The Combination of Low-Cost, Red-Green-Blue (RGB) Image Analysis and Machine Learning to Screen for Barley Plant Resistance to Net Blotch.

Challenges of climate change and growth population are exacerbated by noticeable environmental changes, which can increase the range of plant diseases, for instance, net blotch (NB), a foliar disease which significantly decreases barley (Hordeum vulgare L.) grain yield and quality. A resistant germplasm is usually identified through visual observation and the scoring of disease symptoms; however, this is subjective and time-consuming. Thus, automated, non-destructive, and low-cost disease-scoring approaches are highly relevant to barley breeding. This study presents a novel screening method for evaluating NB severity in barley. The proposed method uses an automated RGB imaging system, together with machine learning, to evaluate different symptoms and the severity of NB. The study was performed on three barley cultivars with distinct levels of resistance to NB (resistant, moderately resistant, and susceptible). The tested approach showed mean precision of 99% for various categories of NB severity (chlorotic, necrotic, and fungal lesions, along with leaf tip necrosis). The results demonstrate that the proposed method could be effective in assessing NB from barley leaves and specifying the level of NB severity; this type of information could be pivotal to precise selection for NB resistance in barley breeding.

Genomic selection in plant breeding: Key factors shaping two decades of progress.

Genomic selection, the application of genomic prediction (GP) models to select candidate individuals, has significantly advanced in the past two decades, effectively accelerating genetic gains in plant breeding. This article provides a holistic overview of key factors that have influenced GP in plant breeding during this period. We delved into the pivotal roles of training population size and genetic diversity, and their relationship with the breeding population, in determining GP accuracy. Special emphasis was placed on optimizing training population size. We explored its benefits and the associated diminishing returns beyond an optimum size. This was done while considering the balance between resource allocation and maximizing prediction accuracy through current optimization algorithms. The density and distribution of single-nucleotide polymorphisms, level of linkage disequilibrium, genetic complexity, trait heritability, statistical machine-learning methods, and non-additive effects are the other vital factors. Using wheat, maize, and potato as examples, we summarize the effect of these factors on the accuracy of GP for various traits. The search for high accuracy in GP-theoretically reaching one when using the Pearson's correlation as a metric-is an active research area as yet far from optimal for various traits. We hypothesize that with ultra-high sizes of genotypic and phenotypic datasets, effective training population optimization methods and support from other omics approaches (transcriptomics, metabolomics and proteomics) coupled with deep-learning algorithms could overcome the boundaries of current limitations to achieve the highest possible prediction accuracy, making genomic selection an effective tool in plant breeding.

Unlocking allelic variation in circadian clock genes to develop environmentally robust and productive crops.

MAIN CONCLUSION: Molecular mechanisms of biological rhythms provide opportunities to harness functional allelic diversity in core (and trait- or stress-responsive) oscillator networks to develop more climate-resilient and productive germplasm. The circadian clock senses light and temperature in day-night cycles to drive biological rhythms. The clock integrates endogenous signals and exogenous stimuli to coordinate diverse physiological processes. Advances in high-throughput non-invasive assays, use of forward- and inverse-genetic approaches, and powerful algorithms are allowing quantitation of variation and detection of genes associated with circadian dynamics. Circadian rhythms and phytohormone pathways in response to endogenous and exogenous cues have been well documented the model plant Arabidopsis. Novel allelic variation associated with circadian rhythms facilitates adaptation and range expansion, and may provide additional opportunity to tailor climate-resilient crops. The circadian phase and period can determine adaptation to environments, while the robustness in the circadian amplitude can enhance resilience to environmental changes. Circadian rhythms in plants are tightly controlled by multiple and interlocked transcriptional-translational feedback loops involving morning (CCA1, LHY), mid-day (PRR9, PRR7, PRR5), and evening (TOC1, ELF3, ELF4, LUX) genes that maintain the plant circadian clock ticking. Significant progress has been made to unravel the functions of circadian rhythms and clock genes that regulate traits, via interaction with phytohormones and trait-responsive genes, in diverse crops. Altered circadian rhythms and clock genes may contribute to hybrid vigor as shown in Arabidopsis, maize, and rice. Modifying circadian rhythms via transgenesis or genome-editing may provide additional opportunities to develop crops with better buffering capacity to environmental stresses. Models that involve clock gene‒phytohormone‒trait interactions can provide novel insights to orchestrate circadian rhythms and modulate clock genes to facilitate breeding of all season crops.

