Method for visually displaying genetic difference between biological individuals through combined graph and generating combined graph

Abstract

The invention relates to the technical field of graph generation for displaying genetic differences, in particular to a method for visually displaying genetic differences among biological individuals through a combined graph and generating the combined graph. Comprising the following steps: S1, selecting a single nucleotide polymorphism (SNP) site from a DNA sample, and obtaining individual SNP information by using a sequencing method; s2, designing a coding rule, and assigning different colors to genotypes of single nucleotide polymorphism (SNP); s3, creating a visual two-dimensional graph according to a coding rule; and S4, verifying whether the created two-dimensional graph can accurately reflect the genetic difference. According to the design of the invention, specific colors are distributed for different genotypes, and the information is integrated into a two-dimensional graph, so that the complex genetic information becomes visual. Therefore, researchers can quickly identify genetic differences among individuals, and non-professionals can conveniently understand basic concepts of genetics.

Description

Technical Field

[0001] The present invention relates to the technical field of graphic generation for displaying genetic differences, and in particular to a method for intuitively displaying genetic differences between biological individuals through combined graphics and a combined graphics generation method. Background Art

[0002] With the development of genomics, we are able to obtain a large amount of genetic information, but this information is often complex and difficult to understand intuitively. For example, DNA (deoxyribonucleic acid) is a biological macromolecule that exists in all known biological cells and is the carrier of genetic information. The DNA molecule consists of two spiral chains, and the nucleotides on these two chains are connected to each other in the form of base pairing. There are four types of bases: adenine (A), cytosine (C), guanine (G) and thymine (T). These bases are arranged in a specific order to form genes, which in turn encode the genetic information of organisms. Single Nucleotide Polymorphism (SNP) refers to DNA sequence polymorphism caused by the variation of a single nucleotide at the genome level. SNP is the most common type of heritable variation in humans, accounting for more than 90% of all known polymorphisms. This variation can be a conversion (substitution between purine and purine or between pyrimidine and pyrimidine) or a transversion (substitution between purine and pyrimidine). SNPs may occur both within gene sequences and in non-coding sequences outside genes. Alleles are genes that control different forms of the same trait at the same position on a pair of homologous chromosomes. They are polymorphic forms of genes that can encode different protein or RNA sequences, thereby affecting the phenotype or physiological characteristics of individuals. The gene sequence of each organism is unique, just like everyone's fingerprint, which can be used to determine the identity of the individual organism. However, it is difficult for non-professionals to understand the seemingly mysterious gene sequence information. Traditional data presentation methods (such as tables or text reports) are difficult to effectively convey the full picture of genetic differences, especially when involving a large number of individuals; researchers, medical workers and the public may need a simple and direct way to understand and compare genetic differences between individuals. Although traditional statistical charts are useful, they have limited expressiveness in terms of genetic diversity, especially when showing the genetic structure of large-scale populations. In summary, a method for intuitively displaying genetic differences between biological individuals and generating combined graphics through combined graphics is provided. Summary of the invention

[0003] The purpose of the present invention is to provide a method for intuitively displaying genetic differences between biological individuals through combined graphics and a combined graphics generation method, so as to solve the problems of complex genetic information and data visualization requirements raised in the above background technology.

[0004] To achieve the above object, the present invention provides a method for intuitively displaying genetic differences between biological individuals through combined graphics and a combined graphics generation method, comprising the following steps:

[0005] S1. Select single nucleotide polymorphism (SNP) sites from DNA samples and use high-throughput sequencing methods to obtain individual SNP information;

[0006] S2. Design coding rules and assign different colors to the genotypes of single nucleotide polymorphisms (SNPs);

[0007] S3, create a visual two-dimensional graph according to the coding rules;

[0008] S4. Verify whether the created two-dimensional graphs can accurately reflect genetic differences.

