Gene snp molecular marker method and system for watermelon breeding

Abstract

The application relates to the technical field of gene SNP molecular markers, in particular to a gene SNP molecular marker method and system for watermelon breeding, which comprises the following steps: extracting DNA solutions of watermelon samples of multiple varieties respectively, and performing SNP typing-based fingerprint construction; obtaining 16S rRNA sequences of watermelon microbial communities, dividing the 16S rRNA sequences into base sequences; determining mutation correlation parameters of the bases; determining a transition probability matrix of the 16S rRNA sequences; correcting sampling step lengths of the 16S rRNA sequences; correcting the transition probability matrix; determining characteristic vectors of the 16S rRNA sequences; determining genetic distances between the 16S rRNA sequences of the watermelon microbial communities and soil samples, constructing a phylogenetic tree, and determining strongly correlated microbial communities of the breeding environment of the watermelon samples of the multiple varieties. The adaptability of the watermelon breeding environment is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of gene SNP molecular markers, in particular to a gene SNP molecular marker method and system for watermelon breeding. BACKGROUND

[0002] The gene SNP (single nucleotide polymorphism) molecular marker technology provides an efficient and accurate genetic analysis tool for watermelon breeding by detecting single nucleotide variations in the genome. The application of gene SNP molecular marker technology in watermelon breeding covers genetic diversity analysis, molecular marker-assisted selection, and other key areas, significantly improving the efficiency of watermelon breeding.

[0003] At present, the fingerprint constructed based on gene SNP molecular marker technology is one of the main technical means for watermelon gene detection and species identification. The fingerprint is used to reveal the site and genotype data between different varieties of watermelon, facilitating the identification of watermelon varieties. However, the fingerprint cannot provide key information about pathogens. It needs to use 16S rRNA sequences to identify the species of microorganisms and bacteria, and construct a phylogenetic tree. Therefore, it is possible to combine SNP data of different varieties of watermelon and 16S rRNA sequences of different strains for correlation analysis, and to mine genetic loci closely related to specific flora, providing guidance and basis for watermelon breeding and variety improvement.

[0004] When constructing a phylogenetic tree based on 16S rRNA of different watermelon strains, the current vector space model algorithm is the mainstream algorithm for constructing phylogenetic trees in non-sequence comparison algorithms. The VSM (Vector Space Model) algorithm based on TF-IDF retrieval technology only calculates the genetic distance between 16S rRNA sequences of the species to be identified and reference species by the frequency of base sequences in the 16S rRNA sequence. However, the influence of random mutations on the frequency of base sequence randomness is ignored, which causes the credibility of the identification result to be low, thereby affecting the joint analysis of SNP points and flora in the breeding environment. SUMMARY

[0005] To solve the above technical problems, the purpose of the present application is to provide a gene SNP molecular marker method and system for watermelon breeding, and the technical solution adopted is as follows:

[0006] In a first aspect, the embodiments of the present application provide a gene SNP molecular marker method for watermelon breeding, which comprises the following steps:

[0007] Extract DNA solutions of multiple watermelon samples of different varieties, obtain SNP typing data of the DNA solutions of the watermelon varieties, and construct a fingerprint of the multiple watermelon samples of different varieties.

[0008] Obtain 16S rRNA sequences of each watermelon flora, divide each 16S rRNA sequence into a fixed-length base sequence, and screen each SNP marker capable of completely distinguishing the multiple varieties from the fingerprint.

[0009] Determine the mutation correlation parameter of each base based on the mutation frequency of the base in the base fragment of the multiple watermelon samples of different varieties at each SNP marker, and the proportion of the number of base fragments of each SNP marker in the fingerprint in the number of base fragments in all SNP markers.

[0010] Analyze the transition probability of each base sequence in each 16S rRNA sequence to determine the transition probability matrix of each 16S rRNA sequence; correct the sampling step of each 16S rRNA sequence by the mutation correlation parameter of all bases; combine the transition probability matrix and the sampling step to correct the transition probability matrix by using a random sampling algorithm.

[0011] Determine the feature vector of each 16S rRNA sequence by using each transition probability in the corrected transition probability matrix and the occurrence frequency of each base sequence in each 16S rRNA sequence.

[0012] Obtain soil samples corresponding to the multiple watermelon samples of different varieties, extract 16S rRNA sequences of the soil samples, determine the genetic distance between the watermelon flora and the 16S rRNA sequences of the soil samples based on the correlation between the feature vectors of the watermelon flora and the 16S rRNA sequences of the soil samples, construct a phylogenetic tree of the breeding environment flora of the multiple watermelon varieties, and determine the strongly correlated flora of the breeding environment of each watermelon sample.

[0013] In one embodiment, the fingerprint is composed of base fragments of each watermelon variety at each SNP marker.

[0014] In one embodiment, dividing each 16S rRNA sequence into a fixed-length base sequence includes:

[0015] For each 16S rRNA sequence, the first four bases are taken as the first base sequence, and then moved with a step size of 1 to obtain base sequences of the same length for each 16S rRNA sequence.

