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17 September 2023

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

,
,
and
1
Department of Botany and Plant Pathology, Oregon State University, Corvallis, OR 97331, USA
2
Molecular & Digital Breeing Group, New Cultivar Innovation, The New Zealand Institute for Plant and Food Research Limited, Private Bag 92169, Auckland 1142, New Zealand
3
State Key Laboratory of Agricultural Genomics, Key Laboratory of Genomics, Ministry of Agriculture, BGI Research, Shenzhen 518083, China
*
Author to whom correspondence should be addressed.
Biomolecules2023, 13(9), 1403;https://doi.org/10.3390/biom13091403
This article belongs to the Special Issue The Genomics Era: From Reference Genomes to Pan-Genomic Graphs
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Abstract

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.

1. Introduction

In recent years, advancements in affordable sequencing platforms and computational resources have helped to generate reference-quality genome assemblies for multiple crop accessions/varieties belonging to a single species. Thus, crop genomics has transitioned to the pan-genomic era. This shift has been made possible by advances in sequencing technology (i.e., short-read Illumina sequencing, long-read PacBio sequencing with low errors, Nanopore sequencing) and bioinformatics tools employed for genomic data processing and genome assemblies (for excellent reviews, see [1,2,3,4,5,6,7]). Whole-genome assembly-based pan-genomes have been reported for a few plant species, including rice [8,9,10], barley [11], wheat [12], maize [13], soybean [14], a wild relative of soybean Glycine soja [15], and brassica [16]. In particular, PacBio HiFi reads proved very useful for high-quality polyploid genome assemblies for peanut [17], wheat [18], oilseed [16], strawberry [19], and potato [20].
The intraspecies genome comparisons of crops suggest extensive structural variation across diverse genotypes that affect both the genomic contents and plant function [14,21,22,23]. The structural variations among genotypes of the same species include insertion deletions (indels) and translocation of the small or large genomic regions that further cause presence–absence variations, copy number variations, chromosomal rearrangements, and variations in repeat sequences (i.e., tandem gene duplications, repetitive sequences in non-coding regions of the genome, transposable elements, centromere repeats, etc.). The conserved genes present in all cultivars/genotypes/subspecies/strains within a species constitute the “core” genome, and the variable genes represent the “dispensable” or “accessory” genome [2,24]. As shown in Figure 1, the accessory genome consists of the “shell genes” (found in most cultivars within a species) and the “cloud genes“ (present in only a small fraction of cultivars of the same species).
Figure 1. A conceptual depiction of the pan-genes across genomes of eight accessions belonging to the same clade. Each ring represents one accession. The top-left “core genes” represent conserved genes across eight accessions. The white section in a ring indicates the absence of ortholog(s). The “soft cores” represent genes found in ≥95% of accessions. The “cloud genes” are found only in one or two taxa. The rest between the “cloud genes” and “soft core genes” are “shell genes”.
Thus, a single reference genome represents only a fraction of the species-wide genomic space, and a pan-genome represents species-wide genomic space [2,13,15,25,26]. Often, the pan-genomes encompass genes found in crop wild relatives and ancestral species [27,28,29,30,31]. We would like to note here that many useful genes lost during crop domestication and extensive plant breeding [32,33,34] may be found in the “dispensable”/accessory genome of any crop species [15,33,35,36,37,38,39]. Thus, the availability of plant pan-genomes allows researchers and breeders to explore important candidate genes for improving crop yield, nutritional quality, and adaptability to changing climatic conditions and diseases. For instance, a few comparative genomic studies have revealed that gene amplification plays a vital role in disease resistance, abiotic stress tolerance, and other agronomic traits associated with plant development, architecture, and yield [40,41,42,43,44,45,46,47,48,49]. In addition, the high-quality pan-genomes also make it possible to study previously inaccessible regions of the eukaryotic genomes, including centromeres, long heterochromatic blocks, rDNA regions, etc., that exhibit low recombination, and provide new insights into crop genome evolution [50].
Recently, many excellent reviews have been published on plant pan-genomes [1,2,51,52,53,54], which focus on pan-genome construction, structure variation detection, challenges associated with polyploid crops, and the application of pan-genomes in crop research. It is important to highlight that none of the published reviews on pan-genomes provide a comprehensive collection of pan-genome tools or resources accessible in the public domain. Thus, our work fills this gap by concentrating on the current landscape of available pan-genome tools and resources tailored to the needs of crop researchers. Here, we review the current tools used for constructing and visualizing crop pan-genome data, public genomic portals/resources hosting pan-genes, pan-genome data, and pan-genome browsers. Furthermore, we highlight a few studies that have exploited a pan-genomic approach for discovering candidate genes associated with important agronomic traits. We also discuss the potential of pan-genome-driven translational research.

