Skip to main content

High-quality chromosome-scale de novo assembly of the Paspalum notatum ‘Flugge’ genome



Paspalum notatum ‘Flugge’ is a diploid with 20 chromosomes (2n = 20) multi-purpose subtropical herb native to South America and has a high ecological significance. It is currently widely planted in tropical and subtropical regions. Despite the gene pool of P. notatum ‘Flugge’ being unearthed to a large extent in the past decade, no details about the genomic information of relevant species in Paspalum have been reported. In this study, the complete genome information of P. notatum was established and annotated through sequencing and de novo assembly of its genome.


The latest PacBio third-generation HiFi assembly and sequencing revealed that the genome size of P. notatum ‘Flugge’ is 541 M. The assembly result is the higher index among the genomes of the gramineous family published so far, with a contig N50 = 52Mbp, scaffold N50 = 49Mbp, and BUSCOs = 98.1%, accounting for 98.5% of the estimated genome. Genome annotation revealed 36,511 high-confidence gene models, thus providing an important resource for future molecular breeding and evolutionary research. A comparison of the genome annotation results of P. notatum ‘Flugge’ with other closely related species revealed that it had a close relationship with Zea mays but not close compared to Brachypodium distachyon, Setaria viridis, Oryza sativa, Puccinellia tenuiflora, Echinochloa crusgalli. An analysis of the expansion and contraction of gene families suggested that P. notatum ‘Flugge’ contains gene families associated with environmental resistance, increased reproductive ability, and molecular evolution, which explained its excellent agronomic traits.


This study is the first to report the high-quality chromosome-scale-based genome of P. notatum ‘Flugge’ assembled using the latest PacBio third-generation HiFi sequencing reads. The study provides an excellent genetic resource bank for gramineous crops and invaluable perspectives regarding the evolution of gramineous plants.

Peer Review reports


Paspalum notatum (P. notatum) ‘Flugge’ is a subtropical grass native to South America belonging to the Poaceae family [1], including diploid and apomictic polyploid biotypes [2]. It has excellent agronomic traits such as fast growth, strong reproductive ability, and resistance to cold, barrenness, high temperature, submergence, and erosion [3,4,5,6]. It has been used for water and soil conservation, environmental protection, ecological restoration, and landscaping, among other uses, thus greatly improving people’s lives amongst other economic and ecological benefits [7, 8]. The grass does not have strict soil type requirements and possesses a strong ability to grow on sandy soils with lower fertility and aridity [9]. These advantages make it the commonly used turfgrass during the warm seasons [10]. It provides a huge feeding value for the livestock industry [11, 12], thus necessitating increased planting in recent years. The grass is widely planted in tropical and subtropical regions. Notably, different environments provide a new source of genetic novelty for P. notatum ‘Flugge’, making it grow rapidly and adapt to various environmental changes [13,14,15].

P. notatum ‘Flugge’ as a very important subtropical grass but its biological researches, especially at the genomic level, is far less than other members of gramineous plants [16,17,18], such as Sorghum bicolor and Zea mays, and the Setaria viridis family. Though it is an important sub-family in the millet tribe, the Paspalinae, there are only a few reports about its genome [19, 20]. Only the reference genome of E. crusgalli in the genus Echinochloa has been reported [21]. The lack of this information restricts our understanding of the evolutionary history of Paspalum and the ability to fully tap the genetic potential of this species for breeding superior varieties, especially in the context of global climate changes [22, 23].

Mapping the genome differences between P. notatum ‘Flugge’ and its related species using a robust phylogenetic framework is a basis for a comprehensive understanding of the evolution of its genes and genomes [24, 25]. Moreover, using Hi-C technology to observe the collinearity between the chromosomes of P. notatum ‘Flugge’ and its related species significantly improves the accuracy and sensitivity of gene evolution research and enables the prediction of more robust genome structure patterns [26, 27]. Using the complete genomics information of P. notatum ‘Flugge’ and that of the closely related species for biological analysis can provide many valuable contributions to the analysis of the differentiation and evolution mechanism of the Poaceae Paspalum. In this study, we obtained the genome information of P. notatum ‘Flugge’ by combing Illumina, Pacbio HiFi, and Hi-C to fully understand its genome content and molecular evolution history. The study also aimed to identify the historical events and continuous changes of the geographical environment, the positive selection of genes, and the systematic evolution of P. notatum ‘Flugge’. The study provides a starting point for evolutionary genomics studies and a new research direction for analyzing the evolutionary relationships between P. notatum ‘Flugge’ and its related species [28, 29].


Genome-survey, sequencing, and assembly

This study evaluated the genome size, repeatability, heterozygosity, and other genome parameters of P. notatum ‘Flugge’ (Fig. 1a) [30]. Quality control results of the offline data revealed 57Gbp of Illumina data, with a GC content of 46.08%. A comparison of 10,000 randomly selected clean reads to the NT library through blasting revealed a 96.12% mapping. K-mer analysis performed to estimate the complexity of the genome further predicted a genome size of 549 M, with 1.16% of repeat sequence and 58.33% of heterozygous sequence.

Fig. 1
figure 1

Plant morphology and Hi-C-assisted genome assembly of P. notatum ‘Flugge’. a Phenotype of the sequenced P. notatum ‘Flugge’ plant. b Hi-C interaction heatmap showing 100-kb resolution super scaffolds

The genome sequence of P. notatum ‘Flugge’ was predicted using the traditional second-generation sequencing (NGS) data assembly method and the third generation HiFi sequencing (Third-Generation Sequencing, TGS) developed by PacBio [31]. Besides TGS making up for some of the shortcomings of NGS in assembly applications, it also did not require PCR amplification, produced ultra-long read lengths, and had no GC preference. Therefore, using PacBio HiFi for genome assembly is an effective assembly strategy. High-quality HiFi reads were obtained after parameter comparison of the output data. The HiFi reads were 1.9Mbp, with an N50 measure of 1.4kbp.

