WO2019242186A1

WO2019242186A1 - Method, apparatus, computer device and storage medium for determining target to be detected

Info

Publication number: WO2019242186A1
Application number: PCT/CN2018/111924
Authority: WO
Inventors: 孙亚洲; 杜刘稳; 陈斌; 牛团结; 肖贡; 郭婷; 曾柳眉; 陈杰
Original assignee: 深圳市达仁基因科技有限公司
Priority date: 2018-06-22
Filing date: 2018-10-25
Publication date: 2019-12-26
Also published as: CN110021365B; CN110021365A

Abstract

Disclosed is a method for determining a target to be detected, comprising: determining a target pathogen operation group to be detected (102); obtaining specific k-mers contained in the target pathogen operation group from the target database (104); determining the specific k-mers contained in each genome contained in the target pathogen operation group (106); processing the specific k-mers contained in each genome to obtain sets of non-coincident specific regions corresponding to each genome (108); obtaining the number of occurrences of each non-coincident specific region contained in the sets of non-coincident specific regions corresponding to each genome in all the sets of non-coincident specific regions (110); selecting a non-coincident specific region that occurs more than a preset number threshold as the target to be detected of the target pathogen operation group (112).

Description

Method, device, computer equipment and storage medium for determining detection target

This application claims the priority of a Chinese patent application filed on June 22, 2018, with the application number 2018106516939, and the application name is "Method, Device, Computer Equipment and Storage Medium for Detecting Targets", its entire content Incorporated by reference in this application.

Technical field

The present application relates to a method, an apparatus, a computer device, and a storage medium for determining a detection target.

Background technique

A target is a special nucleic acid fragment that can be used in PCR reactions (Polymerase Chain Reaction), antibody-antigen reactions, and hybridization probe reactions.

In the traditional technology, if specific detection targets (i.e., characteristic targets) for a certain pathogen or pathogen operating group need to be determined, the physiological, metabolic, genetic and other indicators of the pathogen or pathogen operating group need to be detailed. And detailed long-term research, so it is time-consuming and inefficient. Some existing new technical solutions can already be used to analyze the genome data to find potential detection targets for a certain pathogen or pathogen operating group. However, these current technical solutions are often demanding, such as requiring characteristic targets to exist only in one species operation group, and to exist in the genome of each individual in the species operation group. This lack of any stringent requirements makes it very difficult to find characteristic targets in a pathogen or a group of pathogens.

Summary of the Invention

According to various embodiments disclosed in the present application, a method, an apparatus, a computer device, and a storage medium for determining a detection target are provided.

A method for determining a detection target includes:

Determine the target pathogen operating group to be detected;

Obtain a specific k-mer included in the target pathogen operating group from the target database, where the specific k-mer is a k-mer that satisfies preset specificity conditions, and k-mer refers to a genome of length k sequence;

Determining a specific k-mer included in each genome included in the target pathogen operating group;

Processing the specific k-mer included in each genome to obtain a set of non-overlapping specific regions corresponding to each genome, wherein the set of non-overlapping specific regions includes non-overlapping specific regions;

Obtaining the number of occurrences of each non-overlapping specific region contained in the non-overlapping specific region set corresponding to each genome in the entire non-overlapping specific region set; and

Selecting a non-overlapping specific region where the number of occurrences exceeds a preset number of times as a detection target of the target pathogen operation group.

A device for determining a detection target includes:

A determination module for determining a target pathogen operation group to be detected;

A specific k-mer acquisition module, configured to acquire a specific k-mer included in the target pathogen operation group from a target database, where the specific k-mer is a k-mer that satisfies a preset specific condition, k-mer refers to a genomic sequence of length k; determining a specific k-mer included in each genome included in the target pathogen operating group;

A non-overlapping specific region acquisition module is configured to process specific k-mers contained in each genome to obtain a set of non-overlapping specific regions corresponding to each genome, where the non-overlapping specific region set includes non-overlapping specific regions Specific regions; and

The detection target selection module is used to obtain the number of occurrences of each non-overlapping specific region contained in the set of non-overlapping specific regions corresponding to each genome; A non-overlapping specific region of the number of times threshold is set as a detection target of the target pathogen operation group.

A computer device includes a memory and one or more processors. The memory stores computer-readable instructions. When the computer-readable instructions are executed by the processor, the one or more processors are executed. The following steps: computer readable instructions computer readable instructions to determine a target pathogen operating group to be detected;

One or more non-volatile computer-readable storage media storing computer-readable instructions. When the computer-readable instructions are executed by one or more processors, the one or more processors execute the following steps: computer-readable instructions Computer readable instructions to determine the target pathogen operating group to be detected;

Details of one or more embodiments of the present application are set forth in the accompanying drawings and description below. Other features and advantages of the application will become apparent from the description, the drawings, and the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to explain the technical solutions in the embodiments of the present application or the prior art more clearly, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings in the following description are merely These are some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without paying creative work.

FIG. 1 is a schematic flowchart of a method for determining a detection point according to one or more embodiments.

FIG. 2 is a schematic flow chart before step 102 according to one or more embodiments.

FIG. 3 is a schematic flowchart of step 108 according to one or more embodiments.

FIG. 4 is a schematic flowchart of step 306 according to one or more embodiments.

FIG. 5 is a schematic flow chart after step 306 according to one or more embodiments.

FIG. 6 is a schematic flowchart of step 110 according to one or more embodiments.

FIG. 7 is a schematic flow chart after step 112 according to one or more embodiments.

FIG. 8 is a schematic flowchart of step 704 according to one or more embodiments.

FIG. 9 is a schematic flow chart after step 112 according to another embodiment.

FIG. 10 is a schematic flowchart of step 906 according to one or more embodiments.

FIG. 11 is a schematic flowchart of step 912 according to one or more embodiments.

FIG. 12 is a schematic flowchart of step 1104 according to one or more embodiments.

FIG. 13 is a schematic flowchart of a method for determining a detection point according to another or more embodiments.

FIG. 14 is a schematic flowchart of step 1302 according to one or more embodiments.

FIG. 15 is a schematic flowchart of step 1304 according to one or more embodiments.

FIG. 16 is a structural block diagram of an apparatus for determining a detection point according to one or more embodiments.

FIG. 17 is an internal structural diagram of a computer device according to one or more embodiments.

detailed description

In order to make the technical solution and advantages of the present application more clear and clear, the present application is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the application, and are not used to limit the application.

In one embodiment, as shown in FIG. 1, a method for determining a detection target is provided, including the following steps:

Step 102: Determine a target pathogen operation group to be detected.

A pathogen operating group can represent a genetic unit or a taxonomic unit of different classification levels such as a species, a subspecies, a subtype, a strain or virus strain, or a genus. A pathogen operating group may include one or more Related genomes. The target pathogen operating group refers to a pathogen operating group to be detected. For example, the pathogen operating group to be detected is Staphylococcus aureus, then the target pathogen operating group in step 102 refers to Staphylococcus aureus.

Step 104: Obtain the specific k-mer included in the target pathogen operation group from the target database. The specific k-mer is a k-mer that meets the preset specific conditions. The k-mer refers to a genomic sequence of length k. .

The target database stores a feature target sequence set previously established for each pathogen operating group, and the characteristic target sequence set corresponding to each pathogen operating group includes a specific k-mer corresponding to each pathogen operating group . Therefore, the specific k-mer included in the target pathogen manipulation group can be obtained from the target database. The specific k-mer refers to a k-mer selected from the k-mers included in the target pathogen operation group and meeting a preset specificity condition, that is, a specific k-mer corresponding to the target pathogen operation group. The preset specific condition is a condition set by a technician in advance for selecting a matching k-mer. The preset specific condition may be determined according to a technician's consideration or an actual project requirement.

k-mer refers to a genomic sequence of length k, where k is a natural number. If there are a different deterministic characters in a genomic data, then for a particular k, there may be a total of k-mers with a power of a that are different. For DNA or RNA (ribonucleic acid) sequences, deterministic characters refer to the five bases A (adenine), T (thymine), C (cytosine), G (guanine), and U (uracil); In the case of protein sequences, deterministic characters are defined amino acid characters.

Step 106: Determine a specific k-mer included in each genome included in the target pathogen operating group.

A pathogen operating group can include one or more related genomes. Therefore, the target pathogen operating group includes one or more related genomes, and each genome includes one or more k-mers. Specific k-mers are k-mers that satisfy preset specificity conditions, so each genome contains one or more specific k-mers. Therefore, the specific k-mer contained in each genome contained in the target pathogen operating group to be detected can be determined.

Step 108: The specific k-mer included in each genome is processed to obtain a set of non-overlapping specific regions corresponding to each genome, and the set of non-overlapping specific regions includes non-overlapping specific regions.

After determining the specific k-mer contained in each genome included in the target pathogen manipulation group, the specific k-mer contained in each genome can be processed. After processing the specific k-mer contained in each genome, a set of non-overlapping specific regions corresponding to each genome can be obtained. The set of non-overlapping specific regions corresponding to each genome includes one or more non-overlapping specific regions corresponding to each genome. The difference between a non-overlapping specific region and a specific k-mer is that the specific k-mer has a length limitation and is a sequence of a specific length k, while the non-overlapping specific region does not have any limitation on its length.

Step 110: Obtain the number of occurrences of each non-overlapping specific region included in the non-overlapping specific region set corresponding to each genome in all the non-overlapping specific region sets.

After obtaining the non-overlapping specific region set corresponding to each genome, the number of occurrences of each non-overlapping specific region in each non-overlapping specific region set in each non-overlapping specific region set can be obtained. The set of non-overlapping specific regions corresponding to each genome can be regarded as a small set, and the entire set of non-overlapping specific regions of the entire genome is composed of a complete set of non-overlapping specific regions, that is, the complete set is composed of multiple small sets. . The complete set of non-overlapping specific regions contains all the non-overlap-specific regions in the entire genome included in the operation group of the pathogen. Therefore, each non-overlap-specific region in the set of non-overlap-specific regions of each genome can be obtained. Sexual region, the number of occurrences in the full set of coincident specific regions.

In step 112, a non-overlapping specific region with a number of occurrences exceeding a preset number of times is selected as a detection target of the target pathogen operation group.

After obtaining the number of occurrences of each non-overlapping specific region in the entire set of non-overlapping specific regions, a non-overlapping specific region with an appearance frequency exceeding a preset number of thresholds can be selected as a detection target of the target pathogen operation group . The preset number of times threshold can be set by technicians according to actual project requirements. The selected non-overlapping specific regions may be multiple, or may correspond to multiple genomes.

In the above method for determining the detection target, the specific k-mer of the target pathogen operating group is obtained, and then a non-overlap that meets the preset number of thresholds is selected from the non-overlap specific regions obtained according to the specific k-mer. Specific region, as the detection target of the target pathogen operating group. Because the specific k-mer is determined by probabilistic preset specific conditions, and a set of non-overlapping specific regions is obtained based on the specific k-mer, when the non-overlapping specific regions that meet the conditions are finally selected as the detection target Therefore, this technical solution greatly expands the search range of potential detection targets, increases the flexibility of limiting the search range of detection targets, and improves the efficiency of determining detection targets.

In one embodiment, the preset number of times threshold = (1-Y) * N, Y is a preset first condition threshold, and N is the number of non-overlapping specific region sets.

When selecting a part of non-overlapping specific regions from multiple non-overlapping specific regions as the detection target of the target pathogen operation group, a non-overlapping specific region is selected to be a non-overlapping specific region set. Overlap specific regions. The preset number of times threshold is equal to (1-Y) * N. Y is a preset first condition threshold, and the preset first condition threshold Y can be set to less than 5%, which can be specifically set by a technician according to the actual situation. N is the number of non-overlapping specific region sets, and each genome corresponds to a non-overlapping specific region set, so N is actually the number of genomes contained in the target pathogen operating group. Generally, a non-overlapping specific region does not appear in every genome, so any non-overlapping specific region appears in all non-overlapping-specific region sets less than or equal to N.

In one embodiment, the preset first condition threshold is less than 5%.

In one of the embodiments, the specific k-mer refers to the k-mer in the pathogen operation group in which the number of occurrences in the genome occurrence number index table of the target pathogen operation group meets a preset error condition.

The characteristic target sequence set corresponding to each pathogen operation group includes a specific k-mer that satisfies a preset specific condition in each pathogen operation group. Further, the preset specific condition refers to a k-mer included in a pathogen operation group in which the number of occurrences in the genome occurrence number index table of each target pathogen operation group satisfies a preset error condition. The preset error condition refers to the error condition preset by the technician according to the actual project requirements. The error condition can be a range of regions, that is, the k-mer selected as a specific can be allowed to have a certain error, instead of being completely satisfied. Some strict objective condition.

