Rapid pathogenic microorganism analysis method and system

Abstract

The invention provides a rapid analysis method and system for pathogenic microorganisms, and belongs to the field of bioinformatics, the method comprises the following steps: determining a target microorganism sequencing sequence of the pathogenic microorganisms, and analyzing the target microorganism sequencing sequence to generate a primary result; performing data correction based on the first-level result to obtain a second-level result; performing data expansion based on the second-level result to generate a third-level result; and processing the third-level result according to a filtering and screening rule to obtain a fourth-level result. According to the rapid analysis method and system for the pathogenic microorganisms, the analysis speed of the pathogenic microorganisms can be increased, and the timeliness is improved.

Description

Technical Field

[0001] The present disclosure belongs to the field of bioinformatics, and more specifically, relates to a method and system for rapid analysis of pathogenic microorganisms. Background Art

[0002] Early detection of causative microorganisms in patients with serious infections is essential to inform clinical interventions and administer appropriate targeted antibiotics. However, timely and accurate diagnosis remains extremely challenging for many hospitalized patients. As most infectious syndromes have indistinguishable clinical presentations, broad-based multiplexed diagnostic testing is urgently needed.

[0003] In the field of infection detection, traditional laboratory diagnosis of infectious diseases mainly relies on microscopic examination and cultivation of pathogens in appropriate culture media or cell lines. However, some microorganisms are uncultivable or difficult to culture (such as mycoplasma, Bartonella), while others (such as mycobacteria and molds) may take weeks to grow and form. Accurate molecular detection by PCR provides an alternative diagnostic method to culture, but this method is hypothesis-driven and requires prior knowledge of suspected pathogens. Therefore, how to increase the analysis speed of pathogenic microorganisms and improve timeliness is a problem that needs to be solved at present. Summary of the invention

[0004] The purpose of the present disclosure is to provide a method and system for rapid analysis of pathogenic microorganisms to increase the analysis speed of pathogenic microorganisms and improve timeliness.

[0005] A first aspect of the present disclosure provides a method for rapid analysis of pathogenic microorganisms, comprising: Determine a target microbial sequencing sequence of a pathogenic microorganism, analyze the target microbial sequencing sequence, and generate a primary result; Performing data correction based on the primary result to obtain a secondary result; Perform data expansion based on the secondary results to generate tertiary results; The third-level results are processed according to filtering and screening rules to obtain fourth-level results.

[0006] A second aspect of the embodiments of the present disclosure provides a rapid analysis system for pathogenic microorganisms, comprising: A primary module is used to determine the target microbial sequencing sequence of pathogenic microorganisms, analyze the target microbial sequencing sequence, and generate a primary result; A secondary module, used for performing data correction based on the primary result to obtain a secondary result; A third-level module, used for performing data expansion based on the second-level results to generate third-level results; The fourth level module is used to process the third level results according to filtering and screening rules to obtain fourth level results.

[0007] According to a third aspect of an embodiment of the present disclosure, an electronic device is provided, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of the above-mentioned method for rapid analysis of pathogenic microorganisms when executing the computer program.

[0008] According to a fourth aspect of the embodiments of the present disclosure, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned method for rapid analysis of pathogenic microorganisms are implemented.

[0009] The beneficial effects of the pathogenic microorganism rapid analysis method and system provided by the embodiments of the present disclosure are: On the one hand, the analysis speed of the embodiment of the present disclosure is fast, and the timeliness is much better than traditional microbial culture and other methods; on the other hand, the embodiment of the present disclosure also adopts an optimized analysis strategy, which not only improves the analysis speed but also reduces the analysis time. Therefore, the embodiment of the present disclosure improves the analysis speed of pathogenic microorganisms and improves the timeliness. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0011] Figure 1 A schematic diagram of a process for rapid analysis of pathogenic microorganisms provided in one embodiment of the present disclosure; Figure 2 A schematic diagram of an example process flow of a method for rapid analysis of pathogenic microorganisms provided in one embodiment of the present disclosure; Figure 3 A structural block diagram of a rapid analysis system for pathogenic microorganisms provided in one embodiment of the present disclosure; Figure 4 A schematic block diagram of an electronic device provided by an embodiment of the present disclosure. DETAILED DESCRIPTION

[0012] In the following description, specific details such as specific system structures and technologies are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present disclosure. However, it should be clear to those skilled in the art that the present disclosure may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obstructing the description of the present disclosure with unnecessary details.

