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A high-quality chromosome-level genome assembly of the endangered species Magnolia amoena
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  • Published: 05 March 2026

A high-quality chromosome-level genome assembly of the endangered species Magnolia amoena

  • Yu Liu1 na1,
  • Xing-Jian Liu1 na1,
  • Ke Hu1,
  • Ming-Han Li  ORCID: orcid.org/0000-0002-5187-96121,
  • Zi-Jie Shen1,
  • Xiao-Qin Sun1 &
  • …
  • Rui-Sen Lu1 

Scientific Data , Article number:  (2026) Cite this article

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We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Genome
  • Plant sciences

Abstract

Magnolia amoena (Magnoliaceae), a deciduous tree endemic to eastern China, is valued for its striking floral diversity and traditional medicinal uses, yet remains understudied. To support its conservation and evolutionary research, we generated a high-quality chromosome-scale assembly using DNBSEQ-T7, PacBio HiFi, and Hi-C data. The final assembly spanned 1.87 Gb with a contig N50 of 36.92 Mb, of which 95.73% (1.79 Gb) was anchored onto 19 pseudochromosomes. Repetitive elements occupied 79.55% of the genome, dominated by long terminal repeats (57.72%) and DNA transposons (14.25%). A total of 39,739 protein-coding genes (mean length: 10.21 kb) were predicted, with 86.34% functionally annotated, alongside 270 miRNAs, 631 tRNAs, 668 rRNAs, and 4,133 snRNAs. Comparative genomic analysis across 11 magnoliid species identified 23,244 gene families in M. amoena, of which 1,905 were unique. Phylogenomic reconstruction strongly supported M. amoena as sister to M. biondii, with an estimated divergence time of ~18.5 Mya. Overall, this genome assembly lays a robust foundation for future research into the evolution, adaptation, and conservation of M. amoena.

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Data availability

All data generated for Magnolia amoena in this study have been made publicly available. The chromosome-level genome assembly has been deposited in NCBI GenBank under accession number JBNYVC000000000. Raw sequencing reads (DNBSEQ-T7 short-read, PacBio long-read, Hi-C, Iso-Seq, and RNA-Seq) are available in the NCBI Sequence Read Archive under BioProject accession PRJNA1264003. Genome annotation files are accessible via figshare (https://doi.org/10.6084/m9.figshare.29095601).

Code availability

Software and analysis pipelines were implemented according to the official manuals and published protocols of established bioinformatics tools. Detailed software versions and parameters are provided in the Methods section.

References

  1. Wang, Y. B. et al. Major clades and a revised classification of Magnolia and Magnoliaceae based on whole plastid genome sequences via genome skimming. J. Syst. Evol. 58, 673–695, https://doi.org/10.1111/jse.12588 (2020).

    Google Scholar 

  2. Shankar, U. Primitive angiosperms in the Indian Subcontinent: Taxonomic diversity and geographical distribution of Magnoliaceae Juss. (APG IV). Pleione 14, 137–151, https://doi.org/10.3767/000651904X486214 (2020).

    Google Scholar 

  3. Figlar, R. B. & Nooteboom, H. P. Notes on Magnoliaceae IV. Blumea-Biodiversity, Evolution and Biogeography of Plants 49, 87–100, https://doi.org/10.3767/000651904X486214 (2004).

    Google Scholar 

  4. Xie, H. H. et al. Diversity patterns and conservation gaps of Magnoliaceae species in China. Sci. Total Environ. 813, 152665, https://doi.org/10.1016/j.scitotenv.2021.152665 (2022).

    Google Scholar 

  5. Dong, S. S. et al. Plastid and nuclear phylogenomic incongruences and biogeographic implications of Magnolia s.l. (Magnoliaceae). J. Syst. Evol. 60, 1–15, https://doi.org/10.1111/jse.12727 (2022).

    Google Scholar 

  6. Lee, Y. J. et al. Therapeutic applications of compounds in the Magnolia family. Pharm. Thera. 130, 157–176, https://doi.org/10.1016/j.pharmthera.2011.01.010 (2011).

