ZEAMAP, a Comprehensive Database Adapted to the Maize Multi-Omics Era

Comprehensive approaches to heavy metal bioremediation: Integrating microbial insights and genetic innovations

The increasing contamination of ecosystems with heavy metals (HMs) due to industrial activities raises significant jeopardies to environmental health and human well-being. Addressing this issue, recent advances in the field of bioremediation have highlighted the potential of plant-associated microbiomes and genetically engineered organisms (GEOs) to mitigate HMs pollution. This review explores recent advancements in bioremediation strategies for HMs detoxification, with particular attention to omics technologies such as metagenomics, metabolomics, and metaproteomics in deepening the understanding of microbial interactions and their potential for neutralizing HMs. Additionally, Emerging strategies and technologies in GEOs and microorganism-aided nanotechnology have proven to be effective bioremediation tools, particularly for alleviating HM contamination. Despite the promising strategies developed in laboratory settings, several challenges impede their practical application, including ecological risks, regulatory limitations, and public concerns regarding the practice of genetically modified organisms. A comprehensive approach that involves interdisciplinary research is essential to enhance the efficacy and safety of bioremediation technologies. This approach should be coupled with robust regulatory frameworks and active public engagement to ensure environmental integrity and societal acceptance. This review underscores the importance of developing sustainable bioremediation strategies that align with ecological conservation goals and public health priorities.

Questions and Consequences of Omics in Genetically Engineered Crop Regulation

In 2016, a National Academies of Sciences, Engineering, and Medicine advisory committee proposed omics technologies as one possible adequate response to the regulatory challenges posed by gene editing and synthetic biology. This paper presents a set of questions that would need to be answered to integrate omics experiments and data into crop regulatory systems. These questions concern both experimental practice and how omics-experimental and regulatory systems intersect. We anticipate that the chosen answers to these questions will impact the scientific validity, regulatory burden, and usefulness for forecasting risk in nuanced ways. In doing so, we conclude that the integration of omics technologies into regulatory systems poses an array of more-than-technical dilemmas whose management will require cross-sector collaboration and innovative approaches to socio-technical decision-making.

ZmDnaJ-ZmNCED6 module positively regulates drought tolerance via modulating stomatal closure in maize

Heat Shock Protein plays a vital role in maintaining protein homeostasis and protecting cells from stress stimulation. As one of the HSP40 proteins, DnaJ is a stress response protein widely existing in plant cells. The function and regulatory mechanism of ZmDnaJ, a novel chloroplast-localized type-III HSP40, in maize drought tolerance were characterized. Tissue-specific expression analysis showed that ZmDnaJ is highly expressed in the leaves, and is strongly drought-induced in maize seedlings. Overexpression of ZmDnaJ improved maize drought tolerance by enhancing stomatal closure and increasing ABA content to mediate photosynthesis. In contrast, the CRISPR-Cas9 knockout zmdnaj mutant showed lower relative water content and high sensitivity to drought stress. Moreover, Y2H, BiFC and Co-IP analyses revealed that ZmDnaJ interacts with an ABA synthesis-related protein ZmNCED6 to regulate drought tolerance. Similarly, ZmNCED6 overexpressed lines showed stronger oxidation resistance, enhanced photosynthetic rate, stomatal closure and ABA content, whilst the CRISPR-Cas9 knockout mutant showed sensitive to drought stress. More importantly, ZmDnaJ could regulate key drought tolerance genes (ZmPYL10, ZmPP2C44, ZmEREB65, ZmNCED4, ZmNCED6 and ZmABI5), involved in ABA signal transduction pathways. Taken together, our findings suggest that ZmDnaJ-ZmNCED6 module improves drought tolerance in maize.

Multiomics Research: Principles and Challenges in Integrated Analysis

Multiomics research is a transformative approach in the biological sciences that integrates data from genomics, transcriptomics, proteomics, metabolomics, and other omics technologies to provide a comprehensive understanding of biological systems. This review elucidates the fundamental principles of multiomics, emphasizing the necessity of data integration to uncover the complex interactions and regulatory mechanisms underlying various biological processes. We explore the latest advances in computational methodologies, including deep learning, graph neural networks (GNNs), and generative adversarial networks (GANs), which facilitate the effective synthesis and interpretation of multiomics data. Additionally, this review addresses the critical challenges in this field, such as data heterogeneity, scalability, and the need for robust, interpretable models. We highlight the potential of large language models to enhance multiomics analysis through automated feature extraction, natural language generation, and knowledge integration. Despite the important promise of multiomics, the review acknowledges the substantial computational resources required and the complexity of model tuning, underscoring the need for ongoing innovation and collaboration in the field. This comprehensive analysis aims to guide researchers in navigating the principles and challenges of multiomics research to foster advances in integrative biological analysis.

New perspectives of post-GWAS analyses: From markers to causal genes for more precise crop breeding

Crop breeding advancement is hindered by the imperfection of methods to reveal genes underlying key traits. Genome-wide Association Study (GWAS) is one such method, identifying genomic regions linked to phenotypes. Post-GWAS analyses predict candidate genes and assist in causative mutation (CM) recognition. Here, we assess post-GWAS approaches, address limitations in omics data integration and stress the importance of evaluating associated variants within a broader context of publicly available datasets. Recent advances in bioinformatics tools and genomic strategies for CM identification and allelic variation exploration are reviewed. We discuss the role of markers and marker panel development for more precise breeding. Finally, we highlight the perspectives and challenges of GWAS-based CM prediction for complex quantitative traits.

CGD: a multi-omics database for Chrysanthemum genomic and biological research

Asteraceae is the largest family of dicotyledons and includes Chrysanthemum and Helianthus, two important genera of ornamental plants. The genus Chrysanthemum consists of more than 30 species and contains many economically important ornamental, medicinal, and industrial plants. To more effectively promote Chrysanthemum research, we constructed the CGD, a Chrysanthemum genome database containing a large amount of data and useful tools. The CGD hosts well-assembled reference genome data for six Chrysanthemum species. These genomic data were fully annotated by comparison with various protein and domain data. Transcriptome data for nine different tissues, five flower developmental stages, and five treatments were subsequently added to the CGD. A fully functional 'RNA data' module was designed to provide complete and visual expression profile data. In addition, the CGD also provides many of the latest bioinformatics analysis tools, such as the efficient sgRNA search tool for Chrysanthemum. In conclusion, the CGD provides the latest, richest, and most complete multi-omics resources and powerful tools for Chrysanthemum. Collectively, the CGD will become the central gateway for Chrysanthemum genomics and genetic breeding research and will aid in the study of polyploid evolution.

A stepwise guide for pangenome development in crop plants: an alfalfa (Medicago sativa) case study

BACKGROUND

The concept of pangenomics and the importance of structural variants is gaining recognition within the plant genomics community. Due to advancements in sequencing and computational technology, it has become feasible to sequence the entire genome of numerous individuals of a single species at a reasonable cost. Pangenomes have been constructed for many major diploid crops, including rice, maize, soybean, sorghum, pearl millet, peas, sunflower, grapes, and mustards. However, pangenomes for polyploid species are relatively scarce and are available in only few crops including wheat, cotton, rapeseed, and potatoes.

