Pathogen detection methods and systems

Abstract

This invention provides a pathogen detection method and system, relating to the field of biotechnology. The pathogen detection method provided by this invention includes: a. acquiring metagenomic sequencing data of a sample and annotating the sequence species; b. obtaining the abundance ranking of each microbial species in the sample; c. calculating the sequence dispersion of each microorganism in the sample; d. determining whether the microorganism meets the following conditions: abundance ranking within the top 5; sequence dispersion ≥ 0.95; and belonging to a known pathogen; e. if all conditions in step d are met, it is determined to be a pathogenic microorganism; otherwise, it is determined not to be a pathogenic microorganism. This pathogen detection method is simple, convenient, and highly accurate. Combining three indicators, it can identify the types of pathogenic microorganisms causing disease in the host.

Description

Technical Field

[0001] This invention relates to the field of biotechnology, and in particular to a method and system for detecting pathogens. Background Technology

[0002] Pathogens are generally microorganisms that can cause infectious diseases in humans or animals, including bacteria, fungi, viruses, chlamydia, mycoplasma, parasites, or other vectors. Diseases caused by the transmission of pathogens between humans, animals, and between humans and animals are called infectious diseases. Pathogens can be transmitted through various routes, including air, water, food, contact, soil, mother-to-child transmission, bodily fluids, and fecal-oral routes, and exhibit characteristics such as epidemicity, endemicity, and seasonality. Some pathogens can cause recurrent infections. Infectious diseases caused by pathogen transmission seriously endanger human health. Pandemics of infectious diseases can impose enormous economic burdens, and the economic impact of some infectious disease outbreaks can last for decades. Pathogen detection technologies can help us accurately determine the causes of infectious diseases, understand the transmission characteristics, toxicity, and drug resistance of pathogens, thereby better assisting us in the treatment of infectious diseases and the prevention of their spread.

[0003] Traditional pathogen detection techniques include microscopic observation of pathogen morphology, detection of pathogen-specific antigens, pathogen isolation and culture, and serological experiments. With the development of molecular biology techniques, gene detection technologies such as nucleic acid hybridization, gene chips, and polymerase chain reaction (PCR) have been increasingly widely applied in pathogen detection. Especially in the last 20 years, metagenomic next-generation sequencing (mNGS) technology, combined with powerful bioinformatics platforms, has greatly aided the diagnosis of infectious diseases. Along with the application of mNGS, a series of pathogen detection and annotation software programs, such as Kraken2, Bracken, Centrifuge, and MetaPhlAn, have been developed. However, these software programs also suffer from limitations such as limited functionality (Kracken2, Bracken, Centrifuge, and MetaPhlAn can only perform species sequence annotation and abundance calculation, but cannot perform species quality assessment or pathogen screening) and difficulties in system installation and maintenance. In addition, mNGS has the characteristic of unbiased detection, which can detect all microorganisms in clinical specimens. These microorganisms include a large number of non-pathogens (such as fixed bacteria, background bacteria, hospital or laboratory contamination, etc.). All samples may carry some microorganisms or microbial nucleic acids, but not all of them are pathogenic. Therefore, after species classification, mNGS data still need further analysis and judgment to determine which sequences of pathogens may be pathogenic. At present, metagenomic technology still lacks simple and effective pathogen identification methods.

[0004] In view of this, the present invention is hereby proposed. Summary of the Invention

[0005] The first objective of this invention is to provide a pathogen detection method that can be used for auxiliary screening of clinical pathogens to solve at least one of the above-mentioned problems.

[0006] A second objective of the present invention is to provide a system for pathogen detection.

[0007] In a first aspect, the present invention provides a method for identifying pathogenic microorganisms for non-disease diagnostic purposes, comprising the following steps:

[0008] a. Obtain metagenomic sequencing data of the samples and perform sequence species annotation;

[0009] b. Obtain the abundance ranking of each microbial species in the sample;

[0010] c. Calculate the sequence dispersion of each microorganism in the sample;

[0011] d. Determine whether the microorganism meets the following conditions: abundance ranking within the top 5; sequence dispersion ≥ 0.95; belongs to a known pathogen;

[0012] e. If all the conditions in step d are met, then it is determined to be a pathogenic microorganism; otherwise, it is determined not to be a pathogenic microorganism.

