A method, system, device and medium for analyzing metagenomic third-generation sequencing data

Abstract

This application discloses a method, system, device, and medium for metagenomic third-generation sequencing data analysis, belonging to the field of metagenomic sequencing data processing technology. The analysis method of this application first assembles the preprocessed metagenomic third-generation sequencing data to obtain a contig sequence set; it then collects sequencing sequences that did not participate in the assembly and filters out sequences with a length less than a preset threshold, merging the remaining sequences into the contig sequence set to obtain a merged sequence set; and finally, it performs data analysis based on the merged sequence set. Using the analysis method, system, device, and medium of this application, the utilization rate of metagenomic third-generation sequencing data can be improved, thereby obtaining more, more comprehensive, and more accurate microbial-related information.

Description

Technical Field

[0001] This application relates to the field of metagenomic sequencing data processing technology, and in particular to a method, system, device and medium for metagenomic third-generation sequencing data analysis. Background Technology

[0002] The main working principle of the Flye assembly tool is to construct a repeating graph by finding overlaps between long reads, and finally output a gap-free contig. The design goal is to obtain the optimal and longest contig.

[0003] However, to ensure the accuracy of the assembled genome, Flye sets strict parameter thresholds for constructing the overlap map and tends to interrupt assembly in regions with uneven coverage. This rigorous algorithm design is an advantage when pursuing high-quality single-genome assembly, but it becomes a major technical limitation when dealing with metagenomic samples with complex species composition and huge abundance differences. A large number of HiFi reads from low-abundance microorganisms (especially rare species with an abundance of less than 1%) or insufficient coverage are directly excluded from the assembly process by Flye because they cannot meet the algorithm's minimum overlap or coverage requirements. This directly results in the assembled genome not being able to fully represent the true microbial community diversity in the sample. Summary of the Invention

[0004] To solve at least one of the above-mentioned technical problems, the technical solution adopted in this application is as follows.

[0005] The first aspect of this application provides a method for analyzing metagenomic third-generation sequencing data, including the following steps: The metagenomic third-generation sequencing data were preprocessed; The preprocessed metagenomic third-generation sequencing data were assembled using third-generation sequencing assembly software to obtain a contig sequence set; Sequencing sequences that did not participate in assembly were collected and those shorter than a preset threshold were filtered out. The remaining sequences were merged into the contig sequence set to obtain a merged sequence set. The preset threshold was 400~800bp. Data analysis is performed based on the merged sequence set, including analysis of drug resistance-related genes.

[0006] In some embodiments of this application, the third-generation sequencing assembly software is Flye.

[0007] In some embodiments of this application, the preset threshold is obtained based on analysis of the length of the sequencing sequence that was not assembled.

[0008] In some specific embodiments of this application, the preset threshold is 500bp.

[0009] In some embodiments of this application, the metagenomic third-generation sequencing data is obtained by sequencing DNA samples derived from environmental or water samples using HiFi sequencing technology.

[0010] In some specific embodiments of this application, the environmental samples include, but are not limited to, soil, silt and sediment, and the water samples include, but are not limited to, rivers, lakes and other water bodies, as well as filter membrane samples obtained by filtering any of the water samples.

[0011] HiFi (High-Fidelity Sequencing) is a DNA technology developed by PacBio, based on its Single Molecule Real-Time Sequencing (SMRT) platform. The core of HiFi sequencing technology lies in its unique Cyclic Consensus Sequencing (CCS) mode. By reading the same DNA molecule multiple times, HiFi sequencing can generate high-fidelity reads up to 25kb with an accuracy of up to 99.9%.

[0012] In some embodiments of this application, the data analysis further includes at least one of the following analyses: (1) Species diversity analysis; (2) Analysis of genes related to metabolite synthesis; (3) Microbial co-occurrence network analysis.

[0013] A second aspect of this application provides an analysis system for metagenomic third-generation sequencing data, comprising the following modules: The data input module is used to obtain the metagenomic third-generation sequencing data and to preprocess the metagenomic third-generation sequencing data; An assembly module, connected to the data input module, is used to assemble preprocessed metagenomic third-generation sequencing data using third-generation sequencing assembly software to obtain a contig sequence set; The merging module, connected to the assembly module, is used to obtain the contig sequence set and to collect sequencing sequences that did not participate in the assembly and filter out sequencing sequences with a length less than a preset threshold. The remaining sequences are merged into the contig sequence set to obtain the merged sequence set. The preset threshold is 400~800bp. An analysis module, connected to the merging module, is used to perform data analysis based on the merged sequence set.

