CN115927559A - Construction method of pathogen targeted detection system, primer group, electronic equipment and application - Google Patents
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Abstract
The present disclosure describes a method of constructing a pathogen-targeted detection system, comprising: determining a plurality of pathogens to be detected; constructing a pathogen database based on the plurality of pathogens, the pathogen database including genomic sequences of all strains of each pathogen of the plurality of pathogens; designing a plurality of pathogens to obtain a first candidate primer group set; comparing the first candidate primer group set with a pathogen database to obtain the inclusion of each primer group, and comparing the first candidate primer group set with an NT database to obtain the specificity of each primer group; obtaining a target primer group set; sequences of target regions of various pathogens are obtained based on the target primer group set, and an artificial plasmid set is obtained based on the sequence design of the target regions. According to the present disclosure, a method for constructing a pathogen-targeted detection system capable of improving the quality of primers and quantifying pathogens can be provided.
Description
Technical Field
The invention relates to the field of gene detection, in particular to a construction method of a pathogen targeted detection system, a primer group, electronic equipment and application.
Background
Since the generation of Polymerase Chain Reaction (PCR) technology, a series of new PCR detection techniques have been derived based on conventional PCR. Multiplex PCR (mPCR) refers to a technique for specifically amplifying two or more PCR fragments in one reaction system, and has the advantage that different templates can be simultaneously amplified at one time. With the development of sequencing technologies, especially next generation sequencing technologies (i.e., NGS technologies), it has become possible to detect PCR products with high sensitivity and high resolution, and therefore the upper limit of the number of fragments that can be amplified simultaneously by multiplex PCR has evolved into the limit of the multiplex PCR system. Unlike traditional PCR reaction systems which only carry out 3-5 and at most 15, the realization of thousands or even thousands of ultra-multiplex PCR (high-multiplex PCR or ultra-high-multiplex PCR) is assisted by NGS sequencing, and the formed targeting-NGS technology (i.e. the tNGS technology) gradually plays a role in the fields of infectious disease screening, genetic disease diagnosis, tumor gene detection and the like.
In the multiple PCR application in the field of pathogen qualitative detection, if the specific amplification of gene segments among dozens, hundreds, thousands or even tens of thousands of different species is to be realized in one system, the primers are not simply mixed and amplified, and the specificity of different primers, the specificity of different amplified segments and the amplification conditions are all contents which need to be comprehensively considered. At present, in the design of primers for multiple targeted detection of pathogens, a target gene is generally searched for a pathogen list to be detected, then a primer is designed for the target gene to obtain a primer pool, the primer pool and a sample are used for performing a wet experiment stage (library construction, on-machine sequencing and result analysis), results such as dimers and non-specificity are obtained based on an analysis result, the primer pool is optimized, and then the wet experiment stage is performed, and the steps are repeated until the primer pool meets the detection requirement.
However, for the target pathogen list, personnel are required to look up data and investigate one by one, the step consumes time and energy, and for some pathogens, a proper target gene is difficult to find, and the design difficulty is increased; the designed primers are subjected to wet experiments without pre-evaluation, and need to be continuously optimized through large-batch experiments, so that a large amount of manpower and material resources are consumed, the research and development period is long, and the efficiency is low.
In addition, many pathogens are conditionally pathogenic (e.g., must reach a certain concentration to cause disease), and thus quantitative detection of pathogens is also of clinical significance.
Disclosure of Invention
The present disclosure has been made in view of the above-described state of the art, and an object of the present disclosure is to provide a method for constructing a pathogen-targeted detection system capable of improving primer quality and quantifying a pathogen, a target primer set, an artificial plasmid set, an electronic device, and an application.
To this end, the first aspect of the present disclosure provides a method for constructing a pathogen-targeted detection system, which is a system for performing multiplex PCR detection on a target region of a gene of each pathogen, the method comprising: determining a plurality of pathogens to be detected; constructing a pathogen database based on the plurality of pathogens, the pathogen database including genomic sequences of all strains of each pathogen of the plurality of pathogens; designing a first candidate primer set for each pathogen in the plurality of pathogens using primer3 software, the first candidate primer set comprising a plurality of primer sets, each primer set in the plurality of primer sets comprising an upstream primer and a downstream primer; comparing the first candidate primer group set with the pathogen database to obtain the inclusion of each primer group, and comparing the first candidate primer group set with the NT database to obtain the specificity of each primer group; reserving primer groups with the inclusion larger than a first preset value and the specificity larger than a second preset value in the first candidate primer group set to obtain a second candidate primer group set; sequencing each primer group of the second candidate primer group set according to the occurrence probability of a primer dimer or a hairpin structure by using MFEprimer software, and reserving at least three groups of primer groups with the lowest occurrence probability of the primer dimer or the hairpin structure aiming at each pathogen to obtain a target primer group set; obtaining sequences of target regions of the multiple pathogens based on the target primer set, and designing an artificial plasmid set based on the sequences of the target regions, wherein the artificial plasmid set comprises multiple artificial plasmids with preset copy numbers and capable of combining with each primer set in the target primer set, the artificial plasmids are designed based on the sequences of the target regions and are different from the sequences of the target regions, and the artificial plasmids have different sequences from the sequences of the target regions.
