Method, system, equipment and medium for analyzing cross contamination of pathogenic microorganism sample

Abstract

The invention provides a method, a system, equipment and a medium for analyzing cross contamination of pathogenic microorganism samples, and belongs to the technical field of pathogenic microorganism detection. The method comprises the following steps: obtaining sequencing data of pathogenic microorganism samples in the same batch, and obtaining the sequence number of each pathogenic microorganism species in each pathogenic microorganism sample; aiming at the specific species of the pathogenic microorganisms, respectively calculating the batch ratio # imgabs 0 # value and the batch # imgabs 1 # value of the specific species of the pathogenic microorganisms; and judging whether the species detected in the sample is the cross-contaminant species or not according to the batch ratio # imgabs2 # value and the batch # imgabs3 # value. By utilizing the method, the system, the equipment and the medium, whether the detected pathogenic microorganisms are real species or not can be accurately judged, false positive is effectively reduced, and the method, the system, the equipment and the medium have important application value.

Description

Technical Field

[0001] The present invention relates to the technical field of pathogenic microorganism detection, and in particular to a method, system, equipment and medium for analyzing cross contamination of pathogenic microorganism samples. Background Art

[0002] The current high-throughput sequencing platforms include third-generation sequencing platforms and second-generation sequencing platforms. The third-generation sequencing platform can obtain longer sequencing sequences (up to the level of millions of base pairs), but the sequencing quality is slightly lower; while the second-generation sequencing platform can obtain shorter sequencing reads (hundreds of base pairs) and higher sequencing quality.

[0003] The main advantage of mNGS is unbiased sampling, which makes it possible to widely identify known and unexpected pathogens and even discover new microorganisms. mNGS can also be combined with other targeted methods, such as using primers from conserved 16S ribosomal RNA (16S rRNA) and internal transcribed spacer (ITS) sequences for universal bacterial and fungal detection, respectively, so that these organisms can be identified at the species level. Another advantage of mNGS is that it can provide auxiliary genomic information required for evolutionary tracking, strain identification, and prediction of drug resistance. By counting sequencing reads, mNGS can provide quantitative or semi-quantitative data on the concentration of organisms in a sample, which is useful for studying polymicrobial samples or multiple pathogens associated with a disease.

[0004] Of course, mNGS also faces some technical problems or difficulties. One of the problems is the presence of microbial contaminants in the mNGS test samples, reagents used for processing, or laboratory environments, which can complicate the analysis and interpretation of the results. During routine clinical sample collection, even biopsies from potentially sterile parts of the body may be inadvertently contaminated, which may include contamination of the skin flora during fine needle puncture or oral flora during bronchoalveolar lavage. Therefore, reagent and workflow quality control procedures need to be strictly followed to maintain a testing environment that is as sterile and nucleic acid-free as possible. Negative controls, reagent evaluations, and regular swab tests are required to ensure that laboratory and sample cross-contamination does not produce false positive results. In addition, the laboratory must be familiar with the common microbial flora in a series of clinical samples for each sample type to be tested. Cross-contamination between samples in the same batch means that samples containing high-abundance pathogens cross-contaminate other samples, resulting in false positive results. Cross-contamination between samples may occur during sample collection, transportation, processing, and experimentation. For mNGS, cross-contamination may also be caused by incorrect assignment of high-frequency indices during mixed sequencing, resulting in index hopping. Summary of the invention

[0005] In view of the lack of a bioinformatics method with better performance for distinguishing and filtering cross-contamination of pathogen detection samples in the same batch in the prior art, the present invention aims to provide a method for distinguishing and filtering cross-contamination of samples in pathogen detection, which can reduce the impact of cross-contamination from the perspective of data analysis.

[0006] The first aspect of the present invention provides a method for analyzing cross contamination of pathogenic microorganism samples, comprising the following steps:

[0007] S101, obtained from the same batch Sequencing data of pathogenic microorganism samples, and obtain the number of sequences of each pathogenic microorganism species in each pathogenic microorganism sample;

[0008] S102, for the specific species of pathogenic microorganisms , calculate the batch proportion respectively Values ​​and Batches value:

[0009] ,

[0010] ,

[0011] in, Indicates species In the The proportion of batches in samples value; Indicates species In the The number of sequences in a sample; Indicates species In the batches in samples value; Indicates species The median number of sequences in all pathogenic microorganism samples; , indicating species The number of sequences in each pathogenic microorganism sample and The median of the absolute values ​​of the deviations, if =0, then =0,

[0012] S103, using the following conditions to determine when When greater than 0, species Is it the first Cross contamination species in samples:

[0013] If (1) is greater than a first preset threshold, or (2) is not greater than a first preset threshold and If it is not less than the second preset threshold, the species is considered Non Cross-contamination species in samples;

[0014] Otherwise, species For the Cross-contamination species in the samples.

