CN111118227A - Evaluation method for biomarker combined application - Google Patents
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Abstract
The invention discloses an evaluation method for combined application of biomarkers, which comprises the following steps: collecting a sample and extracting DNA; selecting a specific primer; making qPCR reaction and standard curve; selecting a target source intensity characteristic and a non-target species interference intensity as parameters to evaluate the primer characteristics; calculating the difference value between the upper quartile point of the detected concentration of the specific marker in the target sample library and the lower quartile point of the false positive signal in each non-target species, and realizing the classification of the interference intensity of the non-target species according to the difference value; representing the interference intensity of non-target species to the markers by numbers, randomly selecting m primers from n different markers and forming a group to simulate the laboratory conditions capable of detecting the n markers, calculating corresponding scores of different combinations, and obtaining the evaluation result of the combination according to the scores. The invention can solve the defects of low sensitivity or poor specificity and the like existing in the use of a single marker.
Description
Technical Field
The invention relates to the field of bacteria detection research, in particular to an evaluation method for combined application of biomarkers.
Background
Animal waste contains a large amount of pathogenic microorganisms and enteroviruses discharged from the body, and the water-mediated diseases can be caused by untreated discharge to the environmental water body, thereby bringing great risks to human health. For example, SARs viral infection in 2003 and a novel coronavirus epidemic outbreak in 2020 have the potential to infect humans by "fecal-oral" transmission, causing respiratory diseases. In addition, the water environment is polluted by alcohols, phenols, ammonia, benzene, hydrogen sulfide and the like generated by the decomposition of the excrement in the environment. Therefore, how to accurately identify the fecal pollution in the water environment and make effective pollution prevention measures is a key problem of improving the water quality and protecting the health of the civil water.
The fecal coliform bacteria and the Escherichia coli and other traditional indicator bacteria are used by countries in the world for many years as the earliest developed microbial indexes for reflecting the fecal pollution condition of water bodies. The Chinese surface water environmental quality standard (GB 3838-2002) also comprises a water quality evaluation standard corresponding to the detected concentration of fecal coliform. However, such traditional indicator bacteria are widely present in various animal excreta, and can only be used for evaluating the fecal pollution degree in the water body, and cannot identify pollution sources, and also cannot perform quantitative analysis on each pollution source, so that the microbial pollution prevention and treatment work of the water body stays in a passive mode of 'pollution control by pollution', and the management and treatment cost is greatly increased. In addition, the types of pathogenic bacteria contained in different animal wastes are different, the pathogenic bacteria mechanism difference to human bodies is obvious, and if the microbial pollution sources cannot be accurately identified, the health risk level of the human bodies caused by the waste pollution is difficult to effectively evaluate.
In recent years, developed countries in europe, the united states, and the like have led to the development of a Microbial contamination tracing (MST) technique based on a host-specific biomarker, and the main idea of the technique is to design a specific sequence having a "fingerprint function" for a specific sequence contained in intestinal microorganisms of different species. The fecal pollution source in the water body can be directly identified by detecting the positive/negative result of the specific sequence in the water environment. Meanwhile, the quantitative analysis of the specific sequence can also quantify the relative contribution rate of each pollution source, and effective information is provided for the targeted treatment of the water body fecal pollution.
However, there is no absolute specific marker among the classes of markers that have been developed so far. Meanwhile, many researches have found that, in the stage of designing a specific marker, two indexes of sensitivity (which is the ratio of measuring the capability of a primer for measuring a specific gene fragment and determining the sensitivity to be true positive correctly by the primer capable of being paired with the specific gene fragment) and specificity cannot be considered, and the improvement of one index usually causes the reduction of the other index. Therefore, the pollution source information of the excrement in the water body cannot be accurately obtained by singly using a certain marker. And simultaneously, the markers with different characteristics are combined, so that the defects in the source analysis work can be effectively overcome. However, no technology for analyzing the microbial contamination source by effectively combining different primers according to the characteristics of the primers exists at present.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide an evaluation method for combined application of biomarkers, the method establishes a classification method for measuring the specific interference intensity of different non-target species to the markers, and establishes an optimal combination method for different markers by combining quantitative indexes such as the source intensity characteristics of the markers, the interference intensity of the non-target species and the like, so that the defects of low sensitivity or poor specificity and the like existing in the use of a single marker can be overcome.
