Diversity detection method for dinoflagellate and cysts thereof based on autonomous LSU rDNA and ITS double databases and double molecular markers

Abstract

The invention discloses a dinoflagellate and its cyst diversity detection method based on autonomous LSU rDNA and ITS double databases and double molecular markers, relates to the technical field of molecular biology and marine ecological monitoring, and solves the problems of insufficient coverage of existing public databases and limited resolution of single molecular markers. According to the invention, an LSU rDNA sequence database and an ITS sequence database are constructed, and molecular markers of an LSU D1-D2 region and an ITS1 region are correspondingly amplified respectively, so that combined application of double databases and double markers is realized, and complementarity, comprehensiveness and high-resolution molecular identification is carried out on dinoflagellate and cyst class groups thereof. According to the method, the defects that an existing public database is insufficient in coverage and the resolution ratio of a single molecular marker is limited can be effectively overcome, the detection rate and identification accuracy of the dinoflagellate community are remarkably improved, and scientific support and technical guarantee are provided for dinoflagellate red tide monitoring, marine ecosystem evaluation and environmental risk early warning.

Description

Technical Field

[0001] This invention relates to the fields of molecular biology and marine ecological monitoring technology, and in particular to a method for detecting the diversity of dinoflagellates and their cysts based on a dual database of autonomous LSUrDNA and ITS and dual molecular markers. Background Technology

[0002] In the life cycle of dinoflagellates, dormant cysts are a crucial functional stage for their adaptation to environmental changes, typically formed through sexual reproduction. Once aquatic environmental conditions such as temperature, nutrient concentration, and light reach suitable thresholds, dormant cysts germinate into rapidly reproducing vegetative bodies, becoming a "seed bank" for dinoflagellate red tide outbreaks. Their abundance and distribution directly determine the probability, scale, and duration of potential red tides in the region. Therefore, conducting research on the diversity, abundance, and distribution of dinoflagellates and their cysts is particularly important.

[0003] However, existing dinoflagellate research and detection technologies still have significant limitations. Firstly, traditional cyst identification techniques heavily rely on germination experiments and morphological identification, which are not only time-consuming and labor-intensive but also generally have low cyst germination rates, greatly limiting identification efficiency and species coverage. Secondly, the development of next-generation sequencing technology has promoted the application of rRNA gene fragment-based macrobarcoding technology in dinoflagellate diversity research. However, the effectiveness of this technology is highly dependent on the selection of amplification regions and the completeness and accuracy of the reference database. An ideal DNA barcode fragment should be easy to amplify and sequence, and also possess interspecific discriminative power, i.e., high interspecific variability and low intraspecific variability. Therefore, the selected primers must also meet this requirement to ensure identification resolution. Among common molecular markers in dinoflagellates, SSU rDNA exhibits low interspecific variability and overlapping genetic distances within and between species. Therefore, SSU rDNA is not an ideal molecular marker. Therefore, it is necessary to propose a new method for detecting the diversity of dinoflagellates and their cysts to address the aforementioned problems. Summary of the Invention

[0004] The purpose of this invention is to provide a method for detecting the diversity of dinoflagellates and their cysts based on a dual database of proprietary LSU rDNA and ITS and dual molecular markers, in order to solve the problems of insufficient coverage of existing public databases and limited resolution of single molecular markers.

[0005] This invention provides a method for detecting the diversity of dinoflagellates and their cysts based on a dual database of autonomous LSU rDNA and ITS and dual molecular markers, comprising:

[0006] Step 1: Obtain an encapsulation solution suitable for encapsulated DNA extraction and a filter membrane suitable for cell DNA extraction;

[0007] Step 2: Extract encapsulated DNA from the encapsulation solution and extract cellular DNA from the filter membrane;