[0015] In some embodiments of the present invention, the sequencing data is obtained using second-generation sequencing or third-generation sequencing.

[0016] In some embodiments of the present invention, the pathogenic microorganism sample is selected from one or more of a nasopharyngeal swab sample, an oral swab sample, a sputum sample, a bronchoalveolar lavage fluid sample, a pleural effusion sample, a cerebrospinal fluid sample, a serum sample, a plasma sample, a blood sample, a urine sample, a reproductive tract secretion sample, and a stool sample.

[0017] In some embodiments of the present invention, the first preset threshold is based on the proportion of cross-contamination sample batches in the population. The representative value is selected from the group consisting of the maximum value, the upper quartile, and the upper limit of the 95% confidence interval.

[0018] In some embodiments of the present invention, the presence of cross-contaminated samples in the population means that before batch testing, multiple methods are used to determine that there is no contamination by the species, and samples of the species are detected during batch testing.

[0019] In some other embodiments of the present invention, the first preset threshold can also be based on the batch ratio of the tested samples in the current batch. The representative value of the value is determined, and the representative value can be the mode or the median.

[0020] In some embodiments of the present invention, the second preset threshold is 3.

[0021] In some embodiments of the present invention, the pathogenic microorganism is a bacterium, a virus, a mycoplasma or a chlamydia.

[0022] A second aspect of the present invention provides a system for analyzing cross contamination of pathogenic microorganism samples, comprising the following modules:

[0023] Data input module, used to obtain the same batch The sequencing data of pathogenic microorganism samples includes the sequence number of each species of pathogenic microorganism;

[0024] A data storage module, used for storing reference genome sequences of pathogenic microorganisms;

[0025] A sequence number acquisition module, connected to the data input module and the data storage module respectively, for calculating the sequence number of each pathogenic microorganism species in each pathogenic microorganism sample;

[0026] A calculation module is connected to the sequence number acquisition module and is used to target specific species of pathogenic microorganisms. , calculate the batch proportion respectively Values ​​and Batches value:

[0027] ,

[0028] ,

[0029] in, Indicates species In the The proportion of batches in samples value; Indicates species In the The number of sequences in a sample; Indicates species In the batches in samples value; Indicates species The median number of sequences in all pathogenic microorganism samples; , indicating species The number of sequences in each pathogenic microorganism sample and The median of the absolute values ​​of the deviations, if =0, then =0;

[0030] The judgment module is connected to the calculation module and is used to judge when When greater than 0, species Is it the first Cross contamination species in samples:

[0031] If (1) is greater than a first preset threshold, or (2) is not greater than a first preset threshold and If it is not less than the second preset threshold, the species is considered Non Cross-contamination species in samples;

[0032] Otherwise, species For the Cross-contamination species in samples.

[0033] In some embodiments of the present invention, the sequencing data is obtained using second-generation sequencing or third-generation sequencing.

[0034] The third aspect of the present invention provides a computer device, comprising: a memory for storing a computer program; and a processor for implementing the steps of any method described in the first aspect of the present invention when executing the computer program.

[0035] A fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of any method described in the first aspect of the present invention are implemented.

[0036] Compared with the prior art, the present invention has the following beneficial effects:

[0037] By using the method of the present invention, for the detection of pathogenic microorganism samples in the same batch, it is possible to accurately determine whether the detected pathogenic microorganism is a real species based only on the sequence number, effectively reducing false positives, and having important application value.

[0038] The method, system, device and medium of the present invention can not only be used for clinical pathogenic microorganism detection, but also can be applied to multiple fields such as customs epidemic disease-related pathogen detection.