[0014] In some embodiments of this application, the analysis system further includes a database storage module for storing a database that can be analyzed.

[0015] A third aspect of this application provides a computer device, comprising: a memory for storing a computer program; and a processor for executing the steps of any of the analysis methods described in the first aspect of this application when executing the computer program.

[0016] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of any of the analysis methods described in the first aspect of this application.

[0017] Compared with the prior art, this application has the following advantages: The analytical methods, systems, equipment, and media described in this application can improve the utilization rate of metagenomic third-generation sequencing data, thereby obtaining more, more comprehensive, and more accurate microbial-related information.

[0018] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this application, nor is it intended to limit the scope of this application. Other features of this application will become readily apparent from the following description. Attached Figure Description

[0019] The above and other objects, features, and advantages of exemplary embodiments of this application will become readily apparent from the following detailed description taken in conjunction with the accompanying drawings. Several embodiments of this application are illustrated in the drawings by way of example and not limitation, in which: Figure 1 This paper shows the sequence length distribution of the G2 sample that was not used for assembly in Embodiment 2 of this application; Figure 2 This paper shows the sequence length distribution of the G6 sample that was not used for assembly in Embodiment 2 of this application; Figure 3 This paper shows the sequence length distribution of the G15 sample that was not used for assembly in Embodiment 2 of this application; Figure 4 The results of annotating secondary metabolite synthesis gene clusters based on sequence sets obtained by two methods in Embodiment 2 of this application are shown. Figure 5 The diagram shows a comparison of the number of sequences obtained from G2 samples with different filtering thresholds in Embodiment 3 of this application with the number of ARGs, MGEs, contigs carrying ARGs, plasmids carrying ARGs, and chromosomes carrying ARGs. Figure 6 The diagram shows a comparison of the number of sequences obtained from G6 samples with different filtering thresholds in Embodiment 3 of this application with the number of ARGs, MGEs, contigs carrying ARGs, plasmids carrying ARGs, and chromosomes carrying ARGs. Figure 7 The diagram shows a comparison of the number of sequences obtained from G15 samples with different filtering thresholds in Embodiment 3 of this application with the number of ARGs, MGEs, contigs carrying ARGs, plasmids carrying ARGs, and chromosomes carrying ARGs. Detailed Implementation

[0020] Unless otherwise stated, implied from the context, or as is customary in the art, all parts and percentages in this application are based on weight, and all testing and characterization methods used are concurrent with the filing date of this application. Where applicable, any patent, patent application, or disclosure relating to this application is incorporated herein by reference in its entirety, and its equivalent patent families are also incorporated herein by reference, particularly the definitions of relevant terms in the art disclosed in such documents. If any definition of a specific term disclosed in the prior art is inconsistent with any definition provided in this application, the definition provided in this application shall prevail.

[0021] To make the technical problems, technical solutions and beneficial effects solved by this application clearer, the following detailed description is provided in conjunction with embodiments.

[0022] The following examples are used to illustrate preferred embodiments of this application. Those skilled in the art will understand that the techniques disclosed in the examples represent technologies discovered by the inventors that can be used to implement this application, and therefore can be considered preferred embodiments of this application. However, those skilled in the art should understand from this specification that many modifications can be made to the specific embodiments disclosed herein, still yielding the same or similar results, without departing from the spirit or scope of this application.

[0023] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains, and all materials cited herein and referenced by them are incorporated herein by reference.

[0024] Those skilled in the art will recognize, or can learn through routine experimentation, many equivalents of the specific embodiments of the invention described herein. These equivalents will be included in the claims.

[0025] Unless otherwise specified, the experimental methods used in the following examples are conventional methods. Unless otherwise specified, the instruments and equipment used in the following examples are all conventional laboratory instruments and equipment; unless otherwise specified, the experimental materials used in the following examples were all purchased from conventional biochemical reagent stores.

[0026] Example 1: Metagenomic Third-Generation Sequencing First, microbial DNA was extracted from three original samples (G2, G6, and G15) from the water filter membrane. Extraction was performed using SDS lysis and phenol-chloroform extraction. Purity, concentration, and integrity were checked using Nanodrop, Qubit, and agarose gel electrophoresis, respectively. If the sample quality check results met the requirements, subsequent experiments were carried out. Otherwise, DNA needed to be re-extracted or the existing DNA needed to be purified, depending on the specific reason.