The purpose of the invention is realized by the following technical scheme: a method for assessing the utility of a biomarker panel comprising the steps of:
s1: collecting a sample and extracting DNA;
s2: selecting a specific primer;
s3: making qPCR reaction and standard curve;
s4: in order to identify the concentration distribution characteristics of the primers in different animal excreta, selecting a target source intensity characteristic and a non-target species interference intensity as parameters to evaluate the characteristics of the primers;
s5: calculating the difference value between the upper quartile point of the detected concentration of the specific marker in the target sample library and the lower quartile point of the false positive signal in each non-target species, and realizing the classification of the interference intensity of the non-target species according to the difference value;
s6: a method for establishing a marker combination includes the steps of representing interference intensity of non-target species to markers by numbers, simulating laboratory conditions capable of detecting n markers by selecting m primers from n different markers and forming a group, calculating corresponding scores of different combinations, and obtaining evaluation results of the combinations according to the scores.
Preferably, in step S1, the DNA extraction method is: and (3) adopting a fecal genome extraction kit, uniformly mixing each part of feces, weighing a certain amount of feces, putting the feces into a sterilized centrifuge tube, and further extracting DNA according to the kit specification. The extracted DNA was measured for mass by an ultramicro spectrophotometer and stored in a refrigerator at-20 ℃.
Preferably, in step S3, after selecting specific primers, amplifying the selected host specific primers by using DNA of stool samples of different humans and animals as templates; in order to construct a standard product, PCR amplification is carried out on each specific primer by adopting corresponding sample DNA, and the amplified target product is purified and recovered by a DNA purification kit;
connecting the recovered phage primer amplification products to pGEM-T vectors, connecting other recovered products to pMD 19-TVector vectors, transforming the products to DH5 α competent cells after connection, screening positive clones by using a plate culture medium with 1:1000 ampicillin content, extracting plasmid DNA, simultaneously determining corresponding DNA concentration, calculating the copy number of a target fragment, and preparingThe plasmids were all diluted 10-fold in a gradient of 10-0, 10-1，10-2，10-3，10-4，10-5，10-67 gradients; using each gradient plasmid as template, setting 3 repeats for each gradient, carrying out qPCR amplification, constructing corresponding standard curve by Cq value obtained by reaction, and correlation coefficient (R) of each standard curve2) All are larger than 0.97, and the amplification efficiency is between 90 and 110 percent, which indicates that each standard curve is qualified.
Further, qPCR amplification includes both fluorescent dye and fluorescent probe methods.
Preferably, in step S4, the target source intensity characteristic and the non-target species interference intensity belong to quantitative analysis indexes and are calculated based on the true positive signal intensity of each marker in the target host and the false positive signal intensity obtained in different non-target species.
Preferably, in step S5, the classification of the non-target species interference intensity is implemented according to the difference, and the specific classification method is as follows:
when the difference is greater than 0, the false positive signal in the non-target species is much less than the target source intensity of the marker, and the non-target species is a weak interfering non-target species;
when the difference is less than 0, the false positive signal generated in the non-target species generates moderate interference on the marker, and the non-target species is a moderate interference non-target species;
if the non-target species does not produce a false positive reaction with the marker, the non-target species is defined as an absolute specific non-target species;
if the false positive signal generated in the non-target species is on the same order of magnitude as the true positive signal intensity of the marker, the non-target species is defined as a dominant interfering species.
Preferably, in step S6, the interference intensity of the non-target species to the marker is represented by a number, specifically: "1" represents a non-target species with absolute specificity, "2" represents a weakly interfering non-target species, "3" represents a moderately interfering non-target species, "4" represents a non-target species that produces significant interference with the marker.
