Individual genome simulation data generation method based on graph compression

Abstract

The invention belongs to the field of bioinformatics, and particularly relates to an individual genome simulation data generation method based on graph compression. The invention aims to solve the problem that the accuracy is low when the existing individual genome simulation data is used for predicting the curative effects of drugs on different individuals. The invention provides an individual genome simulation data generation method based on graph compression. The method comprises the following steps: S1, acquiring a historical genome data set; s2, preprocessing graph genome data in the historical genome data set to obtain preprocessed graph genome data; s3, calculating the connectivity of nodes in the preprocessed graph genome data, and performing forward compression processing on the preprocessed graph genome data according to the connectivity of the nodes to obtain forward compressed graph genome data; and S4, generating individual genome simulation data according to the forward compressed graph genome data. The problem that the accuracy is low when existing individual genome simulation data is used for predicting the curative effects of drugs on different individuals is solved.

Description

Technical Field

[0001] The invention belongs to the field of bioinformatics, and in particular relates to a method for generating individual genome simulation data based on graph compression. Background Art

[0002] Individual genome simulation has important value in the field of bioinformatics, especially in drug development. Genome simulation can help researchers gain a deeper understanding of the drug's mechanism of action, thereby accelerating the development of new drugs.

[0003] By simulating individual genomes, we can predict the efficacy of drugs on different individuals and improve the efficiency and accuracy of drug development. In addition, since each person's genome is unique, research based on simulation data and in-depth mining of genetic information is expected to provide patients with more personalized medical solutions and achieve targeted disease prevention and treatment. Genome simulation is also of great significance in efficient mutation detection. In genetic research, it is crucial to detect gene mutations quickly and accurately, and simulation data can be used as an effective data set to help researchers discover new gene mutations and their functional effects. In addition, in the prediction of genetic risk of diseases, by studying the characteristics of simulation data, the risk of individuals suffering from genetic diseases can be assessed, thereby achieving early intervention and prevention. In summary, individual genome simulation plays an irreplaceable and important role in medical research and clinical practice. It can not only promote the development of new drugs, but also promote the development of personalized medicine and precision medicine, and provide strong data support for genetic research and disease prevention.

[0004] However, the existing simulation of individual genomes in previous tools has problems such as arbitrary simulation mechanisms, simple reference genomes, and mutations originating from custom scripts, which leads to the existence of simulation data. Since the types and lengths of artificially introduced mutations are randomly generated, the sites are randomly customized, and there is a lack of biological basis, resulting in low accuracy in predicting the efficacy of drugs on different individuals through simulating individual genomes. Summary of the invention

[0005] The purpose of the present invention is to solve the problem of low accuracy of existing individual genome simulation data when used to predict the efficacy of drugs on different individuals. A method for generating individual genome simulation data based on graph compression is provided, comprising:

[0006] S1: Access to historical genomic datasets;

[0007] The genome data in the historical genome data set is graph genome data;

[0008] The graph genome includes: a node set, an edge set, and a graph

[0009] The nodes represent sequence fragments in the genome

[0010] (k-mer, usually a DNA fragment of length k). These fragments can be of fixed length or can be adjusted according to specific mutation information. The division of nodes in the present invention is already divided in the historical data set;

[0011] The edge represents the connection relationship between nodes;

[0012] The graph represents the overall structure of nodes and edges

[0013] If two fragments are directly connected in the genome, an edge is added between them. The presence of an edge may reflect sequence connectivity, variation (such as insertions or deletions), or structural changes.

[0014] S2: preprocessing the graph genome data in the historical genome data set to obtain preprocessed graph genome data;

[0015] S3: Calculate the connectivity of the nodes in the preprocessed graph genome data, and perform forward compression processing on the preprocessed graph genome data according to the connectivity of the nodes to obtain forward compressed graph genome data;

[0016] S4: Generate individual genome simulation data based on the graph genome data after forward compression.

[0017] The beneficial effects of the present invention are:

[0018] The present invention proposes a method for generating individual genome simulation data based on graph compression. The method utilizes the high-quality data structure of the graph genome to perform graph compression and decompression transformations, and utilizes the interactive information between high- and low-level graphs to realize the output of a large amount of simulation data. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 A schematic flow chart of a method for generating individual genome simulation data based on graph compression according to the present invention. DETAILED DESCRIPTION

[0020] Specific implementation method 1: Combination Figure 1 - Figures illustrating the invention,

[0021] S1: Access to historical genomic datasets;

[0022] The genome data in the historical genome data set is graph genome data;

[0023] The sources of the historical genome dataset are:

[0024] https: / / s3-us-west-2.amazonaws.com / human-pangenomics / pangenomes / freeze / freeze1 / minig raph-cactus / hprc-v1.1-mc-grch38 / hprc-v1.1-mc-grch38.gfa.gz;

[0025] The graph genome refers to a method of representing and storing genome data in the form of a graph structure. Compared with traditional genome sequences, the graph genome not only contains the basic sequence information of the genome, but also can better represent the variation and diversity of the genome.