A target pathogen operating group has an index table of the number of occurrences of the corresponding genome of the pathogen operating group. The number of genomes contained in the target pathogen operation group of the k-mer included in the target pathogen operation group can be known according to the number of genome occurrence index table corresponding to the target pathogen operation group, that is, the target pathogen operation group can be selected. The k-mer in the pathogen operating group whose occurrences in the genome occurrence number index table satisfy the preset error condition, and the selected k-mer is used as the specific k-mer included in the target pathogen operating group.

When selecting the specific k-mer, a certain degree of error is allowed, so the specific sequence representing the target pathogen operation group can be found with a high probability within a certain error range. Because the specificity is determined by the probability preset specificity condition When the non-overlapping specific region set is obtained based on the specific k-mer, and the non-overlapping specific region that meets the conditions is finally selected as the detection target, this technical solution greatly expands the potential detection target. The search range increases the flexibility of limiting the search range of detection targets, and improves the efficiency of determining detection targets.

In one embodiment, before the above step 102, the method further includes the following steps: generating an index table of the number of occurrences of the genome corresponding to the target pathogen operation group, and the genome number index table records that the genome included in the target pathogen operation group contains each The number of k-mer genomes; the genome occurrence index table is stored in the feature target sequence set corresponding to the target pathogen operating group.

The genome is all the genetic information in an organism. This genetic information is stored in the form of a nucleotide sequence. The sum of the genetic material in a complete monomer of an organism (such as an animal or plant individual, or animal or plant cell, or bacterial individual) is the genome. In each pathogen operating group, multiple genomes can be included, and in each genome, multiple k-mers can be included. In the index table of the number of genome occurrences corresponding to each pathogen operation group, the number of k-mers contained in each pathogen operation group has appeared in the genome of the pathogen operation group, that is, the genome number index table records each k- The number of genomes of the k-mer is included in the genome included in the pathogen operating group corresponding to mer.

The table of the number of genomes corresponding to the target pathogen operation group actually records how many genomes included in the target pathogen operation group for each k-mer included in the target pathogen operation. If a k-mer occurs more than once in the same genome, it will still only be counted once in the index table of the number of genome occurrences corresponding to the target pathogen operating group. After obtaining data on how many genomes each k-mer included in the target pathogen operation group has contained in the target pathogen operation group, an index table of the number of occurrences of the genome corresponding to the target pathogen operation group can be established. After the genomic appearance frequency index table corresponding to the target pathogen operating group is established, the genomic appearance frequency index table can be stored into the characteristic target sequence set corresponding to the target pathogen operating group, that is, stored in the target database. After storage, if needed, Using the index table of the number of occurrences of the genome can retrieve data from the database, thereby improving the detection efficiency.

In one embodiment, as shown in FIG. 2, before step 102, the method further includes the following steps:

Step 100: Select a k-mer that satisfies a preset specific condition from a k-mer corresponding to the target pathogen operation group.

Step 101: Store a k-mer that satisfies a preset specific condition into a feature target sequence set corresponding to a target pathogen operation group.

In the target database, a characteristic target sequence set corresponding to the target pathogen operating group is stored, and the target characteristic target sequence set includes a specific k-mer corresponding to the target pathogen operating group. Specific k-mer refers to the selection of k-mers that meet the preset specific conditions from the k-mers included in the target pathogen operation group. When a k-mer that meets the preset specific conditions is selected, that is, the specific k-mer, It is stored in the feature target sequence set corresponding to the target pathogen operation group.

In one embodiment, the k-mer in the specific k-mer satisfies the following two conditions: the number of occurrences in the genome occurrence index table corresponding to the target pathogen operation group meets a first preset error condition; and in the target pathogen The number of occurrences in the genome occurrence number index table corresponding to the operation group and the number of occurrences in the genome occurrence number index table of the complete set meet the second preset error condition. The genome number index table corresponding to the target pathogen operation group records the number of genomes of each k-mer in the genome included in the target pathogen operation group; the genome occurrence number index table of the complete set contains the genomes included in the complete set. The number of genomes of each k-mer.

In the target database, the target pathogen operating group has a corresponding characteristic target sequence set, and the specific k-mer included in the characteristic target sequence set refers to a k-mer that satisfies a preset specific condition. The preset specific condition includes a first preset error condition and a second preset error condition. When the k-mer satisfies these two conditions at the same time, it is considered that the k-mer meets the preset specific condition and the k -mer as a specific k-mer. Further, the number of occurrences of the k-mer in the genome appearance index table of the target pathogen operation group needs to satisfy the first preset error condition, and the number of occurrences of the k-mer in the genome appearance index table of the target pathogen operation group. And the number of occurrences in the genome occurrence number index table of the complete set satisfies the second preset error condition. The complete set refers to the collection of all the high-confidence genomes collected. The high-confidence genome contains both the pathogen's genome and the non-pathogen's genome. For example, high-confidence genomes of symbiotic bacteria, probiotics, humans, animals, and plants. A high-confidence genome refers to a selected genome that meets a preset reliability condition.

The count corresponding to each k-mer recorded in the genome occurrence index table of the complete set represents how many genomes of the k-mer have appeared in the total set. If the k-mer appears multiple times in the same genome, it will only be counted once. The genome number index table of the target pathogen operating group records the number of genomes of each k-mer in the genome contained in the target pathogen operation group, and the genome occurrence index index of the complete episode records the genome included in the complete episode. It contains the number of k-mer genomes.

The selection of the specific k-mer includes two parameters, a preset error condition and a second preset error condition, and thus allows the non-specificity of the specific k-mer within a certain range. Without these two parameters, non-specificity in a certain range cannot be allowed, and it is often difficult to find a specific k-mer for a pathogen operating group. Therefore, by selecting a specific k-mer that allows a certain amount of error, and thereby establishing a set of characteristic target sequences, a specific target that can represent the pathogen's operating group can be found with high probability.

In one embodiment, the first preset error condition is: the ratio of the number of occurrences in the genome occurrence index table of the target pathogen operation group to the number of genomes included in the target pathogen operation group is greater than or equal to the first threshold 1.

The first preset error condition refers to that the sum of the ratio of the number of occurrences recorded in the genome occurrence number index table corresponding to the target pathogen operation group to the number of genomes contained in the target pathogen operation group and the first threshold is greater than or equal to 1. Suppose the target pathogen operating group contains N genomes, and the number of occurrences of a k-mer in the genome occurrence index table of the target pathogen operating group is C1, and the first threshold is P1, then the first preset error condition refers to, C1 / N + P1≥1. The first threshold value P1 represents an acceptable error probability, and can be any value between 0 and 1. The first threshold value can be set by a technician according to the actual project.

In one of these embodiments, the first threshold is less than 5%.

The first threshold is an acceptable error probability. The first threshold may be any value between 0 and 1. The first threshold may be set to a value less than 5%.

In one embodiment, the second preset error condition is: the ratio of the number of occurrences in the genome appearance number index table of the target pathogen operation group to the number of appearances in the genome occurrence number index table of the complete set and the second threshold value And is greater than or equal to 1.

The second preset error condition refers to that the sum of the number of occurrences recorded in the genome appearance number index table corresponding to the target pathogen operating group and the appearance number in the genome episode number index table of the complete set and the sum of the second threshold value is greater than or equal to 1 . Assume that the number of occurrences of a k-mer in the genome occurrence index table of the target pathogen operation group is C1, and the number of occurrences of the k-mer in the genome occurrence index table of the complete set is C2, and the second threshold value is P2, then The second preset error condition refers to C1 / C2 + P2≥1. The second threshold value is the same as the above-mentioned first threshold value, which represents an acceptable error probability, and can be any value between 0 and 1. The second threshold value P2 can also be set by a technician according to the actual project.

In one of these embodiments, the second threshold is less than 5%.

The second threshold value is the same as the first threshold value, which means an acceptable error probability. The second threshold value can also be any value between 0 and 1, and the second threshold value can be set to a value less than 5%. The first threshold and the second threshold may be equal or different.

In one embodiment, before obtaining the sequencing data of the sample, the method further includes: generating a genome occurrence index table of the complete set, and the genome occurrence number index table of the complete set records records of each k-mer included in the genome included in the complete set. The number of genomes; the genome occurrence index table of the complete set is stored in the target database.

In the target database, a characteristic target sequence set corresponding to each pathogen operating group is stored. The full set contains all the high-confidence genomes collected, that is, the full set contains both the high-reliability genomes of multiple pathogen operating groups and the high-reliability genomes of multiple non-pathogenic operating groups. After obtaining the data of how many genomes each k-mer included in each pathogen operation group has contained in the complete set, an index table of the number of occurrences of the genome of the complete set can be generated. The genome occurrences index table of the complete set records how many genomes of the k-mer contained in each pathogen operation group have appeared in the complete set, that is, the genome count index table of the complete set records the genomes that each k-mer contains in the complete set. It contains the number of k-mer genomes.

Therefore, in the genome number table of the complete set, actually how many genomes each k-mer contains in the complete set is recorded, that is, how many genomes each k-mer appears in the entire genome is recorded. However, the number of measurements is the number of genomes, not the number of k-mer occurrences. If a k-mer occurs more than once in the same genome, it will still be counted only once in the genome occurrence index table of the complete set. After obtaining the data of how many genomes each k-mer has appeared in the complete set, an index table of the number of occurrences of the genome for the complete set can be established. The genome occurrence index table of the complete set is different from the genome occurrence index table corresponding to each pathogen operation group. The genome occurrence number index table of the pathogen operation group corresponds to the pathogen operation group, and each pathogen operation group has its corresponding Index table of the number of occurrences of the genome, but only one set of the index of the number of occurrences of the genome in the ensemble is generated, which is for all data. After storing the generated genomic appearance frequency index table of the complete set, if it is needed in the process of detecting the sequencing data, the data can be retrieved from the database, thereby improving the detection efficiency.

In one embodiment, the above step 106 includes: sequentially using each genome included in the target pathogen operating group as a reference genome; locating each specific k-mer included in the target pathogen operating group to the reference genome; and The specific k-mer mapped to the reference genome is the specific k-mer contained in the reference genome.

Multiple target genomes are included in the target pathogen operating group. Each genome can be used as a reference genome in turn, and the specific k-mer included in the target pathogen operating group can be mapped to the reference genome. Because the specific k-mer is a k-mer that is pre-selected to meet the preset specificity conditions, there may be cases where some specific k-mers cannot be mapped to a certain genome. The specific k-mer successfully mapped to the reference genome can be used as the specific k-mer included in the reference genome. Some specific k-mers cannot be mapped to a certain genome, then it can be considered that the specific k-mers are not included in the genome. Therefore, localization can also be considered as reconfirming the specific k-mer included in each genome. Therefore, through this localization operation, the specific confirmation of the specific k-mer contained in each genome is double-checked to increase the probability of fault tolerance.

In one embodiment, the specific k-mer mapped to the reference genome is used as the specific k-mer included in the reference genome, which includes: sequentially selecting a region from the reference genome for comparison with the specific k-mer; when detecting When the similarity between the selected region and the specific k-mer reaches a preset similarity threshold, the specific k-mer is used as the specific k-mer included in the reference genome.

When the specific k-mer included in the target pathogen operating group is mapped to the reference genome to determine whether the specific k-mer belongs to the reference genome, a region can be selected from the reference genome in turn and compared with the specific k-mer. The selected region is a gene sequence. Comparing the selected gene sequence with a specific k-mer, the similarity of the two sequences can be detected. When it is detected that the similarity between the selected region and the specific k-mer reaches a preset similarity threshold, it can be considered that the specific k-mer is included in the reference genome, that is, the specific k-mer can be used as a reference The genome contains a specific k-mer. The reference genome can be regarded as a string of many bases. When compared with the specific k-mer, a sequence of length k can be sequentially compared with the specific k-mer from the string of the reference genome. . If the selected sequence and the specific k-mer have similar strings reaching a preset similarity threshold, the specific k-mer can be considered as a specific k-mer in the reference genome. The preset similarity threshold can be customized by a technician. For example, when the preset similarity threshold is set to 99%, if the specificity of a specific k-mer and a region on the reference genome reaches or exceeds 99%, the specific k-mer is considered to belong to the reference genome.

