Skip to main content

Phylogenetic placement of Ceratophyllum submersum based on a complete plastome sequence derived from nanopore long read sequencing data



Eutrophication poses a mounting concern in today’s world. Ceratophyllum submersum L. is one of many plants capable of living in eutrophic conditions, therefore it could play a critical role in addressing the problem of eutrophication. This study aimed to take a first genomic look at C. submersum.


Sequencing of gDNA from C. submersum yielded enough reads to assemble a plastome. Subsequent annotation and phylogenetic analysis validated existing information regarding angiosperm relationships and the positioning of Ceratophylalles in a wider phylogenetic context.

Peer Review reports


Ceratophyllum submersum L., commonly known as soft hornwort, is a subaquatic plant whose genus is the only extant member in the order of Ceratophyllales, placed as a sister clade to the eudicots [1, 2]. It is native to Europe, Africa and Asia and grows in stagnant freshwater bodies [3]. Morphological features include long, branching stems that can reach up to several metres in length and leaves that are forked into narrow, filament-like segments that grow in multiple whorls around the stem (Fig. 1) [4]. The plant is often green, but can vary in colour from brown to red depending on environmental conditions. Anthocyanins contribute to the colouration of many plant species, and metabolic analyses have detected several derivatives in C. submersum [5,6,7]. It thrives in eutrophic conditions, characterised by low light intensity and high nutrient levels [8, 9]. Eutrophication of aquatic environments is indicated by the accumulation of nutrients albeit other parameters and multiple classification systems exist [10]. Due to anthropogenic effects, the occurrences of eutrophic environments are rising which poses a problem e.g. for greenhouse gas emissions [11]. In aquatic systems, eutrophication induces harmful algal blooms (HABs) which are responsible for environmental hazards like the Oder ecological disaster in 2022 [12]. Due to its capabilities, C. submersum competes with other phototrophic organisms capable of living in eutrophic conditions. This suggests that it may inhibit the formation of HABs despite its vulnerability to them [7].

Fig. 1
figure 1

Ceratophyllum submersum and its habitat. A: The pond (Braunschweig, 52.28062 N / 10.54896 E) where C. submersum was collected, B: Whole C. submersum plant, C: Close up view of C. submersum

While a low coverage skimming report has been performed previously [13], the results appear to be missing in the established sequence databases. Here, we utilised nanopore long-read sequencing to obtain the plastome sequence of C. submersum and provide a thorough annotation of its genetic content. This analysis allowed us to place the species in a phylogenetic context which informs future studies.

Results and discussion

A total of 0.48 Gbp of sequencing data was generated from 94,100 long reads. Out of this, 1,544 reads representing the C. submersum plastome were extracted (Additional file 1, PRJEB62706), accounting for 3.3% of the data. Due to the limitations of the Flye assembler used by ptGAUL, only 752 reads (covering about 1.68% of the total data) were utilised for the assembly, as it can only accommodate up to 50x coverage of the estimated assembly size. Considering these factors, only 0.27 Gbp of sequencing data are required to assemble an approximately 160 kbp sized plastome, provided that the ratio of plastid DNA to nuclear DNA does not exceed 3%. We calculated that about 0.8 Gbp of total genomic sequencing data would be adequate for cases where the plastid DNA content is even lower (1%). High-quality assemblies can be achieved with coverage levels lower than 50, so a smaller amount of data may still be sufficient. We inferred a sequencing goal of 1 Gbp to enable the assembly of a plastome sequence without prior plastid purification. If fresh leaf material is chosen, a higher plastid DNA portion should be achievable, potentially reducing the amount of data needed for successful assembly.

The plastome assembly of C. submersum resulted in a 155,767 bp long sequence with a GC content of 38.26%. In total, 75 unique protein coding genes were annotated (Additional file 2). The C. submersum plastome sequence is 485 bp shorter than the reference plastome sequence of Ceratophyllum demersum L. that has a length of 156,252 bp [14]. While the GC content is similar (C. demersum: 38.22, C. submersum: 38.26) the amount of unique annotated genes in C. submersum is smaller than in C. demersum (75 against 79). After the annotation some predicted coding sequences were flawed (e.g. containing multiple stop codons). Manual evaluation revealed four homopolymeric regions in which a frameshift would correct the annotation. Since homopolymers are a frequent error type in ONT reads, manual correction in those regions is appropriate [15]. The comparison of those regions to all plastome reads suggested a correction of two of these regions, namely the adenine at position 3,094 and the thymines at position 3,143, at position 86,261, and at position 86,262 were inserted.

