Genes involved in auxin biosynthesis, transport and signalling underlie the extreme adventitious root phenotype of the tomato aer mutant.

The use of tomato rootstocks has helped to alleviate the soaring abiotic stresses provoked by the adverse effects of climate change. Lateral and adventitious roots can improve topsoil exploration and nutrient uptake, shoot biomass and resulting overall yield. It is essential to understand the genetic basis of root structure development and how lateral and adventitious roots are produced. Existing mutant lines with specific root phenotypes are an excellent resource to analyse and comprehend the molecular basis of root developmental traits. The tomato aerial roots (aer) mutant exhibits an extreme adventitious rooting phenotype on the primary stem. It is known that this phenotype is associated with restricted polar auxin transport from the juvenile to the more mature stem, but prior to this study, the genetic loci responsible for the aer phenotype were unknown. We used genomic approaches to define the polygenic nature of the aer phenotype and provide evidence that increased expression of specific auxin biosynthesis, transport and signalling genes in different loci causes the initiation of adventitious root primordia in tomato stems. Our results allow the selection of different levels of adventitious rooting using molecular markers, potentially contributing to rootstock breeding strategies in grafted vegetable crops, especially in tomato. In crops vegetatively propagated as cuttings, such as fruit trees and cane fruits, orthologous genes may be useful for the selection of cultivars more amenable to propagation.

Searching molecular biomarkers correlating with BSID-III at 24 months in infants with neonatal hypoxic-ischemic encephalopathy.

An early prediction of outcomes of neonatal hypoxic-ischemic encephalopathy (NE) is of key importance in reducing neonatal mortality and morbidity. The objectives were (i) to analyze the characteristics of miRNA expression and metabolic patterns of neonates with NE and (ii) to assess their predictive performance for neurodevelopmental outcomes. Plasma samples from moderate/severe NE patients (N = 92) of the HYPOTOP study were collected before, during, and after therapeutic hypothermia (TH) and compared to a control group (healthy term infants). The expression of miRNAs and concentrations of metabolites (hypoxia-related and energy, steroid, and tryptophan metabolisms) were analyzed. Neurodevelopmental outcomes were evaluated at 24 months postnatal age using Bayley Scales of Infant Development, ed. III, BSID-III. Differences in miRNA and metabolic profiles were found between NE vs. control infants, abnormal (i.e., mildly and moderately abnormal and severe) vs. normal, and severe vs. non-severe (i.e., normal and mildly and moderately abnormal) BSID-III. 4-Androstene-3,17-dione, testosterone, betaine, xanthine, and lactate were suitable for BSID-III outcome prediction (receiver operating characteristic areas under the curve (AUCs) ≥ 0.6), as well as 68 miRNAs (AUCs of 0.5-0.9). Significant partial correlations of xanthine and betaine levels and the expression of several miRNAs with BSID-III sub-scales were found. Conclusion: We have identified metabolites/miRNAs that might be useful to support the prediction of middle-term neurodevelopmental outcomes of NE. What is known and what is new: • The early prediction of outcomes of neonatal hypoxic-ischemic encephalopathy (NE) is of key importance in reducing neonatal mortality and morbidity. • Alterations of the metabolome and miRNAs had been observed in NE. • We performed miRNA sequencing and quantified selected metabolites (i.e., lactate, pyruvate, ketone bodies, Krebs cycle intermediates, tryptophan pathway, hypoxia-related metabolites, and steroids) by GC- and LC-MS. • Specific miRNAs and metabolites that allow prediction of middle-term neurodevelopmental outcomes of newborns with NE undergoing hypothermia treatment were identified.

The ORGAN SIZE (ORG) locus modulates both vegetative and reproductive gigantism in domesticated tomato.