In one of the embodiments, the specific k-mer mapped to the reference genome is used as the specific k-mer included in the reference genome, including: sequentially selecting from the reference genome a sequence having the same length as the specific k-mer, and selecting The sequence of the same length as the specific k-mer is compared with the specific k-mer; when it is detected that the selected sequence is the same as the specific k-mer, the specific k-mer is used as the specific k included in the reference genome. -mer.

Different from the positioning method used in the previous embodiment to determine whether the similarity between the selected region and the specific k-mer reaches a preset similarity threshold, the length of the specific k-mer and the specific k-mer can be selected from the reference genome in this order Identical sequences were compared with specific k-mers. If it is detected that the selected sequence is the same as the specific k-mer, it is considered that the specific k-mer belongs to the reference genome and is the specific k-mer included in the reference genome. If it is detected that the selected sequence is different from the specific k-mer, the specific k-mer is not considered to belong to the reference genome. That is, there is no case that the selected sequence is similar to the specific k-mer, but only belongs to or does not belong. This positioning method is faster because there is no judgment of similarity errors.

In one embodiment, before determining the target pathogen operation group to be detected, the method further includes: obtaining a pre-selected genome that meets a preset confidence condition as a high-confidence genome; and determining a high degree of confidence included in each pathogen operation group. Confidence genome, as the genome corresponding to each pathogen operating group.

A high-confidence genome refers to a selected genome that meets a preset reliability condition. The preset confidence condition refers to a condition for selecting a genome set by a technician. After obtaining the high-confidence genome, the high-confidence genome contained in each pathogen operating group can be determined, and the corresponding contained genome in each pathogen operating group can be determined. The high-confidence genome can include both the pathogen genome and the non-pathogen genome, such as high-confidence genomes of symbiotic, probiotic, human, animal, and plant. The highly reliable genome can be derived from the RefSeq dataset (RefSeq reference sequence database of the National Center for Biotechnology Information) of the NCBI (RefSeq reference sequence database, non-redundant in a biological sense provided by the National Center for Bioinformatics). Other genes and protein sequences) or other public or private high-confidence genomes.

In one embodiment, satisfying the preset credibility condition includes any of the following: when the proportion of non-deterministic characters contained in the genome sequence is lower than a preset proportion threshold; the sequences belonging to the same chromosome included in the genome sequence When the fragment is below the preset fragment threshold; sequence comparison of a genomic sequence with all other genomic sequences whose genetic relationship meets the preset genetic distance threshold range to determine the full sequence average of the genomic sequence in its similar genomic sequence Coverage percentage, when the average coverage percentage is higher than a preset percentage value.

There are three ways to identify and screen high-confidence genomes:

1. Screen based on the proportion of non-deterministic characters contained in a genomic data. For example, for the DNA genome, the proportion of non-deterministic characters refers to the proportion of non-ACGT characters contained in it. If the proportion of non-ACGT characters in a piece of DNA genome data is too high, then the piece of data is suspected of low confidence. Genome. For DNA or RNA sequences, non-deterministic characters refer to characters other than ACGTU. For protein sequences, non-deterministic characters refer to characters other than certain amino acid characters.

2. Screen based on the number of genomic data fragments included in a complete chromosome. If there are too many fragments that belong to the same chromosome, then the genome is a suspected low-confidence genome.

3. Perform genome-wide sequence alignment of multiple genomes with similar genetic relationships (eg, genetic distance is less than a certain threshold) to determine the average genome-wide coverage percentage of the genome in its similar genomes, and then average based on this whole genome Screening by percentage of coverage: Genomes with a low average percentage of coverage are those that are suspected of having low completion, ie, low confidence. Genetic distance refers to an index that measures the size of the overall genetic difference between species (or individuals).

In one embodiment, as shown in FIG. 3, step 108 includes:

In step 302, a specific k-mer included in each genome is mapped to the genome.

Step 304: Select specific k-mer and / or non-overlapping specific regions contained in each genome in turn for detection.

In step 306, when it is detected that the distance between the selected specific k-mer and / or non-overlapping specific regions on the genome is less than a preset distance threshold, the selected specific k-mer and / or non-overlapping specific regions are detected. The replacement is performed to obtain a non-overlapping specific region after replacement.

Step 308: Obtain a set of non-overlapping specific regions corresponding to each genome according to the finally retained specific k-mer and the replaced non-overlapping specific regions.

After determining the specific k-mer contained in each genome included in the target pathogen operating group, the specific k-mer contained in each genome can be mapped to the genome. Then, two specific k-mers included in the genome can be selected in order to detect whether the distance between the selected two specific k-mers on the genome is less than a preset distance threshold. If so, the two specific k-mers selected will be selected. k-mer for replacement. The replacement method may be to replace the selected two specific k-mers with the smallest region that can cover the selected two specific k-mers to obtain the corresponding non-overlapping specific regions. It may also be a sequence in which the selected two specific k-mers are located on the genome as corresponding non-overlapping specific regions. The preset distance threshold can be a negative number or 0, and is generally set to an integer less than 5.

The specific k-mer in each genome can also be compared with the non-overlapping specific regions in the genome, or the two non-overlapping specific regions in the genome can be compared. When performing alignment comparison, specific k-mer and / or non-overlapping specific regions contained in each genome can be selected for detection. The alignment method is the same as that between two specific k-mers. Check whether the selected two non-overlapping specific regions or the selected specific k-mer and non-overlapping specific regions have a distance on the genome smaller than a preset distance threshold. If so, the selected specific k-mer and non-overlapping specific regions or two selected non-overlapping specific regions are replaced to obtain corresponding non-overlapping specific regions. A set of non-overlapping specific regions corresponding to each genome can be obtained according to the specific k-mer finally retained in each genome and the non-overlapping specific regions after replacement, and a set of non-overlapping specific regions corresponding to each genome Contained are non-overlapping specific regions in the genome.

Assume that A and B are the two specific k-mers selected, A is ACGGTCATC, and B is TCATCCGA. After locating A and B on the genome, if the sequence between A and B is CCC, then the way to replace A and B can be A + CCC + B, that is, the cost of replacing A and B is coincident. The specific region is ACGGTCATCCCTCATCCGA. If there is no sequence between A and B, A and B can be directly spliced, that is, the sequence composed of A + B is the overlapped specific region obtained by replacing A and B. In this example, there is a case where the ends overlap between A and B, that is, the ends of A and the head of B have multiple overlapping characters. Then the replacement method for A and B is to replace the selected two specific k-mers with the smallest area that can cover the selected two specific k-mers, that is, ACGGTCATCCGA. The specific replacement method can be customized by a technician, or selected based on the distance between A and B or the number of overlapping characters.

When processing specific k-mers to obtain non-overlapping specific regions, there may be situations in which two specific k-mers can be replaced to obtain one non-overlapping specific region or three specific k-mers. The replacement of mer can obtain a non-overlapping specific region, or it can also be a non-overlapping specific region obtained by replacing multiple specific k-mers. Therefore, the length of the obtained non-overlapping specific region is not limited. If the distance between the selected two specific k-mers on the genome is not less than a preset distance threshold, no processing is required. After doing this for each genome, a set of non-overlapping specific regions contained in each genome can be obtained. The set of non-overlapping specific regions corresponding to each genome contains the non-overlapping specific regions in the genome.

In one embodiment, the preset distance threshold is less than 5.

The preset distance threshold can be an integer less than 5.

In one embodiment, as shown in FIG. 4, the above step 306 includes:

In step 402, it is detected whether the distance between the selected specific k-mer and / or non-overlapping specific region on the genome is less than or equal to zero, and if yes, step 404 is performed; if not, step 406 is performed.

Step 404: Replace the selected two specific k-mers with the smallest region that can cover the selected two specific k-mers to obtain non-overlapping specific regions.

For each genome, two specific k-mer and / or non-overlapping specific regions are selected from the genome in order to locate on the genome, and these two specific k-mer and / or non-overlapping specificities can be obtained. The distance between regions, which is the number of characters in the distance. When it is detected that the distance between two selected specific k-mer and / or non-overlapping specific regions on the genome is less than a preset distance threshold, the selected two specific k-mer and / or non-overlapping specific regions may be specifically Sexual regions are replaced to obtain corresponding non-overlapping specific regions. When the distance between the two specific k-mer and / or non-overlapping specific regions on the genome is 0, it means that the two specific k-mer and / or non-overlapping specific regions are directly related to each other. Adjacent and connected. When the distance is negative, it means that the selected two specific k-mer and / or non-overlapping specific regions have a certain number of base pairs. When it is detected that the distance between two selected specific k-mer and / or non-overlapping specific regions on the genome is less than or equal to 0, the The replacement method may be to replace the selected two specific k-mer and / or non-overlapping specific regions with the smallest region covering the selected two specific k-mer and / or non-overlapping specific regions. That is, a region replaces the two specific k-mer and / or non-overlapping specific regions, and this region is a non-overlapping specific region obtained according to the two specific k-mer and / or non-overlapping specific regions.

Step 406: Obtain a sequence spaced between the selected two specific k-mer and / or non-overlapping specific regions on the mapped genome.

Step 408: splicing the selected two specific k-mers and the intermediate interval in sequence to obtain a spliced sequence.

Step 410: Replace the selected two specific k-mers with a splicing sequence to obtain a non-overlapping specific region.

When the distance between the selected specific k-mer and / or non-overlapping specific regions on the genome is greater than zero, it means that there are other sequences between the two sequences. Then you can get the sequence of these two sequences in the middle of the genome. The two sequences selected may be two specific k-mers, or one specific k-mer and one non-overlapping specific region, or two non-overlapping specific regions. Thus, the selected two sequences and the intermediate interval sequence can be spliced in sequence to obtain a spliced sequence. After replacing the selected two sequences with the spliced sequence, a non-overlapping specific region can be obtained. And so on, until there is no specific k-mer or specific region whose distance is less than a preset distance threshold.

In one embodiment, as shown in FIG. 5, after step 306, the method further includes:

Step 502: Select specific k-mer and / or non-overlapping specific regions contained in each genome for detection.

In step 504, when it is detected that the distance between the selected specific k-mer and / or non-overlapping specific region on the genome is less than a preset distance threshold, the selected specific k-mer and / or non-overlapping specific region is detected. The replacement is performed to obtain a non-overlapping specific region after replacement.

Step 506: Obtain a set of non-overlapping specific regions corresponding to each genome according to the finally retained specific k-mer and the replaced non-overlapping specific regions.

After processing the specific k-mer, the corresponding non-overlapping specific region can be obtained. The specific k-mer in each genome can be compared with the non-overlapping specific regions in the genome, or the two non-overlapping specific regions in the genome can be compared. The alignment method is the same as that between two specific k-mers. Detect whether the selected two non-overlapping specific regions or the selected specific k-mer and non-overlapping specific intervals are smaller than the preset distance threshold on the genome. If so, the selected specific k-mer and non-overlapping specific regions or two selected non-overlapping specific regions are replaced to obtain corresponding non-overlapping specific regions. According to the specific k-mer finally retained in each genome and the non-overlapping specific regions after replacement, a set of non-overlapping specific regions corresponding to each genome can be obtained. In the set of non-overlapping specific regions corresponding to each genome, Contained are non-overlapping specific regions in the genome.

In one embodiment, as shown in FIG. 6, the above step 110 includes:

In step 602, a set of non-overlapping specific regions corresponding to each genome included in the target pathogen operation group is summarized to obtain a non-overlapping specific region union.

Step 604: Obtain the number of occurrences of each non-overlapping specific region included in the non-overlapping specific region set in the set of non-overlapping specific regions corresponding to each genome.

After obtaining the set of non-overlapping specific regions corresponding to each genome, if the target pathogen operation group contains N genomes, then N sets of non-overlapping specific regions will be obtained. By summing the N non-overlapping specific region sets, a non-overlapping specific region union set can be obtained. When calculating the number of occurrences of each non-overlapping specific region in the entire set of non-overlapping specific regions, it is actually calculating the number of occurrences of each non-overlapping specific region in the non-overlapping specific regions. If a certain non-overlapping specific region has appeared in M genomes, then the number of occurrences of the non-overlapping specific region in the non-overlapping specific region and the concentration will be M.

In one embodiment, as shown in FIG. 7, after step 112, the method further includes:

In step 702, the selected non-overlapping specific region whose number of occurrences exceeds a preset number of times threshold is used as a representative specific region.

In step 704, a total set of representative specific regions is formed according to the obtained representative specific regions.

