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. 2026 Mar 21;13:728. doi: 10.1038/s41597-026-07062-0

The chromosome-level genome assembly of the early monocot species Tofieldia thibetica

Hong Chen 1,#, Xiang-Yu Wang 1,#, Jia-Le Wang 1, Ning-Hua Tan 1,✉, Ling-Yun Chen 1,✉
PMCID: PMC13168421  PMID: 41865025

Abstract

Tofieldia thibetica is a species native to Southwest China. It holds an important phylogenetic position as one of the earliest-diverging lineages within monocots. Here we report a chromosome-level genome assembly of T. thibetica, assembled using PacBio HiFi and Hi-C sequencing data. The 1,641 Mb assembly has a contig N50 of 55.66 Mb, with 1,362 Mb of sequences anchored to 15 pseudochromosomes. Genome annotation predicted 53,411 protein-coding genes, of which 53,075 were functionally annotated. Repetitive sequences account for 79.53% of the genome. This assembly fills a critical genomic gap in Tofieldiaceae and provides an essential resource for phylogenomic and functional studies. This assembly also supports efforts to explore the family’s medicinal potential and to advance our understanding of monocot evolution.

Subject terms: Plant evolution, Taxonomy

Background & Summary

Tofieldia thibetica, a species in the family Tofieldiaceae1, is endemic to Southwest China. It typically inhabits scrublands, grassy slopes, and rock crevices at elevations ranging from 700 to 2,300 meters2. The whole plant of T. thibetica has traditionally been used in medicine for promoting digestion, alleviating pain, detoxifying and reducing swelling, relieving gastric discomfort, treating wind-induced skin rashes, and mitigating injuries from falls or trauma3.

Tofieldia thibetica was treated as a member of Liliaceae in Flora of China2, but it is now classified within the family Tofieldiaceae1,4–8. Tofieldiaceae was recognized as an early-branching lineage within Alismatales, which is sister to core monocots. T. thibetica, characterized by distinctive morphological traits such as free carpels developing into obovoid capsules and seeds featuring a prominent white raphe (Fig. 1a), serves as an ideal model for studying monocot evolution and speciation. However, previous studies have reported conflicting results regarding the phylogenetic relationships among Tofieldiaceae, core Alismatales, and Araceae9,10. The paucity of genomic data of Tofieldiaceae has limited our understanding of the evolution of this family.

Fig. 1.

Fig. 1

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Plant photo (a), genome size (b,c), and ploidy estimation (d) of T. thibetica using flow cytometry, k-mer analysis, and Smudgeplot. In the flow cytometry histograms (b), FL2-H represents the propidium iodide (PI) fluorescence intensity, reflecting relative nuclear DNA content. CP indicates the co-processed sample. From left to right, the first G1 peak represents tomato (Solanum lycopersicum, 1 C = 900 Mbp), the second corresponds to the tested sample, and the third represents maize (Zea mays B73, 1 C = 2.3 Gbp). Tomato and maize were used as internal reference standards. The value 0.03 denotes the coefficient of variation (CV) of the G1 peak, indicating high measurement precision.

Advances in high-throughput sequencing have revolutionized phylogenetic and comparative genomic studies. We generated a chromosome-level genome assembly of T. thibetica by integrating PacBio HiFi long-read sequencing with Hi-C data. This assembly fills a key genomic gap by clarifying the evolutionary placement of this species, supporting functional gene discovery and conservation biology applications, and facilitating broader phylogenetic and adaptive evolution studies within Alismatales and monocots.

Methods

Plant materials and sequencing

Fresh roots, stems, leaves, and inflorescences of T. thibetica were collected from one individual plant in Chuxiong, Yunnan (101.3947°E, 25.0962°N; 1,877 m) on July 17, 2023. After cleaning, tissues were flash-frozen in liquid nitrogen, preserved on dry ice, and subsequently used for genomic DNA extraction using the cetyltrimethylammonium bromide (CTAB) method11 and total RNA extraction using the TRIzol Reagent kit (Invitrogen, USA). A SMRTbell library (insert size ~15 kb) was prepared according to the manufacturer’s protocol (Pacific Biosciences, CA, USA), and long-read sequencing was conducted using the PacBio Revio platform. A Hi-C library was also constructed, followed by sequencing using the BGI DNBSEQ-T7 platform (Beijing Genomic Institute, Shenzhen, China). PacBio Revio HiFi sequencing yielded 88.26 Gb of long reads (Table 1), while Hi-C sequencing generated 264 Gb of raw data, with 262.38 Gb retained after quality filtering with Fastp (v0.23.2)12. Transcriptome sequencing on the DNBSEQ-T7 platform produced 26.60 Gb of raw data, from which 25.46 Gb of high-quality reads were obtained for downstream analyses.