2. Pan-Genome Construction, Visualization, and Data Analysis Tools

The first step in setting up a pan-genome infrastructure is the selection of a diverse set of representative genotypes for sequence assembly that capture as many genetic variants as possible with a limited panel of genotypes [14,21,55,56]. The second step is the sequencing of individual genomes. The high-quality reference genomes are of critical importance for building pan-genome assemblies and a complete pan-gene atlas. Therefore, we see overlap and inter-connection between the genomics and pan-genomics. We envision pan-genomics as a natural extension and outgrowth of genomics, not a different field of study. The third step is the assembly and construction of the pan-genome. Previously a few reviews [1,57,58,59] have been published on several approaches implemented for pan-genome assembly. Here, we briefly describe the basic tenets of three popular methods.
The first approach uses a high-quality reference genome for mapping sequence reads generated from all other genotypes. Iterative refinement allows for a progressive improvement of the assembly with additional data. This strategy can minimize errors by exploiting the information from a high-quality reference genome and limiting the coordinate consolidation issue (Figure 2A). However, this method requires the availability of a high-quality reference genome, which may not be available for all species or strains. Secondly, it is sensitive to misalignment errors or inaccuracies in the reference genome, which can potentially propagate errors throughout the assembly. Likewise, bias towards the reference genome may limit the detection of novel or divergent sequences.
Figure 2. An illustration of three popular approaches currently used for pan-genome construction, including (A) reference-based iterative method, (B) de novo genome assembly, and (C) graph-based pan-genome assembly.
In species without access to a reference genome, de novo assembly of individual genotypes is generated, followed by mapping assembled contigs to each other [27]. The de novo genome assemblies have become a method of choice due to the advances in long-read sequencing and the availability of fast algorithms for aligning long-reads to call structural variants [7]. Conceptually, de novo assembly of multiple high-quality reference genomes and their comparison by pair-wise sequence alignment is arguably the most powerful and accurate approach to detect sequence variants from base-level resolution to novel genomic elements and rearrangements (Figure 2B). However, generating assemblies of polyploid plant genomes is still challenging, as current methods are limited in detecting and phasing heterozygous structural variants that erroneously produce chimeric contigs joining different haplotypes or ignore alternative haplotypes [60,61]. This approach is time-intensive and requires significant computational and bioinformatic resources, especially for large genomes and complex variations. Repeat resolution can be challenging, leading to fragmented assemblies, as it relies heavily on sequencing depth to overcome repetitive regions and complex variations.
The third graph-based approach allows adding any variant to the reference as a node at the genomic location where it is discovered, and then haplotypes are associated with one of the reference genomes used to build the graph. Reads are then realigned to this genome, leading to more accurate mapping. This method can accommodate new genomic data through iterative refinement, allowing for continuous improvement of the pan-genome assembly (Figure 2C). However, graph construction and traversal can be computationally intensive, especially for large and diverse pan-genomes, and require substantial computational resources. Typically, graph complexity increases with the addition of more genomes, potentially impacting scalability and computational efficiency. Nonetheless, the graph-based pan-genomes can represent complex variations, including structural variants and large-scale rearrangements, facilitating the identification of shared and unique genomic regions among individuals or strains and aiding in excellent visualization of pan-genomes. A conceptual visualization of the graph-based pan-genome is shown in Figure 3.
Figure 3. A conceptual view of a pan-genome reference graph carrying chromosomal rearrangements and mapped features. The graph allows views and analysis of whole-genome alignments, pan-gene sets, gene orthology, expression, pathways, function, and aligned synteny to help accelerate knowledge discovery and hypothesis-driven research.
Recently, Shang et al., 2022 [21] have constructed a ‘Super Pan-genome of rice’ containing high-quality assemblies of 251 rice genomes, including 202 accessions of domesticated Asian rice Oryza sativa, 28 accession of Oryza rufipogon (the wild ancestor of O. sativa), 11 accessions of domesticated African rice Oryza glaberrima, and 10 accessions of Oryza barthii (the wild ancestor of O. glaberrima). They used the de novo long-read assembly and a graph-based approach. The Rice Super Pan-genome Information Resource Database (RiceSuperPIRdb) provides access to a reference-free whole-genome multiple sequence alignment for these 251 rice accessions. This resource hosts fully annotated pan-genome graph visualization using the JBrowse genome browser. It facilitates the integration of structural variations, gene annotations, transposable element annotations, pan-genome graphs, and BLAST tools [21].
A few excellent reviews have previously described the development of computational tools for pan-genome visualizations [57,58,62,63]. We note here that genome sequencing technologies and assembly algorithms are rapidly evolving to achieve high accuracy complemented by additional independent mapping approaches, such as optical maps and Hi-C, to validate structural variant calls (i.e., inversions and translocations). It is important to acknowledge that the details and outcomes of each method may vary based on the specific pan-genome assembly tools, parameters, and characteristics of the genomic data employed in the process [64,65]. Here, we compiled a summary of the latest representative tools in Table 1. It is crucial to recognize that the development of pan-genome tools is an active field, and the list could not be exhaustive.
Table 1. A list of popular open-source tools for pan-genome assembly and visualization. All URLs were checked and confirmed to be valid on 13 September 2023.