[0013] In order to make the purpose, technical solutions and advantages of the present disclosure more clear, specific embodiments will be described below in conjunction with the accompanying drawings.

[0014] Please refer to Figure 1 and Figure 2 , Figure 1 A schematic diagram of a rapid analysis method for pathogenic microorganisms provided in one embodiment of the present disclosure is shown in FIG. Figure 2 A schematic diagram of an example process of a rapid analysis method for pathogenic microorganisms provided in one embodiment of the present disclosure, the method comprising: S101: Determine the target microbial sequencing sequence of the pathogenic microorganism, analyze the target microbial sequencing sequence, and generate a primary result.

[0015] In this embodiment, the target microorganism sequencing sequence is a high-throughput sequencing sequence (reads) after dehumanization and deduplication, and the pathogenic microorganisms may include bacterial pathogens. The steps of determining the target microorganism sequencing sequence of pathogenic microorganisms may include quality control analysis, dehumanization and deduplication operations. The species classification analysis of the target microorganism sequencing sequence is the core step of the entire analysis process. The analysis speed of this step determines the timeliness of the entire pathogenic microorganism rapid analysis process; and the accuracy of this step will ultimately determine the sensitivity and specificity of the pathogenic microorganism rapid analysis method.

[0016] From the perspective of timeliness, when analyzing the species classification of target microbial sequencing sequences, the comparison speed of the selected software is the first factor to be considered. Among various types of comparison software, the algorithm based on k-mer fragment sorting is a typical application of using space for time, and is superior to other types of software in terms of speed. Therefore, Kraken2 is currently one of the most commonly used comparison software in the industry, and its analysis speed is 2 to 3 orders of magnitude faster than the classic exhaustive sequence search and comparison software Blast.

[0017] The rapid realization of the analysis of species classification of target microbial sequencing sequences also depends on the size of the database to be compared. In order to improve timeliness and the effective utilization of computing resources, generally, a large nucleic acid sequence database such as nt is not used, but a streamlined database that includes most pathogenic microorganisms is used.

[0018] On the other hand, with the rapid popularization of sequencing technology, the capacity of the nucleic acid sequence database of newly measured species has also shown nearly exponential growth in recent years. For example, the NCBI non-redundant nucleic acid sequence library (nt) currently contains hundreds of millions of sequences after decompression, and the storage space has reached hundreds of GB. It takes more and more time to download and occupy a large amount of storage time for a long time, especially when comparing. It will consume a huge amount of analysis time. High-throughput sequencing itself will generate a large number of reads, and the time consumed for comparison with a huge database will be unacceptable. Therefore, the selection of a streamlined database that takes into account the completeness of pathogen types and the length of analysis time is also critical to the practical feasibility of this embodiment.

[0019] After analyzing the target microbial sequencing sequence based on the k-mer fragment algorithm, a first result is generated, wherein the first result is a preliminary statistical result of species classification.

[0020] S102: Perform data correction based on the primary result to obtain the secondary result.

[0021] In this embodiment, the secondary result is the data after the primary result is corrected.

[0022] Since step S101 uses ultrafast alignment analysis software based on the k-mer fragment algorithm, the purpose of reducing analysis time has been achieved. However, compared with the exhaustive search algorithm software Blast, the disadvantage of this algorithm is that the error rate is higher. After the alignment statistics of tens of millions of reads are completed, the classification statistics of each species will differ greatly. At this time, reasonable algorithms and software can be used for correction.

[0023] Since the comparison in step S101 uses the software Kraken2, the corresponding software Bracken, which estimates abundance based on a probability statistical model, performs primary result correction and abundance statistics to generate secondary results.

[0024] S103: Perform data expansion based on the secondary results to generate tertiary results.