Table 1.

Summary of sequencing data generated for T. thibetica.

Sequence Platform Total reads Total bases (bp) Sequence depth (×)
HiFi reads PacBio Revio 5,527,556 88,262,875,770 55.44
Hi-C reads DNBSEQ-T7 1,759,106,000 262,376,374,000 198.46
RNA reads DNBSEQ-T7 178,140,764 25,460,198,000 —
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Estimate of genome size

Using flow cytometry with tomato (900 Mb) and maize (2.3 Gb) as internal standards, we estimated a genome size of ~1.61 Gb for T. thibetica, based on the sample-to-standard fluorescence intensity ratio of 0.50 (Fig. 1b). K-mer frequency distribution (21-mer) of PacBio HiFi reads was calculated using the count and histogram sub-commands of Jellyfish13. The resulting profile was modeled with GenomeScope2 (v1.0.0)14, which indicated a diploid genome (p = 2) with a haploid size of 1.01 Gb, 59.30% repetitive content, and 1.25% heterozygosity (Fig. 1c). Smudgeplot (v0.1.6)15 analysis (k = 31) also supported diploidy (Fig. 1d). Collectively, these results indicated that T. thibetica has a large, highly repetitive, and moderately heterozygous genome.

De Novo genome assembly and quality assessment

The chromosome-scale genome assembly of T. thibetica was generated using a hierarchical strategy that combined PacBio HiFi long reads and Hi-C data. PacBio HiFi reads were assembled with hifiasm (v0.19.6)16, and redundant sequences were removed using Purge_dups (v1.2.5)17, resulting in 730 contigs with a total length of 1,641 Mb. The contig N50 was 55.66 Mb with a GC content of 43.87%. BUSCO (v5.1.2, embryophyta_odb10; 1,614 single-copy orthologs)18 analysis identified 1,531 complete single-copy genes, corresponding to 98.40% completeness (Table 2). Integration of Hi-C data with Juicer (v1.6)19 and 3D-DNA (v180922)20 anchored 1,362 Mb of sequences (83.0% of the genome) to 15 pseudochromosomes andincreased the scaffold N50 to 93.60 Mb. The final assembly exhibited a GC content of 43.98% and a BUSCO completeness of 95.60% with 1,515 complete single-copy genes (Table 2). Quality was further supported by an LTR Assembly Index (LAI) of 17.68 estimated with LTR_retriever (v2.9.7)21, a base-level quality value (QV) of 63.99, and a k-mer completeness of 83.96% calculated with Merqury (v1.3)22. A genome-wide Hi-C interaction heatmap was generated with HapHiC (v1.0.6)23 (Fig. 2a), and a circos plot was constructed using Circos (v0.69)24 (Fig. 2b).

Table 2.

Statistics for contig-level and chromosome-level genome assemblies of T. thibetica.

Assembly statistic Contig-level assembly Chromosome-level assembly
Number of contigs/chromosomes 730 15
Largest contig/scaffold (bp) 107,953,961 132,319,398
Total length (bp) 1,641,809,050 1,362,655,837
GC content (%) 43.87 43.98
Contig/scaffold N50 (bp) 55,660,170 93,597,260
Contig/scaffold N90 (bp) 21,285,891 73,790,276
Contig/scaffold L50 12 7
Contig/scaffold L90 29 13
BUSCO completeness (%) 98.40 95.60
Complete single-copy BUSCOs 1,531 1,515
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Fig. 2.

Fig. 2

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Chromosome-level assembly and features of T. thibetica. (a) Hi-C contact heatmap displaying chromosomal interactions. (b) Circos plot illustrating genomic features, including (i) pseudochromosomes, (ii) gene density, (iii) GC content, (iv) LTR retrotransposon density, and (v) syntenic links.