Preferably, in step S6, the scores corresponding to the different combinations are calculated by:
calculating the digital representation of the marker with the minimum interference degree in the combination by various non-target animals, namely the minimum interference degree, summing the minimum interference degrees of various non-target animals, and passing the result through a formula
Normalizing the dimensionless;x newrepresents the interference score of the marker combination,xrepresents the sum of the minimum interference scores of each type of non-target species on the marker combination,x maxrepresents the score for the non-target species with the greatest interference with the marker combination,x minrepresents the score that minimally interferes with the marker combination for non-target species.
Preferably, the target source intensity of each marker is subjected to normalization and dimensionless processing to obtain a target source intensity characteristic value; and then summing the obtained target source intensity characteristic value and the interference degree score of the non-target species on the marker combination according to the average weight to obtain the final evaluation result of the marker combination. The evaluation of the marker combination effect introduces the source intensity characteristic of the marker, thereby ensuring that the optimal marker combination not only can accurately quantify the target pollution source, but also can effectively eliminate the interference caused by non-target animals.
Compared with the prior art, the invention has the following advantages and beneficial effects:
1. the invention improves the conventional method for evaluating the interference intensity of non-target species on the marker, and establishes a new primer combination evaluation method. The improved combination evaluation method comprises information of the interference intensity of non-target species to the marker, and simultaneously considers the source intensity characteristic of the marker, so that the combination evaluation of the marker is more comprehensive, the source analysis effects of different numbers and different types of primer combinations can be more visually compared, and the optimal combination of the primers can be further screened out for accurately analyzing the microbial pollution source in the water body and carrying out quantitative analysis on the microbial pollution source.
2. By adopting the primer combination evaluation method, the source analysis effect after the combination of primers with different numbers and different types can be intuitively understood, and an important basis is provided for realizing the analysis of the microbial pollution source through the optimal combination of the primers.
3. The invention establishes a classification method for measuring the specific interference level of non-target species to markers, and solves the defect that the traditional evaluation method ignores the difference of different non-target hosts in the interference of primers.
4. The invention determines the optimal marker combination method according to the characteristics of different markers, and establishes the optimal combination method capable of combining different numbers of markers for identifying the microbial contamination source.
Drawings
Fig. 1 is a sample city distribution map of the present embodiment.
FIG. 2-1 is a graph of the false positive signal distribution of the human marker BacH with various non-target species.
FIG. 2-2 is a graph of the false positive signal distribution of the human marker Bachum with various non-target species.
FIGS. 2-3 are false positive signal profiles of the human marker HF183 against various non-target species.
FIGS. 2-4 are false positive signal profiles of the human marker Hum2 with various classes of non-target species.
FIGS. 2-5 are false positive signal profiles of the human marker Hum163 with various classes of non-target species.
FIGS. 2-6 are false positive signal profiles of the human marker CPQ _056 with various classes of non-target species.
FIGS. 2-7 are false positive signal profiles of the human marker CPQ _064 and various non-target species.
FIG. 3-1 is a graph of false positive signal distribution for the porcine marker Pig-1-Bac with various non-target species.
FIG. 3-2 is a graph of false positive signal distribution for the porcine marker Pig-2-Bac with various non-target species.
Fig. 3-3 is a graph of false positive signal distribution for porcine marker l.
Fig. 3-4 are graphs of false positive signals for the porcine marker p.nd5 and various non-target species.
FIG. 4-1 is a graph of false positive signal distribution for ruminant marker Rum-2-Bac and various non-target species.
Fig. 4-2 is a false positive signal profile of the ruminant marker Bac708 with various non-target species.
Fig. 4-3 are false positive signal profiles of the ruminant marker BacCow with various non-target species.
Fig. 5 is a graph of the detected concentration of the avian marker in the target/non-target species in this example.
FIG. 6 is a schematic diagram of the calculated classification of the interference intensity of the non-target species to the markers in this embodiment.
Fig. 7 is a flowchart of the method of the present embodiment.