[0025] In this embodiment, the tertiary result is a result of data expansion for the secondary result, wherein the secondary result includes bacterial pathogens.

[0026] Considering timeliness, a simplified database is used in step S101. The statistical analysis of bacterial pathogens is sufficient, but for other types of pathogens, such as viruses, fungi, parasites and some special pathogens, the coverage is very limited, which will greatly limit the detection ability of this type of pathogen. Therefore, measures should be taken to conduct supplementary analysis, that is, to expand the data for the secondary results.

[0027] For example, establishing separate databases for pathogens such as viruses, fungi, and parasites, or using a modified and de-redundant full nucleic acid database and using the above database for supplementary analysis can quickly obtain information on uncommon and rare pathogens. For example, some viral and fungal infections, although their infection rates are lower than those of bacteria, are no less important than bacterial infections.

[0028] Therefore, a more comprehensive species database analysis with special settings is used to add viruses, fungi, parasites and special pathogens as well as bacterial pathogens that are not in the secondary results to the secondary results to generate the tertiary results, which are the final statistical results of species classification.

[0029] S104: Process the third-level results according to the filtering and screening rules to obtain fourth-level results.

[0030] In this embodiment, the fourth-level results are the data after filtering and screening the third-level results.

[0031] The third-level results need to be filtered and screened based on the abundance values ​​of each species in the water background sample and all samples tested in the same batch. One of the biggest difficulties in the analysis of target microbial sequencing sequences is to identify and eliminate false positive results in the analysis results. Therefore, reasonable and effective filtering and screening rules can be set to remove false positive results.

[0032] There are many reasons for false positive results, common sources include: 1) environmental bacteria contaminated during collection, transportation, and experiments; 2) engineered bacteria carried by experimental reagents; 3) parasitic bacteria carried by the patient himself, which can be transformed into pathogenic bacteria under certain conditions.

[0033] To remove environmental contaminants, an effective measure is to set up negative control samples. In theory, high-abundance pathogens in negative samples represent laboratory environmental bacteria. By comparing the data results of negative samples and clinical samples and setting a reasonable discrimination threshold, pathogens unique to clinical samples can be screened out.

[0034] Another effective way is to establish a database of laboratory sample species statistics and corresponding clinical information. Through long-term records and statistics, a stable statistically significant baseline of the content of various microorganisms can be obtained, as well as the fluctuation range of the abundance values ​​of various microorganisms in various patients and samples. Among them, locally accumulated data are of great value for setting thresholds.

[0035] From the above, it can be concluded that, on the one hand, the analysis speed of this embodiment is fast and the timeliness is much better than traditional microbial culture and other methods; on the other hand, this embodiment also adopts an optimized analysis strategy, which not only improves the analysis speed but also reduces the analysis time. Therefore, this embodiment improves the analysis speed of pathogenic microorganisms and improves the timeliness.

[0036] In one embodiment of the present disclosure, determining a target microorganism sequencing sequence based on a pathogenic microorganism includes: Performing data quality control analysis on the first data to obtain a first sequence; Dehumanizing the first sequence to obtain a second sequence; The second sequence is deduplicated to obtain the target microbial sequencing sequence.

[0037] In this embodiment, the first data is the offline data, which is the original sequencing data generated in the second generation sequencing technology. At the same time, the offline data is not only sequencing data, but also a part of the metagenomic sequencing.

[0038] Specifically, the steps of performing data quality control analysis on the first data are as follows: First, the first data is analyzed and counted using the quantity and quality statistics software of sequencing data. Through the statistical results, the basic situation of the first sequence can be accurately grasped.

[0039] Secondly, for metagenomic sequencing, the number of reads measured for each sample is a basic indicator. If the number of reads does not reach a certain amount, it is impossible to obtain a basic understanding of the types of microorganisms contained in the sample. According to the current industry-wide rules, the number of sequencing reads for a sample should generally not be less than 20M, or 20 million. As for the upper limit, the more the better, because as the number of reads increases, the measured species categories will increase to a certain extent and will also approach a limit. That is, it depends on the richness of the microbial species contained in the sample type. Therefore, different types of samples have different requirements for the optimal sequencing amount. At present, the industry generally believes that 20M~50M reads is an appropriate range.