Durum wheat heat tolerance loci defined via a north-south gradient.

The global production of durum wheat (Triticum durum Desf.) is hindered by a constant rise in the frequency of severe heat stress events. To identify heat-tolerant germplasm, three different germplasm panels ("discovery," "investigation," and "validation") were studied under a range of heat-stressed conditions. Grain yield (GY) and its components were recorded at each site and a heat stress susceptibility index was calculated, confirming that each 1°C temperature rise corresponds to a GY reduction in durum wheat of 4.6%-6.3%. A total of 2552 polymorphic single nucleotide polymorphisms (SNPs) defined the diversity of the first panel, while 5642 SNPs were polymorphic in the "investigation panel." The use of genome-wide association studies revealed that 36 quantitative trait loci were associated with the target traits in the discovery panel, of which five were confirmed in a "subset" tested imposing heat stress by plastic tunnels, and in the investigation panel. A study of allelic combinations confirmed that Q.icd.Heat.003-1A, Q.icd.Heat.007-1B, and Q.icd.Heat.016-3B are additive in nature and the positive alleles at all three loci resulted in a 16% higher GY under heat stress. The underlying SNPs were converted into kompetitive allele specific PCR markers and tested on the validation panel, confirming that each explained up to 9% of the phenotypic variation for GY under heat stress. These markers can now be used for breeding to improve resilience to climate change and increase productivity in heat-stressed areas.

What plant breeding may (and may not) look like in 2050?

At the turn of 2000 many authors envisioned future plant breeding. Twenty years after, which of those authors' visions became reality or not, and which ones may become so in the years to come. After two decades of debates, climate change is a "certainty," food systems shifted from maximizing farm production to reducing environmental impact, and hopes placed into GMOs are mitigated by their low appreciation by consumers. We revise herein how plant breeding may raise or reduce genetic gains based on the breeder's equation. "Accuracy of Selection" has significantly improved by many experimental-scale field and laboratory implements, but also by vulgarizing statistical models, and integrating DNA markers into selection. Pre-breeding has really promoted the increase of useful "Genetic Variance." Shortening "Recycling Time" has seen great progression, to the point that achieving a denominator equal to "1" is becoming a possibility. Maintaining high "Selection Intensity" remains the biggest challenge, since adding any technology results in a higher cost per progeny, despite the steady reduction in cost per datapoint. Furthermore, the concepts of variety and seed enterprise might change with the advent of cheaper genomic tools to monitor their use and the promotion of participatory or citizen science. The technological and societal changes influence the new generation of plant breeders, moving them further away from field work, emphasizing instead the use of genomic-based selection methods relying on big data. We envisage what skills plant breeders of tomorrow might need to address challenges, and whether their time in the field may dwindle.

Foliar transcriptomes reveal candidate genes for late blight resistance in cultivars of diploid potato Solanum tuberosum L. Andigenum Group.

