Detection method and device for pathogenic microorganisms, terminal equipment and program product
By combining a comprehensive scoring method with multiple dimensions, the problem of insufficient accuracy in pathogen detection in existing technologies has been solved, achieving more accurate pathogen detection.
Patent Information
- Application Number
- CN202512035885.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-30
- Publication Date
- 2026-05-05
AI Technical Summary
Existing methods for detecting pathogenic microorganisms based on sequence alignment suffer from insufficient accuracy.
By identifying multiple sequencing sequences of the sample to be tested, BLASTN alignment is performed. The overall score is calculated by combining measurement indicators such as the number of matches, match length, similarity, and relative abundance, and the target pathogenic microorganism is identified.
It improves the accuracy of pathogen detection and enables a more comprehensive and balanced assessment of the presence of pathogens in the sample being tested.
Smart Images
Figure CN121983127A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of biological detection technology, and in particular to a method, apparatus, terminal equipment and program product for detecting pathogenic microorganisms. Background Technology
[0002] Pathogenic microorganisms are important pathogenic factors causing a range of health problems in humans, including infectious diseases, allergic reactions, diarrhea, and even certain tumors, and can lead to death in severe cases. Therefore, timely and accurate detection of pathogenic microorganisms is of paramount importance for clinical diagnosis, treatment guidance, and infection control.
[0003] Traditional methods for detecting pathogenic microorganisms primarily rely on microbial culture, biochemical identification, serological tests, and molecular diagnostics based on polymerase chain reaction (PCR). With the development of high-throughput sequencing technology, especially the application of third-generation nanopore sequencing, metagenomics-based pathogen detection methods have gradually become a research hotspot. In this approach, bioinformatics alignment tools (such as BLAST) are typically used to match the sequenced sequences with a reference database to infer the possible pathogenic microorganisms present in the sample. However, these traditional bioinformatics methods based on sequence alignment have significant algorithmic limitations, leading to insufficient accuracy in the detection results.
[0004] Therefore, how to accurately detect pathogenic microorganisms is a technical problem that needs to be solved by those skilled in the art. Summary of the Invention
[0005] The purpose of this application is to provide a method, apparatus, terminal equipment, computer-readable storage medium, and computer program product for detecting pathogenic microorganisms, with the aim of improving the accuracy of pathogenic microorganism detection.
[0006] Firstly, this application provides a method for detecting pathogenic microorganisms. The method includes: Identify multiple sequencing sequences corresponding to the sample to be tested; For each sequencing sequence, the sequencing sequence is compared with a reference database using BLASTN to determine the matching sequence that matches the sequencing sequence; For each pathogenic microorganism to be tested, the actual measurement data corresponding to the measurement index is determined based on the matching sequence; the measurement index includes the number of matches, the match length, the similarity, and the relative abundance. Based on the actual measurement data, determine the comprehensive score corresponding to each of the pathogenic microorganisms to be tested; The target pathogenic microorganism corresponding to the sample to be tested is determined based on the comprehensive score of each sample.
[0007] In one embodiment, determining the comprehensive score corresponding to each of the tested pathogenic microorganisms based on the actual measurement data includes: The individual comprehensive evidence value of the pathogenic microorganism is determined by multiplying the actual measurement data corresponding to the measurement index of the pathogenic microorganism to be tested. Obtain the total comprehensive evidence value of all pathogenic microorganisms in the sample to be tested; The overall score of the pathogenic microorganism to be tested is determined based on the individual comprehensive evidence value and the total comprehensive evidence value.
[0008] In one embodiment, determining the multiple sequencing sequences corresponding to the sample to be tested includes: Obtain the original sequence corresponding to the sample to be tested; Interference sequences were removed from the original sequence to obtain multiple sequencing sequences.
[0009] In one embodiment, the interfering sequence includes a sequence with a length less than a preset length threshold, a sequence with an average sequencing quality score lower than a preset quality threshold, and a host sequence.
[0010] In one embodiment, the process of determining the matching length includes: For each matching sequence, the single weighted length is determined based on the original similarity between the matching sequence and the sequencing sequence and the length of the matching bases; The sum of the single-weighted lengths corresponding to each of the matching sequences is determined as the matching length.
[0011] In one embodiment, the process of determining the similarity includes: The similarity is determined based on the ratio of the matching length to the number of matches.