The contigs were subsequently generated based on the phased string graph [32]. The assembled genome (541 M) contained 79 contigs, with an N50 of 52Mbp and a maximum contig size of 125Mbp. The average GC content of the assembled genome was 45.65% (Table 1), which was higher than that of Oryza sativa (43.65%) [30] and Cynodon transvaalensis (43.6%) [32]. The Illumina reads were subsequently compared with the DNA library to evaluate the quality and completeness of the assembly. The comparison yielded 93.77% of the properly mapped reads. Moreover, the single-copy orthologous gene library used to evaluate the completeness of the genetic space revealed a BUSCO [33] of 98.1% of the assembled genome, highlighting that it had good integrity.

Table 1 Summary statistic for the Paspalum notatum ‘Flugge’ genome

Scaffold construction and curation

Hi-C is a high-throughput chromosome conformation capture technology. It utilizes the entire cell nucleus as the research object, fixes and captures the mutual sites in the chromosomes, and then performs high-throughput sequencing to study the spatial distribution of chromatin DNA in the whole genome [34, 35]. A high-resolution chromatin regulatory element interaction map is obtained from the positional relationship. In this study, we generated chromosome-level super scaffolds using the Hi-C data with 60G and genome coverage of 110X. Subsequent analysis of the results of the Hi-C library revealed a genome with a genome size of 540 Mbp and a scaffold N50 of 49 Mbp. Moreover, 514 Mb genome sequences were mapped to 10 chromosomes after Hi-C assisted assembly, accounting for 95.15% of the sequences. The linkages between and within chromosomes was calculated upon completion of the Hi-C assisted assembly to further verify the accuracy of the assembly results. The linkages within the chromosomes were much stronger than between the chromosomes. Moreover, the linkages of chromosomes in a close physical location were much stronger than in a distant physical location (Fig. 1b). These findings suggested that the assembly result was correct. Table 1 summarizes the assembly information.

Genome annotation

The gene functions in the genome are inferred by calculating the homology alignments and predicting its repetitive sequences. In this study, we identified MITEs repetitive sequences and LTR transposable elements, accounting for 60.64 and 46.61% of the total sequence, respectively, using structure prediction methods. The LTR-retrotransposons of Copia and Gypsy was 10.03 and 25.31%, respectively. In addition, there were 2827 single repeats identified in the assembled genome. There were 12 types of ncRNA totaling 3907 ncRNA.

In the same line, 36,511 high-confidence gene models were obtained using RNA-seq and de novo prediction strategies after eliminating gene models containing premature stop codons and frameshifts. The gene models were unevenly distributed on ten chromosomes.

The average gene length was 4029 bp, with each gene containing an average of five exons. The average lengths of CDS, exons, and introns were 1503 bp, 319 bp, and 599 bp, respectively. We also compared Paspalum notatum ‘Flugge’ with five related species, including Puccinellia tenuiflora, Zea mays, Sorghum bicolor, Echinochloa c, Echinochloa h, and Brachypodium distachyon. Zea mays had the largest genome (~ 2.1GB) [36] that was 3.8 times that of Paspalum notatum ‘Flugge’. An assembly of Echinochloa C with the largest number of genes (103853), Brachypodium distachyon, which had the smallest number of genes (30002), and the other four species which had similar numbers of genes, revealed similar average CDS lengths (Table 2). Annotation comparison analysis of five databases used to annotate the genomes annotated 22,900 genes and predicted the functions of different genes and the number and proportion of genes corresponding to them and revealing a data set of 7976 known common genes (Fig. 2).

Table 2 The information of annotated gene models per species for all the species
Fig. 2
figure 2

Venn analysis of five major databases(NR, Swiss-Prot, eggNOG, GO, KEGG) containing gene function annotation information

Gene family and evolution analysis

Collinearity analysis suggested that the chromosomes of P. notatum ‘Flugge’ and Zea mays showed a certain degree of synchronization. The ten chromosomes of P. notatum ‘Flugge’ and ten of Zea mays had a good collinear relationship (Fig. 3), indicating that the chromosomes were conserved after the differentiation of the two species.

Fig. 3
figure 3

Features of the P. notatum ‘Flugge’ and Z. mays genome. a Length of each pseudochromosome (Mb). b Distribution of repetitive sequence. c Distribution of gene density. d Distribution of the GC content (e) P. notatum ‘Flugge’ and Zea mays synteny analysis; the beginning of NC represents the chromosome of Zea mays, while the beginning of CHR represents the chromosome of P. notatum ‘Flugge’

A comparison of P. notatum ‘Flugge’ with the genomes of the six representative species combined with gene family analysis revealed that the 36,511 genes of Paspalum notatum ‘Flugge’ clustered with 25,335 gene families. The maximum number of clusters in Arabidopsis was 30,235. However, all the species included in the analysis shared 7219 gene families (Fig. 4a). The analysis suggested that P. notatum ‘Flugge’ expanded 146 gene families and contracted 807 gene families in the evolution process. GO analysis showed that the expanded gene family types were related to organic-inorganic compound synthesis, DNA biosynthesis, and nucleosides. Notably, gene families related to acid metabolism were the most enriched (Table S1). The gene families were potentially involved in plant growth metabolism and stress resistance, thus conferring P. notatum ‘Flugge’ with strong resistance, fast growth, and strong reproductive ability [37]. A phylogenetic tree was constructed using 5583 single-copy homologous genes, with Arabidopsis thaliana (TAIR10.1 from NCBI) [38] as the out-group. P. notatum ‘Flugge’, Zea mays (v5.0 from NCBI) [39], Echinochloa crusgalli (v2.0 from Bioinplant Lab of Zhejiang University) [40], and Setaria viridis (v2.0 from NCBI) [41] clustered together to form a monophyletic group [42]. Zea mays (maize) was more closely related to P. notatum ‘Flugge’ than the other species, with an estimation that it diverged about 26.1 million years ago (Fig. 4b).