[0016] In one embodiment, the mutation frequency is calculated as follows:

[0017] wherein, is the mutation frequency of base A at the mth SNP marker, and N is the number of watermelon varieties, is the sum of the number of times that base A at the first position in the base fragment of the mth SNP marker of the N watermelon varieties is mutated to other bases and the number of times that other bases are mutated to base A, is the sum of the number of times that base A at the second position in the base fragment of the mth SNP marker of the N watermelon varieties is mutated to other bases and the number of times that other bases are mutated to base A.

[0018] In one embodiment, the determining of the mutation correlation parameter comprises:

[0019] For each SNP marker, the ratio of the number of different base fragments in each SNP marker to the number of different base fragments in all SNP markers is taken as the distinguishing weight of each SNP marker in the fingerprint.

[0020] Mutation correlation parameter of base A The expression of the mutation correlation parameter of base A is:

[0021] wherein, , , are the mutation frequencies of base A at the first, second, and Mth SNP markers, respectively; , , are the distinguishing weights of the first, second, and Mth SNP markers, respectively, and M is the number of SNP markers that can completely distinguish the plurality of varieties.

[0022] In one embodiment, the modifying of the sampling step size of each 16S rRNA sequence comprises:

[0023] The product of the mutation correlation parameter of each base and a preset adjustment scale is calculated, the sum of the mean value of the products of all bases and a preset initial step size is calculated, and the sampling step size of each 16S rRNA sequence is the floor value of the sum.

[0024] In one embodiment, the determining of the feature vector of each 16S rRNA sequence comprises:

[0025] For the modified transition probability matrix of each 16S rRNA sequence, the frequency of each base sequence in the 16S rRNA sequence and the maximum transition probability value in the corresponding row vector in the modified transition probability matrix are taken as the array of each base sequence.

[0026] The array of all base sequences in each 16S rRNA sequence is arranged in the order of base sequence to form a characteristic vector of each 16S rRNA sequence.

[0027] In one embodiment, the genetic distance is calculated as follows:

[0028] wherein, is the genetic distance between the 16S rRNA sequence of the a th watermelon bacterial community and the 16S rRNA sequence of the b th soil sample, is the characteristic vector of the 16S rRNA sequence of the a th watermelon bacterial community, is the characteristic vector of the 16S rRNA sequence of the b th soil sample, and Y() is a cosine similarity calculation function.

[0029] In one embodiment, the strong correlation bacterial community of the breeding environment of each watermelon sample is determined by:

[0030] In the phylogenetic tree, the bacterial community in the branch with a homologous support rate higher than a preset first percentage is screened as the bacterial community that cannot be avoided in the breeding environment change of the N watermelon varieties, and the bacterial community in the branch with a homologous support rate lower than a preset second percentage is screened as the bacterial community that can be avoided in the breeding environment change of the N watermelon varieties, wherein the first percentage is greater than the second percentage.

[0031] In a second aspect, the embodiments of the present application also provide a gene SNP molecular marker system for watermelon breeding, which comprises a memory, a processor, and a computer program stored in the memory and running on the processor, and the processor implements the steps of the method according to any one of the above aspects when executing the computer program.

[0032] The present application has at least the following beneficial effects:

[0033] The application improves the analysis accuracy of the genetic background of varieties by extracting DNA solutions of multiple varieties of watermelons and obtaining SNP typing data, constructs a fingerprint map of the watermelon samples of the multiple varieties, screens SNP markers that completely distinguish the varieties from the fingerprint map, enhances the specificity of marker selection, calculates mutation correlation parameters based on mutation frequencies and proportions of SNP markers, and identifies functional sites that are significantly related to target traits to avoid interference of invalid markers on subsequent analysis; corrects the sampling step length of each 16S rRNA sequence through mutation correlation parameters of all bases, avoids overstepping important random mutations of base sequences in the 16S rRNA sequence due to too large sampling step length, and improves the accuracy of correction of base sequence transfer probability in the 16S rRNA sequence; further, the Monte Carlo sampling MCMC algorithm is used to correct the transfer probability of each base sequence in the 16S rRNA sequence, thereby improving the accuracy of the corresponding feature vector of the 16S rRNA sequence in the vector space, and realizing quantitative evaluation of the influence of the breeding environment on the environment bacteria group through genetic distance calculation of the corresponding feature vector of the 16S rRNA sequence and the soil sample, and improving the accuracy and reliability of the genetic distance calculation; finally, the fingerprint map based on the genetic relationship of the watermelon varieties and the phylogenetic tree revealing the genetic evolution relationship of the bacteria group in the breeding environment of different varieties of watermelons are combined to realize joint analysis of the distinguishing SNP markers and the bacteria group type, which can accurately guide the improvement strategy in the breeding environment of different varieties of watermelons and improve the adaptability of the breeding environment. BRIEF DESCRIPTION OF DRAWINGS

[0034] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present application or the prior art, the drawings needed in the embodiments or the prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and those skilled in the art can obtain other drawings according to these drawings without creative labor.