Detailed Description
The present invention will be described in further detail with reference to examples and drawings, but the present invention is not limited thereto.
Examples
The invention aims to establish a classification method capable of measuring the interference intensity of various non-target species on the specificity of a marker according to the false positive reaction intensity of the marker in the non-target species, and provides an effective marker combination method by combining the source intensity characteristics of the marker, thereby helping research or management personnel to accurately combine various markers according to experimental conditions, accurately analyzing various pollution sources existing in a water body, and quantifying the respective contribution rates of different pollution sources.
The embodiment provides an evaluation method for biomarker combined application based on a quantitative source analysis method of a host specific biomarker primer, and by adopting a qPCR technology to analyze the characteristic performance of the primer in China, wherein the host specific biomarker is developed aiming at a specific gene segment of 16S rRNA of a specific intestinal microorganism in a target host, and can be used for amplifying a shorter primer with a specific nucleotide sequence segment. qPCR refers to a real-time fluorescent quantitative nucleic acid amplification detection system, and is a method for adding fluorescent dye or Taqman probe into a PCR reaction system, monitoring the whole PCR process in real time by using the accumulation of fluorescent signals, and finally carrying out quantitative analysis on a DNA template through a corresponding standard curve.
The specific steps in the evaluation method for combined biomarker application described in this example are described in detail below with reference to fig. 7.
S1: sample Collection and DNA extraction
Fecal sample collection work was developed in 28 cities in china. Wherein 22 cities are located at the east side of the 'black river-Teng Chong line', belong to densely populated areas and have great contribution to the pollution of Chinese water systems, and the distribution of sampling points is shown in figure 1.
The total collection of 506 parts of samples relates to 13 species such as human, pig, cattle, sheep, camel, chicken, duck, goose, cat, dog, rabbit, donkey, horse and the like. Wherein the quantity of human, pig, sheep, cattle, poultry and other common pollution source feces accounts for 78 percent of the total sample quantity. And collecting the sample, storing in a closed ice box, and returning to the laboratory within 48 h.
DNA refers to deoxyribonucleic acid, a biological macromolecule that can constitute genetic instructions to guide biological development and functioning. Has the function of information storage, and can be compared with a blueprint or a recipe. The extraction of the DNA of the excrement adopts a related excrement genome extraction kit, 0.25g (wet weight) of the mixed excrement is weighed and put into a sterilized centrifugal tube, and then the DNA is extracted according to the kit specification. The extracted DNA was measured for mass by an ultramicro spectrophotometer and stored in a refrigerator at-20 ℃.
S2: selection of specific primers
Specificity herein refers to host specificity, i.e., a primer developed for a particular host, with the intention that, ideally, the primer will only amplify a gene fragment of the target host. The characteristic biomarker primers of this example are shown in Table 1.
TABLE 1 characteristic biomarker primers
S3: qPCR reaction and Standard Curve Generation
Using DNA of different human and animal excrement samples as templateAnd amplifying the selected various host specific primers. The primer information is shown in Table 1. qPCR amplification includes two methods, fluorescent dye and fluorescent probe. The fluorescent dye amplification system is 20ul SYBR Premix Ex TaqTM II (2X) (available from TaKaRa) 10 ul; ddH2O, 6.4ul, upstream and downstream primers 0.8ul each, DNA template 2 ul. reaction program ① pre-denaturation, 95 ℃, 30s, ② denaturation, 95 ℃, 5s, ③ annealing extension, 60 ℃, 1min, 40 cycles, fluorescent probe method amplification system 20ul, SuperReal probe PreMix (2X) (from TaKaRa) 10ul, DNA template 2ul, upstream and downstream primers 0.25ul each, probe 1 ul (10nM), ddH2O, 6.4 ul. reaction program, ① pre-denaturation, 95 ℃, 15min, ② denaturation, 95 ℃, 3s, ③ annealing and extension, 60 ℃, 30s and 40 cycles.