BACKGROUND AND AIMS: Gigantism is a key component of the domestication syndrome, a suite of traits that differentiates crops from their wild relatives. Allometric gigantism is strongly marked in horticultural crops, causing disproportionate increases in the size of edible parts such as stems, leaves or fruits. Tomato (Solanum lycopersicum) has attracted attention as a model for fruit gigantism, and many genes have been described controlling this trait. However, the genetic basis of a corresponding increase in size of vegetative organs contributing to isometric gigantism has remained relatively unexplored. METHODS: Here, we identified a 0.4-Mb region on chromosome 7 in introgression lines (ILs) from the wild species Solanum pennellii in two different tomato genetic backgrounds (cv. 'M82' and cv. 'Micro-Tom') that controls vegetative and reproductive organ size in tomato. The locus, named ORGAN SIZE (ORG), was fine-mapped using genotype-by-sequencing. A survey of the literature revealed that ORG overlaps with previously mapped quantitative trait loci controlling tomato fruit weight during domestication. KEY RESULTS: Alleles from the wild species led to lower cell number in different organs, which was partially compensated by greater cell expansion in leaves, but not in fruits. The result was a proportional reduction in leaf, flower and fruit size in the ILs harbouring the alleles from the wild species. CONCLUSIONS: Our findings suggest that selection for large fruit during domestication also tends to select for increases in leaf size by influencing cell division. Since leaf size is relevant for both source-sink balance and crop adaptation to different environments, the discovery of ORG could allow fine-tuning of these parameters.

De novo genome assembly and functional annotation for Fusarium langsethiae.

BACKGROUND: Fusarium langsethiae is a T-2 and HT-2 mycotoxins producing species firstly characterised in 2004. It is commonly isolated from oats in Northern Europe. T-2 and HT-2 mycotoxins exhibit immunological and haemotological effects in animal health mainly through inhibition of protein, RNA and DNA synthesis. The development of a high-quality and comprehensively annotated assembly for this species is therefore essential in providing the molecular understanding and the mechanism of T-2 and HT-2 biosynthesis in F. langsethiae to help develop effective control strategies. RESULTS: The F. langsethiae assembly was produced using PacBio long reads, which were then assembled independently using Canu, SMARTdenovo and Flye. A total of 19,336 coding genes were identified using RNA-Seq informed ab-initio gene prediction. Finally, predicting genes were annotated using the basic local alignment search tool (BLAST) against the NCBI non-redundant (NR) genome database and protein hits were annotated using InterProScan. Genes with blast hits were functionally annotated with Gene Ontology. CONCLUSIONS: We developed a high-quality genome assembly of a total length of 59 Mb and N50 of 3.51 Mb. Raw sequence reads and assembled genome is publicly available and can be downloaded from: GenBank under the accession JAFFKB000000000. All commands used to generate this assembly are accessible via GitHub: https://github.com/FadyMohareb/fusarium_langsethiae .

A near-chromosome level genome assembly of the European hoverfly, Sphaerophoria rueppellii (Diptera: Syrphidae), provides comparative insights into insecticide resistance-related gene family evolution.