[0009] As a further improvement of the technical solution, in S1, a high-throughput sequencing method is used to obtain the SNP information of an individual, comprising the following steps:

[0010] S1.1. Collect biological samples from individuals, extract DNA from the samples by organic extraction method, and use ultraviolet spectrometer to detect the quality and concentration of DNA;

[0011] S1.2, using enzyme digestion to break the DNA into small fragments of a certain length, performing end repair on the DNA fragments to make them blunt-ended, and adding adapter sequences to the DNA fragments;

[0012] S1.3, using polymerase chain reaction to amplify the DNA fragment with the adapter sequence, and removing the non-specific amplification product by gel electrophoresis to obtain a purified library;

[0013] S1.4. Use quantitative PCR to determine the concentration of the library, and fix the library DNA on the sequencing chip to form dense DNA clusters for sequencing;

[0014] S1.5. During the sequencing process, one nucleotide is added each time and the signal change is recorded. After the sequencing is completed, the original sequencing data is output;

[0015] S1.6. Perform quality control on the sequence reads generated during the sequencing process and the related quality score information, remove low-quality sequence reads, use the Smith-Waterman local alignment algorithm to align high-quality sequence reads to the reference genome, and analyze the alignment results to detect the differences between the individual and the reference genome, namely the SNP sites.

[0016] As a further improvement of the technical solution, in S1.6, the Smith-Waterman local alignment algorithm is specifically:

[0017]

[0018] Where H[i][j] represents the best comparison score between the first i SNP sites of the SNP sequence of the first biological individual and the first j SNP sites of the SNP sequence of the second biological individual; H represents the matrix; a i represents the genotype of the first biological individual at the i-th SNP site; b i represents the genotype of the second biological individual at the i-th SNP site; g represents the penalty; s(a i , b i ) represents the evaluation of the i-th element a of the SNP sequence of the first biological individual i and the jth element b in the SNP sequence of the second biological individual i i represents the position index in the SNP sequence of the first biological individual; j represents the position index in the SNP sequence of the second biological individual.

[0019] As a further improvement of the technical solution, in S2, coding rules are designed and different colors are assigned to the genotypes of single nucleotide polymorphisms (SNPs), including the following steps:

[0020] S2.1, selecting SNP sites with higher mutation frequencies in the high-throughput sequencing results obtained in step S1;

[0021] S2.2, sort the selected SNP sites according to their actual positions on the chromosome, and record the position information of each SNP site;

[0022] S2.3. Mark the homozygous dominant genotype in white, the heterozygous genotype in gray, and the homozygous recessive genotype in black.

[0023] As a further improvement of the technical solution, in S2.2, the selected SNP sites are sorted according to their actual positions on the chromosome, comprising the following steps:

[0024] S2.21. Collect all relevant SNP data and preprocess the SNP data;

[0025] S2.22. Put all SNP data on chromosome 1 together, put all SNP data on chromosome 2 together, and so on;

[0026] S2.23. For each chromosome, the SNP sites are sorted according to the physical position of the SNP data on the chromosome, and the SNP sites with smaller position values ​​are ranked first;

[0027] S2.24. Create an index system to quickly locate specific SNP sites. The index includes chromosome number and physical location.

[0028] S2.25. Store the sorted SNP data in a database.

[0029] As a further improvement of the technical solution, in S3, creating a visualized two-dimensional graph according to the encoding rule includes the following steps:

[0030] S3.1. Create a blank two-dimensional grid m×n;

[0031] S3.2, map each SNP site to a cell in the grid according to the order of chromosome position;

[0032] S3.3, assigning a corresponding color to the SNP site in each cell according to the color coding rule defined in step S2.3;

[0033] S3.4, fill each cell in the grid with the assigned color, identify the abnormal SNP sites in the two-dimensional image by the local outlier factor LOF algorithm, and mark the abnormal SNP sites with different colors;

[0034] S3.5. Use graphics editing software to generate the final two-dimensional graphics.

[0035] As a further improvement of the technical solution, in S3.4, the abnormal SNP sites in the two-dimensional pattern are identified by the local outlier factor LOF algorithm, and the abnormal SNP sites are marked with different colors, including the following steps:

[0036] S3.41, normalize the data of SNP sites;

[0037] S3.42, use Euclidean distance to calculate the distance between each SNP site and all other SNP sites;

[0038] S3.43. For each SNP site, find its k nearest neighbors;

[0039] S3.44, calculate the local accessibility density of each SNP site, and calculate the local density of each SNP site;

[0040] S3.45. For each SNP site, calculate its LOF value;

[0041] S3.46. Determine abnormal SNP sites based on LOF values, and use different colors to mark abnormal SNP sites with LOF values ​​higher than the threshold K.