MAIN BODY

In this review, we explore the various methods used in crop pangenome development, discussing the challenges and implications of these techniques based on insights from published pangenome studies. We offer a systematic guide and discuss the tools available for constructing a pangenome and conducting downstream analyses. Alfalfa, a highly heterozygous, cross pollinated and autotetraploid forage crop species, is used as an example to discuss the concerns and challenges offered by polyploid crop species. We conducted a comparative analysis using linear and graph-based methods by constructing an alfalfa graph pangenome using three publicly available genome assemblies. To illustrate the intricacies captured by pangenome graphs for a complex crop genome, we used five different gene sequences and aligned them against the three graph-based pangenomes. The comparison of the three graph pangenome methods reveals notable variations in the genomic variation captured by each pipeline.

CONCLUSION

Pangenome resources are proving invaluable by offering insights into core and dispensable genes, novel gene discovery, and genome-wide patterns of variation. Developing user-friendly online portals for linear pangenome visualization has made these resources accessible to the broader scientific and breeding community. However, challenges remain with graph-based pangenomes including compatibility with other tools, extraction of sequence for regions of interest, and visualization of genetic variation captured in pangenome graphs. These issues necessitate further refinement of tools and pipelines to effectively address the complexities of polyploid, highly heterozygous, and cross-pollinated species.

Molecular mechanisms underlying gene regulatory variation of maize metabolic traits

Variation in gene expression levels is pervasive among individuals and races or varieties, and has substantial agronomic consequences, for example, by contributing to hybrid vigor. Gene expression level variation results from mutations in regulatory sequences (cis) and/or transcription factor (TF) activity (trans), but the mechanisms underlying cis- and/or trans-regulatory variation of complex phenotypes remain largely unknown. Here, we investigated gene expression variation mechanisms underlying the differential accumulation of the insecticidal compounds maysin and chlorogenic acid in silks of widely used maize (Zea mays) inbreds, B73 and A632. By combining transcriptomics and cistromics, we identified 1,338 silk direct targets of the maize R2R3-MYB TF Pericarp color1 (P1), consistent with it being a regulator of maysin and chlorogenic acid biosynthesis. Among these P1 targets, 464 showed allele-specific expression (ASE) between B73 and A632 silks. Allelic DNA-affinity purification sequencing identified 34 examples in which P1 allelic specific binding (ASB) correlated with cis-expression variation. From previous yeast one-hybrid studies, we identified 9 TFs potentially implicated in the control of P1 targets, with ASB to 83 out of 464 ASE genes (cis) and differential expression of 4 out of 9 TFs between B73 and A632 silks (trans). These results provide a molecular framework for understanding universal mechanisms underlying natural variation of gene expression levels, and how the regulation of metabolic diversity is established.

PPGV: a comprehensive database of peach population genome variation

Peach tree is one of the most important fruit trees in the world, and it has been cultivated for more than 7,500 years. In recent years, the genome and population resequencing of peach trees have been published continuously, which has effectively promoted the research of peach tree genetics and breeding. In order to promote the further mining and utilization of these data, we integrated and constructed a comprehensive peach genome and variation database (PPGV, http://peachtree.work/home ). The PPGV contains 10 sets of published peach tree genome data, as well as genomic variation information for 1,378 peach tree samples (the resequencing data of 1,378 samples were aligned with the high-quality genomes of Lovell, CN14 and Chinesecling, respectively, for mutation detection). A variety of useful and flexible tools, such as BLAST, Gene ID Convert, KEGG/GO Enrichment, Primer Design and Gene function, were also specially designed for searching data and assisting in breeding.

HortGenome Search Engine, a universal genomic search engine for horticultural crops

Horticultural crops comprising fruit, vegetable, ornamental, beverage, medicinal and aromatic plants play essential roles in food security and human health, as well as landscaping. With the advances of sequencing technologies, genomes for hundreds of horticultural crops have been deciphered in recent years, providing a basis for understanding gene functions and regulatory networks and for the improvement of horticultural crops. However, these valuable genomic data are scattered in warehouses with various complex searching and displaying strategies, which increases learning and usage costs and makes comparative and functional genomic analyses across different horticultural crops very challenging. To this end, we have developed a lightweight universal search engine, HortGenome Search Engine (HSE; http://hort.moilab.net), which allows for the querying of genes, functional annotations, protein domains, homologs, and other gene-related functional information of more than 500 horticultural crops. In addition, four commonly used tools, including 'BLAST', 'Batch Query', 'Enrichment analysis', and 'Synteny Viewer' have been developed for efficient mining and analysis of these genomic data.

A high-quality haplotype genome of Michelia alba DC reveals differences in methylation patterns and flower characteristics

Michelia alba DC is a highly valuable ornamental plant of the Magnoliaceae family. This evergreen tropical tree commonly grows in Southeast Asia and is adored for its delightful fragrance. Our study assembled the M. alba haplotype genome MC and MM by utilizing Nanopore ultralong reads, Pacbio Hifi long reads and parental second-generation data. Moreover, the first methylation map of Magnoliaceae was constructed based on the methylation site data obtained using Nanopore data. Metabolomic datasets were generated from the flowers of three different species to assess variations in pigment and volatile compound accumulation. Finally, transcriptome data were generated to link genomic, methylation, and morphological patterns to reveal the reasons underlying the differences between M. alba and its parental lines in petal color, flower shape, and fragrance. We found that the AP1 and AP2 genes are crucial in M. alba petal formation, while the 4CL, PAL, and C4H genes control petal color. The data generated in this study serve as a foundation for future physiological and biochemical research on M. alba, facilitate the targeted improvement of M. alba varieties, and offer a theoretical basis for molecular research on Michelia L.

Plant microphenotype: from innovative imaging to computational analysis

The microphenotype plays a key role in bridging the gap between the genotype and the complex macro phenotype. In this article, we review the advances in data acquisition and the intelligent analysis of plant microphenotyping and present applications of microphenotyping in plant science over the past two decades. We then point out several challenges in this field and suggest that cross-scale image acquisition strategies, powerful artificial intelligence algorithms, advanced genetic analysis, and computational phenotyping need to be established and performed to better understand interactions among genotype, environment, and management. Microphenotyping has entered the era of Microphenotyping 3.0 and will largely advance functional genomics and plant science.

LettuceDB: an integrated multi-omics database for cultivated lettuce

Crop genomics has advanced rapidly during the past decade, which generated a great abundance of omics data from multi-omics studies. How to utilize the accumulating data becomes a critical and urgent demand in crop science. As an attempt to integrate multi-omics data, we developed a database, LettuceDB (https://db.cngb.org/lettuce/), aiming to assemble multidimensional data for cultivated and wild lettuce germplasm. The database includes genome, variome, phenome, microbiome and spatial transcriptome. By integrating user-friendly bioinformatics tools, LettuceDB will serve as a one-stop platform for lettuce research and breeding in the future. Database URL: https://db.cngb.org/lettuce/.