[0008] Step 3: High-throughput sequencing of the extracted DNA; specifically, primer pair LSU D1R / LSU 305R is used to amplify the D1-D3 region of the LSU rRNA gene to obtain the first target fragment; primer pair PRIMER B-reverse / ITS300R is used to amplify the ITS1 region of dinoflagellates to obtain the second target fragment; amplicon libraries are constructed based on the first and second target fragments, and high-throughput sequencing is performed on qualified amplicon libraries;

[0009] Step four: Process the high-throughput sequencing results to obtain amplicon sequence variants; annotate the amplicon sequence variants using the BLAST method: compare the ITS1 amplicon sequence with the ITS sequence database; compare the LSU D1-D2 amplicon sequences with the LSU rDNA sequence database; during the annotation process, only amplicon sequence variants identified as dinoflagellates are retained.

[0010] Further, in step one, a grab sampler is used to collect the top 0-3 cm of sediment at the target site, which is then stored at 4°C in the dark. 10 g of wet sample is added to 15-20 mL of filtered sterile seawater and gently stirred. The mixture is then ultrasonically vibrated for 2 min to remove debris particles. The resulting mud-water mixture is filtered sequentially through 200 μm and 10 μm sieves. The residue retained on the sieves is repeatedly washed with sterile filtered seawater until the filtrate is clear and then resuspended. The suspension is then treated with sodium polytungstate density separation to obtain encapsulated concentrates, thereby effectively removing residual cells and debris contamination and obtaining an encapsulated solution suitable for subsequent DNA extraction.

[0011] Further, in step one, surface water samples from the target station are collected and filtered through a polycarbonate membrane with a pore size of 0.22 μm; then the membrane is immersed in 1 mL of lysis buffer and stored at -20°C until DNA extraction.

[0012] Furthermore, in step two, the encapsulation solution is subjected to repeated freeze-thaw treatment with liquid nitrogen to rupture it, and then the encapsulated DNA is extracted according to the kit instructions; the frozen filter membrane is then used to extract cellular DNA.

[0013] Furthermore, in step three, the amplification reaction system is 50 μL, including: 1-3 μL of template DNA with a total amount of less than 200 ng, 1 μL each of 10 μM upstream and downstream primers, 5 μL of 10×Ex Taq buffer, 4 μL of 25 mM dNTP mixture, 0.3 μL of Ex Taq DNA polymerase at 5 U / μL, and the remaining volume is made up with ddH2O.

[0014] Furthermore, in step three, the amplification conditions are set as follows: pre-denaturation at 95℃ for 3 min 30 s; 34 cycles are performed, each cycle consisting of denaturation at 95℃ for 50 s, annealing at 47℃ for 50 s, and extension at 72℃ for 50 s; after the cycle is completed, final extension is performed at 72℃.

[0015] Furthermore, in step four, the obtained sequence is processed using the R software package DADA2, including quality control, redundancy removal, sequence splicing, and chimera removal, ultimately obtaining the amplicon sequence variant.

[0016] Furthermore, in step four, the ITS1 amplified sequence is compared with a pre-constructed ITS sequence database, with a similarity threshold of 97% and a coverage threshold of 90%; the LSU D1-D2 amplified sequences are compared with a pre-constructed LSU rDNA sequence database, with a similarity threshold of 98% and a coverage threshold of 90%.

[0017] Furthermore, in step four, the LSU rDNA sequence database is updated and expanded on the PHYTOPK28-D1D2 database as follows:

[0018] The PHYTOPK28-D1D2 database format was adjusted to meet the requirements of BLAST analysis and comparison.

[0019] Unnecessary sequences for dinoflagellates and phytoplankton research were screened and removed from the PHYTOPK28-D1D2 database. Screening criteria included sequence length integrity, species annotation clarity, and removal of repetitive sequences.

[0020] The LSU sequence of a new species was added to the PHYTOPK28-D1D2 database;

[0021] Phylogenetic tree analysis was used to verify the classification accuracy of sequences and to remove or correct suspicious or erroneous sequences.