BACKGROUND: Sphaerophoria rueppellii, a European species of hoverfly, is a highly effective beneficial predator of hemipteran crop pests including aphids, thrips and coleopteran/lepidopteran larvae in integrated pest management (IPM) programmes. It is also a key pollinator of a wide variety of important agricultural crops. No genomic information is currently available for S. rueppellii. Without genomic information for such beneficial predator species, we are unable to perform comparative analyses of insecticide target-sites and genes encoding metabolic enzymes potentially responsible for insecticide resistance, between crop pests and their predators. These metabolic mechanisms include several gene families - cytochrome P450 monooxygenases (P450s), ATP binding cassette transporters (ABCs), glutathione-S-transferases (GSTs), UDP-glycosyltransferases (UGTs) and carboxyl/choline esterases (CCEs). METHODS AND FINDINGS: In this study, a high-quality near-chromosome level de novo genome assembly (as well as a mitochondrial genome assembly) for S. rueppellii has been generated using a hybrid approach with PacBio long-read and Illumina short-read data, followed by super scaffolding using Hi-C data. The final assembly achieved a scaffold N50 of 87Mb, a total genome size of 537.6Mb and a level of completeness of 96% using a set of 1,658 core insect genes present as full-length genes. The assembly was annotated with 14,249 protein-coding genes. Comparative analysis revealed gene expansions of CYP6Zx P450s, epsilon-class GSTs, dietary CCEs and multiple UGT families (UGT37/302/308/430/431). Conversely, ABCs, delta-class GSTs and non-CYP6Zx P450s showed limited expansion. Differences were seen in the distributions of resistance-associated gene families across subfamilies between S. rueppellii and some hemipteran crop pests. Additionally, S. rueppellii had larger numbers of detoxification genes than other pollinator species. CONCLUSION AND SIGNIFICANCE: This assembly is the first published genome for a predatory member of the Syrphidae family and will serve as a useful resource for further research into selectivity and potential tolerance of insecticides by beneficial predators. Furthermore, the expansion of some gene families often linked to insecticide resistance and selectivity may be an indicator of the capacity of this predator to detoxify IPM selective insecticides. These findings could be exploited by targeted insecticide screens and functional studies to increase effectiveness of IPM strategies, which aim to increase crop yields by sustainably and effectively controlling pests without impacting beneficial predator populations.

A scaffold-level genome assembly of a minute pirate bug, Orius laevigatus (Hemiptera: Anthocoridae), and a comparative analysis of insecticide resistance-related gene families with hemipteran crop pests.

BACKGROUND: Orius laevigatus, a minute pirate bug, is a highly effective beneficial predator of crop pests including aphids, spider mites and thrips in integrated pest management (IPM) programmes. No genomic information is currently available for O. laevigatus, as is the case for the majority of beneficial predators which feed on crop pests. In contrast, genomic information for crop pests is far more readily available. The lack of publicly available genomes for beneficial predators to date has limited our ability to perform comparative analyses of genes encoding potential insecticide resistance mechanisms between crop pests and their predators. These mechanisms include several gene/protein families including cytochrome P450s (P450s), ATP binding cassette transporters (ABCs), glutathione S-transferases (GSTs), UDP-glucosyltransferases (UGTs) and carboxyl/cholinesterases (CCEs). METHODS AND FINDINGS: In this study, a high-quality scaffold level de novo genome assembly for O. laevigatus has been generated using a hybrid approach with PacBio long-read and Illumina short-read data. The final assembly achieved a scaffold N50 of 125,649 bp and a total genome size of 150.98 Mb. The genome assembly achieved a level of completeness of 93.6% using a set of 1658 core insect genes present as full-length genes. Genome annotation identified 15,102 protein-coding genes - 87% of which were assigned a putative function. Comparative analyses revealed gene expansions of sigma class GSTs and CYP3 P450s. Conversely the UGT gene family showed limited expansion. Differences were seen in the distributions of resistance-associated gene families at the subfamily level between O. laevigatus and some of its targeted crop pests. A target site mutation in ryanodine receptors (I4790M, PxRyR) which has strong links to diamide resistance in crop pests and had previously only been identified in lepidopteran species was found to also be present in hemipteran species, including O. laevigatus. CONCLUSION AND SIGNIFICANCE: This assembly is the first published genome for the Anthocoridae family and will serve as a useful resource for further research into target-site selectivity issues and potential resistance mechanisms in beneficial predators. Furthermore, the expansion of gene families often linked to insecticide resistance may be an indicator of the capacity of this predator to detoxify selective insecticides. These findings could be exploited by targeted pesticide screens and functional studies to increase effectiveness of IPM strategies, which aim to increase crop yields by sustainably, environmentally-friendly and effectively control pests without impacting beneficial predator populations.

De Novo Genome Assembly Of Solanum Sitiens Reveals Structural Variation Associated With Drought And Salinity Tolerance.