Step 706: Remove the representative specific region that does not have a biological function from the total set of representative specific regions to obtain a total set of representative specific regions that have biological functions.

Step 708: Use the non-overlapping specific region in the total set of representative specific regions with biological functions as the detection target of the target pathogen operating group.

The biological functions of genes include the storage of genetic information, the replication of genetic information, and the expression of genetic information. After selecting non-overlapping specific regions whose partial occurrences exceed a preset number of thresholds, these selected non-overlapping specific regions can be used as representative specific regions, and the representative specific regions can be composed according to the selected representative specific regions Total collection. The non-overlapping specific regions in the total set of specific regions are screened. The screening method is to remove the representative specific regions that do not have biological functions, and then obtain the total set of representative specific regions that have biological functions. Therefore, these biologically-representative specific-specific regions in the total set of biologically-representative specific-regions can be used as detection targets, that is, non-overlapping specific regions in the total set of biologically-representative-specific-regions as the target pathogen Detection target of the operation group.

In one embodiment, as shown in FIG. 8, step 704 includes:

Step 802: Obtain the gene annotation information of each genome included in the target pathogen operation group from the target database. The gene annotation information includes the position of each known functional region on each genome and corresponding function information.

The gene annotation information is information indicating the position and function of each gene in a genome. Therefore, the gene annotation information of each genome includes the position and corresponding function information of each known functional region on each genome. The position of the region includes the starting and ending positions, plus and minus strands, and sequences. The corresponding functional information includes genes encoding proteins, encoding microRNAs (a type of non-coding single-stranded RNA with a length of about 22 nucleotides encoded by endogenous genes). Molecule), a region encoding a promoter (promoter), a region encoding a regulatory protein that recognizes binding, a replication initiation region, and the like. The gene annotation information of each genome stored in the target database can be obtained from the GenBank gene annotation information of the corresponding genome through the NCBI's GenBank database (NCBI's open source annotated information accounting sequence database), or through the Ensembl database (an A database of genome sequences and annotation information maintained by organizations such as the European Bioinformatics Institute) to obtain gene annotation information of corresponding genomes.

The target database stores the gene annotation information of each genome contained in each pathogen operating group, so the gene annotation information of each genome contained in the target pathogen operating group can be obtained from the target database, and can be obtained Go to the biologically functional area of each genome contained in the target pathogen manipulation group.

In step 804, one representative specific region is selected from the total set of representative specific regions and compared with known functional regions in the entire genome.

Step 806: Remove the representative specific region whose overlapped region length with the known functional region is lower than a preset coincidence threshold to obtain a total set of representative specific regions with biological functions.

Represents the total set of specific regions, which is a union set of non-coincidence specific regions corresponding to non-coincidence specific regions selected according to each genome that exceeds a preset number of times. When determining whether each representative specific region in the total set of representative specific regions has biological function, a representative specific region and a known functional region included in the entire genome of the target pathogen operating group can be selected in order. Align and determine whether the overlapped regions of the selected representative specific region and the known functional region have significant overlap, that is, determine whether the degree of overlap of the two sequences reaches a preset overlap threshold. When the degree of coincidence between the selected representative specific region and the known functional region is lower than the preset coincidence threshold, the representative specific region is considered to have no biological function, and these and the known functional region can be removed. The specific overlap of the regions whose degree of coincidence is lower than a preset coincidence threshold. Therefore, the other representative specific regions whose degree of overlap with the known functional region is higher than a preset coincidence threshold are representative specific regions with biological functions.

When comparing and judging whether the overlapped region of the selected representative specific region and the known functional region has significant overlap, it is judged whether the overlap degree of the two sequences reaches a preset overlap threshold. The preset coincidence threshold for significant coincidence here may be: the length of the coincident region exceeds a certain threshold T1, for example, 12bp, or the length of the coincident region as a percentage of the length of the specific region exceeds a certain threshold T2, for example, 30%, or the coincident region The percentage of the length of the relevant functional region exceeds a certain threshold T3, such as 30%, or the percentage of the total length of all functional regions contained in the specific region to the length of the specific region exceeds a certain threshold T4 , For example 30%.

Step 806 is an optional step, that is, step 806 may not be performed, but is generally recommended to be performed. Because the sequences with biological functions generally do not mutate during the evolution process under the selection pressure, the biologically selected sequences are finally selected as diagnostic targets, which can effectively avoid the emergence of pathogens during the evolution and reproduction of pathogens. Mutations in selected specific regions, ie, changes in sequence. Therefore, the long-term validity and accuracy of the selected detection target can be guaranteed to a certain extent.

In one embodiment, as shown in FIG. 9, after step 112, the method further includes:

In step 902, the genome containing the largest number of non-overlapping specific regions with a number of occurrences exceeding a preset number of times is used as the representative genome.

Step 904: Use the representative specific region set corresponding to the representative genome as the PCR specific region set.

After selecting the non-overlapping specific regions whose occurrences exceed a preset number of times as the detection target of the target pathogen operation group, the selected non-overlapping specific regions can be selected based on the number of occurrences in the genome of the selected non-overlapping specific regions. Representing the genome. Can count the number of non-overlapping specific regions in each genome that exceed the preset number of thresholds, and select the genome that contains the largest number of non-overlapping specific regions in which these occurrences exceed the preset number of thresholds as representatives Genome.

The representative specific area is a non-overlapping specific area selected in all non-overlapping specific area sets that exceeds a preset number of times, that is, there are multiple representative specific areas. There can be multiple representative specific regions selected in each genome, and each genome corresponds to a set of representative specific regions, and the set of representative specific regions contains representative specific regions. The representative genome is actually a genome selected from multiple genomes contained in the target pathogen operating group, then the representative genome also has its corresponding set of representative specific regions. Therefore, after the representative genome is selected, the representative specific region set corresponding to the representative genome can be used as a PCR (polymerase chain reaction) representative specific region set.

Step 906: Select non-overlapping specific regions from the set of PCR-specific regions to generate a set of PCR-specific region pairs that meets the conditions.

Step 908: Select two non-overlapping specific regions in one PCR-specific region pair from the set of eligible PCR-specific region pairs to locate on the representative genome.

One or more non-overlapping specific regions are included in the PCR representative specific region set, and the position of each non-overlapping specific region included in the PCR representative specific region set on the representative genome can be obtained. Thereby, two non-overlapping specific regions whose distances on the genome match a preset distance range can be found, and a set of PCR-specific region pairs meeting the conditions can be generated. The preset distance range D can be (MD-SD, MD + SD). MD can be set to about 1000bp, SD can be set to about 500bp.

Step 910: Use the sequence corresponding to the interval formed by the two non-overlapping specific regions on the representative genome as the interval to be detected.

In step 912, a set of intervals to be detected is formed according to the obtained intervals to be detected.

In step 914, each to-be-detected interval in the set of to-be-detected intervals is screened to obtain a final set of detection primer pairs.

Assuming that A and B are two non-overlapping specific regions whose distances on the genome match the preset distance range, then A and B are eligible PCR-specific region pairs, and A and B are one PCR-specific region. Select two PCR-specific regions in a pair of PCR-specific region pairs from a set of eligible PCR-specific region pairs and locate them on the representative genome. Then, the two selected PCR-specific regions will be on the representative genome. By forming an interval, the sequence corresponding to the interval formed by these two PCR-specific regions can be obtained, and this sequence is taken as the interval to be detected representing the genome. The two PCR-specific regions in each pair of PCR-specific region pairs in the set of eligible PCR-specific region pairs are selected and positioned on the genome, and then each corresponding interval to be detected can be obtained. Set to the interval to be detected. That is, the set of intervals to be detected includes one or more intervals to be detected.

Then, each of the to-be-detected intervals in the set of to-be-detected intervals is screened, and the final set of detection primer pairs can be obtained after the screening. You can use the automatic PCR primer generation tool to select suitable PCR primer pairs during screening, such as Primer3 (a design primer tool). Here, the final detection primer pair set obtained according to the interval to be detected may include multiple PCR primer pairs.

In one embodiment, step 902 includes: obtaining a total set of representative specific regions according to the selected non-overlapping specific regions whose occurrences exceed a preset number of times; selecting a non-overlapping specificity in the total set including the representative specific regions; The genome with the largest number of regions is the representative genome.

After obtaining the number of occurrences of each non-overlapping specific region in the entire set of non-overlapping specific regions, a non-overlapping specific region with an occurrence exceeding a preset number of times can be selected. The selected non-overlapping specific regions are composed to represent the total set of specific regions. The total set of representative specific regions includes one or more non-overlap specific regions whose occurrences in all non-overlap specific region sets exceed a preset number of times. From the total set of representative specific regions, a genome containing the largest number of non-overlapping specific regions (ie, representative specific regions) that occur more than a preset number of times can be selected as the representative genome. That is, the genome containing the largest number of non-overlapping specific regions in the total set of representative specific regions is selected as the representative genome.

In one embodiment, as shown in FIG. 10, step 906 includes:

Step 1002: Obtain the position of each specific region of the PCR in the set of representative regions of the PCR.

In step 1004, two non-overlapping specific regions with a distance in a preset distance range are used as a pair of PCR-specific regions that meet the conditions.

Step 1006: Generate a set of eligible PCR-specific region pairs according to the eligible PCR-specific region pairs.

After obtaining the PCR representative specific region set consisting of the PCR representative specific regions, a non-overlapping specific region that meets a preset distance range can be selected from the PCR representative specific region sets to generate a set of PCR specific region pairs that meets the conditions . The position of each PCR representative specific region included in the PCR representative specific region set can be obtained, that is, each non-overlapping specific region included in the PCR representative specific region set is located on the representative genome. The location of each non-overlapping specific region on the representative genome can be determined. Therefore, the distance between each two non-overlapping specific regions can be obtained, that is, the number of characters separated between each two non-overlapping specific regions. Optional take out two non-overlapping specific regions with a distance that matches the preset distance range.

For example, if the preset separation distance range is set to a range of 700 to 1300, then two non-overlapping specific regions with a separation distance in the range of 700 to 1300 can be selected, that is, two non-overlapping characters with a space of 700 to 1300 characters. Specific region. The two non-overlapping specific regions with a selected distance in the preset distance range are used as a pair of PCR-specific regions that meet the conditions. Further, a set of PCR-specific region pairs meeting the conditions may be generated according to the selected eligible PCR-specific region pairs. That is, one or more eligible PCR-specific region pairs are included in the set of eligible PCR-specific region pairs.

In one embodiment, the preset distance range is greater than 500 bp and less than 1500 bp.

You can set the preset distance range to be greater than 500bp and less than 1500bp.

In one embodiment, in step 910, the positions of the two non-overlapping specific regions selected as the two furthest segments on the representative genome are used as the boundary of the interval to be detected; the boundary of the interval to be detected is formed on the representative genome. The sequence corresponding to the interval is used as the interval to be detected.

After generating a set of qualified PCR-specific region pairs, randomly select two non-overlapping specific regions from the set of eligible PCR-specific region pairs to locate on the representative genome, and obtain the selected two non-overlapping specificities. The location of the region on the representative genome. The positions of the two non-overlapping specific regions selected on the representative genome can be obtained, that is, the position from the head of one of the non-overlapping specific regions to the end of the other non-overlapping specific region . Assume that A and B are the two non-overlapping specific regions selected, A is ACGGTCATC, and B is TCATCCGAG. Locate A and B on the representative genome, and the sequence separated between A and B is AAAATTTTT, then you can obtain the two characters of A and B that are farthest apart on the representative genome are the A characters at the beginning of "ACGGTCATC" G character to the end in "TCATCCGAG". The characters "A" and "G" can be used as the boundaries of the interval to be detected, and then the interval to be detected is actually "ACGGTCATC" + "AAAATTTTT" + "TCATCCGAG". That is, the finally obtained detection interval is "ACGGTCATCAAAATTTTTTCATCCGAG". This processing is performed on two non-overlapping specific regions selected randomly from the set of PCR-specific regions that meet the conditions, and multiple corresponding intervals to be detected can be obtained.

In one embodiment, the positions of the two non-overlapping specific regions selected and the positions of the non-specific regions between the two selected non-overlapping specific regions are marked on the representative genome.

After locating the two non-overlapping specificities on the representative genome, the positions of the two non-overlapping specific regions can also be marked on the representative genome, and the two non-overlapping specific regions can also be recorded. Location between non-specific regions. The non-specific region refers to a specific k-mer that does not belong to the selection, nor does it belong to a non-overlapping specific region formed by the specific k-mer. The positions of non-overlapping specific regions can be used to identify PCR primer pairs. PCR primers need to use non-overlapping specific regions, and the specificity of PCR depends on the primers, so you can mark which belong to non-overlapping specific regions and which belong to non-specific regions.