[0027] For samples that pass the initial testing, a library is constructed. First, large DNA fragments are broken down using g-tubes. These fragments are then enriched and purified using magnetic beads. Damage repair is performed, followed by end repair and the addition of 'A' tags. Finally, adapter ligation completes the library construction. After successful library construction, sequencing is performed.

[0028] Sequencing was performed using the PacBio Sequel II sequencing platform, and the resulting data were HiFi sequences with an accuracy of 0.99 or higher (>Q20).

[0029] Example 2: Assembly of metagenomic third-generation sequencing data For the third-generation sequencing data obtained in Example 1, two methods were used to obtain the sequence set for analysis.

[0030] Method 1: Use only Flye for assembly to obtain the contig sequence set.

[0031] After assembly using this method, the length distribution of sequences not used for assembly in the three samples is statistically analyzed, such as... Figures 1-3 As shown.

[0032] Method 2: First, use Flye to assemble the sequence to obtain the contig sequence set. For the sequences not used for assembly, select sequences longer than 500bp and merge them with the contig sequence set. The merged data is used for further analysis.

[0033] Statistical analysis was performed on the sequence sets obtained by the two methods, and the results are shown in Table 1.

[0034] Table 1: Statistical analysis results of sequence sets obtained by the two methods .

[0035] As shown in Table 1, the contig sequence set obtained using Method 1 contains only 2,000 to 5,000 sequences, while the merged sequence set obtained using Method 2 contains 200,000 to 500,000 sequences—a difference of two orders of magnitude. In other words, the Flye assembly software discards a large number of sequences, which could be crucial for metagenomic analysis.

[0036] Binning analysis was further performed on the sequence sets obtained by the two methods to count the total number of metagenomic assembled genomes (MAGs) and the number of qualified MAGs. The results are shown in Table 2.

[0037] Table 2: Binning analysis results of sequence sets obtained by the two methods .

[0038] As shown in Table 2, after binning analysis, only G6 has qualified MAGs regardless of the method used, and the MAGs obtained by both methods are completely consistent. It should be noted that using method two, with the same number of qualified MAGs, the total number of MAGs is less, meaning fewer failed MAGs are obtained.

[0039] For metagenomic analysis, the field focuses more on metabolism-related gene information. Therefore, the inventors conducted statistical analysis of data obtained using two methods to identify annotated secondary metabolite synthesis gene clusters, the results of which are shown in Table 3 and [Table data would be inserted here]. Figure 4 As shown.

[0040] Table 3: Annotation results of secondary metabolite synthesis gene clusters based on sequence sets obtained by the two methods. .

[0041] From Table 3 and Figure 4 It can be seen that analyzing the sequence set obtained by Method 2 yields more secondary metabolite synthesis gene clusters. Specifically, the number of secondary metabolite synthesis gene clusters obtained from sample G6 increased from 6 to 36, a 5-fold increase; and the number of secondary metabolite synthesis gene clusters obtained from sample G15 increased from 32 to 72, more than a 1-fold increase.

[0042] The inventors further compared the number of drug resistance genes (ARGs) obtained from sequence set analysis based on the two methods, as shown in Table 4.

[0043] Table 4: Number of ARGs obtained from sequence set analysis based on two methods .

[0044] As shown in Table 4, the number of ARGs and MGEs, as well as the number of contigs, plasmids, and chromosomes carrying ARGs, obtained by analyzing the sequence set based on Method 2 are far greater than those obtained by analyzing the contig sequence set based on Method 1. This indicates that using Method 2 to analyze metagenomic third-generation sequencing data can yield more information on drug resistance genes.

[0045] Finally, the inventors also compared the number of open reading frames (ORFs) predicted from sequence sets obtained by the two methods, as shown in Table 5.

[0046] Table 5: Number of ORFs predicted from sequence sets obtained by the two methods .

[0047] As shown in Table 5, analysis using the sequence set from Method 2 can yield more ORFs, and thus more gene information.

[0048] Example 3: The impact of the filtering threshold on the analysis results In this embodiment, for the three samples in Example 2, the sequence sets for analysis were obtained using Method 2. Different filtering thresholds were used each time, and the sequence sets obtained with different thresholds were statistically analyzed. The results are shown in Tables 6 to 8.