Fig. 4
figure 4

Gene family and phylogenetic tree analyses of P. notatum ‘Flugge’ and other representative plant genomes. a Venn diagram of the number of shared gene families. b A phylogenetic tree based on shared single-copy gene families (left), gene family expansions and contractions among P. notatum ‘Flugge’ and seven other species (middle), and Gene family clustering in P. notatum ‘Flugge’ and seven other plant genomes (right). c Genome-wide replication Ks distribution map of P. notatum ‘Flugge’ and its related species. d Genome-wide replication Ks analysis of P. notatum ‘Flugge’

The WGD events are important indices in plant evolution and are thought to be a driving force for plant adaptation to various environments [43, 44]. Changes in the synonymous replacement rate between paralogous genes were used to measure the duplication and loss of genes in the P. notatum ‘Flugge’ genome to explore its evolutionary history during the evolution process. The resultant data suggested that the differentiation of P. notatum ‘Flugge’ and Setaria viridis occurred before the WGD events. Both P. notatum ‘Flugge’ and Setaria viridis experienced a common WGD event when the KS value was 0.32 (Fig. 4c). In addition, the WGD event also occurred when the KS value of P. notatum ‘Flugge’ was 0.7 (Fig. 4d).


The gene and genome data of gramineous plants with excellent agronomic traits are an important resource for comparative genomics and functional omics. Paspalum is an excellent turfgrass whose high-quality chromosome-scale-based genome was assembled for the first time in this study. These findings improve the genomic resource library of gramineous plants and provide an excellent reference for future research on other Paspalum crops. The latest PacBio third-generation HiFi assembly and sequencing revealed that the genome size of P. notatum ‘Flugge’ was 541 M. The assembly result is the higher index among the genomes of grasses published so far, with a contig N50 = 52Mbp, scaffold N50 = 49Mbp, and BUSCOs = 98.1%, accounting for 98.5% of the estimated genome. Notably, the coverage of the assembled genome at the chromosome level was also very high (95.15%) after combining high-throughput sequencing and Hi-C scaffolding. Genome annotation revealed 36,511 high-confidence gene models, thus providing an important resource for future molecular breeding and evolutionary research.

P. notatum ‘Flugge’ belongs to the Poaceae family, with limited data regarding its performance in evolutionary history. Genome collinearity analysis revealed that P. notatum ‘Flugge’ and Zea mays had a good degree of genome collinearity. Both species belong to the Subtrib. Panicinae Reichb and are thus close in phylogeny and genetic relationship. Phylogenetic analyses revealed that P. notatum ‘Flugge’ diverged after Oryza sativa, Puccinellia tenuiflora, Brachypodium distachyon, and before Setaria viridis and Echinochloa crusgalli. These species share the same ancestor with P. notatum ‘Flugge’. The genome information of P. notatum ‘Flugge’ will help clarify the evolutionary process of gramineous species and provide a preliminary understanding of their evolutionary state. P. notatum ‘Flugge’ has good resistance to various stresses and can thus provide important genetic resources against biotic and abiotic stresses for Poaceae crops.


This study is the first to report the high-quality chromosome-scale-based genome of P. notatum ‘Flugge’ assembled using the latest PacBio third-generation HiFi sequencing reads. The genome has a high coverage rate and the higher completeness index among the gramineous genomes that have been published to date. This study provides an excellent genetic resource bank for gramineous crops and crucial perspectives regarding the evolution of gramineous plants.

Experimental procedures

For sequencing of genomic DNA, the sample was collected by a qualified postgraduate in vacuutainer tube, from the well-growing P. notatum ‘Flugge’ (2n = 20), planted in a light incubator in the Grassland Agri-husbandry Research Center. The standard plant followed ethics normswere and complies with Chinese and international regulations.

DNA isolation and sequencing

P. notatum ‘Flugge’ cv. Crowver was selected as the sampling plant. The plant was grown in an incubator at the Qingdao Agricultural University in Shandong, China. Its leaves were sampled in liquid nitrogen followed by genomic DNA extraction using the Tiangen DNA secure kit. Sequencing of the DNA was done by Berry Hekang (Beijing, China) using the PacBio third-generation HiFi assembly sequencing platform. Quality and quantity control of the DNA samples were first done, followed by library preparation of the processed DNA, and the libraries were subjected to PE sequencing using Illumina NovaSeq. Reads containing adapters, duplicates, and a low sequence quality were first filtered, followed by a random selection of 10,000 of the reads for comparison with the NT library using the BLAST tool. There was no significant external contamination detected. Notably, K-mer analysis was performed to estimate the gene size, heterozygosity, and duplication ratio to have a general understanding of the genome in advance.

Genome assembly and quality evaluation

The NanoDrop 2000 spectrophotometer was used to detect the quality of the genomic DNA [45, 46]. The purified genome was subsequently constructed into a SMRTbell library and then sequenced using the Pacbio SMRT technology [32]. The size of the library was detected using Agilent 2100 bioanalyzer. The obtained data was filtered and then processed using the smrtlink software for ccs processing [47,48,49,50]. The hifiasm software was used for assembly, followed by de-hybridization of the contig sequence using the purge-dups software [51, 52]. A single-copy orthologous gene library combined using tblastn, augustus, and hmmer software were finally used to evaluate the integrity of the assembled genome [33, 34, 53,54,55,56].

