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While considerable knowledge exists about the enzymes pivotal for C4 photosynthesis, much less is known about the cis-regulation important for specifying their expression in distinct cell types. Here, we use single-cell-indexed ATAC-seq to identify cell-type-specific accessible chromatin regions (ACRs) associated with C4 enzymes for five different grass species. This study spans four C4 species, covering three distinct photosynthetic subtypes: Zea mays and Sorghum bicolor (NADP-dependent malic enzyme), Panicum miliaceum (NAD-dependent malic enzyme), Urochloa fusca (phosphoenolpyruvate carboxykinase), along with the C3 outgroup Oryza sativa. We studied the cis-regulatory landscape of enzymes essential across all C4 species and those unique to C4 subtypes, measuring cell-type-specific biases for C4 enzymes using chromatin accessibility data. Integrating these data with phylogenetics revealed diverse co-option of gene family members between species, showcasing the various paths of C4 evolution. Besides promoter proximal ACRs, we found that, on average, C4 genes have two to three distal cell-type-specific ACRs, highlighting the complexity and divergent nature of C4 evolution. Examining the evolutionary history of these cell-type-specific ACRs revealed a spectrum of conserved and novel ACRs, even among closely related species, indicating ongoing evolution of cis-regulation at these C4 loci. This study illuminates the dynamic and complex nature of cis-regulatory elements evolution in C4 photosynthesis, particularly highlighting the intricate cis-regulatory evolution of key loci. Our findings offer a valuable resource for future investigations, potentially aiding in the optimization of C3 crop performance under changing climatic conditions. [ABSTRACT FROM AUTHOR]

During the meiosis of many eukaryote species, crossovers tend to occur within narrow regions called recombination hotspots. In plants, it is generally thought that gene regulatory sequences, especially promoters and 5′ to 3′ untranslated regions, are enriched in hotspots, but this has been characterized in a handful of species only. We also lack a clear description of fine-scale variation in recombination rates within genic regions and little is known about hotspot position and intensity in plants. To address this question, we constructed fine-scale recombination maps from genetic polymorphism data and inferred recombination hotspots in 11 plant species. We detected gradients of recombination in genic regions in most species, yet gradients varied in intensity and shape depending on specific hotspot locations and gene structure. To further characterize recombination gradients, we decomposed them according to gene structure by rank and number of exons. We generalized the previously observed pattern that recombination hotspots are organized around the boundaries of coding sequences, especially 5′ promoters. However, our results also provided new insight into the relative importance of the 3′ end of genes in some species and the possible location of hotspots away from genic regions in some species. Variation among species seemed driven more by hotspot location among and within genes than by differences in size or intensity among species. Our results shed light on the variation in recombination rates at a very fine scale, revealing the diversity and complexity of genic recombination gradients emerging from the interaction between hotspot location and gene structure. [ABSTRACT FROM AUTHOR]

Plant genomics and crop breeding are at the intersection of biotechnology and information technology. Driven by a combination of high-throughput sequencing, molecular biology and data science, great advances have been made in omics technologies at every step along the central dogma, especially in genome assembling, genome annotation, epigenomic profiling, and transcriptome profiling. These advances further revolutionized three directions of development. One is genetic dissection of complex traits in crops, along with genomic prediction and selection. The second is comparative genomics and evolution, which open up new opportunities to depict the evolutionary constraints of biological sequences for deleterious variant discovery. The third direction is the development of deep learning approaches for the rational design of biological sequences, especially proteins, for synthetic biology. All three directions of development serve as the foundation for a new era of crop breeding where agronomic traits are enhanced by genome design. [ABSTRACT FROM AUTHOR]

Extensive research has focused on exploring the range of genome sizes in eukaryotes, with a particular emphasis on land plants, where significant variability has been observed. Accurate estimation of genome size is essential for various research purposes, but existing sequence-based methods have limitations, particularly for low-coverage datasets. In this study, we introduce LocoGSE, a novel genome size estimator designed specifically for low-coverage datasets generated by genome skimming approaches. LocoGSE relies on mapping the reads on single copy consensus proteins without the need for a reference genome assembly. We calibrated LocoGSE using 430 low-coverage Angiosperm genome skimming datasets and compared its performance against other estimators. Our results demonstrate that LocoGSE accurately predicts monoploid genome size even at very low depth of coverage (<1X) and on highly heterozygous samples. Additionally, LocoGSE provides stable estimates across individuals with varying ploidy levels. LocoGSE fills a gap in sequence-based plant genome size estimation by offering a user-friendly and reliable tool that does not rely on high coverage or reference assemblies. We anticipate that LocoGSE will facilitate plant genome size analysis and contribute to evolutionary and ecological studies in the field. Furthermore, at the cost of an initial calibration, LocoGSE can be used in other lineages. [ABSTRACT FROM AUTHOR]