[0042] As a further improvement of the technical solution, in S3.45, for each SNP site, the LOF value is calculated as follows:

[0043]

[0044] Where p represents the SNP site; N u(p) represents the u SNP sites closest to p; lrd(p) represents the local reachability density of point p; LOF u(p) represents the local outlier factor of point p; o represents the point that is closer to point p; u represents the number of neighbors used to calculate the local outlier factor.

[0045] As a further improvement of the technical solution, in S4, verifying whether the created two-dimensional graph can accurately reflect the genetic difference comprises the following steps:

[0046] S4.1. Check the two-dimensional graph for missing values, erroneous entries, and other anomalies, and remove SNP sites that appear too low in the sample population;

[0047] S4.2. Confirm that each genotype is correctly assigned the corresponding color. Use a known SNP dataset as test input to check whether the sorting algorithm can correctly sort by chromosome number and physical position.

[0048] S4.3. Calculate the grid size according to the number of SNPs and chromosomes, check whether the calculation result is reasonable, adjust the resolution of the two-dimensional graph, and observe whether the change in grid size meets the display requirements;

[0049] S4.4. Verify that the color mapping of each SNP site is correct through the test program, and test the display effect of the two-dimensional graphics on different devices and platforms;

[0050] S4.5. Compare the 2D figures of different individuals to see whether the color patterns reasonably reflect the known genetic differences;

[0051] S4.6. Use the principal component analysis (PCA) method to evaluate the similarities and differences of color distribution in two-dimensional graphics.

[0052] S4.7. Regenerate the 2D graph using the new SNP dataset and compare it with the previous 2D graph to check the consistency of the results.

[0053] As a further improvement of the technical solution, in S4.6, the principal component analysis PCA method is specifically:

[0054]

[0055] Where X represents the original genetic data matrix; μ represents the genotype mean vector of each SNP locus; σ represents the genotype standard deviation vector of each SNP locus; V k represents a matrix composed of the eigenvectors corresponding to the first k largest eigenvalues; Y represents the projection value of each individual in the principal component space; k represents the index vector.

[0056] Compared with the prior art, the present invention has the following beneficial effects:

[0057] 1. This method of visually displaying genetic differences between biological individuals and generating combined graphics by combining graphics, by assigning specific colors to different genotypes and integrating this information into two-dimensional graphics, makes complex genetic information visualized. This not only helps researchers quickly identify genetic differences between individuals, but also makes it easier for non-professionals to understand the basic concepts of genetics. The color coding method allows even people without a deep genetics background to see at a glance the similarities and differences in genetic characteristics between individuals, enhancing the interpretability and dissemination of genetic information.

[0058] 2. The method for intuitively displaying genetic differences between biological individuals and generating combined graphics through combined graphics also includes a process for verifying the generated two-dimensional graphics to ensure the accuracy and reliability of the data. In addition, by applying the local outlier factor (LOF) algorithm, potential data anomalies can be effectively identified, which helps to discover abnormal patterns in genetic variation at an early stage, such as possible mutations or rare genetic diseases. This method improves the efficiency of data analysis and also provides more reliable basic data for subsequent research. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 is a flow chart of the overall method of the present invention;

[0060] Figure 2 An example diagram of two-dimensional graphics generation for the present invention. DETAILED DESCRIPTION

[0061] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0062] Example 1: Please refer to Figure 1 As shown, this embodiment provides a method for intuitively displaying genetic differences between biological individuals through combined graphics and generating combined graphics, including the following steps:

[0063] S1. Select single nucleotide polymorphism (SNP) sites from DNA samples and use high-throughput sequencing methods to obtain individual SNP information;

[0064] In this embodiment, a high-throughput sequencing method is used to obtain individual SNP information, including the following steps:

[0065] S1.1. Collect biological samples from individuals, usually blood, saliva or tissue samples, extract DNA from the samples using organic extraction methods, and use ultraviolet spectrometer to detect the quality and concentration of the DNA;

[0066] Among them, the purpose of organic extraction is to separate and purify DNA and remove proteins, lipids and other cellular components;