[0013] As a further technical solution, the abundance ranking of each microbial species in the sample is calculated by counting the number of sequences annotated to each species and sorting them from largest to smallest.

[0014] As a further technical solution, the sequence dispersion of the microorganisms is statistically determined by calculating the Simpson index of the sequences annotated to each microorganism. The Simpson index is calculated using sampling with replacement or sampling without replacement. Specifically, the calculation method is as follows: two random samples are taken from the sequences annotated to a certain species. The probability that the two sequences are located in the same region is D. The sequence dispersion is defined as 1-D. The same region is defined as follows: when the genome of a certain species is divided into n windows on average, the sequences drawn in the two samples are located in the same window, which means that they are located in the same region.

[0015] Preferably, the value of n is in the range of 80 to 120; more preferably, the value of n is 100.

[0016] Preferably, the Simpson index is calculated using sampling with replacement.

[0017] As a further technical solution, the sample is a body fluid sample or a tissue sample;

[0018] Preferably, the sample is bronchoalveolar lavage fluid, sputum, percutaneous lung biopsy tissue, bronchoscopic lung biopsy tissue, or nasopharyngeal swab.

[0019] As a further technical solution, the metagenomic sequencing data is metagenomic sequencing data with host sequence data removed;

[0020] Preferably, the software for removing host sequence data includes: bwa, SoapAligner, or bowtie2;

[0021] Preferably, the software used for sequence species annotation includes: blast, kraken2, bracken, centrifuge, or MetaPhlAn.

[0022] As a further technical solution, the metagenomic sequencing data is metagenomic sequencing data with low-quality sequences and adapter sequences removed.

[0023] In a second aspect, the present invention provides a system for pathogen detection, comprising:

[0024] a. Computational processing module: used to perform data analysis on the metagenomic sequencing data of the sample and obtain the abundance and sequence dispersion of microorganisms;

[0025] b. Judgment Module: If a microorganism meets the following criteria, it is judged to be a pathogen:

[0026] Indicator 1: Abundance ranking ≤ 5;

[0027] Indicator 2: Sequence dispersion ≥ 0.95;

[0028] Indicator 3: The microorganism belongs to a known pathogen;

[0029] The Simpson index of microbial sequences is calculated using sampling with replacement or sampling without replacement models to statistically determine the sequence dispersion of microorganisms.

[0030] As a further technical solution, a data extraction module is also included: nucleic acid extraction from the sample, library construction and sequencing to obtain metagenomic sequencing data.

[0031] As a further technical solution, the data analysis includes: removing the host sequence and then annotating the remaining sequence with species information.

[0032] As a further technical solution, the data analysis also includes removing low-quality sequences and adapter sequences from the metagenomic sequencing data.

[0033] Compared with the prior art, the present invention has the following beneficial effects:

[0034] The pathogen detection method provided by this invention is simple, convenient, and highly accurate. It can identify the types of pathogenic microorganisms that cause disease in the host by combining three indicators. Attached Figure Description

[0035] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0036] Figure 1 A comparison of the abundance rankings of pathogens and non-pathogens;

[0037] Figure 2 For comparing sequencing coverage of pathogens and non-pathogens;

[0038] Figure 3 Comparison of average sequencing depth between pathogens and non-pathogens;

[0039] Figure 4 To calculate the dispersion comparison between pathogen and non-pathogen sequences for the sampling with replacement model;

[0040] Figure 5 To calculate the dispersion comparison between pathogen and non-pathogen sequences in the sampling-without-replacement model;

[0041] Figure 6 Venn diagram showing the filtering effect of pathogen screening indicators. Detailed Implementation

[0042] The embodiments and examples of the present invention will be described in detail below. However, those skilled in the art will understand that the following embodiments and examples are for illustrative purposes only and should not be considered as limiting the scope of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention. Unless otherwise specified, conventional conditions or conditions recommended by the manufacturer shall apply. Reagents or instruments whose manufacturers are not specified are all commercially available conventional products.

[0043] In conjunction with this invention, the terms abundance ranking, sequencing coverage, average sequencing depth of species, Simpson index, sampling with replacement, and sampling without replacement are explained in Table 1.

