CN110875082A - Microorganism detection method and device based on targeted amplification sequencing - Google Patents
Microorganism detection method and device based on targeted amplification sequencing Download PDFInfo
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Abstract
A microorganism detection method and a device based on target amplification sequencing. The method of the invention comprises the following steps: obtaining a target amplification sequencing sequence of a sample to be detected; aligning the targeted amplification sequencing sequence to a target sequence database, the target sequence database comprising a plurality of targets, each target for identifying one of a plurality of signatures for a population of microorganisms, each target having a set detection threshold; counting the detected sequence number of each target, wherein the detected sequence number comprises the sequence number only compared to the target and the sequence number obtained by dividing the optimal comparison score by the suboptimal comparison score and is less than a set value, and comparing the detected sequence number of each target with the detected threshold value of each target to obtain the detection result of the microorganism to be detected. The invention has the advantages of high detection precision and flux, wide detection range, high automation degree and low requirement on computer technical conditions.
Description
Technical Field
The invention relates to the technical field of microbial detection, in particular to a microbial detection method and a microbial detection device based on targeted amplification sequencing.
Background
The traditional detection method of microorganisms mainly comprises the following steps: (1) and (6) microscopic examination. Microorganisms characterized in morphology and staining were observed by direct smear staining and then observed by microscope. (2) And (5) detecting and culturing. The culture is tested by selecting appropriate culture medium, pH, culture time, temperature, etc. to provide the necessary conditions for the growth of the specific microorganism. (3) And (4) performing biochemical tests. The biochemical reaction is based on the fact that various microorganisms have different enzyme systems and different metabolites produced by the decomposition of nutrients, so that the microorganisms are identified. (4) And (4) serological identification. According to the specificity of the reaction of the corresponding antigen and the antibody, immune serum containing known specific antibodies is adopted to identify the genus, species and serotype of the separated microorganism to be detected. (5) And (3) detecting antigen and antibody. Unknown antigenic components are tested with known specific antibodies. (6) A method of molecular biology. Since different species of microorganisms have different genes or nucleotide sequences, detection can be carried out by detecting the presence or absence of a gene sequence specific to the microorganism. The conventional methods mainly include nucleic acid hybridization and Polymerase Chain Reaction (PCR).
At present, the detection of microorganisms mainly depends on the traditional detection culture, and the method has long time consumption and high omission ratio. On one hand, the empirical medication increases the economic burden of patients and easily delays the optimal diagnosis and treatment time of the patients, and on the other hand, the abuse of antibiotics can cause serious drug resistance effect. Therefore, a method for detecting microbial infection, which can quickly, accurately and comprehensively identify unknown causes, is urgently needed in the market.
High-throughput sequencing is very beneficial to the detection of infectious pathogens due to its low cost, high throughput, wide detection range and fast time. The targeted amplification technology can be used for simultaneously carrying out targeted amplification on thousands of amplicons. As the technology is a new technology, no mature matching information analysis flow exists. Therefore, a high-quality information analysis process needs to be constructed in the field, so that the detection precision is effectively improved, the report reading is assisted, and the market popularization of the products is facilitated.
Disclosure of Invention
The invention provides a microorganism detection method and a microorganism detection device based on targeted amplification sequencing, which have the advantages of high detection precision and flux, wide detection range, high automation degree and low requirement on computer technical conditions.
According to a first aspect, there is provided in one embodiment a method for targeted amplification sequencing-based detection of microorganisms, comprising:
obtaining a target amplification sequencing sequence of a sample to be detected, wherein the sample to be detected contains at least one characteristic mark of a possible microorganism to be detected, and each characteristic mark comprises at least one target for marking the characteristic mark;
aligning the target amplified sequencing sequence to a target sequence database, the target sequence database comprising a plurality of targets, each target for identifying one of a plurality of signatures of a population of microorganisms, each target having a set detection threshold;
counting the detected sequence number of each target, wherein the detected sequence number comprises the sequence number only compared to the target and the sequence number of which the result of dividing the optimal comparison score by the suboptimal comparison score is less than a set value, the optimal comparison score is the score compared to the target, and the suboptimal comparison score is the score compared to other targets; and
comparing the detection sequence number of each target with the detection threshold value of each target, wherein the detection sequence number is larger than the detection threshold value, namely the detection is carried out, and determining the detection result of each characteristic marker according to the detection result of each target, namely the detection result of the microorganism to be detected is obtained.
Preferably, the detection threshold for each target can be determined by ROC curve method or negative sample detection sequence number distribution method. The ROC curve method comprises the steps of taking a positive sample (a sample which is determined to contain the characteristic mark corresponding to the target) and a negative sample (a sample which is determined not to contain the characteristic mark corresponding to the target) as research objects, obtaining the detected sequence number of the target corresponding to each characteristic mark, drawing an ROC curve according to the detected sequence number of the corresponding target, determining the detected sequence number of the corresponding target based on the point with the optimal ROC curve sensitivity and specificity, and obtaining the detected threshold value of each target.
Preferably, the characteristic markers comprise one or more of pathogen, drug resistance gene and virulence factor; preferably, the pathogen is selected from one or more of bacteria, DNA/RNA viruses, fungi and protozoa.
As a preferred embodiment, each signature comprises at least 3 targets for identifying the signature; preferably, greater than 50% of the targets detected in each signature are considered signature detected.
Preferably, the target sequence database further includes an internal reference target selected from a gene sequence conserved in the host of the microorganism.
Preferably, the population of microorganisms is a syndrome; preferably, the above syndrome is selected from the group consisting of blood influenza infection syndrome, encephalitis meningitis syndrome, respiratory infection syndrome or diarrhea syndrome, more preferably blood flow infection syndrome or encephalitis meningitis syndrome.