Repetitive sequence annotation

Repetitive sequences were identified through a combination of homology-based prediction with RepeatMasker (v4.1.2)25 and de novo identification with RepeatModeler (v2.0.5)26. Transposable elements were further classified using EDTA (v2.1.1)27, and elements that remained unclassified were subjected to deep annotation with DeepTE28. In total, 1,084.71 Mb of repetitive sequences were identified, representing 79.53% of the genome (Table 3). Long terminal repeats (LTRs) were the predominant class, with Copia and Gypsy accounting for 16.04% and 53.71% of the genome, respectively.

Table 3.

Classification and genomic composition of repetitive elements in T. thibetica.

Class Count Masked (bp) Masked (%)
LTR Gypsy 750,392 731,900,128 53.71
Copia 171,450 218,616,531 16.04
unknown 22,660 19,667,807 1.44
LINE 10,343 8,486,789 0.62
TIR 229,264 83,844,818 6.15
Non-LTR 406 275,390 0.02
Non-TIR 37,090 9,454,686 0.69
Repeat fragment 42,840 11,461,359 0.84
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Gene structure annotation

Protein-coding genes in the T. thibetica genome were annotated by integrating multiple approaches: homology-based prediction with GeneWise (v2.4.1)29, transcriptome-based evidence from RNA-seq read alignment with HISAT2 (v2.2.1)30, transcript assembly with StringTie (v2.2.1)31, and ORF prediction with TransDecoder (v5.7.1, https://github.com/TransDecoder/TransDecoder), and ab initio prediction with Augustus (v3.5.0)32. The results were merged using GETA (v2.7.1, https://github.com/chenlianfu/geta), yielding 53,411 protein-coding genes. Gene structural features included an average gene length of 6,847 bp, mean exon length 1,663 bp, mean intron length 5,184 bp, and mean coding sequence length 1,290 bp. BUSCO analysis (v5.1.2, embryophyta_odb10) indicated a completeness of 88.60%.

Non-coding RNA annotation

We employed a suite of computational tools to identify and annotate non-coding RNAs within the T. thibetica genome. tRNAs were predicted with tRNAscan-SE (v2.0.11)33 in eukaryotic mode (default). rRNAs, snoRNAs, and miRNAs were predicted with Infernal (v1.1.4)34 using homology searches against the Rfam database (e-value: 1e-5). The annotation identified 1,840 rRNA genes, 296 tRNA genes, 52 snoRNA genes, and 22 miRNA genes.

Gene functional annotation

Functional annotation of the 53,411 protein-coding genes in T. thibetica was performed using seven databases: EggNOG35, Non-Redundant Protein Sequence Database (Nr), Swiss-Prot36, GO37,38, KEGG39,40, InterPro41, and Pfam42. 53,075 genes (99.37%) were successfully functionally annotated (Table 4). The Nr database achieved the highest annotation coverage at 98.61%, followed by eggNOG at 96.71%, with Swiss-Prot covering 57.31% of genes. For conserved domain prediction, InterPro and Pfam annotated 86.23% and 85.52% of genes, respectively. KEGG pathway annotation was assigned to 57.98% of genes, while 37.64% received GO terms.

Table 4.

Functional annotation statistics of protein-coding genes in T. thibetica.

Database Annotated genes Annotation ratio (%)
Nr 52,667 98.61
EggNOG 51,654 96.71
Swiss-Prot 30,609 57.31
InterPro 46,056 86.23
Pfam 45,677 85.52
KEGG 30,967 57.98
GO 20,101 37.64
Total annotated genes 53,075 99.37
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Data Record

The raw sequencing data of T. thibetica, including PacBio HiFi reads, Hi-C reads, and transcriptome reads, have been deposited in NCBI Sequence Read Archive (SRA) with the following accession codes: PacBio HiFi, SRR3526371543, SRR3526371744, SRR3526371845; Hi-C, SRR3526371346, SRR3526371447; transcriptome, SRR3526371148, SRR3526371249, SRR3526371650. The corresponding BioProject and BioSample accessions are PRJNA1319348 and SAMN51150105, respectively.

