A method for visually displaying genetic differences between biological individuals by combining graphs and a method for generating the combined graphs

Abstract

The present application relates to the technical field of graphical generation of genetic differences, in particular to a method for visually displaying genetic differences between biological individuals by combining graphics and a method for generating combined graphics, comprising the following steps: S1, selecting single nucleotide polymorphism (SNP) sites from DNA samples and using sequencing methods to obtain SNP information of individuals; S2, designing coding rules and assigning different colors to genotypes of single nucleotide polymorphism (SNP); S3, creating a visual two-dimensional graph according to the coding rules; S4, verifying whether the created two-dimensional graph can accurately reflect genetic differences. The present application designs to assign specific colors to different genotypes and integrate these information into two-dimensional graphs, so that complex genetic information becomes visualized. This not only helps researchers to quickly identify genetic differences between individuals, but also facilitates non-professionals to 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 graphic generation method. Background Art

[0002] With the development of genomics, we have been able to obtain a vast amount of genetic information, but this information is often complex and difficult to intuitively understand. For example, DNA (deoxyribonucleic acid) is a biological macromolecule present in the cells of all known organisms and serves as the carrier of genetic information. The DNA molecule consists of two helical chains, with nucleotides linked together in a base-pairing pattern. 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 an organism. Single nucleotide polymorphisms (SNPs) are DNA sequence variations caused by variations in a single nucleotide at the genomic level. SNPs are the most common type of heritable variation in humans, accounting for over 90% of all known polymorphisms. These variations can be transitions (purine-to-purine or pyrimidine-to-pyrimidine substitutions) or transversions (purine-to-pyrimidine substitutions). SNPs can occur within gene sequences or in non-coding sequences outside of 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 and can encode different protein or RNA sequences, thereby affecting the phenotype or physiological characteristics of an individual. The genetic sequence of each organism is unique, just like each person'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 incomprehensible genetic 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. Traditional statistical charts, although useful, have limited expressive power in terms of genetic diversity, especially when displaying the genetic structure of large-scale populations. In summary, a method for intuitively displaying genetic differences between biological individuals and generating combined graphics is provided through combined graphics. 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 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 SNP genotypes;

[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 present technical solution, in S1, a high-throughput sequencing method is used to obtain individual SNP information, comprising the following steps:

[0010] S1.1. Collect biological samples from individuals, extract DNA from the samples using an organic extraction method, and test the quality and concentration of the DNA using an ultraviolet spectrometer;

[0011] 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 blunt-ended, and add adapter sequences to the DNA fragments;

[0012] S1.3. Amplify the DNA fragments with adapter sequences using polymerase chain reaction, and remove nonspecific amplification products by gel electrophoresis to obtain a purified library;

[0013] S1.4. Determine the library concentration using quantitative PCR and immobilize the library DNA onto a sequencing chip to form dense DNA clusters for sequencing.

[0014] S1.5. During the sequencing process, each nucleotide is added 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 associated quality score information, remove low-quality sequence reads, and use the Smith-Waterman local alignment algorithm to align high-quality sequence reads to the reference genome. By analyzing the alignment results, detect the differences between the individual and the reference genome, namely SNP sites.

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

[0017]

[0018] Where H[i][j] represents the best alignment 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 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 present 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. Select SNP sites with higher mutation frequencies from 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 present 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, sort the SNP sites according to their physical location on the chromosome, with the SNP sites with smaller position values ​​at the front.

[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 present technical solution, in S3, creating a visualized two-dimensional graph according to the encoding rules 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 location.

[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 graph using 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 present technical solution, in S3.4, the abnormal SNP sites in the two-dimensional graph are identified by the local outlier factor (LOF) algorithm and marked with different colors, including the following steps:

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

[0037] S3.42. Calculate the distance between each SNP site and all other SNP sites using Euclidean distance;

[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 the LOF value, and use different colors to mark abnormal SNP sites with LOF values ​​higher than the threshold K.

[0042] As a further improvement of this 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 points that are closer to point p; u represents the number of neighbors used to calculate the local outlier factor.

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

[0046] S4.1. Check the two-dimensional graph for missing values, incorrect entries, and other anomalies, and remove SNPs that appear too infrequently 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 based on the number of SNPs and chromosomes, check whether the calculation result is reasonable, adjust the resolution of the 2D graph, and observe whether the change in grid size meets the display requirements;

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

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

[0051] S4.6. Use principal component analysis (PCA) to evaluate the similarities and differences in 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 this 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 site; σ represents the genotype standard deviation vector of each SNP site; V k represents a matrix consisting 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 for visually displaying genetic differences between individuals through combined graphics and generating combined graphics assigns specific colors to different genotypes and integrates this information into two-dimensional graphics, visualizing complex genetic information. This not only helps researchers quickly identify genetic differences between individuals but also facilitates the understanding of basic genetic concepts for non-specialists. The color coding approach allows even those without a deep genetics background to clearly see the similarities and differences in genetic characteristics between individuals, enhancing the interpretability and dissemination of genetic information.