Preferably, the set value is 0.8.
As a preferred technical solution, the target amplification sequencing sequence further comprises a host sequence, and the method further comprises:
and (3) comparing other sequences obtained after the sequence compared with the target sequence database is removed with a host genome database, and calculating the proportion of the host sequence in the sample to be detected for quality control of the detection result.
As a preferred solution, the host genome database comprises two parts, a human reference genome downloaded from the NCBI official website and a yellow genome sequence downloaded from the yellow genome public database official website.
The method comprises the steps of synchronously detecting a plurality of samples to be detected, and graphically outputting the detection result of at least one target in the plurality of samples to be detected which are synchronously detected to determine the existence of cross contamination, wherein the samples to be detected are preferably the same batch of samples of the same type.
According to a second aspect, there is provided in one embodiment a microbial detection apparatus based on targeted amplification sequencing, comprising:
the system comprises an acquisition unit, a sequencing unit and a sequencing unit, wherein the acquisition unit is used for acquiring a target amplification sequencing sequence of a sample to be detected, the sample to be detected contains at least one characteristic mark of a microorganism to be detected, and the characteristic mark comprises at least one target for marking the characteristic mark;
an alignment unit for aligning the target amplification sequencing sequence to a target sequence database, the target sequence database comprising a plurality of targets, each target for identifying one of a plurality of signatures of a population of microorganisms, each target having a set detection threshold;
the counting unit is used for counting the detected sequence number of each target, wherein the detected sequence number comprises the sequence number only compared to the target and the sequence number of which the result of dividing the optimal comparison score by the suboptimal comparison score is less than a set value, the optimal comparison score is the score compared to the target, and the suboptimal comparison score is the score compared to other targets; and
and a comparison unit for comparing the detection sequence number of each target with the detection threshold value of each target, wherein the detection sequence number is larger than the detection threshold value to obtain the detection result, and the detection result of each characteristic mark is determined according to the detection result of each target to obtain the detection result of the microorganism to be detected.
According to a third aspect, an embodiment provides a computer readable storage medium comprising a program executable by a processor to implement the method of the first aspect.
The detection method has the advantages of high detection precision and flux, wide detection range, simple operation and short detection period, and can finish detection within 2 working days (the fastest 24 hours). In addition, the detection cost is low, the expandability is high, and in a preferred embodiment, the method can be widely applied to detection of various syndromes. Aiming at a specific syndrome, related pathogenic microorganisms can be accurately determined.
Drawings
FIG. 1 is a flow chart of a microorganism detection method based on targeted amplification sequencing according to an embodiment of the present invention;
FIG. 2 is a block diagram of a microorganism detection apparatus based on target-oriented amplification sequencing according to an embodiment of the present invention;
FIG. 3 shows the detection results of all cerebrospinal fluid samples of 3 targets of Human _ heresvirus _6 pathogen in the batch according to the present invention, wherein the abscissa represents the Sample Number (Sample) and the ordinate represents the detected sequence Number (Reads Number).
Detailed Description
The present invention will be described in further detail with reference to the following detailed description and accompanying drawings. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention. However, those skilled in the art will readily recognize that some of the features may be omitted or replaced with other elements, materials, methods in different instances.
Furthermore, the features, operations, or characteristics described in the specification may be combined in any suitable manner to form various embodiments. Also, the various steps or actions in the method descriptions may be transposed or transposed in order, as will be apparent to one of ordinary skill in the art. Thus, the various sequences in the specification and drawings are for the purpose of describing certain embodiments only and are not intended to imply a required sequence unless otherwise indicated where such sequence must be followed.
In one embodiment, as shown in FIG. 1, the present invention provides a method for detecting microorganisms based on targeted amplification sequencing, comprising the following steps:
s101: obtaining a target amplification sequencing sequence of a sample to be detected, wherein the sample to be detected contains at least one characteristic mark of a possible microorganism to be detected, and each characteristic mark comprises at least one target for identifying the characteristic mark.
In the embodiment of the present invention, the "sample to be detected", that is, the sample to be detected, may be a clinical sample, including a healthy person sample, such as a healthy person blood sample, a cerebrospinal fluid sample, and the like. Extracting nucleic acid (such as DNA) from the samples by a known technology in the field to obtain nucleic acid sequence fragments in the samples, constructing the nucleic acid library to obtain a nucleic acid library for sequencing, and amplifying target fragments by using specific targeting primers in the library construction process to obtain a targeting amplification sequencing sequence after sequencing. The sequencing platform is not limited, and can be any second generation high throughput sequencing platform, including but not limited to Illumina, Ion Torrent, BGISEQ or MGISEQ sequencing platform, preferably BGISEQ sequencing platform, such as BGISEQ-500, BGISEQ-50 or BGISEQ-2000 sequencing platform, and the like.
The sample to be detected may contain the microorganism to be detected, the sample containing the microorganism to be detected is a positive sample, and the sample containing no microorganism to be detected is a negative sample. The "microorganism to be detected" may be any microorganism such as bacteria, DNA/RNA viruses, fungi and protozoa. In the present invention, since the microorganism to be detected is identified by the corresponding characteristic marker, the positive sample can be regarded as a sample containing the corresponding characteristic marker. For example, sample a contains signature 1, then sample a is a positive sample for the target of signature 1; sample B does not contain signature 2, then sample B is a negative sample for the target of signature 2.