MOTIVATION: Solanum sitiens is a self-incompatible wild relative of tomato, characterised by salt and drought resistance traits, with the potential to contribute through breeding programmes to crop improvement in cultivated tomato. This species has a distinct morphology, classification and ecotype compared to other stress resistant wild tomato relatives such as S. pennellii and S. chilense. Therefore, the availability of a reference genome for S. sitiens will facilitate the genetic and molecular understanding of salt and drought resistance. RESULTS: A high-quality de novo genome and transcriptome assembly for S. sitiens (Accession LA1974) has been developed. A hybrid assembly strategy was followed using Illumina short reads (∼159X coverage) and PacBio long reads (∼44X coverage), generating a total of ∼262 Gbp of DNA sequence. A reference genome of 1,245 Mbp, arranged in 1,483 scaffolds with a N50 of 1.826 Mbp was generated. Genome completeness was estimated at 95% using the Benchmarking Universal Single-Copy Orthologs (BUSCO) and the K-mer Analysis Tool (KAT). In addition, ∼63 Gbp of RNA-Seq were generated to support the prediction of 31,164 genes from the assembly, and to perform a de novo transcriptome. Lastly, we identified three large inversions compared to S. lycopersicum, containing several drought resistance related genes, such as beta-amylase 1 and YUCCA7. AVAILABILITY: S. sitiens (LA1974) raw sequencing, transcriptome and genome assembly have been deposited at the NCBI's Sequence Read Archive, under the BioProject number "PRJNA633104".All the commands and scripts necessary to generate the assembly are available at the following github repository: https://github.com/MCorentin/Solanum_sitiens_assembly. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Tersect: a set theoretical utility for exploring sequence variant data.

SUMMARY: Comparing genomic features among a large panel of individuals across the same species is considered nowadays a core part of the bioinformatics analyses. This typically involves a series of complex theoretical expressions to compare, intersect, extract symmetric differences between individuals within a large set of genotypes. Several publically available tools are capable of performing such tasks; however, due to the sheer size of variants being queried, such tasks can be computationally expensive with a runtime ranging from few minutes up to several hours depending on the dataset size. This makes existing tools unsuitable for interactive data query or as part of genomic data visualization platforms such as genome browsers. Tersect is a lightweight, high-performance command-line utility which interprets and applies flexible set theoretical expressions to sets of sequence variant data. It can be used both for interactive data exploration and as part of a larger pipeline thanks to its highly optimized storage and indexing algorithms for variant data. AVAILABILITY AND IMPLEMENTATION: Tersect was implemented in C and released under the MIT license. Tersect is freely available at https://github.com/tomkurowski/tersect. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

VarGen: an R package for disease-associated variant discovery and annotation.

SUMMARY: Over the past decade, there has been an exponential increase in the amount of disease-related genomic data available in public databases. However, this high-quality information is spread across independent sources and researchers often need to access these separately. Hence, there is a growing need for tools that gather and compile this information in an easy and automated manner. Here, we present 'VarGen', an easy-to-use, customizable R package that fetches, annotates and rank variants related to diseases and genetic disorders, using a collection public databases (viz. Online Mendelian Inheritance in Man, the Functional Annotation of the Mammalian genome 5, the Genotype-Tissue Expression and the Genome Wide Association Studies catalog). This package is also capable of annotating these variants to identify the most impactful ones. We expect that this tool will benefit the research of variant-disease relationships. AVAILABILITY AND IMPLEMENTATION: VarGen is open-source and freely available via GitHub: https://github.com/MCorentin/VarGen. The software is implemented as an R package and is supported on Linux, MacOS and Windows. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Overproduction of ABA in rootstocks alleviates salinity stress in tomato shoots.