[0022] A continuous update mechanism is established to regularly supplement new sequences. The LSU rDNA sequence database includes sequences from dinoflagellates, diatoms, green algae, dinoflagellates, nematocysts, golden algae, and floating algae.

[0023] Furthermore, in step four, the sequences in the ITS sequence database are established based on the NCBI public database. Some sequences are verified for classification accuracy through phylogenetic tree analysis, and suspicious or erroneous sequences are removed or corrected. A continuous update mechanism is established to expand the database with new sequences. The ITS sequence database includes dinoflagellates, diatoms, spirochetes, nematocysts, and Alveolata sequences.

[0024] This invention offers the following advantages: A method for detecting the diversity of dinoflagellates and their cysts based on a proprietary LSU rDNA and ITS dual database and dual molecular markers. By constructing separate LSU rDNA and ITS sequence databases and amplifying molecular markers corresponding to the LSU D1-D2 and ITS1 regions respectively, the invention achieves the combined application of dual databases and dual markers, enabling complementary, comprehensive, and high-resolution molecular identification of dinoflagellates and their cyst groups. This invention effectively overcomes the shortcomings of insufficient coverage in existing public databases and the limited resolution of single molecular markers, significantly improving the detection rate and identification accuracy of dinoflagellate communities. It provides scientific support and technical assurance for the monitoring of dinoflagellate red tides, the assessment of marine ecosystems, and environmental risk early warning. Attached Figure Description

[0025] To more clearly illustrate the technical solution of the present invention, the drawings used in the embodiments will be briefly introduced below. Obviously, those skilled in the art can obtain other drawings based on these drawings without creative effort.

[0026] Figure 1 This is a schematic diagram of the unique and common dinoflagellates detected in the LSU D1-D2 and ITS1 datasets. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention. The technical solutions provided by various embodiments of this invention will be described in detail below with reference to the accompanying drawings.

[0028] Please see Figure 1 This invention provides a method for detecting the diversity of dinoflagellates and their cysts based on a self-developed LSU rDNA and ITS dual database and dual molecular markers, comprising:

[0029] Step 1: Obtain an encapsulation solution suitable for encapsulated DNA extraction and a filter membrane suitable for cellular DNA extraction.

[0030] Specifically, a grab sampler was used to collect surface sediment (0-3 cm) at the target site, which was then stored at 4°C in the dark. Approximately 10 g of wet sample was added to 15-20 mL of filtered sterile seawater and gently stirred. The mixture was then ultrasonically agitated for 2 minutes (with stirring) to remove debris particles. The resulting mud-water mixture was filtered sequentially through 200 μm and 10 μm sieves. The residue retained on the sieves was repeatedly washed with sterile filtered seawater until the filtrate was clear and resuspended. This suspension was then treated with sodium polytungstate (SPT) density separation (Bolch, 1997) to obtain encapsulated concentrates, effectively removing residual cells and debris contamination, and yielding an encapsulation solution suitable for subsequent DNA extraction. Surface water samples were collected from the target site and filtered through a 0.22 μm pore size polycarbonate membrane. These membranes were then immersed in 1 mL of lysis buffer and stored at -20°C until DNA extraction.

[0031] Step 2: Extract encapsulated DNA from the encapsulation solution and extract cellular DNA from the filter membrane.

[0032] Specifically, the extraction of encapsulated DNA involves repeatedly freezing and thawing the enriched encapsulated solution in liquid nitrogen to rupture it, followed by processing according to the kit. The operation was performed according to the instruction manual of the Soil Kit (Macherey Nagel, Düren, Germany). Cellular DNA extraction was performed by using a frozen filter membrane according to the method described by Yuan et al. (2015).

[0033] Step 3: High-throughput sequencing of the extracted DNA; specifically, primer pair LSU D1R / LSU 305R is used to amplify the D1-D3 region of the LSU rRNA gene to obtain the first target fragment; primer pair PRIMER B-reverse / ITS300R is used to amplify the ITS1 region of the dinoflagellates to obtain the second target fragment; amplicon libraries are constructed based on the first and second target fragments, and high-throughput sequencing is performed on qualified amplicon libraries.