[0026] The following is an introduction to the background and concepts of graph genomes

[0027] Genomes are usually represented by linear sequences (i.e., DNA base pair sequences), and the genome of each species has a fixed "reference sequence", i.e., a representative genome. However, real genomes are often very diverse, with a variety of variations, such as single nucleotide polymorphisms (SNPs), insertions / deletions (Indels), structural variations (such as inversions, duplications), etc. Traditional linear reference genomes are difficult to fully capture these variations, especially complex genomic variations;

[0028] Graph genomics attempts to solve this problem through the structure of graphs. Graphs are used here to represent different versions of the genome and the mutation relationships between them, allowing for a more flexible and accurate description of the complexity of the genome.

[0029] The graph genome includes: a node set, an edge set, and a graph

[0030] The nodes represent sequence fragments in the genome

[0031] (k-mer, usually a DNA fragment of length k). These fragments can be of fixed length or can be adjusted according to specific mutation information. The division of nodes in the present invention is already divided in the historical data set;

[0032] The edge represents the connection relationship between nodes;

[0033] The graph represents the overall structure of nodes and edges

[0034] If two fragments are directly connected in the genome, an edge is added between them. The presence of an edge may reflect sequence connectivity, variation (such as insertions or deletions), or structural changes.

[0035] S2: preprocessing the graph genome data in the historical genome data set to obtain preprocessed graph genome data;

[0036] S3: Calculate the connectivity of the nodes in the preprocessed graph genome data, and perform forward compression processing on the preprocessed graph genome data according to the connectivity of the nodes to obtain forward compressed graph genome data;

[0037] S4: Generate individual genome simulation data based on the graph genome data after forward compression;

[0038] Specific implementation method 2: The difference between this implementation method and specific implementation method 1 is that:

[0039] The specific process of preprocessing the graph genome data in the historical genome data set in S2 to obtain the preprocessed graph genome data is as follows:

[0040] S2.1: Invert all nodes of the graph genome in the historical genome dataset to obtain inverted nodes, and add the inverted nodes to the original graph genome to obtain an expanded graph genome.

[0041] S2.2: Select a starting node in the expanded graph genome, use a breadth-first search method based on a queue data structure, search for nodes layer by layer in the expanded graph genome in order of levels, and finally obtain a reachable node set with the starting node as the root;

[0042] The starting node is fixed, is the starting point of the chromosome, has biological significance, and is well known to those skilled in the art. Simulation data of only one chromosome is obtained each time.

[0043] S2.3: According to the reachable node set, the node set that cannot be found is deleted from the expanded graph genome to obtain the deleted graph genome;

[0044] S2.4: Screening and eliminating the obtained deleted graph genome to obtain a pre-processed graph genome;

[0045] The other steps and parameters are the same as those in the first specific implementation.

[0046] Specific implementation method three: This implementation method is different from the specific implementation method one in that:

[0047] In S2.1, all nodes of the graph genome in the historical genome data set are inverted to obtain a set of inverted nodes, and the inverted set of nodes is added to the original graph genome to obtain an expanded graph genome, which is expressed as:

[0048] V'={v'|v'∈V,S'(v')=f(S(v))}, G'=(V∪V′,E∪E')

[0049] In the formula, v represents a node of the graph genome, v' represents a node after negation, V is the set of all nodes of the graph genome, V' represents the set of nodes after negation, E is the set of all edges of the graph genome, E' represents the set of edges composed of negated nodes; f is the negation operation function, S(v) is the state attribute of node v; G' is the expanded graph genome; S'(v') represents the state attribute of node v' after negation; ∪ represents the union;

[0050] The state attributes of the node v include: the number of the node (positive or negative); the base sequence represented by the node (forward or reverse complement) is well known to those skilled in the art;

[0051] In S2.2, a starting node is selected in the expanded graph genome, and a breadth-first search method based on a queue data structure is used to search nodes layer by layer in the expanded graph genome in a hierarchical order, and finally a reachable node set with the starting node as the root is obtained; the specific process is as follows:

[0052] S2.2.1: Select a starting node in the expanded graph genome;

[0053] S2.2.2: Set the level of the selected starting node to 0, search the expanded graph genome for nodes directly connected to the starting node, and obtain the first-level node set; the specific process is:

[0054] The neighboring nodes directly connected to the current node are the first-layer nodes, the unvisited nodes directly connected to the first-layer nodes are the second-layer nodes, and so on, all nodes are divided into k levels.