3. A Survey of Crop Pan-Genome Portals and Data Resources

With technical advances and the affordability of the sequencing and assembly of genomes, we are experiencing a deluge of big data in biology. The plant research community now faces a bigger challenge of making genomic data findable, accessible, interoperable, and reusable (FAIR) [111,112]. Public databases and genomic resources play a crucial role in making genomic data FAIR and provide tools for analyses and visualization of genomic, transcriptomic, proteomic, and metabolomic data [113,114,115,116,117,118,119,120]. Furthermore, the secondary knowledgebases synthesize and curate knowledge graphs, providing information for gene–gene interactions, metabolic networks, and pathways, and providing the tools for analyses of user’s data in the context of plant genome browser or pathways [90,97,114,115,119,121,122,123,124,125,126,127,128,129,130]. Currently, a substantial number of plant genome browsers, amounting to a few hundred, can be accessed through platforms like Plant Ensembl [120], Phytozome [131], and various clade-specific community databases (for a recent review, see [132]). In particular, the pathway databases and species-specific metabolic networks curate data at the species level and, thus, can easily accommodate the knowledge gained from the genome analysis of multiple accessions belonging to the same species. If some of the critical genetic hotspots or genomic loci associated with metabolism or production of specific metabolites production are absent in the reference genome (or not annotated correctly), the pan-genome data can help pathway database biocurators to incorporate data from multiple representative genotypes and build the accurate representation of metabolome present in a species or clade. The availability of pan-genomes would reduce the occurrence of false negatives. However, the availability of plant pan-genome portals is limited and experiencing slow growth. To provide an overview of the current state of crop pan-genomic research, resources, and portals, we compiled Table 2.
Table 2. A list of pan-genome portals and data resources for crops. All URLs were checked and confirmed to be valid on 13 September 2023.
It is clear from Table 2 that crop pan-genome research is at its early stage. The pan-genome browsers are available for a few crops, and thus, most of the data is not supported for user-friendly query, visualization, and analysis of the user’s data. However, the few platforms and genomic databases that support pan-gene analysis in a phylogenomic context and support the user’s query show the potential of pan-genome data for supporting basic research as well as translational applications for crop improvement. Here, we highlight an example of visualizing the pan-gene data for the TILLER ANGLE CONTROL 1 (OsTAC1) transcription factor coding gene from various accessions of cultivated rice O. sativa and other members of Oryza genus at Gramene (https://oryza.gramene.org; accessed on July 20, 2023). OsTAC1 is induced by gravity stimulation and promotes horizontal shoot growth by negatively regulating shoot gravitropism [160]. Thus, it is involved in regulating tiller angle and modulating plant architectural traits of agronomic importance. A comparison of TAC1 protein sequences shows a significant variability at the carboxy-terminal between the domesticated rice cultivars of japonica and indica accessions (see Figure 4A). Indeed, a previously published study has shown that a point mutation in the OsTAC1 gene at the 3′-splicing site of the 1.5-kb intron (‘GGGA’) in japonica rice accessions caused a reduction in the expression of this gene, leading to a smaller tiller angle. This trait was selected in the japonica rice accessions. In contrast, wild rice accessions and indica rice accessions with large tiller angles contain ‘AGGA’ sequences at the 3′-splicing site of the 1.5-kb intron [161]. The OsTAC1 gene is on chromosome 9 in the rice genome and shows high conservation across all rice accessions (Figure 4B). However, we also see that the OsTAC1 gene neighborhood on the left-hand side is not much conserved, and it remains to be explored for candidate genes involved in the functional adaptation of rice accessions.
Figure 4. A pan-gene overview for TAC1 transcription factor (reference gene OsTAC1; Os09g0529300) and its orthologs from various accessions of cultivated rice O. sativa, other members of Oryza genus, and two other monocots maize and sorghum at Gramene oryza pansite. Users can explore (A) a multiple protein sequence alignment of TAC1 orthologs and (B) gene neighborhood conservation.
In addition to the Gramene database, a few more pan-genome portals provide a similar view of pan-genes visualization and analysis tools. For example, the Banana Genome Hub uses the Panache platform to visualize pan-genome data on Musaceae. However, the well-established crop genome portals generally support users in exploring genes and gene families, chromosome structures, synteny, structural variations, gene expression patterns, SNP markers, etc.

