A method for establishing reference thresholds for metagenomic pathogen detection

Abstract

The present invention discloses a method for establishing a reference threshold for metagenomic pathogen detection. The method includes: determining the type of clinical sample; determining its composition and the distribution range of human cell content; setting a gradient negative control; performing multiple batches of repeated tests on the negative control sample according to the metagenomic detection process that the clinical sample to be tested is about to undergo; counting the number of detection sequences of different pathogens in the negative control samples with different human cell concentrations, and obtaining the fluctuation range of the number of detection sequences of the corresponding pathogens in the negative control samples with different human cell concentrations; taking 120% of the upper limit of the fluctuation range of the number of detection sequences of the corresponding pathogens in the negative control samples as the threshold. The present invention can achieve hierarchical discrimination of sample detection results, improve the accuracy of metagenomic pathogen detection results; improve the efficiency of report interpretation; effectively reduce the false positives of metagenomic detection results; and evaluate the contamination in the detection process in real time, guiding laboratory process improvement.

Description

Technical Field

[0001] The present invention relates to the field of biotechnology, and in particular to a method for establishing a reference threshold for metagenomic pathogen detection. Background Art

[0002] With the reduction of the cost of second-generation sequencing and the shortening of detection time, metagenomic detection technology is increasingly being used in the detection of clinical pathogens. Because of its advantages such as unbiasedness and wide coverage of pathogens, it can detect all pathogens in the sample at one time, and does not require cultivation. It can directly detect clinical samples, effectively identify mixed infections and other difficult-to-culture microorganisms, and is of great significance for the detection of clinical infectious pathogens. However, metagenomic methods have also encountered certain bottlenecks in the process of clinical promotion and use, including the problem of low sensitivity of pathogen detection in samples with high human cell content; the problem of how to interpret a large number of pathogens detected by metagenomics; and the identification of colonizing bacteria and pathogenic bacteria detected in respiratory samples. These are the main problems that plague clinicians in using this technology.

[0003] The principle of metagenomics testing is to test all nucleic acids in the sample, and the test results are affected by many factors, including the human cell content in the test sample, the pathogen nucleic acid content, the pathogen type, the size of the pathogen genome, the amount of sequencing data, the test environment, the various reagents and consumables used in the test, the pathogen database, the bioinformatics comparison algorithm, etc. All influencing factors will be presented in the final test results, so how to establish a reasonable detection threshold under different influencing factors and effectively filter the test results is an important link in the accuracy of metagenomics results. Summary of the invention

[0004] The present invention provides a method for establishing a reference threshold for metagenomic test results, which can assist clinicians in filtering metagenomic test results, reduce interference information in the results, and provide clinicians with more accurate test results.

[0005] In a first aspect, the present invention claims a method for establishing a reference threshold for metagenomic pathogen detection.

[0006] The method for establishing a reference threshold for metagenomic pathogen detection claimed in the present invention may include the following steps:

[0007] (1) Determine the types of clinical samples to be tested for metagenomic pathogens.

[0008] (2) Determine the composition and distribution range of human cell content according to the type of clinical sample. n Up to 10 n+mcells / mL; wherein m and n are both positive integers; and the composition does not include pathogens.

[0009] (3) The composition and human cell content of the clinical sample determined in step (2) are distributed in the order of magnitude of 10 n Up to 10 n+m cells / mL, and m negative control samples are set; the composition of the negative control sample is kept as consistent as possible with that of the clinical sample. If a negative clinical sample can be obtained, it is best to use the negative clinical sample as the negative control sample.

[0010] When m=1, the human cell content of the negative control sample is 10 n cells / mL;

[0011] When m≥2, in the order of human cell content from small to large, the human cell content in the first negative control sample is 10 n cells / mL, and the human cell content in the second negative control sample was 10 n+1 cells / mL, and so on, the human cell content in the mth negative control sample is 10 n+m-1 cells / mL; wherein the corresponding reference relationship between the negative control sample and the clinical sample is as follows: the first negative control sample can be used as a human cell content order of magnitude of 10 n Up to 10 n+1 The second negative control sample can be used as a reference negative control for clinical samples with a human cell content of 10 n+1 Up to 10 n+2 The mth negative control sample can be used as a reference negative control for clinical samples with a human cell content of 10 n+m-1 Up to 10 n+m That is, when the human cell content in the clinical sample is on the order of 10 n Up to 10 n+1 cells / mL, the first negative control sample is selected as the negative reference; when the human cell content in the clinical sample is 10 n+1 Up to 10 n+2 When the human cell content in the clinical sample is 10 cells / mL, the second negative control sample is selected as the negative reference; and so on. n+m-1 Up to 10 n+m When the mth negative control sample is selected as the negative reference.