A genome-wide association study identifies genes associated with cuticular wax metabolism in maize

The plant cuticle is essential in plant defense against biotic and abiotic stresses. To systematically elucidate the genetic architecture of maize (Zea mays L.) cuticular wax metabolism, 2 cuticular wax-related traits, the chlorophyll extraction rate (CER) and water loss rate (WLR) of 389 maize inbred lines, were investigated and a genome-wide association study (GWAS) was performed using 1.25 million single nucleotide polymorphisms (SNPs). In total, 57 nonredundant quantitative trait loci (QTL) explaining 5.57% to 15.07% of the phenotypic variation for each QTL were identified. These QTLs contained 183 genes, among which 21 strong candidates were identified based on functional annotations and previous publications. Remarkably, 3 candidate genes that express differentially during cuticle development encode β-ketoacyl-CoA synthase (KCS). While ZmKCS19 was known to be involved in cuticle wax metabolism, ZmKCS12 and ZmKCS3 functions were not reported. The association between ZmKCS12 and WLR was confirmed by resequencing 106 inbred lines, and the variation of WLR was significant between different haplotypes of ZmKCS12. In this study, the loss-of-function mutant of ZmKCS12 exhibited wrinkled leaf morphology, altered wax crystal morphology, and decreased C32 wax monomer levels, causing an increased WLR and sensitivity to drought. These results confirm that ZmKCS12 plays a vital role in maize C32 wax monomer synthesis and is critical for drought tolerance. In sum, through GWAS of 2 cuticular wax-associated traits, this study reveals comprehensively the genetic architecture in maize cuticular wax metabolism and provides a valuable reference for the genetic improvement of stress tolerance in maize.

Rice requires a chromatin remodeler for Polymerase IV-small interfering RNA production and genomic immunity

Transgenes are often spontaneously silenced, which hinders the application of genetic modifications to crop breeding. While gene silencing has been extensively studied in Arabidopsis (Arabidopsis thaliana), the molecular mechanism of transgene silencing remains elusive in crop plants. We used rice (Oryza sativa) plants silenced for a 35S::OsGA2ox1 (Gibberellin 2-oxidase 1) transgene to isolate five elements mountain (fem) mutants showing restoration of transgene expression. In this study, we isolated multiple fem2 mutants defective in a homolog of Required to Maintain Repression 1 (RMR1) of maize (Zea mays) and CLASSY (CLSY) of Arabidopsis. In addition to failing to maintain transgene silencing, as occurs in fem3, in which mutation occurs in NUCLEAR RNA POLYMERASE E1 (OsNRPE1), the fem2 mutant failed to establish transgene silencing of 35S::OsGA2ox1. Mutation in FEM2 eliminated all RNA POLYMERASE IV (Pol-IV)-FEM1/OsRDR2 (RNA-DEPENDENT RNA POLYMERASE 2)-dependent small interfering RNAs (siRNAs), reduced DNA methylation on genome-wide scale in rice seedlings, caused pleiotropic developmental defects, and increased disease resistance. Simultaneous mutation in 2 FEM2 homologous genes, FEM2-Like 1 (FEL1) and FEL2, however, did not affect DNA methylation and rice development and disease resistance. The predominant expression of FEM2 over FEL1 and FEL2 in various tissues was likely caused by epigenetic states. Overexpression of FEL1 but not FEL2 partially rescued hypomethylation of fem2, indicating that FEL1 maintains the cryptic function. In summary, FEM2 is essential for establishing and maintaining gene silencing; moreover, FEM2 is solely required for Pol IV-FEM1 siRNA biosynthesis and de novo DNA methylation.

Transcriptomics, proteomics, and metabolomics interventions prompt crop improvement against metal(loid) toxicity

The escalating challenges posed by metal(loid) toxicity in agricultural ecosystems, exacerbated by rapid climate change and anthropogenic pressures, demand urgent attention. Soil contamination is a critical issue because it significantly impacts crop productivity. The widespread threat of metal(loid) toxicity can jeopardize global food security due to contaminated food supplies and pose environmental risks, contributing to soil and water pollution and thus impacting the whole ecosystem. In this context, plants have evolved complex mechanisms to combat metal(loid) stress. Amid the array of innovative approaches, omics, notably transcriptomics, proteomics, and metabolomics, have emerged as transformative tools, shedding light on the genes, proteins, and key metabolites involved in metal(loid) stress responses and tolerance mechanisms. These identified candidates hold promise for developing high-yielding crops with desirable agronomic traits. Computational biology tools like bioinformatics, biological databases, and analytical pipelines support these omics approaches by harnessing diverse information and facilitating the mapping of genotype-to-phenotype relationships under stress conditions. This review explores: (1) the multifaceted strategies that plants use to adapt to metal(loid) toxicity in their environment; (2) the latest findings in metal(loid)-mediated transcriptomics, proteomics, and metabolomics studies across various plant species; (3) the integration of omics data with artificial intelligence and high-throughput phenotyping; (4) the latest bioinformatics databases, tools and pipelines for single and/or multi-omics data integration; (5) the latest insights into stress adaptations and tolerance mechanisms for future outlooks; and (6) the capacity of omics advances for creating sustainable and resilient crop plants that can thrive in metal(loid)-contaminated environments.

HetFCM: functional co-module discovery by heterogeneous network co-clustering

Functional molecular module (i.e., gene-miRNA co-modules and gene-miRNA-lncRNA triple-layer modules) analysis can dissect complex regulations underlying etiology or phenotypes. However, current module detection methods lack an appropriate usage and effective model of multi-omics data and cross-layer regulations of heterogeneous molecules, causing the loss of critical genetic information and corrupting the detection performance. In this study, we propose a heterogeneous network co-clustering framework (HetFCM) to detect functional co-modules. HetFCM introduces an attributed heterogeneous network to jointly model interplays and multi-type attributes of different molecules, and applies multiple variational graph autoencoders on the network to generate cross-layer association matrices, then it performs adaptive weighted co-clustering on association matrices and attribute data to identify co-modules of heterogeneous molecules. Empirical study on Human and Maize datasets reveals that HetFCM can find out co-modules characterized with denser topology and more significant functions, which are associated with human breast cancer (subtypes) and maize phenotypes (i.e., lipid storage, drought tolerance and oil content). HetFCM is a useful tool to detect co-modules and can be applied to multi-layer functional modules, yielding novel insights for analyzing molecular mechanisms. We also developed a user-friendly module detection and analysis tool and shared it at http://www.sdu-idea.cn/FMDTool.

Identification and characterization of yellow stripe-like genes in maize suggest their roles in the uptake and transport of zinc and iron

BACKGROUND

Yellow Stripe-Like (YSL) proteins are involved in the uptake and transport of metal ions. They play important roles in maintaining the zinc and iron homeostasis in Arabidopsis, rice (Oryza sativa), and barley (Hordeum vulgare). However, proteins in this family have not been fully identified and comprehensively analyzed in maize (Zea mays L.).