After connection, all phage primer amplification products (CPQ _056 and CPQ _ 064) are connected to pGEM-T vectors, other recovery products are connected to pMD 19-T vectors, after connection, all phage primer amplification products are transformed to DH5 α competent cells, a flat culture medium with 1:1000 ampicillin content is adopted to screen positive clones, plasmid DNA is extracted, corresponding DNA concentration is measured at the same time, the copy number of a target fragment is calculated, all prepared plasmids are subjected to 10-time gradient dilution, and the dilution is 10 times that of the plasmids-0, 10-1，10-2，10-3，10-4，10-5，10-67 gradients. qPCR amplification was performed using each gradient plasmid as template, with 3 replicates per gradient. The Cq values obtained by the reaction were used to construct the corresponding standard curve. Correlation coefficient (R) of each standard curve2) All are larger than 0.97, and the amplification efficiency is between 90 and 110 percent, which indicates that each standard curve is qualified.
S4: analysis of the characteristics of the primers
In order to identify the concentration distribution characteristics of the primers in different animal excreta, a target source intensity characteristic and a non-target species interference intensity are selected as parameters to evaluate the characteristics of the primers. These 2 types of parameters belong to quantitative analysis indexes and are calculated based on the true positive signal intensity of each marker in the target host and the false positive signal intensity obtained in different non-target species, and the results are shown in FIGS. 2 to 5.
S5: classification of non-target species interference intensity
To determine the level of interference of non-target species with the primers, each type of slightly and moderately interfering non-target species was identified by calculating the difference (25 th/75th threshold) between the upper quartile (target source 25 th) and the lower quartile (75 th) of false positive signals in each non-target species at the detected concentration of the specific marker in the target sample pool.
When the threshold value of 25th/75th is more than 0, the false positive signal in the non-target species is far less than the target source intensity of the marker, the interference on the marker is small, the generated interference signal can be eliminated by diluting a DNA template and the like, and the non-target species is a weak interference species;
when the 25th/75th threshold is less than 0, false positive signals generated in non-target species can generate moderate interference on the marker, and the interference signals are difficult to remove by diluting or purifying DNA, and the non-target species is moderate interference;
if the non-target species does not produce a false positive reaction with the marker, the species is defined as an absolute specific species;
a species is defined as a dominant interfering species if the false positive signal generated in the non-target species is on the same order of magnitude as the marker true positive signal.
The specific classification result of the interference intensity of the non-target species in this embodiment is shown in fig. 6.
S6: marker combination method
The interference intensity of the non-target species on the marker is represented by a number. "1" represents a non-target species with absolute specificity, "2" represents a weakly interfering non-target species, "3" represents a moderately interfering non-target species, "4" represents a non-target species that produces significant interference with the marker. The intensity of interference of non-target species with each marker is shown in tables 2-5.
TABLE 2 interference intensity of non-target species on human-derived markers
TABLE 3 interference intensity of non-target species on pig-derived markers
TABLE 4 interference intensity of non-target species on ruminant markers
TABLE 5 interference intensity of non-target species on avian markers
Because the experimental conditions of laboratories in different regions are different, and some laboratories may not be able to quantitatively analyze the microbial contamination source by detecting all the above markers, the main purpose of establishing the marker combination method is to provide each laboratory to combine markers according to the existing conditions and select the optimal combination according to the scores obtained by different combinations. The basic idea of marker combination is to formulate the combination by permutation
(1-1) optionally taking m (m is less than or equal to n) primers from n different markers and combining the primers into a group, thereby simulating the laboratory condition that n (n = 1-7) markers can be detected, and giving corresponding scores to different combinations for selection of researchers.
After combination of the markers, interference of non-target species with the combination can be eliminated or reduced by the marker that produces the least cross-reactivity. Therefore, for different marker combinations, only the digital representation of the marker with the minimum interference degree in the combination by each type of non-target animal is required, namely the minimum interference degree, and the minimum interference degree of each type of non-target animal is requiredSumming the results by formula
(2-2) normalizing the dimensionless.