[0055] Prioritize visiting neighboring nodes directly connected to the current node (i.e., visit nodes layer by layer in hierarchical order), and then gradually expand to more distant nodes until all reachable nodes are visited, and obtain a reachable node set with the start node as the root; this is a processing process well known to those skilled in the art;

[0056] S2.2.3: Search the expanded graph genome for nodes directly connected to the upper-layer nodes, and use the new nodes obtained as the next-layer node set, and finally obtain a reachable node set with the start node as the root; the total number of layers is k, where k is a positive integer;

[0057] The k-th layer node set is obtained by searching the k-1th layer node set and the formula is expressed as:

[0058]

[0059] In the formula, Rk represents the set of nodes at the kth layer, R k-1 represents the set of nodes in the k-1th layer, R i represents the set of nodes in the i-th layer; w represents the nodes in the k-th layer, and u represents the nodes in the k-1-th layer; (u,w) represents the edge from node u to node w;

[0060] Note that the edge (u, w) here has a direction, the direction is from u to w, u is the forward connection of w, and w is the backward connection of u. The graph genome structure of the present invention is a directed graph structure;

[0061] In S2.3, based on the reachable node set, the node set that cannot be found is deleted from the expanded graph genome to obtain the deleted graph genome; this is expressed by the formula:

[0062]

[0063] In the formula, G" represents the deleted graph genome.

[0065] The other steps and parameters are the same as those in the first to second embodiments.

[0066] Specific implementation method 4: This implementation method is different from specific implementation methods 1 to 4 in that:

[0067] In S2.4, the obtained deleted graph genome is screened and eliminated to obtain a pre-processed graph genome; the specific process is as follows:

[0068] S2.4.1.: Set the termination node set and eliminate the iteration termination condition;

[0069] S2.4.2: Screen and remove leaf nodes in the deleted graph genome according to the termination node set and the iteration termination condition; obtain the preprocessed graph genome;

[0070] The other steps and parameters are the same as those in the first to third embodiments.

[0071] Specific implementation method 5: This implementation method is different from specific implementation methods 1 to 4 in that:

[0072] The specific process of setting the termination node set and iteration termination condition in S2.4.1 is as follows:

[0073] S2.4.1.1: Set the termination node set T in the deleted graph genome.

[0074] The termination node is also a node with biological significance. The termination node set only includes this termination node, which is the end point of the chromosome and is well known to those skilled in the art.

[0075] Each chromosome is an endpoint, and the endpoint set here is also for a single chromosome. The content of the present invention is all for a chromosome. The total number of chromosomes actually generated can be calculated as 22 autosomes + 2 sex chromosomes + 1 mitochondrial chromosome, a total of 25.

[0076] S2.4.1.2: Set the elimination iteration termination condition, expressed as:

[0077] or So that (v,u)∈E (h)

[0078] In S2.4.2, the leaf nodes in the deleted graph genome are screened and removed according to the termination node set and the iteration termination condition; the preprocessed graph genome is obtained; the specific process is:

[0079] S2.4.2.1: Set initial situation

[0080] G (0) =(V (0) ,E (0) )(V (0) =V”,E (0) =E”)

[0081] S2.4.2.2: Based on the terminal node set T, obtain the leaf node set L that needs to be removed in the hth round (h) ; h∈[0,f-1];

[0082] According to the leaf node set L that needs to be removed in the hth round (h) For the remaining node set V in round h (h) Perform elimination to obtain the remaining node set V in the h+1th round (h+1) and the edge set E consisting of the remaining nodes in the h+1th round (h+1) ,

[0083] When the remaining node set in round f meets the iteration termination condition, the iteration is stopped and the preprocessed graph genome is obtained, which is expressed as:

[0084] and

[0085] V (h+1) =V (h) \L (h)

[0086] E (h+1) =E (h) \{(u,v)|u∈V (h) ,v∈L (h)}

[0087] So that (v,u)∈E (f)}

[0088] In the formula, T represents the set of terminal nodes, V (h) represents the set of remaining nodes in round h, E (h) represents the edge set consisting of the remaining nodes in the hth round, \ represents the removal process; V (f) represents the set of remaining nodes in round f;

[0089] The other steps and parameters are the same as those in Specific Embodiments 1 to 4.