4. Plant Pan-Genomics-Driven Insights for Understanding the Basis of Agronomic Traits

Cereal crops have been the prime subject of agriculture research. Thus, we have matured genomic resources, enriched genome annotations, and genotype and sequence data facilitating comparative genomics and pan-genomics studies (See Table 2). The focus of all categories of genomic research on cereal crops aims to increase the grain yield or plant developmental, physiological, and architectural traits that can support the higher yield. For example, Wang et al. (2022) [109] used rice pan-genome to identify GW5 genes associated with the trait ‘thousand-grain weight’ (TGW) and a novel locus qPH8-1 involved in the regulation of plant height. In another study by Shang et al., 2022 [21], a super pan-genome of rice was constructed helped to identify genetic variants associated with submergence tolerance, seed shattering, and plant architecture [21]. Many important studies have been published on maize, rice, and wheat, and discoveries are being implemented for their improvement.
Notably, more recently, investments are being made in the genomic and pan-genomic research of minor cereal crops, including sorghum and millets, suitable for growing in diverse and marginal lands (see Table 2). These crops have a high degree of in-built tolerance for mitigating the impact of harsh environments, and resistance against many pests and pathogens. For example, foxtail millet (Setaria italica) is a model plant for studying C4 photosynthesis and developing climate-resilient crops. A pan-genomic study of foxtail millet identified an important genetic variation in the promoter region of SiGW3 that is associated with yield improvements [22]. Another study identified 13 marker-trait associations using proso millet (Panicum miliaceum L.) pan-genome [162]. Similarly, Yan et al. (2023) recently constructed a pearl millet pan-genome that helped to identify over 400,000 genomic structural variants and provided insights into heat tolerance. This study also identified the RWP-RK gene conferring enhanced heat tolerance [55]. Another group of previously understudied crops is legumes that have gained from genomic and pan-genomic research and breeding efforts (see Table 2). For example, pigeon pea is an important orphan crop mainly grown by smallholder farmers in the tropics and subtropical regions of the world. It has an in-built tolerance for drought stress and is very productive in marginal land with small inputs. A pan-genome study of pigeon peas identified 225 SNPs associated with nine agronomically important traits. These associations will aid pigeon pea germplasm improvement [154]. In another study, Liu et al. (2022) analyzed 217 mung bean accessions and discovered many novel genes associated with agronomic traits, including an SNP in the candidate genes SWEET10 homolog (jg24043) associated with crude starch content; NRT1/PTR FAMILY 2.13 gene for pod length; a homolog of WUSCHEL-family homeobox gene associated with yield; and a gene presence-absence variation in a multi-gene locus associated with color-related traits [163]. Mung bean is an excellent plant-based source of protein and is grown in temperate, subtropical, and tropical regions.