In the case where the microorganism to be tested is a pathogenic microorganism, the detection result of the present invention is not directly related to a disease, that is, in the case where the detection result of the microorganism of the present invention is obtained, a diagnosis result of a disease or a health condition cannot be directly obtained. Thus, the method of the present invention is not directed to obtaining a diagnosis of a disease or a health condition, but is directed to obtaining the presence or absence of a microorganism of interest (including a pathogenic microorganism) in a sample to be tested. Thus, the method of the present invention is not essentially a disease diagnostic method. For example, in the case where the pathogenic microorganism is hepatitis b virus, it is not possible to directly determine whether the main body of the specimen (e.g., blood) is hepatitis b patient by detecting the presence of hepatitis b virus, because the presence of hepatitis b virus in the blood reflects that the main body of the blood is a carrier of hepatitis b virus, but the carrier of hepatitis b virus may be a carrier of hepatitis b virus with normal liver function and a carrier of hepatitis b virus with impaired liver function. Among hepatitis B virus carriers with normal liver functions, the virus detection results of some carriers can naturally turn negative, and the carrying state is finished; some carriers may be lifelong carriers; some carriers develop hepatitis. Therefore, it is impossible to directly determine whether a subject has hepatitis B or the risk of having hepatitis B based on the presence of hepatitis B virus in blood. Similarly, the detection of other microorganisms to be detected does not directly determine the occurrence or absence of the relevant disease.
In addition, the method of the invention essentially belongs to a bioinformatics analysis method, and the starting point of the method is targeted amplification sequencing sequence data, rather than experimental treatment directly performed on a human body or an in vitro sample of the human body.
In one embodiment of the invention, the microorganism to be tested contains one or more characteristic markers, which are used to characterize the presence of the microorganism to be tested. Such signature markers may be resistance genes, virulence factors, etc. of the pathogen and the corresponding pathogen, such as bacteria, DNA/RNA viruses, fungi, protozoa, etc.
In the present embodiment, the "target amplification sequencing sequence" refers to a sequence obtained by specifically and targeted amplifying a specific nucleic acid fragment (e.g., gene) and sequencing the sequence. By "specific nucleic acid fragment" can be meant a nucleic acid sequence (e.g., a conserved gene sequence) that is unique to a particular microorganism and whose presence is capable of specifically characterizing the presence of the relevant microorganism in a sample. In embodiments of the invention, targeted amplification may be performed according to techniques well known in the art. The targeted amplification can be multiplex amplification under the guidance of multiple primers, namely, corresponding amplification primers are designed aiming at multiple specific nucleic acid fragments simultaneously, and synchronous amplification is realized in the same system.
In the embodiments of the present invention, the target amplification sequencing sequence is also referred to as "target amplification sequencing sequence data", and refers to a sequence obtained by performing target amplification on a microbial genome (preferably, also including a host genome, such as a human genome) by using a target amplification technology, and then sequencing the amplification result, including a series of sequencing reads (reads), including the original sequencing data, and also including the data after further processing. However, in order to improve alignment accuracy, to remove the effects of sequences with shorter linker sequences, shorter read lengths, and lower sequencing quality values, it is preferable to filter the target amplified sequencing sequences. For example, in one embodiment, sequences sharing contiguous 10bp bases with the linker sequence are filtered out; filtering out sequences with read length lower than a certain threshold (default 50 bp); and filtering out sequences having a base percentage of sequencing quality value of less than 5 of more than 50%.
In the present embodiment, the term "target" refers to a sequence used for identifying the existence of a signature, such as a part of a sequence segment in a drug resistance gene or a virulence factor gene, which may be a sequence unique to and conserved in the gene, and each signature may have multiple targets, for example, in the case where the signature is a gene, the targets may be multiple exons (or introns) of the gene. In preferred examples, the signature includes at least 3 targets for identifying the signature. More than 50% of the targets of each signature are considered to be detected, e.g., in the case of 3 targets for a signature, more than 2 (including 2) targets are detected, indicating that the signature is detected. In general, the greater the number of targets used to identify a signature, the more accurate the determination of the presence of that signature in a sample.
S102: aligning the target amplification sequencing sequence to a target sequence database, wherein the target sequence database comprises a plurality of targets, each target is used for identifying one of a plurality of characteristic markers of a group of microorganisms, and each target has a set detection threshold.
In order to obtain a high-quality comparison result, in the embodiment of the present invention, the comparison result may be screened according to a certain principle. In one embodiment of the present invention, the high quality alignment result is obtained by screening according to the following principle: (a) principle one: reserving sequences with the alignment length ratio of more than 90 percent, namely sequences with the length of a reference sequence of more than 90 percent of the full length of the sequences on a single sequence alignment; (b) principle two: reserving sequences with mismatched base number less than 5%, namely sequences with inconsistent base number ratio less than 5% generated by sequencing error on the part of alignment and the reference sequence; (c) principle three: keeping the sequence of the alignment specificity, and if one sequence is aligned in different target areas, screening the specific alignment sequence according to the score difference ratio (namely, the ratio of suboptimal alignment divided by optimal alignment is less than a set value, such as 0.8) of the multiple alignment results to obtain a 'unique' alignment sequence.
In embodiments of the invention, the target sequence database comprises primarily targets for identifying a population of microorganisms (e.g., a syndrome). In preferred embodiments, the target sequence database further comprises an internal reference target, i.e., a conserved sequence in the host genome (e.g., a region on the HFM gene of the human genome) that is present in all host samples and can serve as an internal reference. That is, if the detection result of a sample does not have a microbial target sequence, but has a certain reference target sequence, it can be said that there is no problem in the amplification step in the experiment; if there is no internal reference target sequence, there is no microbial target sequence in the detection result, and it cannot be excluded whether there is no microbial target sequence in the sample originally or the experimental amplification is unsuccessful.
In one embodiment of the invention, at least 3 targets are screened for each pathogen (or drug resistance gene, virulence factor) to be detected for a syndrome, together with internal reference targets to form a target sequence database.