The sequencing took place on a flow cell that was previously used to analyse gDNA from Digitalis purpurea L. Therefore, a phylogenetic tree was calculated to validate clean and distinguishable sequencing data, incorporating our plastome assemblies for both species (D. purpurea data: PRJEB62706) (Fig. 2).

Fig. 2
figure 2

Phylogenetic tree of selected spermatophytes based on plastome protein sequences. The asterisks (*) indicate plastome assemblies generated in this study. Within the angiosperms, the clades are differently coloured. All nodes received full bootstrapping support (100%), except those displaying the actual value. Please see the material and methods section for further description of the tree calculation. Visualisation was done in iTOL 6.7.6 [16]. The full tree including the outgroup Chlamydomonas reinhardtii can be found in the additional files (Additional file 4). GS = gymnosperms. The sketches are licensed by adobe and depositphotos (Standard License:,

Multiple plastome reference sequences were chosen to represent the angiosperm clade as well as Chlamydomonas reinhardtii as outgroup (for full list and references see Additional file 3). The phylogenetic tree (Fig. 2, for full tree see Additional file 4) classifies C. submersum close to its reference C. demersum and our D. purpurea plastome assembly close to the RefSeq D. purpurea plastome sequence generated by Zhao et al. [17]. The angiosperm clade is represented in accordance with the current APG IV classification except for the exact separation/placement of the Magnoliids and the Chloranthales, which is still controversial [2]. This underlines the significance of plastome sequences for modern plant phylogenetics [18].

The plastome assembly and phylogenetic analysis presented in this study provides first steps towards genetic and genomic characterization of Ceratophyllum submersum. Further research is needed to determine its nuclear genome and to explore potential applications of this plant, such as its use as a valuable resource or as an agent to mitigate environmental hazards.

Materials and methods

Plant material, gDNA extraction and sequencing

Ceratophyllum submersum was collected from a small pond in Braunschweig (52.28062 N / 10.54896 E) and kept in cultivation at the Institute of Plant Biology at TU Braunschweig. The artificial pond needs occasional water replenishment. Foliage from surrounding shrubs and dead aquatic plants lead to a high eutrophy (see Fig. 1A). C. submersum shoot tips (Fig. 1C) were harvested for gDNA extraction conducted with a CTAB method [19,20,21]. Short DNA fragments were depleted with the Short Read Eliminator kit (Pacific Biosciences). Library preparation for ONT sequencing was started with 1 µg of DNA that was first repaired with the NEBNext® Companion Module and then processed according to the SQK-LSK109 protocol (Oxford Nanopore Technologies). For ONT sequencing, a R9.4.1 flow cell was used with a MinION. ONT sequencing is one of the leading sequencing technologies in plant genomics [22], and was applied in this project to generate a complete plastome sequence based on long reads. Prior to C. submersum sequencing, the flow cell was already utilised for D. purpurea gDNA sequencing. Processing of raw data was performed with guppy v6.4.6 + ae70e8f ( which internally called minimap2 v2.24-r1122 [23] with default parameters on a graphical processor unit (GPU) in the de.NBI cloud to generate FASTQ files. Guppy was run with the default configuration of dna_r9.4.1_450bps_hac.