Technical Validation

The integrity and accuracy of T. thibetica genome assembly were evaluated through multiple approaches. The Hi-C contact map revealed strong intra-chromosomal interaction signals along the diagonal (Fig. 2), confirming the integrity of the genome structure. The GC content distribution showed no evidence of sequence contamination (Fig. 2b). For genomic completeness assessment, BUSCO analysis demonstrated that 95.6% of complete single-copy genes were assembled from a set of 1,614 conserved orthologs (C: 95.6% [S: 93.9%, D: 1.7%], F: 0.9%, M: 3.5%, n: 1,614). To validate accuracy, Merqury produced a base-level quality value (QV) of 63.99, and LTR_retriever generated an LTR Assembly Index (LAI) of 17.68. These results indicated high accuracy in both base calling and repetitive region assembly. Additionally, 53,075 (99.37%) gene models were successfully annotated against databases including NR, KEGG, EggNOG, GO, Pfam, Swiss-Prot, and InterPro. Taken together, these results provided compelling evidence that the de novo assembled T. thibetica genome is of high quality.

Acknowledgements

We thank Wen-Da Zhang and Pu-Guang Zhao for assistance in data analyses. This work was supported by the National Natural Science Foundation of China (No. 32370242) and the Fundamental Research Funds for the Central Universities (No. 2632024TD04).

Author contributions

L.Y.C. and N.H.T. designed this study; H.C., X.Y.W. and J.L.W. conducted data analyses; H.C. drafted this manuscript. All authors read and revised this manuscript.

Data availability

The chromosome-level genome assembly of T. thibetica has been deposited in NCBI GenBank under the accession number GCA_054954025.151, associated with BioProject PRJNA1404966 and BioSample SAMN54730446. The corresponding genome annotation files are available on Figshare (10.6084/m9.figshare.30058588).

Code availability

No custom code was developed in this study. All code and pipelines employed in data processing were rigorously executed according to the operation manuals and standardized protocols of publicly available bioinformatics tools. Detailed descriptions of software versions, code implementations, and parameter configurations are provided in the Methods section.

Competing interests

The authors declare no competing interests.

Footnotes

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

These authors contributed equally: Hong Chen, Xiang-Yu Wang.

Contributor Information

Ning-Hua Tan, Email: nhtan@cpu.edu.cn.

Ling-Yun Chen, Email: lychen@cpu.edu.cn.