[0012] In one embodiment, the process of determining the relative abundance includes: The relative abundance is determined by the ratio of the number of matches of the pathogenic microorganisms to the total number of matches of all the pathogenic microorganisms to be tested.
[0013] Secondly, this application also provides a device for detecting pathogenic microorganisms. The device includes: The sequencing sequence determination module is used to determine multiple sequencing sequences corresponding to the sample to be tested; The matching sequence determination module is used to perform BLASTN alignment of each sequencing sequence with a reference database to determine the matching sequence that matches the sequencing sequence. The actual measurement data determination module is used to determine the actual measurement data corresponding to the measurement index for each pathogenic microorganism to be tested, based on the matching sequence; the measurement index includes the number of matches, the match length, the similarity, and the relative abundance. The comprehensive score determination module is used to determine the comprehensive score corresponding to each of the actual measurement data. The detection result determination module is used to determine the target pathogenic microorganism corresponding to the sample to be tested based on the comprehensive score.
[0014] Thirdly, this application also provides a terminal device. The terminal device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the method described above.
[0015] Fourthly, this application also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the method described above.
[0016] Fifthly, this application also provides a computer program product. The computer program product includes a computer program that, when executed by a processor, implements the steps of the method described above.
[0017] This application provides a method for detecting pathogenic microorganisms. After determining multiple sequencing sequences corresponding to the sample to be tested, for each sequencing sequence, BLASTN alignment is performed with a reference database to identify matching sequences. For each pathogenic microorganism to be tested, actual measurement data corresponding to four dimensions of measurement indicators—number of matches, match length, similarity, and relative abundance—are determined based on the matching sequences. The actual measurement data corresponding to the four measurement indicators are integrated to calculate a comprehensive score for each pathogenic microorganism to be tested. Finally, the target pathogenic microorganism corresponding to the sample is determined based on each comprehensive score. This multi-dimensional fusion strategy avoids the one-sidedness of a single indicator and can more comprehensively and evenly assess the presence of pathogenic microorganisms in the sample. Therefore, this method can improve the accuracy of pathogenic microorganism detection.
[0018] It is understood that the pathogen detection device, terminal equipment, computer-readable storage medium, and computer program product provided in the embodiments of this application have the same beneficial effects as the pathogen detection method described above, and will not be repeated here. Attached Figure Description
[0019] To more clearly illustrate the technical solutions in the specific embodiments of this application or 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 this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.
[0020] Figure 1 A flowchart illustrating a method for detecting pathogenic microorganisms provided in this application embodiment; Figure 2 A schematic diagram of the structure of a pathogenic microorganism detection device provided in an embodiment of this application; Figure 3 This is a schematic diagram of the structure of a terminal device provided in an embodiment of this application. Detailed Implementation
[0021] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of this application with unnecessary detail.
[0022] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.
[0023] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.
[0024] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."
[0025] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.
[0026] References to "one embodiment" or "some embodiments" in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized. "A plurality" means "two or more."
[0027] This application provides a method for detecting pathogenic microorganisms, which can be executed by the processor of a terminal device when running a corresponding computer program. In this embodiment, the terminal device can be a portable device such as MinION or GridION, and this embodiment is not limited to this.
[0028] Figure 1 The flowchart illustrates a method for detecting pathogenic microorganisms provided in this application embodiment. For ease of explanation, only the parts relevant to this embodiment are shown. The method provided in this embodiment includes the following steps: S100: Identify multiple sequencing sequences corresponding to the sample to be tested.
[0029] The sample to be tested refers to a clinical or environmental sample that requires pathogen detection, such as a user's sputum, blood, cerebrospinal fluid, tissue biopsy, or environmental swabs. The sequence to be tested refers to the data unit determined after nanopore sequencing of the sample to be tested.
[0030] Specifically, a nanopore sequencer is run to perform nanopore sequencing on the sample to be tested, obtaining the raw sequence. The FASTX Toolkit is then used to convert the raw sequence from FASTQ format to FASTA format, yielding the sequencing sequence. It should be noted that this embodiment preferably uses third-generation nanopore sequencing technology. One of the core features that distinguishes third-generation nanopore sequencing technology from second-generation sequencing technology is long-read data, which can provide longer, more continuous, and more complete genome fragments.
[0031] S200: For each sequencing sequence, the sequencing sequence is compared with the reference database using BLASTN to determine the matching sequence that matches the sequencing sequence.