[0034] Specifically, primer pair LSU D1R (5′-ACCCGCTGAATTTAAGCATA-3′, Scholin et al., 1994) / LSU 305R (5′-TTTAAYTCTCTTTYCAAAGTCC-3′, Smith et al., 2017) was used to amplify the D1-D3 region of the LSU rRNA gene, obtaining a first target fragment of approximately 330 bp in length; primer pair PRIMER B-reverse (5′-TAGGTGAACCTGCAGAAGGAT-3′, Medlin et al., 1988) / ITS300R (5′-CACGGAAKTTCTGCARTTCACAATG-3′, Fu et al., 2021) was used to amplify the ITS1 region of dinoflagellates, obtaining a second target fragment of approximately 260 bp in length.

[0035] The amplification reaction system consisted of 50 μL, including: 1-3 μL template DNA (total amount less than 200 ng), 1 μL each of 10 μM forward and reverse primers, 5 μL 10×Ex Taq buffer, 4 μL 25 mM dNTP mixture, 0.3 μL (5 U / μL) Ex Taq DNA polymerase, and the remaining volume was made up with ddH2O. The amplification conditions were set as follows: 95℃ pre-denaturation for 3 min 30 s; 34 cycles, each consisting of 95℃ denaturation for 50 s, 47℃ annealing for 50 s, and 72℃ extension for 50 s; after cycling, a final extension was performed at 72℃. Library construction was carried out according to... Ultra TM DNA Library Prep Kit for The standard procedure was followed. The qualified amplicon libraries were sequenced using an Illumina Miseq 2×300bp sequencing platform.

[0036] Step four: Process the high-throughput sequencing results to obtain amplicon sequence variants; annotate the amplicon sequence variants using the BLAST method: compare the ITS1 amplicon sequence with the ITS sequence database; compare the LSU D1-D2 amplicon sequences with the LSU rDNA sequence database; during the annotation process, only amplicon sequence variants identified as dinoflagellates are retained.

[0037] Specifically, after high-throughput sequencing, the obtained sequences were processed using the R software package DADA2, including quality control, redundancy removal, sequence splicing, and chimera removal, ultimately yielding amplicon sequence variants (ASVs). The obtained ASVs were annotated using the BLAST method: the ITS1 amplified sequence was compared with a self-constructed Internal Transcriptional Spacer (ITS) sequence database, with a similarity threshold of 97% and a coverage threshold of 90%; the LSU D1–D2 amplified sequences were compared with a self-constructed eukaryotic large subunit ribosomal DNA (LSU rDNA) sequence database, with a similarity threshold of 98% and a coverage threshold of 90%. During the annotation process, only ASVs identified as dinoflagellates were retained.

[0038] The similarity threshold is used to ensure a high degree of consistency between the aligned sequence and the reference sequence at the base level, reducing misjudgments caused by random matching or conserved regions. Different gene regions have different evolutionary rates, therefore the threshold settings in this study also vary. For example, the ITS region has greater variation, and a similarity of 97% can provide a relatively reliable species differentiation capability, preserving effective species information while avoiding misjudgments caused by an excessively low threshold. The LSU region is more evolutionarily conserved, so a higher threshold of 98% is used to ensure accuracy.

[0039] The coverage threshold is used to ensure that the alignment results cover the main part of the target sequence, thereby avoiding false positive annotations caused by high similarity of local fragments. Setting a 90% coverage threshold can ensure the reliability of species annotation while avoiding misjudgments caused by excessively short alignment regions, thus improving the overall robustness of the annotation.