Since 1999, The Arabidopsis Information Resource (www.arabidopsis.org) has been curating data about the Arabidopsis thaliana genome. Its primary focus is integrating experimental gene function information from the peer-reviewed literature and codifying it as controlled vocabulary annotations. Our goal is to produce a "gold standard" functional annotation set that reflects the current state of knowledge about the Arabidopsis genome. At the same time, the resource serves as a nexus for community-based collaborations aimed at improving data quality, access, and reuse. For the past decade, our work has been made possible by subscriptions from our global user base. This update covers our ongoing biocuration work, some of our modernization efforts that contribute to the first major infrastructure overhaul since 2011, the introduction of JBrowse2, and the resource's role in community activities such as organizing the structural reannotation of the genome. For gene function assessment, we used gene ontology annotations as a metric to evaluate: (1) what is currently known about Arabidopsis gene function and (2) the set of "unknown" genes. Currently, 74% of the proteome has been annotated to at least one gene ontology term. Of those loci, half have experimental support for at least one of the following aspects: molecular function, biological process, or cellular component. Our work sheds light on the genes for which we have not yet identified any published experimental data and have no functional annotation. Drawing attention to these unknown genes highlights knowledge gaps and potential sources of novel discoveries. [ABSTRACT FROM AUTHOR]

Abiotic stresses are major constraints in crop production, and are accountable for more than half of the total crop loss. Plants overcome these environmental stresses using coordinated activities of transcription factors and phytohormones. Pearl millet an important C4 cereal plant having high nutritional value and climate resilient features is grown in marginal lands of Africa and South-East Asia including India. Among several transcription factors, the basic leucine zipper (bZIP) is an important TF family associated with diverse biological functions in plants. In this study, we have identified 98 bZIP family members (PgbZIP) in pearl millet. Phylogenetic analysis divided these PgbZIP genes into twelve groups (A-I, S, U and X). Motif analysis has shown that all the PgbZIP proteins possess conserved bZIP domains and the exon-intron organization revealed conserved structural features among the identified genes. Cis-element analysis, RNA-seq data analysis, and real-time expression analysis of PgbZIP genes suggested the potential role of selected PgbZIP genes in growth/development and abiotic stress responses in pearl millet. Expression profiling of selected PgbZIPs under various phytohormones (ABA, SA and MeJA) treatment showed differential expression patterns of PgbZIP genes. Further, PgbZIP9, a homolog of AtABI5 was found to localize in the nucleus and modulate gene expression in pearl millet under stresses. Our present findings provide a better understanding of bZIP genes in pearl millet and lay a good foundation for the further functional characterization of multi-stress tolerant PgbZIP genes, which could become efficient tools for crop improvement. [ABSTRACT FROM AUTHOR]

This comprehensive article critically analyzes the advanced biotechnological strategies to mitigate plant drought stress. It encompasses an in-depth exploration of the latest developments in plant genomics, proteomics, and metabolomics, shedding light on the complex molecular mechanisms that plants employ to combat drought stress. The study also emphasizes the significant advancements in genetic engineering techniques, particularly CRISPR-Cas9 genome editing, which have revolutionized the creation of drought-resistant crop varieties. Furthermore, the article explores microbial biotechnology's pivotal role, such as plant growth-promoting rhizobacteria (PGPR) and mycorrhizae, in enhancing plant resilience against drought conditions. The integration of these cutting-edge biotechnological interventions with traditional breeding methods is presented as a holistic approach for fortifying crops against drought stress. This integration addresses immediate agricultural needs and contributes significantly to sustainable agriculture, ensuring food security in the face of escalating climate change challenges. [ABSTRACT FROM AUTHOR]

Extensive research has focused on exploring the range of genome sizes in eukaryotes, with a particular emphasis on land plants, where significant variability has been observed. Accurate estimation of genome size is essential for various research purposes, but existing sequence-based methods have limitations, particularly for low-coverage datasets. In this study, we introduce LocoGSE, a novel genome size estimator designed specifically for low-coverage datasets generated by genome skimming approaches. LocoGSE relies on mapping the reads on single copy consensus proteins without the need for a reference genome assembly. We calibrated LocoGSE using 430 low-coverage Angiosperm genome skimming datasets and compared its performance against other estimators. Our results demonstrate that LocoGSE accurately predicts monoploid genome size even at very low depth of coverage (