RESULTS

In this study, we identified 19 ZmYSLs in the maize genome and analyzed their structural features. The results of a phylogenetic analysis showed that ZmYSLs are homologous to YSLs of Arabidopsis and rice, and these proteins are divided into four independent branches. Although their exons and introns have structural differences, the motif structure is relatively conserved. Analysis of the cis-regulatory elements in the promoters indicated that ZmYSLs might play a role in response to hypoxia and light. The results of RNA sequencing and quantitative real-time PCR analysis revealed that ZmYSLs are expressed in various tissues and respond differently to zinc and iron deficiency. The subcellular localization of ZmYSLs in the protoplast of maize mesophyll cells showed that they may function in the membrane system.

CONCLUSIONS

This study provided important information for the further functional analysis of ZmYSL, especially in the spatio-temporal expression and adaptation to nutrient deficiency stress. Our findings provided important genes resources for the maize biofortification.

IPOP: An Integrative Plant Multi-omics Platform for Cross-species Comparison and Evolutionary Study

The advent of high-throughput sequencing technologies has led to the production of a significant amount of omics data in plants, which serves as valuable assets for conducting cross-species multi-omics comparative analysis. Nevertheless, the current dearth of comprehensive platforms providing evolutionary annotation information and multi-species multi-omics data impedes users from systematically and efficiently performing evolutionary and functional analysis on specific genes. In order to establish an advanced plant multi-omics platform that provides timely, accurate, and high-caliber omics information, we collected 7 distinct types of omics data from 6 monocots, 6 dicots, and 1 moss, and reanalyzed these data using standardized pipelines. Additionally, we furnished homology information, duplication events, and phylostratigraphic stages of 13 species to facilitate evolutionary examination. Furthermore, the integrative plant omics platform (IPOP) is bundled with a variety of online analysis tools that aid users in conducting evolutionary and functional analysis. Specifically, the Multi-omics Integration Analysis tool is available to consolidate information from diverse omics sources, while the Transcriptome-wide Association Analysis tool facilitates the linkage of functional analysis with phenotype. To illustrate the application of IPOP, we conducted a case study on the YTH domain gene family, wherein we observed shared functionalities within orthologous groups and discerned variations in evolutionary patterns across these groups. To summarize, the IPOP platform offers valuable evolutionary insights and multi-omics data to the plant sciences community, effectively addressing the need for cross-species comparison and evolutionary research platforms. All data and modules within IPOP are freely accessible for academic purposes (http://omicstudio.cloud:4012/ipod/).

Two teosintes made modern maize

The origins of maize were the topic of vigorous debate for nearly a century, but neither the current genetic model nor earlier archaeological models account for the totality of available data, and recent work has highlighted the potential contribution of a wild relative, Zea mays ssp. mexicana. Our population genetic analysis reveals that the origin of modern maize can be traced to an admixture between ancient maize and Zea mays ssp. mexicana in the highlands of Mexico some 4000 years after domestication began. We show that variation in admixture is a key component of maize diversity, both at individual loci and for additive genetic variation underlying agronomic traits. Our results clarify the origin of modern maize and raise new questions about the anthropogenic mechanisms underlying dispersal throughout the Americas.

Maize Feature Store: A centralized resource to manage and analyze curated maize multi-omics features for machine learning applications

The big-data analysis of complex data associated with maize genomes accelerates genetic research and improves agronomic traits. As a result, efforts have increased to integrate diverse datasets and extract meaning from these measurements. Machine learning models are a powerful tool for gaining knowledge from large and complex datasets. However, these models must be trained on high-quality features to succeed. Currently, there are no solutions to host maize multi-omics datasets with end-to-end solutions for evaluating and linking features to target gene annotations. Our work presents the Maize Feature Store (MFS), a versatile application that combines features built on complex data to facilitate exploration, modeling and analysis. Feature stores allow researchers to rapidly deploy machine learning applications by managing and providing access to frequently used features. We populated the MFS for the maize reference genome with over 14 000 gene-based features based on published genomic, transcriptomic, epigenomic, variomic and proteomics datasets. Using the MFS, we created an accurate pan-genome classification model with an AUC-ROC score of 0.87. The MFS is publicly available through the maize genetics and genomics database. Database URL  https://mfs.maizegdb.org/.

Advances in alternative splicing identification: deep learning and pantranscriptome

In plants, alternative splicing is a crucial mechanism for regulating gene expression at the post-transcriptional level, which leads to diverse proteins by generating multiple mature mRNA isoforms and diversify the gene regulation. Due to the complexity and variability of this process, accurate identification of splicing events is a vital step in studying alternative splicing. This article presents the application of alternative splicing algorithms with or without reference genomes in plants, as well as the integration of advanced deep learning techniques for improved detection accuracy. In addition, we also discuss alternative splicing studies in the pan-genomic background and the usefulness of integrated strategies for fully profiling alternative splicing.

Exploring Pan-Genomes: An Overview of Resources and Tools for Unraveling Structure, Function, and Evolution of Crop Genes and Genomes

The availability of multiple sequenced genomes from a single species made it possible to explore intra- and inter-specific genomic comparisons at higher resolution and build clade-specific pan-genomes of several crops. The pan-genomes of crops constructed from various cultivars, accessions, landraces, and wild ancestral species represent a compendium of genes and structural variations and allow researchers to search for the novel genes and alleles that were inadvertently lost in domesticated crops during the historical process of crop domestication or in the process of extensive plant breeding. Fortunately, many valuable genes and alleles associated with desirable traits like disease resistance, abiotic stress tolerance, plant architecture, and nutrition qualities exist in landraces, ancestral species, and crop wild relatives. The novel genes from the wild ancestors and landraces can be introduced back to high-yielding varieties of modern crops by implementing classical plant breeding, genomic selection, and transgenic/gene editing approaches. Thus, pan-genomic represents a great leap in plant research and offers new avenues for targeted breeding to mitigate the impact of global climate change. Here, we summarize the tools used for pan-genome assembly and annotations, web-portals hosting plant pan-genomes, etc. Furthermore, we highlight a few discoveries made in crops using the pan-genomic approach and future potential of this emerging field of study.

A single silk- and multiple pollen-expressed PMEs at the Ga1 locus modulate maize unilateral cross-incompatibility

The Gametophyte factor1 (Ga1) locus in maize confers unilateral cross-incompatibility (UCI), and it is controlled by both pollen and silk-specific determinants. Although the Ga1 locus has been reported for more than a century and is widely utilized in maize breeding programs, only the pollen-specific ZmGa1P has been shown to function as a male determinant; thus, the genomic structure of the Ga1 locus and all the determinants that control UCI at this locus have not yet been fully characterized. Here, we used map-based cloning to confirm the determinants of UCI at the Ga1 locus and maize pan-genome sequence data to characterize the genomic structure of the Ga1 locus. The Ga1 locus comprises one silk-expressed pectin methylesterase gene (PME) (ZmGa1F) and eight pollen-expressed PMEs (ZmGa1P and ZmGa1PL1-7). Knockout of ZmGa1F in Ga1/Ga1 lines leads to the complete loss of the female barrier function. The expression of individual ZmGa1PL genes in a ga1/ga1 background endows ga1 pollen with the ability to overcome the female barrier of the Ga1 locus. These findings, combined with genomic data and genetic analyses, indicate that the Ga1 locus is modulated by a single female determinant and multiple male determinants, which are tightly linked. The results of this study provide valuable insights into the genomic structure of the Ga2 and Tcb1 loci and will aid applications of these loci in maize breeding programs.