Hi-C data analysis and chromosome construction

Paspalum notatum ‘Flugge’ leaf tissue (100 mg) was soaked in paraformaldehyde, a cell cross-linking agent, for 15 min to bind DNA. Glycine was then added to the mixture to terminate the chromatin cross-linking reaction, followed by collection and freezing of the treated tissues in liquid nitrogen. The tissues were then ground to powder to extract DNA. Biotin-labeled oligonucleotide ends were added during the end repair, and a covaris breaker was subsequently used to break the extracted DNA recovered into 350 bp fragments [57]. The DNA bound to biotin was then captured and purified using avidin magnetic beads, followed by library construction and sequencing using the Illumina PE150 platform [35]. The raw reads were filtered, followed by a random selection of 10,000 sequencing reads for comparison to the NT library using the BLAST tool to check for cell contamination [52, 58]. The JUICER software was then employed to compare the Hi-C data with the draft genome [34]. The 3D-DNA comparison was subsequently used to analyze the Hi-C library results to obtain valid Hi-C data and generate the chromosome level scaffold of the P. notatum ‘Flugge’ genome [59,60,61].

Genome functional annotation

The RepeatMasker, MITE Hunter, LTRharvest, LTR Finder, LTR retriever, and RepeatModeler software were employed to analyze and predict the repetitive sequences to identify the MITEs and LTR transposable elements following the structure prediction method [62, 63]. The software parameters of LTRharvest and LTR Finder were -similar 90 -vic 10 -seed 20 -seqids yes -minlenltr 100 -maxlenltr 7000 -mintsd 4 -maxtsd 6 -motif TGCA -motifmis And -D 15000 -d 1000 -L 7000 -l 100 -p 20 -C -M 0.9 [64, 65]. The parameters of the RepeatModeler software used to identify the repetitive sequences in the masked genome from scratch were -engine ncbi -pa 60. In the same line, the parameters of the RepeatMasker software used to mask the repetitive sequences in the genome were -s -nolow -norna -gff -engine ncbi -parallel 20 [66].

The tRNAscan-SE software was used to predict the tRNA ab initio rRNA. Other types of ncRNA were searched using the Rfam database. Their specific information was obtained through similarity comparison [67,68,69].

All repetitive regions except tandem repeats were soft-masked for protein-coding gene annotation. The coding sequences of Puccinellia tenuiflora (v1.0 from BIGD) [70], Zea mays (v5.0 from NCBI) [39], Sorghum bicolor (NCBIv3 from NCBI) [71], Brachypodium distachyon (v3.0 from NCBI) [72] and Echinochloa crusgalli (v2.0 from Bioinplant Lab of Zhejiang University) [40] were downloaded. These coding sequences were subjected to Blast (v. 2.2.20) searches against the P. notatum ‘Flugge’ genome. Homologs containing premature stop codons and frameshifts were discarded. P. notatum ‘Flugge’-leaf RNA-seq data were aligned to P. notatum ‘Flugge’ contigs using GeMoMa-1.6.1 [73] and a comprehensive transcriptome database was built using PASA (v. 2.0.1) [74, 75]. Open reading frames were predicted using PASA (v. 2.0.1) and the resulting database was used to train parameters for the following four de novo gene prediction software packages: AUGUSTUS (v. 3.2.2), GeneMarker-ET (v. 4.57) [76], GlimmerHMM (v. 3.0.2), and SNAP. Predictions obtained using these packages were then combined using EVM, then 36,511 genes were retrieved and functionally annotated by blast searches against databases including NR, Swiss-Pro, eggNOG, GO and KEGG. Venn analysis of the five major databases was then performed to obtain more accurate gene functional annotation information.

Comparative analysis

The Mummer software set at nucmer -g 1000 -c 90 -l 200 was employed to perform genome collinearity analysis on P. notatum ‘Flugge’ and its relative species [77, 78], Zea mays, to derive its evolution history. Notably, the OrthoMCL cluster analysis was used to identify the 8 gene protein families(Z.mays, B.distachyon, S.viridis, O.sativa, A.thaliana, P.tenuiflora, E.crusgalli) [79]. An all-vs-all BLAST alignment of all P. notatum ‘Flugge’ gene protein-coding sequences (with 1e-5 as the default e-value) was first performed [80], followed by a calculation of the sequence similarity. The Markov clustering algorithm was then used for cluster analysis (expansion coefficient is 1.5) to obtain the protein family clustering results. Single-copy genes of each species were selected as reference markers, and four-fold degenerate sites were used to construct supergenes because of the imperfect evolutionary research of P. notatum ‘Flugge’. The Mafft software was subsequently used for multiple sequence comparisons of supergenes. A suitable base substitution model was selected, followed by constructing a species-based maximum likelihood (ML) phylogenetic tree and estimating its differentiation time using the RAxML software. The mcmctree tool in the PAML software package (parameters: burn-in = 5,000,000, sample-number = 1,000,000, sample-frequency = 50) was used to estimate differentiation time based on the single-copy gene family [81]. The time calibration point (correction point) was derived from the Timetree website. The Cafe software was subsequently used to analyze the gene families changes between species and then perform a GO functional enrichment analysis on the gene families. The Branch-site model analysis method was employed to detect the positive selection occurring in a specific clade and only affects some sites. A research of P. notatum ‘Flugge’ and its related species was performed to select one-to-one orthology proteins, which were subsequently aligned using the PRANK software set at default. The Gblocks software set at -t = c -e = .ft. -b4 = 5 -d = y [82, 83], was then used to filter the alignment results. The CODEML test in PAML was then used to test the positive selection located in a specific branch and affecting certain sites only. The Chi2 program set at a degree of freedom = 2 in PAML was subsequently used to test the correction of multiple hypotheses [84].

