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Korean J. Pl. Taxon > Volume 53(3); 2023 > Article
PARK, YUN, XI, KWON, and SON: Current status of whole-genome sequences of Korean angiosperms

Abstract

Owing to the rapid development of sequencing technologies, more than 1,000 plant genomes have been sequenced and released. Among them, 69 Korean plant taxa (85 genome sequences) contain at least one whole-genome sequence despite the fact that some samples were not collected in Korea. The sequencing-by-synthesis method (next-generation sequencing) and the PacBio (third-generation sequencing) method were the most commonly used in studies appearing in 65 publications. Several scaffolding methods, such as the Hi-C and 10x types, have also been used for pseudo-chromosomal assembly. The most abundant families among the 69 taxa are Rosaceae (10 taxa), Brassicaceae (7 taxa), Fabaceae (7 taxa), and Poaceae (7 taxa). Due to the rapid release of plant genomes, it is necessary to assemble the current understanding of Korean plant species not only to understand their whole genomes as our own plant resources but also to establish new tools for utilizing plant resources efficiently with various analysis pipelines, including AI-based engines.

INTRODUCTION

A genome sequence consisting of the complete list of nucleotides contains all of the genetic information of an organism (Alberts et al., 2002). Once the genome is deciphered properly, theoretically we may find all information pertaining to an organism, thus providing strong motivation for several early genome projects (Liggett, 2001), including those focusing on Arabidopsis thaliana L. (Martienssen and McCombie 2001; Mitchell-Olds, 2001). In 2000, the first plant whole-genome sequence, five chromosomes of A. thaliana L., was fully assembled and published (The Arabidopsis Genome Initiative, 2000), marking a relatively late start in comparison to those of bacteriophages (Sanger et al., 1977), bacteria (Fleischmann et al., 1995), and fungi (Goffeau et al., 1996). At that time, the A. thaliana genome had 25,498 genes (The Arabidopsis Genome Initiative, 2000), considered to be the most important element to understanding plants (Hutchison et al., 2016). Over time, researchers realized that additional elements, such as non-coding RNAs, also played important roles (Jones-Rhoades et al., 2006; Song et al., 2019) as other aspects by which we could understand plant whole-genome sequences.
Whole-genome sequences have been assembled from small pieces of DNA from organisms due to two major limitations of sequencing: (1) the start position of sequencing cannot be determined in a whole-genome project, and (2) the length of sequences we can obtain is limited, e.g., 500 bp to 1,000 bp when using the Sanger sequencing method. To decipher whole-genome sequences with sufficiently long lengths (e.g., A. thaliana is 119 Mbp (The Arabidopsis Genome Initiative, 2000) and Zea mays L. is 2.3 Gbp (Schnable et al., 2009)), an approach known as the whole-shotgun strategy was adopted after a method based on bacterial artificial chromosome (BAC) sequences (Zhang and Wu, 2001) given the large-scale computing power for genome assembly (Weber and Myers, 1997). After the commercialization of next-generation sequencing (NGS) technologies, which have critically reduced sequencing costs, to promote whole-genome sequencing projects, many plant genome sequences were released.
The National Center for Biotechnology Information (NCBI) has served as a data depository for many plant genome sequences (e.g., Cymbidium goeringii (Rchb.f.) Rchb.f. (Chung et al., 2021)). A centralized database in which to archive plant genome sequences is useful to provide access for comparative genomic analyses. The Plant Genome Database (https://www.plantgenome.info/) is such an example of a centralized database for plant whole-genome sequences (Park et al., 2021a), containing 3,509 genomes originating from 1,431 species (Release 3.0 to be available in March 2023). These genomic resources are important to conduct further various comparative genomic analyses to understand the genomes.
The accumulation of genome data for eukaryotes on a global scale has already begun (Lewin et al., 2018). Synthesizing the current status of whole-genome data on a local scale is necessary to set the stage for future research to ascertain the biodiversity and evolution of Korean plants and to discern applications of genomic resources ultimately to conserve endangered species. In this review, we examine the historical background of genome sequencing for plants, emphasizing the development of various sequencing technologies and the current status of whole genomes for angiosperms in Korea.