[0039] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood by reading the detailed description below with reference to the accompanying drawings. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-limiting manner, in which:

[0041] Figure 1 A schematic diagram of the process flow of the cross-contamination analysis method for detecting pathogenic species in the same batch of samples in Example 1 of the present invention is shown;

[0042] Figure 2 The batch ratio of Klebsiella pneumoniae in each sample in Example 2 of the present invention is shown Value distribution;

[0043] Figure 3 The batches of Klebsiella pneumoniae in each sample in Example 2 of the present invention are shown. Value distribution;

[0044] Figure 4 The distribution of the sequence numbers of Klebsiella pneumoniae in each sample in Example 2 of the present invention is shown. DETAILED DESCRIPTION

[0045] Unless otherwise indicated, implied from the context, or customary in the prior art, all parts and percentages in this invention are based on weight, and the tests and characterization methods used are all synchronized with the filing date of this invention. Where applicable, the contents of any patent, patent application or publication involved in this invention are fully incorporated herein by reference, and their equivalent patent families are also introduced as references. If the definition of a specific term disclosed in the prior art is inconsistent with any definition provided in this invention, the definition of the term provided in this invention shall prevail.

[0046] In order to make the technical problems, technical solutions and beneficial effects solved by the present invention more clearly understood, the present invention is further described in detail below in conjunction with embodiments.

[0047] The following examples are used to demonstrate preferred embodiments of the present invention. It will be appreciated by those skilled in the art that the techniques disclosed in the following examples represent techniques discovered by the inventors that can be used to implement the present invention and therefore can be considered as preferred embodiments of the present invention. However, it will be appreciated by those skilled in the art based on this specification that many modifications may be made to the specific embodiments disclosed herein and still achieve the same or similar results without departing from the spirit or scope of the present invention.

[0048] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs, and the disclosure and materials cited therein are hereby incorporated by reference.

[0049] Those skilled in the art will recognize, or be able to ascertain using no more than routine experimentation, many technical equivalents to the specific embodiments of the invention described herein. Such equivalents are intended to be encompassed by the claims.

[0050] The experimental methods in the following examples are conventional methods unless otherwise specified. The instruments and equipment used in the following examples are conventional laboratory instruments and equipment unless otherwise specified; the experimental materials used in the following examples are purchased from conventional biochemical reagent stores unless otherwise specified.

[0051] Example 1 Analysis of cross contamination of pathogenic species detection samples from the same batch

[0052] refer to Figure 1 The present invention provides a method for analyzing cross contamination of pathogenic microorganism samples in the same batch, referring to Figure 1 , each step is described in detail as follows:

[0053] S101, obtained from the same batch The sequencing data of pathogenic microorganism samples were collected, and the number of sequences of each pathogenic microorganism species in each pathogenic microorganism sample was obtained.

[0054] For a species , can be expressed as a vector of sequence numbers, that is, , where n is the total number of pathogenic microorganism samples, For the Species in the samples The sequence number, it should be noted that, Can be 0, that is, Species not present in samples .

[0055] S102, for the specific species of pathogenic microorganisms , calculate the batch proportion respectively Values ​​and Batches value:

[0056] ,

[0057] ,

[0058] in,

[0059] Indicates species In the The proportion of batches in samples value;

[0060] Indicates species In the batches in samples value;

[0061] Indicates species The median number of sequences in all pathogenic microorganism samples;

[0062] , indicating species The number of sequences in each pathogenic microorganism sample and The median of the absolute values ​​of the deviations, if =0, then =0. That is, calculate When the value is, the species is first calculated The absolute value of the deviation between the number of sequences in each pathogenic microorganism sample and the median is then taken as the median of this set of absolute values. As the denominator, if the calculated If the value is 0, the corresponding The value is set to 0, which means that the default does not deviate from the median.

[0063] It should be noted that the robust zscore method is used here to calculate the batch value, compared to the normal The value calculation method is more accurate, less susceptible to outliers, more robust, and more reliable. When , it means that the number of sequences is higher than the median of all pathogenic microorganism samples, which is an outlier and is more likely to be a real species detection signal.

[0064] S103, using the following conditions to determine when When greater than 0, species Is it the first Cross contamination species in samples:

[0065] If (1) is greater than a first preset threshold, or (2) is not greater than a first preset threshold and If it is not less than the second preset threshold, the species is considered Non Cross-contamination species in samples;

[0066] Otherwise, species For the Cross-contamination species in the samples.

[0067] Species Is it the first The cross contamination species in a sample depends on and Two values:

[0068] when When it is greater than 2% (the first preset threshold), it indicates that the species In the The batch ratio in each sample is high and the probability of cross-contamination is low. It needs to be retained for subsequent analysis as the real detected species.