Establishing combinations by identifying the strength of cross-reactivity of non-target species with the marker may result in an excessive concern about the effect of the combination on the exclusion of interference by non-target animals, while ignoring the sensitivity of the marker combination to detection of target species, i.e. the appearance of false negative results due to the low overall target source strength of the marker combination. Therefore, the evaluation of the effect of the marker combination needs to introduce the source intensity characteristic of the marker, so that the optimal marker combination can not only accurately quantify the target pollution source, but also effectively eliminate the interference caused by non-target animals. The target source intensity characteristics (the average detected concentration of the specific labeled primers in the target host sample library) of each marker are shown in FIGS. 2-1 to 2-7, 3-1 to 3-4, 4-1 to 4-3, and 5, and the target source intensity is subjected to normalization and dimensionless treatment by referring to the formula 2-2, and the results are shown in tables 6 and 7.
TABLE 6 human marker target source intensity eigenvalues
TABLE 7 non-human marker target Source Strength eigenvalue
And summing the obtained target source intensity characteristic value and the interference degree of the non-target species to the marker combination according to the average weight to obtain the evaluation result of the marker combination.
In the embodiment, for example, the human-derived microbial contamination is identified, and if only the markers in 4 are selected for combination in a laboratory, two combinations of BacH, Bachum, HF183, Hum2 and Hum163, CPQ _056 and CPQ _064 are randomly selected, evaluated respectively and the optimal combination is selected.
For the combinations BacH, BacHum, HF183, Hum2, the degree of interference of the non-target species on the combinations is shown in table 8.
TABLE 8 interference degree of non-target species on BacH, Bachum, HF183, Hum2
And selecting various non-target species to sum the minimum interference degrees of the marker combinations: sum =2+1+1+1+2+1+1+1+1+4+1+2=18
The combined interference degree score is as follows:
the source intensity score of the combined target is:
the combined effect scores were: z = X + Y =0.68
For combinations Hum163, CPQ _056, CPQ _064, the degree of interference of non-target species on the combination is shown in table 9.
TABLE 9 degree of interference of non-target species on BacH, HF183, CPQ _056, CPQ _064
And selecting various non-target species to sum the minimum interference degrees of the marker combinations: sum =2+1+1+1+1+2+1+1+1+3+1+1=16
The combined interference degree score is as follows:
the source intensity score of the combined target is:
the combined effect scores were: z = X + Y =0.58
From the above scoring results, it can be seen that the combined effect of BacH, BacHum, HF183, and Hum2 was superior to that of the combination of Hum163, CPQ _056, and CPQ _064 when the contamination of the human-derived microorganism was quantitatively analyzed using the four marker combinations.
By adopting the primer combination evaluation method provided by the embodiment, the source analysis effect after the combination of primers with different numbers and different types can be intuitively understood, and an important basis is provided for realizing the analysis of the microbial pollution source through the optimal combination of the primers.
The above embodiments are preferred embodiments of the present invention, but the present invention is not limited to the above embodiments, and any other changes, modifications, substitutions, combinations, and simplifications which do not depart from the spirit and principle of the present invention should be construed as equivalents thereof, and all such changes, modifications, substitutions, combinations, and simplifications are intended to be included in the scope of the present invention.
Claims (9)
1. A method for assessing the utility of a biomarker panel comprising the steps of:
s1: collecting a sample and extracting DNA;
s2: selecting a specific primer;
s3: making qPCR reaction and standard curve;
s4: in order to identify the concentration distribution characteristics of the primers in different animal excreta, selecting a target source intensity characteristic and a non-target species interference intensity as parameters to evaluate the characteristics of the primers;
s5: calculating the difference value between the upper quartile point of the detected concentration of the specific marker in the target sample library and the lower quartile point of the false positive signal in each non-target species, and realizing the classification of the interference intensity of the non-target species according to the difference value;
s6: a method for establishing a marker combination includes the steps of representing interference intensity of non-target species to markers by numbers, simulating laboratory conditions capable of detecting n markers by selecting m primers from n different markers and forming a group, calculating corresponding scores of different combinations, and obtaining evaluation results of the combinations according to the scores.