HISTORY OF SEQUENCING TECHNOLOGIES UTILIZED FOR DECIPHERING PLANT GENOMES

Sequencing technologies have been improved based on the requirements of whole-genome projects. First, it is now possible to increase the number of reads when conducting a whole-genome assembly given the greater computing power. As an example, the human genome project initiated by Craig Venter ended with the generation of 14.8 billion base-pairs (~5× coverage) for de novo assembly (Venter et al., 2001). Second, improved de novo assembly results are now possible by increasing the length of NGS raw reads. The first advance triggered the development of NGS technologies, including pyrosequencing (Fakhrai-Rad et al., 2002), sequencing by synthesis (Fuller et al., 2009), and sequencing by ligation (Smith et al., 2010). These technologies produced a great many reads in comparison to the Sanger sequencing method, though the reads are shorter than those generated by the Sanger sequencing method, apart from those that use pyrosequencing technology. After several plant genome sequences, including A. thaliana (The Arabidopsis Genome Initiative, 2000), Populus trichocarpa Torr. & A. Gray ex. Hook. (Tuskan et al., 2006), Vitis vinifera L. (Jaillon et al., 2007), and Z. mays (Schnable et al., 2009), were deciphered by the Sanger sequencing method, the cucumber (Cucumis sativus L.) genome was successfully assembled mainly based on sequencing by a synthesis method (commonly known as the Illumina method) (Huang et al., 2009). Although the NGS read length is relatively short, it has been shown that plant genome sequences can be assembled based on early short-read sequences (36-bp reads) generated by NGS technology. On the other hand, the 22-Gbp plant genome of Pinus taeda L. was successfully assembled (Zimin et al., 2014) based on pyrosequencing, implying that the de novo assembly of very large plant genomes is also possible with read lengths similar to those by the Sanger sequencing method.
As more plant genome sequences continue to be sequenced based on NGS technologies at a much lower cost in comparison to the Sanger sequencing method, the short length has become a critical hurdle hindering the realization of high-quality genome assemblies. New sequencing technologies after NGS technologies, referred to as third-generation sequencing (TGS) technologies, have been developed, with high-throughput sequencing reads longer than 10 kb, much longer than those by pyrosequencing and even the Sanger sequencing method (Terminology 1). Currently, the average length of the most recent single-molecular real-time (SMRT) sequencing method by Pacific Biosciences (PacBio) is around 10-25 kb with high accuracy (Hon et al., 2020) and the nanopore sequencing method by Oxford Nanopore Technologies (ONT) (referred to as Nanopore hereafter) produces ultra-long reads (>100 kb) (Amarasinghe et al., 2020).
Terminology 1
Next-generation sequencing (NGS) technologies: NGS technologies refer to major sequencing technologies which have been developed to overcoming the limitations of Sanger sequencing technologies addressed in the late 1990s. The first commercialized NGS technology is a pyrosequencing method known as the 454 technology method initially. In the early phase, it generated less than one million 150-bp reads but eventually could generate several million reads of which the length exceeds 1 kb (GS-Flx). This technology was officially ended in 2017. The sequencing-by-synthesis (SBS) technology was commercialized in 2007 with several million 36-bp reads per lane. This approach provided low-cost data, but each read was too short in comparison to those by the pyrosequencing method. Human genomes were successfully re-sequenced with data from this method in 2008 and 2009 (Bentley et al., 2008; Wang et al., 2008; Kim et al., 2009) and the cucumber genome was successfully assembled de novo with short-read data together with reads by the Sanger sequencing method (Huang et al., 2009). Currently this method provides a hundred million 151-bp reads per lane with the latest version of SBS technology (NovaSeq-6000). Other technologies such as sequencing-by-ligation developed by Applied Bioscience have also been commercialized, known as SOLiD (Miles et al., 2013), but disappeared after several years due to failures to address quality control issues.
