirable trait that reduces the market value of the stored crop. Regular darkening (RD) pintos darken faster after harvest and accumulate higher level of proanthocyanidins (PAs) compared to slow darkening (SD) cultivars. Although the markers cosegregating with the SD trait have been known for some time, the SLOW DARKENING (Sd) gene identity had not been proven.Here, we identified Psd as a candidate for controlling the trait. Genetic complementation, transcript abundance, metabolite analysis, and inheritance study confirmed that Psd is the Sd gene. Psd is another allele of the P (Pigment) gene, whose loss-of-function alleles result in a white seed coat. Psd encodes a bHLH transcription factor with two transcript variants but only one is involved in PA biosynthesis. An additional glutamate residue in the activation domain, and/or an arginine to histidine substitution in the bHLH domain of the Psd-1 transcript in the SD cultivar is likely responsible for the reduced activity of this allele compared to the allele in a RD cultivar, leading to reduced PA accumulation.Overall, we demonstrate that a novel allele of P, Psd , is responsible for the SD phenotype, and describe the development of new, gene-specific, markers that could be utilized in breeding to resolve an economically important issue for bean producers.

KnowPulse: A Web-Resource Focused on Diversity Data for Pulse Crop Improvement.

KnowPulse (https://knowpulse.usask.ca) is a breeder-focused web portal for pulse breeders and geneticists. With a focus on diversity data, KnowPulse provides information on genetic markers, sequence variants, phenotypic traits and germplasm for chickpea, common bean, field pea, faba bean, and lentil. Genotypic data is accessible through the genotype matrix tool, displayed as a marker-by-germplasm table of genotype calls specific to germplasm chosen by the researcher. It is also summarized on genetic marker and sequence variant pages. Phenotypic data is visualized in trait distribution plots: violin plots for quantitative data and histograms for qualitative data. These plots are accessible through trait, germplasm, and experiment pages, as well as through a single page search tool. KnowPulse is built using the open-source Tripal toolkit and utilizes open-source tools including, but not limited to, species-specific JBrowse instances, a BLAST interface, and whole-genome CViTjs visualizations. KnowPulse is constantly evolving with data and tools added as they become available. Full integration of genetic maps and quantitative trait loci is imminent, and development of tools exploring structural variation is being explored.

Tripal v3: an ontology-based toolkit for construction of FAIR biological community databases.

Community biological databases provide an important online resource for both public and private data, analysis tools and community engagement. These sites house genomic, transcriptomic, genetic, breeding and ancillary data for specific species, families or clades. Due to the complexity and increasing quantities of these data, construction of online resources is increasingly difficult especially with limited funding and access to technical expertise. Furthermore, online repositories are expected to promote FAIR data principles (findable, accessible, interoperable and reusable) that presents additional challenges. The open-source Tripal database toolkit seeks to mitigate these challenges by creating both the software and an interactive community of developers for construction of online community databases. Additionally, through coordinated, distributed co-development, Tripal sites encourage community-wide sustainability. Here, we report the release of Tripal version 3 that improves data accessibility and data sharing through systematic use of controlled vocabularies (CVs). Tripal uses the community-developed Chado database as a default data store, but now provides tools to support other data stores, while ensuring that CVs remain the central organizational structure for the data. A new site developer can use Tripal to develop a basic site with little to no programming, with the ability to integrate other data types using extension modules and the Tripal application programming interface. A thorough online User's Guide and Developer's Handbook are available at http://tripal.info, providing download, installation and step-by-step setup instructions.