Abiotic stresses are major constraints in crop production, and are accountable for more than half of the total crop loss. Plants overcome these environmental stresses using coordinated activities of transcription factors and phytohormones. Pearl millet an important C4 cereal plant having high nutritional value and climate resilient features is grown in marginal lands of Africa and South-East Asia including India. Among several transcription factors, the basic leucine zipper (bZIP) is an important TF family associated with diverse biological functions in plants. In this study, we have identified 98 bZIP family members (PgbZIP) in pearl millet. Phylogenetic analysis divided these PgbZIP genes into twelve groups (A-I, S, U and X). Motif analysis has shown that all the PgbZIP proteins possess conserved bZIP domains and the exon-intron organization revealed conserved structural features among the identified genes. Cis-element analysis, RNA-seq data analysis, and real-time expression analysis of PgbZIP genes suggested the potential role of selected PgbZIP genes in growth/development and abiotic stress responses in pearl millet. Expression profiling of selected PgbZIPs under various phytohormones (ABA, SA and MeJA) treatment showed differential expression patterns of PgbZIP genes. Further, PgbZIP9, a homolog of AtABI5 was found to localize in the nucleus and modulate gene expression in pearl millet under stresses. Our present findings provide a better understanding of bZIP genes in pearl millet and lay a good foundation for the further functional characterization of multi-stress tolerant PgbZIP genes, which could become efficient tools for crop improvement.

Plant viruses infect a wide range of commercially important crop plants and cause significant crop production losses worldwide. Numerous alterations in plant physiology related to the reprogramming of gene expression may result from viral infections. Although conventional integrated pest management-based strategies have been effective in reducing the impact of several viral diseases, continued emergence of new viruses and strains, expanding host ranges, and emergence of resistance-breaking strains necessitate a sustained effort toward the development and application of new approaches for virus management that would complement existing tactics. RNA interference-based techniques, and more recently, clustered regularly interspaced short palindromic repeats (CRISPR)-based genome editing technologies have paved the way for precise targeting of viral transcripts and manipulation of viral genomes and host factors. In-depth knowledge of the molecular mechanisms underlying the development of disease would further expand the applicability of these recent methods. Advances in next-generation/high-throughput sequencing have made possible more intensive studies into host−virus interactions. Utilizing the omics data and its application has the potential to expedite fast-tracking traditional plant breeding methods, as well as applying modern molecular tools for trait enhancement, including virus resistance. Here, we summarize the recent developments in the CRISPR/Cas system, transcriptomics, endogenous RNA interference, and exogenous application of dsRNA in virus disease management. [ABSTRACT FROM AUTHOR]

This comprehensive article critically analyzes the advanced biotechnological strategies to mitigate plant drought stress. It encompasses an in-depth exploration of the latest developments in plant genomics, proteomics, and metabolomics, shedding light on the complex molecular mechanisms that plants employ to combat drought stress. The study also emphasizes the significant advancements in genetic engineering techniques, particularly CRISPR-Cas9 genome editing, which have revolutionized the creation of drought-resistant crop varieties. Furthermore, the article explores microbial biotechnology’s pivotal role, such as plant growth-promoting rhizobacteria (PGPR) and mycorrhizae, in enhancing plant resilience against drought conditions. The integration of these cutting-edge biotechnological interventions with traditional breeding methods is presented as a holistic approach for fortifying crops against drought stress. This integration addresses immediate agricultural needs and contributes significantly to sustainable agriculture, ensuring food security in the face of escalating climate change challenges.

This document is an editorial published in the journal Frontiers in Plant Science. It discusses the importance of plant genomics and pathogenomics in improving crop disease resistance. The editorial highlights the challenges posed by increasing demand for safe and diverse food, climate change, and the emergence of new aggressive plant pathogens. It emphasizes the progress made in genome sequencing and the identification of resistance genes, as well as the potential of genomics research combined with genome editing approaches for crop improvement. The document also mentions specific studies on topics such as terpenoid biosynthesis in peach plants, RNA interference-mediated approaches for disease resistance, and genomics research on wheat rust diseases. Overall, the editorial emphasizes the importance of understanding the interactions between plants and pathogens for developing sustainable strategies to prevent and control plant diseases and ensure future food security. [Extracted from the article]