Third-generation sequencing (TGS) technologies: TGS technologies were developed to meet long-read requirements (more than that by the Sanger sequencing method) in a high-throughput manner (similar to NGS technologies). From the long period of stabilization of TGS technologies, the single-molecular real-time (SMRT) sequencing method by Pacific Biosciences (PacBio) and the method developed by Oxford Nanopore Technology (ONT) have been successfully commercialized, providing a large number of long reads (>10 kb). One weak point of TGS is its low base-pair accuracy; HiFi reads provided by PacBio increased this accuracy so that additional polishing is not required now (Hon et al., 2020); while Nanopore (ONT) still requires a polishing process based on NGS sequences (Amarasinghe et al., 2020) but provides much longer reads than that by PacBio.
Based on 65 genome publications covering 72 plant genomes (Table 1), the corresponding sequencing methods were investigated (Fig. 1). Most of the investigated genome projects utilized multiple methods, especially for those that adopted TGS methods requiring a base pair polishing process supported by NGS technologies. Only two genomes (2.77%), A. thaliana (The Arabidopsis Genome Initiative, 2000) and Arabidopsis lyrata (L.) O'Kane & Al-Shehbaz (Hu et al., 2011), were sequenced with only the Sanger sequencing method (Fig. 1, Table 1). Thirteen genomes were sequenced together with pyrosequencing and MiSeq technologies, which provide longer sequences than the typical NGS technologies; however, these methods were selected not due to the large genome (i.e., P. taeda (Zimin et al., 2014)) but because of available optimized sequencing technologies when these projects were conducted. Since the commercialization of TGS technologies, most genomes have been sequenced with PacBio or Nanopore (Fig. 1). Because Nanopore was commercialized later than PacBio, there have been fewer genomes deciphered using this method compared to those by PacBio (Fig. 1).
After assembling whole-genome sequences from raw reads successfully, additional processes for chromosomal-level assembly, including Hi-C (Lieberman-Aiden et al., 2009), 10X Genomics Chromium (termed 10× hereafter) (Weisenfeld et al., 2017), and Bionano (Bocklandt et al., 2019), referred to as the scaffolding method hereafter, were conducted for 20 genomes (23.53%; the Rosa rugosa Thunb. genome project used both Hi-C and 10× technologies) (Fig. 1) (Chen et al., 2021). The most frequently used technology is Hi-C (65.00%) (Fig. 1), which has been utilized in a wide range of genomic studies (Kong and Zhang, 2019). The remaining scaffolding methods, including old-fashioned techniques, i.e., the BAC-end (Osoegawa et al., 2001) and Fosmid-end (Williams et al., 2012) sequencing methods, showed to be rarely used (Fig. 1). Based on the ratio between N50 and the total length, recent scaffolding methods including Hi-C, 10×, and Bionano presented better efficiency of assembly with some exceptional cases, i.e., Erysimum cheiranthoides L. and Prunus davidiana Carrière (Fig. 2). In addition, the ratio does not present a correlation with the total genome length (R2 = 0.0397), strongly suggesting that the N50 lengths of plant genomes depend on the species (Terminology 2).
Terminology 2
N50 length: The genome assembly displayed various lengths of contigs or scaffolds due to the limitation of de novo assembly, particularly the assembly of repetitive sequences. Hence, the quality of genome assembly can be estimated for the N50 length, defined as the length of the contig or scaffold for which longer contigs or scaffolds explain half of the assembled sequence. This reflects how well long sequences were assembled in comparison to average or even median lengths by focusing on relatively long assembled sequences. This approach is more effective for the assembled sequences based on long reads generated by TGS.