Plastome assembly and annotation

The FASTQ files were subjected to a plastome assembly with ptGAUL using standard parameters [23,24,25,26]. As reference the C. demersum plastome from the NCBI RefSeq database (release 216) was used [14, 27]. Since the ptGAUL results consist of two assemblies (one per path) the OGDRAW plastome maps were compared between the reference and the assemblies to decide which one is closer to the reference. The C. submersum assembly needed to be reverse complemented and the sequence start was adjusted to the C. demersum sequence. GC contents of the assembly were calculated using the script [28]. For annotation, GeSeq was used (see Additional file 5 for parameters) [29,30,31,32,33,34,35,36,37,38]. Protein coding genes were extracted from the resulting GBSON.json file using our own script [39]. Frameshifts in the coding sequences of incorrectly translated peptide sequences were identified in two steps. First, all open reading frames (ORFs) were annotated by EMBOSS sixpack. Then, NCBI blastx results were mapped against the ORFs to specify the location of the possible frameshifts [40, 41]. Further evaluation of these frameshifts was conducted with the help of a read mapping. All plastome reads (FASTQ) were mapped against the plastome sequence (FASTA) with minimap2 (v2.24-r1122, parameters: -ax map-ont --secondary = no -t 27) to generate a SAM file [23]. From that, a BAM file and its corresponding index file was generated with samtools 1.10 [42]. The mapping was then analysed in the Integrative Genomics Viewer (IGV) 2.16.1 [43]. The assembly underwent correction only if supported by more than a quarter and a minimum of ten reads.

Data preparation prior to submission was performed by extracting all the read IDs from the ‘new_filter_gt3000.fa’, generated by ptGAUL, which contains all the identified plastome reads. The plastome reads were extracted from the FAST5 and FASTQ raw read datasets via the ‘fast5_subset’ command from the ont_fast5_api tool and our custom script (both using default parameters) [39, 44].

Phylogenetic analysis

Reference data retrieval and phylogenetic supermatrix tree construction was performed by our newly developed Python pipeline PAPAplastomes (Pipeline for the Automatic Phylogenetic Analysis of plastomes). It integrates established external tools for complex steps (Additional file 6) [45]. Reference species, closely related species of interest, and outgroup species can be specified via a config file. First, NCBI RefSeq plastome data is downloaded and the reference plastome peptide sequences are extracted from this collection. These peptide sequences are further combined with the peptide sequences derived from the assemblies representing plastomes of interest. The pre-OrthoFinder trimming step removes potential paralogs with the exact same sequence, sequences which contain asterisks, and sequences that are shorter than 10 amino acids (this threshold value is adjustable). Next, OrthoFinder v2.5.4 [46,47,48,49] is applied. Post-Orthofinder processing includes four steps. Removal of outlier sequences (first step), deletion of orthogroups missing the species of interest or their references (second step), removal of orthogroups harbouring fewer species than the pre-defined outgroup species (third step), and paralog cleaning (fourth step). These steps are explained in more detail below. First step: Since the OrthoFinder results include phylogenetic trees of each orthogroup, outlier identification is conducted with the help of the Python module Dendropy v4.5.2 [50]. Per orthogroup the edge length for each taxon is accessed except for outgroup species. Based on these lengths, outliers are identified by the 1.5*IQR method (inter quartile range) i.e. sequences with a distance larger than 1.5 times the variation are excluded. Second step: The species of an orthogroup are listed and if pre-defined species are all either present or absent, the orthogroup will be kept. Otherwise, the orthogroup will be discarded. This is suggested for closely related species i.e. from the same genus and can be specified by the user in the config file. Third step: Orthogroups consisting solely of the outgroup species are exempt from this criterion. Fourth step: Among remaining paralogs within one species, only the longest sequence is kept.

The cleaned orthogroups are then aligned with MAFFT v7.453 (--maxiterate 1000 --localpair) [51, 52] and alignments are concatenated. Phylogenetic supermatrix tree calculation is performed by IQ-TREE (multicore version 1.6.12 for Linux 64-bit built Aug 15 2019; with the ‘-nt AUTO -bb 1000’ options, seed: 291,752) based on the concatenated alignment of all orthogroups [53,54,55].


Before sequencing C. submersum, the flow cell had already been utilised for sequencing gDNA of D. purpurea. Insufficient DNA availability hindered the complete sequencing and assembly of C. submersum’s nuclear genome. Future optimisations in DNA extraction methods dedicated to small aquatic plants could overcome this limitation.