[0090] Specific implementation method 6: This implementation method is different from specific implementation methods 1 to 5 in that:

[0091] In S3, the connectivity of the nodes in the preprocessed graph genome data is calculated, and the preprocessed graph genome data is forward compressed according to the connectivity of the nodes to obtain the forward compressed graph genome data; the specific process is:

[0092] S3.1: Calculate the connectivity of all nodes in the preprocessed graph genome;

[0093] The connectivity of the node is the sum of the number of parent nodes and child nodes of the node;

[0094] The parent node of the node is the node to which the node is forward connected, and the child node of the node is the node to which the node is backward connected;

[0095] In an undirected graph, the degree is the number of edges connected to a node; in a directed graph, the degree can be further divided into in-degree (the number of parent nodes) and out-degree (the number of child nodes). The preprocessed graph genome in the present invention is a directed graph, so the connectivity of a node in the present invention is the sum of the number of parent nodes and child nodes of the node;

[0096] S3.2: The first-class sub-nodes in the preprocessed graph genome are determined according to the connectivity of all nodes, and all the first-class sub-nodes constitute a first-class sub-node set;

[0097] The first-type child node is a node with only one parent node. The parent node of the first-type child node is called a first-type parent node. All first-type parent nodes constitute a first-type parent node set.

[0098] S3.3: Use the first-class parent node to inherit all the connection information of the corresponding first-class child node; obtain the inherited first-class parent node,

[0099] The formula is:

[0100] if v c With unique v p

[0101] Where B represents the backward connection information; v c Represents the first type of child node, v p represents the first-class parent node, Indicates the first type of parent node after inheritance;

[0102] S3.4: Remove the first type of child nodes in the preprocessed graph genome to obtain a removed graph genome;

[0103] At the same time, the corresponding first-category parent node after inheritance creates a merged node set;

[0104] Among them, the first type of parent node after the i-th inheritance The created merge node set is represented as

[0105] The merged node set records the inheritance information of the first type of child nodes that are removed;

[0106] The existence of this set ensures that the merge relationship between nodes is traceable and maintains information consistency. Furthermore, when a child node contains the merge information of other nodes (i.e., a child node has a merge node set), these merge sets will also be merged into the parent node set. This process makes node merging transitive and completes closure isolation, so that the relationship between nodes is gradually compressed through multiple rounds of iterations until no node has a unique parent node, and finally a unified parent node set is formed. C(v c ) is v c The merged node set.

[0107] C′(v p )=C(v p )∪{v c}∪C(v c )

[0108] S3.5: Calculate the connectivity of all nodes in the removed graph genome.

[0109] According to the connectivity of all nodes, determine whether all nodes in the removed graph genome have no unique parent node.

[0110] If all the nodes in the removed graph genome do not have a unique parent node, the forward compressed graph genome data is obtained;

[0111] When any node in the removed graph genome has a unique parent node, return to S3.2;

[0112] It is well known to those skilled in the art to determine whether all nodes in the removed graph genome have no unique parent node based on the connectivity of all nodes. The connectivity of a node includes the number of parent nodes that the node has.

[0113] ; Other steps and parameters are the same as those in Specific Implementation Methods 1 to 5.

[0114] Specific implementation method 7: This implementation method is different from specific implementation methods 1 to 6 in that:

[0115] The specific process of generating individual genome simulation data according to the forward compressed graph genome data in S4 is as follows:

[0116] S4.1: annotate the merged child node set corresponding to each inherited first-category parent node in the forward compressed graph genome in the preprocessed graph genome; and obtain the annotated preprocessed graph genome;

[0117] Among them, the node corresponding to the inherited first-category parent node in the preprocessed graph genome is called the second-category parent node;

[0118] The node number of the second-class parent node is marked; for example, the first-class parent node inherited in the forward compressed graph genome The first parent node after inheritance The merge node set created is

[0119] The corresponding node in the preprocessed graph genome is v p , marked with v p ( )

[0120] S4.2: Based on the annotated preprocessed graph genome, construct a subgraph genome with the second type of parent node as the first node and n terminal nodes as the last nodes;

[0121] In the graph genome after forward compression, each inherited parent node represents a subgraph genome;

[0122] Among them, the first type of parent node after the i-th inheritance The subgraph genome represented by

[0123] A subgraph genome has a second-class parent node and n terminal nodes, n∈T; assuming that there are a total of m inherited parent nodes in the forward compressed graph genome, then the m parent nodes corresponding to the annotated preprocessed graph genome have m subgraphs; each subgraph corresponds to an inherited parent node;