CURRENT STATUS OF WHOLE GENOMES OF KOREAN ANGIOSPERM SPECIES

There are official lists of native and naturalized Korean plant species, including (1) the Database of National Species List of Korea (Park et al., 2020a), (2) the Standard List of Korean plant species (http://www.nature.go.kr/kpni), and (3) The Northeastern Asia Biodiversity Institute’s List of Korean Vascular Plants. All three lists suggest approximately 4,500 native and naturalized plant species in Korea, also presenting utility as a filter to select published plant genome sequences to investigate the current status of plant genomes of native and naturalized Korean plant species. We used the Northeastern Asia Biodiversity Institute’s List of Korean Vascular Plants, selecting 85 plant genomes originating from 69 taxa as Korean angiosperm species genomes from the Plant Genome Database (https://www.plantgenome.info/), which contains available plant whole genome-sequences (Park et al., 2021a) (Table 1). The difference between the numbers of plant genome sequences and the taxa indicates that multiple genomes of some plant taxa were sequenced, making this a good resource to understand intraspecific variations at the level of the genome. We excluded more than 1,700 A. thaliana genomes (Ossowski et al., 2008; Ashelford et al., 2011; Cao et al., 2011; Gan et al., 2011; Long et al., 2013; Schmitz et al., 2013; The 1001 Genomes Consortium, 2016; Zou et al., 2017) due to the extremely large number of genomes in this case.
Despite the time gap between the releases of genome sequences and the corresponding publication dates, only 15.29% (13 out of 85 genomes) of genomes do not have any publication. Moreover, five species, Spirodela polyrhiza (L.) Schleid., Diospyros lotus L., Glycine soja Sieb. and Zucc., Castanea mollissima Blume, and Boehmeria nivea (L.) Gaudich., contained three genomes while six species, Panax ginseng C. A. Mey., Corylus heterophylla Fisch. ex Trautv., Setaria viridis (L.) P. Beauv., Typha latifolia L., Pyrus pyrifolia (Burm. f.) Nakai., and Ziziphus jujuba Mill., covered two genomes (Table 1), making them good candidates for understanding intraspecific variations at the genome-wide level. Remarkably, only two genome publications with accessible genome sequences, Gastrodia elata Blume and Codonopsis lanceolata (Siebold. & Zucc.) Trautv., described the whole genome of Korean angiosperm species (Table 1), indicating that most genomes filtered by the Korean angiosperm species list described in this paper were sequenced based on samples outside of Korea. As the importance of biological resources increases, as exemplified in the Nagoya Protocol (Buck and Hamilton, 2011), additional efforts to obtain whole-genome sequences of these species using the samples isolated in Korea and to prepare assembled sequences (i.e., unfortunately, while the G. soja genome was sequenced in Korea (Kim et al., 2010), no assembled sequence is available.) are required to maximize the benefits of the low sequencing costs and feasible bioinformatic analyses.
Sixty-nine taxa were classified into 17 orders and 30 families. Rosales covering four families (Moraceae, Rosaceae, Rhamnaceae, and Utricaceae) contained the largest number of genomes, and Poales covered the second largest from Poaceae to Typhaceae, while Boraginales, Caryophyllales, Cucurbitales, Solanales, and Vitales had only one genome (Fig. 3). At the family level, Rosaceae (10 taxa), Brassicaceae (7 taxa), Fabaceae (7 taxa), and Poaceae (7 taxa) contained a large number of genomes among the 30 families (Fig. 3). This taxonomical bias was expected due to various technical factors associated with genome sequencing projects, including the genome size and ploidy. Most taxa are economically important species. Based on current sequencing technologies, especially TGS technologies, many of these problems have already been overcome; e.g., the hexaploid large genome of wheat (Triticum aestivum L.) was sequenced and successfully assembled at the pseudo-chromosomal level (International Wheat Genome Sequencing Consortium, 2018). Rosaceae contained ten genomes originating from five genera, specifically Prunus, Rosa, Fragaria, Malus, and Pyrus and Poaceae, had nine genomes from the six genera, displaying large taxonomic coverage (Fig. 3, Table 1). These genomes are good candidates for understanding the genomic features of the aforementioned families.

GENOMIC STATISTICS OF 85 NATIVE AND NATURALIZED KOREAN PLANT GENOMES

The genome length and GC ratio of 85 native and naturalized Korean plant genomes were investigated, indicating average genome lengths of Araliaceae, Caryophyllaceae, and Orchidaceae of 3.20, 2.60, and 2.03 Gbp, respectively; the standard deviation of the Orchidaceae genome length was largest at 1.70 Gbp, while that of the Salicaceae genome is smallest at 16.79 Mbp (Fig. 4). Three genomes from Orchidaceae showed that two of the three genomes were around 1 Gbp; while one was 3 Gbp in length, with Salicaceae covering two genomes from the same genus, Populus. The trend of genome size variations along with the families is congruent with the findings of earlier work (Šmarda et al., 2014). In addition, the standard deviation of the GC ratio in Zosteraceae is extremely large, 4.05%, despite the fact that the two genomes are from the same genus, Zostera (Table 1). Araceae, Fabaceae, and Poaceae displayed that the standard deviation of the GC ratio ranges from 1.18 to 1.25% (Fig. 4), which is a large value in comparison to the other families. These variations in GC ratio are similar to those in a previous study that investigated variations of the GC ratio of the whole genome in a monocot species (Šmarda et al., 2014). The number of genomes in each family is small to present a corresponding trend, therefore they provide a glimpse of family-specific genomic features through these numbers as an indicator. This indicates that further genomic studies of Korean plants are necessary.

UTILIZATION OF KOREAN ANGIOSPERM GENOME SEQUENCES

More than 1,000 plant whole genomes have been sequenced, meaning that we can investigate their characteristics in detail. However, grants and proper human resources are still required for us to present the potential or direct economic value of this resource. Genomics-assisted breeding, a good example of the utilization of whole genomes, can be conducted based on genome-wide association studies to target useful phenotypes for breeding (Ahmar et al., 2020, 2021). To shorten the breeding time and increase the efficiency of the process, genomic editing is a viable upcoming strategy, with modifications to genetic elements to achieve helpful characteristics for humans using CRISPR-associated protein 9 (Lee et al., 2019). Legal regulations pertaining to genetically modified organisms, considered a main suppression factor, have become positive, accepting genome-edited plants in comparison to genetically modified organisms (Sprink et al., 2022). This development will promote the potential usage of plant whole-genome sequences because the techniques mentioned above commonly require whole genomes.
Native plant resources have been utilized to develop a range of useful products, including medicines over many years due to effective compounds such as aspirin, an acetyl salicylic acid from willow bark (Norn et al., 2009), and paclitaxel extracted from the bark of Taxus brevifolia Nutt. (Bose et al., 2020). In addition, various natural product medicines have also been developed based on plant extracts (Ngo et al., 2013) as well as traditional medicines, which have been utilized for several thousand years (Ansari and Inamdar, 2010), reflecting the commercial usage of plant resources. Whole-genome sequences can be analyzed to predict useful phytocompounds because they contain all enzymes involved in biochemical synthesis theoretically (Kang et al., 2020; Park et al., 2020b), suggesting another useful feature of Korean native plant genomes.