MDSi: Multi-omics Database for Setaria italica

BACKGROUND

Foxtail millet (Setaria italica) harbors the small diploid genome (~ 450 Mb) and shows the high inbreeding rate and close relationship to several major foods, feed, fuel and bioenergy grasses. Previously, we created a mini foxtail millet, xiaomi, with an Arabidopsis-like life cycle. The de novo assembled genome data with high-quality and an efficient Agrobacterium-mediated genetic transformation system made xiaomi an ideal C₄ model system. The mini foxtail millet has been widely shared in the research community and as a result there is a growing need for a user-friendly portal and intuitive interface to perform exploratory analysis of the data.

RESULTS

Here, we built a Multi-omics Database for Setaria italica (MDSi, http://sky.sxau.edu.cn/MDSi.htm ), that contains xiaomi genome of 161,844 annotations, 34,436 protein-coding genes and their expression information in 29 different tissues of xiaomi (6) and JG21 (23) samples that can be showed as an Electronic Fluorescent Pictograph (xEFP) in-situ. Moreover, the whole-genome resequencing (WGS) data of 398 germplasms, including 360 foxtail millets and 38 green foxtails and the corresponding metabolic data were available in MDSi. The SNPs and Indels of these germplasms were called in advance and can be searched and compared in an interactive manner. Common tools including BLAST, GBrowse, JBrowse, map viewer, and data downloads were implemented in MDSi.

CONCLUSION

The MDSi constructed in this study integrated and visualized data from three levels of genomics, transcriptomics and metabolomics, and also provides information on the variation of hundreds of germplasm resources that can satisfies the mainstream requirements and supports the corresponding research community.

BnIR: A multi-omics database with various tools for Brassica napus research and breeding

In the post-genome-wide association study era, multi-omics techniques have shown great power and potential for candidate gene mining and functional genomics research. However, due to the lack of effective data integration and multi-omics analysis platforms, such techniques have not still been applied widely in rapeseed, an important oil crop worldwide. Here, we report a rapeseed multi-omics database (BnIR; http://yanglab.hzau.edu.cn/BnIR), which provides datasets of six omics including genomics, transcriptomics, variomics, epigenetics, phenomics, and metabolomics, as well as numerous "variation-gene expression-phenotype" associations by using multiple statistical methods. In addition, a series of multi-omics search and analysis tools are integrated to facilitate the browsing and application of these datasets. BnIR is the most comprehensive multi-omics database for rapeseed so far, and two case studies demonstrated its power to mine candidate genes associated with specific traits and analyze their potential regulatory mechanisms.

Machine Learning-Assisted Approaches in Modernized Plant Breeding Programs

In the face of a growing global population, plant breeding is being used as a sustainable tool for increasing food security. A wide range of high-throughput omics technologies have been developed and used in plant breeding to accelerate crop improvement and develop new varieties with higher yield performance and greater resilience to climate changes, pests, and diseases. With the use of these new advanced technologies, large amounts of data have been generated on the genetic architecture of plants, which can be exploited for manipulating the key characteristics of plants that are important for crop improvement. Therefore, plant breeders have relied on high-performance computing, bioinformatics tools, and artificial intelligence (AI), such as machine-learning (ML) methods, to efficiently analyze this vast amount of complex data. The use of bigdata coupled with ML in plant breeding has the potential to revolutionize the field and increase food security. In this review, some of the challenges of this method along with some of the opportunities it can create will be discussed. In particular, we provide information about the basis of bigdata, AI, ML, and their related sub-groups. In addition, the bases and functions of some learning algorithms that are commonly used in plant breeding, three common data integration strategies for the better integration of different breeding datasets using appropriate learning algorithms, and future prospects for the application of novel algorithms in plant breeding will be discussed. The use of ML algorithms in plant breeding will equip breeders with efficient and effective tools to accelerate the development of new plant varieties and improve the efficiency of the breeding process, which are important for tackling some of the challenges facing agriculture in the era of climate change.

ZmHOX32 is related to photosynthesis and likely functions in plant architecture of maize

HOX32, a member of the HD-ZIP III family, functions in the leaf morphogenesis and plant photosynthesis. However, the regulatory mechanism of HOX32 in maize has not been studied and the regulatory relationship in photosynthesis is unclear. We conducted a comprehensive study, including phylogenetic analysis, expression profiling at both transcriptome and translatome levels, subcellular localization, tsCUT&Tag, co-expression analysis, and association analysis with agronomic traits on HOX32 for the dissection of the functional roles of HOX32. ZmHOX32 shows conservation in plants. As expected, maize HOX32 protein is specifically expressed in the nucleus. ZmHOX32 showed constitutively expression at both transcriptome and translatome levels. We uncovered the downstream target genes of ZmHOX32 by tsCUT&Tag and constructed a cascaded regulatory network combining the co-expression networks. Both direct and indirect targets of ZmHOX32 showed significant gene ontology enrichment in terms of photosynthesis in maize. The association study suggested that ZmHOX32 plays an important role in regulation of plant architecture. Our results illustrate a complex regulatory network of HOX32 involving in photosynthesis and plant architecture, which deepens our understanding of the phenotypic variation in plants.

Unravelling structural, functional, evolutionary and genetic basis of SWEET transporters regulating abiotic stress tolerance in maize

Sugars Will Eventually be Exported Transporters (SWEETs) are the novel sugar transporters widely distributed among living systems. SWEETs play a crucial role in various bio-physiological processes, viz., plant developmental, nectar secretion, pollen development, and regulation of biotic and abiotic stresses, in addition to their prime sugar-transporting activity. Thus, in-depth structural, evolutionary, and functional characterization of maize SWEET transporters was performed for their utility in maize improvement. The mining of SWEET genes in the latest maize genome release (v.5) showed an uneven distribution of 20 ZmSWEETs. The comprehensive structural analyses and docking of ZmSWEETs with four sugars, viz., fructose, galactose, glucose, and sucrose, revealed frequent amino acid residues forming hydrogen (asparagine, valine, serine) and hydrophobic (tryptophan, glycine, and phenylalanine) interactions. Evolutionary analyses of SWEETs showed a mixed lineage with 50-100 % commonality of ortho-groups and -sequences evolved under strong purifying selection (Ka/Ks < 0.5). The duplication analysis showed non-functionalization (ZmSWEET18 in B73) and neo- and sub-functionalization (ZmSWEET3, ZmSWEET6, ZmSWEET9, ZmSWEET19, and ZmSWEET20) events in maize. Functional analyses of ZmSWEET genes through co-expression, in silico expression and qRT-PCR assays showed the relevance of ZmSWEETs expression in regulating drought, heat, and waterlogging stress tolerances in maize. The first ever ZmSWEET-regulatory network revealed 286 direct (ZmSWEET-TF: 140 ZmSWEET-miRNA: 146) and 1226 indirect (TF-TF: 597; TF-miRNA: 629) edges. The present investigation has given new insights into the complex transcriptional and post-transcriptional regulation and the regulatory and functional relevance of ZmSWEETs in assigning stress tolerance in maize.