Pan-genome studies have become increasingly important for understanding the genetic diversity of major crops from tropical regions, including cassava (Manihot esculenta) and banana (Musa spp.). These crops are a vital component of diverse ecosystems and play essential roles in the livelihoods of local communities. Insights gained from genomic and pan-genome research are being used for improving banana resistance against Fusarium wilt to safeguard the global banana industry [99,164]. Similarly, the pan-genome of cassava (Manihot esculenta), a staple crop for millions of people in Africa, is helping to score genomic variations, particularly in genes associated with disease resistance and starch biosynthesis [151]. More studies are being performed on important fruits and vegetables (i.e., banana, apple, tomato, melon, citrus, and grape) and oilseed crops (i.e., Brassica, soybean, sunflower, etc.) listed in Table 2. A few studies have uncovered new genes and rare alleles that regulate secondary metabolites associated with color and flavor [46,49,165], pathogen resistance [43,47,56,79,145,165,166,167], and abiotic stress tolerance [41,42,55]. These findings foster an understanding of the genetic basis of diverse traits in domesticated crops and offer promising prospects for introducing candidate genes (for disease resistance and quality traits) through molecular breeding or precise genome editing into elite cultivars.
In conclusion, the acquisition of pan-genomes holds immense potential for pursuing fundamental questions related to the evolution of crop genomes as well as for breeding high-yielding crops that are resilient to a range of biotic and abiotic stresses. Utilizing pan-genomes enables researchers to (1) identify gains and losses of genetic regions and structural variations strongly associated with desirable fitness phenotypes such as abiotic stress and disease tolerance, growth and development, yield, biomass, and performance. (2) Identify gains and losses in protein-coding regions and/or epigenetic features between crops and closely related species. (3) Identify pathways, gene networks, expression profiles, and transcript isoforms that correlate to given major and minor quantitative trait loci (QTLs) with desired phenotypes and adaptation traits. (4) Project functional and phenotype homologs from a well-studied species onto a new/less-studied species through whole genome comparisons and synteny. (5) Advance plant breeding efforts by mapping/querying/visualizing public or personal project data to build or test hypotheses, discover markers, and gain knowledge. In summary, harnessing the full potential of pan-genomes for targeted crop breeding is an important goal of the plant research and breeding community. The integration of diverse genomic data and their visualization in the context of pan-genome is crucial for supporting marker-assisted selection, genomic selection, and gene editing efforts towards developing crops that can adapt and thrive in a range of climates and environments, securing global food security and sustainability (see Figure 5).
Figure 5. Plant pan-genome browsers can help to integrate heterogeneous omics data to understand gene function, genome evolution and speciation; to establish genotype to phenotype connections; and enable genomic selection, genome editing, and phenotype prediction to support and sustain agriculture production.