[0012] (4) The m negative control samples determined in step (3) are subjected to multiple batches of multiple repeat tests according to the metagenomic detection process that will be performed on the clinical samples to be tested (including the experimental environment, reagents, consumables, bioinformatics analysis process, etc. are all the same).

[0013] (5) The negative control samples of different batches and different repeated tests under the same human cell content are recorded as negative control samples A, and the detection sequence numbers of different pathogens detected in the negative control sample A are standardized and counted according to the sequencing data volume of 20Mreads to obtain the fluctuation range of the detection sequence numbers of the corresponding pathogens in the negative control sample A. This step can form the fluctuation range of the detection sequence numbers of different pathogens under the human cell content, and can also form the fluctuation range of the detection sequence numbers of different pathogens within the continuous variation range of the human cell content.

[0014] (6) Based on the corresponding reference relationship in step (3), find a clinical sample at a human cell concentration corresponding to the negative control sample A, recorded as clinical sample A; the reference threshold of different pathogens when the clinical sample A is subjected to metagenomic pathogen detection is: 120% of the upper limit of the fluctuation range of the detection sequence number of the corresponding pathogen in the negative control sample A obtained in step (5).

[0015] Further, in step (2), a total of N clinical samples of the type determined in step (1), where N is a positive integer greater than or equal to 30, can be taken, and the human nucleic acid content in the N clinical samples can be measured by a commercial fluorescent PCR kit to determine the distribution range of the human cell content in the clinical samples of this type. n Up to 10 n+m cells / mL.

[0016] Furthermore, in step (2), the composition of the clinical sample can be determined according to its type by researching clinical-related books and literature.

[0017] Furthermore, in step (4), the multiple batches and multiple repetitions may be: at least three batches of negative control samples with different human cell contents are tested, and each batch is tested at least 10 times. Accordingly, in step (5), the total number of samples summarized under each human cell content is not less than 30.

[0018] In a second aspect, the present invention claims a metagenomic pathogen detection method.

[0019] The metagenomic pathogen detection method claimed in the present invention may include:

[0020] Step 1: According to the method described in the first aspect above, reference thresholds for different pathogens are established for metagenomic pathogen detection in different clinical samples corresponding to different orders of magnitude of human cell content.

[0021] Step 2: Perform metagenomic testing on the clinical samples to be tested, and standardize the obtained data according to the sequencing data volume of 20Mreads. In the same batch of tests, a negative control sample with a known human cell content is added as an intra-batch negative control.

[0022] Step 3: Detecting the content of human cells in the clinical sample to be tested.

[0023] Step 4: Compare the test results of the negative control samples of the same batch in step 2 with the fluctuation range of the detection sequence number of the pathogen in the negative control samples under the corresponding human cell content determined in step (5). If the standardized data of the detection sequence number of the detected pathogens are all within the corresponding fluctuation range or do not exceed 120% of the upper limit of the fluctuation range, it is considered that the negative control is under control in this test result; if the standardized data of the detection sequence number of the pathogen detected in the negative control sample exceeds 120% of the upper limit of the corresponding fluctuation range, it is considered that the negative control of this test is abnormal, indicating that there is contamination in this experiment, and it is recommended to retest to find out the cause.

[0024] Step 5: For the batch of negative controls in step 2, the standardized data of the detection sequence number of the pathogen in the clinical sample to be tested detected in the batch is compared with the corresponding reference threshold determined in step 1. If the standardized data of the detection sequence number of a certain pathogen detected in the clinical sample to be tested exceeds 120% of the upper limit of the corresponding fluctuation range, it is considered that the candidate clinical sample to be tested contains the pathogen; otherwise, it is considered that the clinical sample to be tested does not contain the pathogen.