[0040] Then, the quality information of the measured reads is given by the second-generation sequencing technology through the quality value of each base in the fastq file. The sequencing quality can be statistically analyzed and quality controlled through relevant software (such as fastqc or fastp). The overall control of sequencing quality is generally achieved through the indicator Q value. The Q20 percentage refers to the percentage of bases with a sequencing error probability of less than 1%, and the Q30 percentage refers to the percentage of bases with a sequencing error probability of less than one thousandth. At present, it is generally believed that if the Q30 ratio is not less than 85%, the quality of this sequencing is considered to meet the high quality requirements.

[0041] Finally, reads with low quality values ​​are removed through quality control software, thereby improving the overall Q30 ratio so that the filtered reads meet the minimum quality requirements.

[0042] Specifically, the reasons for dehumanizing the first sequence are as follows: Although the current pathogen mNGS experiments generally have experimental processing steps to remove human origin, these experimental steps are often difficult to completely remove human sequences in the sample. Therefore, the first data often still contains a large number of human sequences, sometimes higher than 95%. Removing human sequences can not only avoid wasting a lot of computing resources in the subsequent analysis process and reduce analysis time, but also increase the proportion of pathogenic microorganisms, which is conducive to more prominent display of pathogens during sorting and visualization.

[0043] However, it takes time to compare human genomes. One of the characteristics of high-throughput sequencing is that the amount of nucleic acid sequence data is very large, so this problem will be more prominent. If this step takes too long and seriously affects the overall timeliness, it will inevitably be more costly than profitable. Therefore, choosing appropriate comparison software and setting reasonable parameters to achieve the best balance between analysis accuracy and time consumption can achieve the best benefits. Through many attempts, the comparison software bowtie2 is currently a better choice for comparison and filtering of human genomes.

[0044] Specifically, the steps for deduplication of the second sequence are as follows: The first data contains a large number of identical amplified sequences, which not only reduce the efficiency of effective comparison in subsequent species classification analysis, but also interfere with the statistics of species abundance in the results. Therefore, de-duplication operations are required before performing species classification statistics.

[0045] At present, there are many softwares with deduplication functions, and the corresponding speeds will also be different. In the case of little impact on the overall timeliness, you can choose software that is as easy to operate, easy to install, and small in size as possible. For example, seqkit software has many basic functions for sequence processing, which has been used in the previous quality control link. The software also has a deduplication sub-function. Using this sub-function for deduplication is not only easy to operate and fast, but also does not require the installation of additional software. It is simple and easy to operate with good operability.

[0046] From the above, it can be concluded that the extraction and evaluation of effective data, the rapid and accurate removal of human genome source sequences and repetitive sequences caused by amplification can increase the overall signal-to-noise ratio and improve the utilization of computing hardware resources.

[0047] In one embodiment of the present disclosure, the target microorganism sequencing sequence is analyzed to generate a primary result, including: The first method is used to compare the target microbial sequencing sequences to obtain the primary results.

[0048] In this embodiment, the first method is the k-mer segmentation method, which uses the k-mer segmentation principle to achieve rapid alignment of the target microbial sequencing sequence. It refers to the application of relevant software (such as Kraken2, etc.) and the k-mer principle to automatically and quickly segment reads into k-mer sequences, thereby achieving the purpose of rapid alignment.

[0049] In one embodiment of the present disclosure, data correction is performed based on the primary result to obtain the secondary result, including: The second method is used to perform data correction on the primary result to obtain the secondary result.

[0050] In this embodiment, the second method refers to using machine learning software to perform data correction on the preliminary statistics of species classification, that is, applying relevant software (such as Bracken, etc.) to analyze and correct the preliminary statistical results obtained by k-mer calculation in the previous step.

[0051] In one embodiment of the present disclosure, data expansion is performed based on the secondary results to generate tertiary results, including: Determine the species database and conduct additional analysis on non-bacterial pathogenic microorganisms; Perform data expansion on the secondary results to generate tertiary results; Among them, non-bacterial pathogens include viruses, fungi, parasites and special pathogens.