In one embodiment, as shown in FIG. 11, step 912 includes:

In step 1102, a PCR primer tool is used to screen each to-be-detected interval in the set of to-be-detected intervals to obtain a set of candidate PCR primer pairs.

After selecting the sequences corresponding to the two non-overlapping specific regions in the region representing the genome as the test intervals, in this way, multiple test intervals are obtained, and then each test interval is screened. For example, PCR can be applied. The primer tool screens each to-be-detected interval in the set of to-be-detected intervals. The PCR primer tool may be Primer3. Thereby, a part of the interval to be detected can be selected, and one or more candidate PCR primer pair sets can be obtained.

Step 1104: Select a specific primer pair for the target pathogen operation group from the candidate PCR primer pair set, and generate a specific primer pair set corresponding to the target pathogen operation group.

A pair of PCR primers automatically generated by most existing automatic PCR primer generation tools can only satisfy the specificity of the primers in the region to be detected, and cannot guarantee the specificity in other regions. At the same time, some automatic PCR primer generation tools cannot refer to specific regions within the interval to be detected to mark messages. Therefore, it is necessary to further determine the specificity of the obtained candidate PCR primers to the primers in the collection. Therefore, a specific primer pair for the target pathogen operating group can be selected from the obtained candidate PCR primer pair set, and a specific primer pair set corresponding to the target pathogen operating group can be generated. The genomes in the complete set that are not in the target pathogen operating group can be used as alignment reference genomes, and the two primers in an alternative PCR primer pair can be aligned with the sequences in the alignment reference genome and mapped to the alignment. On the reference genome. When judging whether the mapping is successful, the primer pair of the aligned reference genome can be compared with the sequence corresponding to the position of the aligned reference genome. When the sequence similarity reaches a preset similarity threshold, it is determined that the primer pair is successfully located. The candidate PCR primer pairs that have been successfully mapped are determined to be not specific primer pairs corresponding to the target pathogen operating group, and the pair of primers are removed from the set of candidate PCR primer pairs. A set of specific primer pairs corresponding to the target pathogen operating group can be generated based on the selected specific primer pairs.

Step 1106: Select a primer pair in the specific primer pair set that meets the preset primer conditions as the final detection primer pair.

Step 1108: Generate a set of final detection primer pairs based on the final detection primer pairs.

After selecting specific primer pairs for the target pathogen operating group, further screening can be performed. A primer pair is selected from the specific primer pair set that meets the preset primer conditions, and these selected primer pairs are used as the final detection primer pair, thereby generating a corresponding final detection primer pair set.

In one embodiment, as shown in FIG. 12, step 1104 includes:

In step 1202, a full set is obtained from the target database, and the full set contains a plurality of collected high-confidence genomes.

In step 1204, a genome that is not included in the target pathogen operating group is obtained through the complete set, and is used as a comparison reference genome.

A full set is stored in the target database, and the full set contains multiple collected high-confidence genomes. A high-confidence genome refers to a selected genome that meets a preset reliability condition. Therefore, the genomes included in the target pathogen operating group can be known, so that the genomes included in the target pathogen operating group can be removed from the full set, and other genomes that are not in the target pathogen operating group can be used as reference reference genomes. The alignment reference genome is not included in the target pathogen manipulation group.

In step 1206, primer pairs are selected from the set of candidate PCR primer pairs in order to map the reference genome.

In step 1208, the selected primer pair is compared with the sequence corresponding to the location of the aligned reference genome. When the sequence similarity reaches a preset similarity threshold, it is determined that the primer pair is successfully located.

Determine if the primer pair is aligned. When referring to a reference genome, a primer pair selected to be aligned to a reference gene may be compared with a sequence corresponding to a position where the primer pair is located to be aligned to the reference genome. When it is detected that the similarity between the primer pair and the sequence corresponding to the position reaches a preset similarity threshold, it is determined that the primer pair is positioned successfully. The preset similarity threshold can be set by a technician, for example, the preset similarity can be set to 95%, 99%. Then when it is detected that the similarity between the primer pair and the sequence corresponding to the position reaches 95% or 99%, it is determined that the primer pair is successfully mapped to the reference genome.

In step 1210, a primer pair that satisfies a preset alignment condition is removed from the primer pair determined to be successfully located to obtain a specific primer pair corresponding to the target pathogen operation group.

Step 1212: Generate a set of specific primer pairs corresponding to the target pathogen operating group according to the specific primer pairs.

Select one primer pair from the set of candidate PCR primer pairs in turn to locate the reference genome, and determine whether the selected primer pair has been successfully mapped to the comparative reference genome. From the primer pairs determined to be successfully located, the primer pairs that meet the preset alignment conditions are removed, and the specific primer pairs corresponding to the target pathogen operating group are left. A set of specific primer pairs corresponding to the target pathogen operating group can be generated from the specific primer pairs. That is, the set of specific primer pairs corresponding to the target pathogen operating group includes one or more specific primer pairs corresponding to the target pathogen operating group.

In one embodiment, the preset comparison conditions include at least one of the following: two primers of the selected primer pair are located on the same chromosome of the same genome at the same time; the distance between the two primers of the selected primer pair is The distance range is set; a preset number of base sequences exists at the 3 ′ end of any primer of the selected primer pair, and the base sequences of the primer pair located at the position of the aligned reference genome are the same.

Primer pairs that meet the above conditions are labeled as non-specific primer pairs. What needs to be selected is to remove the primer pairs that meet the preset alignment conditions from the primer pairs determined to be successfully mapped to the aligned reference genome. These primer pairs marked as non-specific primer pairs will be removed. Specific primer pairs are required. The preset distance range is a range interval D, D can float within the range (MD-SD, MD + SD), where MD is generally 1000bp and SD is generally 500dp. k is generally greater than 0.5.

In one embodiment, meeting the preset primer conditions includes at least one of the following: primer length is between 17 and 28 bp; primer annealing temperature is between 52 and 58 degrees Celsius; GC percentage is between 40% and 60%; primer The 3 'end of the primer is C, G, CG, or GC; the G / C of the last 5 bases of the 3' end of the primer does not exceed 3, and the last 5 bases of the 3 'end of the primer does not contain more than 2 consecutive C or G; does not contain repeats or single nucleic acid repeats; there is no 3 'end complementary between the two primers, or self-complementation of a single primer.

In one embodiment, as shown in FIG. 13, a method for determining a detection target is provided, including the following steps:

In step 1302, a characteristic target sequence set of the target pathogen operation group is established.

As shown in FIG. 14, step 1302 includes:

Step 1302A: Collection and sorting of high-confidence genomes.

In the target database, a feature target sequence set corresponding to each pathogen operation group can be stored. When establishing the characteristic target sequence set corresponding to each pathogen operating group, it is necessary to collect and sort high-reliability genomic data first. The high-confidence genome can include both the pathogen genome and the non-pathogen genome, such as high-confidence genomes of symbiotic, probiotic, human, animal, and plant. The highly reliable genome can be derived from the RefSeq dataset (RefSeq reference sequence database of the National Center for Biotechnology Information) of the NCBI (RefSeq reference sequence database, non-redundant in a biological sense provided by the National Center for Bioinformatics). Other genes and protein sequences) or other public or private high-confidence genomes.

There are three ways to identify and screen high-confidence genomes:

3. Perform genome-wide sequence alignment of multiple genomes with similar genetic relationships (eg, genetic distance is less than a certain threshold) to determine the average genome-wide coverage percentage of the genome in its similar genomes, and then average based on this whole genome Screening by percentage of coverage: Genomes with a low average percentage of coverage are those that are suspected of having low completion, ie, low confidence. Genetic distance refers to an index that measures the size of the overall genetic difference between species (or individuals). All high-confidence genomes collected can be collectively referred to as the complete set.

In step 1302B, the genome included in the target pathogen operating group is determined.

A high-confidence genome refers to a selected genome that meets a preset reliability condition. After obtaining the high-confidence genome, the high-confidence genome contained in a pathogen operating group can be determined, and the corresponding contained genome in a pathogen operating group can be determined. The genome contained in the target pathogen operating group can thus be determined. The target pathogen operating group refers to a pathogen operating group to be detected. For example, the pathogen operating group to be detected is Staphylococcus aureus, then the target pathogen operating group in step 102 refers to Staphylococcus aureus.

In step 1302C, an index table of the number of occurrences of the genome of the complete set is generated.

Using the corpus, a genomic appearance index table of the corpus can be generated. In the genomic appearance index table of the corpus, it is recorded how many genomes of each corpus in the corpus appeared in the corpus. k-mer refers to a genomic sequence of length k. k can be defined by itself, and the range can generally be set between 11 and 32. If there are a different deterministic characters in a genomic data, then for a specific k, there may be a total of k different powers of k.

For example, for DNA genomic data, DNA has a total of four different deterministic characters of ACGT, then for a particular k, there are 4 possible k-th different k-mers. For a genome of length n, there may be at most n-k + 1 different k-mers. But because a genome contains repeating regions, in general, a k-mer with a length of n characters will be much smaller than n-k + 1. Therefore, if the ordinary k-mer counting method is used, a given k-mer may appear multiple times and may be counted multiple times in a given genome. In the genome occurrence index table of the complete set, which is different from the previous method, if a k-mer occurs more than once in a genome, the genome occurrence index table of the complete set still counts only once. Therefore, the count corresponding to a k-mer in the resulting k-mer genome occurrence number index table represents how many genomes the k-mer has appeared in the total set.

If a DNA or RNA genomic sequence is used, because of the reverse complementarity of the nucleic acid sequence, after a k-mer A appears, its reverse complementary sequence A 'should also be considered to have appeared, so both A and A' should be Record into the table. In the subsequent steps, if the k-mer of the DNA or RNA sequence is targeted, when a k-mer 'A is mentioned to do some operation, it is also considered that its reverse complementary sequence A' is also mentioned and performed by default Corresponding processing operation.

In step 1302D, an index table of the number of occurrences of the genome corresponding to the target pathogen operation group is generated.

The index table of the number of occurrences of the genome of the target pathogen operating group is different from the index table of the number of occurrences of the genome of the complete set in step 1302C. The complete set of genome occurrence index table records the complete set, that is, how many genomes a k-mer has appeared in the entire pathogen operating group, that is, how many genomes a k-mer has appeared in the complete set. However, the index table of the number of occurrences of the genome corresponding to the target pathogen operation group is corresponding to the target pathogen operation group. It records the k-mers contained in the target pathogen operation group and how many genomes have appeared in the target pathogen operation group.

In step 1302E, a specific k-mer table corresponding to the target pathogen operation group is generated.

The specific k-mer table corresponding to the target pathogen operation group records the k-mers in the target pathogen operation group that satisfy the preset specific conditions, that is, the specific k-mer. The specific k-mer is a k-mer selected from the k-mers that meets the preset specificity conditions. The selection of a specific k-mer must meet the following two conditions:

1. If the target pathogen operation group contains N genomes, and the number of occurrences of a k-mer in the genome occurrence index table corresponding to the target pathogen operation group is C ₁ , then the condition must be met: C ₁ / N + P ₁ ≥ 1. That is, the ratio of the number of occurrences in the genome appearance index table of the target pathogen operation group to the number of genomes included in the target pathogen operation group and the first threshold is greater than or equal to 1, where the first threshold P ₁ is usually less than 5%. .

2. If a k-mer appears in the genome appearance index table corresponding to the target pathogen operation group as C ₁ , and the k-mer appears in the genome episode appearance index table of the complete set as C ₂ , then The condition needs to be met: C ₁ / C ₂ + P ₂ ≥1, that is, the ratio of the number of occurrences in the genome occurrence index table of the target pathogen operating group to the occurrence number in the genome occurrence index table of the complete set and the second threshold Is greater than or equal to 1. Wherein the second threshold value P ₂ is usually less than 5%.

The first threshold value P ₁ and the second threshold value P ₂ may be equal to or different from each other. When the specific k-mer is selected, the two parameters of the first threshold P ₁ and the second threshold P ₂ are added, allowing an error rate within a certain range, that is, allowing the non-specificity of the specific k-mer within a certain range. . Without these two parameters, non-specificity in a certain range cannot be allowed, and it is often difficult to find a specific k-mer for a certain pathogen operating group.