[0025] Step 5 can get the list of pathogen detection results. Then the interpreter can interpret according to the patient's clinical information and the list of test results, and finally give a test report.

[0026] Furthermore, in step 2, the process of performing metagenomic detection on the clinical sample to be tested may also include the following step: adding an internal reference sequence to the clinical sample to be tested for quality control.

[0027] There is no crossover (mutual matching) sequence between the internal reference sequence and the pathogen nucleic acid sequence and the human nucleic acid sequence library.

[0028] Furthermore, in step 3, the content of human nucleic acid in the clinical sample to be tested can be measured by a commercial fluorescent PCR kit, thereby determining the content of human cells in the clinical sample to be tested.

[0029] Alternatively, in step 3, the content of human cells in the clinical sample to be tested can be converted according to the number of specific detection sequences of the internal reference sequence in the clinical sample to be tested. The detailed conversion scheme refers to the Chinese invention patent (invention name: Method and device for quantitative detection of metagenomic pathogens based on internal reference, patent publication number: CN111607639A), and the specific formula is as follows:

[0030] The number of internal reference specific detection sequences = (internal reference sequence size × internal reference concentration × total sequence number × internal reference characteristic coefficient) / (human genome size × human nucleic acid concentration), where the internal reference characteristic coefficient is a constant.

[0031] In a third aspect, the present invention claims a system for performing metagenomic pathogen detection.

[0032] The system for performing metagenomic pathogen detection claimed in the present invention may contain:

[0033] (A) Reagents and / or instruments required for metagenomic pathogen detection.

[0034] (B) The device comprises a data input module, a threshold storage module, a data comparison module and a conclusion output module.

[0035] The data input module is configured to collect data on the number of detection sequences of the pathogen in the clinical sample to be tested obtained by the method described in the second aspect above, which is normalized according to the sequencing data volume of 20Mreads.

[0036] The threshold storage module is configured to store reference thresholds for different pathogens when performing metagenomic pathogen detection on different clinical samples with different orders of magnitude of human cell content, established according to the method described in the first aspect above.

[0037] The data comparison module is configured to receive the standardized data of the number of detection sequences of the pathogens in the clinical sample to be tested sent from the data input module, call the reference thresholds of different pathogens when performing metagenomic pathogen detection on the clinical samples corresponding to the corresponding human cell content order of magnitude stored in the threshold storage module, and then compare the standardized data of the number of detection sequences of each pathogen in the clinical sample to be tested with the reference threshold of the corresponding pathogen.

[0038] The conclusion output module is configured to receive the comparison result sent by the data comparison module, and then output a conclusion according to the comparison result.

[0039] In a fourth aspect, the present invention claims a computer readable storage medium.

[0040] The computer-readable storage medium claimed in the present invention stores a computer program for executing the following steps:

[0041] Collecting data after the number of detection sequences of the pathogen in the clinical sample to be tested obtained by the method described in the second aspect above is standardized according to the sequencing data volume of 20Mreads;

[0042] Comparing the standardized data of the number of detection sequences of each pathogen in the clinical sample to be tested with the reference threshold of the corresponding pathogen established by the method described in the first aspect above;

[0043] A conclusion is output according to the comparison result.

[0044] In the third and fourth aspects, the conclusions may be output as follows: if the standardized data of the detection sequence number of a certain pathogen in the clinical sample to be tested is higher than the reference threshold of the corresponding pathogen, it is considered that the candidate clinical sample to be tested contains the pathogen; otherwise, it is considered that the clinical sample to be tested does not contain the pathogen.

[0045] In a specific embodiment of the present invention, the clinical sample is specifically cerebrospinal fluid. The negative control sample is specifically artificial cerebrospinal fluid with human cells added. The artificial cerebrospinal fluid is a commercially available sterile artificial cerebrospinal fluid, specifically a product of Beijing Coolaibo Technology Co., Ltd., item number SL6630X-500mL, and the main components of the artificial cerebrospinal fluid include inorganic salts, ions and a specific pH range. The human cells are Hela cells. The order of magnitude distribution of the human cell content in the cerebrospinal fluid sample is 10 3 Up to 10 6 cells / mL, so there are 3 negative control samples, which are respectively supplemented with 10 3 cells / mL, 10 4 cells / mL and 10 5 cells / mL of the artificial cerebrospinal fluid of human cells (wherein the content of human cells is the final content in the negative control sample). The fluctuation range of the detection sequence number of 20 common laboratory contaminants and / or 20 common pathogens in cerebrospinal fluid in the negative control sample is shown in Table 1.