[0052] In this embodiment, when performing supplementary analysis on viruses, fungi, parasites and special pathogens, this embodiment examines that the comparison software centrifuge provides a redundant and comprehensive nucleic acid library, which can be supported by common small servers (with a memory of about 150G). Compared with the 1T memory required by the nt library used by kraken2, the applicability is greatly improved, especially for small companies or research groups. However, the process of using the centrifuge full nucleic acid library for comparison statistics is still very time-consuming, which will double or even more the analysis time.

[0053] In order not to affect the analysis timeliness, this example deeply analyzes the algorithm design principle of the centrifuge software and attempts to accelerate from three levels: 1) The analysis process is changed from serial to parallel. While doing the Kraken2 analysis, the Centrifuge analysis is submitted, so that the server computing resources can be more fully utilized; 2) The process of centrifuge analysis of a batch of samples one by one was changed to: first merge the reads and mark them according to the samples, then do only one centrifuge analysis, and then split the results of each sample from the results and perform statistics separately. Because the whole nucleic acid database needs to be read twice during centrifuge analysis, this process is a very time-consuming and rate-limiting step; 3) The centrifuge analysis process has also been modified. Previously, according to the official standard analysis process of the software, it was divided into two steps: "classification" and "statistics". Both steps required reading the entire nucleic acid database first, so it took a lot of time. In this embodiment, the process was modified to only run the first step, and the second step was to analyze and count the results of the first step by writing a script to directly obtain the statistical results, thereby omitting the second step of the original process and greatly reducing the time consumed. The execution process of the statistical script is divided into three steps: a) read the classification result file and record the number of reads of each species; b) ignore the bacterial data in the results, and select the results of viruses, fungi, and parasites to calculate the rpm value; c) merge the bacterial results with kraken2 into the final results of all species.

[0054] It can be concluded from the above that this embodiment is applicable to the detection of various known pathogens, including bacteria, viruses, fungi and parasites, and the detection range is far greater than other traditional detection methods.

[0055] In one embodiment of the present disclosure, the method for rapid analysis of pathogenic microorganisms further comprises: The filtering and screening rules are to filter and screen according to the abundance value; The calculation formula of abundance value is as follows:

[0056] in, Represents the number of pathogen high-throughput sequencing sequences, Represents the total number of high-throughput sequencing sequences.

[0057] After the initial process design was completed and run, this embodiment found a thorny problem: the number of pathogens and suspected pathogens given in the analysis was still too large, often reaching more than a dozen. If all were given, for doctors, there would be too many results to choose from and they would have no way to analyze and judge, so the value of the report would be greatly reduced.

[0058] To this end, this embodiment has formulated the following screening rules after repeated, careful consideration and attempts: 1) rpm sorted from high to low; 2) Among bacteria, viruses, fungi, and parasites (four major categories), the highest value is generally selected as the reported pathogen, and the first k (k<3) pathogens with rpm values ​​greater than 70% of the highest value are also added as reported pathogens; 3) Among the pathogens with a rpm value less than 70% of the maximum value, select the first k (k≤3) pathogens as suspected pathogens and report them; 4) For the above pathogens and suspected pathogens, if their rpm value in the sample is less than 5 times their rpm value in the water sample, they shall be ignored; 5) For the above pathogens and suspected pathogens, if the rpm value in the sample is less than the mean + standard deviation of all samples in this batch, it shall be ignored; 6) Special pathogens do not rely on rpm from high to low, refer to rules 4) and 5).

[0059] After repeated testing and debugging, this rule has achieved good results.

[0060] The value of k can be adjusted.

[0061] It can be concluded from the above that this embodiment can achieve screening and filtering of false positive results in the final result data of species classification statistics.

[0062] In one embodiment of the present disclosure, the method for rapid analysis of pathogenic microorganisms further comprises: The four-level results are split according to clinical samples, and independent data tables are generated and uploaded to the review platform.