[0044] Table 1. Glossary

[0045]

[0046]

[0047] In a first aspect, the present invention provides a method for identifying pathogenic microorganisms for non-disease diagnostic purposes, comprising the following steps:

[0048] a. Obtain metagenomic sequencing data of the samples and perform sequence species annotation;

[0049] b. Obtain the abundance ranking of each microbial species in the sample;

[0050] c. Calculate the sequence dispersion of each microorganism in the sample;

[0051] d. Determine whether the microorganism meets the following conditions: abundance ranking within the top 5; sequence dispersion ≥ 0.95; belongs to a known pathogen;

[0052] e. If all the conditions in step d are met, then it is determined to be a pathogenic microorganism; otherwise, it is determined not to be a pathogenic microorganism.

[0053] It should be noted that, in this invention, metagenomic sequencing data can be second-generation metagenomic sequencing data. In this invention, "known pathogens" refers to known pathogens that match the patient's disease and can cause the corresponding disease. For example, for patients with respiratory diseases (samples may include: bronchoalveolar lavage fluid, sputum, percutaneous lung biopsy tissue, bronchoscopic lung biopsy tissue, or nasopharyngeal swabs), in order to identify the pathogens causing the disease, the scope of "known pathogens" includes all known pathogens that can cause respiratory-related diseases.

[0054] The pathogen detection method provided by this invention is simple, convenient, and highly accurate. It can identify the types of pathogenic microorganisms that cause disease in the host by combining three indicators, and can be used as an auxiliary screening method for pathogens in clinical samples.

[0055] It should be noted that the pathogen detection method provided by this invention is not for disease diagnosis and treatment purposes. For example, it can be used to obtain samples from diagnosed patients, identify the pathogens causing the patient's disease using the detection method of this invention, and obtain information such as the correspondence between pathogens and patient symptoms in a simple, efficient and accurate manner.

[0056] In some preferred embodiments, the abundance ranking of each microbial species in the sample is calculated by counting the number of sequences annotated to each species and sorting them from largest to smallest.

[0057] In some preferred embodiments, the sequence dispersion of the microorganisms is statistically determined by calculating the Simpson index of the sequences annotated to each microorganism. The Simpson index can be calculated using sampling with replacement or sampling without replacement. Specifically, the calculation method is as follows: one sequence is randomly selected twice from all sequences annotated to a certain species. The probability that the two selected sequences are located in the same region is D. The sequence dispersion is defined as 1-D. The same region is defined as follows: if the genome of a certain species is divided into n windows on average, and the sequences selected twice are located in the same window, then they are located in the same region. The value of n ranges from 80 to 120. In some preferred embodiments, the value of n is 100.

[0058] In this invention, the Simpson's index of microorganisms can be calculated using either sampling with replacement or sampling without replacement models to statistically analyze the sequence dispersion of microorganisms. The inventors have found that calculating the Simpson's index for each microorganism to statistically analyze the sequence dispersion of a species is not highly sensitive to the amount of sequencing data. Other methods for measuring dispersion, such as variance, standard deviation, and range, are all related to the amount of sequencing data. Therefore, this invention preferably uses the Simpson's index to calculate sequence dispersion. Furthermore, the inventors have found that the Simpson's index calculated using the sampling with replacement model has superior identification power for pathogen identification in this invention.

[0059] In some preferred embodiments, the sample is a body fluid sample or a tissue sample;

[0060] Preferably, the sample is bronchoalveolar lavage fluid, sputum, percutaneous lung biopsy tissue, bronchoscopic lung biopsy tissue, or nasopharyngeal swab.

[0061] In some preferred embodiments, the metagenomic sequencing data is metagenomic sequencing data with host sequence data removed.

[0062] Preferably, the software for removing host sequence data includes, but is not limited to, bwa, SoapAligner, or bowtie2, with bwa being the preferred choice.

[0063] Preferably, the software for sequence species annotation includes, but is not limited to: blast, kraken2, bracken, centrifuge, or MetaPhlAn, with blast being the preferred option.

[0064] Based on the characteristics of BWA (fast speed but high memory consumption) and BLAST (low memory consumption but slow alignment speed), this invention adopts a two-stage sequence species annotation algorithm, which ensures both the speed of the process and controls memory consumption, thereby reducing the excessive hardware requirements of the process.