[0069] when When it is less than 2%, the probability of cross contamination is high, please check further Value, if If it is not less than 3 (the second preset threshold), the species is considered No. If the number of sequences in a sample is much larger than the overall median, the filtering status is No (passed) and it needs to be retained in subsequent analysis as a truly detected species.

[0070] In addition to the above two cases, is not greater than a first preset threshold and is less than the second preset threshold, then the species For the The cross-contamination species in the samples, the species in the subsequent analysis For the Sequences in the samples need to be filtered out to prevent false positive results.

[0071] Example 2 Cross-contamination analysis of sequencing data of bronchoalveolar lavage fluid samples from the same batch

[0072] In this example, 25 samples were selected for testing, including 20 clinical bronchoalveolar lavage fluid samples, 5 standard samples and 1 NTC template-free negative control sample. The sample information is shown in Table 1.

[0073] Table 1 Sample information table

[0074]

[0075] Targeted multiple panels were used for detection, and the species included in the panel were specifically amplified, then mixed into a sequencing library, and nanopore third-generation sequencing was performed to obtain preliminary species sequence number results. On this basis, a cross-contamination discrimination algorithm was used for filtering and marking, and the detection effect of the method for analyzing cross-contamination of pathogenic microorganism samples of the present invention was further evaluated.

[0076] The specific data processing steps are as follows:

[0077] (1) Data preprocessing.

[0078] Preprocess the fastq data files of each sample obtained by sequencing to obtain valid data. Each fastq file contains the sequencing sequence of the sample, also known as reads. Use porechop software to remove sequencing adapters (default parameters). Use Filtlong software to process low-quality and short sequences, and filter and retain sequences with an average sequencing quality score (controlled by the --min_mean_q parameter) of not less than 70 points and a length (controlled by the --min_length parameter) of not less than 80bp.

[0079] (2) Reference genome alignment count.

[0080] Use minimap2 software to align the preprocessed sequence to the reference genome sequence containing the target region within the product detection range, and also to the host reference genome. The minimap2 alignment mode parameter is "-x map-ont" to support Nanopore 3rd generation nanopore data. Filter out results with alignment similarity less than 90% or alignment coverage (the percentage of sequence alignment region to total sequence length) less than 80%. Finally, select the reference sequence species classification with the highest alignment score as the species classification of the input sequencing reads. Finally, count the reads of all species classifications to obtain the number of sequences of all species for a single sample.

[0081] For the species sequence number results of all samples, cross-contamination analysis was performed using the method of Example 1.

[0082] For Klebsiella pneumoniae, the number of sequences and batch proportions in each sample Values ​​and Batches The values ​​are Figure 2 , Figure 3 and Figure 4 shown.

[0083] Among them, the proportion of Klebsiella pneumoniae in the batches of samples numbered b02 and b07 The values ​​are relatively high, reaching 49.7843% and 43.6413% respectively, which can be directly considered as the real detected species. For other samples (including standard products), the batch ratio The values ​​are all below 2%.

[0084] Further use of batches The values ​​were further judged and it was found that the batches of Klebsiella pneumoniae in the five standards (b21~b25) The values ​​are all greater than 3, and are judged to be real detected species rather than cross-contaminated species, which is consistent with the actual situation.

[0085] The results of cross-contamination analysis of Acinetobacter baumannii in all samples are shown in Table 2 .

[0086] Table 2 Results of cross-contamination analysis of Acinetobacter baumannii in various samples

[0087]

[0088] The results of cross-contamination analysis of Klebsiella pneumoniae in all samples are shown in Table 3.

[0089] Table 3 Results of cross-contamination analysis of Klebsiella pneumoniae in each sample

[0090]

[0091] The results of the cross-contamination analysis of Streptococcus pneumoniae in all samples are shown in Table 4.

[0092] Table 4 Results of cross contamination analysis of Streptococcus pneumoniae in various samples

[0093]

[0094] The results of the cross-contamination analysis of Mycoplasma pneumoniae in all samples are shown in Table 5.

[0095] Table 5 Results of cross contamination analysis of Mycoplasma pneumoniae in each sample

[0096]

[0097] The results of cross-contamination analysis of influenza A virus in all samples are shown in Table 6.

[0098] Table 6 Results of cross contamination analysis of influenza A virus in various samples

[0099]

[0100] The results of cross contamination analysis of Staphylococcus aureus in all samples are shown in Table 7.