[0058] 2. This method for visually displaying genetic differences between individuals and generating combined graphics also includes a verification process for 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 facilitates the early detection of abnormal patterns in genetic variation, such as possible mutations or rare genetic diseases. This method improves the efficiency of data analysis and 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 generated by the present invention. DETAILED DESCRIPTION

[0061] The following will provide a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall 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 SNP information of an individual, including the following steps:

[0065] S1.1. Collect a biological sample from an individual, usually blood, saliva, or tissue sample, extract DNA from the sample using an organic extraction method, and test the quality and concentration of the DNA using an ultraviolet spectrometer;

[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 blunt-ended, 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 more easily connect with other DNA fragments or vectors (such as plasmids, phages, etc.);

[0069] S1.3. Amplifying DNA fragments with adapter sequences using polymerase chain reaction (PCR) and removing nonspecific amplification products by gel electrophoresis to obtain a purified library, wherein a library represents a set of pre-processed and prepared DNA fragments;

[0070] When amplifying DNA fragments with adapter sequences, the presence of adapters must be taken into account in the design of PCR primers. Typically, primers are designed to perfectly match the adapter sequences, ensuring that the PCR reaction specifically targets the DNA fragments with adapters. This design ensures that only DNA fragments with successfully attached adapters are amplified, thereby excluding DNA fragments without adapters. Gel electrophoresis utilizes the charge carried by biomacromolecules, which migrate toward electrodes of opposite charge under the action of an electric field. Molecules of different sizes experience different resistances and migration speeds, and can therefore be separated.

[0071] S1.4. Determine the library concentration using quantitative PCR and immobilize the library DNA onto a 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 and can therefore be used to quantify the amount of initial DNA template;

[0073] S1.5. During the sequencing process, each nucleotide is added 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 and their associated quality score information generated during sequencing, remove low-quality sequence reads, and use the Smith-Waterman local alignment algorithm to align high-quality sequence reads to the reference genome. By analyzing the alignment results, detect differences between the individual and the reference genome, namely, 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 global alignment results. The Smith-Waterman algorithm allows insertion and deletion operations and handles these operations through a penalty mechanism, making the algorithm adaptable to different types of mutations, such as single nucleotide polymorphisms (SNPs) and insertions and deletions (InDels). The Smith-Waterman local alignment algorithm is specifically as follows:

[0076]

[0077] Where H[i][j] represents the best alignment 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 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 SNP genotypes;

[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 high mutation frequencies from the high-throughput sequencing results obtained in step S1. These SNP sites should cover multiple chromosomes to fully reflect the genetic characteristics of the individual;

[0081] S2.2. Sort the selected SNPs according to their actual location on the chromosome. This 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 last. The location information of each SNP is 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, sort the SNP sites according to their physical location on the chromosome, with the SNP sites with smaller position values ​​at the front.

[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 position index;

[0088] Creating an index structure includes defining the 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 entry 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 entry 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 the 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 visual 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. can be flexibly adjusted according to actual conditions:

[0095]

[0096]

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

[0098] S3.1. Create a blank two-dimensional grid m×n. This grid is used to store all SNP loci. The number of rows and columns in 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 order of chromosome location. The number of cells depends on the total number of SNP sites to be displayed. If more or fewer SNP sites need to be displayed, the number of cells can be changed by adjusting the resolution of the 2D 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 display 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 in density from their surrounding neighbors. This 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 is possible to very intuitively show which points are considered abnormal. This method makes data visualization clearer and easier to understand, helping users 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. Calculate the distance between each SNP site and all other SNP sites using Euclidean distance;

[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 as 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 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 outlier 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. Abnormal SNP sites with LOF values ​​higher than a threshold K are marked with different colors.

[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 the 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 dominant, 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 horizontally and vertically, a total of 1225 small grids, to represent the differences in these 1225 alleles, we can intuitively demonstrate the genetic differences between biological individuals in these 1225 alleles by using the three different colors of each small grid: white, gray, and black.