According to the construction method, the primer3 software is used for designing each pathogen to obtain the first candidate primer group set, and compared with the method that the target area of each pathogen is searched firstly and then the primer is designed in a targeted manner, batch processing and execution can be realized, and the efficiency is improved; the method can be used for carrying out inclusiveness evaluation on the first candidate primer group set by constructing a pathogen database containing genome sequences of all strains of each pathogen, and meanwhile, the first candidate primer group set can be subjected to specificity evaluation through an NT (nucleotide sequence) library, so that a second candidate primer group set can be obtained through screening; and (3) screening primer dimers or hairpin structures of the second candidate primer group set by MFEprimer software, so that primer groups with high occurrence rate of the primer dimers or hairpin structures in the second candidate primer group set can be screened out, and finally, a target primer group set is obtained by reserving the primer groups. In addition, based on the finally obtained target primer group set, a sequence of a target region of each pathogen can be obtained, and then based on the sequence of the target region, an artificial plasmid set can be designed, wherein the artificial plasmid set comprises a plurality of artificial plasmids which can be combined with each primer group in the target primer group set, and the artificial plasmids are designed based on the sequence of the target region and are different from the sequence of the target region, so that the artificial plasmids can be combined with the primer groups and can be distinguished from the sequence of the target region; on the other hand, the content of the artificial plasmid can be a predetermined copy number, that is, the addition amount of the artificial plasmid is known, and then the amount of the corresponding pathogen can be obtained by subsequently comparing the number of the target regions combined with the primer sets and the number of the artificial plasmids combined with the primer sets, so that the pathogen can be quantitatively detected.
In the construction method related to the present disclosure, optionally, the artificial plasmid is designed by adding a sequence, reducing a partial sequence, or replacing a partial sequence based on the sequence of the target region. Therefore, the artificial plasmid corresponding to each pathogen can be designed according to the sequence of the target region by means of adding the sequence, reducing the partial sequence or replacing the partial sequence.
In the construction method related to the present disclosure, optionally, the step of constructing the pathogen database includes: for each pathogen in the plurality of pathogens, the genome sequences of all strains below each pathogen classification level are downloaded from NCBI, viPR, patic, IRD and VEuPathDB databases based on species information for each pathogen to obtain the pathogen database, wherein the species information includes generic class phylum information, txid numbers and serotypes. Thus, a pathogen database can be constructed that contains the genomic sequences of all strains of each pathogen.
In a construction method contemplated by the present disclosure, a first set of candidate primer sets for each pathogen of the plurality of pathogens is optionally designed using primer3 software across the entire genome of the plurality of pathogens, wherein 200 sets of primers are designed per 100kb of the genome. In this case, primer3 software is used for designing primers in the whole genome range of pathogens, so that compared with a mode of looking up data and researching and determining target genes of each pathogen and then designing primers in a targeted manner, the efficiency can be improved, and the design difficulty can be reduced under the condition that suitable target genes are difficult to find.
In the construction method according to the present disclosure, optionally, the first preset value is 95%, and the second preset value is 95%. Therefore, the primer groups in the target primer group set can be ensured to have good inclusion and specificity.
The second aspect of the present disclosure provides a target primer set and an artificial plasmid set, which are constructed by the construction method provided by the first aspect of the present disclosure. According to the target primer group set and the artificial plasmid set provided by the disclosure, each pathogen is designed through primer3 software to obtain a first candidate primer group set, and compared with the method that a target area of each pathogen is searched first and then a primer is designed in a targeted manner, batch processing and execution can be realized, and the efficiency is improved; the method can be used for carrying out inclusiveness evaluation on the first candidate primer group set by constructing a pathogen database containing genome sequences of all strains of each pathogen, and meanwhile, the first candidate primer group set can be subjected to specificity evaluation through an NT (nucleotide sequence) library, so that a second candidate primer group set can be obtained through screening; and (3) screening primer dimers or hairpin structures of the second candidate primer group set by MFEprimer software, so that primer groups with high occurrence rate of the primer dimers or hairpin structures in the second candidate primer group set can be screened out, and finally, a target primer group set is obtained by reserving the primer groups. In addition, based on the finally obtained target primer group set, a sequence of a target region of each pathogen can be obtained, and then based on the sequence of the target region, an artificial plasmid set can be designed, wherein the artificial plasmid set comprises a plurality of artificial plasmids which can be combined with each primer group in the target primer group set, and the artificial plasmids are designed based on the sequence of the target region and are different from the sequence of the target region, so that the artificial plasmids can be combined with the primer groups and can be distinguished from the sequence of the target region; on the other hand, the content of the artificial plasmid can be a predetermined copy number, that is, the addition amount of the artificial plasmid is known, and then the amount of the corresponding pathogen can be obtained by subsequently comparing the number of the target regions combined with the primer sets and the number of the artificial plasmids combined with the primer sets, so that the pathogen can be quantitatively detected.
A third aspect of the present disclosure provides an electronic device, comprising a processor and a memory, wherein the memory stores one or more readable instructions, and when the one or more readable instructions are executed by the processor, the method for constructing a pathogen-targeted detection system provided by the first aspect of the present disclosure is implemented. Thus, a high quality primer set and an artificial plasmid set for quantification can be obtained by the electronic device of the present disclosure.
A fourth aspect of the present disclosure provides a set of target primer sets and a set of artificial plasmids as provided in the second aspect of the present disclosure or an electronic device as provided in the third aspect of the present disclosure for use in pathogen detection.
In the application to which the present disclosure relates, optionally, the method includes: using the target primer group set and the artificial plasmid set to build a library of a sample to obtain a library; sequencing the library to obtain sequencing data, and filtering the sequencing data by using a quality value to obtain filtered sequencing data, wherein the filtered sequencing data comprises a plurality of reads (reads); and comparing the filtered sequencing data with the sequences of the target regions of the various pathogens, wherein if the reads are matched with the sequences of the target regions, the reads are judged to be the sequences of the corresponding pathogens, and the detection result is judged to be positive in detection of the corresponding pathogens. Thus, the sequencing data of the amplification products of the target primer set can be subjected to quality value filtering, and whether the detection result of each pathogen is positive or not can be obtained from the sequencing data.