For a pathogen operating group, if n specific k-mers are found, assuming that the occurrence of P ₁ in condition (1) of this step is randomly distributed in each genome of the pathogen operating group, then actually for the pathogen The probability of false negatives in the operation group was less than or equal to P ₁ ⁿ . For n that is large enough, the likelihood of false negatives occurring here will be extremely small. At the same time, if n 'specific k-mers are actually detected in the operating group of the pathogen, assuming that the occurrence of P ₂ in the condition (2) of this step is randomly distributed in each other genome of the operating group other than the pathogen, In fact, the probability of false positives for the pathogen operation group is actually less than or equal to P ₁ ^{n '} (that is, the power of n' to P ₂ ). For n 'large enough, the probability of false positives that can occur here is extremely small. The false negative rate refers to the proportion of positives that produce a negative test result in the test, that is, the conditional probability that a negative test result exists considering the condition being searched for.

Step 1304: Determine a detection target of the target pathogen operation group.

As shown in FIG. 15, step 1304 includes:

In step 1304A, each genome included in the target pathogen operating group is used as a reference genome in turn, and each specific k-mer included in the target pathogen operating group is mapped to the reference genome.

Each genome included in the target pathogen operating group is used as a reference genome in turn, and each specific k-mer in the specific k-mer table corresponding to the target pathogen operating group is mapped to the reference genome. Since the specific k-mer is a k-mer selected in advance to meet the preset specificity conditions, there may be cases where some specific k-mers cannot be mapped to the genome. The specific k-mer successfully mapped to the reference genome can be used as the specific k-mer included in the reference genome. Some specific k-mers cannot be mapped to a certain genome, then it can be considered that the specific k-mers are not included in the genome. Therefore, the localization here can be considered as confirming the specific k-mer included in each genome again. Because there may be a displacement situation, through this positioning operation, the specific k-mer contained in each genome is reconfirmed to increase the probability of fault tolerance.

Step 1304B, sequentially select a region from the reference genome and compare it with the specific k-mer. When it is detected that the similarity between the selected region and the specific k-mer reaches a preset similarity threshold, the specific k-mer is taken as The reference genome contains a specific k-mer.

In step 1304C, two specific k-mers included in the reference genome are sequentially selected for detection.

In step 1304D, when it is detected that the distance between the selected two specific k-mers on the reference genome is less than a preset distance threshold, the selected two specific k-mers are replaced to obtain a non-overlapping specific region.

Step 1304E: Generate a set of non-overlapping specific regions corresponding to each genome from each of the non-overlapping-specific regions in each genome obtained.

The two specific k-mers included in the genome can be selected in order to detect whether the distance between the selected two specific k-mers on the genome is less than a preset distance threshold, and if so, the selected two specific k-mers will be selected. -mer for replacement. When it is detected that the distance between two selected specific k-mers on the genome is less than or equal to 0, the replacement of the two specific k-mers may be to cover the selected two specific k-mers. The smallest region of mer replaces the two specific k-mers selected. That is, a region replaces the two specific k-mers, and this region is a non-overlapping specific region obtained based on the two specific k-mers. It may also be a sequence in which the selected two specific k-mers are located on the genome as corresponding non-overlapping specific regions. The preset distance threshold can be a negative number or 0, and is generally set to an integer less than 5. When the distance between the two specific k-mers selected on the genome is 0, it means that the two specific k-mers selected are directly adjacent and connected to each other. When the distance is negative, it means that the two specific k-mers selected have a certain number of base pairs.

Step 1304F: Obtain the number of occurrences of each non-overlapping specific region included in the non-overlapping specific region set corresponding to each genome in the entire non-overlapping specific region set.

After obtaining the non-overlapping specific region set corresponding to each genome, the number of occurrences of each non-overlapping specific region in each non-overlapping specific region set in each non-overlapping specific region set can be obtained. The set of non-overlapping specific regions corresponding to each genome can be regarded as a small set, and the entire set of non-overlapping specific regions of the entire genome is composed of a complete set of non-overlapping specific regions, that is, the complete set is composed of multiple small sets. . The full set of non-overlapping specific regions contains all non-overlapping specific regions in the genome, so each non-overlapping specific region in the set of non-overlapping specific regions of each genome can be obtained. The number of occurrences in the regional ensemble.

Assume that a set of N non-overlapping specific regions corresponding to N genomes is obtained. In general, a non-overlapping specific region does not appear in every genome, so each non-overlapping specific region is in all non-overlapping specific regions. The number of occurrences in the set of coincident specific regions is generally less than or equal to N.

In step 1304G, a non-overlapping specific region with a number of occurrences exceeding a preset number of times is selected as a detection target of the target pathogen operation group.

The selected non-overlapping specific regions have the following characteristics: (1) the length is generally much longer than the k-mer obtained from the characteristic target sequence manipulated by the target pathogen; (2) basically all are included in the target pathogen manipulation group It has appeared in all genomes; (3) It has basically not appeared in genomes that are not the target pathogen operating group. These characteristics can meet the technical needs of the detection target used in most diagnostic detection technologies. Therefore, the selected non-overlapping specific regions are simply screened according to the technical requirements of the detection target of a specific diagnostic detection technology (such as meeting the conditions of length, CG content percentage, annealing temperature, etc.), and finally can be obtained The non-overlapping specific regions that meet the needs of the detection target technology constitute the final set of detection targets that can be used to detect the target pathogen operating group. Based on the sequences in the set of detection targets, users can synthesize and manufacture molecular probes suitable for this particular diagnostic detection technology.

In step 1304H, the selected non-overlapping specific region with a number of occurrences exceeding a preset number threshold is taken as the representative specific region, and the genome containing the most non-overlapping specific region with the number of occurrences exceeding a preset number threshold is taken as a representative genome.

Step 1304I: The representative specific region set corresponding to the representative genome is used as the PCR specific region set.

After selecting the non-overlapping specific regions whose occurrences exceed a preset number of times as the detection target of the target pathogen operation group, the selected non-overlapping specific regions can be selected based on the number of occurrences in the genome of the selected non-overlapping specific regions. Representing the genome. Can count the number of non-overlapping specific regions in each genome that exceed the preset number of thresholds, and select the genome that contains the largest number of non-overlapping specific regions in which these occurrences exceed the preset number of thresholds as representatives Genome. In fact, the non-overlapping specific region contained in the representative genome is the detection target of the identified target pathogen operating group.

Before step 1304I, some non-overlapping specific regions that do not have biological functions need to be removed to obtain representative specific regions that have biological functions. For each genome contained in the target pathogen operating group, the gene annotation information of each genome can be obtained from the target database. The gene annotation information is information indicating the position and function of each gene in a genome. Therefore, the gene annotation information of each genome includes the position and corresponding function information of each known functional region on each genome. For example, the GenBank gene annotation information of the genome is obtained through the NCBI's GenBank database, or the genome annotation information of the genome is obtained through the Ensembl database. The type of gene annotation information includes the position of any known functional region on the genome and the functional information corresponding to the region. Positions include start and stop positions, plus and minus strands, sequences, etc. Functional information refers to, for example, genes encoding proteins, genes encoding microRNA, regions encoding promoters, regions encoding regulatory proteins for recognition and binding, and replication initiation regions.

According to the obtained gene annotation information of each genome in the target pathogen operation group, each non-overlapping specific region included in each representative genome can be screened. The representative specific region is a non-overlapping specific region whose appearance frequency exceeds a preset number of times. When determining whether each representative specific region has a biological function, a representative specific region may be sequentially selected for comparison with a known functional region included in the entire genome of the target pathogen operating group, and the selected Whether the overlapping region of the specific region and the known functional region is significantly overlapped, that is, it is judged whether the overlap degree of the two sequences reaches a preset overlap threshold. When the degree of coincidence between the selected representative specific region and the known functional region is lower than the preset coincidence threshold, the representative specific region is considered to have no biological function, and these and the known functional region can be removed. The specific overlap of the regions whose degree of coincidence is lower than a preset coincidence threshold. Therefore, the other representative specific regions whose degree of overlap with a known functional region is higher than a preset coincidence threshold are representative specific regions with biological functions.

Step 1304J: Select non-overlapping specific regions from the set of PCR specific regions that meet the preset distance distance range to generate a set of PCR-specific region pairs that meet the conditions.

Step 1304K: Select two non-overlapping specific regions in a PCR-specific region pair from the set of eligible PCR-specific region pairs to locate on the representative genome, and place the selected two non-overlapping specific regions on the representative genome. The sequence corresponding to the interval is used as the interval to be detected.

In step 1304L, screening is performed for each interval to be detected to obtain a final detection primer pair set.

Select two PCR-specific regions in a pair of PCR-specific region pairs from a set of eligible PCR-specific region pairs and locate them on the representative genome. Then, the two selected PCR-specific regions will be on the representative genome. By forming an interval, the sequence corresponding to the interval formed by these two PCR-specific regions can be obtained, and this sequence is taken as the interval to be detected representing the genome. The two PCR-specific regions in each pair of PCR-specific region pairs in the set of eligible PCR-specific region pairs are selected and positioned on the genome, and then each corresponding interval to be detected can be obtained. Set to the interval to be detected. That is, the set of intervals to be detected includes one or more intervals to be detected.

After obtaining multiple detection intervals in this way, screening is performed on each detection interval. For example, PCR primer tools can be used to screen each detection interval in the set of detection intervals. The PCR primer tool may be Primer3. Thereby, a part of the interval to be detected can be selected, and one or more candidate PCR primer pair sets can be obtained. A pair of PCR primers automatically generated by most existing automatic PCR primer generation tools can only satisfy the specificity of the primers in the region to be detected, and cannot guarantee the specificity in other regions. At the same time, some automatic PCR primer generation tools cannot refer to specific regions within the interval to be detected to mark messages. Therefore, it is necessary to further determine the specificity of the obtained candidate PCR primers to the primers in the collection. Therefore, a specific primer pair for the target pathogen operating group can be selected from the obtained candidate PCR primer pair set, and a specific primer pair set corresponding to the target pathogen operating group can be generated.

Here, the genomes in the complete set that do not belong to the target pathogen operating group can be used as alignment reference genomes respectively, and the two primers in one candidate PCR primer pair can be compared with the sequences in the alignment reference genome and combined. Map to the aligned reference genome. When judging whether the mapping is successful, the primer pair of the aligned reference genome can be compared with the sequence corresponding to the position of the aligned reference genome. When the sequence similarity reaches a preset similarity threshold, the primer pair is determined to be successful. The candidate PCR primer pairs that have been successfully located are determined to be not specific primer pairs corresponding to the target pathogen operating group, and the team primers are removed from the set of candidate PCR primer pairs. A set of specific primer pairs corresponding to the target pathogen operating group can be generated based on the selected specific primer pairs. After selecting specific primer pairs for the target pathogen operating group, further screening can be performed. A primer pair that meets the preset primer conditions is selected from the specific primer pair set, and these selected primer pairs are used as the final detection primer pair to generate a corresponding final detection primer pair set.

Meet the preset primer conditions including at least one of the following: the length of the primer is between 17 and 28bp; the annealing temperature of the primer is between 52 and 58 degrees Celsius; the GC percentage is between 40% and 60%; the 3 'end of the primer is C, G, CG, or GC; no more than 3 G / C in the last 5 bases of the 3 'end of the primer, no more than 2 consecutive C or G in the last 5 bases of the 3' end of the primer; no Repeats or single nucleic acid repeats; there is no 3 'end complementarity between two primers, or self-complementation of a single primer.

For a selected target pathogen operation group, the process in step 1302 needs to be run before the process in step 1304 is run. If the genomic data of the pathogen, or background genome data is updated, then steps 1302 and 1304 need to be rerun.

It should be understood that although the steps in the flowcharts of FIG. 1 to FIG. 15 are sequentially displayed in accordance with the instructions of the arrows, these steps are not necessarily performed in the order indicated by the arrows. Unless explicitly stated in this document, the execution of these steps is not strictly limited, and these steps can be performed in other orders. Moreover, at least a part of the steps in the figure may include multiple sub-steps or stages. These sub-steps or stages are not necessarily executed at the same time, but may be executed at different times. The execution order of these sub-steps or stages It is not necessarily performed sequentially, but may be performed in turn or alternately with at least a part of other steps or sub-steps or stages of other steps.