[0049] Table 6: The effect of different filtering thresholds on the number of sequences obtained from G2 samples .

[0050] Table 7: The effect of different filtering thresholds on the number of sequences obtained from G6 samples .

[0051] Table 8: Effect of different filtering thresholds on the number of sequences obtained from G15 samples .

[0052] As shown in Tables 6-8, the higher the filtering threshold, the fewer sequences are retained, and the larger the average length and N50. Conversely, the lower the filtering threshold, the more sequences are retained, and the smaller the average length and N50.

[0053] Furthermore, the ARG was analyzed based on the merged sequence sets obtained with different thresholds, and the results are shown in Tables 9 to 11.

[0054] Table 9: Number of ARGs obtained from the merged sequence set analysis of G2 samples based on different filtering thresholds .

[0055] Table 10: Number of ARGs obtained from the merged sequence set analysis of G6 samples based on different filtering thresholds .

[0056] Table 11: Number of ARGs obtained from merged sequence set analysis of G15 samples based on different filtering thresholds .

[0057] As shown in Table 9, for the G2 sample, within the filtering threshold range of 300-400, the number of detected ARGs remained stable at 106, the number of MGEs remained stable at 24, and the number of contigs, plasmids, and chromosomes carrying ARGs remained stable at 86, 26, and 31, respectively. Within the filtering threshold range of 500-700, the number of detected ARGs decreased slightly, remaining stable at 105, while the number of MGEs remained unchanged at 24. The number of contigs, plasmids, and chromosomes carrying ARGs decreased by 1, becoming 85, 25, and 30, respectively. When the filtering threshold was increased to 800, the number of detected ARGs further decreased by 1. The number of ARGs decreased from 1 to 104, while the number of MGEs, contigs carrying ARGs, and plasmids remained unchanged. However, the number of chromosomes carrying ARGs decreased by 1. When the filtering threshold was increased to 1500, the number of detected ARGs decreased to 94, the number of MGEs decreased to 21, and the number of contigs, plasmids, and chromosomes carrying ARGs decreased to 74, 19, and 26, respectively. When the filtering threshold was further increased to 2000, the number of detected ARGs decreased to 87, the number of MGEs decreased to 19, and the number of contigs and plasmids carrying ARGs further decreased to 69 and 17, respectively.

[0058] For the G2 sample, the number of sequences corresponding to different filtering thresholds was compared with the number of ARGs, MGEs, contigs carrying ARGs, plasmids carrying ARGs, and chromosomes carrying ARGs. Figure 5 As can be seen, when the filtering threshold is less than 800, the number of ARGs, MGEs, contigs carrying ARGs, plasmids carrying ARGs, and chromosomes carrying ARGs remain unchanged, while the number of sequences increases significantly, leading to increased computational resources required for the analysis. When the filtering threshold is greater than 800, the number of detected ARGs decreases.

[0059] As shown in Table 10, for sample G6, within a filtering threshold range of 300-800, the number of detected ARGs remained stable at 57, the number of MGEs remained stable at 5, and the number of ARG-carrying contigs, plasmids, and chromosomes were 40, 12, and 15, respectively. When the filtering threshold range was increased to 1500 or 2000, the number of detected ARGs decreased to 56, the number of ARG-carrying contigs decreased to 39, and the others did not change significantly.

[0060] For the G6 sample, the number of sequences corresponding to different filtering thresholds was compared with the number of ARGs, MGEs, contigs carrying ARGs, plasmids carrying ARGs, and chromosomes carrying ARGs. Figure 6As can be seen, similar conclusions can be drawn: when the filtering threshold is less than 800, the number of ARGs, MGEs, contigs carrying ARGs, plasmids carrying ARGs, and chromosomes carrying ARGs remain unchanged, while the number of sequences increases significantly, leading to increased computational resources required for analysis. When the filtering threshold is greater than 800, the number of detected ARGs decreases. As shown in Table 11, for the G15 sample, within the filtering threshold range of 300-500, the number of detected ARGs remained stable at 100, the number of MGEs remained stable at 7, and the number of ARG-carrying contigs, plasmids, and chromosomes remained stable at 83, 19, and 37, respectively. Within the filtering threshold range of 600-800, the number of detected ARGs decreased slightly, remaining stable at 98, the number of MGEs also decreased by 1 to 6, the number of ARG-carrying contigs and plasmids decreased by 2 to 81 and 17, respectively, and the number of ARG-carrying chromosomes remained unchanged at 37. When the filtering threshold range was increased to 1500, the number of detected ARGs decreased to 94, the number of ARG-carrying contigs and plasmids decreased to 77 and 15, respectively, and the number of ARG-carrying chromosomes decreased by 1. When the filtering threshold range was further increased to 2000, the number of detected ARGs, the number of ARG-carrying contigs, and the number of plasmids further decreased.