[0065] In some preferred embodiments, the metagenomic sequencing data is metagenomic sequencing data with low-quality sequences and adapter sequences removed to avoid interference from impurity sequences.

[0066] In a second aspect, the present invention provides a system for pathogen detection, comprising:

[0067] a. Computational processing module: used to perform data analysis on the metagenomic sequencing data of the sample and obtain the abundance and sequence dispersion of microorganisms;

[0068] b. Judgment Module: If a microorganism meets the following criteria, it is judged to be a pathogen:

[0069] Indicator 1: Abundance ranking ≤ 5;

[0070] Indicator 2: Sequence dispersion ≥ 0.95;

[0071] Indicator 3: The microorganism belongs to a known pathogen;

[0072] The Simpson index of microbial sequences is calculated using sampling with replacement or sampling without replacement models to statistically determine the sequence dispersion of microorganisms.

[0073] This system for pathogen detection is simple, can accurately detect pathogens, and can be used as an auxiliary screening tool for pathogens in clinical samples.

[0074] In some preferred embodiments, a data extraction module is also included: nucleic acid extraction from the sample, library construction and sequencing to obtain metagenomic sequencing data.

[0075] In some preferred embodiments, the data analysis includes removing the host sequence and then annotating the remaining sequence by species, which helps to shorten the analysis time.

[0076] In some preferred embodiments, the data analysis further includes removing low-quality sequences and adapter sequences from the metagenomic next-generation sequencing data.

[0077] The present invention will be further illustrated below with specific embodiments and comparative examples. However, it should be understood that these embodiments are merely for the purpose of more detailed illustration and should not be construed as limiting the present invention in any way.

[0078] Example 1

[0079] This invention utilizes 26 bronchoalveolar lavage fluid samples (from 22 patients with respiratory infectious diseases and 4 healthy individuals) for metagenomic next-generation sequencing. Host sequence removal and pathogen sequence identification are performed using alignment software and 27,000 pathogen genomes collected and organized from the NCBI database. Furthermore, by statistically analyzing and comparing the differences between known pathogens in patients and other microorganisms, a relatively accurate pathogen screening method is established.

[0080] 1.1 Sample Information

[0081] Table 2

[0082]

[0083]

[0084] 1.2 Sample Sequencing Process

[0085] The purity and integrity of the original samples were analyzed by agarose gel electrophoresis (AGE), and the DNA concentration was precisely quantified using Qubit. Qualified DNA samples were randomly fragmented into approximately 350 bp fragments using a Covaris ultrasonic disruptor. The entire library preparation process included end repair, A-tailing, sequencing adapter addition, purification, and PCR amplification. After library construction, preliminary quantification was performed using Qubit 2.0, diluting the library to 2 ng / µL. Subsequently, the insert size of the library was detected using an Agilent 2100. Once the insert size met expectations, the effective concentration of the library was accurately quantified using Q-PCR (effective concentration > 3 nM) to ensure library quality. After passing the library test, different libraries were pooled according to the effective concentration and target data volume requirements before Illumina HiSeq sequencing.

[0086] 1.3 Establishment of mNGS data processing workflow, database, quality control indicators and screening methods

[0087] The objective of this experiment was to establish a rapid pathogen detection workflow with low hardware dependence. Furthermore, the samples used in this experiment were all derived from human bodily fluids or tissues, and the sequencing data included not only microbial data but also a large amount of human DNA sequences. Therefore, this invention first compared the time and memory consumption when using three software programs—bwa, bowtie2, and blast—to align sample sequencing data with human reference sequences (version HG38) and with human and 27,000 microbial reference sequences (from the NCBI RefSeq database). It was found that, with 2GB of sequencing data (containing both human and microbial sequences), bwa had the best alignment rate and runtime when aligning with human reference sequences, with a memory consumption of approximately 15GB (as shown in Table 3). When aligning with human and 27,000 microbial reference sequences, bwa's memory consumption exceeded 100GB in all three software programs (as shown in Table 4; bowtie2 was not evaluated due to its long index construction time). After removing sequences that aligned to human references from the sequencing data, the remaining sequences were compared with 27,000 microbial reference sequences using BLAST, a process that took no more than 10 minutes and consumed less than 10GB of memory. Therefore, using BWA software to remove host sequences and then using BLAST to identify microbial species in the remaining sequences not only ensures fast execution time but also solves the problem of excessive memory usage spikes.