The hitchhikers' guide to RNA sequencing and functional analysis

DNA and RNA sequencing technologies have revolutionized biology and biomedical sciences, sequencing full genomes and transcriptomes at very high speeds and reasonably low costs. RNA sequencing (RNA-Seq) enables transcript identification and quantification, but once sequencing has concluded researchers can be easily overwhelmed with questions such as how to go from raw data to differential expression (DE), pathway analysis and interpretation. Several pipelines and procedures have been developed to this effect. Even though there is no unique way to perform RNA-Seq analysis, it usually follows these steps: 1) raw reads quality check, 2) alignment of reads to a reference genome, 3) aligned reads' summarization according to an annotation file, 4) DE analysis and 5) gene set analysis and/or functional enrichment analysis. Each step requires researchers to make decisions, and the wide variety of options and resulting large volumes of data often lead to interpretation challenges. There also seems to be insufficient guidance on how best to obtain relevant information and derive actionable knowledge from transcription experiments. In this paper, we explain RNA-Seq steps in detail and outline differences and similarities of different popular options, as well as advantages and disadvantages. We also discuss non-coding RNA analysis, multi-omics, meta-transcriptomics and the use of artificial intelligence methods complementing the arsenal of tools available to researchers. Lastly, we perform a complete analysis from raw reads to DE and functional enrichment analysis, visually illustrating how results are not absolute truths and how algorithmic decisions can greatly impact results and interpretation.

Unleashing the power within short-read RNA-seq for plant research: Beyond differential expression analysis and toward regulomics

RNA-seq has become a state-of-the-art technique for transcriptomic studies. Advances in both RNA-seq techniques and the corresponding analysis tools and pipelines have unprecedently shaped our understanding in almost every aspects of plant sciences. Notably, the integration of huge amount of RNA-seq with other omic data sets in the model plants and major crop species have facilitated plant regulomics, while the RNA-seq analysis has still been primarily used for differential expression analysis in many less-studied plant species. To unleash the analytical power of RNA-seq in plant species, especially less-studied species and biomass crops, we summarize recent achievements of RNA-seq analysis in the major plant species and representative tools in the four types of application: (1) transcriptome assembly, (2) construction of expression atlas, (3) network analysis, and (4) structural alteration. We emphasize the importance of expression atlas, coexpression networks and predictions of gene regulatory relationships in moving plant transcriptomes toward regulomics, an omic view of genome-wide transcription regulation. We highlight what can be achieved in plant research with RNA-seq by introducing a list of representative RNA-seq analysis tools and resources that are developed for certain minor species or suitable for the analysis without species limitation. In summary, we provide an updated digest on RNA-seq tools, resources and the diverse applications for plant research, and our perspective on the power and challenges of short-read RNA-seq analysis from a regulomic point view. A full utilization of these fruitful RNA-seq resources will promote plant omic research to a higher level, especially in those less studied species.

Genome-wide dissection of changes in maize root system architecture during modern breeding

Appropriate root system architecture (RSA) can improve maize yields in densely planted fields, but little is known about its genetic basis in maize. Here we performed root phenotyping of 14,301 field-grown plants from an association mapping panel to study the genetic architecture of maize RSA. A genome-wide association study identified 81 high-confidence RSA-associated candidate genes and revealed that 28 (24.3%) of known root-related genes were selected during maize domestication and improvement. We found that modern maize breeding has selected for a steeply angled root system. Favourable alleles related to steep root system angle have continuously accumulated over the course of modern breeding, and our data pinpoint the root-related genes that have been selected in different breeding eras. We confirm that two auxin-related genes, ZmRSA3.1 and ZmRSA3.2, contribute to the regulation of root angle and depth in maize. Our genome-wide identification of RSA-associated genes provides new strategies and genetic resources for breeding maize suitable for high-density planting.

Bioinformatics in Plant Breeding and Research on Disease Resistance

In the context of plant breeding, bioinformatics can empower genetic and genomic selection to determine the optimal combination of genotypes that will produce a desired phenotype and help expedite the isolation of these new varieties. Bioinformatics is also instrumental in collecting and processing plant phenotypes, which facilitates plant breeding. Robots that use automated and digital technologies to collect and analyze different types of information to monitor the environment in which plants grow, analyze the environmental stresses they face, and promptly optimize suboptimal and adverse growth conditions accordingly, have helped plant research and saved human resources. In this paper, we describe the use of various bioinformatics databases and algorithms and explore their potential applications in plant breeding and for research on plant disease resistance.

Genome sequencing reveals evidence of adaptive variation in the genus Zea

Maize is a globally valuable commodity and one of the most extensively studied genetic model organisms. However, we know surprisingly little about the extent and potential utility of the genetic variation found in wild relatives of maize. Here, we characterize a high-density genomic variation map from 744 genomes encompassing maize and all wild taxa of the genus Zea, identifying over 70 million single-nucleotide polymorphisms. The variation map reveals evidence of selection within taxa displaying novel adaptations. We focus on adaptive alleles in highland teosinte and temperate maize, highlighting the key role of flowering-time-related pathways in their adaptation. To show the utility of variants in these data, we generate mutant alleles for two flowering-time candidate genes. This work provides an extensive sampling of the genetic diversity of Zea, resolving questions on evolution and identifying adaptive variants for direct use in modern breeding.

Integration of multi-omics technologies for crop improvement: Status and prospects

With the rapid development of next-generation sequencing (NGS), multi-omics techniques have been emerging as effective approaches for crop improvement. Here, we focus mainly on addressing the current status and future perspectives toward omics-related technologies and bioinformatic resources with potential applications in crop breeding. Using a large amount of omics-level data from the functional genome, transcriptome, proteome, epigenome, metabolome, and microbiome, clarifying the interaction between gene and phenotype formation will become possible. The integration of multi-omics datasets with pan-omics platforms and systems biology could predict the complex traits of crops and elucidate the regulatory networks for genetic improvement. Different scales of trait predictions and decision-making models will facilitate crop breeding more intelligent. Potential challenges that integrate the multi-omics data with studies of gene function and their network to efficiently select desirable agronomic traits are discussed by proposing some cutting-edge breeding strategies for crop improvement. Multi-omics-integrated approaches together with other artificial intelligence techniques will contribute to broadening and deepening our knowledge of crop precision breeding, resulting in speeding up the breeding process.