Availability of data and materials

The datasets generated and analysed during the current study are available under PRJEB62706. Scripts developed for the data analysis are available from Codeberg (, and GitHub (



Coding sequence


Genomic DNA


Graphical processing unit




Harmful algal bloom


NCBI Reference Sequence Database


Interquartile range


Oxford Nanopore Technologies


  1. Les DH. The origin and affinities of the ceratophyllaceae. Taxon. 1988;37(2):326–45.

    Article  Google Scholar 

  2. The Angiosperm Phylogeny Group, Chase MW, Christenhusz MJM, Fay MF, Byng JW, Judd WS, et al. An update of the angiosperm phylogeny group classification for the orders and families of flowering plants: APG IV. Bot J Linn Soc. 2016;181(1):1–20.

    Article  Google Scholar 

  3. POWO. Plants of the World Online. Facilitated by the royal botanic gardens, Kew. Plants of the World Online. 2023. Accessed 16 Apr 2023

  4. Düll R, Kutzelnigg H. Taschenlexikon der Pflanzen Deutschlands und angrenzender Länder: die häufigsten mitteleuropäischen Arten im Porträt. 7th ed. Wiebelsheim: Quelle & Meyer; 2011. p. 932.

    Google Scholar 

  5. Les DH. In: Systematics and evolution of Ceratophyllum L. (Ceratophyllaceae): a monograph (taxonomy, flavonoids, aquatic plants, phytogeography, variation). Columbus: The Ohio State University; 1986.

    Google Scholar 

  6. Les DH. Ceratophyllaceae. In: K Kubitzki, JG Rohwer, V Bittrich, editors. Flowering plants dicotyledons. Berlin, Heidelberg: Springer, Berlin Heidelberg; 1993. p. 246–50.

    Chapter  Google Scholar 

  7. Ujvárosi AZ, Riba M, Garda T, Gyémánt G, Vereb G, M-Hamvas M, et al. Attack of Microcystis aeruginosa bloom on a Ceratophyllum submersum field: ecotoxicological measurements in real environment with real microcystin exposure. Sci Total Environ. 2019;662:735–45.

    Article  PubMed  Google Scholar 

  8. Zartes Hornblatt. (Ceratophyllum submersum). Accessed 5 Apr 2023.

  9. Ellenberg H. Vegetation Mitteleuropas mit den Alpen. 6., vollstandig neu bearbeitete und stark erweiterte Auflage. Radebeul: Verlag Eugen Ulmer; 2010.

    Google Scholar 

  10. Ayele HS, Atlabachew M. Review of characterization, factors, impacts, and solutions of Lake eutrophication: lesson for lake Tana, Ethiopia. Environ Sci Pollut Res. 2021;28(12):14233–52.

    Article  CAS  Google Scholar 

  11. Li Y, Shang J, Zhang C, Zhang W, Niu L, Wang L, et al. The role of freshwater eutrophication in greenhouse gas emissions: a review. Sci Total Environ. 2021;768:144582.

    Article  CAS  PubMed  Google Scholar 

  12. European Commission: Joint Research Centre. An EU analysis of the ecological disaster in the Oder River of 2022: lessons learned and research based recommendations to avoid future ecological damage in EU rivers, a joint analysis from DG ENV, JRC and the EEA. Publications Office of the European Union. 2023. Accessed 16 Apr 2023.

  13. Alsos IG, Lavergne S, Merkel MKF, Boleda M, Lammers Y, Alberti A, et al. The treasure vault can be opened: large-scale genome skimming works Well using herbarium and silica gel dried material. Plants. 2020;9(4):432.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Moore MJ, Bell CD, Soltis PS, Soltis DE. Using plastid genome-scale data to resolve enigmatic relationships among basal angiosperms. Proc Natl Acad Sci. 2007;104(49):19363–8.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Delahaye C, Nicolas J. Sequencing DNA with nanopores: troubles and biases. PLoS ONE. 2021;16(10):e0257521.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Letunic I, Bork P. Interactive tree of life (iTOL) v5: an online tool for phylogenetic tree display and annotation. Nucleic Acids Res. 2021;49(W1):W293–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Zhao F, Liu B, Liu S, Min DZ, Zhang T, Cai J, et al. Disentangling a 40-year-old taxonomic puzzle: the phylogenetic position of Mimulicalyx (Lamiales). Bot J Linn Soc. 2023;201(2):135–53.