Characterization of major resistance (R) genes to late blight (LB) -caused by the oomycete Phytophthora infestans- is very important for potato breeding. The objective of this study was to identify novel genes for resistance to LB from diploid Solanum tuberosum L. Andigenum Group (StAG) cultivar accessions. Using comparative analysis with a edgeR bioconductor package for differential expression analysis of transcriptomes, two of these accessions with contrasting levels of resistance to LB were analyzed using digital gene expression data. As a result, various differentially expressed genes (P ≤ 0.0001, Log2FC ≥ 2, FDR < 0.001) were noted. The combination of transcriptomic analysis provided 303 candidate genes that are overexpressed and underexpressed, thereby giving high resistance to LB. The functional analysis showed differential expression of R genes and their corresponding proteins related to disease resistance, NBS-LRR domain proteins, and specific disease resistance proteins. Comparative analysis of specific tissue transcriptomes in resistant and susceptible genotypes can be used for rapidly identifying candidate R genes, thus adding novel genes from diploid StAG cultivar accessions for host plant resistance to P. infestans in potato.

Alternative Splicing Variation: Accessing and Exploiting in Crop Improvement Programs.

Alternative splicing (AS) is a gene regulatory mechanism modulating gene expression in multiple ways. AS is prevalent in all eukaryotes including plants. AS generates two or more mRNAs from the precursor mRNA (pre-mRNA) to regulate transcriptome complexity and proteome diversity. Advances in next-generation sequencing, omics technology, bioinformatics tools, and computational methods provide new opportunities to quantify and visualize AS-based quantitative trait variation associated with plant growth, development, reproduction, and stress tolerance. Domestication, polyploidization, and environmental perturbation may evolve novel splicing variants associated with agronomically beneficial traits. To date, pre-mRNAs from many genes are spliced into multiple transcripts that cause phenotypic variation for complex traits, both in model plant Arabidopsis and field crops. Cataloguing and exploiting such variation may provide new paths to enhance climate resilience, resource-use efficiency, productivity, and nutritional quality of staple food crops. This review provides insights into AS variation alongside a gene expression analysis to select for novel phenotypic diversity for use in breeding programs. AS contributes to heterosis, enhances plant symbiosis (mycorrhiza and rhizobium), and provides a mechanistic link between the core clock genes and diverse environmental clues.

Challenges for crop improvement.

The genetic improvement of crops faces the significant challenge of feeding an ever-increasing population amidst a changing climate, and when governments are adopting a 'more with less' approach to reduce input use. Plant breeding has the potential to contribute to the United Nations Agenda 2030 by addressing various sustainable development goals (SDGs), with its most profound impact expected on SDG2 Zero Hunger. To expedite the time-consuming crossbreeding process, a genomic-led approach for predicting breeding values, targeted mutagenesis through gene editing, high-throughput phenomics for trait evaluation, enviromics for including characterization of the testing environments, machine learning for effective management of large datasets, and speed breeding techniques promoting early flowering and seed production are being incorporated into the plant breeding toolbox. These advancements are poised to enhance genetic gains through selection in the cultigen pools of various crops. Consequently, these knowledge-based breeding methods are pursued for trait introgression, population improvement, and cultivar development. This article uses the potato crop as an example to showcase the progress being made in both genomic-led approaches and gene editing for accelerating the delivery of genetic gains through the utilization of genetically enhanced elite germplasm. It also further underscores that access to technological advances in plant breeding may be influenced by regulations and intellectual property rights.

Harnessing genome-wide genetic diversity, population structure and linkage disequilibrium in Ethiopian durum wheat gene pool.