[0046] The present invention provides a method for establishing a reference threshold for pathogen detection based on metagenomics, which is individually set according to the type of pathogen, genome size and other characteristics, and has higher filtering accuracy; the metagenomics detection threshold establishment scheme provided by the present invention can dynamically monitor the environmental microbial changes in the metagenomics detection laboratory, and can more accurately correct the metagenomics pathogen detection results; the present invention establishes the reference threshold for pathogens by setting a negative control, and processes it according to the clinical sample detection process, which can effectively eliminate the interference of reagents, consumables, environment and other pathogenic microorganisms that appear in the process of bioinformatics comparison on the detection results; the negative control selected by the present invention includes a group of negative samples with different human cell contents, and the setting of the human cell content is set according to the actual human cell content distribution of the sample type to be tested, and has good equivalence; the negative control samples set by the present invention are processed according to exactly the same process as the samples to be tested, which can reflect the interference effect in the detection of real samples to the greatest extent.

[0047] Beneficial effects brought by the technical solution of the present invention:

[0048] 1) The present invention can establish corresponding reference thresholds for metagenomic detection of pathogen sequences for samples of different sample types and different human cell contents, which can achieve stratified discrimination of sample detection results and improve the accuracy of metagenomic pathogen detection results.

[0049] 2) The solution provided by the present invention can effectively reduce the reporting interpretation cycle of metagenomic pathogen detection results and improve the efficiency of report interpretation; through the technical solution of the present invention, it can be further applied to the automated interpretation process to further shorten the reporting cycle.

[0050] 3) The present invention sets the reference interval by filtering negative control samples and processes them according to a process that is completely consistent with clinical sample testing. This can effectively filter reagents, consumables, and the environment in the metagenomic pathogen detection process and microbial contamination in the bioinformatics comparison process, thereby reducing false positives in metagenomic detection results.

[0051] 4) The present invention can dynamically monitor the pollutants in the entire detection process, evaluate the pollution in the detection process in real time, and guide laboratory process improvements. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 The distribution statistics of human cell content in 56 cerebrospinal fluid clinical samples are presented.

[0053] Figure 2 The results show the detection of pathogens in simulated negative cerebrospinal fluid samples at different human cell concentrations.

[0054] Figure 3Demonstration of the filtration status of pathogens detected in clinical samples. DETAILED DESCRIPTION

[0055] The present invention is further described in detail below in conjunction with specific embodiments, and the examples provided are only for illustrating the present invention, rather than for limiting the scope of the present invention. The examples provided below can be used as a guide for further improvements by those of ordinary skill in the art, and do not constitute a limitation of the present invention in any way.

[0056] The experimental methods in the following examples, unless otherwise specified, are all conventional methods, and are performed according to the techniques or conditions described in the literature in the field or according to the product instructions. The materials, reagents, etc. used in the following examples, unless otherwise specified, can all be obtained from commercial channels.

[0057] Example 1: Method for establishing reference thresholds for metagenomic pathogen detection

[0058] In this example, cerebrospinal fluid samples are selected for demonstration, and the main process is as follows:

[0059] 1. Determination of human nucleic acid content in cerebrospinal fluid clinical samples: 56 cerebrospinal fluid clinical samples were randomly selected (subjects were informed and agreed), and the human nucleic acid content was measured using the "Human Housekeeping Gene DNA Quantitative Detection Kit (Fluorescence PCR Method)" produced by Jiangsu Hongwei Tesi Pharmaceutical Technology Co., Ltd., and converted into human cell numbers for statistics. The results showed that the content of human cells in cerebrospinal fluid samples was concentrated in 10 3 -10 6 cells / mL, such as Figure 1 shown.