FURTHER DIRECTIONS FOR KOREAN ANGIOSPERM GENOME SEQUENCES

Currently, more than a hundred plant whole genomes have been sequenced per year, a rate much faster than that of ten years ago, when NGS technologies were merely utilized for the de novo assembly of plant genomes. Hence, more Korean angiosperm genomes will also be available from our own genome projects and genome projects conducted outside of Korea. We can consider two main strategies for a database for archive and utilization of Korean angiosperm genomes. First, to expand the coverage of Korean angiosperm genomes, additional Korean angiosperm genomes, especially endemic species which can be utilized commercially or which may be valuable in research, can be sequenced. Second, additional individuals or populations of Korean angiosperm species can be sequenced to understand genome-wide intraspecific variations (Slavov et al., 2012; The 3,000 Rice Genomes Project, 2014; Gulyaev et al., 2022) as well as functional gene families (Kim et al., 2021a, 2021b), as useful phenotypes of plant resources show differences in an intraspecific manner (Moore et al., 2014; Aspinwall et al., 2015; Ren et al., 2020). In addition, all of these genomes can be managed under the environment of a standardized integrated platform to analyze them further, such as the web-based genomic analysis platform Galaxy (Giardine et al., 2005; Blankenberg et al., 2010) or the Genome Information System (GeIS; https://geis.infoboss.co.kr/), which have been utilized in various genomics studies (Lee et al., 2020; Park et al., 2020c, 2021b).

ACKNOWLEDGMENTS

This study was carried out with the support of the InfoBoss Research Grant (IBG-0042).

NOTES

CONFLICTS OF INTEREST
The authors declare that there are no conflicts of interest.