Yanyang Liu, Henan Academy of Agricultural Sciences (HNAAS), China; Landraces are an important genetic source for transferring valuable novel genes and alleles required to enhance genetic variation. Therefore, information on the gene pool's genetic diversity and population structure is essential for the conservation and sustainable use of durum wheat genetic resources. Hence, the aim of this study was to assess genetic diversity, population structure, and linkage disequilibrium, as well as to identify regions with selection signature. Five hundred (500) individuals representing 46 landraces, along with 28 cultivars were evaluated using the Illumina Infinium 25K wheat SNP array, resulting in 8,178 SNPs for further analysis. Gene diversity (GD) and the polymorphic information content (PIC) ranged from 0.13-0.50 and 0.12-0.38, with mean GD and PIC values of 0.34 and 0.27, respectively. Linkage disequilibrium (LD) revealed 353,600 pairs of significant SNPs at a cut-off (r2 > 0.20, P < 0.01), with an average r2 of 0.21 for marker pairs. The nucleotide diversity (π) and Tajima's D (TD) per chromosome for the populations ranged from 0.29-0.36 and 3.46-5.06, respectively, with genome level, mean π values of 0.33 and TD values of 4.43. Genomic scan using the Fst outlier test revealed 85 loci under selection signatures, with 65 loci under balancing selection and 17 under directional selection. Putative candidate genes co-localized with regions exhibiting strong selection signatures were associated with grain yield, plant height, host plant resistance to pathogens, heading date, grain quality, and phenolic content. The Bayesian Model (STRUCTURE) and distance-based (principal coordinate analysis, PCoA, and unweighted pair group method with arithmetic mean, UPGMA) methods grouped the genotypes into five subpopulations, where landraces from geographically non-adjoining environments were clustered in the same cluster. This research provides further insights into population structure and genetic relationships in a diverse set of durum wheat germplasm, which could be further used in wheat breeding programs to address production challenges sustainably.

Comparative transcriptome profiling provides insights into the growth promotion activity of Pseudomonas fluorescens strain SLU99 in tomato and potato plants.

The use of biocontrol agents with plant growth-promoting activity has emerged as an approach to support sustainable agriculture. During our field evaluation of potato plants treated with biocontrol rhizobacteria, four bacteria were associated with increased plant height. Using two important solanaceous crop plants, tomato and potato, we carried out a comparative analysis of the growth-promoting activity of the four bacterial strains: Pseudomonas fluorescens SLU99, Serratia plymuthica S412, S. rubidaea AV10, and S. rubidaea EV23. Greenhouse and in vitro experiments showed that P. fluorescens SLU99 promoted plant height, biomass accumulation, and yield of potato and tomato plants, while EV23 promoted growth in potato but not in tomato plants. SLU99 induced the expression of plant hormone-related genes in potato and tomato, especially those involved in maintaining homeostasis of auxin, cytokinin, gibberellic acid and ethylene. Our results reveal potential mechanisms underlying the growth promotion and biocontrol effects of these rhizobacteria and suggest which strains may be best deployed for sustainably improving crop yield.

Modification of Fatty Acid Profile and Oil Contents Using Gene Editing in Oilseed Crops for a Changing Climate.

Mutation breeding based on various chemical and physical mutagens induces and disrupts non-target loci. Hence, large populations were required for visual screening, but desired plants were rare and it was a further laborious task to identify desirable mutants. Generated mutant had high defect due to non-targeted mutation, with poor agronomic performance. Mutation techniques were augmented by targeted induced local lesions in genome (TILLING) facilitating the selection of desirable germplasm. On the other hand, gene editing through CRISPR/Cas9 allows knocking down genes for site-directed mutation. This handy technique has been exploited for the modification of fatty acid profile. High oleic acid genetic stocks were obtained in a broad range of crops. Moreover, genes involved in the accumulation of undesirable seed components such as starch, polysaccharide, and flavors were knocked down to enhance seed quality, which helps to improve oil contents and reduces the anti-nutritional component.

Exploiting genetic and genomic resources to enhance productivity and abiotic stress adaptation of underutilized pulses.