[0035] Figure 1 The step flow chart of the method for watermelon breeding gene SNP molecular marker provided by an embodiment of the present application is shown in the figure.

[0036] Figure 2 The fingerprint map construction flow chart is shown in the figure.

[0037] Figure 3 The DNA extraction flow chart is shown in the figure.

[0038] Figure 4 The PCR amplification flow chart is shown in the figure. DETAILED DESCRIPTION

[0039] For further elaboration of the technical means and effects taken by the present application to achieve the predetermined object, the following describes the specific implementation, structure, features and effects of the SNP molecular marker method and system for watermelon breeding according to the present application in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0040] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0041] The specific scheme of the SNP molecular marker method and system for watermelon breeding provided by the present application is described below in combination with the accompanying drawings.

[0042] Please refer to Figure 1 , which shows the step flowchart of the SNP molecular marker method for watermelon breeding provided by one embodiment of the present application. The method comprises the following steps:

[0043] Step one, extract the DNA solution of N varieties of watermelon samples respectively, obtain the SNP typing data of the DNA solution of each watermelon variety, and construct the fingerprint of N varieties of watermelon samples.

[0044] The fingerprint construction flowchart of watermelon samples is shown in Figure 2 , which specifically comprises:

[0045] S1, sowing: cultivate and sow N varieties of watermelon seedlings, N=10 in this embodiment, and the implementer can set it according to the actual situation, which is not limited in this embodiment;

[0046] S2, sampling: collect 80mg~120mg of fresh tissue of each variety of watermelon leaf at the true leaf stage, 100mg in this embodiment, add it into a 2mL centrifuge tube with a steel column, freeze it quickly by liquid nitrogen, and label each centrifuge tube to ensure that each watermelon variety corresponds to one centrifuge tube, then use a grinder to run at a set frequency until it is ground into powder, ensuring that the leaves are fully ground and broken;

[0047] In another embodiment, one piece of true leaf of each watermelon variety is placed in a 1.5mL centrifuge tube, then it is quickly frozen in a liquid nitrogen tank, and a tissue grinder is used to grind it into powder.

[0048] S3, DNA extraction, the DNA extraction flowchart is shown in Figure 3 :

[0049] S31, extraction: add 1 mL CTAB extraction buffer, mix well, then place the centrifuge tube in a 65°C water bath for 30 minutes. Shake the centrifuge tube several times during the water bath to promote cell lysis.

[0050] S32, separation: after cooling the sample solution in the water bath at room temperature for 1-2 minutes, add 500 μL of chloroform-isoamyl alcohol mixture (241), mix well, then place in a centrifuge with a speed setting of 10,000-12,000 rpm and centrifuge for 5-10 minutes. In this example, after cooling the sample solution in the water bath at room temperature for 1 minute, 500 μL of chloroform-isoamyl alcohol mixture (241) was added, mixed well, then placed in a centrifuge with a speed setting of 10,000 rpm and centrifuged for 5 minutes.

[0051] S33, precipitation: since the DNA is in the upper solution and the protein, watermelon tissue fragments and other impurities are in the lower precipitate, the supernatant is aspirated into a new 1.5 mL centrifuge tube, an equal volume of isopropanol solution is added, and the mixture is mixed well by vigorous shaking. Set the speed to 12,000 rpm in the centrifuge and centrifuge for 10 minutes. After the centrifuge stops, discard the supernatant in the centrifuge tube.

[0052] S34, washing: add 1 mL of 75% ethanol solution to the centrifuge tube containing only the DNA precipitate, invert several times to resuspend the DNA, and remove the residual isopropanol. After washing, retain the DNA precipitate and discard the solution. Repeat the washing 1-3 times and discard the excess liquid. In this example, the washing is repeated 2 times.

[0053] S35, dissolution: invert the centrifuge tube at room temperature for 2 minutes, use a blotting paper to absorb the excess 75% ethanol solution, then place the centrifuge tube in a fume hood to promote the evaporation of the residual ethanol. Add 100 μL of ddH2O to dissolve the DNA precipitate. Store the DNA solution at -20°C.

[0054] S4: PCR (Polymerase Chain Reaction) amplification, the PCR amplification flow chart is shown in Figure 4

[0055] S41, design primers and probes: select one SNP marker information to synthesize primers. Add the adapter sequence GAAGGTGACCAAGTTCATGCT to the 5' end of the forward primer (Forward 1) in each set of primers, and add the adapter sequence GAAGGTCGGAGTCAACGGATT to the 5' end of the forward primer (Forward 2) sequence. The reverse primer sequence is not changed. In this example ;

[0056] ​S42, dilute the DNA solution and set up the reaction system: dilute the DNA solution to 40-60 ng / μL , and the KASP reaction system is 5 , the KASP reaction reagent is 5 , the concentration of the DNA solution is 50 ng / μL; the primer mixture is 0.14 ;

[0057] S43, obtain SNP typing data: perform PCR amplification on a real-time fluorescence quantitative PCR instrument and read the fluorescence signal, ensure that the SNP primer screened can be effectively genotyped, and after the fluorescence quantitative PCR is completed, use Real-Time PCR Software for genotyping analysis; KASP (Kompetitive Allele Specific PCR) is a commonly known technology in the field of gene detection, and the specific process will not be described again.