    Google Scholar 

  7. Xu, J. W. & Xu, H. Magnolol: Chemistry and biology. Ind. Crop. Prod. 205, 117493, https://doi.org/10.1016/j.indcrop.2023.117493 (2023).

    Google Scholar 

  8. Rauf, A. et al. Honokiol: A review of its pharmacological potential and therapeutic insights. Phytomedicine 90, 153647, https://doi.org/10.1016/j.phymed.2021.153647 (2021).

    Google Scholar 

  9. Wang, Y. L. et al. Magnolia sinostellata and relatives (Magnoliaceae). Phytotaxa 154, 47–58, https://doi.org/10.11646/phytotaxa.154.1.3 (2013).

    Google Scholar 

  10. Rivers, M., Beech, E., Murphy, L. & Oldfield, S. The Red List of Magnoliaceae- revised and extended. (Botanic Gardens Conservation International, 2016).

  11. Liu, D., Chu, L. & Yang, Y. Genetic diversity of rare and endangered plant Magnolia amoena. Chin. J. Appl. Ecol. 15, 1139–1142, https://www.cjae.net/CN/Y2004/V/I7/1139 (2004).

    Google Scholar 

  12. Nanjing University of Chinese Medicine. Zhong Yao Da Ci Dian 2nd edn Vol. 1 (Shanghai Scientific & Technical Publishers, 2006).

  13. China Expert Workshop. Magnolia amoena. The IUCN Red List of Threatened Species 2014: e.T32423A2818554. https://doi.org/10.2305/IUCN.UK.2014-1.RLTS.T32423A2818554.en (2014)

  14. Ministry of Ecology and Environment of the People’s Republic of China & Chinese Academy of Sciences. China Biodiversity Red List: Higher Plants Volume (2020). 577 (2023).

  15. Ma, H. F., Sima, Y. K. & Xiang, W. Composition analysis of the volatile chemicals in Magnolia amoena. Yunnan For. Sci. Technol. 4, 65–67, https://doi.org/10.16473/j.cnki.xblykx1972.2001.04.014 (2001).

    Google Scholar 

  16. Yu, Z. J., Yi, G. M., Shan, W. & Du, J. Introduction and domestication of Magnolia amoena. Pract. For. Technol. 3, 8–9, https://doi.org/10.13456/j.cnki.lykt.2008.01.014 (2008).

    Google Scholar 

  17. Sun, Q. M., Dou, J., Liu, X. J. & Liu, X. W. Investigation on the Magnoliaceae Plant Resources in East China. J. Anhui Agri. Sci. 36, 14956–14957+15018, https://doi.org/10.13989/j.cnki.0517-6611.2008.34.103 (2008).

    Google Scholar 

  18. Wang, C. Y., Liu, X. L. & Yu, C. The genetic diversity analysis of subgenus Yulania and its related species. Mol. Plant Breed. 18, 3786–3796, https://doi.org/10.13271/j.mpb.018.003786 (2020).

    Google Scholar 

  19. Doyle, J. J. & Doyle, J. L. A rapid DNA isolation procedure for small quantities of fresh leaf tissue. Phytochem. Bull. 19, 11–15 (1987).

    Google Scholar 

  20. Chen, S. F., Zhou, Y. Q., Chen, Y. R. & Gu, J. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 34, i884–i890, https://doi.org/10.1093/bioinformatics/bty560 (2018).

    Google Scholar 

  21. Marçais, G. & Kingsford, C. A fast, lock-free approach for efficient parallel counting of occurrences of k-mers. Bioinformatics 27, 764–770, https://doi.org/10.1093/bioinformatics/btr011 (2011).

    Google Scholar 

  22. Vurture, G. W. et al. GenomeScope: fast reference-free genome profiling from short reads. Bioinformatics 33, 2202–2204, https://doi.org/10.1093/bioinformatics/btx153 (2017).

    Google Scholar 

  23. 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. Methods 18, 170–175, https://doi.org/10.1038/s41592-020-01056-5 (2021).