In the application to which the present disclosure relates, optionally, the method includes: comparing the filtered sequencing data with the artificial plasmid set, wherein if the reads are matched with the sequences of the artificial plasmids, the reads are judged to be the artificial plasmids of the corresponding pathogens; calculating the reads number matched with the sequence of the target region and marking as the reads number of the target region, calculating the reads number matched with the artificial plasmid and marking as the reads number of the artificial plasmid; obtaining the content of the plurality of pathogens based on the predetermined copy number, the target region reads number and the artificial plasmid reads number. Thus, the content of each pathogen can be obtained.
According to the present disclosure, a method for constructing a pathogen-targeted detection system capable of improving primer quality and quantifying pathogens, a target primer set, an artificial plasmid assembly, an electronic device, and applications thereof can be provided.
Drawings
Fig. 1 shows a flow chart of a method of construction of a pathogen targeted detection system according to examples of the present disclosure.
Fig. 2 shows a schematic diagram of a scenario involving primer sets in combination with a target region and an artificial plasmid according to an example of the present disclosure.
Detailed Description
Hereinafter, preferred embodiments of the present disclosure will be described in detail with reference to the accompanying drawings. In the following description, the same components are denoted by the same reference numerals, and redundant description thereof is omitted. The drawings are schematic and the ratio of the dimensions of the components and the shapes of the components may be different from the actual ones.
It is noted that, as used herein, the terms "comprises," "comprising," or any other variation thereof, such that a process, method, system, article, or apparatus that comprises or has a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include or have other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
In addition, the subtitles and the like referred to in the following description of the present invention are not intended to limit the content or the scope of the present invention, and serve only as a cue for reading. Such a subtitle should neither be understood as a content for segmenting an article, nor should the content under the subtitle be limited to only the scope of the subtitle.
A first aspect of the present disclosure relates to a method of constructing a pathogen-targeted detection system (hereinafter simply referred to as "construction method"). The pathogen targeted detection system constructed according to the disclosed construction method can improve the quality of the primer group and can carry out quantitative detection on pathogens.
The second aspect of the present disclosure relates to a target primer set and an artificial plasmid collection (described later) constructed by the construction method of the first aspect of the present disclosure.
A third aspect of the present disclosure relates to an electronic device (described later) capable of executing the construction method of the first aspect of the present disclosure.
A fourth aspect of the present disclosure relates to a set of target primer sets and a set of artificial plasmids of the second aspect of the present disclosure, and an application of the electronic device of the third aspect of the present disclosure in detecting a pathogen (described later).
The pathogen targeted detection system of the present disclosure may be a system for multiplex PCR detection of target regions of genes of various pathogens. Pathogens are generic terms for agents that cause or transmit disease, including viruses, bacteria, fungi, and parasites. By detecting the existence condition of pathogen genes in the sample, the infection condition of the pathogen can be diagnosed in an auxiliary way, and the subsequent symptomatic treatment is facilitated. The multiplex PCR is also called multiplex primer PCR or composite PCR, and is a PCR reaction in which more than two pairs of primers are added to the same PCR reaction system to simultaneously amplify a plurality of nucleic acid fragments.
In the present disclosure, a target region of each pathogen gene may be detected, and the target region may be a partial region selected from the pathogen genes. By detecting the target region, the detection result of the pathogen can be obtained.
The following describes a construction method, a target primer set, an artificial plasmid set, an electronic device, and applications of a pathogen targeted detection system according to various aspects of the present disclosure, with reference to the accompanying drawings.
Fig. 1 shows a flow chart of a method of construction of a pathogen targeted detection system in accordance with an example of the present disclosure.
In this embodiment, as shown in fig. 1, the method for constructing the pathogen targeting detection system may include: determining a plurality of pathogens to be detected (step S10); constructing a pathogen database based on a plurality of pathogens (step S20); designing a first candidate primer set for each pathogen of the plurality of pathogens (step S30); comparing the first candidate primer group set with the pathogen database to obtain the inclusion of each primer group, and comparing the first candidate primer group set with the NT database to obtain the specificity of each primer group (step S40); reserving primer groups with the inclusion larger than a first preset value and the specificity larger than a second preset value in the first candidate primer group set to obtain a second candidate primer group set (step S50); sequencing each primer group of the second candidate primer group set according to the occurrence probability of the primer dimer or the hairpin structure, and reserving at least one group of primer groups with the lowest occurrence probability of the primer dimer or the hairpin structure for each pathogen to obtain a target primer group set (step S60); sequences of target regions of a plurality of pathogens are obtained based on the target primer set, and an artificial plasmid set is designed based on the sequences of the target regions (step S70). The specificity refers to whether a sequence matched by the primer belongs to a target pathogen sequence, and the inclusion refers to whether the primer can capture all sequences of the target pathogen in the database. Therefore, an artificial plasmid set of various pathogens can be obtained, and a pathogen targeted detection system is obtained.
In some examples, as described above, the method of constructing a pathogen-targeted detection system may include determining a plurality of pathogens to be detected (step S10).
In step S10, in some examples, determining multiple pathogens to be detected may refer to determining a detection object, and a list of pathogens to be detected may be determined according to different detection requirements. For example, where it is desired to detect the course of a pathogen associated with respiratory infection, the plurality of pathogens to be detected may be a plurality of pathogens associated with respiratory infection. In some examples, the plurality of pathogens to be detected may include respiratory tract-related pathogens such as mucor racemosus, escherichia coli, enterobacter cloacae, pseudomonas putida, proteus vulgaris, staphylococcus aureus, mycobacterium intracellulare, burkholderia cepacia, echovirus, coxsackievirus a, coxsackievirus B, rhinovirus B, enterovirus C, and enterovirus D68. Thus, a list of pathogens to be detected can be obtained.