[0067] S1.2, use enzyme digestion to break the DNA into small fragments of a certain length, perform end repair on the DNA fragments to make them flat ends, and add adapter sequences to the DNA fragments to facilitate subsequent PCR amplification and sequencing;

[0068] Among them, enzyme digestion refers to the method of using restriction endonucleases to cut DNA and break it into small fragments of a certain length; linker sequences can help DNA fragments to be more easily connected with other DNA fragments or vectors (such as plasmids, bacteriophages, etc.);

[0069] S1.3, using polymerase chain reaction (PCR) to amplify DNA fragments with adapter sequences, and removing non-specific amplification products by gel electrophoresis to obtain a purified library, wherein the library represents a group of DNA fragments that have been pre-processed and prepared;

[0070] Among them, when amplifying DNA fragments with adapter sequences, the design of PCR primers must take the presence of adapters into consideration. Usually, primers are designed to completely match the adapter sequences, which ensures that the PCR reaction specifically amplifies DNA fragments with adapters. This design ensures that only DNA fragments that have been successfully attached with adapters will be amplified, thereby excluding DNA fragments without adapters. Gel electrophoresis utilizes the charge carried by biological macromolecules, which moves toward the oppositely charged electrode under the action of an electric field. Molecules of different sizes encounter different resistances and have different migration speeds, so they can be separated.

[0071] S1.4. Use quantitative PCR to determine the concentration of the library, and fix the library DNA on the sequencing chip to form dense DNA clusters for sequencing;

[0072] Among them, quantitative PCR utilizes the principles of traditional PCR technology, but adds the ability to monitor DNA amplification in real time. By adding fluorescent markers (fluorescent dyes or fluorescent probes) to the PCR reaction, the changes in the fluorescent signal during the amplification process can be monitored in real time. The intensity of the fluorescent signal is proportional to the starting copy number of the DNA template, so it can be used to quantify the amount of the initial DNA template;

[0073] S1.5. During the sequencing process, one nucleotide is added each time and the signal change is recorded. After the sequencing is completed, the original sequencing data is output;

[0074] S1.6. Perform quality control on the sequence reads generated during sequencing and the quality score information associated therewith, remove low-quality sequence reads, use the Smith-Waterman local alignment algorithm to align high-quality sequence reads to the reference genome, and analyze the alignment results to detect the differences between the individual and the reference genome, i.e., SNP sites;

[0075] Among them, the Smith-Waterman algorithm focuses on finding the best matching region in the sequence. Even if the overall sequence does not match completely, it can find high similarity in the local region. This is particularly useful for genome alignment, because there may be a large number of insertions, deletions and mutations in the genome, which will lead to unsatisfactory results of global alignment; the Smith-Waterman algorithm allows insertion and deletion operations, and handles these operations through a penalty mechanism, so that the algorithm can adapt to different types of mutations, such as single nucleotide polymorphisms (SNPs), insertions and deletions (InDels), etc.; the Smith-Waterman local alignment algorithm is specifically as follows:

[0076]

[0077] Where H[i][j] represents the best comparison score between the first i SNP sites of the SNP sequence of the first biological individual and the first j SNP sites of the SNP sequence of the second biological individual; H represents the matrix; a i represents the genotype of the first biological individual at the i-th SNP site; b i represents the genotype of the second biological individual at the i-th SNP site; g represents the penalty, which is used to penalize insertion or deletion operations in the sequence alignment algorithm; s(a i , b i ) represents the evaluation of the i-th element a of the first biological individual SNP sequence i and the jth element b in the SNP sequence of the second biological individual i i represents the position index in the SNP sequence of the first biological individual; j represents the position index in the SNP sequence of the second biological individual.