[0124] S4.3: Select the first inherited parent node corresponding to the start node in the graph genome after forward compression And the first parent node after inheritance corresponding to the terminal node According to the first parent node after inheritance And the first parent node after inheritance Perform a deep search on the graph genome after forward compression to obtain the individual genome compression path;

[0125] The start node and the end node are the starting point and the end point of the chromosome, which have biological significance and are well known to those skilled in the art. The purpose of this step is to select the individual genome compression path from the start point to the end point of the chromosome in the forward compressed graph genome;

[0126] Because the number of nodes in the forward-compressed graph genome is smaller, given the starting point and end point, the space complexity of finding a path in the forward-compressed graph genome is smaller than that of finding a path in the uncompressed graph genome, and the time and computing resources required are less.

[0127] S4.4: decompress the individual compressed genome path obtained in S4.3 to obtain the individual genome path; perform base processing on the individual genome path to obtain the individual genome structure data in the standard FASTA file format as the individual genome simulation data;

[0128] The other steps and parameters are the same as those in Specific Embodiments 1 to 6.

[0129] Specific implementation eight: This implementation differs from specific implementations one to seven in that:

[0130] In S4.3, the first type of inherited parent node corresponding to the start node is selected in the graph genome after forward compression. And the first parent node after inheritance corresponding to the terminal node According to the first parent node after inheritance And the first parent node after inheritance Perform a deep search on the graph genome after forward compression to obtain the individual genome compression path;

[0131] The specific process is:

[0132] S4.3.1: Use bitwise storage nodes to define the access status of each inherited first-class parent node in the forward compressed graph genome.

[0133] When the bit operation storage node of the node is 1, it means that the node has been visited, and when the bit operation storage node of the node is 0, it means that the node has not been visited; this is a processing process known to those skilled in the art.

[0134] S4.3.2: Select the first inherited parent node of the starting node in the graph genome after forward compression

[0135] Select the first parent node after inheritance corresponding to the terminal node in the graph genome after forward compression

[0136] According to the access status of each inherited first-category parent node in the forward-compressed graph genome, a deep search is performed in the forward-compressed graph genome to obtain an individual genome compression path;

[0137] The individual genome compression path includes m inherited first-category parent nodes, where m is a positive integer;

[0138] The starting point of the individual genome compression path is represented as As the starting point, the end point of the individual genome compression path is expressed as

[0139] The specific process is:

[0140] That is, each node has an attribute value. If it has not been visited, it is recorded as 0. If it has been visited once, it is recorded as 1. If it is visited again, then 1 is carried forward to 0, and the access status of all nodes that are 1 will also return to 0. The principle of starting the search again when encountering a duplicate node during the search process is to finally find a arrive Individual genome compression pathways;

[0141] The other steps and parameters are the same as those in the first to seventh embodiments.

[0142] Specific implementation method 9: This implementation method is different from specific implementation methods 1 to 8 in that:

[0143] In S4.4, the individual compressed genome path obtained in S4.3 is decompressed to obtain the individual genome path; the individual genome path is subjected to base processing to obtain the individual genome structure data in a standard FASTA file format as the individual genome simulation data. The specific process is as follows:

[0144] S4.4.1: convert the m inherited first-class parent nodes in the individual compressed genome path obtained in S4.3 into m individual genome subpaths;

[0145] Among them, the first type of parent node after the i-th inheritance The corresponding individual genome subpathway is denoted as Pi;

[0146] S4.4.2: merge the m individual genome subpaths to obtain the individual genome path;

[0147] S4.4.3: Perform base processing on the individual genome path to obtain the individual genome structure data in the standard FASTA file format as the individual genome simulation data.

[0148] The individual genome path is the path from the start node to the end node in the preprocessed graph genome: through the directed path in the graph genome, a continuous node path is extracted from the start node to the end node.

[0149] The base conversion process refers to integrating the base information of each node in these paths into a standard DNA sequence.

[0150] Finally, the processed DNA sequence is saved in FASTA format; which is well known to those skilled in the art.

[0151] The other steps and parameters are the same as those in Specific Embodiments 1 to 8.