In some examples, as described above, the method of constructing a pathogen-targeted detection system may include constructing a pathogen database based on a plurality of pathogens (step S20).
In step S20, in some examples, after determining a plurality of pathogens to be detected (detection objects), a pathogen database may be constructed based on the detection objects.
In some examples, the pathogen database can be used for the evaluation of the inclusion of primer sets (described in detail later).
In some examples, a pathogen database may be constructed for determining species information for each of a plurality of pathogens to be detected. The species information for each pathogen may include information on phyla of the generic disciplines, tax id numbers, and serotype information. In some examples, the tax id number may be derived from a Taxonomy database. Taxomy, also known as NCBI biological classification database, the planning classification and nomenclature of all the organisms in NCBI public sequence database (https:// www.ncbi.nlm.nih.gov /), which assigns a number, i.e. tax id, to each species, and the tax id correspondence table for each species can be found in ftp:// ftp.ncbi.nih.gov/pub/taxomy/taxdmp.zip.
In some examples, the genomic sequences of all strains below the classification level of each pathogen may be downloaded from NCBI databases (https:// www.ncbi.nlm.nih.gov /), viPR databases (https:// www.viprbrc.org /), PATRIC databases (https:// patricbrc.org /), IRD databases (https:// www.fludborg /), and VEuPathDB databases (https:///. Verathdb.org /), based on the phylum of the genus subject, txid number, and serotype information for each pathogen. Species information such as phylum information of the generic class of disciplines, txid numbers and serotype information can be used as key words to detect from the database, and genome sequences of all strains of each pathogen can be downloaded.
In some examples, the genome sequences of all strains of each pathogen may be assembled into a pathogen database. In other words, the pathogen database includes the genomic sequences of all strains of each pathogen in the plurality of pathogens.
In some examples, as described above, the method of constructing a pathogen-targeted detection system may include designing a first set of candidate primer sets for each pathogen in a plurality of pathogens (step S30).
In step S30, in some examples, after determining a plurality of pathogens to be detected (detection objects), a primer set (first candidate primer set) for each pathogen may be designed for the detection objects.
In some examples, step S20 and step S30 are not in strict chronological order. Step S20 may be performed first, step S30 may be performed first, or step S20 and step S30 may be performed simultaneously.
In some examples, primers may be designed for each pathogen using primer3 software (https:// githiub.com/primer 3-org/primer3, version 2.6.1). This allows batch execution by software, and improves the efficiency of primer design.
In some examples, primer3 software may be used to design primers across the entire genome of each pathogen. In this case, primer3 software is used to design primers in the whole genome of pathogens, which can improve efficiency and reduce design difficulty for some cases in which it is difficult to find suitable target genes, compared with the way of looking up data, investigating and determining target genes of each pathogen and then designing primers in a targeted manner.
In some examples, the number of primers designed by primer3 may be set according to the sequence length of the reference genome, 200 pairs of primers are designed per 100Kbp length of the reference genome, and 2000 pairs of primers are designed per 1Mbp length of the reference genome, resulting in a first candidate primer set. The first candidate primer set includes a plurality of primer sets, each of which includes an upstream primer and a downstream primer.
In some examples, the parameters for primer design using primer3 software may further include:
the length range of the amplification product region was set as: 180 to 900;
TM values (melting temperature of oligonucleotide) set as: 58 ℃ to 62 ℃;
GC content, set to: 40 to 60;
the primer length is set as follows: 17 to 24;
the optimal length of the primer is set to be 20;
the primer pair output number setting was set according to pathogen genome size (200 pairs of primers were designed per 100Kbp length of reference genome).
In some examples, as described above, the method for constructing the pathogen-targeted detection system may include comparing the first set of candidate primer sets with a pathogen database to obtain the inclusion of each primer set, and comparing the first set of candidate primer sets with an NT library to obtain the specificity of each primer set (step S40).
In step S40, in some examples, a database of pathogens may be utilized to assess the inclusion of each set of primers in the first set of candidate primer sets. The primer inclusion is used for evaluating whether a primer set covers all strains under the corresponding pathogen classification level, and the inclusion of 100% means that the primer set can amplify gene sequences of all strains under the corresponding pathogen classification level. In some examples, each set of primers in the first set of candidate primer sets may be aligned to the pathogen database using a blastn short sequence alignment (blastn-short) to obtain a result of the inclusion of each set of primers.
In some examples, a database of pathogens may be utilized to assess the inclusion of each set of primers in the first set of candidate primer sets. The specificity of the primers means that one primer set is used for evaluating whether one primer set can amplify to a non-corresponding species or pathogen, and the specificity is 100% means that the primer set can only amplify to the gene sequence of the corresponding pathogen. In some examples, each set of primers in the first set of candidate primer sets may be aligned to the NT library using a blastn short sequence alignment (blastn-short) to obtain a result of the specificity of each set of primers. The NT library (Nucleotide Sequence Database) is currently the most comprehensive Database of genetic information of species.
In some examples, as described above, the method for constructing a pathogen targeted detection system may include retaining primer sets included in the first candidate primer set that are greater than a first predetermined value and have a specificity greater than a second predetermined value, resulting in a second candidate primer set (step S50). In this case, each group of primers in the first candidate primer group set can be screened according to the inclusion and the specificity to obtain a second candidate primer group set with higher inclusion and specificity.