[0040] ITS sequences in nuclear genes, because they do not encode proteins, are subject to less evolutionary selection pressure and have a faster evolutionary rate, giving them a significant advantage in species resolution. In particular, the ITS1 region has shown to have significantly better identification efficiency and primer universality than ITS2, and its shorter amplified fragments and lower GC content contribute to improved sequencing success rates and data quality. However, ITS1 exhibits significant intraspecific variation among individuals of the same species, and the number of sequences available for reference in public databases is relatively limited. LSU rDNA has a more complete reference sequence, with greater interspecific genetic distances compared to SSU rDNA, resulting in higher resolution. The D1–D2 region is widely used due to its high sequence variability and rich information content, demonstrating good resolution in species identification. Furthermore, this region retains some conserved sequences, facilitating the design of universal primers, thus balancing amplification specificity and community detection rate. However, for some dinoflagellates, the interspecific genetic distance of LSU rDNA is small, making it difficult to completely distinguish species. Nevertheless, the LSU database has a relatively rich sequence pool, so combining it with highly variable sequences in the ITS region can compensate for this limitation, achieving more comprehensive and reliable species identification. Regarding databases, the currently available public database PR2 lacks sequences from both the LSU and ITS regions, resulting in a significant deficiency in the resolution of dinoflagellates. While the NCBI database has abundant sequence resources, its annotation error rate is high, and some taxonomic information is incorrect or outdated, limiting its ability to accurately identify dinoflagellates.

[0041] This invention establishes two types of proprietary databases: a eukaryotic ITS sequence database and an LSU rDNA sequence database. The LSU rDNA sequence database is an updated and expanded version of the PHYTOPK28-D1D2 database published by Grzebyk et al. (2017). First, the original database format was not directly usable for BLAST analysis, so its format was adjusted to meet practical comparison requirements. Second, the original database contained many sequences not closely related to dinoflagellates and phytoplankton research (such as fungi). Unnecessary sequences were removed through screening, making the database more suitable for dinoflagellate and related phytoplankton research. Screening criteria included sequence length integrity, clear species annotation, and removal of repetitive sequences. Simultaneously, phylogenetic tree analysis was used to verify the classification accuracy of the sequences, and questionable or erroneous sequences were removed or corrected to ensure species coverage and data reliability. Furthermore, the database content is continuously expanded through algal division and sequencing, establishing a continuous update mechanism to regularly supplement new sequences and ensure that the database content remains synchronized with the latest advancements in dinoflagellate community research. The LSU rDNA sequence database contains sequences from dinoflagellates, diatoms, green algae, cephalopods, nematocysts, golden algae, and floating algae.

[0042] The sequences in the ITS sequence database of this invention are mainly sourced from the NCBI public database. Most sequences have undergone phylogenetic tree analysis to verify their classification accuracy, and suspicious or erroneous sequences have been removed or corrected. Furthermore, the database is regularly expanded with new sequences, establishing a continuous update mechanism to ensure its accuracy and timeliness. The ITS sequence database includes sequences from dinoflagellates, diatoms, dinoflagellates, nematocysts, and Alveolata.

[0043] Using the method of this invention, samples were analyzed, and the resulting LSU rDNA dataset yielded 1620 successfully annotated ASVs, of which 1308 ASVs were identified as dinoflagellates and used for subsequent analysis, while the remaining 312 ASVs were mainly annotated as diatoms, green algae, and other groups. The resulting ITS1 dataset yielded 649 successfully annotated ASVs, all of which were identified as dinoflagellates. Based on the annotation from these two databases, the LSU rDNA dataset identified 196 dinoflagellate species belonging to 78 genera; the ITS1 dataset identified 118 dinoflagellate species belonging to 48 genera. The detection results showed that only 59 species could be detected simultaneously by both amplification regions. Specifically, the LSU rDNA dataset independently detected 137 species, and the ITS dataset independently detected 59 species, see [see details]. Figure 1 .

