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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.

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[This corrects the article DOI: 10.3389/fpls.2024.1328966.].

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Genomic data serve as an invaluable resource for unraveling the intricacies of the higher plant systems, including the constituent elements within and among species. Through various efforts in genomic data archiving, integrative analysis and value-added curation, the National Genomics Data Center (NGDC), which is a part of the China National Center for Bioinformation (CNCB), has successfully established and currently maintains a vast amount of database resources. This dedicated initiative of the NGDC facilitates a data-rich ecosystem that greatly strengthens and supports genomic research efforts. Here, we present a comprehensive overview of central repositories dedicated to archiving, presenting, and sharing plant omics data, introduce knowledgebases focused on variants or gene-based functional insights, highlight species-specific multiple omics database resources, and briefly review the online application tools. We intend that this review can be used as a guide map for plant researchers wishing to select effective data resources from the NGDC for their specific areas of study. Supplementary Information: The online version contains supplementary material available at 10.1007/s42994-023-00134-4.

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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.

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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.

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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.

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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.

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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.

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The FAIR data principle as a commitment to support long-term research data management is widely accepted in the scientific community. However, although many established infrastructures provide comprehensive and long-term stable services and platforms, a large quantity of research data is still hidden. Currently, high-throughput plant genomics and phenomics technologies are producing research data in abundance, the storage of which is not covered by established core databases. This concerns the data volume, for example, time series of images or high-resolution hyperspectral data; the quality of data formatting and annotation, e.g., with regard to structure and annotation specifications of core databases; uncovered data domains; or organizational constraints prohibiting primary data storage outside institutional boundaries. To share these potentially dark data in a FAIR way and master these challenges the ELIXIR Germany/de.NBI service Plant Genomic and Phenomics Research Data Repository (PGP) implements an on-premise approach, which allows research data to be kept in place and wrapped in FAIR-aware software infrastructure. In this chapter, the e!DAL infrastructure software and the PGP repository are presented as best practice on how to easily setup FAIR-compliant and intuitive research data services.

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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.

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It has recently been shown that structural variants (SV) can have a higher impact on gene expression variation compared to single nucleotide variants (SNV) in different plant species. Additionally, SV were associated with phenotypic variation in several crops. However, compared to the established SV detection based on short-read sequencing, less approaches were described for linked-read based SV calling. We therefore evaluated the performance of six linked-read SV callers compared to an established short-read SV caller based on simulated linked-reads in tetraploid potato. The objectives of our study were to i) compare the performance of SV callers based on linked-read sequencing to short-read sequencing, ii) examine the influence of SV type, SV length, haplotype incidence (HI), as well as sequencing coverage on the SV calling performance in the tetraploid potato genome, and iii) evaluate the accuracy of detecting insertions by linked-read compared to short-read sequencing. We observed high break point resolutions (BPR) detecting short SV and slightly lower BPR for large SV. Our observations highlighted the importance of short-read signals provided by Manta and LinkedSV to detect short SV. Manta and NAIBR performed well for detecting larger deletions, inversions, and duplications. Detected large SV were weakly influenced by the HI. Furthermore, we illustrated that large insertions can be assembled by Novel-X. Our results suggest the usage of the short-read and linked-read SV callers Manta, NAIBR, LinkedSV, and Novel-X based on at least 90x linked-read sequencing coverage to ensure the detection of a broad range of SV in the tetraploid potato genome.

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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.

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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.

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SciApps is an open-source, web-based platform for processing, storing, visualizing, and distributing genomic data and analysis results. Built upon the Tapis (formerly Agave) platform, SciApps brings users TB-scale of data storage via CyVerse Data Store and over one million CPUs via the Extreme Science and Engineering Discovery Environment (XSEDE) resources at Texas Advanced Computing Center (TACC). SciApps provides users ways to chain individual jobs into automated and reproducible workflows in a distributed cloud and provides a management system for data, associated metadata, individual analysis jobs, and multi-step workflows. This chapter provides examples of how to (1) submitting, managing, constructing workflows, (2) using public workflows for Bulked Segregant Analysis (BSA), (3) constructing a Data Analysis Center (DAC), and Data Coordination Center (DCC) for the plant ENCODE project.

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Optical mapping plays an important role in plant genomics, particularly in plant genome assembly and large-scale structural variation detection. While DNA sequencing provides base-by-base nucleotide information, optical mapping shows the physical locations of selected enzyme restriction sites in a genome. The long single-molecule maps produced by optical mapping make it a useful auxiliary technique to DNA sequencing, which generally cannot span large and complex genomic regions. Although optical mapping, therefore, offers unique advantages to researchers, there are few dedicated tools to assist in optical mapping analyses. In this chapter, we present runBNG2, a successor of runBNG to help optical-mapping data analysis for diverse datasets.

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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.

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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.