Fig. 1.
Sequencing methods used in whole-genome projects focusing on native and naturalized Korean plant species: the X-axis presents the sequencing methods used in genome projects with four-color tagged legends denoting their classification. The Y-axis displays the number of genomes for each method.
kjpt-53-3-181f1.jpg
Fig. 2.
Genome assembly properties, the ratio of N50 and total lengths and total lengths of genomes using scaffolding technologies the Xaxis displays species names in a genome project with two-color tags denoting the classification of the scaffolding technologies. Bars indicate the genome length of each species and the line graph shows the ratio of N50 and the total lengths.
kjpt-53-3-181f2.jpg
Fig. 3.
Taxonomical distribution of 69 native and naturalized Korean plant taxa containing whole-genome sequences: (A) graph displaying the order-level distribution, and (B) graph showing the family-level distribution. Dotted lines represent the relationship between families and orders.
kjpt-53-3-181f3.jpg
Fig. 4.
Family-level genomic properties, total length and GC ratio: the X-axis shows families containing at least one whole genome used in this paper. Bars show the average genome length with the error bars of the standard deviation. The line represents the average GC ratio of genomes with the error bars of the standard deviation.
kjpt-53-3-181f4.jpg
Table 1.
List of plant whole genome sequences of Korean angiosperms.
No. Order Family Species Cultivar/Strain Method Genome size (bp) No. of scaffolds N50 (bp) GC ratio (%) No. of genes Reference
1 Alismatales Araceae Lemna minor - HiSeq MiSeq 763,364,415 6,868 335,126 44.81 N/A Van Hoeck et al. (2015)
2 Alismatales Araceae Spirodela polyrhiza 7498 454 BAC-end 132,009,443 207 5,765,642 42.45 N/A Wang et al. (2014)
3 Alismatales Araceae Spirodela polyrhiza 9509 HiSeq Irys 136,591,703 20 7,641,483 42.30 N/A Michael et al. (2017)
4 Alismatales Araceae Spirodela polyrhiza Sp9504 N/A 137,175,874 16,051 14,533 42.21 N/A Unpublished
5 Alismatales Zosteraceae Zostera marina - HiSeq 203,914,448 2,228 485,578 38.34 20,450 Olsen et al. (2016)
6 Alismatales Zosteraceae Zoysia japonica Nagirizaki HiSeq 334,384,427 11,786 2,370,062 44.08 N/A Tanaka et al. (2016)
7 Apiales Apiaceae Centella asiatica BB-174 10x Hi-C 430,216,894 8,739 50,798,654 34.17 N/A Pootakham et al. (2021)
8 Apiales Apiaceae Oenanthe javanica - HiSeq 1,650,714,995 150,335 32,514 35.44 N/A Liu et al. (2021a)
9 Apiales Araliaceae Panax ginseng - HiSeq 3,414,349,854 83,074 108,708 34.94 N/A Xu et al. (2017)
10 Apiales Araliaceae Panax ginseng - HiSeq 2,984,993,682 9,845 3,641,815 34.84 59,352 Kim et al. (2018)
11 Asparagales Orchidaceae Gastrodia elata EJP_2020-GE1012 HiSeq PacBio Hi-C 1,046,143,939 514 50,595,616 34.27 N/A Bae et al. (2022)
12 Asparagales Orchidaceae Gastrodia elata f. glauca - HiSeq 1,060,984,162 3,768 4,911,943 34.52 N/A Yuan et al. (2018)
13 Asparagales Orchidaceae Cymbidium goeringii - HiSeq PacBio Hi-C 3,990,519,457 19,377 178,198,413 33.77 29,556 Chung et al. (2021)
14 Asterales Asteraceae Artemisia annua - HiSeq 454 PacBio 1,792,856,094 39,400 104,891 35.34 N/A Shen et al. (2018)
15 Asterales Campanulaceae Codonopsis lanceolata NIHHS 239928 Nanopore HiSeq 1,347,489,827 22,630 82,893 37.21 N/A Jang et al. (2023)
16 Asterales Campanulaceae Codonopsis pilosula - N/A 937,709,907 1,154 2,556,440 37.13 N/A Unpublished
17 Boraginales Boraginaceae Lithospermum erythrorhizon - Nanopore HiSeq 366,684,367 2,451 315,765 35.15 32,360 Auber et al. (2020)
18 Brassicales Brassicaceae Arabidopsis lyrata MN47 Sanger 206,667,935 695 24,464,547 36.08 32,550 Hu et al. (2011)
19 Brassicales Brassicaceae Arabidopsis thaliana Col-0 Sanger 119,145,879 5 23,459,830 36.03 48,113 Kaul et al. (2000)