    Article  Google Scholar 

  18. Gitzendanner MA, Soltis PS, Yi TS, Li DZ, Soltis DE. Plastome Phylogenetics: 30 years of inferences into plant evolution. In: advances in botanical research. Elsevier; 2018. p. 293–313. Accessed 9 Aug 2022.

  19. Pucker B. Plant DNA extraction and preparation for ONT sequencing. 2020 Mar 21. Accessed 14 Feb 2023.

  20. Siadjeu C, Pucker B, Viehöver P, Albach DC, Weisshaar B. High Contiguity de novo genome sequence assembly of Trifoliate Yam (Dioscorea dumetorum) using long read sequencing. Genes. 2020;11(3):274.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Pucker B, Rückert C, Stracke R, Viehöver P, Kalinowski J, Weisshaar B. Twenty-five years of propagation in suspension cell culture results in substantial alterations of the Arabidopsis Thaliana Genome. Genes. 2019;10(9):671.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Pucker B, Irisarri I, de Vries J, Xu B. Plant genome sequence assembly in the era of long reads: Progress, challenges and future directions. Quant Plant Biol. 2022;3:e5.

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Zhou W, Armijos C, Lee C, Lu R, Wang J, Ruhlman T, et al. Plastid genome assembly using long-read data (ptGAUL). bioRxiv. 2022.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Shen W, Le S, Li Y, Hu F. SeqKit: a cross-platform and ultrafast toolkit for FASTA/Q file manipulation. PLOS ONE. 2016;11(10):e0163962.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Kolmogorov M, Yuan J, Lin Y, Pevzner PA. Assembly of long, error-prone reads using repeat graphs. Nat Biotechnol. 2019;37(5):540–6.

    Article  CAS  PubMed  Google Scholar 

  27. O’Leary NA, Wright MW, Brister JR, Ciufo S, Haddad D, McVeigh R, et al. Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation. Nucleic Acids Res. 2016;44(D1):D733–45.

    Article  PubMed  Google Scholar 

  28. Pucker B, Holtgräwe D, Rosleff Sörensen T, Stracke R, Viehöver P, Weisshaar B. A De novo genome sequence assembly of the arabidopsis thaliana accession niederzenz-1 displays presence/absence variation and strong synteny. PLOS ONE. 2016;11(10):e0164321.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Tillich M, Lehwark P, Pellizzer T, Ulbricht-Jones ES, Fischer A, Bock R, et al. GeSeq – versatile and accurate annotation of organelle genomes. Nucleic Acids Res. 2017;45(W1):W6–11.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Laslett D. ARAGORN, a program to detect tRNA genes and tmRNA genes in nucleotide sequences. Nucleic Acids Res. 2004;32(1):11–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Kent WJ. BLAT —The BLAST -Like Alignment Tool. Genome Res. 2002;12(4):656–64.

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Small I, Ian Castleden. Chloë: Organelle annotator. 2020. Accessed 2020.

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Wheeler TJ, Eddy SR. nhmmer: DNA homology search with profile HMMs. Bioinformatics. 2013;29(19):2487–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Edgar RC. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 2004;32(5):1792–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Greiner S, Lehwark P, Bock R. OrganellarGenomeDRAW (OGDRAW) version 1.3.1: expanded toolkit for the graphical visualization of organellar genomes. Nucleic Acids Res. 2019;47(W1):W59–64.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Abascal F, Zardoya R, Telford MJ. TranslatorX: multiple alignment of nucleotide sequences guided by amino acid translations. Nucleic Acids Res. 2010;38(suppl2):W7–13.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Chan PP, Lin BY, Mak AJ, Lowe TM. tRNAscan-SE 2.0: improved detection and functional classification of transfer RNA genes. Nucleic Acids Res. 2021;49(16):9077–96.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Meckoni SN. small scripts codeberg repository. 2023. Accessed 30 May 2023.

  40. Madeira F, Pearce M, Tivey ARN, Basutkar P, Lee J, Edbali O, et al. Search and sequence analysis tools services from EMBL-EBI in 2022. Nucleic Acids Res. 2022;50(W1):W276–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. NCBI blastx [Internet]. Bethesda (MD). : National Library of Medicine (US), National Center for Biotechnology Information. Accessed 8 June 2023.