The duplicate age distribution method was used to detect WGD events. Blastp was used to compare the longest protein sequence of genes in the genome of P. notatum ‘Flugge’. The MCScanX software was subsequently used to filter the comparison results [85], and the Yn00 tool in the PAML software package was used to calculate the synonymous replacement rate. A density distribution map based on the Ks values of all paralog gene pairs and Ks values of ortholog gene pairs between the genomes of P. notatum ‘Flugge’, Setaria viridis, and other related species was then drawn using Matlab [43].

Availability of data and materials

All data generated and analyzed during this current study are available in the Grassland Agri-husbandry Research Center, Qingdao Agricultural University with permission from the Competent Authority. All sequencing data were submitted in NCBI Database having BioProject ID PRJNA789418 and details of software used are in Table S2. Biological materials used in this study available from the corresponding author.



Next-Generation Sequencing


Circular Consensus Sequencing


Benchmarking Universal Single-Copy Orthologs


High-through chromosome conformation capture


Miniature inverted repeat transposable elements


Long terminal repeat


Long terminal repeat retrotransposons


Non-coding RNA


NCBI nucleotide sequences


Gene Ontology


Kyoto Encyclopedia of Genes and Genomes


Whole genome duplications


  1. Ortiz JPA, Revale S, Siena LA, Podio M, Delgado L, Stein J, et al. A reference floral transcriptome of sexual and apomictic Paspalum notatum. BMC Genomics. 2017;18(1):318.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  2. de Oliveira FA, Vigna BBZ, da Silva CC, Favero AP, de Matta FP, Azevedo ALS, et al. Coexpression and transcriptome analyses identify active Apomixis-related genes in Paspalum notatum leaves. BMC Genomics. 2020;21(1):78.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  3. Peterson PM, Romaschenko K, Johnson G. A classification of the Chloridoideae (Poaceae) based on multi-gene phylogenetic trees. Mol Phylogenet Evol. 2010;55(2):580–98.

    CAS  PubMed  Article  Google Scholar 

  4. Watson L, Dallwitz MJ, Weiller C, Wilson K. The grass genera of the world. N Z J Ecol. 1992;16(2):151–2.

    Google Scholar 

  5. Beard JB-B, Harriet J. Beard’s turfgrass encyclopedia for golf courses, grounds, lawns, sports fields; 2005.

    Google Scholar 

  6. Peterson P, Columbus T, Pennington S. Classification and biogeography of New World grasses: Chloridoideae. Aliso. 2007;23(1):580–94.

    Article  Google Scholar 

  7. Turgeon AJ. Turfgrass management: Turfgrass management; 1991.

    Google Scholar 

  8. Cheng, J. Wang J: Bermuda grass as feedstock for biofuel production: A review.

  9. Foster JL, Adesogan AT, Carter JN, Blount AR, Myer RO, Phatak SC. Intake, digestibility, and nitrogen retention by sheep supplemented with warm-season legume hays or soybean meal. J Anim Sci. 2009;87(9):2891–8.

    CAS  PubMed  Article  Google Scholar 

  10. Hirata M, Pakiding W. Spatiotemporal Dynamics in Herbage Mass and Tiller Density in a Bahiagrass (Paspalum notatum Flugge) Pasture under Cattle Grazing : Results from 4-year Monitoring in Permanent Quadrats. Jpn J Grassland Sci. 2004;50(2):201–4.

    Google Scholar 

  11. Agriculture; data on agriculture reported by researchers at Federal University Rio Grande do Sul (reproductive analyses of intraspecific Paspalum Notatum Flugge hybrids). Agriculture Week 2020.

  12. Agriculture; Findings from Federal University in the Area of agriculture described (nitrogen use efficiency and forage production in intraspecific hybrids of Paspalum Notatum Flugge). Chemicals & Chemistry 2019.

  13. Espinoza F, Pessino SC, Quarin CL, Valle EM. Effect of pollination timing on the rate of apomictic reproduction revealed by RAPD markers in paspalum notatum. Ann Bot. 2002;89(2):165–70.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  14. Sandhu S, Altpeter F. Co-integration, co-expression and inheritance of unlinked minimal transgene expression cassettes in an apomictic turf and forage grass (Paspalum notatum Flugge). Plant Cell Rep. 2008;27(11):1755–65.

    CAS  PubMed  Article  Google Scholar 

  15. Gaut BS. Evolutionary dynamics of grass genomes. New Phytol. 2010;154(1):15–28.

    Article  Google Scholar 

  16. Cotton JL, Wysocki WP, Clark LG, Kelchner SA, Pires JC, Edger PP, et al. Resolving deep relationships of PACMAD grasses: a phylogenomic approach. BMC Plant Biol. 2015;15:178.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  17. Grass phylogeny working G, II. New grass phylogeny resolves deep evolutionary relationships and discovers C4 origins. New Phytol. 2012;193(2):304–12.

    Article  Google Scholar 

  18. Bocchini M, Galla G, Pupilli F, Bellucci M, Albertini E. The vesicle trafficking regulator PN_SCD1 is demethylated and overexpressed in florets of apomictic Paspalum notatum genotypes. Sci Rep. 2018;8(3030):1–11.