In some examples, the first preset value may be 95%. In some examples, the second preset value may be 95%. In other words, each primer of the second set of candidate primer sets needs to have an inclusion and specificity greater than 95%.
In some examples, a blastn short sequence alignment (blastn-short) and a pathogen database and NT library may be used for alignment, species information of matching sequences may be determined according to annotation information of the matching sequences, evaluation results of the inclusion and specificity of each group of primers in the first candidate primer group set may be ranked, and a primer group having the best evaluation results of the inclusion and specificity and satisfying greater than 95% of both the specificity and the inclusion may be selected as the second candidate primer group set. Specific parameters of specificity and inclusion may be as shown in table 1 below:
TABLE 1
In some examples, preferably, a primer set with a specificity and an inclusion both equal to 100% may be selected as the second candidate primer set. Thus, a primer set with perfect matching of specificity and inclusion can be obtained.
In some examples, as described above, the method for constructing the pathogen targeted detection system may include ranking each primer set of the second candidate primer set according to occurrence probability of primer dimer or hairpin structure, and reserving at least one primer set with lowest occurrence probability of primer dimer or hairpin structure for each pathogen to obtain the target primer set (step S60).
In some examples, MFEprimer (https:// www.mfeprimer.com /) software can be used to examine the occurrence probability of primer dimers and the occurrence probability of hairpin structures for each primer group in the second set of candidate primer groups, and the primer groups can be sorted according to the occurrence probability, with at least three primer groups being retained for each pathogen, to obtain the set of target primer groups. Under the condition, at least 3 target regions can be detected for each pathogen, whether the pathogen is detected or not can be judged subsequently according to the detection result of at least 3 target regions, the detection accuracy can be improved, the primer which does not meet the requirement can be removed in advance by adding the link before the primer is tested in a wet experiment, the research and development period is shortened, the test frequency of the wet experiment is reduced, and the efficiency is improved. In some instances, preferably, 5-10 primer sets may be retained for each pathogen, resulting in a target primer set combination. For example, each pathogen may retain 5, 6, 7, 8, 9, or 10 primer sets. In this case, the accuracy of detection can be further improved.
In some examples, the sequence of the targeted pathogen amplified by one primer set is defined as a target region, and the sequence overlap between different target regions of each pathogen is checked, and if the sequence overlap is too large, the judgment performance of the targeted analysis system may be affected, and the primer set may be deleted.
In some examples, as described above, the method for constructing the pathogen-targeted detection system may include obtaining sequences of target regions of a plurality of pathogens based on the set of target primer sets, and designing a set of artificial plasmids based on the sequences of the target regions (step S70).
In some examples, electronic PCR amplification (ePCR, usearchah 11.0.667_ i86linux 32) can be performed using a set of primer sets of interest, yielding information on the sequence of all target regions (information on the sequence of the target region of each of a plurality of pathogens).
In some examples, artificial plasmid sets can be designed based on information on the sequence of the target region of each pathogen of the plurality of pathogens. The artificial plasmid collection may include a plurality of artificial plasmids, each artificial plasmid corresponding to a sequence of each target region. In some examples, each artificial plasmid, like the corresponding target region, is capable of binding to the corresponding primer set. In some examples, the artificial plasmid may have a sequence that differs from the sequence of the corresponding target region. In other words, the sequences of the artificial plasmid and the corresponding target region may have differences, and the remaining sequences are identical to the sequences of the corresponding target region. The sequence of the artificial plasmid can be designed based on the sequence of the target region but is different from the sequence of the target region. For example, in some examples, the artificial plasmid may be designed by adding, subtracting, or replacing a partial sequence to the sequence of the target region based on the sequence of the corresponding target region. Preferably, various artificial plasmids can be designed by replacing a partial sequence based on the sequence of the target region of each pathogen, thereby enabling data analysis to be facilitated.
In some examples, when detecting multiple pathogens in a sample, a set of target primer sets and a set of artificial plasmids can be added simultaneously to construct a library (pooling). In this case, on one hand, the target primer set is specific to multiple pathogens, and when the target primer set is used in an experimental process, a sample to be tested usually contains only one, two (or even zero) genes of the pathogens, so that only a small part of the primer sets in the target primer set can find the template for combination, and the rest of the primer sets cannot find the template for combination, and at this time, primer dimers may be generated; on the other hand, the artificial plasmid and the target region have different sequences, and therefore, the artificial plasmid and the target region can be distinguished from each other in the subsequent data analysis.
Fig. 2 shows a schematic diagram of a scenario involving the binding of a primer set to a target region and an artificial plasmid according to an example of the present disclosure. The effect of reducing primer dimer of the artificial plasmid can be understood by fig. 2, and as shown in fig. 2, when the target region, the primer and the artificial plasmid set exist in the system, the artificial plasmid and the target region both have a primer binding region, so that the primer can anneal to the artificial plasmid and the target region at the same time, and a target region product and an artificial plasmid product can be obtained after extension. It will be appreciated that due to the sequence differences between the artificial plasmid and the target region, the target region products can be distinguished from the artificial plasmid products when the sequence alignment is performed after sequencing.
In some examples, each of the artificial plasmids may have both ends respectively coupled to the upstream primer and the downstream primer of one primer set, and the artificial plasmids include a plurality of differential sequences, one differential sequence within 15bp from the sequence coupled to the upstream primer and one differential sequence within 15bp from the sequence coupled to the downstream primer. In this case, when a pathogen has multiple target regions, if there are overlapping regions between the selected multiple target regions for a pathogen, then the sequence of the artificial plasmid and the sequence of the target region can be easily distinguished because there are different sequences at both ends of the artificial plasmid, thereby reducing the occurrence of false positives.