To determine whether root-supplied ABA alleviates saline stress, tomato (Solanum lycopersicum L. cv. Sugar Drop) was grafted onto two independent lines (NCED OE) overexpressing the SlNCED1 gene (9-cis-epoxycarotenoid dioxygenase) and wild type rootstocks. After 200 days of saline irrigation (EC = 3.5 dS m-1 ), plants with NCED OE rootstocks had 30% higher fruit yield, but decreased root biomass and lateral root development. Although NCED OE rootstocks upregulated ABA-signalling (AREB, ATHB12), ethylene-related (ACCs, ERFs), aquaporin (PIPs) and stress-related (TAS14, KIN, LEA) genes, downregulation of PYL ABA receptors and signalling components (WRKYs), ethylene synthesis (ACOs) and auxin-responsive factors occurred. Elevated SlNCED1 expression enhanced ABA levels in reproductive tissue while ABA catabolites accumulated in leaf and xylem sap suggesting homeostatic mechanisms. NCED OE also reduced xylem cytokinin transport to the shoot and stimulated foliar 2-isopentenyl adenine (iP) accumulation and phloem transport. Moreover, increased xylem GA3 levels in growing fruit trusses were associated with enhanced reproductive growth. Improved photosynthesis without changes in stomatal conductance was consistent with reduced stress sensitivity and hormone-mediated alteration of leaf growth and mesophyll structure. Combined with increases in leaf nutrients and flavonoids, systemic changes in hormone balance could explain enhanced vigour, reproductive growth and yield under saline stress.

Robust detection of point mutations involved in multidrug-resistant Mycobacterium tuberculosis in the presence of co-occurrent resistance markers.

Tuberculosis disease is a major global public health concern and the growing prevalence of drug-resistant Mycobacterium tuberculosis is making disease control more difficult. However, the increasing application of whole-genome sequencing as a diagnostic tool is leading to the profiling of drug resistance to inform clinical practice and treatment decision making. Computational approaches for identifying established and novel resistance-conferring mutations in genomic data include genome-wide association study (GWAS) methodologies, tests for convergent evolution and machine learning techniques. These methods may be confounded by extensive co-occurrent resistance, where statistical models for a drug include unrelated mutations known to be causing resistance to other drugs. Here, we introduce a novel 'cannibalistic' elimination algorithm ("Hungry, Hungry SNPos") that attempts to remove these co-occurrent resistant variants. Using an M. tuberculosis genomic dataset for the virulent Beijing strain-type (n = 3,574) with phenotypic resistance data across five drugs (isoniazid, rifampicin, ethambutol, pyrazinamide, and streptomycin), we demonstrate that this new approach is considerably more robust than traditional methods and detects resistance-associated variants too rare to be likely picked up by correlation-based techniques like GWAS.

MapOptics: a light-weight, cross-platform visualization tool for optical mapping alignment.

SUMMARY: Bionano optical mapping is a technology that can assist in the final stages of genome assembly by lengthening and ordering scaffolds in a draft assembly by aligning the assembly to a genomic map. However, currently, tools for visualization are limited to use on a Windows operating system or are developed initially for visualizing large-scale structural variation. MapOptics is a lightweight cross-platform tool that enables the user to visualize and interact with the alignment of Bionano optical mapping data and can be used for in depth exploration of hybrid scaffolding alignments. It provides a fast, simple alternative to the large optical mapping analysis programs currently available for this area of research. AVAILABILITY AND IMPLEMENTATION: MapOptics is implemented in Java 1.8 and released under an MIT licence. MapOptics can be downloaded from https://github.com/FadyMohareb/mapoptics and run on any standard desktop computer equipped with a Java Virtual Machine (JVM). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Transcriptome and phytohormone changes associated with ethylene-induced onion bulb dormancy.