[0078] S2. Design coding rules and assign different colors to the genotypes of single nucleotide polymorphisms (SNPs);

[0079] In this embodiment, a coding rule is designed and different colors are assigned to the genotypes of single nucleotide polymorphisms (SNPs), including the following steps:

[0080] S2.1, select SNP sites with higher mutation frequency in the high-throughput sequencing results obtained in step S1, and these SNP sites should cover multiple chromosomes so as to fully reflect the genetic characteristics of the individual;

[0081] S2.2. Sort the selected SNP sites according to their actual positions on the chromosome, which means that if a SNP is located at the beginning of chromosome 1, it will be ranked first; while SNPs located at the ends of other chromosomes will be ranked at the end, and the position information of each SNP site will be recorded;

[0082] The selected SNP sites are sorted according to their actual positions on the chromosome, including the following steps:

[0083] S2.21. Collect all relevant SNP data, including chromosome number, physical location, reference base and variant base, and preprocess the SNP data;

[0084] S2.22. Put all SNP data on chromosome 1 together, put all SNP data on chromosome 2 together, and so on;

[0085] S2.23. For each chromosome, the SNP sites are sorted according to the physical position of the SNP data on the chromosome, and the SNP sites with smaller position values ​​are ranked first;

[0086] S2.24. Create an index system to quickly locate specific SNP sites. The index includes chromosome number and physical location.

[0087] Wherein, creating the index system includes creating an index structure, building a chromosome index, and building a physical location index;

[0088] Creating an index structure includes defining index fields (the index should contain two key fields: chromosome number and physical location) and selecting the index type (hash table);

[0089] Constructing the chromosome index includes initializing the index structure (creating an index item for each chromosome) and adding records (adding the physical location of each SNP and its related information (such as SNP ID, genotype, etc.) to the index item of the corresponding chromosome);

[0090] Building a physical position index includes sorting (for each chromosome, sorting the SNP data by physical position) and creating a position index (creating an index for each SNP on a chromosome, recording its physical position and its position in the data set (such as row number));

[0091] S2.25. storing the sorted SNP data in a database;

[0092] S2.3. Mark the homozygous dominant genotype (such as YY or BB) in white, the heterozygous genotype (such as Yy or Bb) in gray, and the homozygous recessive genotype (such as yy or bb) in black.

[0093] S3. Create a visualized two-dimensional graph according to the coding rules, and allow the number of SNP sites, the shape of the two-dimensional graph, the size of the two-dimensional graph, the number of small grids in the two-dimensional graph, the color of the small grids, etc. to be flexibly adjusted according to actual conditions;

[0094] Among them, the number of SNP sites, the shape of the two-dimensional graph, the size of the two-dimensional graph, the number of small grids in the two-dimensional graph, the color of the small grids, etc. are allowed to be flexibly adjusted according to actual conditions:

[0095]

[0096]

[0097] In this embodiment, creating a visualized two-dimensional graph according to the encoding rule includes the following steps:

[0098] S3.1. Create a blank two-dimensional grid m×n. This grid is used to place all SNP loci information. The number of rows and columns of the grid depends on the number of chromosomes and the number of SNP loci on each chromosome. The size of the two-dimensional graph (i.e., width and height) can be adjusted according to display requirements.

[0099] S3.2. Map each SNP site to a cell in the grid according to the position order of the chromosome. The number of small grids depends on the total number of SNP sites that need to be displayed. If more or fewer SNP sites need to be displayed, the number of small grids can be changed by adjusting the resolution of the two-dimensional graph.

[0100] S3.3, assigning a corresponding color to the SNP site in each cell according to the color coding rule defined in step S2.3;

[0101] S3.4. Fill each cell in the grid with the assigned color. In this way, each SNP site is assigned a color, and the entire graph can intuitively show the genetic differences between different individuals. The color can be adjusted as needed to ensure contrast and visual effect. The local outlier factor LOF algorithm is used to identify abnormal SNP sites in the two-dimensional graph and mark them with different colors.

[0102] Among them, the LOF algorithm is a density-based anomaly detection method, which means that it can effectively detect points that are significantly different from their surrounding neighbors in density, which is very useful for identifying points that appear abnormal in a local environment, especially when the data is unevenly distributed; the LOF algorithm can adapt to changes in data density in different regions. It not only considers the density of the point itself, but also compares the density of the point relative to its neighbors. Therefore, even if there are multiple clusters of different densities in a data set, LOF can effectively detect abnormal points; by marking abnormal points with different colors, it can be very intuitive to show which points are considered abnormal. This method makes data visualization clearer and easier to understand, helping users to quickly identify and understand abnormal patterns in the data; identifying abnormal SNP sites in two-dimensional graphics through the local anomaly factor LOF algorithm and marking the abnormal SNP sites with different colors includes the following steps:

[0103] S3.41, normalize the data of SNP sites;

[0104] S3.42, use Euclidean distance to calculate the distance between each SNP site and all other SNP sites;

[0105] S3.43. For each SNP site, find its k nearest neighbors (k is a predetermined parameter);

[0106] S3.44. Calculate the local accessibility density of each SNP site (by calculating the maximum distance from the site to its nearest neighbor), and calculate the local density of each SNP site, that is, the inverse of the average accessibility distance of its k nearest neighbors;

[0107] S3.45. For each SNP site, calculate its LOF value. The LOF value is the ratio of the local density of the point to the average of the local density of its nearest neighboring points. If this ratio is greater than 1, it means that the point is more likely to be an abnormal SNP site than its neighbors.

[0108] Furthermore, for each SNP site, the LOF value is calculated as follows:

[0109]

[0110] Where p represents the SNP site; N u(p) represents the u SNP sites closest to p; lrd(p) represents the local reachability density of point p; LOF u(p) represents the local outlier factor of point p; o represents the point that is closer to point p; u represents the number of neighbors used to calculate the local outlier factor;

[0111] S3.46. Determine abnormal SNP sites based on LOF values, that is, points with LOF values ​​much greater than 1 are considered abnormal SNP sites, and use different colors to mark abnormal SNP sites with LOF values ​​higher than the threshold K;

[0112] S3.5. Use graphics editing software to generate the final two-dimensional graphics. The generated two-dimensional graphics are as follows: Figure 2 As shown;

[0113] For example, the gene that controls the color of pea cotyledons can be yellow (Y) or green (y). Y and y are a pair of alleles with three genotypes: YY, Yy, and yy. Y is a dominant gene, so the corresponding pea cotyledon colors are yellow, yellow, and green. The B gene locus that controls the color of cow hair has BB and Bb for black and bb for red. B and b are a pair of alleles with three genotypes: BB, Bb, and bb. Because this gene is a dominant gene, the corresponding cow hair colors are black, black, and red. In other words, each allele has three different genotypes. If we use a table with 35 columns in both horizontal and vertical directions and a total of 1,225 small grids to represent the differences in 1,225 alleles, we can intuitively display the genetic differences between biological individuals on these 1,225 alleles through the three different colors of white, gray, and black in each small grid.

[0114] S4. Verify whether the created two-dimensional graphs can accurately reflect genetic differences;

[0115] In this embodiment, verifying whether the created two-dimensional graph can accurately reflect the genetic difference includes the following steps:

[0116] S4.1. Check the two-dimensional graph for missing values, erroneous entries, and other abnormalities, and remove SNP sites that appear too low in the sample population (e.g., less than 1%).

[0117] S4.2. Confirm that each genotype (homozygous dominant, heterozygous, homozygous recessive) is correctly assigned the corresponding color. Use the known SNP data set as test input to check whether the sorting algorithm can correctly sort by chromosome number and physical position.

[0118] S4.3. Calculate the grid size according to the number of SNPs and chromosomes, check whether the calculation result is reasonable, adjust the resolution of the two-dimensional graph, and observe whether the change in grid size meets the display requirements;

[0119] S4.4. Verify that the color mapping of each SNP site is correct through the test program, and test the display effect of the two-dimensional graphics on different devices and platforms to ensure color consistency;

[0120] The specific test procedures are as follows:

[0121]

[0122]

[0123] S4.5. Compare the 2D figures of different individuals to see whether the color patterns reasonably reflect the known genetic differences;

[0124] S4.6. Use the principal component analysis (PCA) method to evaluate the similarities and differences of color distribution in two-dimensional graphics.