20 Brassicales Brassicaceae Arabis glabra - N/A 171,129,789 250 5,712,653 36.01 N/A Unpublished
21 Brassicales Brassicaceae Capsella bursa-pastoris - HiSeq MiSeq 268,430,517 8,186 627,605 35.74 52,528 Kasianov et al. (2017)
22 Brassicales Brassicaceae Erysimum cheiranthoides - HiSeq PacBio Hi-C 177,180,559 223 22,409,365 36.29 N/A Züst et al. (2020)
23 Brassicales Brassicaceae Isatis tinctoria - N/A 756,237,117 37,877 85,498 36.89 116,152 Unpublished
24 Brassicales Brassicaceae Rorippa islandica - N/A 390,742,222 430 3,103,162 35.67 65,406 Unpublished
25 Caryophyllales Caryophyllaceae Silene noctiflora OPL-1.1 N/A 2,598,026,552 79,767 59,004 37.95 N/A Unpublished
26 Cucurbitales Cucurbitaceae Gynostemma pentaphyllum JGL-2020 HiSeq BGISeq PacBio Hi-C 582,948,444 578 50,780,587 32.86 N/A Huang et al. (2021)
27 Ericales Actinidiaceae Actinidia rufa Fuchu HiSeq PacBio 677,239,177 501 15,941,708 35.44 52,342 Tahir et al. (2022)
28 Ericales Ebenaceae Diospyros lotus Kunsenshi N/A 945,758,782 8,975 653,513 36.35 N/A Unpublished
29 Ericales Ebenaceae Diospyros lotus W01 N/A 617,726,390 142 40,720,603 36.52 N/A Unpublished
30 Ericales Ebenaceae Diospyros lotus Yz01 N/A 630,098,508 42 42,671,757 36.57 N/A Unpublished
31 Fabales Fabaceae Amphicarpaea edgeworthii AE-2020 BGISeq Nanopore 299,059,313 24 27,224,559 32.06 N/A Liu et al. (2021b)
32 Fabales Fabaceae Glycine soja F N/A 975,918,537 320 48,777,390 34.78 57,501 Unpublished
33 Fabales Fabaceae Glycine soja USDA:GRIN:PI 483463 HiSeq PacBio Bionano 985,259,865 306 48,820,272 34.94 N/A Valliyodan et al. (2019)
34 Fabales Fabaceae Glycine soja W05 HiSeq PacBio Hi-C 863,568,428 33,170 404,776 33.56 50,337 Xie et al. (2019)
35 Fabales Fabaceae Medicago ruthenica Xinghe HiSeq PacBio Hi-C 904,130,090 650 99,386,155 35.89 N/A Yin et al. (2021)
36 Fabales Fabaceae Vicia sativa KSR5 HiSeq PacBio 1,541,180,487 54,083 90,105 34.96 N/A Shirasawa et al. (2021b)
37 Fabales Fabaceae Vigna minima - HiSeq PacBio 486,496,346 2,940 25,346,430 34.19 N/A Naito et al. (2022)
38 Fagales Betulaceae Betula pendula - HiSeq SOLiD 435,914,794 5,644 239,696 35.74 86,599 Salojärvi et al. (2017)
39 Fagales Betulaceae Corylus heterophylla CHET03 346,499,040 381 2,025,119 35.99 N/A Unpublished
40 Fagales Betulaceae Corylus heterophylla R026 Nanopore Hi-C 370,750,808 951 31,328,411 35.84 N/A Zhao et al. (2021)
41 Fagales Fagaceae Castanea crenata Ginyose HiSeq PacBio 721,168,657 781 1,595,543 35.14 N/A Shirasawa et al. (2021c)
42 Fagales Fagaceae Castanea mollissima N11-1 HiSeq PacBio Hi-C 833,240,550 112 57,343,431 35.11 N/A Wang et al. (2020)
43 Fagales Fagaceae Castanea mollissima Vanuxem 454 MiSeq 725,180,808 14,110 101,575 35.06 34,314 Staton et al. (2020)
44 Fagales Fagaceae Castanea mollissima Hubei HiSeq PacBio 785,529,252 2,707 944,461 35.21 36,479 Xing et al. (2019)
45 Fagales Fagaceae Quercus mongolica Qm_2020SYAU HiSeq PacBio Hi-C 809,993,317 321 66,735,633 35.84 N/A Ai et al. (2022)
46 Fagales Juglandaceae Juglans mandshurica - HiSeq 558,070,702 13,809 496,923 36.26 N/A Bai et al. (2018)
47 Lamiales Lamiaceae Perilla citriodora - N/A 618,797,171 29,924 2,413,193 35.38 155,867 Unpublished
48 Lamiales Oleaceae Fraxinus mandshurica - HiSeq MiSeq 454 830,473,688 297,505 30,144 34.96 N/A Sollars et al. (2017)
49 Lamiales Oleaceae Fraxinus sieboldiana - HiSeq MiSeq 454 744,777,403 709,960 1,987 36.21 N/A Sollars et al. (2017)
50 Lamiales Orobanchaceae Phtheirospermum japonicum Okayama N/A 1,226,606,249 10,559 1,109,341 36.03 30,299 Unpublished
51 Malpighiales Euphorbiaceae Euphorbia esula - N/A 1,124,886,465 1,633,094 1,035 34.80 N/A Unpublished
52 Malpighiales Salicaceae Populus davidiana NFU_1 N/A 417,659,603 1,565 562,472 33.56 N/A Unpublished
53 Malpighiales Salicaceae Populus simonii - HiSeq PacBio 441,407,051 369 19,598,675 33.65 N/A Wu et al. (2020)