References

  • 1.Lin, C. T., Liu, B., Zhao, M. J., Ma, K. P. & Ji, L. Q. Catalogue of life China: Towards an index of known species present in China. The Innovation life3, 100140, 10.59717/j.xinn-life.2025.100141 (2025). [Google Scholar]
  • 2.Editorial Committee of Flora of China. In Flora Reipublicae Popularis Sinicae Vol. 14 P. 10 (Science Press, 1980).
  • 3.Sichuan Academy of Natural Resources Sciences. Tofieldia thibetica. Sichuan Plant Resources Information Network http://www.scpri.ac.cn/mplant/#!/list/specinfo/2357/1 (1978).
  • 4.Li, X. X. & Zhou, Z. K. The higher-level phylogeny of monocots based on matK, rbcL and 18S rDNA sequences. J. Syst. Evol.45, 113–133 (2007). [Google Scholar]
  • 5.Davis, J. I. et al. A phylogeny of the monocots, as inferred from rbcL and atpA sequence variation, and a comparison of methods for calculating jackknife and bootstrap values. Syst. Bot.29, 467–510, 10.1600/0363644041744365 (2004). [Google Scholar]
  • 6.Davis, J. I. et al. Are mitochondrial genes useful for the analysis of monocot relationships? Taxon55, 857–870, 10.2307/25065681 (2006). [Google Scholar]
  • 7.Holloway, S. J. & Friedman, W. E. Embryological features of Tofieldia glutinosa and their bearing on the early diversification of monocotyledonous plants. Ann. Bot.102, 167–182, 10.1093/aob/mcn084 (2008). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Azuma, H. & Tobe, H. Molecular phylogenetic analyses of Tofieldiaceae (Alismatales): family circumscription and intergeneric relationships. J. Plant Res.124, 349–357, 10.1007/s10265-010-0387-5 (2011). [DOI] [PubMed] [Google Scholar]
  • 9.Chen, L. Y. et al. Phylogenomic analyses of Alismatales shed light into adaptations to aquatic environments. Mol. Biol. Evol.39, msac079, 10.1093/molbev/msac079 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Luo, Y. et al. Plastid phylogenomic analyses resolve Tofieldiaceae as the root of the early diverging monocot order Alismatales. Genome Biol. Evol.8, 932–945, 10.1093/gbe/evv260 (2016). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Porebski, S., Bailey, L. G. & Baum, B. R. Modification of a CTAB DNA extraction protocol for plants containing high polysaccharide and polyphenol components. Plant Mol. Biol. Rep.15, 8–15, 10.1007/BF02772108 (1997). [Google Scholar]
  • 12.Chen, S. F., Zhou, Y. Q., Chen, Y. R. & Gu, J. Fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics34, i884–i890, 10.1093/bioinformatics/bty560 (2018). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Marçais, G. & Kingsford, C. A fast, lock-free approach for efficient parallel counting of occurrences of k-mers. Bioinformatics27, 764–770, 10.1093/bioinformatics/btr011 (2011). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Vurture, G. W. et al. GenomeScope: fast reference-free genome profiling from short reads. Bioinformatics33, 2202–2204, 10.1093/bioinformatics/btx153 (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Ranallo-Benavidez, T. R., Jaron, K. S. & Schatz, M. C. GenomeScope 2.0 and Smudgeplot for reference-free profiling of polyploid genomes. Nat. Commun.11, 1432, 10.1038/s41467-020-14998-3 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Cheng, H. Y., Concepcion, G. T., Feng, X. W., Zhang, H. W. & Li, H. Haplotype-resolved de novo assembly using phased assembly graphs with hifiasm. Nat. Methods18, 170–175, 10.1038/s41592-020-01056-5 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Guan, D. F. et al. Identifying and removing haplotypic duplication in primary genome assemblies. Bioinformatics36, 2896–2898, 10.1093/bioinformatics/btaa025 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Simão, F. A., Waterhouse, R. M., Ioannidis, P., Kriventseva, E. V. & Zdobnov, E. M. BUSCO: assessing genome assembly and annotation completeness with single-copy orthologs. Bioinformatics31, 3210–3212, 10.1093/bioinformatics/btv351 (2015). [DOI] [PubMed] [Google Scholar]
  • 19.Durand, N. C. et al. Juicer provides a one-click system for analyzing loop-resolution Hi-C experiments. Cell Syst.3, 95–98, 10.1016/j.cels.2016.07.002 (2016). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Dudchenko, O. et al. De novo assembly of the Aedes aegypti genome using Hi-C yields chromosome-length scaffolds. Science356, 92–95, 10.1126/science.aal3327 (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Ou, S. & Jiang, N. LTR_retriever: a highly accurate and sensitive program for identification of long terminal repeat retrotransposons. Plant Physiol.176, 1410–1422, 10.1104/pp.17.01310 (2018). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Rhie, A., Walenz, B. P., Koren, S. & Phillippy, A. M. Merqury: reference-free quality, completeness, and phasing assessment for genome assemblies. Genome Biol.21, 245, 10.1186/s13059-020-02134-9 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Zeng, X. F. et al. Chromosome-level scaffolding of haplotype-resolved assemblies using Hi-C data without reference genomes. Nat. Plants10, 1184–1200, 10.1038/s41477-024-01755-3 (2024). [DOI] [PubMed] [Google Scholar]
  • 24.Krzywinski, M. et al. Circos: an information aesthetic for comparative genomics. Genome Res.19, 1639–1645, 10.1101/gr.092759.109 (2009). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Tarailo-Graovac, M. & Chen, N. S. Using RepeatMasker to identify repetitive elements in genomic sequences. Curr. Protoc. Bioinformatics4, 4.10.1–4.10.14, 10.1002/0471250953.bi0410s25 (2004). [DOI] [PubMed] [Google Scholar]