In the present embodiment, the term "a group of microorganisms" refers to a syndrome. Such syndrome may be selected from, for example, bloodstream infection syndrome, encephalitis meningitis syndrome, respiratory tract infection syndrome, or diarrhea syndrome, more preferably bloodstream infection syndrome or encephalitis meningitis syndrome.
In the embodiment of the invention, each target has a set detection threshold. To determine the detection threshold for each target, for each signature (pathogen and its resistance gene or virulence factor), the detection threshold can be determined using a receiver operating characteristic curve (ROC curve) method. In one embodiment of the present invention, for a pathogen (or a drug-resistant gene, a virulence factor), more than 30 positive samples and more than 800 negative samples (samples in which the pathogen or the drug-resistant gene, or the virulence factor is not detected) are used to obtain the detected sequence number of the target corresponding to the pathogen (or the drug-resistant gene, or the virulence factor) in each sample, an ROC curve is drawn according to the detected sequence number of the corresponding target, and the detected sequence number of the corresponding target is obtained based on the point at which the sensitivity and specificity of the ROC curve are optimal, so as to obtain the detected threshold value of each target. An alternative method of determining the detection threshold for each target further comprises determining the detection threshold using a distribution of negative sample detection sequence numbers. For example, assume that for the first target of a pathogen, there are 30 positive samples and 800 negative samples. The detected values of 800 negative samples are fitted by a distribution function (such as normal distribution), and the parameters of the distribution are calculated, so that 95% quantile points of the distribution are calculated and used as the detection threshold value of the target.
In the embodiment of the present invention, the sample to be tested is usually derived from host tissue fluid (e.g., blood or cerebrospinal fluid), therefore, a component of the sample nucleic acid is host nucleic acid, after the target amplification, a part of the host source sequence exists in the sequencing result, which provides more comprehensive information for enriching the detection result index. Specifically, in one embodiment of the present invention, other sequences after the sequences aligned to the target sequence database are removed are aligned to the host genome database, and the proportion of the host sequences in the sample to be detected is calculated for quality control of the detection result. For example, when the alignment length of the sequence reaches 80%, the sequence is determined as the host sequence. In one embodiment of the present invention, if the ratio of host sequences in a sample result is high (e.g., greater than 50%), and the internal reference ratio is small (e.g., less than 10%), and the target detection result does not reach the detection threshold, the sample is indicated to fail amplification and needs to be redone. A typical but non-limiting host genome sequence database comprises the human reference genome (hg19) downloaded from the NCBI official website and the yellow genome sequence downloaded from the yellow genome public database official website.
S103: counting the detected sequence number of each target, wherein the detected sequence number comprises the sequence number only compared to the target and the sequence number of which the result of dividing the optimal comparison score by the suboptimal comparison score is smaller than a set value, the optimal comparison score is the score compared to the target, and the suboptimal comparison score is the score compared to other targets.
In the embodiment of the present invention, the "detected sequence number" is also referred to as "unique alignment sequence number", and includes two parts: (a) if a sequence is aligned to only the target (i.e., "current target"), then that sequence is considered a detected sequence for the target; (b) if a sequence is aligned to the target (i.e., "current target") and is also aligned to other targets, but the current target is the optimal alignment target, i.e., the target with the highest alignment score, and the alignment score to the next best alignment target (i.e., the second highest alignment score) divided by the alignment score to the optimal alignment target is less than the predetermined value, such a sequence is also considered to be the only alignment sequence for the current target. The sequence satisfying any of the above (a) and (b) is referred to as "the number of detected sequences" of the current target.
In a preferred embodiment of the invention, the above stated value is 0.8, i.e. sequences with less than 0.8 of the result of the sub-optimal alignment divided by the optimal alignment are also considered "uniquely aligned sequences". For example, a sequence that has a score of 50 for alignment to the a target and a score of 30 for alignment to the B target is considered the only aligned sequence for the a target since 30/50< 0.8. The number of all unique aligned sequences of the A target is the "number of unique aligned sequences".
In the embodiment of the present invention, the "detected sequence number" or "unique aligned sequence number" may be raw data, i.e., the number of original sequences, or may be data obtained by normalizing or standardizing the number of original sequences, and such normalized or standardized "detected sequence number" or "unique aligned sequence number" may be referred to as "standardized unique aligned sequence number (SDSMRN)". The normalized or normalized values are smaller than the raw data and are easy to be statistically and graphically displayed.
For example, in one embodiment, normalization is performed on 1 million pieces of off-line data, such as 2000000 pieces of sequence for a sample and 500 pieces of "unique aligned sequence number" (raw data) for target a, then 500 × 1000000/2000000 to 250 pieces of "normalized unique aligned sequence number" (i.e., all off-line data is normalized with 1000000). Further, the normalization calculation may also include a log calculation, such as taking a base-10 log. For statistical and graphical presentation, the base 10 log value of the "unique alignment sequence number" or "normalized unique alignment sequence number" can be used as the "detected sequence number". In short, whatever normalization or normalization is performed on the raw data, the result can be regarded as "detected sequence number".
S104: comparing the detection sequence number of each target with the detection threshold value of each target, wherein the detection sequence number is larger than the detection threshold value, namely the detection is carried out, and determining the detection result of each characteristic marker according to the detection result of each target, namely the detection result of the microorganism to be detected is obtained.