[0032] A reference database refers to a pre-constructed collection containing a large number of known pathogenic microorganism characteristic sequences. In a specific instance, a reference database can be an open-source database or a database specifically designed for pathogenic microorganisms. For example, a pre-designed reference database covers 8,565 pathogens, including 4,441 viruses, 3,785 bacteria, 239 fungi, and 100 parasites; including the complete genomes of viruses and marker genes for bacteria, fungi, and parasites; and 5,956 antibiotic resistance genes.
[0033] Specifically, for each sequencing sequence, the sequencing sequence is compared with a reference database using BLASTN to determine the matching reference genome fragment, thus obtaining the matching sequence.
[0034] BLASTN alignment is a classic bioinformatics algorithm used to perform local similarity searches between an unknown nucleic acid sequence (sequencing sequence) and a reference database. A matching sequence refers to a reference genome fragment in the reference database that, after BLASTN alignment, is determined to have significant similarity to the sequencing sequence.
[0035] The parameter values used when performing BLAST alignment can be set as follows: The E-value (expected value threshold, evalue) is 1e-5; the number of reported results (max_target_seqs), i.e. the number of reference genome fragments with the highest similarity, is limited to 1; the number of execution threads (num_threads) is 20, i.e. 20 computing threads are used for parallel alignment.
[0036] S300: For each pathogenic microorganism to be tested, the actual measurement data corresponding to the measurement index is determined based on the matching sequence; the measurement index includes the number of matches, the length of the match, the similarity, and the relative abundance.
[0037] The pathogenic microorganism to be tested refers to the pathogenic microorganism that needs to be determined to be present in the sample to be tested, that is, the pathogenic microorganism that appears at least once in the matching sequence list. The pathogenic microorganism to be tested includes Proteus mirabilis, Chikungunya virus, and Proteus mirabilis, etc. This embodiment does not limit the specific type of pathogenic microorganism to be tested.
[0038] In this embodiment, the measurement indicators include the number of matches, match length, similarity, and relative abundance.
[0039] Here, the number of matches (M) refers to the number of times the sequencing sequence corresponding to the test sample matches the reference sequence in the reference database; the match length (Sum, L) refers to the sum of the weighted lengths of each single match of all the matching sequences of the pathogenic microorganism; the similarity (Average, S) refers to the weighted strength of evidence for each match of the pathogenic microorganism; and the relative abundance (reads_ratio, A) refers to the normalized proportion of the number of valid matches of the pathogenic microorganism in the test sample to the total number of valid matches of all detected pathogenic microorganisms in the test sample.
[0040] For each pathogenic microorganism to be tested, a measurement index corresponding to each pathogenic microorganism is determined, and the actual measurement data corresponding to each measurement index of each pathogenic microorganism is determined by statistical calculation of the matching sequences.
[0041] S400: Based on the actual measurement data, determine the comprehensive score corresponding to each pathogenic microorganism to be tested.
[0042] The comprehensive score represents the proportion of comprehensive evidence for a certain pathogenic microorganism to the total evidence in the sample, which means the comprehensive relative probability that a certain pathogenic microorganism is the main pathogen in the sample. The comprehensive score ranges from 0 to 1. In the same sample, the sum of the comprehensive scores of all detected pathogenic microorganisms is 1.
[0043] In this embodiment, the actual measurement data corresponding to the number of matches, match length, similarity and relative abundance of each pathogenic microorganism to be tested are obtained. The comprehensive score corresponding to each actual measurement data is calculated, thus obtaining the comprehensive score corresponding to the pathogenic microorganism to be tested.
[0044] S500: Determine the target pathogenic microorganism corresponding to the sample to be tested based on the comprehensive score.
[0045] In practical applications, after determining the comprehensive score corresponding to each pathogenic microorganism to be tested, the comprehensive scores are arranged in descending order from high to low, and the pathogenic microorganism corresponding to the maximum comprehensive score is determined as the target pathogenic microorganism; the target pathogenic microorganism represents the suspected pathogen in the sample to be tested.
[0046] In a specific example, assuming the maximum all-ratio score for each pathogenic microorganism corresponding to sample P1 is 0.94, the target pathogen is identified as *Proteus mirabilis* based on this score. Similarly, assuming the maximum all-ratio score for each pathogenic microorganism corresponding to sample E1 is 0.99, the target pathogen is identified as chikungunya virus. Likewise, assuming the maximum all-ratio score for each pathogenic microorganism corresponding to sample S35 is 0.94, the target pathogen is identified as *Proteus mirabilis*. The suspected pathogens identified in the samples according to this embodiment are the same as those identified using traditional methods, such as microbial culture or metagenomic pipelines.