Recent advances in high-throughput sequencing have enabled the development of a novel approach to the evaluation of genome stability and integrity. The depth of the coverage signal at a particular genome location may point to DNA integrity loss in the region. In this work, the previously developed metric of local genomic integrity, which estimates the uniformity of the coverage signal, has been transformed to a quantitative trait, and genetic variants associated with coverage signal uniformity in the flax genome have been sought. Quantitative Trait Loci (xQTLs; where x is the designation of an arbitrary quantitative parameter associated with a particular genome region, for example, the levels of gene expression, the degree of coverage by ribosomes, etc.) have been analyzed to identify genomic regions that most likely contribute to the loss of genomic integrity and may be involved in the maintenance of genome stability. The analysis invokes information on the whole-genome sequence assembly of 100 flax samples and enables the identification of genes presumably implicated in maintenance of genomic integrity in flax and, possibly, in plants in general. It also reveals novel processes associated with the maintenance of genomic integrity. [ABSTRACT FROM AUTHOR]

Abstract Background Massive parallel sequencing technologies have enabled the elucidation of plant phylogenetic relationships from chloroplast genomes at a high pace. These include members of the family Rhamnaceae. The current Rhamnaceae phylogenetic tree is from 13 out of 24 Rhamnaceae chloroplast genomes, and only one chloroplast genome of the genus Ventilago is available. Hence, the phylogenetic relationships in Rhamnaceae remain incomplete, and more representative species are needed. Results The complete chloroplast genome of Ventilago harmandiana Pierre was outlined using a hybrid assembly of long- and short-read technologies. The accuracy and validity of the final genome were confirmed with PCR amplifications and investigation of coverage depth. Sanger sequencing was used to correct for differences in lengths and nucleotide bases between inverted repeats because of the homopolymers. The phylogenetic trees reconstructed using prevalent methods for phylogenetic inference were topologically similar. The clustering based on codon usage was congruent with the molecular phylogenetic tree. The groups of genera in each tribe were in accordance with tribal classification based on molecular markers. We resolved the phylogenetic relationships among six Hovenia species, three Rhamnus species, and two Ventilago species. Our reconstructed tree provides the most complete and reliable low-level taxonomy to date for the family Rhamnaceae. Similar to other higher plants, the RNA editing mostly resulted in converting serine to leucine. Besides, most genes were subjected to purifying selection. Annotation anomalies, including indel calling errors, unaligned open reading frames of the same gene, inconsistent prediction of intergenic regions, and misannotated genes, were identified in the published chloroplast genomes used in this study. These could be a result of the usual imperfections in computational tools, and/or existing errors in reference genomes. Importantly, these are points of concern with regards to utilizing published chloroplast genomes for comparative genomic analysis. Conclusions In summary, we successfully demonstrated the use of comprehensive genomic data, including DNA and amino acid sequences, to build a reliable and high-resolution phylogenetic tree for the family Rhamnaceae. Additionally, our study indicates that the revision of genome annotation before comparative genomic analyses is necessary to prevent the propagation of errors and complications in downstream analysis and interpretation.

[Purpose/Significance] Advances in single-cell sequencing and high-throughput technology have made it possible for plant genomics to accumulate large quantities of data describing multidimensional genomic-wide molecular phenotypes at low cost. As powerful data mining tools, deep learning techniques can be utilized to further predict and interpret the acquired molecular phenotypes. In recent studies, deep learning has been shown to yield significant results in plant genomics and crop breeding research. However, a complete review of deep learning applications in plant genomics is lacking. [Method/Process] The input to deep learning applied to genomics is usually biological sequences and molecular phenotypes as predictor and target variables, respectively. We introduced the workflow from four views: input data pre-processing includes retrieval, coding, and splitting; model construction and training includes the selection of model architecture and hyperparameters; model evaluation and interpretability. Specifically, this paper introduces the background of deep learning approaches, including the latest graph neural networks; then it discusses two prominent issues in the intersection of genomics and deep learning with respect to gene characterization and protein characterization: 1) how to model the flow of information from plant genomic DNA sequences to molecular phenotypes; and 2) how deep learning models can be utilized to identify functional variation in natural populations? Specifically, the paper summarizes the current status of deep learning applications in related fields, which include deep learning and DNA and gene characterization research, interpretability of deep learning in genomics applications, graph neural networks in genomics, deep learning and genomic variation research, deep learning in protein prediction, ALPHAFOLD in protein prediction, deep learning and crop breeding research, and unsupervised learning in genomics and protein characterization. [Results/Conclusions] This article summarizes how traditional deep-learning algorithms, graph deep-learning, generative adversarial networks and interpretable AI are applied in current research in order to address these two problems. Finally, the prospects for deep learning in future plant genomics research and crop improvement are discussed. Overall, deep learning has provided better results than conventional methods in many genomics research directions, and the application of deep learning in genomics has yielded early applications of scientific and economic significance. Deep learning offers two distinct advantages: 1) end-to-end learning, with the ability to integrate multiple pre-processing steps into a single model; and 2) multimodal data processing capabilities that can handle extremely heterogeneous data in genomics. The advancement of deep learning has the potential to expand new research perspectives in genomics and crop breeding, and to facilitate larger-scale association studies in both phenotypic and genotypic genomics as algorithms become more accurate.