Control of dormancy and sprouting in onion bulbs is commercially important for postharvest management. Although ethylene application is sometimes used to extend dormancy, the underlying mechanisms regulating dormancy transition remain unclear. Since the sprout leaves emerge from the bulb baseplate, we used this tissue to assess the impact of ethylene treatment and storage time on the hormone profile and the transcriptome. Reads from 30 libraries were assembled and annotated, with 94,840 unigenes retained after filtering. The de novo transcriptome assembly was of high quality and continuity (N50: 1809 bp, GC content: 36.21 %), and was used to analyse differential expression and Gene Onotologies. Across two years, applied ethylene resulted in delayed dormancy break and reduced post-dormancy sprout vigour. Ethylene supplementation enhanced endogenous ethylene production and caused a transient climacteric-like increase in respiration. Significant changes in hormone and associated transcript profiles occurred through storage and in response to ethylene. In particular, abscisic acid (ABA) and its metabolite phaseic acid (PA) increased under ethylene during the longer dormancy period; however, cytokinin increases observed during storage appeared largely independent of ethylene treatment. Several hormone-related transcripts showed differential expression over time and/or in response to ethylene. Expression of ethylene biosynthesis (ACO), receptor (EIN4) and transcription factor (EIL3) genes were modified by ethylene, as were ABA biosynthesis genes such NCED, and cytokinin biosynthesis genes such as LOG and CKX. We conclude that ethylene substantially modifies expression of genes in several phytohormone pathways, and some of these changes may underlie the dormancy-extending effects of exogenous ethylene.

BIFURCATE FLOWER TRUSS: a novel locus controlling inflorescence branching in tomato contains a defective MAP kinase gene.

A mutant line, bifurcate flower truss (bif), was recovered from a tomato genetics programme. Plants from the control line produced a mean of 0.16 branches per truss, whereas the value for bif plants was 4.1. This increase in branching was accompanied by a 3.3-fold increase in flower number and showed a significant interaction with exposure to low temperature during truss development. The control line and bif genomes were resequenced and the bif gene was mapped to a 2.01 Mbp interval on chromosome 12; all coding region polymorphisms in the interval were surveyed, and five candidate genes displaying altered protein sequences were detected. One of these genes, SlMAPK1, encoding a mitogen-activated protein (MAP) kinase, contained a leucine to stop codon mutation predicted to disrupt kinase function. SlMAPK1 is an excellent candidate for bif because knock-out mutations of an Arabidopsis orthologue MPK6 were reported to have increased flower number. An introgression browser was used to demonstrate that the origin of the bif genomic DNA at the BIF locus was Solanum galapagense and that the SlMAPK1 null mutant is a naturally occurring allele widespread only on the Galápagos Islands. This work strongly implicates SlMAPK1 as part of the network of genes controlling inflorescence branching in tomato.

PhyTB: Phylogenetic tree visualisation and sample positioning for M. tuberculosis.

BACKGROUND: Phylogenetic-based classification of M. tuberculosis and other bacterial genomes is a core analysis for studying evolutionary hypotheses, disease outbreaks and transmission events. Whole genome sequencing is providing new insights into the genomic variation underlying intra- and inter-strain diversity, thereby assisting with the classification and molecular barcoding of the bacteria. One roadblock to strain investigation is the lack of user-interactive solutions to interrogate and visualise variation within a phylogenetic tree setting. RESULTS: We have developed a web-based tool called PhyTB ( http://pathogenseq.lshtm.ac.uk/phytblive/index.php ) to assist phylogenetic tree visualisation and identification of M. tuberculosis clade-informative polymorphism. Variant Call Format files can be uploaded to determine a sample position within the tree. A map view summarises the geographical distribution of alleles and strain-types. The utility of the PhyTB is demonstrated on sequence data from 1,601 M. tuberculosis isolates. CONCLUSION: PhyTB contextualises M. tuberculosis genomic variation within epidemiological, geographical and phylogenic settings. Further tool utility is possible by incorporating large variants and phenotypic data (e.g. drug-resistance profiles), and an assessment of genotype-phenotype associations. Source code is available to develop similar websites for other organisms ( http://sourceforge.net/projects/phylotrack ).

Probabilistic Modelling of the Food Matrix Effects on Curcuminoid's In Vitro Oral Bioaccessibility.