[0047] Of the 40 samples to be tested, 23 samples were found to contain pathogenic microorganisms according to the method of this embodiment, which is the same as the detection results of the traditional technical solution. This method can also detect pathogens such as Streptococcus pneumoniae and Granullicatella adiacens in 5 samples that are not identified by traditional methods. This method can also identify antibiotic resistance genes in the samples to be tested.
[0048] It should be noted that in practical applications, a visualization engine can also be invoked to generate corresponding charts based on the actual measurement data and comprehensive score of each pathogenic microorganism to be tested.
[0049] In addition, after identifying the target pathogenic microorganism corresponding to the sample to be tested, if a known antibiotic resistance gene with important clinical significance is detected in the target pathogenic microorganism, a notification can be sent via a red pop-up window.
[0050] This application provides a method for detecting pathogenic microorganisms. After determining multiple sequencing sequences corresponding to the sample to be tested, for each sequencing sequence, BLASTN alignment is performed with a reference database to identify matching sequences. For each pathogenic microorganism to be tested, actual measurement data corresponding to four dimensions of measurement indicators—number of matches, match length, similarity, and relative abundance—are determined based on the matching sequences. The actual measurement data corresponding to the four indicators are integrated to calculate a comprehensive score for each pathogenic microorganism to be tested. Finally, the target pathogenic microorganism corresponding to the sample is determined based on each comprehensive score. This multi-dimensional fusion strategy avoids the one-sidedness of a single indicator and can more comprehensively and evenly assess the presence of pathogenic microorganisms in the sample. Therefore, this method can improve the accuracy of pathogenic microorganism detection.
[0051] Based on the above embodiments, this embodiment further explains and optimizes the technical solution. Specifically, in this embodiment, based on each actual measurement data, a comprehensive score corresponding to each pathogenic microorganism to be tested is determined, including: The individual comprehensive evidence value of the pathogen to be tested is determined by multiplying the actual measurement data corresponding to the measurement index of the pathogen to be tested. Obtain the total comprehensive evidence value of all pathogenic microorganisms in the sample to be tested; The overall score of the pathogenic microorganism to be tested is determined based on the individual comprehensive evidence value and the total comprehensive evidence value.
[0052] Specifically, for each pathogenic microorganism to be tested, the actual measurement data corresponding to its four measurement indicators, namely the number of matches, the length of the match, the similarity, and the relative abundance, are multiplied together to obtain the individual comprehensive evidence value (all_sum).
[0053] In a specific instance, all_sum_i = M_i × L_i × S_i × A_i; where i represents the i-th pathogenic microorganism to be tested.
[0054] Specifically, the total aggregate evidence value (Σall_sum) represents the sum of the individual aggregate evidence values corresponding to all the pathogenic microorganisms in the sample.
[0055] In a specific instance, Σall_sum = all_sum_1 + all_sum_2 + … + all_sum_n; where n represents the total number of pathogenic microorganisms detected in the sample.
[0056] Specifically, the overall score (all_ratio) of the pathogen to be tested is determined based on the ratio of the individual comprehensive evidence value (all_sum) to the total comprehensive evidence value (Σall_sum).
[0057] In a specific instance, all_ratio_i = all_sum_i / Σall_sum, which is the comprehensive score corresponding to the i-th pathogenic microorganism to be tested.
[0058] The method described in this embodiment can efficiently and conveniently determine the comprehensive score corresponding to the pathogenic microorganism to be tested, thereby improving the accuracy and efficiency of pathogenic microorganism detection.
[0059] Based on the above embodiments, this embodiment further explains and optimizes the technical solution. Specifically, in this embodiment, multiple sequencing sequences corresponding to the sample to be tested are determined, including: Obtain the original sequence corresponding to the sample to be tested; Interference sequences were removed from the original sequence to obtain multiple sequencing sequences.
[0060] The raw sequence refers to the DNA or RNA sequence data that has been directly read and preliminarily converted from the sample to be tested using a nanopore sequencer, without any processing.
[0061] Interference sequences refer to sequences that can hinder or reduce the accuracy and efficiency of subsequent pathogen detection.