This document is a corrigendum for an article titled "LocoGSE, a sequence-based genome size estimator for plants" published in Frontiers in Plant Science. The corrigendum addresses an error in Figure 2 of the original article, where the same formula was displayed on all panels instead of varying across lineages. The corrected Figure 2 and its caption are provided in the corrigendum. The authors apologize for the error and state that it does not affect the scientific conclusions of the article. The corrigendum also includes information about the publisher's note, the corresponding author, the date of publication, and citation details. [Extracted from the article]

Ksenia Krasileva is an Assistant Professor at UC Berkley, studying innate immunity in plants. Ksenia's work combines plant genomics and plant-microbe interactions with new technologies, spanning basic studies and translational research in agriculture. In this interview Ksenia shares her experience with research and leading a lab, as well as thoughts on innovations in publishing.

Background: Massive parallel sequencing technologies have enabled the elucidation of plant phylogenetic relationships from chloroplast genomes at a high pace. These include members of the family Rhamnaceae. The current Rhamnaceae phylogenetic tree is from 13 out of 24 Rhamnaceae chloroplast genomes, and only one chloroplast genome of the genus Ventilago is available. Hence, the phylogenetic relationships in Rhamnaceae remain incomplete, and more representative species are needed. Results: The complete chloroplast genome of Ventilago harmandiana Pierre was outlined using a hybrid assembly of long- and short-read technologies. The accuracy and validity of the final genome were confirmed with PCR amplifications and investigation of coverage depth. Sanger sequencing was used to correct for differences in lengths and nucleotide bases between inverted repeats because of the homopolymers. The phylogenetic trees reconstructed using prevalent methods for phylogenetic inference were topologically similar. The clustering based on codon usage was congruent with the molecular phylogenetic tree. The groups of genera in each tribe were in accordance with tribal classification based on molecular markers. We resolved the phylogenetic relationships among six Hovenia species, three Rhamnus species, and two Ventilago species. Our reconstructed tree provides the most complete and reliable low-level taxonomy to date for the family Rhamnaceae. Similar to other higher plants, the RNA editing mostly resulted in converting serine to leucine. Besides, most genes were subjected to purifying selection. Annotation anomalies, including indel calling errors, unaligned open reading frames of the same gene, inconsistent prediction of intergenic regions, and misannotated genes, were identified in the published chloroplast genomes used in this study. These could be a result of the usual imperfections in computational tools, and/or existing errors in reference genomes. Importantly, these are points of concern with regards to utilizing published chloroplast genomes for comparative genomic analysis. Conclusions: In summary, we successfully demonstrated the use of comprehensive genomic data, including DNA and amino acid sequences, to build a reliable and high-resolution phylogenetic tree for the family Rhamnaceae. Additionally, our study indicates that the revision of genome annotation before comparative genomic analyses is necessary to prevent the propagation of errors and complications in downstream analysis and interpretation. [ABSTRACT FROM AUTHOR]

Plants, the primary producers of our planet, have evolved from simple aquatic life to very complex terrestrial habitat. This habitat transition coincides with evolution of enormous chemical diversity, collectively termed as 'Plant Specialised Metabolisms (PSMs)', to cope the environmental challenges. Plant glycosylation is an important process of metabolic diversification of PSMs to govern their in planta stability, solubility and inter/intra-cellular transport. Although, individual category of PSMs (terpenoids, phenylpropanoids, flavonoids, saponins, alkaloids, phytohormones, glucosinolates and cyanogenic glycosides) have been well studied; nevertheless, deeper insights of physiological functioning and genomic aspects of plant glycosylation/deglycosylation processes including enzymatic machinery (CYPs, GTs, and GHs) and regulatory elements are still elusive. Therefore, this review discussed the paradigm shift on genomic background of enzymatic machinery, transporters and regulatory mechanism of 'Plant Specialised Glycosides (PSGs)'. Current efforts also update the fundamental understanding about physiological, evolutionary and adaptive role of glycosylation/deglycosylation processes during the metabolic diversification of PSGs. Additionally, futuristic considerations and recommendations for employing integrated next-generation multi-omics (genomics, transcriptomics, proteomics and metabolomics), including gene/genome editing (CRISPR-Cas) approaches are also proposed to explore commercial potential of PSGs. Plants produce a number of specialised metabolites to deal with their evolutionally and adaptive challenges. Addition (glycosylation) and removal (deglycosylation) of sugar moiety in these specialised metabolites is very crucial for their functional attributes. This comprehensive review will strengthen our understanding about the role of glycosylation on plant specialised metabolites and respective enzyme families and regulatory elements. [ABSTRACT FROM AUTHOR]