The bioaccessibility of bioactive compounds plays a major role in the nutritional value of foods, but there is a lack of systematic studies assessing the effect of the food matrix on bioaccessibility. Curcuminoids are phytochemicals extracted from Curcuma longa that have captured public attention due to claimed health benefits. The aim of this study is to develop a mathematical model to predict curcuminoid's bioaccessibility in biscuits and custard based on different fibre type formulations. Bioaccessibilities for curcumin-enriched custards and biscuits were obtained through in vitro digestion, and physicochemical food properties were characterised. A strong correlation between macronutrient concentration and bioaccessibility was observed (p = 0.89) and chosen as a main explanatory variable in a Bayesian hierarchical linear regression model. Additionally, the patterns of food matrix effects on bioaccessibility were not the same in custards as in biscuits; for example, the hemicellulose content had a moderately strong positive correlation to bioaccessibility in biscuits (p = 0.66) which was non-significant in custards (p = 0.12). Using a Bayesian hierarchical approach to model these interactions resulted in an optimisation performance of r2 = 0.97 and a leave-one-out cross-validation score (LOOCV) of r2 = 0.93. This decision-support system could assist the food industry in optimising the formulation of novel food products and enable consumers to make more informed choices.

A chromosome-level genome assembly of Solanum chilense, a tomato wild relative associated with resistance to salinity and drought.

Introduction: Solanum chilense is a wild relative of tomato reported to exhibit resistance to biotic and abiotic stresses. There is potential to improve tomato cultivars via breeding with wild relatives, a process greatly accelerated by suitable genomic and genetic resources. Methods: In this study we generated a high-quality, chromosome-level, de novo assembly for the S. chilense accession LA1972 using a hybrid assembly strategy with ~180 Gbp of Illumina short reads and ~50 Gbp long PacBio reads. Further scaffolding was performed using Bionano optical maps and 10x Chromium reads. Results: The resulting sequences were arranged into 12 pseudomolecules using Hi-C sequencing. This resulted in a 901 Mbp assembly, with a completeness of 95%, as determined by Benchmarking with Universal Single-Copy Orthologs (BUSCO). Sequencing of RNA from multiple tissues resulting in ~219 Gbp of reads was used to annotate the genome assembly with an RNA-Seq guided gene prediction, and for a de novo transcriptome assembly. This chromosome-level, high-quality reference genome for S. chilense accession LA1972 will support future breeding efforts for more sustainable tomato production. Discussion: Gene sequences related to drought and salt resistance were compared between S. chilense and S. lycopersicum to identify amino acid variations with high potential for functional impact. These variants were subsequently analysed in 84 resequenced tomato lines across 12 different related species to explore the variant distributions. We identified a set of 7 putative impactful amino acid variants some of which may also impact on fruit development for example the ethylene-responsive transcription factor WIN1 and ethylene-insensitive protein 2. These variants could be tested for their ability to confer functional phenotypes to cultivars that have lost these variants.

Field phenotyping for African crops: overview and perspectives.

Improvements in crop productivity are required to meet the dietary demands of the rapidly-increasing African population. The development of key staple crop cultivars that are high-yielding and resilient to biotic and abiotic stresses is essential. To contribute to this objective, high-throughput plant phenotyping approaches are important enablers for the African plant science community to measure complex quantitative phenotypes and to establish the genetic basis of agriculturally relevant traits. These advances will facilitate the screening of germplasm for optimum performance and adaptation to low-input agriculture and resource-constrained environments. Increasing the capacity to investigate plant function and structure through non-invasive technologies is an effective strategy to aid plant breeding and additionally may contribute to precision agriculture. However, despite the significant global advances in basic knowledge and sensor technology for plant phenotyping, Africa still lags behind in the development and implementation of these systems due to several practical, financial, geographical and political barriers. Currently, field phenotyping is mostly carried out by manual methods that are prone to error, costly, labor-intensive and may come with adverse economic implications. Therefore, improvements in advanced field phenotyping capabilities and appropriate implementation are key factors for success in modern breeding and agricultural monitoring. In this review, we provide an overview of the current state of field phenotyping and the challenges limiting its implementation in some African countries. We suggest that the lack of appropriate field phenotyping infrastructures is impeding the development of improved crop cultivars and will have a detrimental impact on the agricultural sector and on food security. We highlight the prospects for integrating emerging and advanced low-cost phenotyping technologies into breeding protocols and characterizing crop responses to environmental challenges in field experimentation. Finally, we explore strategies for overcoming the barriers and maximizing the full potential of emerging field phenotyping technologies in African agriculture. This review paper will open new windows and provide new perspectives for breeders and the entire plant science community in Africa.