[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 differences includes the following steps:

[0116] S4.1. Check the two-dimensional graph for missing values, incorrect entries, and other anomalies, and remove SNPs that appear too rarely 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 a known SNP dataset 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 based on the number of SNPs and chromosomes, check whether the calculation result is reasonable, adjust the resolution of the 2D graph, and observe whether the change in grid size meets the display requirements;

[0119] S4.4. Verify the correct color mapping for each SNP locus through a test program and test the display of 2D graphics on different devices and platforms to ensure color consistency;

[0120] The specific test procedures are as follows:

[0121]

[0122]

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

[0124] S4.6. Use principal component analysis (PCA) to evaluate the similarities and differences in 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 large amount 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 in the data by retaining the main components of the data (that is, the directions with large variance). In color distribution analysis, this helps to reduce unnecessary variation caused by measurement errors or other factors, thereby making the color distribution pattern clearer. The specific principal component analysis PCA method is:

[0126]

[0127] Where X represents the original genetic data matrix, which contains the SNP genotype information of each individual; μ represents the genotype mean vector of each SNP site; σ represents the genotype standard deviation vector of each SNP site; 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;

[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 basic principles, main features, and advantages of the present invention are shown and described above. It should be understood by those skilled in the art that the present invention is not limited to the above-described embodiments. The above-described embodiments and descriptions are merely preferred examples of the present invention and are not intended to limit the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention, and such changes and modifications fall within the scope of the invention claimed.

Claims

1. A method for intuitively displaying genetic differences between biological individuals through combined graphics and a combined graphics generation method, characterized by: 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 SNP genotypes; In S2, coding rules are designed and different colors are assigned to the genotypes of single nucleotide polymorphisms (SNPs), including the following steps: S2.

1. Select SNP sites with higher mutation frequencies from 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; 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; In S3, creating a visual two-dimensional graph according to the encoding rules includes the following steps: S3.

1. Create a blank 2D grid ; S3.

2. Map each SNP site to a cell in the grid according to the order of chromosome location. 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 graph using 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; In S3.4, the abnormal SNP sites in the two-dimensional graph are identified by the local outlier factor (LOF) algorithm and marked with different colors, including the following steps: S3.

41. Normalize the data of SNP sites; S3.

42. Calculate the distance between each SNP site and all other SNP sites using Euclidean distance; 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 the LOF value, and use different colors to mark abnormal SNP sites with LOF values ​​higher than the threshold K.

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 using an organic extraction method, and test the quality and concentration of the DNA using an ultraviolet spectrometer; 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 blunt-ended, and add adapter sequences to the DNA fragments; S1.

3. Amplify the DNA fragments with adapter sequences using polymerase chain reaction, and remove nonspecific amplification products by gel electrophoresis to obtain a purified library; S1.

4. Determine the library concentration using quantitative PCR and immobilize the library DNA onto a sequencing chip to form dense DNA clusters for sequencing. S1.

5. During the sequencing process, each nucleotide is added 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 associated quality score information, remove low-quality sequence reads, and use the Smith-Waterman local alignment algorithm to align high-quality sequence reads to the reference genome. By analyzing the alignment results, detect the differences between the individual and the reference genome, namely 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: ； in, The first SNP sequence of the first biological individual The SNP site is related to the SNP sequence of the second individual. The best alignment score of each SNP site; represents a matrix; Indicates that the first biological individual is in Genotypes at SNP sites; Indicates that the second individual Genotypes at SNP sites; Indicates penalty points; Indicates the evaluation of the first biological individual SNP sequence Elements The first SNP sequence in the second biological individual Elements The matching score between Represents the position index in the SNP sequence of the first biological individual; 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 S2.2, 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, sort the SNP sites according to their physical location on the chromosome, with the SNP sites with smaller position values ​​at the front. 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.

5. 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 S3.45, for each SNP site, the LOF value is calculated as follows: ； in, Indicates SNP site; Represents closest SNP sites; Indicates a point The local reachable density of Indicates a point The local anomaly factor of Represents point Points that are closer; Indicates the number of neighbors used when calculating the local outlier factor.

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 S4, verifying whether the created two-dimensional graph can accurately reflect genetic differences includes the following steps: S4.

1. Check the two-dimensional graph for missing values, incorrect entries, and other anomalies, and remove SNPs that appear too infrequently 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 based on the number of SNPs and chromosomes, check whether the calculation result is reasonable, adjust the resolution of the 2D graph, and observe whether the change in grid size meets the display requirements; S4.

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

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

6. Use principal component analysis (PCA) to evaluate the similarities and differences in 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.

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 S4.6, the principal component analysis PCA method is specifically as follows: ； in, represents the original genetic data matrix; Represents the genotype mean vector of each SNP site; Represents the genotype standard deviation vector of each SNP site; Indicates the previous The matrix composed of the eigenvectors corresponding to the largest eigenvalues; Represents the projection value of each individual in the principal component space; Represents an index vector.

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