Wheat genomic study for genetic improvement of traits in China

Bread wheat (Triticum aestivum L.) is a major crop that feeds 40% of the world's population. Over the past several decades, advances in genomics have led to tremendous achievements in understanding the origin and domestication of wheat, and the genetic basis of agronomically important traits, which promote the breeding of elite varieties. In this review, we focus on progress that has been made in genomic research and genetic improvement of traits such as grain yield, end-use traits, flowering regulation, nutrient use efficiency, and biotic and abiotic stress responses, and various breeding strategies that contributed mainly by Chinese scientists. Functional genomic research in wheat is entering a new era with the availability of multiple reference wheat genome assemblies and the development of cutting-edge technologies such as precise genome editing tools, high-throughput phenotyping platforms, sequencing-based cloning strategies, high-efficiency genetic transformation systems, and speed-breeding facilities. These insights will further extend our understanding of the molecular mechanisms and regulatory networks underlying agronomic traits and facilitate the breeding process, ultimately contributing to more sustainable agriculture in China and throughout the world.

A pan-Zea genome map for enhancing maize improvement

Background

Maize (Zea mays L.) is at the vanguard facing the upcoming breeding challenges. However, both a super pan-genome for the Zea genus and a comprehensive genetic variation map for maize breeding are still lacking.

Results

Here, we construct an approximately 6.71-Gb pan-Zea genome that contains around 4.57-Gb non-B73 reference sequences from fragmented de novo assemblies of 721 pan-Zea individuals. We annotate a total of 58,944 pan-Zea genes and find around 44.34% of them are dispensable in the pan-Zea population. Moreover, 255,821 common structural variations are identified and genotyped in a maize association mapping panel. Further analyses reveal gene presence/absence variants and their potential roles during domestication of maize. Combining genetic analyses with multi-omics data, we demonstrate how structural variants are associated with complex agronomic traits.

Conclusions

Our results highlight the underexplored role of the pan-Zea genome and structural variations to further understand domestication of maize and explore their potential utilization in crop improvement.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13059-022-02742-7.

Plant pan-genomics: recent advances, new challenges, and roads ahead

Pan-genomics can encompass most of the genetic diversity of a species or population and has proved to be a powerful tool for studying genomic evolution and the origin and domestication of species, and for providing information for plant improvement. Plant genomics has greatly progressed because of improvements in sequencing technologies and the rapid reduction of sequencing costs. Nevertheless, pan-genomics still presents many challenges, including computationally intensive assembly methods, high costs with large numbers of samples, ineffective integration of big data, and difficulty in applying it to downstream multi-omics analysis and breeding research. In this review, we summarize the definition and recent achievements of plant pan-genomics, computational technologies used for pan-genome construction, and the applications of pan-genomes in plant genomics and molecular breeding. We also discuss challenges and perspectives for future pan-genomics studies and provide a detailed pipeline for sample selection, genome assembly and annotation, structural variation identification, and construction and application of graph-based pan-genomes. The aim is to provide important guidance for plant pan-genome research and a better understanding of the genetic basis of genome evolution, crop domestication, and phenotypic diversity for future studies.

Interactive Web-based Annotation of Plant MicroRNAs with iwa-miRNA

MicroRNAs (miRNAs) are important regulators of gene expression. The large-scale detection and profiling of miRNAs have been accelerated with the development of high-throughput small RNA sequencing (sRNA-Seq) techniques and bioinformatics tools. However, generating high-quality comprehensive miRNA annotations remains challenging due to the intrinsic complexity of sRNA-Seq data and inherent limitations of existing miRNA prediction tools. Here, we present iwa-miRNA, a Galaxy-based framework that can facilitate miRNA annotation in plant species by combining computational analysis and manual curation. iwa-miRNA is specifically designed to generate a comprehensive list of miRNA candidates, bridging the gap between already annotated miRNAs provided by public miRNA databases and new predictions from sRNA-Seq datasets. It can also assist users in selecting promising miRNA candidates in an interactive mode, contributing to the accessibility and reproducibility of genome-wide miRNA annotation. iwa-miRNA is user-friendly and can be easily deployed as a web application for researchers without programming experience. With flexible, interactive, and easy-to-use features, iwa-miRNA is a valuable tool for the annotation of miRNAs in plant species with reference genomes. We also illustrate the application of iwa-miRNA for miRNA annotation using data from plant species with varying genomic complexity. The source codes and web server of iwa-miRNA are freely accessible at http://iwa-miRNA.omicstudio.cloud/.

Genome-Wide Identification and Evolution Analysis of the Gibberellin Oxidase Gene Family in Six Gramineae Crops

The plant hormones gibberellins (GAs) regulate plant growth and development and are closely related to the yield of cash crops. The GA oxidases (GAoxs), including the GA2ox, GA3ox, and GA20ox subfamilies, play pivotal roles in GAs' biosynthesis and metabolism, but their classification and evolutionary pattern in Gramineae crops remain unclear. We thus conducted a comparative genomic study of GAox genes in six Gramineae representative crops, namely, Setaria italica (Si), Zea mays (Zm), Sorghum bicolor (Sb), Hordeum vulgare (Hv), Brachypodium distachyon (Bd), and Oryza sativa (Os). A total of 105 GAox genes were identified in these six crop genomes, belonging to the C19-GA2ox, C20-GA2ox, GA3ox, and GA20ox subfamilies. Based on orthogroup (OG) analysis, GAox genes were divided into nine OGs and the number of GAox genes in each of the OGs was similar among all tested crops, which indicated that GAox genes may have completed their family differentiations before the species differentiations of the tested species. The motif composition of GAox proteins showed that motifs 1, 2, 4, and 5, forming the 2OG-FeII_Oxy domain, were conserved in all identified GAox protein sequences, while motifs 11, 14, and 15 existed specifically in the GA20ox, C19-GA2ox, and C20-GA2ox protein sequences. Subsequently, the results of gene duplication events suggested that GAox genes mainly expanded in the form of WGD/SD and underwent purification selection and that maize had more GAox genes than other species due to its recent duplication events. The cis-acting elements analysis indicated that GAox genes may respond to growth and development, stress, hormones, and light signals. Moreover, the expression profiles of rice and maize showed that GAox genes were predominantly expressed in the panicles of the above two plants and the expression of several GAox genes was significantly induced by salt or cold stresses. In conclusion, our results provided further insight into GAox genes' evolutionary differences among six representative Gramineae and highlighted GAox genes that may play a role in abiotic stress.

gcaPDA: a haplotype-resolved diploid assembler

Background

Generating chromosome-scale haplotype resolved assembly is important for functional studies. However, current de novo assemblers are either haploid assemblers that discard allelic information, or diploid assemblers that can only tackle genomes of low complexity.

Results

Here, Using robust programs, we build a diploid genome assembly pipeline called gcaPDA (gamete cells assisted Phased Diploid Assembler), which exploits haploid gamete cells to assist in resolving haplotypes. We demonstrate the effectiveness of gcaPDA based on simulated HiFi reads of maize genome which is highly heterozygous and repetitive, and real data from rice.

Conclusions

With applicability of coping with complex genomes and fewer restrictions on application than most of diploid assemblers, gcaPDA is likely to find broad applications in studies of eukaryotic genomes.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04591-4.