[0061] For the G15 sample, the number of sequences corresponding to different filtering thresholds was compared with the number of ARGs, MGEs, contigs carrying ARGs, plasmids carrying ARGs, and chromosomes carrying ARGs. Figure 7 As can be seen, similar conclusions can be drawn: when the filtering threshold is less than 800, the number of ARGs, MGEs, contigs carrying ARGs, plasmids carrying ARGs, and chromosomes carrying ARGs remain unchanged, while the number of sequences increases significantly, leading to increased computational resources required for analysis. When the filtering threshold is greater than 800, the number of detected ARGs decreases.

[0062] The above results indicate that when the filtering threshold is too high, such as reaching 1500 or even 2000, it will cause ARG false negatives; when the filtering threshold is below 500, the number of detected ARGs does not increase, indicating that a threshold of 500 is sufficient to detect all ARGs. Considering both computational load and detection rate, setting the filtering threshold to 500-800 is appropriate. A threshold of 500 results in the highest detection rate, while a threshold of 800 provides the highest efficiency.

[0063] Furthermore, it should be understood that after reading the foregoing contents of this application, those skilled in the art can make various alterations or modifications to this application, and these equivalent forms also fall within the scope defined by the appended claims.

Claims

1. A method for analyzing metagenomic third-generation sequencing data, characterized in that, Includes the following steps: The metagenomic third-generation sequencing data were preprocessed; The preprocessed metagenomic third-generation sequencing data were assembled using third-generation sequencing assembly software to obtain a contig sequence set; Sequencing sequences that did not participate in assembly were collected and those shorter than a preset threshold were filtered out. The remaining sequences were merged into the contig sequence set to obtain a merged sequence set. The preset threshold was 400~800bp. Data analysis is performed based on the merged sequence set, including analysis of drug resistance-related genes.

2. The analytical method according to claim 1, characterized in that, The third-generation sequencing assembly software is Flye.

3. The analytical method according to claim 1, characterized in that, The preset threshold is obtained based on the analysis of the length of the sequencing sequence that did not participate in assembly.

4. The analytical method according to claim 3, characterized in that, The preset threshold is 500bp.

5. The analytical method according to any one of claims 1 to 4, characterized in that, The metagenomic third-generation sequencing data were obtained by sequencing DNA samples derived from environmental or water samples using HiFi sequencing technology.

6. The analytical method according to claim 5, characterized in that, The data analysis also includes at least one of the following analyses: (1) Species diversity analysis; (2) Analysis of genes related to metabolite synthesis; (3) Microbial co-occurrence network analysis.

7. A system for analyzing metagenomic third-generation sequencing data, characterized in that, Includes the following modules: The data input module is used to obtain the metagenomic third-generation sequencing data and to preprocess the metagenomic third-generation sequencing data; An assembly module, connected to the data input module, is used to assemble preprocessed metagenomic third-generation sequencing data using third-generation sequencing assembly software to obtain a contig sequence set; The merging module, connected to the assembly module, is used to obtain the contig sequence set and to collect sequencing sequences that did not participate in the assembly and filter out sequencing sequences with a length less than a preset threshold. The remaining sequences are merged into the contig sequence set to obtain the merged sequence set. The preset threshold is 400~800bp. An analysis module, connected to the merging module, is used to perform data analysis based on the merged sequence set, the data analysis including the analysis of drug resistance-related genes.

8. The analysis system according to claim 7, characterized in that, It also includes a database storage module for storing databases that can be analyzed.

9. A computer device, characterized in that, include: Memory, used to store computer programs; A processor, configured to execute the steps of the analysis method according to any one of claims 1 to 6 when executing the computer program.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the analysis method according to any one of claims 1 to 6.

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