[0088] Table 3. Alignment results of different software with hg38 sequences.

[0089]

[0090] Table 4. Comparison of different software with 27,000 pathogen sequences of hg38+.

[0091]

[0092] Based on the above analysis, the raw data was first filtered using the FASTP software to remove low-quality sequences, adapter sequences, and to perform data volume statistics. The remaining high-quality sequences were then processed using the BWA software to remove host sequences and the BAST software to annotate the sequence species.

[0093] The present invention compiled the following data to examine the significant differences between pathogenic and non-pathogenic microorganisms in the samples.

[0094] a) Abundance ranking:

[0095] The number of sequences for each species in the tested samples was counted and ranked. By statistically ranking the abundance of all microorganisms in their samples, we found that the abundance of most pathogenic microorganisms was within the top 5 (e.g., Figure 1 (As shown).

[0096] b) Species coverage:

[0097] The sequencing coverage of all microorganisms in all samples was statistically analyzed. By comparing pathogenic and non-pathogenic microorganisms, we found that although there were significant differences between the two (Mannwhitneyu test p-value 2.95e-17), ... Figure 2 As shown, this indicator cannot clearly distinguish between pathogenic microorganisms and non-pathogenic microorganisms.

[0098] c) Average sequencing depth of species:

[0099] The average sequencing depth of all microorganisms in all samples was calculated. By comparing pathogenic and non-pathogenic microorganisms, we found significant differences (Mannwhitneyu test p-value of 8.34e-34), such as... Figure 3 As shown, this indicator cannot clearly distinguish between pathogenic and non-pathogenic microorganisms.

[0100] d) Species sequencing sequence dispersion

[0101] The dispersion of species sequences was statistically analyzed by calculating the Simpson index for each microorganism using both sampling with and without replacement models. By comparing the dispersion of pathogens and non-pathogens, it was found that both models clearly distinguished between pathogens and non-pathogens. Figure 4 and Figure 5 As shown. Using the Mann-Whitney test, it was found that the difference between the two groups of microorganisms was greater in the sampling with replacement model (p-value of 0 for the sampling with replacement model and p-value of 2.03e-148 for the sampling without replacement model).

[0102] mNGS possesses the characteristic of unbiased detection, enabling the detection of all microorganisms in clinical specimens. These microorganisms include a large number of non-pathogens (such as locator bacteria, background bacteria, and hospital or laboratory contaminants). Therefore, by reviewing relevant literature and databases, a relatively comprehensive database of known respiratory pathogens was constructed. The database construction process is as follows:

[0103] (1) Select microorganisms related to human respiratory diseases based on the pathogenic objects and pathogenic names recorded in the NPRC database. Determine the classification level of the microorganisms (genus, species, or strain) based on the foreign language names, taxonomic status, and NCBI database records in the NPRC database.

[0104] (2) The Chinese names of human respiratory system-related microorganisms recorded in the "Expert Consensus on Clinical Application and Result Interpretation of Metagenomic Sequencing Technology in Respiratory System Infections" were manually translated into English, and the classification level of the microorganisms was determined based on the NCBI database to determine whether the microorganisms were genus, species or strain, etc.

[0105] (3). Combine the data from (1) and (2) and process them according to the classification hierarchy of species in the ncbi database as follows: if the classification hierarchy of a microorganism is genus, then all species under that genus are marked as known pathogens; if the classification hierarchy of a microorganism is species, then that species is marked as a known pathogen; if the classification hierarchy of a microorganism is strain, then the species to which that microorganism belongs is marked as a known pathogen.

[0106] Based on the above analysis, the following pathogen classification model was constructed:

[0107] A microbial species in a sample is considered a pathogen if it meets the following criteria: it ranks among the top 5 most abundant microbial species in the sample (including the 5th).

[0108] If the sequence dispersion is ≥0.95, the microbial species belongs to the species in the known pathogen library constructed in this invention. Otherwise, the microorganism is not a pathogen.