[0044] Combining data from the LSU and ITS1 datasets, a total of 11 potentially toxic dinoflagellates were identified. Among them, the LSU rDNA dataset identified 8 species and the ITS dataset identified 9 species. Only 5 potentially toxic dinoflagellate species could be detected by both amplification regions simultaneously, as shown in Table 1.

[0045] In addition, this invention also identified 82 previously unreported cyst-producing species, of which 48 species were detected in the LSU dataset and 41 species were detected in the ITS1 dataset.

[0046] The results clearly demonstrate that the LSU D1-D2 and ITS1 molecular markers are significantly complementary in the species detection of dinoflagellates and their cysts. Combined with our self-constructed eukaryotic LSU rDNA sequence database and ITS sequence database, the dual-marker and dual-database approach can be implemented. This combined approach effectively overcomes the limitations of single molecular markers or single databases, significantly improving the detection rate, accuracy, and comprehensiveness of species annotation for dinoflagellates and their cysts.

[0047] Table 1. Potential toxic dinoflagellate species detected based on LSU D1-D2 and ITS1 datasets.

[0048]

[0049]

[0050] Example 1: Diversity of Dinoflagellate Cysts in the Pacific and Arctic Oceans

[0051] Ninety-one sampling sites were established across ten regions, from the subtropical Pacific to the Arctic. Surface sediments were collected at the target sites using grab samplers. Samples were processed, DNA was extracted, and high-throughput sequencing, data processing, and analysis were performed according to the procedures described above.

[0052] The embodiments of the present invention described above do not constitute a limitation on the scope of protection of the present invention.

Claims

1. A method for detecting the diversity of dinoflagellates and their cysts based on a self-developed LSU rDNA and ITS dual database and dual molecular markers, characterized in that, include: Step 1: Obtain an encapsulation solution suitable for encapsulated DNA extraction and a filter membrane suitable for cell DNA extraction; Step 2: Extract encapsulated DNA from the encapsulation solution and extract cellular DNA from the filter membrane; Step 3: High-throughput sequencing of the extracted DNA; specifically, primer pair LSU D1R / LSU 305R is used to amplify the D1-D3 region of the LSUrRNA gene to obtain the first target fragment; primer pair PRIMER B-reverse / ITS300R is used to amplify the ITS1 region of the dinoflagellate to obtain the second target fragment; amplicon libraries are constructed based on the first and second target fragments, and high-throughput sequencing is performed on qualified amplicon libraries; Step four: Process the high-throughput sequencing results to obtain amplicon sequence variants; annotate the amplicon sequence variants using the BLAST method: compare the ITS1 amplicon sequence with the ITS sequence database; compare the LSUD1-D2 amplicon sequence with the LSU rDNA sequence database; during the annotation process, only amplicon sequence variants identified as dinoflagellates are retained.

2. The method for detecting the diversity of dinoflagellates and their cysts based on a dual database of autonomous LSU rDNA and ITS and dual molecular markers as described in claim 1, characterized in that, In step one, a grab sampler is used to collect the top 0-3 cm of sediment at the target site, which is then stored at 4°C in the dark. 10 g of wet sample is added to 15-20 mL of filtered sterile seawater and gently stirred. The mixture is then ultrasonically vibrated for 2 min to remove debris particles. The resulting mud-water mixture is then filtered sequentially through 200 μm and 10 μm sieves. The residue trapped on the sieves is repeatedly washed with sterile filtered seawater until the filtrate is clear and then resuspended. The suspension is then treated with sodium polytungstate density separation to obtain encapsulated concentrates, thereby effectively removing residual cells and debris contamination and obtaining an encapsulated solution suitable for subsequent DNA extraction.

3. The method for detecting the diversity of dinoflagellates and their cysts based on a dual database of autonomous LSU rDNA and ITS and dual molecular markers as described in claim 1, characterized in that, In step one, surface water samples were collected from the target station and filtered through a polycarbonate membrane with a pore size of 0.22 μm. The membrane was then immersed in 1 mL of lysis buffer and stored at -20°C until DNA extraction.