5. Outlook, Opportunities, and Innovations in Plant Pan-Genome Research

Plant genomes are often very large and complex, making it difficult to produce high-quality genome assemblies with accurate gene annotations. For over last three decades, genotypic variations have been captured using various genetic markers, including RFLP, RAPD, SNPs, SSRs, microsatellite markers, etc., to establish connections between genotype and phenotype to aid plant breeding and cultivar improvement [168,169,170,171,172,173]. However, the advances in the next-generation sequencing and computational methods required for processing large-scale genomic and transcriptomic data have facilitated rapid and cost-effective whole-genome sequencing [7,174,175] and are now driving pan-genomic research. For the first time in history, researchers are able to explore intra- and inter-species structural variations at the resolution of nucleotide sequence level. We expect a significant expansion in pan-genomes availability across a broader range of plant species. The pan-genomes facilitate the identification of genes conserved across species and genes unique to specific species or a subset of accessions. The availability of species-specific or genus-level pan-genomes of crops is crucial for understanding the dynamics of their genome evolution, including the impact of artificial selection and domestication, crop diversification, and adaptation under varied environments [50,174,176,177]. For instance, a super-pan-genome of the Citrullus genus comprising 346 cultivated watermelon accessions and 201 wild accessions suggested that a duplication of the sugar transporter gene ClTST2 was likely selected during domestication for higher fruit sweetness, and the wild accessions harbor many genes related to disease resistance [178].
In addition, plant pan-genomes can help to advance the fundamental understanding of the plant kingdom at various scales (see Figure 5). It can aid in improving functional annotations of genes and genomes [179,180] and provide insights into specific roles and interactions of different genetic elements. Furthermore, pan-genomes can help in understanding the evolution of metabolic diversity across diverse taxonomic clades [97]. This knowledge has implications for improving crop traits and developing more resilient and sustainable agricultural practices. Moreover, by analyzing the genomic diversity represented in pan-genomes, scientists can understand the distribution and composition of vegetation across different environments. This information is critical for conservation efforts, ecological studies, and developing strategies to protect and sustainably manage plant resources.
Finally, we see a great application of pan-genome data in improving gene annotations and identifying evolutionary conserved sets of genes associated with important agronomic traits. Likewise, comparative genomic studies can help to identify and annotate clade-specific unique genes that determine metabolite compositions of important fruit and vegetable crops or other categories. These specialized metabolic pathways and associated entities can be easily curated in the pathway databases [97,121,123,124,128,181,182]. The workflows for interspecies gene family comparisons, GO annotations, and standard protocols for gene biocuration are very efficient and established; they can easily accommodate the insights gained from intra-species comparisons. For instance, the availability of whole genome sequences of plants has contributed tremendously to the knowledge of gene duplications, gene family evolution, and functional diversification of homologous genes [179,180,183]. Gene Ontology (GO) and Plant Ontology (PO) annotations have played a central role in accessing the potential gene functions [184,185,186]. Furthermore, the comprehensive analysis of plant transcriptomes has helped us to link genes with potential biological processes, pathways, and responses to biotic and abiotic stress or stimulants [179,180,187,188,189]. Integrating pan-genomes with other omics, such as transcriptomics, epigenomics, proteomics, and phenomics data, will enable a comprehensive understanding of gene regulation and functional mechanisms underlying important agronomic traits. In conclusion, as shown in Figure 5, pan-genomic research drives functional genomics, evolutionary studies, and biodiversity exploration and holds great potential for crop improvement, environmental conservation, and sustainable agriculture.