    CAS  Google Scholar 

  19. Hittalmani S, Mahesh HB, Shirke MD, Biradar H, Uday G, Aruna YR, et al. Genome and transcriptome sequence of finger millet (Eleusine coracana (L.) Gaertn.) provides insights into drought tolerance and nutraceutical properties. BMC Genomics. 2017;18(1):465.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  20. Zhang H, Hall N, Goertzen LR, Bi B, Chen CY, Peatman E, et al. Development of a goosegrass (Eleusine indica) draft genome and application to weed science research. Pest Manag Sci. 2019;75(10):2776–84.

    CAS  PubMed  Article  Google Scholar 

  21. Tanaka H, Hirakawa H, Kosugi S, Nakayama S, Ono A, Watanabe A, et al. Sequencing and comparative analyses of the genomes of zoysiagrasses. DNA Res. 2016;23(2):171–80.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  22. Carballo J, Santos BACM, Zappacosta D, Garbus I, Selva JP, Gallo CA, et al. A high-quality genome of Eragrostis curvula grass provides insights into Poaceae evolution and supports new strategies to enhance forage quality. Sci Rep. 2019;9:10250.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  23. Cannarozzi G, Plaza-Wuthrich S, Esfeld K, Larti S, Wilson YS, Girma D, et al. Genome and transcriptome sequencing identifies breeding targets in the orphan crop tef (Eragrostis tef). BMC Genomics. 2014;15:581.

    PubMed  PubMed Central  Article  Google Scholar 

  24. Xu CC, Ge YM, Wang JB. Molecular basis underlying the successful invasion of hexaploid cytotypes of Solidago canadensis L.: insights from integrated gene and miRNA expression profiling. Ecol Evol. 2019;9(8):4820–52.

    PubMed  PubMed Central  Article  Google Scholar 

  25. VanBuren R, Wai CM, Keilwagen J, Pardo J. A chromosome-scale assembly of the model desiccation tolerant grass Oropetium thomaeum. Plant Direct. 2018;2(11):e00096.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  26. Clayton WD. Flora of tropical east {Africa}-{Gramineae}; 1970.

    Google Scholar 

  27. Vanburen R, Bryant D, Edger PP, Tang H, Burgess D, Challabathula D, et al. Single-molecule sequencing of the desiccation-tolerant grass Oropetium thomaeum. Nature. 2015;527(7579):508.

    CAS  PubMed  Article  Google Scholar 

  28. Ye CY, Wu D, Mao L, Jia L, Qiu J, Lao S, et al. The genomes of the allohexaploid Echinochloa crus-galli and its progenitors provide insights into Polyploidization-driven adaptation. Mol Plant. 2020;13(9):1298–310.

    CAS  PubMed  Article  Google Scholar 

  29. Guo C, Wang YN, Yang AG, He J, Xiao CW, Lv SH, et al. The Coix genome provides insights into Panicoideae evolution and papery Hull domestication. Mol Plant. 2020;13(2):309–20.

    CAS  PubMed  Article  Google Scholar 

  30. Du H, Yu Y, Ma Y, Gao Q, Cao Y, Chen Z, et al. Sequencing and de novo assembly of a near complete indica rice genome. Nat Commun. 2017;8:15324.

    PubMed  PubMed Central  Article  Google Scholar 

  31. Simao FA, Waterhouse RM, Ioannidis P, Kriventseva EV, Zdobnov EM. BUSCO: assessing genome assembly and annotation completeness with single-copy orthologs. Bioinformatics. 2015;31(19):3210–2.

    CAS  PubMed  Article  Google Scholar 

  32. Cheng H, Concepcion GT, Feng X, Zhang H, Li H. Haplotype-resolved de novo assembly using phased assembly graphs with hifiasm. Nat Methods. 2021;18(2):170–5.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  33. Seppey M, Manni M, Zdobnov EM. BUSCO: assessing genome assembly and annotation completeness. Methods Mol Biol. 2019;1962:227–45.

    CAS  PubMed  Article  Google Scholar 

  34. Durand NC, Shamim MS, Machol I, Rao SSP, Huntley MH, Lander ES, et al. Juicer provides a one-click system for analyzing loop-resolution hi-C experiments. Cell Syst. 2016;3(1):95–8.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  35. Dudchenko O, Batra SS, Omer AD, Nyquist SK, Hoeger M, Durand NC, et al. De novo assembly of the Aedes aegypti genome using hi-C yields chromosome-length scaffolds. Science. 2017;356(6333):92–5.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  36. Gaut BS, Doebley JF. DNA sequence evidence for the segmental allotetraploid origin of maize. Proc Natl Acad Sci U S A. 1997;94(13):6809–14.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  37. Feder ME, Hofmann GE. Heat-shock proteins, molecular chaperones, and the stress response: evolutionary and ecological physiology. Annu Rev Physiol. 1999;61:243–82.

    CAS  PubMed  Article  Google Scholar 

  38. Sloan DB, Wu Z, Sharbrough J. Correction of persistent errors in Arabidopsis reference mitochondrial genomes. Plant Cell. 2018;30(3):525–7.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  39. Sun S, Zhou Y, Chen J, Shi J, Zhao H, Zhao H, et al. Extensive intraspecific gene order and gene structural variations between Mo17 and other maize genomes. Nat Genet. 2018;50(9):1289–95.

    CAS  PubMed  Article  Google Scholar 

  40. Guo L, Qiu J, Ye C, Jin G, Mao L, Zhang H, et al. Echinochloa crus-galli genome analysis provides insight into its adaptation and invasiveness as a weed. Nat Commun. 2017;8(1):1031.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  41. Peter M, Thielen ALP, Player RA, Bowden KV, Lawton TJ, Wisecaver JH. Reference Genome for the Highly Transformable Setaria viridis ME034V; 2020.

