Haplotype-resolved chromosomal-level genome assembly of Chrysaora achlyos (black sea nettle)

1. Weissbourd, B. et al. A genetically tractable jellyfish model for systems and evolutionary neuroscience. Cell 184, 5854–5868. e5820 (2021).

   Google Scholar 

2. Tinta, T., Zhao, Z., Bayer, B. & Herndl, G. J. Jellyfish detritus supports niche partitioning and metabolic interactions among pelagic marine bacteria. Microbiome 11, 156 (2023).

   Google Scholar 

3. Irvine, T., Costello, J. H., Gemmell, B. J., Sutherland, K. R. & Colin, S. P. Impactful feeding ecology of a predatory guild of oceanic jellies. bioRxiv, 2024.2008. 2019.608650 (2024).

4. Tinta, T., Klun, K. & Herndl, G. J. The importance of jellyfish–microbe interactions for biogeochemical cycles in the ocean. Limnology and Oceanography 66, 2011–2032 (2021).

   Google Scholar 

5. Peng, S. et al. Microbiota regulates life-cycle transition and nematocyte dynamics in jellyfish. Iscience 26 (2023).

6. López‐Martínez, J. et al. Multiple reproduction forms in the polyps of the cannonball jellyfish Stomolophus sp. 2: Probable life‐cycle reversal. Journal of Experimental Zoology Part A: Ecological and Integrative Physiology 339, 239–252 (2023).

   Google Scholar 

7. Karabulut, A. et al. The architecture and operating mechanism of a cnidarian stinging organelle. Nature Communications 13, 3494 (2022).

   Google Scholar 

8. Park, S. et al. The nematocyst’s sting is driven by the tubule moving front. Journal of The Royal Society Interface 14, 20160917 (2017).

   Google Scholar 

9. Bosch-Belmar, M. et al. Jellyfish impacts on marine aquaculture and fisheries. Reviews in Fisheries Science & Aquaculture 29, 242–259 (2020).

   Google Scholar 

10. Báez, J. C. et al. Effects of environmental conditions and jellyfish blooms on small pelagic fish and fisheries from the Western Mediterranean Sea. Estuarine, Coastal and Shelf Science 264, 107699 (2022).

    Google Scholar 

11. Sweetman, A. K. & Chapman, A. First assessment of flux rates of jellyfish carcasses (jelly-falls) to the benthos reveals the importance of gelatinous material for biological C-cycling in jellyfish-dominated ecosystems. Frontiers in Marine Science 2, 47 (2015).

    Google Scholar 

12. Sweetman, A. K. et al. Jellyfish decomposition at the seafloor rapidly alters biogeochemical cycling and carbon flow through benthic food‐webs. Limnology and Oceanography 61, 1449–1461 (2016).

    Google Scholar 

13. Gold, D. A. et al. The genome of the jellyfish Aurelia and the evolution of animal complexity. Nature ecology & evolution 3, 96–104 (2019).

    Google Scholar 

14. Kim, H.-M. et al. The genome of the giant Nomura’s jellyfish sheds light on the early evolution of active predation. BMC biology 17, 28 (2019).

    Google Scholar 

15. Ohdera, A. et al. Box, stalked, and upside-down? Draft genomes from diverse jellyfish (Cnidaria, Acraspeda) lineages: Alatina alata (Cubozoa), Calvadosia cruxmelitensis (Staurozoa), and Cassiopea xamachana (Scyphozoa). GigaScience 8, giz069 (2019).

    Google Scholar 

16. Li, Y. et al. Chromosome-level reference genome of the jellyfish Rhopilema esculentum. GigaScience 9, giaa036 (2020).

    Google Scholar 

17. Martin, J. W., Gershwin, L.-A., Burnett, J. W., Cargo, D. G. & Bloom, D. A. Chrysaora achlyos, a remarkable new species of scyphozoan from the Eastern Pacific. The Biological Bulletin 193, 8–13 (1997).

    Google Scholar 

18. Radwan, F. F., Gershwin, L.-A. & Burnett, J. W. Toxinological studies on the nematocyst venom of Chrysaora achlyos. Toxicon 38, 1581–1591 (2000).

    Google Scholar 

19. Pal, K., Forcato, M. & Ferrari, F. Hi-C analysis: from data generation to integration. Biophysical reviews 11, 67–78 (2019).

    Google Scholar 

20. Xia, W.-X. et al. High-continuity genome assembly of the jellyfish Chrysaora quinquecirrha. Zoological Research 42, 130 (2021).

    Google Scholar 

21. Nisa, S. A. et al. Jellyfish venom proteins and their pharmacological potentials: A review. International Journal of Biological Macromolecules 176, 424–436 (2021).

    Google Scholar 

22. van Berkum, N. L. et al. Hi-C: a method to study the three-dimensional architecture of genomes. J Vis Exp, https://doi.org/10.3791/1869 (2010).

23. Chen, S., Zhou, Y., Chen, Y. & Gu, J. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 34, i884–i890 (2018).

    Google Scholar 

24. Marçais, G. & Kingsford, C. A fast, lock-free approach for efficient parallel counting of occurrences of k-mers. Bioinformatics 27, 764–770 (2011).