[0060] 2. According to the composition information of cerebrospinal fluid samples obtained from the survey, it mainly includes human cells, inorganic salts, various ions, etc. Artificial cerebrospinal fluid (Beijing Coolbo Technology Co., Ltd., product number SL6630X-500mL) (including inorganic salts, ions and appropriate pH value) plus Hela cells were selected as the composition of simulated cerebrospinal fluid, and the cell concentrations were 10 3 cells / mL,10 4 cells / mL,10 5 cells / mL of simulated cerebrospinal fluid negative sample.

[0061] 3. The simulated cerebrospinal fluid negative samples prepared above were tested according to the detection process of cerebrospinal fluid clinical samples. Three batches of tests were performed for each concentration, and 10 replicates were set for each batch. The samples were sequenced according to the MGISEQ-2000 platform library construction and machine operation instructions, and the pathogen detection result list was generated after data analysis according to the bioinformatics analysis process.

[0062] 4. The types of pathogens detected in 30 simulated cerebrospinal fluid negative samples at different cell concentrations and the detection values ​​after standardization based on 20Mreads sequencing data volume were counted. The statistical results show: 3 A total of 3892 pathogens, including bacteria and fungi, were compared in the simulated cerebrospinal fluid negative samples with a concentration of 10 cells / mL; 4 A total of 3581 pathogens, including bacteria and fungi, were compared in the simulated cerebrospinal fluid negative samples with 10 cells / mL; 5 A total of 1706 pathogens, including bacteria and fungi, were identified in the negative simulated cerebrospinal fluid samples with a concentration of 144 cells / mL.

[0063] 5. Count the 30 test values ​​of each pathogen at different cell concentrations to determine the range of its test value. Figure 2 As shown in Table 1 below, 20 common laboratory contaminants and 20 common pathogens in cerebrospinal fluid obtained from the above statistical results are selected as representatives for display. The corresponding detection reference value range is as follows.

[0064] Table 1. Fluctuation range of pathogen detection sequence at different cell concentrations (standardized)

[0065]

[0066]

[0067]

[0068] 6. Select 5 cerebrospinal fluid samples with known clinical results and 1 sample with a cell concentration of 10 4 The negative simulated cerebrospinal fluid samples with a concentration of cells / mL were tested to verify the accuracy of the reference interval established above. The cerebrospinal fluid metagenomics detection process was followed to detect and generate a list of test results. At the same time, 5 samples were subjected to human nucleic acid qPCR quantitative detection, and the quantitative results and clinical results are shown in Table 2 below.

[0069] Table 2. Information of clinical samples of 5 cerebrospinal fluids

[0070]

[0071]

[0072] 7. Compare the negative control sample test results list with the established 10 4The pathogen detection sequence number fluctuation range of cells / mL was compared and filtered. The detection values ​​of all pathogens were within the set reference range, indicating that the negative control of this test result was qualified and the clinical sample test results could be further analyzed.

[0073] 8. At a certain human cell concentration, 120% of the upper limit of the fluctuation range of the number of detection sequences of pathogens in the corresponding negative control sample standardized at 20Mreads sequencing data volume is used as the threshold. The test result list of 5 clinical samples is compared and filtered with 120% (threshold) of the upper limit of the fluctuation range of the number of pathogen sequences within the corresponding human cell content range. If the number of standardized detection sequences of a certain pathogen in the clinical sample at 20Mreads sequencing data volume exceeds 120% of the upper limit of the corresponding fluctuation range, it is considered that the candidate in the clinical sample contains the pathogen; otherwise, it is considered that the clinical sample does not contain the pathogen. The comparison results are as follows: Figure 3 As shown in Table 3, most of the pathogen detection sequence numbers are within the set reference interval. According to the filtered results, the pathogen detection in 5 clinical samples was statistically analyzed, and the results are shown in Table 3.

[0074] Table 3. Results of 5 cerebrospinal fluid clinical samples after filtration

[0075]

[0076]

[0077]

[0078] Note: * in the table indicates that the corresponding bacteria currently have no Chinese name, but they belong to Cryptococcus, so they are represented by *.

[0079] 9. The above filtering results are further interpreted and judged according to the patient's clinical information, and finally the test result report is given. The report results show that the 5 samples are consistent with the clinical diagnosis results.