    Google Scholar 

  42. Koonin EV. Orthologs, paralogs, and evolutionary genomics. Annu Rev Genet. 2005;39:309–38.

    CAS  PubMed  Article  Google Scholar 

  43. Hahn MW, De Bie T, Stajich JE, Nguyen C, Cristianini N. Estimating the tempo and mode of gene family evolution from comparative genomic data. Genome Res. 2005;15(8):1153–60.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  44. Sharpton TJ, Stajich JE, Rounsley SD, Gardner MJ, Wortman JR, Jordar VS, et al. Comparative genomic analyses of the human fungal pathogens Coccidioides and their relatives. Genome Res. 2009;19(10):1722–31.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  45. Koren S, Rhie A, Walenz BP, Dilthey AT, Bickhart DM, Kingan SB, et al. De novo assembly of haplotype-resolved genomes with trio binning. Nat Biotechnol. 2018;36:1174–82.

  46. Korf I. Gene finding in novel genomes. BMC bioinformatics. 2004;5:59.

    PubMed  PubMed Central  Article  Google Scholar 

  47. Nurk S, Walenz BP, Rhie A, Vollger MR, Logsdon GA, Grothe R, et al. HiCanu: accurate assembly of segmental duplications, satellites, and allelic variants from high-fidelity long reads. Genome Res. 2020;30(9):1291–305.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  48. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, et al. The sequence alignment/map format and SAMtools. Bioinformatics. 2009;25(16):2078–9.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  49. Li H, Durbin R. Fast and accurate short read alignment with burrows-wheeler transform. Bioinformatics. 2009;25(14):1754–60.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  50. Walker BJ, Abeel T, Shea T, Priest M, Abouelliel A, Sakthikumar S, et al. Pilon: an integrated tool for comprehensive microbial variant detection and genome assembly improvement. PLoS One. 2014;9(11):e112963.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  51. Ramirez F, Bhardwaj V, Arrigoni L, Lam KC, Gruning BA, Villaveces J, et al. High-resolution TADs reveal DNA sequences underlying genome organization in flies. Nat Commun. 2018;9(1):189.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  52. Guan D, McCarthy SA, Wood J, Howe K, Wang Y, Durbin R. Identifying and removing haplotypic duplication in primary genome assemblies. Bioinformatics. 2020;36(9):2896–8.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  53. Li H. Minimap2: pairwise alignment for nucleotide sequences. Bioinformatics. 2018;34(18):3094–100.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  54. Quinlan AR. BEDTools: The Swiss-Army Tool for Genome Feature Analysis. Curr Protocols Bioinform. 2014;47:11 12 11–34.

    Article  Google Scholar 

  55. Roach MJ, Schmidt SA, Borneman AR. Purge Haplotigs: allelic contig reassignment for third-gen diploid genome assemblies. BMC bioinformatics. 2018;19(1):460.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  56. Jiao Y, Peluso P, Shi J, Liang T, Stitzer MC, Wang B, et al. Improved maize reference genome with single-molecule technologies. Nature. 2017;546(7659):524–7.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  57. Kim D, Langmead B, Salzberg SL. HISAT: a fast spliced aligner with low memory requirements. Nat Methods. 2015;12(4):357–60.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  58. Jarvis DE, Ho YS, Lightfoot DJ, Schmockel SM, Li B, Borm TJA, et al. The genome of Chenopodium quinoa (vol 542, pg 307, 2017). Nature. 2017;545(7655):510.

    CAS  PubMed  Article  Google Scholar 

  59. Mascher M, Gundlach H, Himmelbach A, Beier S, Twardziok SO, Wicker T, et al. A chromosome conformation capture ordered sequence of the barley genome. Nature. 2017;544(7651):426.

    Article  CAS  Google Scholar 

  60. Teh BT, Lim K, Yong CH, Ng CCY, Rao SR, Rajasegaran V, et al. The draft genome of tropical fruit durian (Durio zibethinus). Nat Genet. 2017;49(11):1633.

    CAS  PubMed  Article  Google Scholar 

  61. Bickhart DM, Rosen BD, Koren S, Sayre BL, Hastie AR, Chan S, et al. Single-molecule sequencing and chromatin conformation capture enable de novo reference assembly of the domestic goat genome. Nat Genet. 2017;49(4):643.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  62. Majoros WH, Pertea M, Salzberg SL. TigrScan and GlimmerHMM: two open source ab initio eukaryotic gene-finders. Bioinformatics. 2004;20(16):2878–9.

    CAS  PubMed  Article  Google Scholar 

  63. Stanke M, Steinkamp R, Waack S, Morgenstern B. AUGUSTUS: a web server for gene finding in eukaryotes. Nucleic Acids Res. 2004;32(Web Server issue):W309–12.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  64. Ter-Hovhannisyan V, Lomsadze A, Chernoff YO, Borodovsky M. Gene prediction in novel fungal genomes using an ab initio algorithm with unsupervised training. Genome Res. 2008;18(12):1979–90.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  65. Haas BJ, Salzberg SL, Wei Z, Pertea M. Automated eukaryotic gene structure annotation using EVidenceModeler and the program to assemble spliced alignments. Genome Biol. 2008;9(1):R7.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  66. McGinnis S, Madden TL. BLAST: at the core of a powerful and diverse set of sequence analysis tools. Nucleic Acids Res. 2004;32(Web Server issue):W20–5.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  67. Xie C, Mao X, Huang J, Ding Y, Wu J, Dong S, et al. KOBAS 2.0: a web server for annotation and identification of enriched pathways and diseases. Nucleic Acids Res. 2011;39(Web Server issue):W316–22.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  68. Lowe TM, Eddy SR. tRNAscan-SE: a program for improved detection of transfer RNA genes in genomic sequence. Nucleic Acids Res. 1997;25(5):955–64.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  69. Griffiths-Jones S, Bateman A, Marshall M, Khanna A, Eddy SR. Rfam: an RNA family database. Nucleic Acids Res. 2003;31(1):439–41.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  70. Zhang W, Liu J, Zhang Y, Qiu J, Li Y, Zheng B, et al. A high-quality genome sequence of alkaligrass provides insights into halophyte stress tolerance. Sci China Life Sci. 2020;63(9):1269–82.