In some examples, the length of the difference sequence may be 5bp. This facilitates the differentiation of the artificial plasmid from the target region.
In some examples, the known copy number of each artificial plasmid in the set of artificial plasmids can be a predetermined copy number. In some examples, the predetermined copy number may be 200 to 400 copies. Thus, it is possible to add an appropriate amount of artificial plasmid, i.e., without excessively affecting the binding of the primer to the target region, and also to achieve the effect of consuming the primer.
In some examples, the copy number of the artificial plasmid added to the sample to be tested at the time of library construction is known. Thereby, quantification of the target region can be facilitated. Specifically, when a target primer group set and an artificial plasmid set (the addition amount is a preset copy number) are added into a sample to be detected for library construction, in the PCR amplification process, under an ideal condition, the amplification efficiency of the primer group and a certain target region is the same as the amplification efficiency of the corresponding artificial plasmid and the target region; the library obtained after library construction may contain a sequence of a target region of a certain pathogen and a sequence of an artificial plasmid corresponding to the target region, and after sequencing on an online machine and analyzing sequencing data, the Reads number (read number) of the target region and the Reads number of the artificial plasmid can be obtained; at this time, the copy number of the target region can be calculated by the relationship "target region copy number/target region Reads number = artificial plasmid copy number (i.e. predetermined copy number, which is known)/artificial plasmid read number", and finally the copy number of the pathogen, that is, the content of the pathogen in the sample to be tested is obtained. Thus, the quantitative detection of multiple pathogens in the sample can be performed. It is understood that the quantitative determination refers to a relative quantification, i.e., the relative size of the contents of multiple pathogens in a sample can be derived from the artificial plasmid.
In summary, according to the construction method of the first aspect of the present disclosure, a pathogen-targeted detection system capable of improving the quality of a primer set and quantitatively detecting a pathogen can be constructed.
A second aspect of the present disclosure relates to an electronic device. The electronic device may include a processor and a memory, and the memory may store one or more readable instructions, wherein the one or more readable instructions, when executed by the processor, enable the method of constructing the pathogen targeted detection system described above in this embodiment. Thus, the sequences of the target primer set and the sequences of the artificial plasmid set can be obtained by the electronic device.
A third aspect of the present disclosure relates to a computer-readable medium, which may store a computer program, and when the computer program is executed by a processor, the method for constructing the pathogen targeting detection system described above in this embodiment can be implemented. Therefore, the sequences of the target primer group set and the sequences of the artificial plasmid set can be obtained through processing by the electronic equipment.
A fourth aspect of the present disclosure relates to an application, which may include: the first aspect of the disclosure relates to the application of the construction method of the pathogen targeting detection system in pathogen detection; and/or the use of an electronic device according to the second aspect of the present disclosure for pathogen detection; and/or the use of a computer readable medium according to the third aspect of the present disclosure in pathogen detection.
In some examples, in applications related to the disclosure of the fourth aspect, the specific steps may include: and/or using the combination of the target primer group set and the artificial plasmid to construct a library of the sample, sequencing and analyzing to obtain qualitative detection results and quantitative detection results of the multiple pathogens in the sample.
Specifically, in some examples, in the application related to the fourth aspect of the present disclosure, the target primer set and the artificial plasmid set may be used to library the sample, so as to obtain a library; sequencing the library to obtain sequencing data, filtering the sequencing data by using a quality value to obtain filtered sequencing data, wherein the filtered sequencing data comprises a plurality of reads (reads); and comparing the filtered sequencing data with sequences of target regions of various pathogens, wherein if a certain reads is the same as the sequence of a certain target region, the reads is judged to be the sequence of the pathogen corresponding to the target region, and the detection result is judged to be positive for the detection of the corresponding pathogen.
In some examples, the filtered sequencing data may also be compared to a set of artificial plasmids, where a reads is an artificial plasmid for the pathogen to which the artificial plasmid corresponds if the reads has the same sequence as the artificial plasmid.
In some examples, the same number of reads as the sequence of the target region can be calculated and labeled as the number of reads of the target region, the same number of reads as the artificial plasmid can be calculated and labeled as the number of reads of the artificial plasmid; the content of various pathogens was obtained based on the copy number (i.e., the predetermined copy number) of the artificial plasmid added at the time of library construction, the number of reads of the target region, and the number of reads of the artificial plasmid.
Specifically, when a target primer group set and an artificial plasmid set (the addition amount is a preset copy number) are added into a sample to be detected for library construction, in the PCR amplification process, under an ideal condition, the amplification efficiency of the primer group and a certain target region is the same as the amplification efficiency of the corresponding artificial plasmid and the target region; the library obtained after library construction may contain a sequence of a target region of a certain pathogen and a sequence of an artificial plasmid corresponding to the target region, and after sequencing on an online machine and analyzing sequencing data, the Reads number (read number) of the target region and the Reads number of the artificial plasmid can be obtained; at this time, the copy number of the target region can be calculated by the relationship "target region copy number/target region Reads number = artificial plasmid copy number (i.e. predetermined copy number, which is known)/read number of artificial plasmid", and finally the copy number of the pathogen, that is, the content of the pathogen in the sample to be tested, is obtained. Thus, the quantitative detection of multiple pathogens in the sample can be performed. It is understood that the quantitative determination refers to a relative quantification, i.e., the relative size of the contents of multiple pathogens in a sample can be derived from the artificial plasmid.