Through step S103, the detected sequence number of each target of each feature marker (pathogen and its drug resistance gene or virulence factor) in the sample to be detected is obtained, and then, whether each pathogen (or drug resistance gene or virulence factor) is detected or not is automatically judged according to the interpretation logic, so as to generate a detection result table. In one embodiment of the invention, the interpretation logic is as follows: (a) for each target, detecting if the detected sequence number is larger than a detection threshold value; (b) at least 3 targets are set for each pathogen (or drug resistance gene and virulence factor), and when more than 50 percent of the targets are detected, the pathogen (or drug resistance gene and virulence factor) is considered to be detected; (c) if the pathogen (or drug resistance gene, virulence factor) is also detected in the negative control, then the pathogen (or drug resistance gene, virulence factor) is a false positive result and no detection is reported.
In a preferred embodiment of the present invention, a plurality of specimens are simultaneously detected, and the detection result of at least one target in the simultaneously detected plurality of specimens is graphically output to determine the presence or absence of cross-contamination. The multiple samples to be detected are from the same batch of samples of the same type, if a negative sample is taken as a control in the same batch of samples of the same type aiming at a certain target, more than one target is detected in the negative control sample, the batch pollution is considered to occur, and the result reliability is low; if the target is not detected in all negative control samples, the batch-to-batch contamination is not considered to occur, and the result reliability is high.
In the final application scene of the invention, the report can be displayed in PDF document format, and also can be displayed in HTML format, so that the interactivity in HTML format is better.
The technical advantages of the method of the invention are embodied in that: the method is an innovative technology and has the advantages of short detection period, low detection cost and strong expandability.
The value of the method in clinical application is shown as follows: the invention establishes an automatic analysis process for microorganism detection based on targeted amplification sequencing, performs targeted amplification sequencing on microorganisms in a sample to be detected, analyzes off-line sequences to obtain the detected sequence number of a target, and judges whether a pathogen or a drug-resistant gene and a virulence factor are detected according to a reading rule, thereby having great value for quickly determining unknown pathogenic microorganisms in a specific syndrome sample.
The method of the invention provides an efficient automated analysis process. The method has the advantages of high accuracy, high speed and low cost of the result of detecting the pathogenic microorganisms in the sample, can realize automation in the whole process, and automatically generates a high-quality result report by taking the original sequencing data as a data source. The method has good control on the I/O resources and the memory resources of the computer. The pipeline technology replaces the traditional mode of taking files as information exchange, and the mode of cutting file blocks is taken as a slow solution for big data calculation, so that the method can adapt to the operating system environment of any Unix/Linux in theory.
The method of the invention has simple and visual report. The interpretation logic of the present invention minimizes the detection of false positives from both sensitivity and specificity considerations. The detection report has the characteristics of simplicity and easy reading, and the visualization result and the pathogenic microorganism information are both beneficial to improving the reading efficiency.
As shown in fig. 2, an embodiment of the present invention further provides a microorganism detection apparatus based on targeted amplification sequencing, corresponding to the microorganism detection method based on targeted amplification sequencing of the present invention, including: an obtaining unit 201, configured to obtain a target amplification sequencing sequence of a sample to be detected, where the sample to be detected includes at least one feature marker of a microorganism to be detected that may be present, and each feature marker includes at least one target for identifying the feature marker; an alignment unit 202 for aligning the target amplification sequencing sequence to a target sequence database, the target sequence database comprising a plurality of targets, each target for identifying one of a plurality of signatures of a population of microorganisms, each target having a set detection threshold; a counting unit 203, configured to count detected sequence numbers of each target, where the detected sequence numbers include sequence numbers that are only compared to the target and sequence numbers that are less than a set value obtained by dividing a suboptimal comparison score by an optimal comparison score, where the optimal comparison score is a score compared to the target, and the suboptimal comparison score is a score compared to other targets; and a comparing unit 204 for comparing the number of detection sequences of each target with a detection threshold of each target, wherein the detection is performed if the number of detection sequences is greater than the detection threshold, and the detection result of each feature marker is determined according to the detection result of each target, thereby obtaining the detection result of the microorganism to be detected.
Accordingly, an embodiment of the present invention provides a computer-readable storage medium including a program, which is executable by a processor to implement a microorganism detection method according to the present invention.
Those skilled in the art will appreciate that all or part of the functions of the various methods in the above embodiments may be implemented by hardware, or may be implemented by computer programs. When all or part of the functions of the above embodiments are implemented by a computer program, the program may be stored in a computer-readable storage medium, and the storage medium may include: a read only memory, a random access memory, a magnetic disk, an optical disk, a hard disk, etc., and the program is executed by a computer to realize the above functions. For example, the program may be stored in a memory of the device, and when the program in the memory is executed by the processor, all or part of the functions described above may be implemented. In addition, when all or part of the functions in the above embodiments are implemented by a computer program, the program may be stored in a storage medium such as a server, another computer, a magnetic disk, an optical disk, a flash disk, or a removable hard disk, and may be downloaded or copied to a memory of a local device, or may be version-updated in a system of the local device, and when the program in the memory is executed by a processor, all or part of the functions in the above embodiments may be implemented.
The technical solutions of the present invention are described in detail below by way of examples, and it should be understood that the examples are only illustrative and should not be construed as limiting the scope of the present invention.
Examples
76 clinical samples were obtained from the hospital, 38 each of cerebrospinal fluid and whole blood samples. The process flow overview is as follows: (a) the batch was subjected to experimental treatment (targeted amplification) and negative control samples were used for control quality control: UP335CSFNC1-DB25 (cerebrospinal fluid negative control, artificial cerebrospinal fluid + hela cells), TargetFPVOAAASE-2 (whole blood negative control, hela cells). (b) And then sequencing by using a BGISEQ-500 sequencing platform, performing quality control pretreatment on the generated data, comparing the data with a target sequence database to obtain a sequence comparison result file, calculating the number of detected sequences, and finally generating a text and graphical readable result file. (c) And (3) comparing the data which are not compared with the target with the reference genome of the host to obtain the host sequence proportion, and combining the output results of other steps to generate the composition statistical data of each sample.