[0058] S5: Construct the fingerprint of N watermelon varieties: according to the primer information, fluorescence information and typing results, count the genotype of each SNP site, and use MEGAX software to convert the site information into sequence information, and then use the SNP markers that are successfully typed by KASP to construct the fingerprint of N watermelon varieties.

[0059] The fingerprint diagram is shown in Table 1, wherein X(1), X(2), X(N-1), X(N) respectively represent the 1st, 2nd, N-1th and Nth watermelon varieties, J1, J2, Ja, JH-1 and JH respectively represent the 1st, 2nd, a th, H-1th and Hth SNP marker, and each watermelon variety has a corresponding base fragment under each SNP marker.

[0060] Table 1: Fingerprint diagram

[0061]

[0062] Step two, obtain the 16S rRNA sequence of each watermelon flora, divide each 16S rRNA sequence into a fixed length of base sequence; and screen each SNP marker capable of completely distinguishing N varieties from the fingerprint diagram.

[0063] In this embodiment, the 16S rRNA sequence of each watermelon flora is accessed from the NCBI database, and each 16S rRNA sequence is grouped and processed, and the grouping process is as follows:

[0064] ​​Specifically, each 16S rRNA sequence is divided into base sequences with a length of 4, and the first four bases are taken as the first base sequence, and then each 16S rRNA sequence of each melon flora is divided into base sequences by moving in steps of 1. Because mutations of 16S rRNA sequences of floras caused by changes in breeding environments of different melon varieties should be reflected on the most important four bases A, G, C, and T.

[0065] It should be noted that the grouping of 16S rRNA sequences is to quantify them for mapping into a vector space, and dividing each 16S rRNA sequence into base sequences in the form of grouping can better distinguish the differences between gene sequences of different types of floras at a higher granularity, because different types of floras also have a certain genetic relationship for melon breeding, that is, different floras in the constructed phylogenetic tree may belong to different directions of evolution of the same population, and the genetic differences caused by different directions of evolution are often reflected in local base fragments.

[0066] Because random mutations occur more or less in a random way at the molecular level, and the selection of gene recombination determines the direction of evolution. This leads to a certain randomness of 16S rRNA sequences of different floras when grouping bases. In order to eliminate this randomness and restore the real distribution of base sequences in 16S rRNA sequences, the embodiment is based on the random occurrence of random mutations, and uses the Monte Carlo sampling MCMC algorithm to correct the frequency of each base sequence, thereby improving the accuracy of the corresponding feature vector of 16S rRNA sequence in the vector space. The specific correction process is as follows:

[0067] First, M SNP markers capable of completely distinguishing N melon varieties are selected from the constructed fingerprint of genetic relationships of N melon varieties. The selection can be determined by the heterozygosity and homozygosity of SNP markers on different melon variety genotypes, which is a well-known technology in the field of SNP molecular markers, and will not be described in detail in this embodiment.

[0068] The M SNP markers can completely distinguish the N melon varieties, indicating that the maximum difference in gene sequences in the gene detection process of the N melon varieties is on the M SNP markers. The evolution between different floras encountered in melon breeding is closely related to the breeding environment, and changes in the breeding environment of different melon varieties cause the floras in the environment to mutate in different directions. These mutations are closely related to the M SNP markers on different melon varieties. Therefore, the embodiment sets M as the threshold of the number of state transitions when determining and correcting the frequency of base sequences in the future.

[0069] Step three, determining the mutation correlation parameter of each base based on the mutation frequency of the base in the base segment of each SNP marker of the N watermelon samples and the proportion of the number of the base segment of each SNP marker in the number of the base segment in the M SNP markers in the fingerprint.

[0070] The greater the fingerprint similarity of the two watermelon varieties in the fingerprint, the higher the similarity of the gene sequences between the watermelon varieties, the smaller the mutation of the gene sequences, and the smaller the probability of the mutation of the base sequence of the flora caused by the breeding environment. The base segments of the two watermelon varieties on the M SNP markers reflect the mutation of the gene segments caused by the change of the breeding environment of the two watermelon varieties.

[0071] The more the bases of the two watermelon varieties with the most similar fingerprints change among the M SNP markers, the more the changes of the genes between the two watermelon varieties are reflected. For example, taking M=2 for illustration, the base segments of the two watermelon varieties with the most similar fingerprints on the first SNP marker are AT and AC, and the base segments on the second SNP marker are CG and CT. T and G are the main changes of the two watermelon varieties on the SNP markers, indicating that the differences in the gene detection of the two watermelon varieties mainly reflect on the bases T and G.