4. The method for detecting the diversity of dinoflagellates and their cysts based on a dual database of autonomous LSU rDNA and ITS and dual molecular markers as described in claim 1, characterized in that, In step two, the encapsulation solution is subjected to repeated freeze-thaw cycles in liquid nitrogen to rupture it, and then the encapsulated DNA is extracted according to the kit instructions; the frozen filter membrane is then used to extract cellular DNA.

5. The method for detecting the diversity of dinoflagellates and their cysts based on a dual database of autonomous LSU rDNA and ITS and dual molecular markers as described in claim 1, characterized in that, In step three, the amplification reaction system is 50 μL, including: 1–3 μL of template DNA with a total amount of less than 200 ng, 1 μL each of 10 μM upstream and downstream primers, 5 μL of 10×Ex Taq buffer, 4 μL of 25 mM dNTP mixture, 0.3 μL of Ex Taq DNA polymerase at 5 U / μL, and the remaining volume is made up with ddH2O.

6. The method for detecting the diversity of dinoflagellates and their cysts based on a dual database of autonomous LSU rDNA and ITS and dual molecular markers as described in claim 1, characterized in that, In step three, the amplification conditions are set as follows: pre-denaturation at 95℃ for 3 min 30 s; 34 cycles are performed, with each cycle consisting of denaturation at 95℃ for 50 s, annealing at 47℃ for 50 s, and extension at 72℃ for 50 s; after the cycle is completed, the final extension is performed at 72℃.

7. The method for detecting the diversity of dinoflagellates and their cysts based on a dual database of autonomous LSU rDNA and ITS and dual molecular markers as described in claim 1, characterized in that, In step four, the obtained sequence is processed using the R software package DADA2, including quality control, redundancy removal, sequence splicing, and chimera removal, ultimately obtaining the amplicon sequence variant.

8. The method for detecting the diversity of dinoflagellates and their cysts based on a dual database of autonomous LSU rDNA and ITS and dual molecular markers as described in claim 1, characterized in that, In step four, the ITS1 amplified sequence is compared with a pre-constructed ITS sequence database, with a similarity threshold of 97% and a coverage threshold of 90%; the LSU D1-D2 amplified sequences are compared with a pre-constructed LSU rDNA sequence database, with a similarity threshold of 98% and a coverage threshold of 90%.

9. The method for detecting the diversity of dinoflagellates and their cysts based on a dual database of autonomous LSU rDNA and ITS and dual molecular markers as described in claim 1, characterized in that, In step four, the LSU rDNA sequence database is updated and expanded on the PHYTOPK28-D1D2 database as follows: The PHYTOPK28-D1D2 database format was adjusted to meet the requirements of BLAST analysis and comparison. Unnecessary sequences for research on dinoflagellates and phytoplankton in the PHYTOPK28-D1D2 database were screened and removed. Screening criteria included sequence length integrity, species annotation clarity, and removal of repetitive sequences. The LSU sequence of a new species was added to the PHYTOPK28-D1D2 database; Phylogenetic tree analysis was used to verify the classification accuracy of sequences and to remove or correct suspicious or erroneous sequences. A continuous update mechanism is established to regularly supplement new sequences. The LSU rDNA sequence database includes sequences from dinoflagellates, diatoms, green algae, dinoflagellates, nematocysts, golden algae, and floating algae.

10. The method for detecting the diversity of dinoflagellates and their cysts based on a dual database of autonomous LSU rDNA and ITS and dual molecular markers as described in claim 1, characterized in that, In step four, the sequences in the ITS sequence database are established based on the NCBI public database. Some sequences are verified for classification accuracy through phylogenetic tree analysis, and suspicious or erroneous sequences are removed or corrected. A continuous update mechanism is established to expand the database with new sequences. The ITS sequence database includes dinoflagellates, diatoms, spirochetes, nematocysts, and Alveolata sequences.

Images