The process flow of the present embodiment is detailed as follows:
(1) detection threshold determination for targets
The microbial genome and the human genome of the clinical sample of the embodiment are subjected to targeted amplification by using a targeted amplification technology, the amplification result is sequenced by using a BGISEQ-500 sequencing platform to obtain a machine-off sequence, and then the machine-off sequence is compared with a target sequence library to obtain the sequence number of each target. At least 3 targets have been screened for each pathogen (or drug resistance gene, virulence factor) detected for syndrome, together with an internal reference target (HFM gene) to make up a target sequence database.
For determining the detection threshold value of each target, more than 30 positive samples and more than 800 negative samples (samples without the detection of the pathogen or the drug resistance gene or the virulence factor) are used for each pathogen (or the drug resistance gene or the virulence factor), the detection sequence number of the target corresponding to the pathogen (or the drug resistance gene or the virulence factor) in each sample is obtained, and the detection threshold value of each target is determined according to a receiver operating characteristic curve (ROC curve) method.
(2) Host genome database collation
After the target amplification, partial human sequences exist in the sequencing result, more comprehensive information is provided for enriching the detection result indexes, the sequencing data is compared with the human reference genome sequence, and the human sequence ratio is calculated. The host genome database constructed in this example comprises two parts, a human reference genome (hg19) downloaded from the NCBI official website and a yellow genome sequence downloaded from the yellow genome public database official website.
(3) Parameter selection
The detailed configuration parameters are shown in table 1 below:
TABLE 1
(4) Data quality control
After sequencing the clinical sample after targeted amplification by using a sequencing platform BGISEQ-500, filtering original sequencing data from three aspects:
the first aspect is as follows: the sequence sharing 10bp contiguous bases with the linker sequence was filtered.
The second aspect is that: sequences read below a certain threshold (default 50bp) are filtered.
The third aspect is that: filtering sequences with more than 50% of bases with sequencing quality value less than 5.
(5) Target sequence database comparison and quality control
Comparing the sequence filtered in the step (4) with the target sequence database obtained in the step (1), then performing quality control on the comparison result, and obtaining a high-quality comparison result according to the following screening principle:
principle one: sequences that are greater than 90% of the length of the alignment are retained, i.e., the length of the reference sequence in a single sequence alignment is equal to 90% of the full length of the sequence.
Principle two: sequences with mismatched bases less than 5% are retained, i.e., the proportion of bases on the alignment that are inconsistent with the reference sequence due to sequencing errors is less than 5%.
Principle three: keeping the sequence of the alignment specificity, and if one sequence is aligned in different target areas, screening the specific alignment sequence according to the score difference ratio (namely, the ratio of suboptimal alignment divided by optimal alignment is less than 0.8) of multiple alignment results to obtain a 'unique' alignment sequence.
(6) Statistics of host sequences
And (3) removing the sequences aligned to the target sequence library in the step (5) from the filtered sequences in the step (4), and then aligning to the host reference genome file obtained in the step (2). When the alignment length of the sequence reaches 80%, the sequence is judged as the host sequence.
(7) Target annotation analysis
Counting the following indexes for the detected target according to the comparison result of the target sequence database in the step (5):
(a) normalized alignment sequence number (SDMRN): aligning the normalized sequence numbers of the target region.
(b) Normalized unique alignment sequence number (SDSMRN): the normalized number of sequences uniquely aligned to the target region is indicated by "number of detected sequences" in this example, which indicates the index and the number of detected sequences in the target region.
(8) Automatic interpretation of results
And (4) obtaining the target detection sequence number of each pathogen (or drug resistance gene and virulence factor) in the sample according to the step (7), automatically judging whether each pathogen (or drug resistance gene and virulence factor) is detected according to the interpretation logic, and generating a detection result table. The interpretation logic is as follows:
(a) and for each target, detecting if the detected sequence number is greater than the detection threshold value.
(b) At least 3 targets are required to be set for each pathogen (or drug resistance gene and virulence factor), and the pathogen (or drug resistance gene and virulence factor) is detected when more than 50 percent of the targets are detected.
(c) If the pathogen (or drug resistance gene, virulence factor) is also detected in the negative control, then the pathogen (or drug resistance gene, virulence factor) is a false positive result and no detection is reported.
In the detection result table, only the result that the sum of the detected sequence numbers of the pathogen (or drug resistance gene and virulence factor) targets is more than 10 is displayed.
(9) Result visualization
And counting the detection condition of each detected target in the same batch of samples, and judging whether the pollution condition in the batch exists or not.
(10) Report generation
Automatically generating a tex format report based on a latex language and converting the tex format report into a detection analysis report in a pdf document format, wherein the report display content comprises basic information and sample information of a detected person, and the following detection results:
(a) and (3) identifying the microorganisms: a list of all the microorganisms detected for a particular syndrome is shown, classified as bacteria, viruses, fungi, parasites, and +/-indicates whether the sample detected the microorganism.
(b) And (3) identification result of drug resistance virulence: displaying a list of all drug resistance genes and virulence factors detected by a specific syndrome product, and using +/-to indicate whether the drug resistance genes or the virulence factors are detected by a sample. Only when the microorganism corresponding to the drug resistance gene or the virulence factor is also detected, the detection of the drug resistance gene or the virulence factor is reported.
Table 2 shows the results of the detection of a sample (accession number UP335CSF17XH0055-DB19), "detected or not (negative control result by filtration)" indicates that the sample detected the microorganism (or a drug resistance gene, virulence factor) if P. According to table 2, it can be seen that the number of target detection sequences 2 and 3 of microorganism Human _ heresvirus _6 is significantly higher, and the detection of the microorganism is automatically judged.