[0101] Table 7 Results of cross contamination analysis of Staphylococcus aureus in each sample

[0102]

[0103] The results of cross-contamination analysis of Enterococcus faecium in all samples are shown in Table 8.

[0104] Table 8 Results of cross contamination analysis of Enterococcus faecium in each sample

[0105]

[0106] As shown in Tables 2 to 8, the compliance rates of the cross-contamination analysis and judgment results of Acinetobacter baumannii, Klebsiella pneumoniae, Streptococcus pneumoniae, Mycoplasma pneumoniae, influenza A virus, Staphylococcus aureus and Enterococcus faecium in all samples were 100%, indicating that the method for analyzing the cross-contamination of pathogenic microorganism samples in the same batch in Example 1 can effectively detect the cross-contaminated species in the pathogenic microorganism samples.

[0107] In addition, it should be understood that after reading the above teachings of the present invention, those skilled in the art may make various changes or modifications to the present invention, and these equivalent forms also fall within the scope defined by the claims attached to the present invention.

Claims

1. A method for analyzing cross contamination of pathogenic microorganism samples, characterized in that: The following steps are involved: S101, obtained from the same batch Sequencing data of pathogenic microorganism samples, and obtain the number of sequences of each pathogenic microorganism species in each pathogenic microorganism sample; S102, for the specific species of pathogenic microorganisms , calculate the batch proportion respectively Values ​​and Batches value: ， ， in, Indicates species In the The proportion of batches in samples value; Indicates species In the The number of sequences in a sample; Indicates species In the batches in samples value; Indicates species The median number of sequences in all pathogenic microorganism samples; , indicating species The number of sequences in each pathogenic microorganism sample and The median of the absolute values ​​of the deviations, if =0, then =0, S103, using the following conditions to determine when When greater than 0, species Is it the first Cross contamination species in samples: If (1) is greater than a first preset threshold, or (2) is not greater than a first preset threshold and If it is not less than the second preset threshold, the species is considered Non Cross-contamination species in samples; Otherwise, species For the Cross-contamination species in the samples.

2. The method according to claim 1, characterized in that The sequencing data is obtained using second-generation sequencing or third-generation sequencing.

3. The method according to claim 1, characterized in that The pathogenic microorganism sample is selected from one or more of a nasopharyngeal swab sample, an oral swab sample, a sputum sample, an alveolar lavage fluid sample, a pleural effusion sample, a cerebrospinal fluid sample, a serum sample, a plasma sample, a blood sample, a urine sample, a reproductive tract secretion sample, and a stool sample.

4. The method according to claim 1, characterized in that: The first preset threshold is based on the proportion of cross-contamination sample batches in the population The representative value is selected from the group consisting of the maximum value, the upper quartile, and the upper limit of the 95% confidence interval.

5. The method according to claim 1, characterized in that The second preset threshold is 3.

6. The method according to any one of claims 1 to 5, characterized in that: The pathogenic microorganism is bacteria, virus, mycoplasma or chlamydia.

7. A system for analyzing cross contamination of pathogenic microorganism samples, characterized in that: Includes the following modules: Data input module, used to obtain the same batch The sequencing data of pathogenic microorganism samples includes the sequence number of each species of pathogenic microorganism; A data storage module, used for storing reference genome sequences of pathogenic microorganisms; A sequence number acquisition module, connected to the data input module and the data storage module respectively, for calculating the sequence number of each pathogenic microorganism species in each pathogenic microorganism sample; A calculation module is connected to the sequence number acquisition module and is used to target specific species of pathogenic microorganisms. , calculate the batch proportion respectively Values ​​and Batches value: ， ， in, Indicates species In the The proportion of batches in samples value; Indicates species In the The number of sequences in a sample; Indicates species No. batches in samples value; Indicates species The median number of sequences in all pathogenic microorganism samples; , indicating species The number of sequences in each pathogenic microorganism sample and The median of the absolute values ​​of the deviations, if =0, then =0, The judgment module is connected to the calculation module and is used to judge when When greater than 0, species Is it the first Cross contamination species in samples: If (1) is greater than a first preset threshold, or (2) is not greater than a first preset threshold and If it is not less than the second preset threshold, the species is considered Non Cross-contamination species in samples; Otherwise, species For the Cross-contamination species in the samples.

8. The system according to claim 7, characterized in that The sequencing data is obtained using second-generation sequencing or third-generation sequencing.

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

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

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