[0062] In this embodiment, after obtaining the original sequence corresponding to the sample to be tested, quality control software such as Filtlong is called to delete the interfering sequences in the original sequence to obtain the sequencing sequence. That is, the sequencing sequence is the sequence after deleting the interfering sequences from the original sequence.
[0063] Therefore, this embodiment uses sequencing sequences to detect pathogenic microorganisms, which can eliminate interfering factors and improve the accuracy and efficiency of pathogenic microorganism detection.
[0064] Based on the above embodiments, this embodiment further explains and optimizes the technical solution. Specifically, in this embodiment, the interfering sequences include sequences with a length less than a preset length threshold, sequences with an average sequencing quality score lower than a preset quality threshold, and host sequences.
[0065] The preset length threshold is a pre-defined critical value representing short sequences; in this embodiment, the preset length threshold can be set to 500 bp. It should be noted that sequences shorter than the preset length threshold carry limited information, making reliable alignment and species identification difficult. Therefore, after determining the original sequences, the length of each original sequence is checked, and sequences shorter than the preset length threshold (with fewer bases than the preset length threshold) are deleted. For example, in quality control software (such as Filtlong), the parameter -min_length 500 is set to delete all sequences shorter than 500 bases.
[0066] It should be noted that each base in the original sequence is assigned a base average sequencing quality score, called the Q-score, which represents the confidence level of the nanopore sequencer in correctly identifying that base. The preset quality threshold is a pre-defined critical value representing low confidence; in this embodiment, the preset quality threshold can be set to 90, indicating a base identification confidence level of approximately 99.9%. A base average sequencing quality score lower than the preset quality threshold indicates that the base itself has low confidence and cannot be used for accurate identification; therefore, sequences with a base average sequencing quality score lower than the preset quality threshold in the original sequence must be deleted. For example, in quality control software (such as Filtlong), the parameter -min_mean_q 90 can be set to delete all sequences with a base average sequencing quality score lower than 90 (Q-score < 90).
[0067] The host sequence refers to the DNA sequence derived from the provider of the sample to be tested (such as a human). It is understandable that the large volume of host sequence data will severely dilute the sequence proportion of the pathogenic microorganisms being tested, making it difficult to detect low-abundance pathogens, and consuming significant computational resources in subsequent alignment and detection, thus slowing down the analysis. In this embodiment, after determining the original sequence, the host sequence is screened out using Minimap2 software and then deleted.
[0068] In this embodiment, sequencing sequences are obtained by deleting sequences whose length is less than a preset length threshold, sequences whose average sequencing quality score is lower than a preset quality threshold, and host sequences from the original sequence. This ensures the validity of the sequencing sequences used in subsequent analyses, thereby improving the accuracy and efficiency of pathogen detection.
[0069] Based on the above embodiments, this embodiment further explains and optimizes the technical solution. Specifically, in this embodiment, the process of determining the matching length includes: For each matching sequence, the single weighting length is determined based on the original similarity between the matching sequence and the sequencing sequence and the length of the matching bases. The sum of the single-weighted lengths corresponding to each matching sequence is used to determine the matching length.
[0070] Here, original similarity refers to the proportion of bases that match between the sequencing sequence and its matching sequence; matching base length refers to the length of bases that match between the sequencing sequence and its matching sequence.
[0071] For each successful BLASTN match, i.e., for each matching sequence, a single-weighted length is determined; the single-weighted length = original similarity × matching base length. Specifically, for a given pathogenic microorganism, the sum of the single-weighted lengths corresponding to all its matching sequences is determined as the matching length.
[0072] In a specific instance, the matching length L = Σ[Identity(%) × Alignment Length]; where [Identity(%) represents the original similarity to each matching sequence; Alignment Length represents the length of the matching bases to each matching sequence.]
[0073] The matching length is determined according to the method of this embodiment, and the matching length is adjusted in real time by the original similarity to ensure the accuracy of the comprehensive score calculated based on the matching length and improve the accuracy of pathogen detection.
[0074] Based on the above embodiments, this embodiment further explains and optimizes the technical solution. Specifically, in this embodiment, the process of determining similarity includes: Similarity is determined by the ratio of match length to the number of matches.