The penstemons are ornamental annual flowering plants native to the Intermountain West and Rocky Mountains and commonly used for urban landscaping. Elite commercial penstemons are generally susceptible to abiotic stresses, including drought, root rot, cold, and high salinity. Firecracker penstemon (Penstemon eatonii), however, is much more tolerant to these stresses than most elite cultivars. Importantly, firecracker penstemon has been reported to hybridize with many other penstemons and therefore provides the opportunity to develop more tolerant elite cultivars through strategic crossing. To facilitate the study and utilization of firecracker penstemon, we sequenced and annotated the genome of a P. eatonii accession collected from Utah, USA. We also performed low-coverage, whole-genome sequencing of 26 additional accessions from three different varieties of P. eatonii. This chromosome-scale genome assembly is the most contiguous and complete Penstemon genome sequenced to date., (© The American Genetic Association. 2024.)

While considerable knowledge exists about the enzymes pivotal for C 4 photosynthesis, much less is known about the cis- regulation important for specifying their expression in distinct cell types. Here, we use single-cell-indexed ATAC-seq to identify cell-type-specific accessible chromatin regions (ACRs) associated with C 4 enzymes for five different grass species. This study spans four C 4 species, covering three distinct photosynthetic subtypes: Zea mays and Sorghum bicolor (NADP-dependent malic enzyme), Panicum miliaceum (NAD-dependent malic enzyme), Urochloa fusca (phosphoenolpyruvate carboxykinase), along with the C 3 outgroup Oryza sativa . We studied the cis- regulatory landscape of enzymes essential across all C 4 species and those unique to C 4 subtypes, measuring cell-type-specific biases for C 4 enzymes using chromatin accessibility data. Integrating these data with phylogenetics revealed diverse co-option of gene family members between species, showcasing the various paths of C 4 evolution. Besides promoter proximal ACRs, we found that, on average, C 4 genes have two to three distal cell-type-specific ACRs, highlighting the complexity and divergent nature of C 4 evolution. Examining the evolutionary history of these cell-type-specific ACRs revealed a spectrum of conserved and novel ACRs, even among closely related species, indicating ongoing evolution of cis -regulation at these C 4 loci. This study illuminates the dynamic and complex nature of cis -regulatory elements evolution in C 4 photosynthesis, particularly highlighting the intricate cis- regulatory evolution of key loci. Our findings offer a valuable resource for future investigations, potentially aiding in the optimization of C 3 crop performance under changing climatic conditions., Competing Interests: Competing interests statement:R.J.S. is a co-founder of REquest Genomics, LLC, a company that provides epigenomic services.

Genome sequencing for agriculturally important Rosaceous crops has made rapid progress both in completeness and annotation quality. Whole genome sequence and annotation gives breeders, researchers, and growers information about cultivar specific traits such as fruit quality and disease resistance, and informs strategies to enhance postharvest storage. Here we present a haplotype-phased, chromosomal level genome of Malus domestica, 'WA 38', a new apple cultivar released to market in 2017 as Cosmic Crisp®. Using both short and long read sequencing data with a k-mer based approach, chromosomes originating from each parent were assembled and segregated. This is the first pome fruit genome fully phased into parental haplotypes in which chromosomes from each parent are identified and separated into their unique, respective haplomes. The two haplome assemblies, 'Honeycrisp' originated HapA and 'Enterprise' originated HapB, are about 650 Megabases each, and both have a BUSCO score of 98.7% complete. A total of 53,028 and 54,235 genes were annotated from HapA and HapB, respectively. Additionally, we provide genome-scale comparisons to 'Gala', 'Honeycrisp', and other relevant cultivars highlighting major differences in genome structure and gene family circumscription. This assembly and annotation was done in collaboration with the American Campus Tree Genomes project that includes 'WA 38' (Washington State University), 'd'Anjou' pear (Auburn University), and many more. To ensure transparency, reproducibility, and applicability for any genome project, our genome assembly and annotation workflow is recorded in detail and shared under a public GitLab repository. All software is containerized, offering a simple implementation of the workflow., (© The Author(s) 2024. Published by Oxford University Press on behalf of The Genetics Society of America.)