[0080] After filtering the cerebrospinal fluid clinical samples according to the reference interval and threshold established by the present invention through this embodiment (if the number of standardized detection sequences of a certain pathogen in the clinical sample exceeds 120% of the upper limit of the corresponding pathogen reference interval, it is considered that the clinical sample is a candidate containing the pathogen; otherwise, it is considered that the clinical sample does not contain the pathogen), the number of pathogen types finally obtained is significantly reduced compared to before filtering, and the pathogen detection after filtration is interpreted and judged. The final determined test results are very consistent with the clinical diagnosis results, indicating that the present invention has a good effect on improving the accuracy of metagenomic pathogen detection.

[0081] The present invention has been described in detail above. It will be apparent to those skilled in the art that the present invention may be implemented in a wide range under equivalent parameters, concentrations and conditions without departing from the spirit and scope of the present invention and without the need for unnecessary experimentation. Although the present invention provides specific embodiments, it should be understood that further improvements may be made to the present invention. In short, according to the principles of the present invention, this application intends to include any changes, uses or improvements to the present invention, including changes made by conventional techniques known in the art that depart from the scope disclosed in this application. Applications of some of the basic features may be made within the scope of the following appended claims.

Claims

1. A method for establishing a reference threshold for metagenomic pathogen detection, comprising the following steps: (1) Determine the types of clinical samples to be tested for metagenomic pathogens; (2) Determine the composition and distribution range of human cell content according to the type of clinical sample. n Up to 10 n+m cells / mL; among them, Both m and n are positive integers; (3) The composition and human cell content of the clinical sample determined in step (2) are distributed in the order of magnitude of 10 n Up to 10 n+m cells / mL, and m negative control samples are set; the composition of the negative control samples is consistent with that of the clinical samples; When m=1, the human cell content of the negative control sample is 10 n cells / mL; When m≥2, in the order of human cell content from small to large, the human cell content in the first negative control sample is 10 n cells / mL, and the human cell content in the second negative control sample was 10 n+1 cells / mL, and so on, the human cell content in the mth negative control sample is 10 n+m-1 cells / mL; wherein the corresponding reference relationship between the negative control sample and the clinical sample is as follows: the first negative control sample is used as the corresponding human cell content order of magnitude of 10 n Up to 10 n+1 The reference negative control of the clinical sample with a content of 10 cells / mL, and the second negative control sample as the corresponding human cell content n+1 Up to 10 n+2 The mth negative control sample is used as the reference negative control of the clinical sample, and the mth negative control sample is used as the corresponding human cell content of 10 n+m-1 Up to 10 n+m A reference negative control of the clinical sample; (4) performing multiple batches and multiple repeat tests on the m negative control samples determined in step (3) according to the metagenomic testing process to be performed on the clinical samples to be tested; (5) The negative control samples of different batches and different repeated tests under the same human cell content are recorded as negative control samples A, and the detection sequence numbers of different pathogens detected in the negative control sample A are standardized and counted according to the sequencing data volume of 20Mreads to obtain the fluctuation range of the detection sequence numbers of the corresponding pathogens in the negative control sample A; (6) Based on the corresponding reference relationship in step (3), find a clinical sample at a human cell concentration corresponding to the negative control sample A, recorded as clinical sample A; the reference threshold of different pathogens when the clinical sample A is subjected to metagenomic pathogen detection is: 120% of the upper limit of the fluctuation range of the detection sequence number of the corresponding pathogen in the negative control sample A obtained in step (5).