In some examples, the pathogen-targeted detection system constructed by the construction method according to the first aspect of the present disclosure and the analysis system for analyzing and detecting the sequencing result may be collectively referred to as pathogen-targeted sequencing. Specifically, the pathogen targeted detection system can comprise the steps of primer design, primer evaluation, primer optimization, target database construction and the like, and a comprehensive and systematically efficient pathogen multi-targeted sequencing method system can be obtained by analyzing and detecting sequencing results.
The construction method, the primer set, the electronic device and the application provided in the present disclosure will be described in detail with reference to the following examples, but they should not be construed as limiting the scope of the present invention.
[ example 1]
1. Pathogen selection and primer design
This example is for 14 total respiratory tract-associated pathogens, as shown in table 2 below:
TABLE 2
Genomic sequences of each pathogen in the table were downloaded from NCBI (https:// www.ncbi.nlm.nih.gov /), viPR (https:// www.viprbrc.org /), PATRIC (https:// patribrc.org /), IRD (https:// www.flu.org), VEuPathDB (https:// veupathdb.org /), for a total of 6357 sequences. And based on the reference genomic sequence of each pathogen of interest, the primer3 tool is installed locally to design all potential primer pairs in a whole genome in bulk, yielding 23000 primer pairs in total.
1.2 Primer evaluation
The primer is subjected to specificity, inclusion and dimer evaluation before a wet experiment, so that primer dimer phenomenon and non-specific amplification phenomenon in the wet experiment can be reduced in advance, and the sensitivity of primer detection is improved.
And evaluating the specificity and the inclusion of the primers by adopting a blastn short sequence comparison (blastn-short), comparing the primers with a constructed pathogen target database, and determining the species information of the matched sequences according to the annotation information of the matched sequences. As mentioned above, specificity refers to whether the sequence matched by the primer belongs to the target pathogen sequence, and inclusion refers to whether the primer can capture all the sequences of the target pathogen in the database. Finally, the primer pair with the specificity and the inclusion both of which are more than 95 percent is taken as the candidate primer pair of the target pathogen. See table 1, supra, for specific inclusion calculations. 23000 primer pairs were evaluated in a comprehensive manner, and 1643 primer pairs were obtained by screening according to the specificity and the inclusion of which are greater than 95%, and the following tables 3 to 6 show the evaluation results of 5 primer pairs listed for each pathogen:
TABLE 3
TABLE 4
TABLE 5
TABLE 6
In tables 3 to 6, the first pair of primers of Mucor racemosus of number 01 in Table 2 is denoted by the primer number "P-01-1", the second pair of primers of Mucor racemosus of number 01 in Table 2 is denoted by the primer number "P-01-2", the first pair of primers of Escherichia coli of number 02 in Table 2 is denoted by the primer number "P-02-1", and the schematic rules of the other numbers in the tables are the same, and will not be described again.
And (3) carrying out dimer recognition on the 1643 pairs of screened primer pairs by adopting MFEprimer software, finding out primer pairs with potential combination, and removing to obtain 103 pairs of primer pairs, wherein each pathogen has 7 pairs of primer pairs (3 pairs to 10 pairs) on average. Thus, the primer panel of the pathogen list was constructed. The MFEprimer software runs the command line as follows:
#shell
# mfeprimer dimer -i primer.fa -o dimer.txt -m 1 -p
# mfeprimer hairpin -i primer.fa -o hairpin.txt。
1.3 Determination of the sequence of the target region
The 103 primer pairs obtained in 1.2 and 6357 pathogen sequences downloaded in 1.1 are subjected to electronic PCR amplification (usearch 11.0.667_ i86linux 32), redundancy removal is performed based on 99% sequence similarity, and 2147 amplicon sequences are finally obtained, so that the target region sequence database of the pathogen list of the embodiment is formed. The Usearch tool runs the following commands:
#shell
# usearch11.0.667_i86linux32 -search_pcr geneset.fa -db primer.fa -strand both -maxdiffs 1 -minamp 80 -maxamp 800 -pcrout species.hits.txt -ampout amplicons.fasta。
meanwhile, artificial plasmids are respectively designed according to the sequence of each target region, and the sequences of the artificial plasmids are the same as the sequences of the target regions except that the artificial plasmids respectively have 5bp difference sequences at the positions close to the upstream primer and the downstream primer. As an internal reference, a set of artificial plasmids corresponding to the target regions of the pathogen list of this example was composed.
[ example 2]
2.1 Preparation of Experimental samples
Based on the multiple targeting primers panel (103 pairs of primers) based on the respiratory tract-related pathogens constructed in example 1, 2 samples of known pathogens, 1 negative control sample and 2 samples of unknown pathogens were selected to enter a wet experiment, and artificial plasmids corresponding to all primers were added to a wet experiment reaction system together with the primers. Single-ended 100bp sequencing (SE 100) was performed using the illumina miniseq sequencing platform. Sample information is as follows in table 7:
TABLE 7
2.2 Sample sequencing data quality control
And (4) quality control is carried out on the data of the off-line machine by using software fastp, and the parameters are set to be-q 30 and-u 10, so that high-quality reads are ensured. The reads numbers before and after sample quality control are shown in the following table. And (3) redundancy of the controlled reads is removed by using software vsearch (2.21.1), the running time is shortened, and the comparison efficiency is improved. The results are shown in table 8 below:
TABLE 8
2.3 Sequencing data determination
The short reads (the length of the reads is less than the sum of the lengths of the positive primer and the negative primer) generated by the combination of the primers can be judged as a dimer, the dimer in the reads is eliminated, and the rest reads are judged as a target pathogen, a plasmid and non-specific amplification. For each sample, reads of 3 or more targets were detected among 5 targets, and the sample was judged to be positive, otherwise, the sample was judged to be negative. The statistics of the determination results are shown in table 9 below, and the determination results of the reads of the respective samples are shown in tables 10 to 13 below.