Underutilized pulses and their wild relatives are typically stress tolerant and their seeds are packed with protein, fibers, minerals, vitamins, and phytochemicals. The consumption of such nutritionally dense legumes together with cereal-based food may promote global food and nutritional security. However, such species are deficient in a few or several desirable domestication traits thereby reducing their agronomic value, requiring further genetic enhancement for developing productive, nutritionally dense, and climate resilient cultivars. This review article considers 13 underutilized pulses and focuses on their germplasm holdings, diversity, crop-wild-crop gene flow, genome sequencing, syntenic relationships, the potential for breeding and transgenic manipulation, and the genetics of agronomic and stress tolerance traits. Recent progress has shown the potential for crop improvement and food security, for example, the genetic basis of stem determinacy and fragrance in moth bean and rice bean, multiple abiotic stress tolerant traits in horse gram and tepary bean, bruchid resistance in lima bean, low neurotoxin in grass pea, and photoperiod induced flowering and anthocyanin accumulation in adzuki bean have been investigated. Advances in introgression breeding to develop elite genetic stocks of grass pea with low ß-ODAP (neurotoxin compound), resistance to Mungbean yellow mosaic India virus in black gram using rice bean, and abiotic stress adaptation in common bean, using genes from tepary bean have been carried out. This highlights their potential in wider breeding programs to introduce such traits in locally adapted cultivars. The potential of de-domestication or feralization in the evolution of new variants in these crops are also highlighted.

Partial least squares enhance multi-trait genomic prediction of potato cultivars in new environments.

It is of paramount importance in plant breeding to have methods dealing with large numbers of predictor variables and few sample observations, as well as efficient methods for dealing with high correlation in predictors and measured traits. This paper explores in terms of prediction performance the partial least squares (PLS) method under single-trait (ST) and multi-trait (MT) prediction of potato traits. The first prediction was for tested lines in tested environments under a five-fold cross-validation (5FCV) strategy and the second prediction was for tested lines in untested environments (herein denoted as leave one environment out cross validation, LOEO). There was a good performance in terms of predictions (with accuracy mostly > 0.5 for Pearson's correlation) the accuracy of 5FCV was better than LOEO. Hence, we have empirical evidence that the ST and MT PLS framework is a very valuable tool for prediction in the context of potato breeding data.

Genetic Diversity of Durum Wheat (Triticum turgidum L. ssp. durum, Desf) Germplasm as Revealed by Morphological and SSR Markers.

Ethiopia is considered a center of origin and diversity for durum wheat and is endowed with many diverse landraces. This research aimed to estimate the extent and pattern of genetic diversity in Ethiopian durum wheat germplasm. Thus, 104 durum wheat genotypes representing thirteen populations, three regions, and four altitudinal classes were investigated for their genetic diversity, using 10 grain quality- and grain yield-related phenotypic traits and 14 simple sequence repeat (SSR) makers. The analysis of the phenotypic traits revealed a high mean Shannon diversity index (H' = 0.78) among the genotypes and indicated a high level of phenotypic variation. The principal component analysis (PCA) classified the genotypes into three groups. The SSR markers showed a high mean value of polymorphic information content (PIC = 0.50) and gene diversity (h = 0.56), and a moderate number of alleles per locus (Na = 4). Analysis of molecular variance (AMOVA) revealed a high level of variation within populations, regions, and altitudinal classes, accounting for 88%, 97%, and 97% of the total variation, respectively. Pairwise genetic differentiation and Nei's genetic distance analyses identified that the cultivars are distinct from the landrace populations. The distance-based (Discriminant Analysis of Principal Component (DAPC) and Minimum Spanning Network (MSN)) and model-based population stratification (STRUCTURE) methods of clustering grouped the genotypes into two clusters. Both the phenotypic data-based PCA and the molecular data-based DAPC and MSN analyses defined distinct groupings of cultivars and landraces. The phenotypic and molecular diversity analyses highlighted the high genetic variation in the Ethiopian durum wheat gene pool. The investigated SSRs showed significant associations with one or more target phenotypic traits. The markers identify landraces with high grain yield and quality traits. This study highlights the usefulness of Ethiopian landraces for cultivar development, contributing to food security in the region and beyond.

Inbreeding Effects on the Performance and Genomic Prediction for Polysomic Tetraploid Potato Offspring Grown at High Nordic Latitudes.