[0072] Further, the change of each base among the M SNP markers is calculated to determine the correlation degree of each base with the mutation of the genes of different watermelon varieties, specifically:

[0073] Taking the mth SNP marker in the M SNP markers as an example, the number of changes of each base of the N watermelon varieties on the mth SNP marker is counted. The number of changes is the sum of the number of times that the same base at each position mutates into other bases or other bases mutate into this base, for example, taking N=5 for illustration, the base segments of the five watermelon varieties on the mth SNP marker are AA, AT, CG, TT and AA. The base A at the first position mutates into the base C once and the base T mutates into the base A once, so the number of changes of the base A at the first position is 2.

[0074] Based on the number of changes of the base A at the two positions in the base segment of the mth SNP marker, the mutation frequency of the base A on the mth SNP marker is determined, and the specific calculation method is:

[0075] In the formula, f m(A) is the mutation frequency of the base A on the mth SNP marker, N is the number of watermelon varieties, is the sum of the number of times that the base A at the first position in the base segment of the mth SNP marker mutates into other bases and other bases mutate into the base A, ​The sum of the number of times that the base A at the second position in the base fragment of the mth SNP marker is mutated to other bases and the number of times that the other bases are mutated to the base A for N watermelon varieties.

[0076] The mutation frequencies of each base at each SNP marker are obtained by using the same calculation method as the mutation frequency of the base A at the mth SNP marker, wherein each SNP marker is each SNP marker in the M SNP markers.

[0077] Further, for the mth SNP marker in the M SNP markers, the mutation frequency at the mth SNP marker is weighted according to the strength of the ability of the mth SNP marker to distinguish between watermelon varieties in the phylogenetic tree of the watermelon varieties.

[0078] First, for the M SNP markers in the fingerprint map, the ratio of the number of different base fragments in each SNP marker to the number of different base fragments in the M SNP markers is taken as the distinguishing weight of each SNP marker.

[0079] Subsequently, the mutation frequency of each base at each SNP marker and the distinguishing weight of each SNP marker are combined to determine the mutation association parameter of each base at the M SNP markers, and the mutation association parameter of the base A is represented as , and the specific calculation method is:

[0080] ; wherein , , are the mutation frequencies of the base A at the 1st, 2nd, and Mth SNP markers, respectively; , , are the distinguishing weights of the 1st, 2nd, and Mth SNP markers, respectively, and M is the number of SNP markers that can completely distinguish N watermelon varieties.

[0081] Step four, analyze the transition probability of each base sequence in each 16S rRNA sequence to determine the transition probability matrix of each 16S rRNA sequence; correct the sampling step length of each 16S rRNA sequence through the mutation association parameter of all bases; combine the transition probability matrix and the sampling step length to correct the transition probability matrix using a random sampling algorithm.

[0082] Due to the genetic variation of different bacterial flora in different breeding environments of different watermelon varieties, the random mutation of the base sequence in the 16S rRNA sequence mainly reflects the base sequence mutation. At the same time, the greater the base mutation correlation parameter, the stronger the correlation between the base mutation and the change of the breeding environment of different varieties of watermelon. The more different the frequency change of such base in the 16S rRNA sequence, the faster the frequency change, the higher the probability of random mutation of the base affected by the genetic direction, and the smaller the sampling step length is required to avoid missing important base changes; the slower the frequency change, the lower the probability of random mutation of the base affected by the genetic direction, and a larger sampling step length can be used without missing important base changes.

[0083] Based on the above analysis, the sampling step length of the 16S rRNA sequence of each watermelon bacterial flora is corrected by the mutation correlation parameter of all bases. Taking the a-th 16S rRNA sequence as an example, the corrected sampling step length is denoted as , and the specific calculation method is as follows:

[0084]

[0085]

[0086] In the formula, , the adjustment scale of base A to the sampling step length is obtained by using the same calculation method as the adjustment scale of base A to the sampling step length, is the adjustment scale of base T to the sampling step length, is the adjustment scale of base G to the sampling step length, is the adjustment scale of base C to the sampling step length, is the mutation correlation parameter of base A, representing the influence degree of the random mutation of base A affected by the genetic direction on the sampling step length, is a floor function, is a preset initial step length, 0.3, is a preset adjustment scale, and in the present embodiment , , and the implementer can set them according to the actual situation, which is not limited in the present embodiment.

[0087] Further, the present embodiment determines the correction frequency of the base sequence based on M SNP markers using MCMC sampling to eliminate the influence of the randomness of genetic mutation on the frequency of the base sequence.

[0088] First, in order to ensure the diversity of the bacterial flora possibly encountered in the process of watermelon breeding and not to limit the position of random mutation, the distribution of random noise caused by random mutation is set as the maximum entropy distribution when MCMC sampling is performed to determine the noise distribution.