[0125] Among them, one of the main advantages of PCA is that it can effectively reduce the dimension of data. When processing the color distribution of two-dimensional graphics, the original data may contain a lot of color information. PCA can reduce these high-dimensional data to a lower dimension (such as two dimensions) while retaining the main variation information, which makes the data easier to visualize and simplifies subsequent analysis. PCA can effectively remove noise from the data by retaining the main components of the data (i.e., the directions with larger variance). In color distribution analysis, this helps to reduce unnecessary variations caused by measurement errors or other factors, thereby making the color distribution pattern clearer. The specific PCA method of principal component analysis is:

[0126]

[0127] Where X represents the original genetic data matrix, which contains the genotype information of each individual's SNP locus; μ represents the genotype mean vector of each SNP locus; σ represents the genotype standard deviation vector of each SNP locus; V k represents a matrix composed of eigenvectors corresponding to the first k largest eigenvalues; Y represents the projection value of each individual in the principal component space; k represents the index vector;

[0128] S4.7. Regenerate the 2D graph using the new SNP dataset and compare it with the previous 2D graph to check the consistency of the results.

[0129] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments, and the above embodiments and descriptions are only preferred examples of the present invention, and are not intended to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, and these changes and improvements all fall within the scope of the present invention to be protected.

Claims

1. A method for intuitively displaying genetic differences between biological individuals through combined graphics and a combined graphics generation method, characterized in that: The following steps are involved: S1. Select single nucleotide polymorphism (SNP) sites from DNA samples and use high-throughput sequencing methods to obtain individual SNP information; S2. Design coding rules and assign different colors to the genotypes of single nucleotide polymorphisms (SNPs); S3, create a visual two-dimensional graph according to the coding rules; S4. Verify whether the created two-dimensional graphs can accurately reflect genetic differences.

2. The method for intuitively displaying genetic differences between biological individuals through combined graphics and generating combined graphics according to claim 1, characterized in that: In S1, a high-throughput sequencing method is used to obtain individual SNP information, including the following steps: S1.

1. Collect biological samples from individuals, extract DNA from the samples by organic extraction method, and use ultraviolet spectrometer to detect the quality and concentration of DNA; S1.2, using enzyme digestion to break the DNA into small fragments of a certain length, performing end repair on the DNA fragments to make them blunt-ended, and adding adapter sequences to the DNA fragments; S1.3, using polymerase chain reaction to amplify the DNA fragment with the adapter sequence, and removing the non-specific amplification product by gel electrophoresis to obtain a purified library; S1.

4. Use quantitative PCR to determine the concentration of the library, and fix the library DNA on the sequencing chip to form dense DNA clusters for sequencing; S1.

5. During the sequencing process, one nucleotide is added each time and the signal change is recorded. After the sequencing is completed, the original sequencing data is output; S1.

6. Perform quality control on the sequence reads generated during the sequencing process and the related quality score information, remove low-quality sequence reads, use the Smith-Waterman local alignment algorithm to align high-quality sequence reads to the reference genome, and analyze the alignment results to detect the differences between the individual and the reference genome, namely the SNP sites.

3. The method for intuitively displaying genetic differences between biological individuals through combined graphics and generating combined graphics according to claim 2, characterized in that: In S1.6, the Smith-Waterman local alignment algorithm is specifically: Where H[i][j] represents the best comparison score between the first i SNP sites of the SNP sequence of the first biological individual and the first j SNP sites of the SNP sequence of the second biological individual; H represents the matrix; a i represents the genotype of the first biological individual at the i-th SNP site; b i represents the genotype of the second biological individual at the i-th SNP site; g represents the penalty; s(a i , b i ) represents the evaluation of the i-th element a of the first biological individual SNP sequence i and the jth element b in the SNP sequence of the second biological individual i The matching score between them; i represents the position index in the SNP sequence of the first biological individual; j represents the position index in the SNP sequence of the second biological individual.

4. The method for intuitively displaying genetic differences between biological individuals through combined graphics and generating combined graphics according to claim 1, characterized in that: In S2, a coding rule is designed and different colors are assigned to the genotypes of the single nucleotide polymorphism (SNP), including the following steps: S2.1, selecting SNP sites with higher mutation frequencies in the high-throughput sequencing results obtained in step S1; S2.2, sort the selected SNP sites according to their actual positions on the chromosome, and record the position information of each SNP site; S2.

3. Mark the homozygous dominant genotype in white, the heterozygous genotype in gray, and the homozygous recessive genotype in black.

5. The method for intuitively displaying genetic differences between biological individuals through combined graphics and generating combined graphics according to claim 4, characterized in that: In S2.2, the selected SNP sites are sorted according to their actual positions on the chromosome, including the following steps: S2.