  42. 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.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Robinson JT, Thorvaldsdóttir H, Winckler W, Guttman M, Lander ES, Getz G, et al. Integrative genomics viewer. Nat Biotechnol. 2011;29(1):24–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. ont_fast5_api github repository. nanoporetech. 2022. Accessed 30 May 2023.

  45. Meckoni SN. PAPAplastomes. 2023. Accessed 26 June 2023.

  46. Emms DM, Kelly S. OrthoFinder: phylogenetic orthology inference for comparative genomics. Genome Biol. 2019;20(1):238.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Emms DM, Kelly S. OrthoFinder: solving fundamental biases in whole genome comparisons dramatically improves orthogroup inference accuracy. Genome Biol. 2015;16(1):157.

    Article  PubMed  PubMed Central  Google Scholar 

  48. Emms DM, Kelly S. STRIDE: Species Tree Root inference from gene duplication events. Mol Biol Evol. 2017;34(12):3267–78.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Emms DM, Kelly S. STAG: species tree inference from all genes. Evolut Biol. 2018.

    Article  Google Scholar 

  50. Sukumaran J, Holder MT. DendroPy: a Python library for phylogenetic computing. Bioinformatics. 2010;26(12):1569–71.

    Article  CAS  PubMed  Google Scholar 

  51. Katoh K. MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform. Nucleic Acids Res. 2002;30(14):3059–66.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Katoh K, Standley DM. MAFFT multiple sequence alignment Software Version 7: improvements in performance and usability. Mol Biol Evol. 2013;30(4):772–80.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Nguyen LT, Schmidt HA, von Haeseler A, Minh BQ. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol Biol Evol. 2015;32(1):268–74.

    Article  CAS  PubMed  Google Scholar 

  54. Kalyaanamoorthy S, Minh BQ, Wong TKF, von Haeseler A, Jermiin LS. ModelFinder: fast model selection for accurate phylogenetic estimates. Nat Methods. 2017;14(6):587–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Minh BQ, Nguyen MAT, von Haeseler A. Ultrafast approximation for phylogenetic bootstrap. Mol Biol Evol. 2013;30(5):1188–95.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references


We thank Dr. Christiane Evers (Institute for Plant Biology, TU Braunschweig) and Walter Wimmer (Lower Saxony Department for Water, Coastal and Nature Conservation) for excellent support in taxonomically classifying several specimens. We are also grateful to Sarah Winnier for improving the language of this article. This work was supported by the BMBF-funded de.NBI Cloud within the German Network for Bioinformatics Infrastructure (031A532B, 031A533A, 031A533B, 031A534A, 031A535A, 031A537A, 031A537B, 031A537C, 031A537D, 031A538A). We acknowledge support by the Open Access Publication Funds of Technische Universität Braunschweig.


Open Access funding enabled and organized by Projekt DEAL.

Author information

Authors and Affiliations



SNM, BN, and BP contributed to the concept and design of the studies, wet and dry lab work and manuscript writing. All authors have read the final version of the manuscript and agree to its submission to BMC Research Notes.

Corresponding author

Correspondence to Boas Pucker.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare 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

Additional file 1.

 FASTQ stats of the Ceratophyllum submersum plastome reads. The stats were calculated with the help of the script (

Additional file 2. 

Ceratophyllum submersum plastome map derived from OGDRAW.

 Additional file 3.

 List of references of the reference plastomes. The data was extracted by PAPAplastomes and originates from the RefSeq database.

Additional file 4. 

Full phylogenetic tree with outgroup species Chlamydomonas reinhardtii.

Additional file 5.

 Exact parameters for the GeSeq run.

Additional file 6.

 PAPAplastome workflow chart.

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

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Meckoni, S.N., Nass, B. & Pucker, B. Phylogenetic placement of Ceratophyllum submersum based on a complete plastome sequence derived from nanopore long read sequencing data. BMC Res Notes 16, 187 (2023).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI:


  • ONT
  • Angiosperms
  • Ceratophyllaceae
  • Ceratophyllales plastome
  • Plastomics
  • Chloroplast-genome phylogenomics
  • Phylogenetic pipeline
  • Supermatrix tree