In one embodiment, as shown in FIG. 16, a device for determining a detection target is provided, including:

A determining module 1602, configured to determine a target pathogen operation group to be detected;

The specific k-mer acquisition module 1604 is used to acquire the specific k-mer included in the target pathogen operation group from the target database. The specific k-mer is a k-mer that satisfies preset specific conditions. Refers to a genomic sequence of length k; determines the specific k-mer contained in each genome contained in the target pathogen operating group;

A non-overlapping specific region acquisition module 1606 is configured to process specific k-mers contained in each genome to obtain a set of non-overlapping specific regions corresponding to each genome. The non-overlapping specific region set includes non-overlapping specific regions. Sexual area; and

Detection target selection module 1608, used to obtain the number of occurrences of each non-overlapping specific region contained in the set of non-overlapping specific regions corresponding to each genome in the entire set of non-overlapping specific regions; taking the number of occurrences exceeding a preset The non-overlap specific region of the number of times threshold is used as the detection target of the target pathogen operation group.

In one embodiment, the specific k-mer acquisition module 1604 is further configured to sequentially use each genome included in the target pathogen operation group as a reference genome; and use each specific k-mer included in the target pathogen operation group as a reference genome. Mapping to a reference genome; and a specific k-mer included in the reference genome as a specific k-mer included in the reference genome.

In one embodiment, the specific k-mer acquisition module 1604 is further configured to sequentially select a region from the reference genome for comparison with the specific k-mer; and when the selected region is detected with the specific k-mer When the similarity reaches a preset similarity threshold, the specific k-mer is used as the specific k-mer included in the reference genome.

In one embodiment, the specific k-mer acquisition module 1604 is further configured to sequentially select a sequence with the same length as the specific k-mer from the reference genome, and select the selected sequence with the same length as the specific k-mer from The specific k-mer is compared; and when the selected sequence is detected to be the same as the specific k-mer, the specific k-mer is used as the specific k-mer included in the reference genome.

In one embodiment, the above-mentioned device further includes a data creation module (not shown in the figure) for obtaining a pre-selected genome that meets a preset confidence condition as a high-confidence genome; and determining each pathogen operation The group includes a high-confidence genome as the genome corresponding to each pathogen operating group.

In one embodiment, satisfying the preset credibility condition includes any of the following: when the proportion of non-deterministic characters contained in the genome sequence is lower than a preset proportion threshold; the sequences belonging to the same chromosome included in the genome sequence When a fragment is below a preset fragment threshold; and performing a sequence comparison between a genomic sequence and all other genomic sequences whose genetic relationship meets a preset genetic distance threshold range to determine the full sequence of the genomic sequence in a similar genomic sequence Average coverage percentage, when the average coverage percentage is higher than a preset percentage value.

In one embodiment, the above-mentioned non-overlapping specific region acquisition module 1606 is further configured to locate a specific k-mer included in each genome onto the genome; and sequentially select the specific k-mer included in each genome. And / or the non-overlapping specific region is detected; when it is detected that the selected specific k-mer and / or non-overlapping specific region has a distance on the genome that is less than a preset distance threshold, the selected specific Replacement of specific k-mer and / or non-overlapping specific regions to obtain the non-overlapping specific regions after replacement; and corresponding to each genome according to the specific k-mer and the non-overlapping specific regions after replacement. Of non-coincidence specific regions.

In one embodiment, the above-mentioned non-overlapping specific region acquisition module 1606 is further configured to select a specific k-mer and / or non-overlapping specific region contained in each genome for detection; when the selected specific k- When the distance between the mer and / or non-overlapping specific region on the genome is less than a preset distance threshold, the selected specific k-mer and / or non-overlapping specific region are replaced to obtain the replaced non-overlapping specific region. ; And obtaining a set of non-overlapping specific regions corresponding to each genome according to the finally retained specific k-mer and the replaced non-overlap-specific regions and the replaced non-overlap-specific regions.

In one embodiment, the above-mentioned non-overlapping specific region acquisition module 1606 is further configured to enable energy detection when the detected specific k-mer and / or non-overlapping specific region distance on the genome is less than or equal to zero. Covering the smallest region of the selected two specific k-mers and replacing the selected two specific k-mers to obtain non-overlapping specific regions; when the selected specific k-mer and / or non-overlapping specific regions are detected When the distance on the genome is greater than zero, the sequence of the two specific k-mers and / or non-overlapping specific regions spaced on the located genome is obtained; the two specific k-mers and the intermediate space are selected. The sequences are spliced in order to obtain the spliced sequence; and the two specific k-mers selected are replaced with the spliced sequence to obtain non-overlapping specific regions.

In one embodiment, the preset distance threshold is less than 5.

In one embodiment, the detection target selection module 1608 is further configured to summarize a set of non-overlapping specific regions corresponding to each genome included in the target pathogen operation group, to obtain a union of non-overlapping specific regions; And obtaining the number of times that each non-overlap specific region included in the set of non-overlap specific regions corresponding to each genome appears in the non-overlap specific regions.

In one embodiment, the preset first condition threshold is less than 5%.

In one embodiment, the above-mentioned device further includes a primer screening module (not shown in the figure), configured to use the non-overlapping specific region selected by the occurrence number exceeding a preset number threshold as the representative specific region; according to each obtained The composition of representative specific regions represents the total set of representative specific regions; the representative specific regions that do not have biological functions are removed from the total set of representative specific regions to obtain the total set of representative specific regions that have biological functions; and Non-overlapping specific regions in the total set of sexual regions are used as detection targets for the target pathogen manipulation group.

In one embodiment, the primer screening module is further configured to obtain the gene annotation information of each genome included in the target pathogen operation group from the target database, and the gene annotation information includes each known Location of functional regions and corresponding functional information; sequentially selecting a representative specific region from the total set of representative specific regions for comparison with known functional regions in the entire genome; and removing known functional regions The overlapped region has a representative specific region whose length is less than a preset coincidence threshold, and a total set of representative specific regions with biological functions is obtained.

In one embodiment, the primer screening module is further configured to use the genome containing the largest number of non-overlapping specific regions that exceed the preset number of times as the representative genome; and use the set of representative specific regions corresponding to the representative genome as PCR. Represents a specific region set; select non-overlapping specific regions from a set of PCR specific regions that meet a preset distance range to generate a set of eligible PCR-specific region pairs; from a set of eligible PCR-specific region pairs Select two PCR-specific regions in a PCR-specific region pair to locate on the representative genome; use the sequence corresponding to the interval formed by the two selected PCR-specific regions on the representative genome as the interval to be detected; The detection intervals constitute a set of intervals to be detected; and each of the intervals to be detected in the set of intervals to be detected is screened to obtain a final set of detection primer pairs.

In one embodiment, the primer screening module is further configured to obtain a total set of representative specific regions according to the selected non-overlapping specific regions whose occurrences exceed a preset number of times; and select a non-overlapping region that includes the total set of representative specific regions. The genome with the most specific regions is the representative genome.

In one embodiment, the above primer screening module is further used to obtain the position of each PCR representative specific region in the representative genome in the set of PCR representative specific regions; the two PCR representatives whose distances match a preset distance range The specific region is used as a qualified PCR specific region pair; and a set of qualified PCR specific region pairs is generated based on the qualified PCR specific region pair.

In one embodiment, the primer screening module is further configured to use the position of two non-overlapping specific regions in a selected pair of PCR-specific regions on the representative genome as the two segments that are furthest apart from each other as the boundary of the interval to be detected. ; And a sequence corresponding to the interval formed by the boundary of the interval to be detected representing the genome is taken as the interval to be detected.

In one embodiment, the above primer screening module is further configured to mark the positions of the selected two non-overlapping specific regions on the representative genome and the positions of the non-specific regions between the two selected non-overlapping specific regions. .

In one embodiment, the above primer screening module is further configured to use the PCR primer tool to screen each to-be-detected interval in the set of intervals to be detected to obtain a set of candidate PCR primer pairs; and select from the set of candidate PCR primer pairs. For the specific primer pair of the target pathogen operating group, generate a specific primer pair set corresponding to the target pathogen operating group; and select the primer pair in the specific primer pair set that meets the preset primer conditions as the final detection primer pair; according to the final Detection primer pairs generate a final set of detection primer pairs.

In one embodiment, the above primer screening module is further configured to obtain a complete set from a target database, and the complete set contains a plurality of collected high-confidence genomes; the complete set is obtained through the complete set and is not included in the target pathogen operating group The genome is used as an alignment reference genome; a primer pair is selected from the set of alternative PCR primer pairs in order to locate the alignment reference genome; the selected primer pair is compared with a sequence corresponding to the position of the alignment reference genome, and when the sequences are similar When the degree reaches the preset similarity threshold value, the primer pair is determined to be successfully positioned; the primer pair that satisfies the preset alignment condition is removed from the primer pair determined to be successfully positioned to obtain a specific primer pair corresponding to the target pathogen operation group; and A set of specific primer pairs corresponding to the target pathogen operating group is generated based on the specific primer pairs.

In one embodiment, the primer screening module is further configured to compare the selected primer pair with a sequence corresponding to the position of the aligned reference genome, and determine the primer pair when the sequence similarity reaches a preset similarity threshold. Positioning succeeded.

In one embodiment, the k-mer in the specific k-mer satisfies the following two conditions: the number of occurrences in the genome occurrence index table corresponding to the target pathogen operation group meets a first preset error condition; and in the target pathogen The number of occurrences in the genome occurrence index table corresponding to the operation group and the number of occurrences in the genome occurrence index table of the complete set meet the second preset error condition; the genome number index table corresponding to the target pathogen operation group records the target pathogen operation The genome contained in the group contains the number of genomes of each k-mer; the genome occurrence index table of the complete set records the number of genomes of each k-mer contained in the genome of the complete set.

In one of these embodiments, the first threshold is less than 5%.

In one of these embodiments, the second threshold is less than 5%.

For the specific limitation of the device for determining the detection target, refer to the foregoing limitation on the method for determining the detection target, which is not repeated here. Each module in the above device for determining a detection target can be implemented in whole or in part by software, hardware, and a combination thereof. The above-mentioned modules may be embedded in the hardware in or independent of the processor in the computer device, or may be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. 17. The computer device includes a processor, a memory, a network interface, and a database connected through a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer-readable instructions, and a database. The internal memory provides an environment for the operation of the operating system and computer-readable instructions in a non-volatile storage medium. The database of the computer equipment is used to store data for determining the detection target. The network interface of the computer device is used to communicate with an external terminal through a network connection. The computer-readable instructions are executed by a processor to implement a method for determining a detection target.

Those skilled in the art can understand that the structure shown in FIG. 17 is only a block diagram of a part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer equipment to which the scheme of the present application is applied. The specific computer equipment may be Include more or fewer parts than shown in the figure, or combine certain parts, or have a different arrangement of parts.

A computer device includes a memory and one or more processors. The memory stores computer-readable instructions. When the computer-readable instructions are executed by the processor, the method for determining a detection target provided in any embodiment of the present application is implemented. step.

Computer-readable instructions computer-readable instructions computer-readable instructions computer-readable instructions One or more non-volatile computer-readable storage media storing computer-readable instructions, when the computer-readable instructions are executed by one or more processors , So that one or more processors implement the steps of the method for determining a detection target provided in any embodiment of the present application.

A person of ordinary skill in the art can understand that all or part of the processes in the methods of the foregoing embodiments can be implemented by using computer-readable instructions to instruct related hardware. The computer-readable instructions can be stored in a non-volatile computer. In the readable storage medium, the computer-readable instructions, when executed, may include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous chain (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

The technical features of the above embodiments can be arbitrarily combined. In order to make the description concise, all possible combinations of the technical features in the above embodiments have not been described. However, as long as there is no contradiction in the combination of these technical features, it should be It is considered to be the range described in this specification.

The above-mentioned embodiments only express several implementation manners of the present application, and the description thereof is more specific and detailed, but cannot be understood as a limitation on the scope of the invention patent. It should be noted that, for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can be made, and these all belong to the protection scope of the present application. Therefore, the protection scope of this application patent shall be subject to the appended claims.