Remarking: non-specific means that the number of reads of the same pair of primers is locked, but not the target species; others refer to the number of reads that fail to match the primers as required in the sample (1. Reads do not match the primers; 2.Reads can match the primers but belong to a different primer pair).
TABLE 9
Watch 10
TABLE 11
TABLE 12
Watch 13
2.5 Sample detection result determination
As shown in Table 14 below, it can be seen that the plasmids corresponding to the primers were detected, indicating that the amplification performance of the primers is expected. S1 was used as a negative control, and a small amount of pathogen was detected (considered as interference), and 4 other samples were analyzed on this basis: for samples with known S2 and S3 pathogens, the corresponding pathogens were correctly detected, while the S4 sample was detected as M.intracellulare and the S5 sample was detected as Coxsackie virus A. It can be seen that staphylococcus aureus is detected in all samples (S1 negative control samples are also present, and the quantitative result is close to that of the S1 negative control samples, but staphylococcus aureus is common environmental bacteria, so that staphylococcus aureus in the samples S2-S5 can be determined to be caused by experimental environmental pollution).
TABLE 14
In conclusion, the pathogen targeted detection system constructed by the construction method of the present disclosure can improve the quality of primers and can quantitatively detect pathogens when it is applied to multiplex PCR detection of target regions of genes of various pathogens.
While the present disclosure has been described in detail above with reference to the drawings and the embodiments, it should be understood that the above description does not limit the present disclosure in any way. Variations and changes may be made as necessary by those skilled in the art without departing from the true spirit and scope of the disclosure, which fall within the scope of the disclosure.
Claims (10)
1. A construction method of a pathogen targeted detection system, which is a system for performing multiplex PCR detection on target regions of genes of various pathogens, is characterized by comprising the following steps:
determining a plurality of pathogens to be detected;
constructing a pathogen database based on the plurality of pathogens, the pathogen database including genomic sequences of all strains of each pathogen of the plurality of pathogens;
designing a first candidate primer set for each pathogen in the plurality of pathogens using primer3 software, the first candidate primer set comprising a plurality of primer sets, each primer set in the plurality of primer sets comprising an upstream primer and a downstream primer;
comparing the first candidate primer group set with the pathogen database to obtain the inclusion of each primer group, and comparing the first candidate primer group set with the NT database to obtain the specificity of each primer group;
reserving primer groups with the inclusion larger than a first preset value and the specificity larger than a second preset value in the first candidate primer group set to obtain a second candidate primer group set;
sequencing each primer group of the second candidate primer group set according to the occurrence probability of a primer dimer or a hairpin structure by using MFEprimer software, and reserving at least three groups of primer groups with the lowest occurrence probability of the primer dimer or the hairpin structure aiming at each pathogen to obtain a target primer group set;
obtaining sequences of target regions of the multiple pathogens based on the target primer group set, and designing an artificial plasmid set based on the sequences of the target regions, wherein the artificial plasmid set comprises multiple artificial plasmids with preset copy numbers and capable of combining with each primer group in the target primer group set, the artificial plasmids are designed based on the sequences of the target regions and are different from the sequences of the target regions, and the artificial plasmids have different sequences from the sequences of the target regions.
2. The method of claim 1, wherein the artificial plasmid is designed by adding a sequence, reducing a partial sequence, or replacing a partial sequence based on the sequence of the target region.
3. The method of constructing according to claim 1, wherein the step of constructing the pathogen database comprises:
for each pathogen in the plurality of pathogens, downloading from NCBI, viPR, PATRIC, IRD, and VEuPathDB databases genomic sequences of all strains below each pathogen classification level based on each pathogen's species information to obtain the pathogen database,
wherein the species information includes information of phylum of generic class of disciplines, txid number, and serotype.
4. The method of claim 1, wherein the primer3 software is used to design a first candidate primer set for each pathogen of the plurality of pathogens across the entire genome of the plurality of pathogens, wherein 200 sets of primers are designed per 100kb of genome.
5. The building method according to claim 1, wherein the first preset value is 95% and the second preset value is 95%.
6. A set of target primer sets and a set of artificial plasmids constructed by the construction method according to any one of claims 1 to 5.
7. An electronic device comprising a processor and a memory, the memory having stored thereon one or more readable instructions that, when executed by the processor, implement a method of constructing the pathogen targeted detection system of any one of claims 1 to 5.
8. Use of the set of target primer sets and the set of artificial plasmids of claim 6 or the electronic device of claim 7 in pathogen detection.
9. The use according to claim 8, comprising:
using the target primer group set and the artificial plasmid set to build a library for a sample to obtain a library;
sequencing the library to obtain sequencing data, and filtering the sequencing data by using a mass value to obtain filtered sequencing data, wherein the filtered sequencing data comprises a plurality of reads (reads);
and comparing the filtered sequencing data with sequences of target areas of various pathogens, wherein if the reads are matched with the sequences of the target areas, the reads are judged to be the sequences of the corresponding pathogens, and the detection result is judged to be positive in detection of the corresponding pathogens.
10. The use according to claim 9, comprising:
comparing the filtered sequencing data with the artificial plasmid set, wherein if the reads are matched with the sequences of the artificial plasmids, the reads are judged to be the artificial plasmids of the corresponding pathogens;
calculating the reads number matched with the sequence of the target region and marking as the reads number of the target region, and calculating the reads number matched with the artificial plasmid and marking as the reads number of the artificial plasmid;
obtaining the content of the plurality of pathogens based on the predetermined copy number, the target region reads number and the artificial plasmid reads number.
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