TABLE 2
FIG. 3 shows the results of detection of 3 targets of microorganism Human _ heresvirus _6 in all cerebrospinal fluid samples within a batch, wherein the solid dots represent clinical samples and the open dots represent negative control samples. Referring to FIG. 3, it can be seen that the microorganism is not significantly detected in other samples, and thus cross-contamination can be excluded to further confirm the detection of the microorganism.
The present invention has been described in terms of specific examples, which are provided to aid understanding of the invention and are not intended to be limiting. For a person skilled in the art to which the invention pertains, several simple deductions, modifications or substitutions may be made according to the idea of the invention.
Claims (10)
1. A method for detecting microorganisms based on targeted amplification sequencing, the method comprising:
obtaining a target amplification sequencing sequence of a sample to be detected, wherein the sample to be detected contains at least one characteristic mark of a possible microorganism to be detected, and each characteristic mark comprises at least one target for identifying the characteristic mark;
aligning the targeted amplification sequencing sequences to a target sequence database, the target sequence database comprising a plurality of targets, each target for identifying one of a plurality of signatures for a population of microorganisms, each target having a set detection threshold;
counting the detected sequence number of each target, wherein the detected sequence number comprises the sequence number only compared to the target and the sequence number of which the result of dividing the optimal comparison score by the suboptimal comparison score is less than a set value, the optimal comparison score is the score compared to the target, and the suboptimal comparison score is the score compared to other targets; and
and comparing the detection sequence number of each target with the detection threshold value of each target, wherein the detection sequence number is larger than the detection threshold value, namely the detection is carried out, and determining the detection result of each characteristic marker according to the detection result of each target, namely the detection result of the microorganism to be detected.
2. The method for detecting a microorganism according to claim 1, wherein the detection threshold of the target is determined by an ROC curve method or a negative sample detection sequence number distribution method.
3. The method for detecting microorganisms according to claim 1, wherein the characteristic marker comprises one or more of a pathogen, a drug resistance gene, a virulence factor; preferably, the pathogen is selected from one or more of bacteria, DNA/RNA viruses, fungi and protozoa;
preferably, each signature comprises at least 3 targets for identifying the signature; preferably, greater than 50% of the targets detected in each signature are considered signature detected.
4. The method for detecting a microorganism according to claim 1, wherein the target sequence database further comprises an internal reference target selected from conserved gene sequences of a host of the microorganism.
5. The method of claim 1, wherein the population of microorganisms is a syndrome; preferably, the syndrome is selected from the group consisting of bloodstream infection syndrome, encephalitis meningitis syndrome, respiratory tract infection syndrome, or diarrhea syndrome, more preferably bloodstream infection syndrome or encephalitis meningitis syndrome.
6. The method for detecting a microorganism according to claim 1, wherein the set value is 0.8.
7. The method for detecting microorganisms of claim 1, wherein the targeted amplification sequencing sequence further comprises a host sequence, the method further comprising:
comparing other sequences after the sequence compared to the target sequence database is removed to a host genome database, and calculating the proportion of the host sequence in the sample to be detected for quality control of a detection result; preferably, the host genome database comprises both the human reference genome downloaded from the NCBI official website and the yellow genome sequence downloaded from the yellow genome public database official website.
8. The method for detecting microorganisms according to claim 1, comprising simultaneously detecting a plurality of specimens, and graphically outputting a detection result of at least one target in the simultaneously detected plurality of specimens to determine the presence or absence of cross contamination; preferably, the samples to be detected are the same batch of samples of the same type.
9. A device for detecting microorganisms based on targeted amplification sequencing, the device comprising:
the system comprises an acquisition unit, a sequencing unit and a sequencing unit, wherein the acquisition unit is used for acquiring a target amplification sequencing sequence of a sample to be detected, the sample to be detected contains at least one characteristic mark of a microorganism to be detected, and the characteristic mark comprises at least one target for identifying the characteristic mark;
an alignment unit for aligning the targeted amplification sequencing sequence to a target sequence database, the target sequence database comprising a plurality of targets, each target for identifying one of a plurality of signatures for a population of microorganisms, each target having a set detection threshold;
the counting unit is used for counting the detected sequence number of each target, wherein the detected sequence number comprises the sequence number only compared to the target and the sequence number of which the result of dividing the optimal comparison score by the suboptimal comparison score is less than a set value, the optimal comparison score is the score compared to the target, and the suboptimal comparison score is the score compared to other targets; and
and the comparison unit is used for comparing the detection sequence number of each target with the detection threshold value of each target, the detection is carried out when the detection sequence number is larger than the detection threshold value, and the detection result of each characteristic mark is determined according to the detection result of each target, so that the detection result of the microorganism to be detected is obtained.