54 Poales Poaceae Brachypodium sylvaticum - N/A 358,283,154 629 38,764,466 46.38 50,263 Unpublished
55 Poales Poaceae Echinochloa crus-galli STB08 HiSeq PacBio 1,486,609,408 4,534 1,802,240 45.70 N/A Guo et al. (2017)
56 Poales Poaceae Eleusine indica HZ-2018 HiSeq 492,270,386 24,072 233,459 44.42 N/A Zhang et al. (2019)
57 Poales Poaceae Miscanthus sacchariflorus - HiSeq 2,074,815,175 105,321 37,711 45.94 N/A De Vega et al. (2021)
58 Poales Poaceae Miscanthus sinensis - HiSeq Hi-C 2,079,430,866 14,431 88,510,541 45.77 89,486 Mitros et al. (2020)
59 Poales Poaceae Setaria viridis A10 HiSeq pacBio 395,731,502 14 46,702,114 46.17 52,459 Mamidi et al. (2020)
60 Poales Poaceae Setaria viridis ME034V HiSeq Nanopore 397,031,521 9 46,382,547 45.99 N/A Thielen et al. (2020)
61 Poales Poaceae Themeda triandra sf006 N/A 889,201,528 3,179 600,859 46.24 N/A Unpublished
62 Poales Poaceae Zizania latifolia HSD2 HiSeq 603,989,347 4,522 604,864 42.72 N/A Guo et al. (2015)
63 Poales Typhaceae Typha latifolia L0001 N/A 214,326,387 81 14,472,745 37.76 N/A Unpublished
64 Poales Typhaceae Typha latifolia SDW-2020 HiSeq PacBio 287,194,721 1,158 8,705,712 38.07 N/A Widanagama et al. (2022)
65 Rosales Moraceae Ficus erecta - HiSeq PacBio Genetic map 595,834,738 2,455 697,200 34.32 111,921 Shirasawa et al. (2020)
66 Rosales Rosaceae Fragaria nipponica - 454 HiSeq 206,414,979 215,024 1,275 38.44 87,803 Hirakawa et al. (2014)
67 Rosales Rosaceae Fragaria orientalis - 454 HiSeq 214,184,046 323,163 722 38.11 99,674 Hirakawa et al. (2014)
68 Rosales Rosaceae Malus baccata - HiSeq 703,023,564 47,474 743,357 38.13 45,900 Chen et al. (2019)
69 Rosales Rosaceae Prunus yedoensis var. nudiflora* - HiSeq PacBio Fosmid-end 319,209,792 4,016 145,140 37.65 41,294 Baek et al. (2018)
70 Rosales Rosaceae Prunus yeonesis - HiSeq PacBio 690,105,700 4,571 918,183 37.93 N/A Shirasawa et al. (2019)
71 Rosales Rosaceae Prunus davidiana ST HiSeq PacBio Hi-C 243,913,910 127 28,110,464 37.55 N/A Tan et al. (2021)
72 Rosales Rosaceae Pyrus pyrifolia Nijisseiki BGISeq PacBio 503,888,133 114 7,676,629 37.34 N/A Shirasawa et al. (2021a)
73 Rosales Rosaceae Pyrus pyrifolia Cuiguan HiSeq PacBio 541,340,367 428 27,968,327 37.36 42,559 Gao et al. (2021)
74 Rosales Rosaceae Rosa lucieae - N/A 786,105,075 500,476 10,695 38.73 N/A Unpublished
75 Rosales Rosaceae Rosa multiflora - HiSeq MiSeq 739,637,845 83,189 90,830 38.91 67,380 Nakamura et al. (2018)
76 Rosales Rosaceae Rosa rugosa - PacBio 10x Hi-C 401,223,225 43 61,014,187 39.31 39,704 Chen et al. (2021)
77 Rosales Rhamnaceae Ziziphus jujuba Dongzao HiSeq BAC 437,753,511 4,789 25,259,912 33.40 37,526 Liu et al. (2014)
78 Rosales Rhamnaceae Ziziphus jujuba Junzao HiSeq 362,583,438 47 27,412,306 32.95 N/A Huang et al. (2016)
79 Rosales Rhamnaceae Ziziphus jujuba var. spinosa AT0 HiSeq PacBio Hi-C 405,636,987 538 30,278,369 33.07 24,146 Shen et al. (2021)
80 Rosales Urticaceae Boehmeria nivea - 344,616,830 12,775 1,094,501 35.25 N/A Unpublished
81 Rosales Urticaceae Boehmeria nivea ZZ1 HiSeq 316,026,324 330 17,838,734 35.20 N/A Liu et al. (2018)
82 Rosales Urticaceae Boehmeria nivea Zhongsizhu HiSeq Nanopore PacBio 266,599,076 154,955 48,874 36.94 N/A Wang et al. (2021a)
83 Rosales Urticaceae Boehmeria nivea var. tenacissima - HiSeq Nanopore PacBio 270,213,153 135 19,552,154 35.19 N/A Wang et al. (2021a)
84 Solanales Convolvulaceae Cuscuta australis HiSeq PacBio 262,630,465 218 3,625,894 36.40 18,157 Sun et al. (2018)
85 Vitales Vitaceae Vitis amurensis IBCAS1988 HiSeq PacBio Hi-C Bionano 603,559,200 3,040 26,085,413 34.36 N/A Wang et al. (2021b)

Asterisks indicate Korean endemic species (Chung et al., 2023).

N/A, not available.

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