2. A metagenomic pathogen detection method, comprising: Step 1: establishing reference thresholds for different pathogens when performing metagenomic pathogen detection on different clinical samples with different orders of magnitude of human cell content according to the method of claim 1; Step 2: Perform metagenomic testing on the clinical samples to be tested, and standardize the obtained data according to the 20Mreads sequencing data volume, and add a negative control sample with a known human cell content in the same batch of tests as an intra-batch negative control; Step 3: Detecting the content of human cells in the clinical sample to be tested; Step 4: Compare the test results of the negative control samples of the same batch in step 2 with the fluctuation range of the detection sequence number of the pathogen in the negative control samples under the corresponding human cell content determined in step (5). If the standardized data of the detection sequence number of the detected pathogens are all within the corresponding fluctuation range or do not exceed 120% of the upper limit of the fluctuation range, it is considered that the negative control in this test result is under control; if the standardized data of the detection sequence number of the pathogen detected in the negative control sample exceeds 120% of the upper limit of the corresponding fluctuation range, it is considered that the negative control of this test is abnormal and retested; Step 5: For the batch of negative controls in step 2, the standardized data of the number of detection sequences of the pathogens in the clinical samples to be tested detected in the batch are compared with the corresponding reference threshold determined in step 1; The method is a non-disease diagnosis and treatment method.

3. The method according to claim 1 or 2, characterized in that: In step (2), a total of N clinical samples of the type determined in step (1), where N is a positive integer greater than or equal to 30, are taken, and the human nucleic acid content in the N clinical samples is measured by real-time fluorescence quantitative PCR to determine the distribution range of the human cell content in the clinical samples of this type. n Up to 10 n+m cells / mL.

4. The method according to claim 1 or 2, characterized in that: In step (4), the multiple batches and multiple repetitions are: at least three batches of the negative control samples with different human cell contents are tested, and each batch is tested at least 10 times.

5. The method according to claim 2, characterized in that: In step 2, the process of performing metagenomic testing on the clinical sample to be tested also includes the following steps: adding an internal reference sequence to the clinical sample to be tested for quality control.

6. The method according to claim 2, characterized in that: In step 3, the content of human nucleic acid in the clinical sample to be tested is measured by fluorescent PCR method, so as to determine the content of human cells in the clinical sample to be tested.

7. The method according to claim 5, characterized in that: In step 3, the content of human cells in the clinical sample to be tested is converted according to the number of specific detection sequences of the internal reference sequence in the clinical sample to be tested.

8. A system for performing metagenomic pathogen detection, comprising: (A) Reagents and / or instruments required for metagenomic pathogen detection; (B) a device, comprising a data input module, a threshold storage module, a data comparison module and a conclusion output module; The data input module is configured to collect data of the number of detection sequences of the pathogen in the clinical sample to be tested obtained by the method of any one of claims 2 to 7, which is normalized according to the sequencing data volume of 20 Mreads; The threshold storage module is configured to store reference thresholds for different pathogens when performing metagenomic pathogen detection on different clinical samples corresponding to different orders of magnitude of human cell content, established according to the method of any one of claims 1 to 7; The data comparison module is configured to receive the standardized data of the number of detection sequences of the pathogens in the clinical sample to be tested sent by the data input module, call the reference thresholds of different pathogens when performing metagenomic pathogen detection on the clinical sample corresponding to the order of magnitude of the human cell content stored in the threshold storage module, and then compare the standardized data of the number of detection sequences of each pathogen in the clinical sample to be tested with the reference threshold of the corresponding pathogen; The conclusion output module is configured to receive the comparison result sent by the data comparison module, and then output a conclusion according to the comparison result.

9. The system according to claim 8, characterized in that: The conclusion is output as follows: if the standardized data of the detection sequence number of a certain pathogen in the clinical sample to be tested is higher than the reference threshold of the corresponding pathogen, it is considered that the candidate clinical sample to be tested contains the pathogen; otherwise, it is considered that the clinical sample to be tested does not contain the pathogen.

10. A computer-readable storage medium storing a computer program for executing the following steps: Collecting data after the number of detection sequences of the pathogen in the clinical sample to be tested obtained by the method of any one of claims 2 to 7 is standardized according to the sequencing data volume of 20 Mreads; Comparing the standardized data of the number of detection sequences of each pathogen in the clinical sample to be tested with the reference threshold value of the corresponding pathogen established according to any one of the methods of claims 1 to 7; A conclusion is output according to the comparison result.

11. The computer-readable storage medium according to claim 10, wherein: The conclusion is output as follows: if the standardized data of the detection sequence number of a certain pathogen in the clinical sample to be tested is higher than the reference threshold of the corresponding pathogen, it is considered that the candidate clinical sample to be tested contains the pathogen; otherwise, it is considered that the clinical sample to be tested does not contain the pathogen.

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