    Google Scholar 

  24. Durand, N. C. et al. Juicer provides a one-click system for analyzing loop-resolution Hi-C experiments. Cell Syst. 3, 95–98, https://doi.org/10.1016/j.cels.2016.07.002 (2016).

    Google Scholar 

  25. Dudchenko, O. et al. De novo assembly of the Aedes aegypti genome using Hi-C yields chromosome-length scaffolds. Science 356, 92–95, https://doi.org/10.1126/science.aal3327 (2017).

    Google Scholar 

  26. Durand, N. C. et al. Juicebox provides a visualization system for Hi-C contact maps with unlimited zoom. Cell Syst. 3, 99–101, https://doi.org/10.1016/j.cels.2015.07.012 (2016).

    Google Scholar 

  27. Flynn, J. M. et al. RepeatModeler2 for automated genomic discovery of transposable element families. P. Nat. A. Sci. USA 117, 9451–9457, https://doi.org/10.1073/pnas.1921046117 (2020).

    Google Scholar 

  28. Xu, Z. & Wang, H. LTR_FINDER: an efficient tool for the prediction of full-length LTR retrotransposons. Nucleic Acids Res. 35, W265–W268, https://doi.org/10.1093/nar/gkm286 (2007).

    Google Scholar 

  29. Ellinghaus, D., Kurtz, S. & Willhoeft, U. LTRharvest, an efficient and flexible software for de novo detection of LTR retrotransposons. BMC Bioinformatics 9, 1–14, https://doi.org/10.1186/1471-2105-9-18 (2008).

    Google Scholar 

  30. Abrusán, G., Grundmann, N., DeMester, L. & Makalowski, W. TEclass—a tool for automated classification of unknown eukaryotic transposable elements. Bioinformatics 25, 1329–1330, https://doi.org/10.1093/bioinformatics/btp084 (2009).

    Google Scholar 

  31. 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, https://doi.org/10.1038/s41587-019-0201-4 (2019).

    Google Scholar 

  32. Li, H. Minimap and miniasm: fast mapping and de novo assembly for noisy long sequences. Bioinformatics 32, 2103–2110, https://doi.org/10.1093/bioinformatics/btw152 (2016).

    Google Scholar 

  33. Pertea, M. et al. StringTie enables improved reconstruction of a transcriptome from RNA-seq reads. Nat. Biotechnol. 33, 290–295, https://doi.org/10.1038/nbt.3122 (2015).

    Google Scholar 

  34. Kuo, R. I. et al. Illuminating the dark side of the human transcriptome with long read transcript sequencing. BMC Genomics 21, 1–22, https://doi.org/10.1186/s12864-020-07123-7 (2020).

    Google Scholar 

  35. Jiang, S. R. et al. A high-quality haplotype genome of Michelia alba DC reveals differences in methylation patterns and flower characteristics. Mol. Hort. 4, 23, https://doi.org/10.1186/s43897-024-00098-z (2024).

    Google Scholar 

  36. Dong, S. S. et al. The genome of Magnolia biondii Pamp. provides insights into the evolution of Magnoliales and biosynthesis of terpenoids. Hortic. Res. 8, https://doi.org/10.1038/s41438-021-00471-9 (2021).

  37. Zhou, L. J. et al. The genome of Magnolia hypoleuca provides a new insight into cold tolerance and the evolutionary position of magnoliids. Front. Plant Sci. 14, 1108701, https://doi.org/10.3389/fpls.2023.1108701 (2023).

    Google Scholar 

  38. Yin, Y. P. et al. The chromosome-scale genome of Magnolia officinalis provides insight into the evolutionary position of magnoliids. Iscience 24, https://doi.org/10.1016/j.isci.2021.102997 (2021).

  39. Cai, L. et al. The chromosome-scale genome of Magnolia sinica (Magnoliaceae) provides insights into the conservation of plant species with extremely small populations (PSESP). GigaScience 13, giad110, https://doi.org/10.1093/gigascience/giad110 (2024).