Machine Learning Methods for Automatic Segmentation of Images of Field- and Glasshouse-Based Plants for High-Throughput Phenotyping.

Image segmentation is a fundamental but critical step for achieving automated high- throughput phenotyping. While conventional segmentation methods perform well in homogenous environments, the performance decreases when used in more complex environments. This study aimed to develop a fast and robust neural-network-based segmentation tool to phenotype plants in both field and glasshouse environments in a high-throughput manner. Digital images of cowpea (from glasshouse) and wheat (from field) with different nutrient supplies across their full growth cycle were acquired. Image patches from 20 randomly selected images from the acquired dataset were transformed from their original RGB format to multiple color spaces. The pixels in the patches were annotated as foreground and background with a pixel having a feature vector of 24 color properties. A feature selection technique was applied to choose the sensitive features, which were used to train a multilayer perceptron network (MLP) and two other traditional machine learning models: support vector machines (SVMs) and random forest (RF). The performance of these models, together with two standard color-index segmentation techniques (excess green (ExG) and excess green-red (ExGR)), was compared. The proposed method outperformed the other methods in producing quality segmented images with over 98%-pixel classification accuracy. Regression models developed from the different segmentation methods to predict Soil Plant Analysis Development (SPAD) values of cowpea and wheat showed that images from the proposed MLP method produced models with high predictive power and accuracy comparably. This method will be an essential tool for the development of a data analysis pipeline for high-throughput plant phenotyping. The proposed technique is capable of learning from different environmental conditions, with a high level of robustness.

Modeling the spatial-spectral characteristics of plants for nutrient status identification using hyperspectral data and deep learning methods.

Sustainable fertilizer management in precision agriculture is essential for both economic and environmental reasons. To effectively manage fertilizer input, various methods are employed to monitor and track plant nutrient status. One such method is hyperspectral imaging, which has been on the rise in recent times. It is a remote sensing tool used to monitor plant physiological changes in response to environmental conditions and nutrient availability. However, conventional hyperspectral processing mainly focuses on either the spectral or spatial information of plants. This study aims to develop a hybrid convolution neural network (CNN) capable of simultaneously extracting spatial and spectral information from quinoa and cowpea plants to identify their nutrient status at different growth stages. To achieve this, a nutrient experiment with four treatments (high and low levels of nitrogen and phosphorus) was conducted in a glasshouse. A hybrid CNN model comprising a 3D CNN (extracts joint spectral-spatial information) and a 2D CNN (for abstract spatial information extraction) was proposed. Three pre-processing techniques, including second-order derivative, standard normal variate, and linear discriminant analysis, were applied to selected regions of interest within the plant spectral hypercube. Together with the raw data, these datasets were used as inputs to train the proposed model. This was done to assess the impact of different pre-processing techniques on hyperspectral-based nutrient phenotyping. The performance of the proposed model was compared with a 3D CNN, a 2D CNN, and a Hybrid Spectral Network (HybridSN) model. Effective wavebands were selected from the best-performing dataset using a greedy stepwise-based correlation feature selection (CFS) technique. The selected wavebands were then used to retrain the models to identify the nutrient status at five selected plant growth stages. From the results, the proposed hybrid model achieved a classification accuracy of over 94% on the test dataset, demonstrating its potential for identifying nitrogen and phosphorus status in cowpea and quinoa at different growth stages.