[0075] Specifically, similarity represents the weighted strength of evidence for each match of a pathogen being tested, and is a comprehensive quality indicator that integrates match length and accuracy. Specifically, for a given pathogen being tested, for each matched sequence, the single-match weighted length is determined based on the original similarity between the matched sequence and the sequenced sequence, and the length of the matched bases; the sum of the single-match weighted lengths corresponding to each matched sequence is determined as the match length; the match length is divided by the number of matches of the pathogen being tested to obtain the similarity. That is, similarity S = L / M; Where L represents the matching length, which is L = Σ [Identity(%) × Alignment Length]; M is the number of matches corresponding to the pathogenic microorganism to be tested.
[0076] In this embodiment, considering that when a pathogenic microorganism has many matches (a large M value), but each matching sequence is short or has low similarity, the calculated similarity will be very small, thus reducing the overall score of the pathogenic microorganism. Therefore, this method can ensure the accuracy of the subsequent calculation of the overall score based on similarity, thereby improving the accuracy of pathogenic microorganism detection.
[0077] Based on the above embodiments, this embodiment further explains and optimizes the technical solution. Specifically, in this embodiment, the process of determining the relative abundance includes: The relative abundance is determined by the ratio of the number of matches of the pathogen to be tested to the total number of matches of all pathogens to be tested.
[0078] The number of matches (M) for the pathogenic microorganism to be tested refers to the number of times the sequencing sequence corresponding to the test sample matches the reference sequence in the reference database; a match means that the similarity between the sequencing sequence and the reference sequence reaches a preset similarity threshold. Multiple or redundant matches of the same sequencing sequence are considered as one match, i.e., the number of matches is 1.
[0079] The total number of matches for each pathogenic microorganism to be tested refers to the sum of the number of matches corresponding to each pathogenic microorganism to be tested, that is, the total number of matches for each pathogenic microorganism to be tested Mtotal=M1+M2+...+Mn, where n represents the number of pathogenic microorganisms to be tested detected; Mi represents the number of matches corresponding to the i-th pathogenic microorganism to be tested.
[0080] Relative abundance refers to the normalized proportion of the number of valid matches of the target pathogen in the test sample to the total number of valid matches of all detected target pathogens in the test sample. It quantifies the share of the target pathogen among all currently detected target pathogens, reflecting its relative importance among all target pathogens.
[0081] Specifically, the relative abundance is determined by the ratio of the number of matches of the pathogenic microorganisms to the total number of matches of all pathogenic microorganisms to be tested.
[0082] Suppose that after analyzing a sputum sample, three different pathogenic microorganisms are detected. The number of matches (M) for each pathogenic microorganism is as follows: Streptococcus pneumoniae: M1=150; Haemophilus influenzae: M2=30; Oral streptococci: M3=20; The total number of matches for each pathogenic microorganism to be tested, Mtotal, is 150 + 30 + 20 = 200. The relative abundance A of each pathogenic microorganism to be tested is as follows: The relative abundance of Streptococcus pneumoniae, A1 = 150 / 200 = 0.75; The relative abundance of Haemophilus influenzae, A2 = 30 / 200 = 0.15; The relative abundance of oral streptococci A3 = 20 / 200 = 0.10.
[0083] In this embodiment, the relative abundance of the pathogenic microorganism to be tested is further taken into account as its relative importance among all pathogenic microorganisms to be tested. This method can ensure the accuracy of the comprehensive score calculated based on the relative abundance, thereby improving the accuracy of pathogenic microorganism detection.
[0084] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.
[0085] It should be noted that the information collection process (such as the facial image collection process, fingerprint information collection process, etc.) / feature extraction process involved in this application is carried out with the user's knowledge and permission. That is, the information collection process / feature extraction process complies with the requirements of laws and regulations and does not constitute an act that harms the public interest.
[0086] Figure 2 The diagram shown is a schematic representation of a pathogen detection device provided in an embodiment of this application. Figure 2 As shown, the pathogen detection device of this embodiment includes a sequencing sequence determination module 210, a matching sequence determination module 220, an actual measurement data determination module 230, a comprehensive score determination module 240, and a detection result determination module 250; wherein, Sequencing sequence determination module 210 is used to determine multiple sequencing sequences corresponding to the sample to be tested; The matching sequence determination module 220 is used to perform BLASTN alignment between the sequencing sequence and a reference database for each sequencing sequence to determine the matching sequence that matches the sequencing sequence. The actual measurement data determination module 230 is used to determine the actual measurement data corresponding to the measurement indicators for each pathogenic microorganism to be tested based on the matching sequence; the measurement indicators include the number of matches, the match length, the similarity, and the relative abundance. The comprehensive score determination module 240 is used to determine the comprehensive score corresponding to each pathogenic microorganism to be tested based on the actual measurement data. The test result determination module 250 is used to determine the target pathogenic microorganism corresponding to the sample to be tested based on the comprehensive score.