    Google Scholar 

  40. Li, H. Protein-to-genome alignment with miniprot. Bioinformatics 39, btad014, https://doi.org/10.1093/bioinformatics/btad014 (2023).

    Google Scholar 

  41. Stanke, M., Diekhans, M., Baertsch, R. & Haussler, D. Using native and syntenically mapped cDNA alignments to improve de novo gene finding. Bioinformatics 24, 637–644, https://doi.org/10.1093/bioinformatics/btn013 (2008).

    Google Scholar 

  42. Burge, C. & Karlin, S. Prediction of complete gene structures in human genomic DNA. J. Mol. Biol. 268, 78–94, https://doi.org/10.1006/jmbi.1997.0951 (1997).

    Google Scholar 

  43. Delcher, A. L., Bratke, K. A., Powers, E. C. & Salzberg, S. L. Identifying bacterial genes and endosymbiont DNA with Glimmer. Bioinformatics 23, 673–679, https://doi.org/10.1093/bioinformatics/btm009 (2007).

    Google Scholar 

  44. Holt, C. & Yandell, M. MAKER2: an annotation pipeline and genome-database management tool for second-generation genome projects. BMC Bioinformatics 12, 1–14, https://doi.org/10.1186/1471-2105-12-491 (2011).

    Google Scholar 

  45. Haas, B. J. et al. Automated eukaryotic gene structure annotation using EVidenceModeler and the Program to Assemble Spliced Alignments. Genome Biol. 9, 1–22, https://doi.org/10.1186/gb-2008-9-1-r7 (2008).

    Google Scholar 

  46. The UniProt Consortium. UniProt: the universal protein knowledgebase. Nucleic Acids Res. 46, 2699–2699, https://doi.org/10.1093/nar/gky092 (2018).

    Google Scholar 

  47. Kanehisa, M. & Goto, S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28, 27–30, https://doi.org/10.1093/nar/28.1.27 (2000).

    Google Scholar 

  48. Ashburner, M. et al. Gene ontology: tool for the unification of biology. Nat. Genet. 25, 25–29, https://doi.org/10.1038/75556 (2000).

    Google Scholar 

  49. Buchfink, B., Xie, C. & Huson, D. H. Fast and sensitive protein alignment using DIAMOND. Nat. Methods 12, 59–60, https://doi.org/10.1038/nmeth.3176 (2015).

    Google Scholar 

  50. Eddy, S. R. A new generation of homology search tools based on probabilistic inference. Genome Informatics 2009: Genome Informatics Series Vol. 23, 205–211, https://doi.org/10.1142/9781848165632_0019 (World Scientific, 2009).

  51. Finn, R. D. et al. Pfam: the protein families database. Nucleic Acids Res. 42, D222–D230, https://doi.org/10.1093/nar/gkt1223 (2014).

    Google Scholar 

  52. Aramaki, T. et al. KofamKOALA: KEGG Ortholog assignment based on profile HMM and adaptive score threshold. Bioinformatics 36, 2251–2252, https://doi.org/10.1093/bioinformatics/btz859 (2020).

    Google Scholar 

  53. Lowe, T. M. & Eddy, S. R. tRNAscan-SE: a program for improved detection of transfer RNA genes in genomic sequence. Nucleic Acids Res. 25, 955–964, https://doi.org/10.1093/nar/25.5.955 (1997).

    Google Scholar 

  54. Lagesen, K. et al. RNAmmer: consistent and rapid annotation of ribosomal RNA genes. Nucleic Acids Res. 35, 3100–3108, https://doi.org/10.1093/nar/gkm160 (2007).

    Google Scholar 

  55. Nawrocki, E. P. & Eddy, S. R. Infernal 1.1: 100-fold faster RNA homology searches. Bioinformatics 29, 2933–2935, https://doi.org/10.1093/bioinformatics/btt509 (2013).

    Google Scholar 

  56. Emms, D. M. & Kelly, S. OrthoFinder: phylogenetic orthology inference for comparative genomics. Genome Biol. 20, 1–14, https://doi.org/10.1186/s13059-019-1832-y (2019).