[0089] Then, for each base sequence, taking the u-th base sequence as an example, the probability of the u-th base sequence mutating to the remaining base sequences is calculated, so as to construct the transition probability matrix in the sampling process.

[0090] Specifically, for the 16S rRNA sequence of each watermelon bacterial flora, the frequency of the u-th base sequence appearing in the 16S rRNA sequence and the adjacent next base sequence each time is counted to determine the transition probability of the u-th base sequence mutating to the v-th base sequence , and the specific calculation method is as follows:

[0091] ; in the formula, is the transition probability of the u-th base sequence mutating to the v-th base sequence in the 16S rRNA sequence of each watermelon bacterial flora, is the frequency of the u-th base sequence and the v-th base sequence appearing continuously in the 16S rRNA sequence of each watermelon bacterial flora, and the v-th base sequence is the adjacent next base sequence of the u-th base sequence, is the frequency of the v-th base sequence appearing in the 16S rRNA sequence of each watermelon bacterial flora.

[0092] Subsequently, the transition probability between any two base sequences in the 16S rRNA sequence of each watermelon bacterial flora is calculated, and the transition probability matrix of each 16S rRNA sequence is constructed. The construction of transition probability and transition probability matrix is a known technology, and will not be described in detail in this embodiment.

[0093] Then, based on the transition probability matrix of the 16S rRNA sequence of each watermelon bacterial flora, the sampling step and the threshold of the number of state transitions, combined with the maximum entropy distribution, the MCMC sampling algorithm is used to generate the probability sampling result of the transition probability between the base sequences in each 16S rRNA sequence, which is used as the correction result of each transition probability. The probability sampling result is a transition probability matrix equal in size to the transition probability matrix of the corresponding 16S rRNA sequence, that is, the transition probability matrix after the correction of the transition probability matrix of each 16S rRNA sequence. Wherein, MCMC sampling is a commonly used technology in the field of probability distribution, and the specific process will not be described in detail.

[0094] It should be understood that the base sequence of the 16S rRNA sequence is divided into a conservative region and a hypervariable region, and each row in the corrected transition probability matrix is the probability that the base sequence can mutate to form other base sequences after eliminating random noise. At the same time, the larger the correction result of the transition probability, the more likely the base sequence is located in the hypervariable region, and the low-frequency variation in the hypervariable region can contain more evolutionary or environmental adaptation information, and the possibility of transition between the corresponding base sequences actually occurring on the 16S rRNA sequence of different bacterial flora is higher. On the other hand, the base sequence in the conservative region is highly similar between different species, and the mutation rate is low during evolution; the conservative region where the high-frequency base sequence is located has rich information in the public database, and the species relationship can be quickly identified by comparing these regions.

[0095] Step five, using each transition probability in the corrected transition probability matrix and the occurrence frequency of each base sequence in each 16S rRNA sequence, determine the feature vector of each 16S rRNA sequence.

[0096] For the corrected transition probability matrix of the 16S rRNA sequence of each watermelon bacterial flora, the occurrence frequency P of each base sequence in each 16S rRNA sequence and the maximum transition probability value in the corresponding row vector in the corrected transition probability matrix of each base sequence are combined to form an array of each base sequence. Through the array, the rich information of the conservative region in each 16S rRNA sequence and the evolutionary or environmental adaptation information contained in the base sequence of the hypervariable region are jointly expressed.

[0097] As an example, the correction result of the transition probability of the 1st base sequence to the 2nd base sequence in the 1st 16S rRNA sequence is the largest, and the array of the 1st base sequence is represented as , wherein, is the occurrence frequency of the 1st base sequence in the 1st 16S rRNA sequence, is the corrected transition probability of the 1st base sequence to the 2nd base sequence in the 1st 16S rRNA sequence.

[0098] The array of each base sequence is taken as an element in the corresponding feature vector of the 16S rRNA sequence where the base sequence is located in the vector space, and the arrays of all base sequences are arranged in order in the 16S rRNA sequence where the base sequence is located. The feature vector corresponding to the 16S rRNA sequence of each watermelon bacterial flora is obtained.

[0099] Step six, obtaining soil samples corresponding to the N varieties of watermelon samples, extracting the 16S rRNA sequences of the soil samples; based on the correlation between the characteristic vectors of the watermelon flora and the 16S rRNA sequences of the soil samples, determining the genetic distance between the watermelon flora and the 16S rRNA sequences of the soil samples, constructing a phylogenetic tree of the N varieties of watermelon breeding environment flora, and determining the strongly correlated flora of the breeding environment of each variety of watermelon sample.

[0100] Random soil sampling is performed from the sampling environment in which each watermelon variety is sown, each soil sample has a label for correspondence with the watermelon variety, and the 16S rRNA sequences of the soil samples are extracted. The same method as the characteristic vectors corresponding to the 16S rRNA sequences of each watermelon flora is used to obtain the characteristic vectors corresponding to the 16S rRNA sequences of each soil sample.