2. The method for evaluating the combined use of biomarkers according to claim 1, wherein in step S1, the DNA is extracted by: and (3) adopting a fecal genome extraction kit, uniformly mixing each part of feces, weighing a certain amount of feces, putting the feces into a sterilized centrifuge tube, and further extracting DNA according to the kit specification.
3. The method for evaluating the combined application of biomarkers according to claim 1, wherein in step S3, after the specific primers are selected, the selected host specific primers of each type are amplified using fecal sample DNA of different human and animal as templates; in order to construct a standard product, PCR amplification is carried out on each specific primer by adopting corresponding sample DNA, and the amplified target product is purified and recovered by a DNA purification kit;
connecting the recovered phage primer amplification products to pGEM-T vectors, connecting other recovered products to pMD 19-TVector vectors, after the connection is finished, transforming the products to DH5 α competent cells, screening positive clones by adopting a plate culture medium with 1:1000 ampicillin content, extracting plasmid DNA, simultaneously determining corresponding DNA concentration, calculating the copy number of a target fragment, diluting the prepared plasmids by 10 times of gradient to 10-0, 10-1，10-2，10-3，10-4，10-5，10-67 gradients; and (3) performing qPCR amplification by using each gradient plasmid as a template and setting 3 repeats for each gradient, constructing a corresponding standard curve by using Cq values obtained by reaction, wherein the correlation coefficient of each standard curve is greater than 0.97, and the amplification efficiency is between 90 and 110 percent, which indicates that each standard curve is qualified.
4. The method of claim 3, wherein the qPCR amplification comprises both fluorescent dye and fluorescent probe.
5. The method for evaluating the combined application of biomarkers according to claim 1, wherein in step S4, the target source intensity characteristics and the non-target species interference intensities belong to quantitative analysis indexes and are calculated based on the true positive signal intensity of each marker in the target host and the false positive signal intensity obtained in different non-target species.
6. The method for evaluating combined application of biomarkers according to claim 1, wherein in step S5, the classification of the interference intensity of non-target species is performed according to the difference value by the following specific classification methods:
when the difference is greater than 0, the false positive signal in the non-target species is much less than the target source intensity of the marker, and the non-target species is a weak interfering non-target species;
when the difference is less than 0, the false positive signal generated in the non-target species generates moderate interference on the marker, and the non-target species is a moderate interference non-target species;
if the non-target species does not produce a false positive reaction with the marker, the non-target species is defined as an absolute specific non-target species;
if the false positive signal generated in the non-target species is on the same order of magnitude as the true positive signal intensity of the marker, the non-target species is defined as a dominant interfering species.
7. The method for evaluating the combined application of biomarkers according to claim 1, wherein in step S6, the interference intensity of non-target species to markers is represented by a number, specifically: "1" represents a non-target species with absolute specificity, "2" represents a weakly interfering non-target species, "3" represents a moderately interfering non-target species, "4" represents a non-target species that produces significant interference with the marker.
8. The method for evaluating the combined use of biomarkers according to claim 1, wherein in step S6, the scores corresponding to different combinations are calculated by:
calculating the digital representation of the marker with the minimum interference degree in the combination by various non-target animals, namely the minimum interference degree, summing the minimum interference degrees of various non-target animals, and passing the result through a formula
Normalizing the dimensionless;x newrepresents the interference score of the marker combination,xrepresents the sum of the minimum interference scores of each type of non-target species on the marker combination,x maxrepresents the score for the non-target species with the greatest interference with the marker combination,x minrepresents the score that minimally interferes with the marker combination for non-target species.
9. The evaluation method of the combined application of biomarkers according to claim 1, wherein the target source intensity of each marker is subjected to normalization and dimensionless processing to obtain a target source intensity characteristic value; and then summing the obtained target source intensity characteristic value and the interference degree score of the non-target species on the marker combination according to the average weight to obtain the final evaluation result of the marker combination.
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