Claims

A method for determining a detection target includes:

Determine the target pathogen operating group to be detected;

Obtain a specific k-mer included in the target pathogen operating group from the target database, where the specific k-mer is a k-mer that satisfies preset specificity conditions, and k-mer refers to a genome of length k sequence;

Determining a specific k-mer included in each genome included in the target pathogen operating group;

Processing the specific k-mer included in each genome to obtain a set of non-overlapping specific regions corresponding to each genome, wherein the set of non-overlapping specific regions includes non-overlapping specific regions;

Obtaining the number of occurrences of each non-overlapping specific region contained in the non-overlapping specific region set corresponding to each genome in the entire non-overlapping specific region set; and

Selecting a non-overlapping specific region where the number of occurrences exceeds a preset number of times as a detection target of the target pathogen operation group.
The method according to claim 1, wherein the determining a specific k-mer included in each genome included in the target pathogen operating group comprises:

Sequentially using each genome included in the target pathogen operating group as a reference genome;

Mapping each specific k-mer included in the target pathogen manipulation group to the reference genome; and

The specific k-mer localized to the reference genome is used as the specific k-mer included in the reference genome.
The method according to claim 1, before the determining the target pathogen operation group to be detected, further comprising:

Obtaining a pre-selected genome that meets a preset confidence condition as a high-confidence genome; and

The high-confidence genome included in each pathogen operating group is determined as the genome corresponding to each pathogen operating group.
The method according to claim 3, wherein satisfying the preset credibility condition comprises any one of the following:

When the proportion of non-deterministic characters contained in the genomic sequence is lower than a preset proportion threshold;

When the sequence fragments belonging to the same chromosome contained in the genomic sequence are below a preset fragment threshold;

Perform a sequence comparison between a certain genomic sequence and all other genomic sequences whose genetic relationship meets the preset genetic distance threshold range to determine the average full coverage percentage of the genome sequence in similar genomic sequences. When the average coverage percentage is high When preset percentage value.
The method according to claim 1, wherein processing the specific k-mer included in each genome to obtain a set of non-overlapping specific regions corresponding to each genome, comprising:

Map the specific k-mer contained in each genome to that genome;

Sequentially selecting the specific k-mer and / or the non-overlapping specific region contained in each genome for detection;

When it is detected that the distance between the selected specific k-mer and / or non-overlapping specific region on the genome is less than a preset distance threshold, the selected specific k-mer and / or non-overlapping specific region is performed. Replacement to obtain the non-overlapping specific region after replacement; and

A set of non-overlapping specific regions corresponding to each genome is obtained according to the specific retained k-mer and the non-overlapping specific regions after replacement.
The method according to claim 5, wherein replacing the selected two specific k-mers to obtain a non-overlapping specific region comprises:

When the distance between the selected specific k-mer and / or non-overlapping specific region on the genome is detected to be less than or equal to zero, then the smallest region covering the selected two specific k-mers is replaced with the selected two. Specific k-mers to obtain non-overlapping specific regions;

When the distance between the selected specific k-mer and / or non-overlapping specific region on the genome is greater than zero, the selected two specific k-mer and / or non-overlapping specific regions are obtained. Intermediately spaced sequences on a mapped genome;

Splicing the selected two specific k-mers and the intermediate interval sequence in sequence to obtain a spliced sequence; and

The selected two specific k-mers are replaced with a splicing sequence to obtain a non-overlapping specific region.
The method according to claim 5, wherein the preset distance threshold is less than 5.
The method according to claim 1, wherein the obtaining the number of occurrences of each non-overlapping specific region included in the non-overlapping specific region set corresponding to each genome in the entire non-overlapping specific region set, include:

Summarize the set of non-overlapping specific regions corresponding to each genome included in the target pathogen operating group to obtain a union of non-overlapping specific regions; and

The number of occurrences of each non-overlapping specific region contained in the set of non-overlapping specific regions corresponding to each genome in the non-overlapping specific regions is obtained.
The method according to claim 1, wherein the preset number of times threshold = (1-Y) * N, Y is a preset first condition threshold, and N is a number of non-overlapping specific region sets.
The method according to claim 9, wherein the preset first condition threshold is less than 5%.
The method according to claim 1, further comprising: after selecting the non-overlapping specific region in which the number of occurrences exceeds a preset number of times as a detection target of the target pathogen operation group, further comprising:

Using the selected non-overlapping specific region with a number of occurrences exceeding a preset number threshold as a representative specific region;

According to each representative specific region obtained, a total set of representative specific regions is formed;

Removing the representative specific regions that do not have biological functions from the total set of representative specific regions to obtain a total set of representative specific regions that have biological functions; and

A non-overlapping specific region in the total set of representative specific regions with biological functions is used as a detection target of the target pathogen operating group.
The method according to claim 11, characterized in that the step of removing the representative specific regions that do not have biological functions from the total set of representative specific regions and obtaining the total set of representative specific regions that have biological functions comprises:

Obtaining the gene annotation information of each genome included in the target pathogen operating group from the target database, the gene annotation information including the location of each known functional region on each genome and the corresponding Function information

Select one representative specific region from the total set of representative specific regions in turn to compare with known functional regions in the entire genome; and

The representative specific region whose length of the overlapping region with the known functional region is lower than a preset coincidence threshold is removed to obtain a total set of representative specific regions with biological functions.
The method according to claim 1, further comprising: after selecting the non-overlapping specific region in which the number of occurrences exceeds a preset number of times as a detection target of the target pathogen operation group, further comprising:

Taking the genome containing the largest number of non-overlapping specific regions with a number of occurrences exceeding a preset number of times as a representative genome;

Using the representative specific region set corresponding to the representative genome as the PCR specific region set;

Selecting non-overlapping specific regions from the set of PCR specific regions that meet a preset distance range to generate a set of PCR specific region pairs that meets the conditions;

Selecting two non-overlapping specific regions in one PCR-specific region pair from the set of eligible PCR-specific region pairs to locate on the representative genome;

Using the sequence corresponding to the interval formed by the two non-overlapping specific regions on the representative genome as the interval to be detected;

Forming a set of to-be-detected intervals based on the obtained to-be-detected intervals; and

Screening each to-be-detected interval in the set of to-be-detected intervals to obtain a final set of detection primer pairs.
The method according to claim 13, characterized in that the non-overlapping specific regions matching a preset distance range are selected from the set of PCR specific regions to generate a set of PCR specific region pairs that meets the conditions, include:

Obtaining the position of each PCR representative specific region in the PCR representative specific region set in the representative genome;

Use two PCR-representing specific regions that are separated by a preset distance range as eligible PCR-specific region pairs; and

A set of eligible PCR-specific region pairs is generated based on the eligible PCR-specific region pairs.
The method according to claim 14, wherein the preset distance range is greater than 500 bp and less than 1500 bp.
The method according to claim 13, wherein the screening each detected interval in the set of intervals to be detected to obtain a final set of detection primer pairs comprises:

Using a PCR primer tool to screen each to-be-detected interval in the set of to-be-detected intervals to obtain an alternative set of PCR primer pairs;

Selecting a specific primer pair for the target pathogen operating group from the candidate PCR primer pair set, and generating a specific primer pair set corresponding to the target pathogen operating group;

Selecting a primer pair in the specific primer pair set that meets the preset primer conditions as the final detection primer pair; and

A set of final detection primer pairs is generated based on the final detection primer pairs.
The method according to claim 16, characterized in that said selecting a specific primer pair for said target pathogen operation group from said set of candidate PCR primer pairs, and generating a corresponding one of said target pathogen operation group A collection of specific primer pairs, including:

Obtaining a full set from the target database, the full set containing a plurality of collected high-confidence genomes;

Obtaining a genome that is not included in the target pathogen operating group through the complete set as a reference genome for comparison;

Sequentially selecting primer pairs from the set of candidate PCR primer pairs to locate the aligned reference genome;

Comparing the selected primer pair with a sequence corresponding to the position of the aligned reference genome, and when the sequence similarity reaches a preset similarity threshold, it is determined that the primer pair is successfully positioned;

Removing a primer pair that satisfies a preset alignment condition from the primer pair determined to be successfully located to obtain a specific primer pair corresponding to the target pathogen operating group; and

A set of specific primer pairs corresponding to the target pathogen operating group is generated according to the specific primer pairs.
The method according to claim 17, wherein the preset comparison condition comprises at least one of the following:

Two primers of the selected primer pair are located on the same chromosome of the same genome at the same time;

A distance between two primers of the selected primer pair is within a preset distance range;

A preset number of base sequences exist at the 3 ′ end of any one of the selected primer pairs, and the base sequences of the primer pairs located at the positions of the aligned reference genomes are the same.
The method according to claim 16, wherein the meeting the preset primer conditions comprises at least one of the following:

Primer length is between 17 and 28bp;

Primer annealing temperature is between 52 and 58 degrees Celsius;

GC percentage is between 40% and 60%;

The 3 'end of the primer is C, G, CG, or GC;

The G / C of the last 5 bases of the 3 ′ end of the primer does not exceed 3, and the last 5 bases of the 3 ′ end of the primer does not contain more than 2 consecutive C or G; it does not contain repeats or single nucleic acid repeats ;

There is no 3 'end complementarity between two primers or self-complementation of a single primer.
The method according to claim 1, wherein the k-mer in the specific k-mer satisfies the following two conditions:

The number of occurrences in the genome occurrence index table corresponding to the target pathogen operating group meets a first preset error condition; the number of appearances in the genome occurrence index table corresponding to the target pathogen operating group, and the number of genome occurrences in the complete set The number of occurrences in the index table satisfies a second preset error condition;

The genome number index table corresponding to the target pathogen operation group records the number of genomes of each k-mer included in the genome contained in the target pathogen operation group; the genome occurrence number index table of the complete set records The included genome contains the number of genomes per k-mer.
The method according to claim 20, characterized in that the first preset error condition is: a ratio of the number of occurrences in the genome occurrence index table of the target pathogen operating group to the number of genomes included in the target pathogen operating group The sum with the first threshold is greater than or equal to 1.
The method according to claim 21, wherein the first threshold value is less than 5%.
The method according to claim 20, characterized in that the second preset error condition is: the number of occurrences in the genome occurrence index table of the target pathogen operating group and the occurrence number in the genome occurrence index table of the complete set The sum of the ratio and the second threshold is greater than or equal to 1.
The method according to claim 23, wherein the second threshold is less than 5%.
A device for determining a detection target includes:

A determination module for determining a target pathogen operation group to be detected;

A specific k-mer acquisition module, configured to acquire a specific k-mer included in the target pathogen operation group from a target database, where the specific k-mer is a k-mer that satisfies a preset specific condition, k-mer refers to a genomic sequence of length k; determining a specific k-mer included in each genome included in the target pathogen operating group;

A non-overlapping specific region acquisition module is configured to process specific k-mers contained in each genome to obtain a set of non-overlapping specific regions corresponding to each genome, where the non-overlapping specific region set includes non-overlapping specific regions Specific regions; and

The detection target selection module is used to obtain the number of occurrences of each non-overlapping specific region contained in the set of non-overlapping specific regions corresponding to each genome; A non-overlapping specific region of the number of times threshold is set as a detection target of the target pathogen operation group.
A computer device includes a memory and one or more processors. The memory stores computer-readable instructions. When the computer-readable instructions are executed by the one or more processors, the one or more processors are caused. Each processor performs the following steps:

Determine the target pathogen operating group to be detected;

Obtain a specific k-mer included in the target pathogen operating group from the target database, where the specific k-mer is a k-mer that satisfies preset specificity conditions, and k-mer refers to a genome of length k sequence;

Determining a specific k-mer included in each genome included in the target pathogen operating group;

Processing the specific k-mer included in each genome to obtain a set of non-overlapping specific regions corresponding to each genome, wherein the set of non-overlapping specific regions includes non-overlapping specific regions;

Obtaining the number of occurrences of each non-overlapping specific region contained in the non-overlapping specific region set corresponding to each genome in the entire non-overlapping specific region set; and

Selecting a non-overlapping specific region where the number of occurrences exceeds a preset number of times as a detection target of the target pathogen operation group.
One or more non-volatile computer-readable storage media storing computer-readable instructions, which when executed by one or more processors, cause the one or more processors to perform the following steps:

Determine the target pathogen operating group to be detected;

Obtain a specific k-mer included in the target pathogen operating group from the target database, where the specific k-mer is a k-mer that satisfies preset specificity conditions, and k-mer refers to a genome of length k sequence;

Determining a specific k-mer included in each genome included in the target pathogen operating group;

Processing the specific k-mer included in each genome to obtain a set of non-overlapping specific regions corresponding to each genome, wherein the set of non-overlapping specific regions includes non-overlapping specific regions;

Obtaining the number of occurrences of each non-overlapping specific region contained in the non-overlapping specific region set corresponding to each genome in the entire non-overlapping specific region set; and

Selecting a non-overlapping specific region where the number of occurrences exceeds a preset number of times as a detection target of the target pathogen operation group.