Inbreeding depression (ID) is caused by increased homozygosity in the offspring after selfing. Although the self-compatible, highly heterozygous, tetrasomic polyploid potato (Solanum tuberosum L.) suffers from ID, some argue that the potential genetic gains from using inbred lines in a sexual propagation system of potato are too large to be ignored. The aim of this research was to assess the effects of inbreeding on potato offspring performance under a high latitude and the accuracy of the genomic prediction of breeding values (GEBVs) for further use in selection. Four inbred (S1) and two hybrid (F1) offspring and their parents (S0) were used in the experiment, with a field layout of an augmented design with the four S0 replicated in nine incomplete blocks comprising 100, four-plant plots at Umeå (63°49'30″ N 20°15'50″ E), Sweden. S0 was significantly (p < 0.01) better than both S1 and F1 offspring for tuber weight (total and according to five grading sizes), tuber shape and size uniformity, tuber eye depth and reducing sugars in the tuber flesh, while F1 was significantly (p < 0.01) better than S1 for all tuber weight and uniformity traits. Some F1 hybrid offspring (15-19%) had better total tuber yield than the best-performing parent. The GEBV accuracy ranged from -0.3928 to 0.4436. Overall, tuber shape uniformity had the highest GEBV accuracy, while tuber weight traits exhibited the lowest accuracy. The F1 full sib's GEBV accuracy was higher, on average, than that of S1. Genomic prediction may facilitate eliminating undesired inbred or hybrid offspring for further use in the genetic betterment of potato.

Evolutionary dynamics and adaptive benefits of deleterious mutations in crop gene pools.

Mutations with deleterious consequences in nature may be conditionally deleterious in crop plants. That is, while some genetic variants may reduce fitness under wild conditions and be subject to purifying selection, they can be under positive selection in domesticates. Such deleterious alleles can be plant breeding targets, particularly for complex traits. The difficulty of distinguishing favorable from unfavorable variants reduces the power of selection, while favorable trait variation and heterosis may be attributable to deleterious alleles. Here, we review the roles of deleterious mutations in crop breeding and discuss how they can be used as a new avenue for crop improvement with emerging genomic tools, including HapMaps and pangenome analysis, aiding the identification, removal, or exploitation of deleterious mutations.

Biofortification to avoid malnutrition in humans in a changing climate: Enhancing micronutrient bioavailability in seed, tuber, and storage roots.

Malnutrition results in enormous socio-economic costs to the individual, their community, and the nation's economy. The evidence suggests an overall negative impact of climate change on the agricultural productivity and nutritional quality of food crops. Producing more food with better nutritional quality, which is feasible, should be prioritized in crop improvement programs. Biofortification refers to developing micronutrient -dense cultivars through crossbreeding or genetic engineering. This review provides updates on nutrient acquisition, transport, and storage in plant organs; the cross-talk between macro- and micronutrients transport and signaling; nutrient profiling and spatial and temporal distribution; the putative and functionally characterized genes/single-nucleotide polymorphisms associated with Fe, Zn, and ß-carotene; and global efforts to breed nutrient-dense crops and map adoption of such crops globally. This article also includes an overview on the bioavailability, bioaccessibility, and bioactivity of nutrients as well as the molecular basis of nutrient transport and absorption in human. Over 400 minerals (Fe, Zn) and provitamin A-rich cultivars have been released in the Global South. Approximately 4.6 million households currently cultivate Zn-rich rice and wheat, while ~3 million households in sub-Saharan Africa and Latin America benefit from Fe-rich beans, and 2.6 million people in sub-Saharan Africa and Brazil eat provitamin A-rich cassava. Furthermore, nutrient profiles can be improved through genetic engineering in an agronomically acceptable genetic background. The development of "Golden Rice" and provitamin A-rich dessert bananas and subsequent transfer of this trait into locally adapted cultivars are evident, with no significant change in nutritional profile, except for the trait incorporated. A greater understanding of nutrient transport and absorption may lead to the development of diet therapy for the betterment of human health.

Modeling genotype × environment interaction for single and multitrait genomic prediction in potato (Solanum tuberosum L.).