[0152] Specific implementation method 10: This implementation method is different from specific implementation methods 1 to 9 in that:

[0153] The specific process of converting the j inherited first-class parent nodes in the individual compressed genome path into the j-th individual genome sub-path in S4.4.1 is as follows:

[0154] S4.4.1.1: Select the first parent node after the jth inheritance and the first parent node after the j+1th inheritance

[0155] S4.4.1.2: Based on the first parent node after j+1 inheritance The corresponding second-category parent node v in the annotated preprocessed graph genome pj+1 , determine the end point of the jth individual genome subpath Pj;

[0156] S4.4.1.3: Obtain the jth individual genome subpath Pj according to the end point of the jth individual genome subpath Pj;

[0157] The first parent node after j+1 inheritance in S4.4.1.2 The corresponding second-category parent node v in the annotated preprocessed graph genome pj+1 , determine the end point of the j-th individual genome subpath Pj; the specific process is:

[0158] We call the first-class parent node after inheritance high-dimensional nodes, and the second-class parent node low-dimensional nodes, because a high-dimensional node may be connected to several high-dimensional nodes in the compressed graph, and the subgraph inside the high-dimensional node may be connected to different high-dimensional graphs through different terminal nodes in the subgraph. Therefore, the terminal node of the subgraph represented by the high-dimensional node must be determined by a certain high-dimensional node behind the high-dimensional node. For example, the first-class parent nodes after inheritance of two connections and pass Sure Subgraph representing the genome

[0159] Selected subgraph genome The terminal node of is the end point of the j-th individual genome subpath Pj.

[0160] In S4.4.1.3, the j-th individual genome subpath Pj is obtained according to the end point of the j-th individual genome subpath Pj; the specific process is:

[0161] Subgraph Genome Directly perform unrestricted forward random search to ensure that the search efficiency is not reduced by additional conditions. The output low-dimensional subgraph path end point must converge to the subgraph start node, which is the representative node in the high-level graph.

[0162] That is, we know the end point and starting point of the individual genome subpath, and through the search algorithm we can get a reverse individual genome subpath, and then invert the node path of the reverse search to get the jth individual genome subpath Pj. This path inversion operation is based on the reversibility of graph path search to ensure the logical consistency of the low-dimensional path.

[0163] By analogy, all individual genome sub-paths P1 to Pm are finally obtained, and then all the individual genome sub-paths are merged to obtain the individual genome path.

[0164] The other steps and parameters are the same as those in Specific Implementations 1 to 9.

[0165] The above only describes the preferred embodiments of the present invention. It should be understood that the present invention is not limited to the above specific implementation methods. Although the present invention has been disclosed as above in the preferred embodiments, it is not used to limit the present invention. Any technician familiar with the profession can make some changes or modify the technical contents disclosed above into equivalent embodiments with equivalent changes without departing from the scope of the technical solution of the present invention. However, any simple modification, equivalent replacement and improvement made to the above embodiments without departing from the content of the technical solution of the present invention, based on the technical essence of the present invention, within the spirit and principles of the present invention, still fall within the protection scope of the technical solution of the present invention.

Claims

1. A method for generating individual genome simulation data based on graph compression, characterized in that: include: S1: Access to historical genomic datasets; The genome data in the historical genome data set is graph genome data; The graph genome includes: a node set, an edge set and a graph; The nodes represent sequence fragments in the genome; The edge represents the connection relationship between nodes; The graph represents an overall structure consisting of nodes and edges; S2: preprocessing the graph genome data in the historical genome data set to obtain preprocessed graph genome data; S3: Calculate the connectivity of the nodes in the preprocessed graph genome data, and perform forward compression processing on the preprocessed graph genome data according to the connectivity of the nodes to obtain forward compressed graph genome data; S4: Generate individual genome simulation data based on the graph genome data after forward compression.

2. The method for generating individual genome simulation data based on graph compression according to claim 1, characterized in that: The specific process of preprocessing the graph genome data in the historical genome data set in S2 to obtain the preprocessed graph genome data is as follows: S2.1: Invert all nodes of the graph genome in the historical genome dataset to obtain inverted nodes, and add the inverted nodes to the original graph genome to obtain an expanded graph genome. S2.2: Select a starting node in the expanded graph genome, use a breadth-first search method based on a queue data structure, search for nodes layer by layer in the expanded graph genome in order of levels, and finally obtain a reachable node set with the starting node as the root; S2.3: According to the reachable node set, the node set that cannot be found is deleted from the expanded graph genome to obtain the deleted graph genome; S2.4: Screen and eliminate the deleted graph genome to obtain a preprocessed graph genome.