    Google Scholar 

  57. Edgar, R. C. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 32, 1792–1797, https://doi.org/10.1093/nar/gkh340 (2004).

    Google Scholar 

  58. Capella-Gutiérrez, S., Silla-Martínez, J. M. & Gabaldón, T. trimAl: a tool for automated alignment trimming in large-scale phylogenetic analyses. Bioinformatics 25, 1972–1973, https://doi.org/10.1093/bioinformatics/btp348 (2009).

    Google Scholar 

  59. Stamatakis, A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30, 1312–1313, https://doi.org/10.1093/bioinformatics/btu033 (2014).

    Google Scholar 

  60. Yang, Z. H. PAML 4: phylogenetic analysis by maximum likelihood. Mol. Biol. Evol. 24, 1586–1591, https://doi.org/10.1093/molbev/msm088 (2007).

    Google Scholar 

  61. Liu, Y. Genbank http://identifiers.org/insdc:JBNYVC000000000 (2025).

  62. Liu, Y. Genome assembly and annotation of Magnolia amoena. figshare. Dataset. https://doi.org/10.6084/m9.figshare.29095601.v2 (2025).

  63. NCBI Sequence Read Archive https://identifiers.org/ncbi/insdc.sra:SRR33610905 (2025).

  64. NCBI Sequence Read Archive https://identifiers.org/ncbi/insdc.sra:SRR33610904 (2025).

  65. NCBI Sequence Read Archive https://identifiers.org/ncbi/insdc.sra:SRR33610903 (2025).

  66. NCBI Sequence Read Archive https://identifiers.org/ncbi/insdc.sra:SRR33610898 (2025).

  67. NCBI Sequence Read Archive https://identifiers.org/ncbi/insdc.sra:SRR33610902 (2025).

  68. NCBI Sequence Read Archive https://identifiers.org/ncbi/insdc.sra:SRR33610901 (2025).

  69. NCBI Sequence Read Archive https://identifiers.org/ncbi/insdc.sra:SRR33610900 (2025).

  70. NCBI Sequence Read Archive https://identifiers.org/ncbi/insdc.sra:SRR33610899 (2025).

  71. Manni, M. et al. BUSCO update: novel and streamlined workflows along with broader and deeper phylogenetic coverage for scoring of eukaryotic, prokaryotic, and viral genomes. Mol. Biol. Evol. 38, 4647–4654, https://doi.org/10.1093/molbev/msab199 (2021).

    Google Scholar 

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Acknowledgements

This work was supported by the Jiangsu Forestry Science and Technology Innovation and Promotion Project (LYKJ[2025]06), Special Fund Project for Forestry Development in Jiangsu Province, and the Jiangsu Key Laboratory for Conservation and Utilization of Plant Resources (JSPKLB202401).

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  1. These authors contributed equally: Yu Liu, Xing-Jian Liu.

Authors and Affiliations

  1. Jiangsu Key Laboratory for Conservation and Utilization of Plant Resources, Institute of Botany, Jiangsu Province and Chinese Academy of Sciences, Nanjing, 210014, China

    Yu Liu, Xing-Jian Liu, Ke Hu, Ming-Han Li, Zi-Jie Shen, Xiao-Qin Sun & Rui-Sen Lu

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Contributions

R.S.L. conceived and designed the research; Y.L. and X.J.L. collected the samples; Y.L., K.H., M.H.L. and Z.J.S. conducted the data analysis; X.Q.S. provided valuable suggestions throughout the study; Y.L., X.J.L. and R.S.L. wrote the manuscript. All authors read and approved the final version of the manuscript.

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Correspondence to Rui-Sen Lu.

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Tables S1-S4, Figure S1 (download PDF )

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Liu, Y., Liu, XJ., Hu, K. et al. A high-quality chromosome-level genome assembly of the endangered species Magnolia amoena. Sci Data (2026). https://doi.org/10.1038/s41597-026-06973-2

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  • Received: 26 May 2025

  • Accepted: 25 February 2026

  • Published: 05 March 2026

  • DOI: https://doi.org/10.1038/s41597-026-06973-2

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