  • 26.Flynn, J. M. et al. RepeatModeler2 for automated genomic discovery of transposable element families. Proc. Natl. Acad. Sci.117, 9451–9457, 10.1073/pnas.1921046117 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Ou, S. J. et al. Benchmarking transposable element annotation methods for creation of a streamlined, comprehensive pipeline. Genome Biol.20, 275, 10.1186/s13059-019-1905-y (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Yan, H. D., Bombarely, A. & Li, S. DeepTE: a computational method for de novo classification of transposons with convolutional neural network. Bioinformatics36, 4269–4275, 10.1093/bioinformatics/btaa519 (2020). [DOI] [PubMed] [Google Scholar]
  • 29.Birney, E., Clamp, M. & Durbin, R. GeneWise and Genomewise. Genome Res.14, 988–995, 10.1101/gr.1865504 (2004). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Kim, D., Paggi, J. M., Park, C., Bennett, C. & Salzberg, S. L. Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nat. Biotechnol.37, 907–915, 10.1038/s41587-019-0201-4 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Pertea, M. et al. StringTie enables improved reconstruction of a transcriptome from RNA-seq reads. Nat. Biotechnol.33, 290–295, 10.1038/nbt.3122 (2015). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Stanke, M. et al. AUGUSTUS: ab initio prediction of alternative transcripts. Nucleic Acids Res.34, W435–W439, 10.1093/nar/gkl200 (2006). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Chan, P. P., Lin, B. Y., Mak, A. J. & Lowe, T. M. tRNAscan-SE 2.0: improved detection and functional classification of transfer RNA genes. Nucleic Acids Res.49, 9077–9096, 10.1093/nar/gkab688 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Nawrocki, E. P. & Eddy, S. R. Infernal 1.1: 100-fold faster RNA homology searches. Bioinformatics29, 2933–2935, 10.1093/bioinformatics/btt509 (2013). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Huerta-Cepas, J. et al. eggNOG 5.0: a hierarchical, functionally and phylogenetically annotated orthology resource based on 5090 organisms and 2502 viruses. Nucleic Acids Res.47, D309–D314, 10.1093/nar/gky1085 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Bairoch, A. & Apweiler, R. The SWISS-PROT protein sequence database and its supplement TrEMBL in 2000. Nucleic Acids Res.28, 45–48, 10.1093/nar/28.1.45 (2000). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.The Gene Ontology Consortium. The gene ontology resource: 20 years and still GOing strong. NucleicAcids Res.47, D330–D338, 10.1093/nar/gky1055 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Ashburner, M. et al. Gene ontology: tool for the unification of biology. Nat. Genet.25, 25–29, 10.1038/75556 (2000). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Kanehisa, M. & Goto, S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res.28, 27–30, 10.1093/nar/28.1.27 (2000). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Kanehisa, M., Sato, Y., Kawashima, M., Furumichi, M. & Tanabe, M. KEGG as a reference resource for gene and protein annotation. Nucleic Acids Res.44, D457–D462, 10.1093/nar/gkv1070 (2016). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.Mitchell, A. L. et al. InterPro in 2019: improving coverage, classification and access to protein sequence annotations. Nucleic Acids Res.47, D351–D360, 10.1093/nar/gky1100 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Mistry, J. et al. Pfam: The protein families database in 2021. Nucleic Acids Res.49, D412–D419, 10.1093/nar/gkaa913 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.NCBI Sequence Read Archivehttps://identifiers.org/insdc.sra:SRR35263715.
  • 44.NCBI Sequence Read Archivehttps://identifiers.org/insdc.sra:SRR35263717.
  • 45.NCBI Sequence Read Archivehttps://identifiers.org/insdc.sra:SRR35263718.
  • 46.NCBI Sequence Read Archivehttps://identifiers.org/insdc.sra:SRR35263713.
  • 47.NCBI Sequence Read Archivehttps://identifiers.org/insdc.sra:SRR35263714.
  • 48.NCBI Sequence Read Archivehttps://identifiers.org/insdc.sra:SRR35263711.
  • 49.NCBI Sequence Read Archivehttps://identifiers.org/insdc.sra:SRR35263712.
  • 50.NCBI Sequence Read Archivehttps://identifiers.org/insdc.sra:SRR35263716.
  • 51.NCBI Genome Assembly for Tofieldia thibetica isolate wild_individual_1, GenBank accession GCA_054954025.1, https://www.ncbi.nlm.nih.gov/datasets/genome/GCA_054954025.1/ (2026).

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The chromosome-level genome assembly of T. thibetica has been deposited in NCBI GenBank under the accession number GCA_054954025.151, associated with BioProject PRJNA1404966 and BioSample SAMN54730446. The corresponding genome annotation files are available on Figshare (10.6084/m9.figshare.30058588).

No custom code was developed in this study. All code and pipelines employed in data processing were rigorously executed according to the operation manuals and standardized protocols of publicly available bioinformatics tools. Detailed descriptions of software versions, code implementations, and parameter configurations are provided in the Methods section.

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