[0101] Further, the characteristic vector corresponding to the 16S rRNA sequence of the a-th watermelon flora is denoted as , wherein , , are the arrays of the 1st, 2nd, and Lth base sequences obtained by grouping the a 16S rRNA sequences, and L is the number of base sequences obtained by grouping the 16S rRNA sequences of the a-th watermelon flora. Based on the correlation between the characteristic vectors of the watermelon flora and the 16S rRNA sequences of the soil samples, the genetic distance between the watermelon flora and the 16S rRNA sequences of the soil samples is determined, and the specific calculation method is as follows:

[0102] ; in the formula, is the genetic distance between the 16S rRNA sequence of the a-th watermelon flora and the 16S rRNA sequence of the b-th soil sample, is the characteristic vector of the 16S rRNA sequence of the a-th watermelon flora, is the characteristic vector of the 16S rRNA sequence of the b-th soil sample, and Y() is a cosine similarity calculation function.

[0103] It should be noted that the implementer can select other feasible similarity calculation methods, such as Pearson correlation coefficient, etc., which are not limited in the present embodiment. Compared with directly calculating the cosine similarity or other distance metrics of two characteristic vectors, the genetic distance calculation formula constructed in the present embodiment not only conforms to the cognition that the genetic distance is large and the correlation of the characteristic vectors is small, but also meets the requirements of non-negativity, symmetry, and triangle inequality in molecular biology.

[0104] The genetic distance between the extracted 16S rRNA sequence of each soil sample and the 16S rRNA sequence of each watermelon flora is calculated according to the above steps, so as to construct a phylogenetic tree containing N varieties of watermelon breeding environment flora. The construction of the phylogenetic tree is a known technology, and the specific process is not described herein. In the phylogenetic tree, the homology support rate between the species in the soil sample and the watermelon flora can be displayed. The greater the homology support rate, the more likely it is to belong to the same genus.

[0105] Therefore, the soil samples corresponding to the flora located on the same branch as each watermelon flora are counted, and the specific watermelon variety is determined according to the label of the soil sample, thereby determining all flora in the breeding environment of each watermelon variety. According to the homology support rate of the branch where all flora of all watermelon varieties are located, the flora determined on the branch with a greater homology support rate indicates that the credibility of mutation evolution is higher as the watermelon breeding environment changes, and the correlation with the M SNP sites is stronger. That is, watermelon varieties with similar gene sequences may also have the same flora on a certain branch. In this way, by setting the homology support rate, the flora that may exist in the breeding environment of N varieties of watermelon are effectively segmented, and the correlation analysis of microbial flora-SNP markers is realized.

[0106] Since the greater the homology support rate, the greater the possibility that the flora in the soil sample is consistent with the watermelon flora, in this embodiment, the flora in the branch with a homology support rate above a preset first percentage is selected as the flora that cannot be avoided in the change of the N watermelon breeding environment, and is recorded as a strongly correlated flora. The flora in the branch with a homology support rate below a preset second percentage is selected as the flora that can be avoided in the change of the N watermelon breeding environment, thereby guiding the improvement of the breeding environment of different varieties of watermelon, for example, by establishing a reactor through soil microenvironment directional improvement technology or carrying out biological carbon-inoculant cooperation to guide the improvement of the breeding environment. The first percentage is greater than the second percentage, and in this embodiment, the first percentage is 90% and the second percentage is 75%. The implementer can set it according to the actual situation, which is not limited in this embodiment.

[0107] Based on the same inventive concept as the above method, the present embodiment also provides a gene SNP molecular marker system for watermelon breeding, which comprises a memory, a processor, and a computer program stored in the memory and running on the processor. The processor executes the computer program to implement the steps of any one of the above gene SNP molecular marker methods for watermelon breeding.

[0108] It should be noted that the above-mentioned order of the embodiments of the present application is merely for description, and does not represent the advantages and disadvantages of the embodiments. And the above describes specific embodiments of the present application. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-task processing and parallel processing are also possible or can be advantageous.

[0109] Each of the embodiments in the specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other. Each embodiment focuses on the difference from other embodiments.

[0110] The above only describes the preferred embodiments of the present application, and does not limit the present application. Any modification, equivalent replacement, improvement, etc. made within the principles of the present application shall be included in the protection scope of the present application.