[0087] The pathogen detection device provided in this application embodiment has the same beneficial effects as the pathogen detection method described above.
[0088] In one embodiment, the comprehensiveness score determination module 240 includes: The Individual Composite Evidence Value Determination Submodule determines the individual composite evidence value of the pathogenic microorganism to be tested based on the product of the actual measurement data corresponding to the measurement indicators of the pathogenic microorganism to be tested. The Total Synthesis Evidence Value Determination Submodule is used to obtain the total synthesis evidence value of all pathogenic microorganisms to be tested in the sample. The comprehensive score determination submodule is used to determine the comprehensive score of the pathogenic microorganism to be tested based on the individual comprehensive evidence value and the total comprehensive evidence value.
[0089] In one embodiment, the sequencing sequence determination module 210 includes: The original sequence acquisition submodule is used to acquire the original sequence corresponding to the sample to be tested; The deletion submodule is used to remove interfering sequences from the original sequence, resulting in multiple sequencing sequences.
[0090] In one embodiment, the interfering sequences include sequences with a length less than a preset length threshold, sequences with an average sequencing quality score lower than a preset quality threshold, and host sequences.
[0091] In one embodiment, the actual measurement data determination module 230 includes: The single-weighted length determination submodule is used to determine the single-weighted length of the base combination. The matching length determination submodule is used to determine the matching length by summing the single weighted lengths corresponding to each matching sequence.
[0092] In one embodiment, the actual measurement data determination module 230 includes: The similarity determination submodule is used to determine similarity based on the ratio of match length to the number of matches.
[0093] In one embodiment, the actual measurement data determination module 230 includes: The relative abundance determination submodule is used to determine the relative abundance based on the ratio of the number of matches of the pathogen to be tested to the total number of matches of all pathogens to be tested.
[0094] It should be noted that the information interaction and execution process between the above-mentioned devices / units are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, and they will not be repeated here.
[0095] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.
[0096] Figure 3 This is a schematic diagram of the structure of a terminal device provided in an embodiment of this application. Figure 3 As shown, the terminal device 300 of this embodiment includes a memory 310, a processor 320, and a computer program 330 stored in the memory 310 and executable on the processor 320; when the processor 320 executes the computer program 330, it implements the steps in the above-mentioned embodiments of the detection methods for pathogenic microorganisms; or when the processor 320 executes the computer program 330, it implements the functions of each module / unit in the above-mentioned embodiments of the devices.
[0097] For example, computer program 330 can be divided into one or more modules / units, one or more of which are stored in memory 310 and executed by processor 320 to implement the method of the embodiments of this application. One or more modules / units can be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of computer program 330 in terminal device 300. For example, computer program 330 can be divided into a sequencing sequence determination module, a matching sequence determination module, an actual measurement data determination module, a comprehensive score determination module, and a detection result determination module, with the specific functions of each module as follows: The sequencing sequence determination module is used to determine multiple sequencing sequences corresponding to the sample to be tested; The matching sequence determination module is used to compare the sequencing sequence with a reference database using BLASTN for each sequencing sequence to determine the matching sequence that matches the sequencing sequence. The actual measurement data determination module is used to determine the actual measurement data corresponding to the measurement indicators for each pathogenic microorganism to be tested, based on the matching sequence; the measurement indicators include the number of matches, the match length, the similarity, and the relative abundance. The comprehensive score determination module is used to determine the comprehensive score corresponding to each pathogenic microorganism to be tested based on the actual measurement data. The test result determination module is used to determine the target pathogenic microorganism corresponding to the sample to be tested based on the comprehensive score.
[0098] In applications, terminal device 300 can be a computing device such as a desktop computer, laptop, handheld computer, or cloud server. Terminal device 300 may include, but is not limited to, memory 310 and processor 320. Those skilled in the art will understand that... Figure 3 This is merely an example of a terminal device and does not constitute a limitation on the terminal device. It may include more or fewer components than shown, or combine certain components, or different components. For example, a terminal device may also include input / output devices, network access devices, buses, etc.; among which, input / output devices may include cameras, audio acquisition / playback devices, displays, etc.; network access devices may include communication modules for wireless communication with external devices.
[0099] In applications, the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor.