    Google Scholar 

25. Vurture, G. W. et al. GenomeScope: fast reference-free genome profiling from short reads. Bioinformatics 33, 2202–2204 (2017).

    Google Scholar 

26. Cheng, H., Concepcion, G. T., Feng, X., Zhang, H. & Li, H. Haplotype-resolved de novo assembly using phased assembly graphs with hifiasm. Nature methods 18, 170–175 (2021).

    Google Scholar 

27. Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. bioinformatics 25, 1754–1760 (2009).

    Google Scholar 

28. Durand, N. C. et al. Juicer provides a one-click system for analyzing loop-resolution Hi-C experiments. Cell systems 3, 95–98 (2016).

    Google Scholar 

29. Dudchenko, O. et al. De novo assembly of the Aedes aegypti genome using Hi-C yields chromosome-length scaffolds. Science 356, 92–95 (2017).

    Google Scholar 

30. Marçais, G. et al. MUMmer4: A fast and versatile genome alignment system. PLoS Comput Biol 14, e1005944, https://doi.org/10.1371/journal.pcbi.1005944 (2018).

    Google Scholar 

31. Bao, W., Kojima, K. K. & Kohany, O. Repbase Update, a database of repetitive elements in eukaryotic genomes. Mobile Dna 6, 11 (2015).

    Google Scholar 

32. Price, A. L., Jones, N. C. & Pevzner, P. A. De novo identification of repeat families in large genomes. Bioinformatics 21, i351–i358 (2005).

    Google Scholar 

33. Jurka, J. et al. Repbase Update, a database of eukaryotic repetitive elements. Cytogenetic and genome research 110, 462–467 (2005).

    Google Scholar 

34. Chen, N. Using Repeat Masker to identify repetitive elements in genomic sequences. Current protocols in bioinformatics 5, 4.10. 11–14.10. 14 (2004).

    Google Scholar 

35. Ellinghaus, D., Kurtz, S. & Willhoeft, U. LTRharvest, an efficient and flexible software for de novo detection of LTR retrotransposons. BMC bioinformatics 9, 18 (2008).

    Google Scholar 

36. Benson, G. Tandem repeats finder: a program to analyze DNA sequences. Nucleic acids research 27, 573–580 (1999).

    Google Scholar 

37. Xu, Z. & Wang, H. LTR_FINDER: an efficient tool for the prediction of full-length LTR retrotransposons. Nucleic acids research 35, W265–W268 (2007).

    Google Scholar 

38. Kim, D., Paggi, J. M., Park, C., Bennett, C. & Salzberg, S. L. Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nature biotechnology 37, 907–915 (2019).

    Google Scholar 

39. Kovaka, S. et al. Transcriptome assembly from long-read RNA-seq alignments with StringTie2. Genome biology 20, 278 (2019).

    Google Scholar 

40. 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 (2008).

    Google Scholar 

41. Keilwagen, J., Hartung, F. & Grau, J. GeMoMa: homology-based gene prediction utilizing intron position conservation and RNA-seq data. Gene prediction: Methods and protocols, 161–177 (2019).

42. Haas, B. J. et al. Automated eukaryotic gene structure annotation using EVidenceModeler and the Program to Assemble Spliced Alignments. Genome Biol 9, R7, https://doi.org/10.1186/gb-2008-9-1-r7 (2008).

    Google Scholar 

43. Korf, I. Gene finding in novel genomes. BMC bioinformatics 5, 59 (2004).

    Google Scholar 

44. Mulder, N. & Apweiler, R. InterPro and InterProScan: tools for protein sequence classification and comparison. Methods Mol Biol 396, 59–70, https://doi.org/10.1007/978-1-59745-515-2_5 (2007).

    Google Scholar 

45. Ashburner, M. et al. Gene ontology: tool for the unification of biology. Nature genetics 25, 25–29 (2000).

    Google Scholar 

46. NCBI Sequence Read Archive https://identifiers.org/ncbi/insdc.sra:SRP607485 (2025).

47. Li, Y.-Y. whole genome shotgun sequencing project. GenBank https://identifiers.org/ncbi/insdc:JBVTIV000000000 (2026).

48. Li, Y.-Y. whole genome shotgun sequencing project. GenBank https://identifiers.org/insdc:JBVTIW000000000 (2026).

49. Li, Y.-Y. Haplotype-resolved chromosomal-level genome assembly of Chrysaora achlyos. figshare. Dataset. https://doi.org/10.6084/m9.figshare.30020194 (2025).

    Google Scholar 

50. Li, H. New strategies to improve minimap2 alignment accuracy. Bioinformatics 37, 4572–4574, https://doi.org/10.1093/bioinformatics/btab705 (2021).

    Google Scholar 

51. Astashyn, A. et al. Rapid and sensitive detection of genome contamination at scale with FCS-GX. Genome Biol 25, 60, https://doi.org/10.1186/s13059-024-03198-7 (2024).

    Google Scholar 

52. 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, https://doi.org/10.1186/s13059-020-02134-9 (2020).

    Google Scholar 

53. 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. Bioinformatics 31, 3210–3212, https://doi.org/10.1093/bioinformatics/btv351 (2015).

    Google Scholar 

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