[Purpose/Significance] Advances in single-cell sequencing and high-throughput technology have made it possible for plant genomics to accumulate large quantities of data describing multidimensional genomic-wide molecular phenotypes at low cost. As powerful data mining tools, deep learning techniques can be utilized to further predict and interpret the acquired molecular phenotypes. In recent studies, deep learning has been shown to yield significant results in plant genomics and crop breeding research. However, a complete review of deep learning applications in plant genomics is lacking. [Method/Process] The input to deep learning applied to genomics is usually biological sequences and molecular phenotypes as predictor and target variables, respectively. We introduced the workflow from four views: input data pre-processing includes retrieval, coding, and splitting; model construction and training includes the selection of model architecture and hyperparameters; model evaluation and interpretability. Specifically, this paper introduces the background of deep learning approaches, including the latest graph neural networks; then it discusses two prominent issues in the intersection of genomics and deep learning with respect to gene characterization and protein characterization: 1) how to model the flow of information from plant genomic DNA sequences to molecular phenotypes; and 2) how deep learning models can be utilized to identify functional variation in natural populations? Specifically, the paper summarizes the current status of deep learning applications in related fields, which include deep learning and DNA and gene characterization research, interpretability of deep learning in genomics applications, graph neural networks in genomics, deep learning and genomic variation research, deep learning in protein prediction, ALPHAFOLD in protein prediction, deep learning and crop breeding research, and unsupervised learning in genomics and protein characterization. [Results/Conclusions] This article summarizes how traditional deep-learning algorithms, graph deep-learning, generative adversarial networks and interpretable AI are applied in current research in order to address these two problems. Finally, the prospects for deep learning in future plant genomics research and crop improvement are discussed. Overall, deep learning has provided better results than conventional methods in many genomics research directions, and the application of deep learning in genomics has yielded early applications of scientific and economic significance. Deep learning offers two distinct advantages: 1) end-to-end learning, with the ability to integrate multiple pre-processing steps into a single model; and 2) multimodal data processing capabilities that can handle extremely heterogeneous data in genomics. The advancement of deep learning has the potential to expand new research perspectives in genomics and crop breeding, and to facilitate larger-scale association studies in both phenotypic and genotypic genomics as algorithms become more accurate. [ABSTRACT FROM AUTHOR]

Bacteria communities associated with plants have been given increasing consideration because they are arguably beneficial to their host plants. To understand the ecological and evolutionary impact of these mutualistic associations, it is important to explore the vast unknown territory of bacterial genomic diversity and their functional contributions associated with the major branches of the tree-of-life. Arguably, this aim can be achieved by profiling bacterial communities by applying high throughput sequencing approaches, besides establishing model plant organisms to test key predictions. This study utilized the Illumina Miseq reads of bacterial 16S rRNA sequences to determine the bacterial diversity associated with the endosphere of the leaves of the highly specialized rock spleenwort Asplenium delavayi (Aspleniaceae). By documenting the bacterial communities associated with ferns collected in natural occurrence and cultivation, this study discovered the most species-rich bacterial communities associated with terrestrial ferns reported until now. Despite the substantial variations of species diversity and composition among accessions, a set of 28 bacterial OTUs was found to be shared among all accessions. Functional analyses recovered evidence to support the predictions that changes in bacterial community compositions correspond to functional differentiation. Given the ease of cultivating this species, Asplenium delavayi is introduced here as a model organism to explore the ecological and evolutionary benefits created by mutualistic associations between bacteria and ferns. [ABSTRACT FROM AUTHOR]

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Bacteria communities associated with plants have been given increasing consideration because they are arguably beneficial to their host plants. To understand the ecological and evolutionary impact of these mutualistic associations, it is important to explore the vast unknown territory of bacterial genomic diversity and their functional contributions associated with the major branches of the tree-of-life. Arguably, this aim can be achieved by profiling bacterial communities by applying high throughput sequencing approaches, besides establishing model plant organisms to test key predictions. This study utilized the Illumina Miseq reads of bacterial 16S rRNA sequences to determine the bacterial diversity associated with the endosphere of the leaves of the highly specialized rock spleenwort Asplenium delavayi (Aspleniaceae). By documenting the bacterial communities associated with ferns collected in natural occurrence and cultivation, this study discovered the most species-rich bacterial communities associated with terrestrial ferns reported until now. Despite the substantial variations of species diversity and composition among accessions, a set of 28 bacterial OTUs was found to be shared among all accessions. Functional analyses recovered evidence to support the predictions that changes in bacterial community compositions correspond to functional differentiation. Given the ease of cultivating this species, Asplenium delavayi is introduced here as a model organism to explore the ecological and evolutionary benefits created by mutualistic associations between bacteria and ferns.