Claims

1. A gene SNP molecular marker method for watermelon breeding, characterized in that, The method comprises the following steps: DNA solutions of multiple varieties of watermelon samples are extracted respectively, SNP typing data of the DNA solutions of the varieties of watermelon samples are obtained, and a fingerprint of the multiple varieties of watermelon samples is constructed; 16S rRNA sequences of each watermelon flora are obtained, each 16S rRNA sequence is divided into a fixed-length base sequence, each SNP marker capable of completely distinguishing the multiple varieties is screened out from the fingerprint; Based on the mutation frequency of the base fragments of the multiple varieties of watermelon samples on the each SNP marker and the proportion of the number of base fragments of the each SNP marker in the fingerprint in the number of base fragments of all the SNP markers, a mutation correlation parameter of each base is determined; The transition probability of each base sequence in each 16S rRNA sequence is analyzed, a transition probability matrix of each 16S rRNA sequence is determined, the sampling step of each 16S rRNA sequence is corrected based on the mutation correlation parameter of all bases, and the transition probability matrix is corrected by using a random sampling algorithm in combination with the transition probability matrix and the sampling step; Each transition probability in the corrected transition probability matrix and the appearance frequency of each base sequence in each 16S rRNA sequence are used to determine a feature vector of each 16S rRNA sequence; Corresponding soil samples of the multiple varieties of watermelon samples are obtained, 16S rRNA sequences of the soil samples are extracted, genetic distances between the watermelon flora and the 16S rRNA sequences of the soil samples are determined based on the correlation between the feature vectors of the 16S rRNA sequences of the watermelon flora and the soil samples, a phylogenetic tree of the breeding environment flora of the multiple varieties of watermelon is constructed, and a strongly correlated flora of the breeding environment of each variety of watermelon sample is determined.

2. The method for the genetic SNP molecular marker for watermelon breeding according to claim 1, wherein, The fingerprint is composed of base fragments of each watermelon variety at each SNP marker.

3. The method for the genetic SNP molecular marker for watermelon breeding according to claim 1, wherein, The division of each 16S rRNA sequence into a fixed-length base sequence comprises the following steps: For each 16S rRNA sequence, the first four bases are taken as the first base sequence, and then moved by a step of 1 to obtain base sequences of the same length of each 16S rRNA sequence.

4. The method for genetic SNP molecular marker for watermelon breeding according to claim 1, wherein, The calculation method of the mutation frequency comprises the following steps: wherein, is the mutation frequency of base A at the mth SNP marker, and N is the number of watermelon varieties, is the sum of the number of times that base A at the first position in the base fragment of the mth SNP marker is mutated to other bases and that other bases are mutated to base A in N watermelon varieties, is the sum of the number of times that base A at the second position in the base fragment of the mth SNP marker is mutated to other bases and that other bases are mutated to base A in N watermelon varieties.

5. The method for genetic SNP molecular marker for watermelon breeding according to claim 1, wherein, The determination of the mutation correlation parameter comprises the following steps: For each SNP marker, the ratio of the number of different base fragments in each SNP marker to the number of different base fragments in all SNP markers is taken as the distinguishing weight of each SNP marker in the fingerprint. Mutation association parameter for base A The expression for the mutation association parameter for base A is: ; wherein, , , are the mutation frequencies of base A at the 1st, 2nd, Mth SNP markers, respectively; , , are the discrimination weights of the 1st, 2nd, Mth SNP markers, respectively, and M is the number of SNP markers that can completely discriminate the plurality of varieties.

6. The method for genetic SNP molecular marker for watermelon breeding according to claim 1, wherein, The correction of the sampling step of each 16S rRNA sequence comprises the following steps: The product of the mutation correlation parameter of each base and a preset adjustment scale is calculated, the sum of the mean value of the product of all bases and a preset initial step is calculated, and the sampling step of each 16S rRNA sequence is the floor value of the sum.

7. The method for the genetic SNP molecular marker of watermelon breeding according to claim 1, wherein, The determination of the feature vector of each 16S rRNA sequence comprises the following steps: The array of each base sequence is composed of the frequency of each base sequence in the 16S rRNA sequence and the maximum transition probability value in the corresponding row vector in the modified transition probability matrix of each 16S rRNA sequence; The characteristic vector of each 16S rRNA sequence is composed of the array of all base sequences in each 16S rRNA sequence according to the arrangement order of the base sequences.

8. The method for genetic SNP molecular marker for watermelon breeding according to claim 1, wherein, The genetic distance is calculated in the following manner: wherein, is the genetic distance between the 16S rRNA sequence of the a-th watermelon bacterial community and the 16S rRNA sequence of the b-th soil sample, is the feature vector of the 16S rRNA sequence of the a-th watermelon bacterial community, is the feature vector of the 16S rRNA sequence of the b-th soil sample, and Y() is a cosine similarity calculation function.

9. The method for genetic SNP molecular marker for watermelon breeding according to claim 4, wherein, The strong correlation flora of the breeding environment of each watermelon sample is determined by: In the phylogenetic tree, the flora in the branch with a homologous support rate higher than a preset first percentage is screened as the flora that cannot be avoided in the breeding environment change of the N watermelon varieties, and the flora in the branch with a homologous support rate lower than a preset second percentage is screened as the flora that can be avoided in the breeding environment change of the N watermelon varieties, wherein the first percentage is greater than the second percentage. 10.A gene SNP molecular marker system for watermelon breeding, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, The processor implements the steps of the method of any one of claims 1-9 when executing the computer program.

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