21. Collect all relevant SNP data and preprocess the SNP data; S2.

22. Put all SNP data on chromosome 1 together, put all SNP data on chromosome 2 together, and so on; S2.

23. For each chromosome, the SNP sites are sorted according to the physical position of the SNP data on the chromosome, and the SNP sites with smaller position values ​​are ranked first; S2.

24. Create an index system to quickly locate specific SNP sites. The index includes chromosome number and physical location. S2.

25. Store the sorted SNP data in a database.

6. The method for intuitively displaying genetic differences between biological individuals through combined graphics and generating combined graphics according to claim 1, characterized in that: In S3, creating a visualized two-dimensional graph according to the encoding rules includes the following steps: S3.

1. Create a blank two-dimensional grid m×n; S3.2, map each SNP site to a cell in the grid according to the order of chromosome position; S3.3, assigning a corresponding color to the SNP site in each cell according to the color coding rule defined in step S2.3; S3.4, fill each cell in the grid with the assigned color, identify the abnormal SNP sites in the two-dimensional image by the local outlier factor LOF algorithm, and mark the abnormal SNP sites with different colors; S3.

5. Use graphics editing software to generate the final two-dimensional graphics.

7. The method for intuitively displaying genetic differences between biological individuals through combined graphics and generating combined graphics according to claim 6, characterized in that: In S3.4, the abnormal SNP sites in the two-dimensional pattern are identified by the local outlier factor LOF algorithm, and the abnormal SNP sites are marked with different colors, including the following steps: S3.41, normalize the data of SNP sites; S3.42, use Euclidean distance to calculate the distance between each SNP site and all other SNP sites; S3.

43. For each SNP site, find its k nearest neighbors; S3.44, calculate the local accessibility density of each SNP site, and calculate the local density of each SNP site; S3.

45. For each SNP site, calculate its LOF value; S3.

46. Determine abnormal SNP sites based on LOF values, and use different colors to mark abnormal SNP sites with LOF values ​​higher than the threshold K.

8. The method for intuitively displaying genetic differences between biological individuals through combined graphics and generating combined graphics according to claim 6, characterized in that: In S3.45, for each SNP site, the LOF value is calculated as follows: Where p represents the SNP site; N u(p) represents the u SNP sites closest to p; lrd(p) represents the local reachability density of point p; LOF u(p) represents the local outlier factor of point p; o represents the point that is closer to point p; u represents the number of neighbors used to calculate the local outlier factor.

9. The method for intuitively displaying genetic differences between biological individuals through combined graphics and generating combined graphics according to claim 1, characterized in that: In S4, verifying whether the created two-dimensional graph can accurately reflect the genetic differences includes the following steps: S4.

1. Check the two-dimensional graph for missing values, erroneous entries, and other anomalies, and remove SNP sites that appear too low in the sample population; S4.

2. Confirm that each genotype is correctly assigned the corresponding color. Use a known SNP dataset as test input to check whether the sorting algorithm can correctly sort by chromosome number and physical position. S4.

3. Calculate the grid size according to the number of SNPs and chromosomes, check whether the calculation result is reasonable, adjust the resolution of the two-dimensional graph, and observe whether the change in grid size meets the display requirements; S4.

4. Verify that the color mapping of each SNP site is correct through the test program, and test the display effect of the two-dimensional graphics on different devices and platforms; S4.

5. Compare the 2D figures of different individuals to see whether the color patterns reasonably reflect the known genetic differences; S4.

6. Use the principal component analysis (PCA) method to evaluate the similarities and differences of color distribution in two-dimensional graphics; S4.

7. Regenerate the 2D graph using the new SNP dataset and compare it with the previous 2D graph to check the consistency of the results.

10. The method for intuitively displaying genetic differences between biological individuals through combined graphics and generating combined graphics according to claim 8, characterized in that: In S4.6, the principal component analysis PCA method is specifically: Where X represents the original genetic data matrix; μ represents the genotype mean vector of each SNP locus; σ represents the genotype standard deviation vector of each SNP locus; V k represents a matrix composed of the eigenvectors corresponding to the first k largest eigenvalues; Y represents the projection value of each individual in the principal component space; k represents the index vector.

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