In this study, we extend research on genomic prediction (GP) to polysomic polyploid plant species with the main objective to investigate single-trait (ST) and multitrait (MT) multienvironment (ME) models using field trial data from 3 locations in Sweden [Helgegården (HEL), Mosslunda (MOS), Umeå (UM)] over 2 years (2020, 2021) of 253 potato cultivars and breeding clones for 5 tuber weight traits and 2 tuber flesh quality characteristics. This research investigated the GP of 4 genome-based prediction models with genotype × environment interactions (GEs): (1) ST reaction norm model (M1), (2) ST model considering covariances between environments (M2), (3) ST M2 extended to include a random vector that utilizes the environmental covariances (M3), and (4) MT model with GE (M4). Several prediction problems were analyzed for each of the GP accuracy of the 4 models. Results of the prediction of traits in HEL, the high yield potential testing site in 2021, show that the best-predicted traits were tuber flesh starch (%), weight of tuber above 60 or below 40 mm in size, and the total tuber weight. In terms of GP, accuracy model M4 gave the best prediction accuracy in 3 traits, namely tuber weight of 40-50 or above 60 mm in size, and total tuber weight, and very similar in the starch trait. For MOS in 2021, the best predictive traits were starch, weight of tubers above 60, 50-60, or below 40 mm in size, and the total tuber weight. MT model M4 was the best GP model based on its accuracy when some cultivars are observed in some traits. For the GP accuracy of traits in UM in 2021, the best predictive traits were the weight of tubers above 60, 50-60, or below 40 mm in size, and the best model was MT M4, followed by models ST M3 and M2.

Finger millet RNA-seq reveals differential gene expression associated with tolerance to aluminum toxicity and provides novel genomic resources.

Eleusine coracana, finger millet, is a multipurpose crop cultivated in arid and semi-arid regions of Africa and Asia. RNA sequencing (RNA-seq) was used in this study to obtain valuable genomic resources and identify genes differentially expressed between Al-tolerant and Al-susceptible genotypes. Two groups of finger millet genotypes were used: Al-tolerant (215836, 215845, and 229722) and Al-susceptible (212462, 215804 and 238323). The analysis of the RNA-seq data resulted in 198,546 unigenes, 56.5% of which were annotated with significant hits in one or more of the following six databases: NR (48.8%), GO (29.7%), KEGG (45%), PlantTFDB (19.0%), Uniprot (49.2%), and NT (46.2%). It is noteworthy that only 220 unigenes in the NR database had significant hits against finger millet sequences suggesting that finger millet's genomic resources are scarce. The gene expression analysis revealed that 322 genes were significantly differentially expressed between the Al-tolerant and Al-susceptible genotypes, of which 40.7% were upregulated while 59.3% were downregulated in Al-tolerant genotypes. Among the significant DEGs, 54.7% were annotated in the GO database with the top hits being ATP binding (GO:0005524) and DNA binding (GO:0003677) in the molecular function, DNA integration (GO:0015074) and cell redox homeostasis in the biological process, as well as cellular anatomical entity and intracellular component in the cellular component GO classes. Several of the annotated DEGs were significantly enriched for their corresponding GO terms. The KEGG pathway analysis resulted in 60 DEGs that were annotated with different pathway classes, of which carbohydrate metabolism and signal transduction were the most prominent. The homologs of a number of significant DEGs have been previously reported as being associated with Al or other abiotic stress responses in various crops, including carboxypeptidase SOL1, HMA3, AP2, bZIP, C3H, and WRKY TF genes. A more detailed investigation of these and other DEGs will enable genomic-led breeding for Al tolerance in finger millet. RNA-seq data analysis also yielded 119,073 SNP markers, the majority of which had PIC values above 0.3, indicating that they are highly informative. Additionally, 3,553 single-copy SSR markers were identified, of which trinucleotide SSRs were the most prevalent. These genomic resources contribute substantially to the enrichment of genomic databases for finger millet, and facilitate future research on this crop.