    CAS  PubMed  Article  Google Scholar 

  71. Song Y, Chen Y, Lv J, Xu J, Zhu S, Li M. Comparative chloroplast genomes of Sorghum species: sequence divergence and phylogenetic relationships. Biomed Res Int. 2019;2019:5046958.

    PubMed  PubMed Central  Google Scholar 

  72. Huo N, Vogel JP, Lazo GR, You FM, Ma Y, McMahon S, et al. Structural characterization of Brachypodium genome and its syntenic relationship with rice and wheat. Plant Mol Biol. 2009;70(1–2):47–61.

    CAS  PubMed  Article  Google Scholar 

  73. Gremme G, Steinbiss S, Kurtz S. GenomeTools: a comprehensive software library for efficient processing of structured genome annotations. IEEE/ACM Trans Comput Biol Bioinform. 2013;10(3):645–56.

    PubMed  Article  Google Scholar 

  74. Storz G. An expanding universe of noncoding RNAs. Science. 2002;296(5571):1260–3.

    CAS  PubMed  Article  Google Scholar 

  75. Li L, Stoeckert CJ Jr, Roos DS. OrthoMCL: identification of ortholog groups for eukaryotic genomes. Genome Res. 2003;13(9):2178–89.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  76. Nawrocki EP, Eddy SR. Infernal 1.1: 100-fold faster RNA homology searches. Bioinformatics. 2013;29(22):2933–5.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  77. Krzywinski M, Schein J, Birol I, Connors J, Gascoyne R, Horsman D, et al. Circos: an information aesthetic for comparative genomics. Genome Res. 2009;19(9):1639–45.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  78. Harris RS: Improved pairwise alignment of genomic DNA. Dissertations & Theses - Gradworks 2007.

    Google Scholar 

  79. Wang Y, Tang H, Debarry JD, Tan X, Li J, Wang X, et al. MCScanX: a toolkit for detection and evolutionary analysis of gene synteny and collinearity. Nucleic Acids Res. 2012;40(7):e49.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  80. Bailey JA, Eichler EE. Primate segmental duplications: crucibles of evolution, diversity and disease. Nat Rev Genet. 2006;7(7):552–64.

    CAS  PubMed  Article  Google Scholar 

  81. Lynch M, Conery JS. The evolutionary fate and consequences of duplicate genes. Science. 2000;290(5494):1151–5.

    CAS  PubMed  Article  Google Scholar 

  82. Jiao Y, Wickett NJ, Ayyampalayam S, Chanderbali AS, Landherr L, Ralph PE, et al. Ancestral polyploidy in seed plants and angiosperms. Nature. 2011;473(7345):97–100.

    CAS  PubMed  Article  Google Scholar 

  83. Yang JZNRZ. Evaluation of an improved branch-site likelihood method for detecting positive selection at the molecular level. Mol Biol Evol. 2005;12:2472–9.

    Google Scholar 

  84. Kumar S, Nei M, Dudley J, Tamura K. MEGA: a biologist-centric software for evolutionary analysis of DNA and protein sequences. Brief Bioinform. 2008;9(4):299–306.

    CAS  PubMed  Article  Google Scholar 

  85. Hahn MW, Han MV, Han SG. Gene family evolution across 12 Drosophila genomes. PLoS Genet. 2007;3(11):e197.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

Download references


The author would like to thank Professor Guofeng Yang, Professor Zengyu Wang, and Professor Juan Sun (Professor of Grassland Science, Qingdao Agricultural University) for their help in data analysis and article writing. Thank you for the scientific research funding provided by the College of Grassland Science of Qingdao Agricultural University. Thanks for the experimental help provided by Beijing Berry and Kang.


This study was supported by the National Nature Science Foundation of China (U1906201), Shandong Forage Research System (SDAIT-23-01), China Agriculture Research System (CARS-34) and the First Class Grassland Science Discipline Program of Shandong Province (1619002), China.

Author information

Authors and Affiliations



ZY, ZW and GY conceived and designed this research. ZY analyzed data and wrote the manuscript. ZY, HL, YC and JS executed the data analyses. JS participated in the discussionof the results. LM, AW, FM, QW, XY and LC collected samples. GY, HS, YG contributed to the evaluation and discussion of the results and manuscript revisions. All authors have read and approved the final version.

Corresponding author

Correspondence to Guofeng Yang.

Ethics declarations

Ethics approval and consent to participate

P. notatum ‘Flugge’ is not endangered or a protected species in China, and it was purchased from Crovo and planted in a light incubator. The seeds are sorted and selected by Professor Guofeng Yang. All the study procedures were carried out in accordance with relevant guidelines.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Yan, Z., Liu, H., Chen, Y. et al. High-quality chromosome-scale de novo assembly of the Paspalum notatum ‘Flugge’ genome. BMC Genomics 23, 293 (2022).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI:


  • Paspalum notatum ‘Flugge’
  • Genome
  • De novo assembly
  • Genome annotation