3. The method for generating individual genome simulation data based on graph compression according to claim 2, characterized in that: In S2.1, all nodes of the graph genome in the historical genome data set are inverted to obtain a set of inverted nodes, and the inverted set of nodes is added to the original graph genome to obtain an expanded graph genome, which is expressed as: V'={v'|v'∈V,S'(v')=f(S(v))}, G'=(V∪V',E∪E') In the formula, v represents a node of the graph genome, v′ represents a node after negation, V is the set of all nodes of the graph genome, V′ represents the set of nodes after negation, E is the set of all edges of the graph genome, and E′ represents the set of edges composed of negated nodes; f is the negation operation function, S(v) is the state attribute of node v; G′ is the expanded graph genome; S′(v') represents the state attribute of node v′ after negation; ∪ represents the union; In S2.2, a starting node is selected in the expanded graph genome, and a breadth-first search method based on a queue data structure is used to search nodes layer by layer in the expanded graph genome in a hierarchical order, and finally a reachable node set with the starting node as the root is obtained; the specific process is as follows: S2.2.1: Select a starting node in the expanded graph genome; S2.2.2: Set the level of the selected starting node to 0, search the expanded graph genome for nodes directly connected to the starting node, and obtain the first-level node set; S2.2.3: Search the expanded graph genome for nodes directly connected to the upper-layer nodes, and use the new nodes obtained as the next-layer node set, and finally obtain a reachable node set with the start node as the root; the total number of layers is k, where k is a positive integer; Among them, the k-th layer node set is obtained by searching the k-1th layer node set and is expressed by the formula: In the formula, R k represents the set of nodes at the kth layer, R k-1 represents the set of nodes in the k-1th layer, R i represents the set of nodes in the i-th layer; w represents the nodes in the k-th layer, and u represents the nodes in the k-1-th layer; (u,w) represents the edge from node u to node w; In S2.3, based on the reachable node set, the node set that cannot be found is deleted from the expanded graph genome to obtain the deleted graph genome; this is expressed by the formula: In the formula, G” represents the deleted graph genome.

4. The method for generating individual genome simulation data based on graph compression according to claim 3, characterized in that: In S2.4, the obtained deleted graph genome is screened and eliminated to obtain a pre-processed graph genome; the specific process is as follows: S2.4.1.: Set the termination node set and eliminate the iteration termination condition; S2.4.2: Screen and remove the leaf nodes in the deleted graph genome according to the termination node set and the iteration termination condition to obtain the preprocessed graph genome.

5. The method for generating individual genome simulation data based on graph compression according to claim 4, characterized in that: The specific process of setting the termination node set and iteration termination condition in S2.4.1 is as follows: S2.4.1.1: Set the termination node set T in the deleted graph genome. S2.4.1.2: Set the elimination iteration termination condition, expressed as: or So that (v,u)∈E (h) In S2.4.2, the leaf nodes in the deleted graph genome are screened and removed according to the termination node set and the iteration termination condition; the preprocessed graph genome is obtained; the specific process is: S2.4.2.1: Set initial situation G (0) =(V (0) ,E (0) )(V (0) =V”,E (0) "=E”) S2.4.2.2: Based on the terminal node set T, obtain the leaf node set L that needs to be removed in the hth round (h) ; h∈[0,f-1]; According to the leaf node set L that needs to be removed in the hth round (h) For the remaining node set V in round h (h) Perform elimination processing to obtain the remaining node set V in the h+1th round (h+1) and the edge set E consisting of the remaining nodes in the h+1th round (h+1) , When the remaining node set in round f meets the iteration termination condition, the iteration is stopped and the preprocessed graph genome is obtained, which is expressed as: V (h+1) =V (h) \L (h) E (h+1) =E (h) \{(u,v)∣u∈V (h) ,v∈L (h) } In the formula, T represents the set of terminal nodes, V (h) represents the set of remaining nodes in round h, E (h) represents the edge set consisting of the remaining nodes in the hth round, \ represents the removal process; V (f) Represents the set of remaining nodes in round f.

6. The method for generating individual genome simulation data based on graph compression according to claim 5, characterized in that: In S3, the connectivity of the nodes in the preprocessed graph genome data is calculated, and the preprocessed graph genome data is forward compressed according to the connectivity of the nodes to obtain the forward compressed graph genome data; the specific process is: S3.1: Calculate the connectivity of all nodes in the preprocessed graph genome; The connectivity of the node is the sum of the number of parent nodes and child nodes of the node; The parent node of the node is the node to which the node is forward connected, and the child node of the node is the node to which the node is backward connected; S3.2: The first-class sub-nodes in the preprocessed graph genome are determined according to the connectivity of all nodes, and all the first-class sub-nodes constitute a first-class sub-node set; The first-type child node is a node with only one parent node. The parent node of the first-type child node is called a first-type parent node, and all first-type parent nodes constitute a first-type parent node set; S3.3: Use the first-class parent node to inherit the connection information of the corresponding first-class child node; obtain the inherited first-class parent node, which is expressed by the formula: if v c With unique v p Where B represents the backward connection information; v c Represents the first type of child node, v p represents the first-class parent node, Indicates the first type of parent node after inheritance; S3.4: Remove the first-class child nodes in the preprocessed graph genome, and create a merged node set for the corresponding inherited first-class parent nodes; Get the graph genome after elimination; Among them, the first type of parent node after the i-th inheritance The created merge node set is represented as The merged node set records the inheritance information of the first type of child nodes that are removed; S3.5: Calculate the connectivity of all nodes in the removed graph genome, and determine whether all nodes in the removed graph genome have no unique parent node based on the connectivity of all nodes. If all the nodes in the removed graph genome do not have a unique parent node, the forward compressed graph genome data is obtained; When any node in the removed graph genome has a unique parent node, return to S3.