[0109] Analysis of 26 samples used in this invention identified a total of 27,312 microorganisms (22 of which were known pathogens, and the remaining 27,290 were non-pathogens). We classified all microorganisms using the aforementioned model, as shown in Tables 5 and 6. Figure 6 As shown, 18 out of 22 known pathogens were identified as pathogens, with a detection rate of 81.82% (18 / 22), and 27,238 out of 27,290 non-pathogens were identified as non-pathogens, with a consistency rate of 99.79% ((18+27238) / 27312).

[0110] Table 5

[0111]

[0112] Table 6. Statistical data on pathogen-related data

[0113]

[0114]

[0115] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for identifying pathogenic microorganisms for non-disease diagnostic purposes, characterized in that, Includes the following steps: a. Obtain metagenomic sequencing data of the samples and perform sequence species annotation; b. Obtain the abundance ranking of each microbial species in the sample; c. Calculate the sequence dispersion of each microorganism in the sample; d. Determine whether the microorganism meets the following conditions: sorted by sequence number from largest to smallest, and its abundance ranking is within the top 5; sequence dispersion ≥ 0.95; it belongs to a known pathogen; e. If all the conditions in step d are met, then it is determined to be a pathogenic microorganism; otherwise, it is determined not to be a pathogenic microorganism. The sequence dispersion of the microorganisms is statistically determined by calculating the Simpson index of the sequences annotated to each microorganism. The Simpson index is calculated using either sampling with replacement or sampling without replacement. Specifically, one sequence is randomly selected twice from all sequences annotated to a species. The probability that the two selected sequences are located in the same region is D. The sequence dispersion is defined as 1-D. The same region is defined as follows: if the genome of a species is divided into n windows on average, and the sequences selected twice are located in the same window, then they are considered to be in the same region. The value of n ranges from 80 to 120.

2. The method for identifying pathogenic microorganisms according to claim 1, characterized in that, n takes the value 100.

3. The method for identifying pathogenic microorganisms according to claim 1, characterized in that, The Simpson index is calculated using sampling with replacement.

4. The method for identifying pathogenic microorganisms according to claim 1, characterized in that, The sample is a body fluid sample or a tissue sample.

5. The method for identifying pathogenic microorganisms according to claim 4, characterized in that, The samples were bronchoalveolar lavage fluid, sputum, percutaneous lung biopsy tissue, bronchoscopic lung biopsy tissue, or nasopharyngeal swabs.

6. The method for identifying pathogenic microorganisms according to claim 1, characterized in that, The metagenomic sequencing data is metagenomic sequencing data with host sequence data removed.

7. The method for identifying pathogenic microorganisms according to claim 6, characterized in that, Software for removing host sequence data includes: bwa, SoapAligner, or bowtie2; Software for sequence species annotation includes: blast, kraken2, bracken, centrifuge, or MetaPhlAn.

8. The method for identifying pathogenic microorganisms according to claim 1, characterized in that, The metagenomic sequencing data are metagenomic sequencing data after removing low-quality sequences and adapter sequences.

9. A system for pathogen detection, characterized in that, include: a. Computational processing module: used to perform data analysis on the metagenomic sequencing data of the samples and obtain the abundance and sequence dispersion of microorganisms; b. Judgment Module: If a microorganism meets the following criteria, it is judged to be a pathogen: Indicator 1: Sort by sequence number from largest to smallest, with abundance ranking ≤5; Indicator 2: Sequence dispersion ≥ 0.95; Indicator 3: The microorganism belongs to a known pathogen; The sequence dispersion of the microorganisms is statistically determined by calculating the Simpson index of the sequences annotated to each microorganism. The Simpson index is calculated using either sampling with replacement or sampling without replacement. Specifically, one sequence is randomly selected twice from all sequences annotated to a species. The probability that the two selected sequences are located in the same region is D. The sequence dispersion is defined as 1-D. The same region is defined as follows: if the genome of a species is divided into n windows on average, and the sequences selected twice are located in the same window, then they are considered to be in the same region. The value of n ranges from 80 to 120.

10. The system according to claim 9, characterized in that, It also includes a data extraction module: extracting nucleic acids from samples, constructing libraries, and sequencing to obtain metagenomic sequencing data.

11. The system according to claim 9, characterized in that, The data analysis includes: removing the host sequence and then annotating the remaining sequence with species information.

12. The system according to claim 9, characterized in that, The data analysis process also includes removing low-quality sequences and adapter sequences from the metagenomic sequencing data.

Images