It is important to emphasize that pan-genome construction and whole genome-level comparative analysis require substantial computational infrastructure and expertise in various facets, including sequence generation, genome assembly and annotation, and subsequent bioinformatic analyses and data visualization. In general, all these tasks are beyond the capacity of individual research laboratories, and thus, they require extensive infrastructure and bioinformatic support from their institutions and public databases. Public data repositories, databases, genomic resources, and secondary knowledgebases play an essential role in aiding the community of researchers in providing ontologies [184], archiving and annotating genomic data, and supporting analysis and visualization of omics data to support data-driven hypotheses and making experimental plans [90,97,115,128]. Here, we have reviewed the resources and tools available to the plant research community for pan-genomic research.
We note here that the resources and tools to support researchers in exploring pan-genomic data are limited and at an early developmental stage. We have witnessed a growing number of publications on crop pan-genomes in the last five years (see Table 2); however, the pan-genomic and genomic diversity data for the majority of the crops are stored and archived with no associated tools/features required for visualization and effective use by other researchers. Thus, advancement and innovations in pan-genomic data visualization, analysis tools, and additional biocuration of genomic data are needed to facilitate meaningful intraspecies and interspecies genomic comparisons. Community biocuration plays an essential role in making sense of the big data and ensuring quality controls at various steps [190]. It could allow researchers to study pan-genomes more thoroughly and identify genes and genomic regions associated with important traits. Equally important is integrating crop pan-genomes in comprehensive knowledgebases that host analyzed and annotated genomic and pathway data for ensuring the FAIR data policy implementations [111,132].
From a technical aspect, integrating machine learning and artificial intelligence techniques will expedite the analysis of complex pan-genomic datasets, aid in identifying patterns, and accelerate the construction of predictive models and gene biocuration. Furthermore, the genomic resources are more mature for the model plants and major crop species [132]. However, the necessary financial and infrastructure support for minor crops, fruit and vegetable crops, and orphan crops is still insufficient [191,192,193]. Although the cost of sequencing a single genome has decreased significantly in recent years, sequencing multiple genomes can still be prohibitive, a significant barrier to conducting pan-genome research in poorly studied crops or orphaned crops. We expect that this review will help researchers find appropriate tools and resources relevant to pan-genome construction and analysis. Secondly, it will help to understand and evaluate the strategies employed for pan-genomic studies in crops and encourage them to seek necessary collaborations within the community. We take this opportunity to advocate for increased funding for developing the infrastructure, tools, and biocuration of genomic data. Furthermore, we hope this review can help students and young researchers learn about the status and the future potential of pan-genomics.

Author Contributions

S.N. conceptualized the initial study plan and coordinated the collaboration with all the authors. S.N., C.H.D., S.K.S. and P.J. conducted a review of the literature and contributed to writing the manuscript. S.N., C.H.D. and P.J. created all the figures for the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

SN acknowledges funding from the National Aeronautics and Space Administration (NASA), USA #80NSSC22K0891. PJ acknowledges funding from NASA #80NSSC22K0855 and National Science Foundation, USA #2029854.

Data Availability Statement

We have provided all the data within this manuscript.

Acknowledgments

S.N. and P.J. thank the members of the Gramene project team at Cold Spring Harbor Laboratory, the Ensembl Plants team at European Bioinformatics Institute, and the human Reactome project and the Ontario Institute for Cancer Research for their support and interest in strong cross-platform data integration. S.K.S. acknowledges support from the 10KP project (https://db.cngb.org/10kp) and China National GeneBank (CNGB; https://www.cngb.org/). C.D. thanks the bioinformatics teams at The New Zealand Institute for Plant and Food Research Limited (PFR), breeders at Kiwifruit Breeding Centre (KBC), and researchers at China Agricultural University (CAU) for their support.

Conflicts of Interest

The authors declare no conflict of interest.

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