Advances in "Omics" Approaches for Improving Toxic Metals/Metalloids Tolerance in Plants

Food safety has emerged as a high-urgency matter for sustainable agricultural production. Toxic metal contamination of soil and water significantly affects agricultural productivity, which is further aggravated by extreme anthropogenic activities and modern agricultural practices, leaving food safety and human health at risk. In addition to reducing crop production, increased metals/metalloids toxicity also disturbs plants' demand and supply equilibrium. Counterbalancing toxic metals/metalloids toxicity demands a better understanding of the complex mechanisms at physiological, biochemical, molecular, cellular, and plant level that may result in increased crop productivity. Consequently, plants have established different internal defense mechanisms to cope with the adverse effects of toxic metals/metalloids. Nevertheless, these internal defense mechanisms are not adequate to overwhelm the metals/metalloids toxicity. Plants produce several secondary messengers to trigger cell signaling, activating the numerous transcriptional responses correlated with plant defense. Therefore, the recent advances in omics approaches such as genomics, transcriptomics, proteomics, metabolomics, ionomics, miRNAomics, and phenomics have enabled the characterization of molecular regulators associated with toxic metal tolerance, which can be deployed for developing toxic metal tolerant plants. This review highlights various response strategies adopted by plants to tolerate toxic metals/metalloids toxicity, including physiological, biochemical, and molecular responses. A seven-(omics)-based design is summarized with scientific clues to reveal the stress-responsive genes, proteins, metabolites, miRNAs, trace elements, stress-inducible phenotypes, and metabolic pathways that could potentially help plants to cope up with metals/metalloids toxicity in the face of fluctuating environmental conditions. Finally, some bottlenecks and future directions have also been highlighted, which could enable sustainable agricultural production.

CottonGVD: A Comprehensive Genomic Variation Database for Cultivated Cottons

Cultivated cottons are the most important economic crop, which produce natural fiber for the textile industry. In recent years, the genetic basis of several essential traits for cultivated cottons has been gradually elucidated by decoding their genomic variations. Although an abundance of resequencing data is available in public, there is still a lack of a comprehensive tool to exhibit the results of genomic variations and genome-wide association study (GWAS). To assist cotton researchers in utilizing these data efficiently and conveniently, we constructed the cotton genomic variation database (CottonGVD; http://120.78.174.209/ or http://db.cngb.org/cottonGVD). This database contains the published genomic information of three cultivated cotton species, the corresponding population variations (SNP and InDel markers), and the visualized results of GWAS for major traits. Various built-in genomic tools help users retrieve, browse, and query the variations conveniently. The database also provides interactive maps (e.g., Manhattan map, scatter plot, heatmap, and linkage disequilibrium block) to exhibit GWAS and expression GWAS results. Cotton researchers could easily focus on phenotype-associated loci visualization, and they are interested in and screen for candidate genes. Moreover, CottonGVD will continue to update by adding more data and functions.

Genome-Wide Analysis of the MADS-Box Gene Family in Maize: Gene Structure, Evolution, and Relationships

The MADS-box gene family is one of the largest families in plants and plays an important roles in floral development. The MADS-box family includes the SRF-like domain and K-box domain. It is considered that the MADS-box gene family encodes a DNA-binding domain that is generally related to transcription factors, and plays important roles in regulating floral development. Our study identified 211 MADS-box protein sequences in the Zea mays proteome and renamed all the genes based on the gene annotations. All the 211 MADS-box protein sequences were coded by 98 expressed genes. Phylogenetic analysis of the MADS-box genes showed that all the family members were categorized into five subfamilies: MIKC-type, Mα, Mβ, Mγ, and Mδ. Gene duplications are regarded as products of several types of errors during the period of DNA replication and reconstruction; in our study all the 98 MADS-box genes contained 22 pairs of segmentally duplicated events which were distributed on 10 chromosomes. We compared expression data in different tissues from the female spikelet, silk, pericarp aleurone, ear primordium, leaf zone, vegetative meristem, internode, endosperm crown, mature pollen, embryo, root cortex, secondary root, germination kernels, primary root, root elongation zone, and root meristem. According to analysis of gene ontology pathways, we found a total of 41 pathways in which MADS-box genes in maize are involved. All the studies we conducted provided an overview of MADS-box gene family members in maize and showed multiple functions as transcription factors. The related research of MADS-box domains has provided the theoretical basis of MADS-box domains for agricultural applications.

Genome-wide association study reveals the genetic architecture of root hair length in maize

Background

Root hair, a special type of tubular-shaped cell, outgrows from root epidermal cell and plays important roles in the acquisition of nutrients and water, as well as interactions with biotic and abiotic stress. Although many genes involved in root hair development have been identified, genetic basis of natural variation in root hair growth has never been explored.

Results

Here, we utilized a maize association panel including 281 inbred lines with tropical, subtropical, and temperate origins to decipher the phenotypic diversity and genetic basis of root hair length. We demonstrated significant associations of root hair length with many metabolic pathways and other agronomic traits. Combining root hair phenotypes with 1.25 million single nucleotide polymorphisms (SNPs) via genome-wide association study (GWAS) revealed several candidate genes implicated in cellular signaling, polar growth, disease resistance and various metabolic pathways.

Conclusions

These results illustrate the genetic basis of root hair length in maize, offering a list of candidate genes predictably contributing to root hair growth, which are invaluable resource for the future functional investigation.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-07961-z.

Tripal, a community update after 10 years of supporting open source, standards-based genetic, genomic and breeding databases

Online, open access databases for biological knowledge serve as central repositories for research communities to store, find and analyze integrated, multi-disciplinary datasets. With increasing volumes, complexity and the need to integrate genomic, transcriptomic, metabolomic, proteomic, phenomic and environmental data, community databases face tremendous challenges in ongoing maintenance, expansion and upgrades. A common infrastructure framework using community standards shared by many databases can reduce development burden, provide interoperability, ensure use of common standards and support long-term sustainability. Tripal is a mature, open source platform built to meet this need. With ongoing improvement since its first release in 2009, Tripal provides full functionality for searching, browsing, loading and curating numerous types of data and is a primary technology powering at least 31 publicly available databases spanning plants, animals and human data, primarily storing genomics, genetics and breeding data. Tripal software development is managed by a shared, inclusive governance structure including both project management and advisory teams. Here, we report on the most important and innovative aspects of Tripal after 11 years development, including integration of diverse types of biological data, successful collaborative projects across member databases, and support for implementing FAIR principles.

The utility of metabolomics as a tool to inform maize biology

With the rise of high-throughput omics tools and the importance of maize and its products as food and bioethanol, maize metabolism has been extensively explored. Modern maize is still rich in genetic and phenotypic variation, yielding a wide range of structurally and functionally diverse metabolites. The maize metabolome is also incredibly dynamic in terms of topology and subcellular compartmentalization. In this review, we examine a broad range of studies that cover recent developments in maize metabolism. Particular attention is given to current methodologies and to the use of metabolomics as a tool to define biosynthetic pathways and address biological questions. We also touch upon the use of metabolomics to understand maize natural variation and evolution, with a special focus on research that has used metabolite-based genome-wide association studies (mGWASs).

Using precision phenotyping to inform de novo domestication

An update on the use of precision phenotyping to assess the potential of lesser cultivated species as candidates for de novo domestication or similar development for future agriculture.