Third-generation long-read sequencing is transforming plant genomics. Oxford Nanopore Technologies and Pacific Biosciences are offering competing long-read sequencing technologies and enable plant scientists to investigate even large and complex plant genomes. Sequencing projects can be conducted by single research groups and sequences of smaller plant genomes can be completed within days. This also resulted in an increased investigation of genomes from multiple species in large scale to address fundamental questions associated with the origin and evolution of land plants. Increased accessibility of sequencing devices and user-friendly software allowsmore researchers to get involved in genomics. Current challenges are accurately resolving diploid or polyploid genome sequences and better accounting for the intra-specific diversity by switching from the use of single reference genome sequences to a pangenome graph. [ABSTRACT FROM AUTHOR]

Summary: Recent advances in plant genomics are scaling up gene expression profiling from the individual level to the population level, making transcriptomics a more powerful tool while deciphering the genome function. This study developed an efficient 3′RNA‐seq method, Simplified Poly(A) Anchored Sequencing (SiPAS), to perform large‐scale experiments of gene expression quantification. Aside from being cost‐effective, by conducting a comprehensive performance assessment of SiPAS in hexaploid wheat, we demonstrated that SiPAS is highly sensitive, accurate, and reproducible while quantifying gene expression. Our method is anticipated to boost studies of population transcriptomics in plants and improve our understanding of genome biology. [ABSTRACT FROM AUTHOR]

Third-generation long-read sequencing is transforming plant genomics. Oxford Nanopore Technologies and Pacific Biosciences are offering competing long-read sequencing technologies and enable plant scientists to investigate even large and complex plant genomes. Sequencing projects can be conducted by single research groups and sequences of smaller plant genomes can be completed within days. This also resulted in an increased investigation of genomes from multiple species in large scale to address fundamental questions associated with the origin and evolution of land plants. Increased accessibility of sequencing devices and user-friendly software allows more researchers to get involved in genomics. Current challenges are accurately resolving diploid or polyploid genome sequences and better accounting for the intra-specific diversity by switching from the use of single reference genome sequences to a pangenome graph.

The history of the series is described by three successive Editors. Each one reports how he has been approached, his conception of the different volumes and gives an overview of the topics which have been treated. The first period, from creation of the series in 1963 by Professor Reginald Preston to 2006, was covered by Professor James (Jim) Callow who became Editor after Professor Harold Woolhouse, in 1985. During this period, 44 volumes were published, essentially eclectic, covering most of the fields in plant biology. Under James Callow's editorship, the frequency of issues increased up to two/year and a few thematic volumes were published. When he decided to stop in 2006, Dr. Jean Claude Kader and Dr. Michel Delseny were appointed. They maintained a rate of two to three issues/year, alternating eclectic and thematic volumes. They together published 16 volumes and resigned in 2012. Finally, they were replaced by Professors Jean-Pierre Jacquot and Pierre Gadal. They published only thematic volumes and the rate increased to four-five issues/year. [ABSTRACT FROM AUTHOR]

Meiosis is a key feature of sexual reproduction. During meiosis homologous chromosomes replicate, recombine, and randomly segregate, followed by the segregation of sister chromatids to produce haploid cells. The unique genotypes of recombinant gametes are an essential substrate for the selection of superior genotypes in natural populations and in plant breeding. In this review we summarize current knowledge on meiosis in diverse monocot and dicot crop species and provide a comprehensive resource of cloned meiotic mutants in six crop species (rice, maize, wheat, barley, tomato, and Brassica species). Generally, the functional roles of meiotic proteins are conserved between plant species, but we highlight notable differences in mutant phenotypes. The physical lengths of plant chromosomes vary greatly; for instance, wheat chromosomes are roughly one order of magnitude longer than those of rice. We explore how chromosomal distribution for crossover recombination can vary between species. We conclude that research on meiosis in crops will continue to complement that in Arabidopsis, and alongside possible applications in plant breeding will facilitate a better understanding of how the different stages of meiosis are controlled in plant species. [ABSTRACT FROM AUTHOR]