2.

7. The method for generating individual genome simulation data based on graph compression according to claim 6, characterized in that: The specific process of generating individual genome simulation data according to the forward compressed graph genome data in S4 is as follows: S4.1: Annotate the merged child node set corresponding to each inherited first-category parent node in the forward compressed graph genome in the preprocessed graph genome; Get the annotated preprocessed graph genome; Among them, the node corresponding to the inherited first-category parent node in the preprocessed graph genome is called the second-category parent node; S4.2: Based on the annotated preprocessed graph genome, construct a subgraph genome with the second type parent node as the first node and n terminal nodes as the last nodes; n is a positive integer; In the graph genome after forward compression, each inherited parent node represents a subgraph genome; Among them, the first type of parent node after the i-th inheritance The subgraph genome represented by S4.3: Select the first inherited parent node corresponding to the start node in the graph genome after forward compression And the first parent node after inheritance corresponding to the terminal node According to the first parent node after inheritance And the first parent node after inheritance Perform a deep search on the graph genome after forward compression to obtain the individual genome compression path; S4.4: Decompress the individual compressed genome path obtained in S4.3 to obtain the individual genome path; perform base conversion on the individual genome path to obtain the individual genome structure data in the standard FASTA file format as the individual genome simulation data.

8. The method for generating individual genome simulation data based on graph compression according to claim 7, characterized in that: In S4.3, the first type of inherited parent node corresponding to the start node is selected in the graph genome after forward compression. And the first parent node after inheritance corresponding to the terminal node According to the first parent node after inheritance And the first parent node after inheritance Perform a deep search on the graph genome after forward compression to obtain the individual genome compression path; The specific process is: S4.3.1: Use bitwise storage nodes to define the access status of each inherited first-class parent node in the forward compressed graph genome. S4.3.2: Select the first parent node of the inherited type corresponding to the starting node in the graph genome after forward compression Select the first parent node after inheritance corresponding to the terminal node in the graph genome after forward compression According to the access status of each inherited first-category parent node in the forward-compressed graph genome, a deep search is performed in the forward-compressed graph genome to obtain an individual genome compression path; The individual genome compression path includes m inherited first-category parent nodes, where m is a positive integer; The starting point of the individual genome compression path is represented as As the starting point, the end point of the individual genome compression path is expressed as 9. The method for generating individual genome simulation data based on graph compression according to claim 8, characterized in that: In S4.4, the individual compressed genome path obtained in S4.3 is decompressed to obtain the individual genome path; the individual genome path is subjected to base processing to obtain the individual genome structure data in a standard FASTA file format as the individual genome simulation data. The specific process is as follows: S4.4.1: Convert the m inherited first-class parent nodes in the individual compressed genome path obtained in S4.3 into m individual genome subpaths; Among them, the first type of parent node after the i-th inheritance The corresponding individual genome subpathway is denoted as Pi; S4.4.2: merge the m individual genome subpaths to obtain the individual genome path; S4.4.3: Perform base processing on the individual genome path to obtain the individual genome structure data in the standard FASTA file format as the individual genome simulation data.

10. The method for generating individual genome simulation data based on graph compression according to claim 9, characterized in that: In S4.4.1, the j inherited first-class parent nodes in the individual compressed genome path are converted into the j-th individual genome sub-path; the specific process is: S4.4.1.1: Select the first parent node after the jth inheritance and the first parent node after the j+1th inheritance S4.4.1.2: Based on the first parent node after j+1 inheritance The corresponding second-category parent node v in the annotated preprocessed graph genome pj+1 , determine the end point of the jth individual genome subpath Pj; S4.4.1.3: Obtain the jth individual genome subpath Pj according to the end point of the jth individual genome subpath Pj.

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