10. A computer-readable storage medium, characterized by comprising a program executable by a processor to implement the method of any one of claims 1-8.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112687343A (en) * | 2020-12-31 | 2021-04-20 | 杭州柏熠科技有限公司 | Nanopore sequencing-based broad-spectrum pathogenic microorganism and drug resistance analysis system |
| CN113284560A (en) * | 2021-04-28 | 2021-08-20 | 广州微远基因科技有限公司 | Pathogenic detection background microorganism judgment method and application |
| WO2021196357A1 (en) * | 2020-04-02 | 2021-10-07 | 上海之江生物科技股份有限公司 | Method and device for obtaining species-specific consensus sequences of microorganisms and application |
| CN114187968A (en) * | 2020-09-15 | 2022-03-15 | 深圳华大生命科学研究院 | Sterility detection method based on NGS technology |
Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1876844A (en) * | 2005-05-17 | 2006-12-13 | 埃佩多夫阵列技术股份有限公司 | Identification and/or quantification method of nucleotide sequence(s) elements specific of genetically modified plants on arrays |
| US20110152109A1 (en) * | 2009-12-21 | 2011-06-23 | Gardner Shea N | Biological sample target classification, detection and selection methods, and related arrays and oligonucleotide probes |
| CN102628082A (en) * | 2012-04-10 | 2012-08-08 | 凯晶生物科技(苏州)有限公司 | Method for qualitatively and quantitatively detecting nucleic acid based on high-flux sequencing technology |
| CN105525033A (en) * | 2014-09-29 | 2016-04-27 | 天津华大基因科技有限公司 | Method and device for detecting microorganisms in blood |
| CN105671150A (en) * | 2016-01-29 | 2016-06-15 | 江汉大学 | Method for qualitative and quantitative detection of microorganisms in human body |
| CN106282356A (en) * | 2016-08-30 | 2017-01-04 | 天津诺禾医学检验所有限公司 | A kind of method and device based on amplicon secondary order-checking point mutation detection |
| US20170002432A1 (en) * | 2015-06-30 | 2017-01-05 | uBiome, Inc. | Method and system for diagnostic testing |
| CN106372459A (en) * | 2016-08-30 | 2017-02-01 | 天津诺禾致源生物信息科技有限公司 | Method and device for detecting copy number variation based on amplicon next generation sequencing |
| US20170191116A1 (en) * | 2014-06-04 | 2017-07-06 | Quest Diagnostics Investments Incorporated | Method for direct microbial identification |
| US20180080068A1 (en) * | 2016-09-16 | 2018-03-22 | Fluxion Biosciences, Inc. | Methods and Systems for Ultra-Sensitive Detection of Genomic Alterations |
| CN108304693A (en) * | 2018-01-23 | 2018-07-20 | 元码基因科技（北京）股份有限公司 | Utilize the method for high-flux sequence data analysis Gene Fusion |
| CN108334750A (en) * | 2018-04-19 | 2018-07-27 | 江苏先声医学诊断有限公司 | A kind of macro genomic data analysis method and system |
-
2018
- 2018-09-04 CN CN201811027464.6A patent/CN110875082B/en active Active
Patent Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1876844A (en) * | 2005-05-17 | 2006-12-13 | 埃佩多夫阵列技术股份有限公司 | Identification and/or quantification method of nucleotide sequence(s) elements specific of genetically modified plants on arrays |
| US20110152109A1 (en) * | 2009-12-21 | 2011-06-23 | Gardner Shea N | Biological sample target classification, detection and selection methods, and related arrays and oligonucleotide probes |
| CN102628082A (en) * | 2012-04-10 | 2012-08-08 | 凯晶生物科技(苏州)有限公司 | Method for qualitatively and quantitatively detecting nucleic acid based on high-flux sequencing technology |
| US20170191116A1 (en) * | 2014-06-04 | 2017-07-06 | Quest Diagnostics Investments Incorporated | Method for direct microbial identification |
| CN105525033A (en) * | 2014-09-29 | 2016-04-27 | 天津华大基因科技有限公司 | Method and device for detecting microorganisms in blood |
| US20170002432A1 (en) * | 2015-06-30 | 2017-01-05 | uBiome, Inc. | Method and system for diagnostic testing |
| CN107849599A (en) * | 2015-06-30 | 2018-03-27 | 优比欧迈公司 | method and system for diagnostic test |
| CN105671150A (en) * | 2016-01-29 | 2016-06-15 | 江汉大学 | Method for qualitative and quantitative detection of microorganisms in human body |
| CN106282356A (en) * | 2016-08-30 | 2017-01-04 | 天津诺禾医学检验所有限公司 | A kind of method and device based on amplicon secondary order-checking point mutation detection |
| CN106372459A (en) * | 2016-08-30 | 2017-02-01 | 天津诺禾致源生物信息科技有限公司 | Method and device for detecting copy number variation based on amplicon next generation sequencing |
| US20180080068A1 (en) * | 2016-09-16 | 2018-03-22 | Fluxion Biosciences, Inc. | Methods and Systems for Ultra-Sensitive Detection of Genomic Alterations |
| CN108304693A (en) * | 2018-01-23 | 2018-07-20 | 元码基因科技（北京）股份有限公司 | Utilize the method for high-flux sequence data analysis Gene Fusion |
| CN108334750A (en) * | 2018-04-19 | 2018-07-27 | 江苏先声医学诊断有限公司 | A kind of macro genomic data analysis method and system |
Non-Patent Citations (2)
| Title |
|---|
| CHEN CHEN 等: "The microbiota continuum along the female reproductive tract and its relation to uterine-related diseases", 《NATURE COMMUNICATIONS》 * |
| 姚雪 等: "宏基因组靶向测序分析皮肤表面微生物群落方法优化", 《生物技术通报》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2021196357A1 (en) * | 2020-04-02 | 2021-10-07 | 上海之江生物科技股份有限公司 | Method and device for obtaining species-specific consensus sequences of microorganisms and application |
| CN114187968A (en) * | 2020-09-15 | 2022-03-15 | 深圳华大生命科学研究院 | Sterility detection method based on NGS technology |
| CN112687343A (en) * | 2020-12-31 | 2021-04-20 | 杭州柏熠科技有限公司 | Nanopore sequencing-based broad-spectrum pathogenic microorganism and drug resistance analysis system |
| CN113284560A (en) * | 2021-04-28 | 2021-08-20 | 广州微远基因科技有限公司 | Pathogenic detection background microorganism judgment method and application |
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