[0063] In this embodiment, the rapid analysis method of pathogenic microorganisms is used for clinical infection microorganism detection. The ultimate goal of the project is to provide patients with clear and reliable detection reports of microbial infection sources. The sequencing and analysis in the background are generally carried out in batches of multiple samples. Therefore, before the final review and report generation, the data of each patient needs to be separated, and an independent data table needs to be generated and uploaded to the review and reporting platform.

[0064] Therefore, the individual result data of each clinical sample is picked out because the previous filtering and screening often requires the comparison of other samples in the same batch and negative control samples. The subsequent review and report generation require the data of each sample to be viewed and operated separately, so the combined results need to be split into independent data for each sample.

[0065] Corresponding to the rapid analysis method of pathogenic microorganisms in the above embodiment, Figure 3 This is a structural block diagram of a rapid analysis system for pathogenic microorganisms provided by an embodiment of the present disclosure. For ease of explanation, only the parts related to the embodiment of the present disclosure are shown. Figure 3 The pathogenic microorganism rapid analysis system 20 includes: a primary module 21, a secondary module 22, a tertiary module 23 and a quaternary module 24.

[0066] Among them, the primary module 21 is used to determine the target microbial sequencing sequence of the pathogenic microorganism, analyze the target microbial sequencing sequence, and generate a primary result; A secondary module 22 is used to perform data correction based on the primary result to obtain a secondary result; The third-level module 23 is used to perform data expansion based on the second-level results to generate third-level results; The fourth level module 24 is used to process the third level results according to the filtering and screening rules to obtain the fourth level results.

[0067] In one embodiment of the present disclosure, the primary module 21 is specifically used for: Performing data quality control analysis on the first data to obtain a first sequence; Dehumanizing the first sequence to obtain a second sequence; The second sequence is deduplicated to obtain the target microbial sequencing sequence.

[0068] In one embodiment of the present disclosure, the primary module 21 is further used for: The first method is used to compare the target microbial sequencing sequences to obtain the primary results.

[0069] In one embodiment of the present disclosure, the secondary module 22 is specifically used for: The second method is used to perform data correction on the primary result to obtain the secondary result.

[0070] In one embodiment of the present disclosure, the tertiary module 23 is specifically used for: Determine the species database and conduct additional analysis on non-bacterial pathogenic microorganisms; Perform data expansion on the secondary results to generate tertiary results; Among them, non-bacterial pathogens include viruses, fungi, parasites and special pathogens.

[0071] In one embodiment of the present disclosure, the pathogenic microorganism rapid analysis system 20 further includes: a screening module and a separation module; A screening module is used for filtering and screening according to the abundance value; The calculation formula of abundance value is as follows:

[0072] in, Represents the number of pathogen high-throughput sequencing sequences, Represents the total number of high-throughput sequencing sequences.

[0073] The splitting module is used to split the four-level results according to clinical samples, generate independent data tables, and upload them to the review platform.

[0074] See also Figure 4 , Figure 4 A schematic block diagram of an electronic device provided by an embodiment of the present disclosure. Figure 4 The electronic device 300 in the embodiment shown may include: one or more processors 301, one or more input devices 302, one or more output devices 303 and one or more memories 304. The processors 301, input devices 302, output devices 303 and memories 304 communicate with each other via a communication bus 305. The memory 304 is used to store computer programs, which include program instructions. The processor 301 is used to execute the program instructions stored in the memory 304. The processor 301 is configured to call the program instructions to execute the functions of each module / unit in the above-mentioned system embodiments, such as Figure 3 The functions of modules 21 to 24 are shown.

[0075] It should be understood that in the embodiment of the present disclosure, the processor 301 may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0076] The input device 302 may include a touch panel, a fingerprint collection sensor (for collecting the user's fingerprint information and fingerprint direction information), a microphone, etc., and the output device 303 may include a display (LCD, etc.), a speaker, etc.

[0077] The memory 304 may include a read-only memory and a random access memory, and provide instructions and data to the processor 301. A portion of the memory 304 may also include a non-volatile random access memory. For example, the memory 304 may also store information about the device type.