[0100] In applications, memory can be an internal storage unit of a terminal device, such as its hard drive or RAM; it can also be an external storage device, such as a plug-in hard drive, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card; or it can include both internal and external storage units. Memory is used to store operating systems, applications, boot loaders, data, and other programs, such as computer program code. Memory can also be used to temporarily store data that has been output or will be output.
[0101] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, can implement the steps in the above-described method embodiments.
[0102] This application implements all or part of the processes in the methods of the above embodiments, which can be accomplished by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or some intermediate form. The computer-readable medium can include at least: any entity or device capable of carrying the computer program code to a terminal device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, such as a USB flash drive, a portable hard drive, a magnetic disk, or an optical disk.
[0103] The computer-readable storage medium provided in this application embodiment has the same beneficial effects as the above-described method for detecting pathogenic microorganisms.
[0104] This application also provides a computer program product, including a computer program that, when executed by a processor, can implement the steps in the various method embodiments described above.
[0105] The computer program product provided in this application embodiment has the same beneficial effects as the above-mentioned method for detecting pathogenic microorganisms.
[0106] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.
[0107] Those skilled in the art will recognize that the device and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.
[0108] In the embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interface, or the device may be indirectly coupled or communicated, and may be electrical, mechanical, or other forms.
[0109] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application 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 of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.
Claims
1. A method for detecting pathogenic microorganisms, characterized in that, The method includes: Identify multiple sequencing sequences corresponding to the sample to be tested; For each sequencing sequence, the sequencing sequence is compared with a reference database using BLASTN to determine the matching sequence that matches the sequencing sequence; For each pathogenic microorganism to be tested, the actual measurement data corresponding to the measurement index is determined based on the matching sequence; the measurement index includes the number of matches, the match length, the similarity, and the relative abundance. Based on the actual measurement data, determine the comprehensive score corresponding to each of the pathogenic microorganisms to be tested; The target pathogenic microorganism corresponding to the sample to be tested is determined based on the comprehensive score of each sample.
2. The method according to claim 1, characterized in that, The determination of the comprehensive score corresponding to each of the tested pathogenic microorganisms based on the actual measurement data includes: The individual comprehensive evidence value of the pathogenic microorganism is determined by multiplying the actual measurement data corresponding to the measurement index of the pathogenic microorganism to be tested. Obtain the total comprehensive evidence value of all pathogenic microorganisms in the sample to be tested; The overall score of the pathogenic microorganism to be tested is determined based on the individual comprehensive evidence value and the total comprehensive evidence value.
3. The method according to claim 1, characterized in that, The determination of multiple sequencing sequences corresponding to the sample to be tested includes: Obtain the original sequence corresponding to the sample to be tested; Interference sequences were removed from the original sequence to obtain multiple sequencing sequences.
4. The method according to claim 3, characterized in that, The interfering sequences include sequences with a length less than a preset length threshold, sequences with an average sequencing quality score lower than a preset quality threshold, and host sequences.
5. The method according to any one of claims 1 to 4, characterized in that, The process of determining the matching length includes: For each matching sequence, the single weighted length is determined based on the original similarity between the matching sequence and the sequencing sequence and the length of the matching bases; The sum of the single-weighted lengths corresponding to each of the matching sequences is determined as the matching length.
6. The method according to claim 5, characterized in that, The process of determining the similarity includes: The similarity is determined based on the ratio of the matching length to the number of matches.
7. The method according to claim 5, characterized in that, The process of determining the relative abundance includes: The relative abundance is determined by the ratio of the number of matches of the pathogenic microorganisms to the total number of matches of all the pathogenic microorganisms to be tested.
8. A device for detecting pathogenic microorganisms, characterized in that, The device includes: The sequencing sequence determination module is used to determine multiple sequencing sequences corresponding to the sample to be tested; The matching sequence determination module is used to perform BLASTN alignment of each sequencing sequence with a reference database to determine the matching sequence that matches the sequencing sequence. The actual measurement data determination module is used to determine the actual measurement data corresponding to the measurement index for each pathogenic microorganism to be tested, based on the matching sequence; the measurement index includes the number of matches, the match length, the similarity, and the relative abundance. The comprehensive score determination module is used to determine the comprehensive score corresponding to each of the actual measurement data. The detection result determination module is used to determine the target pathogenic microorganism corresponding to the sample to be tested based on the comprehensive score.
9. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1 to 7.
10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.