[0078] In a specific implementation, the processor 301, input device 302, and output device 303 described in the embodiments of the present disclosure can execute the implementation methods described in the first and second embodiments of the rapid analysis method of pathogenic microorganisms provided in the embodiments of the present disclosure, and can also execute the implementation methods of the electronic device described in the embodiments of the present disclosure, which will not be repeated here.

[0079] In another embodiment of the present disclosure, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by the processor, all or part of the processes in the above-mentioned embodiment method are implemented, and the computer program can also be completed by instructing the relevant hardware through the computer program. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed by the processor, the steps of each of the above-mentioned method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium, etc.

[0080] The computer-readable storage medium may be an internal storage unit of the electronic device of any of the aforementioned embodiments, such as a hard disk or memory of the electronic device. The computer-readable storage medium may also be an external storage device of the electronic device, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device. Furthermore, the computer-readable storage medium may also include both an internal storage unit of the electronic device and an external storage device. The computer-readable storage medium is used to store computer programs and other programs and data required by the electronic device. The computer-readable storage medium may also be used to temporarily store data that has been output or is to be output.

[0081] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this disclosure.

[0082] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the electronic devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0083] In the several embodiments provided in the present application, it should be understood that the disclosed electronic devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces or units, or it can be an electrical, mechanical or other form of connection.

[0084] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiments of the present disclosure.

[0085] In addition, each functional unit in each embodiment of the present disclosure may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0086] The above are only specific embodiments of the present disclosure, but the protection scope of the present disclosure is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in the present disclosure, and these modifications or replacements should be included in the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure should be based on the protection scope of the claims.

Claims

1. A method for rapid analysis of pathogenic microorganisms, characterized in that: include: Determine a target microbial sequencing sequence of a pathogenic microorganism, analyze the target microbial sequencing sequence, and generate a primary result; Performing data correction based on the primary result to obtain a secondary result; Perform data expansion based on the secondary results to generate tertiary results; The third-level results are processed according to filtering and screening rules to obtain fourth-level results.

2. The method for rapid analysis of pathogenic microorganisms according to claim 1, characterized in that: The method of determining the target microorganism sequencing sequence based on the pathogenic microorganism comprises: Performing data quality control analysis on the first data to obtain a first sequence; Dehumanizing the first sequence to obtain a second sequence; The second sequence is deduplicated to obtain the target microbial sequencing sequence.

3. The method for rapid analysis of pathogenic microorganisms according to claim 1, characterized in that: The target microorganism sequencing sequence is analyzed to generate a primary result, including: The first method is used to compare the target microorganism sequencing sequence to obtain the primary result.

4. The method for rapid analysis of pathogenic microorganisms according to claim 1, characterized in that: The performing of data correction based on the primary result to obtain the secondary result includes: The second method is used to perform data correction on the primary result to obtain the secondary result.

5. The method for rapid analysis of pathogenic microorganisms according to claim 1, characterized in that: The data expansion based on the secondary result to generate the tertiary result includes: Determine the species database and conduct additional analysis on non-bacterial pathogenic microorganisms; Performing data expansion on the secondary results to generate the tertiary results; Among them, the non-bacterial pathogenic microorganisms include viruses, fungi, parasites and special pathogens.

6. The method for rapid analysis of pathogenic microorganisms according to claim 5, characterized in that: Also includes: The filtering and screening rule is to perform filtering and screening according to abundance value; The calculation formula of abundance value is as follows: in, Represents the number of pathogen high-throughput sequencing sequences, Represents the total number of high-throughput sequencing sequences.

7. The method for rapid analysis of pathogenic microorganisms according to claim 1, characterized in that: Also includes: The four-level results are split according to clinical samples, generating independent data tables, and uploaded to the review platform.

8. A rapid analysis system for pathogenic microorganisms, characterized in that: include: A primary module is used to determine the target microbial sequencing sequence of pathogenic microorganisms, analyze the target microbial sequencing sequence, and generate a primary result; A secondary module, used for performing data correction based on the primary result to obtain a secondary result; A third-level module, used for performing data expansion based on the second-level results to generate third-level results; The fourth level module is used to process the third level results according to filtering and screening rules to obtain fourth level results.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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