Construction method of pathogenic microorganism genome database

Abstract

The invention relates to the technical field of construction of genome databases, in particular to a construction method of a pathogenic microorganism genome database. The method comprises the following steps: carrying out pathogen characteristic point location analysis on a pathogen microorganism genome sequence to obtain pathogen characteristic point location data; carrying out physiological and biochemical characteristic analysis on a pathogenic microorganism genome sequence according to the pathogenic characteristic point data, carrying out genome evolution relation simulation, and then carrying out prepositive relation linkage identification to obtain prepositive relation linkage data; performing relation nested structure analysis on the prepositive relation linkage data, and performing relation coding index construction to obtain a linkage relation coding index; and carrying out dynamic retrieval condition matching on the linkage relationship coding index, and carrying out genome database construction to obtain the pathogenic microorganism genome database. The construction technology of the genome database is improved, so that the construction technology of the genome database is more perfect.

Description

Technical Field

[0001] The present invention relates to the technical field of genome database construction, and in particular to a method for constructing a pathogenic microorganism genome database. Background Art

[0002] The threat of pathogenic microorganisms to human health has attracted more and more attention. The construction of pathogenic microorganism genome databases has become an important foundation for studying the pathogenic mechanism of pathogenic microorganisms, drug target discovery, disease prevention and control, and other fields. Pathogenic microorganisms include various pathogens such as bacteria, fungi, viruses, and parasites, which can cause a wide range of infectious diseases or chronic diseases, such as tuberculosis, AIDS, malaria, and influenza. With the continuous advancement of high-throughput gene sequencing technology, scientists can obtain the genome information of pathogenic microorganisms more accurately, and through in-depth analysis of this information, reveal the genetic diversity, mutation patterns, transmission paths, and interactions between pathogenic microorganisms and hosts. At present, major scientific research institutions and database platforms around the world have launched genome information collection and construction projects for pathogenic microorganisms, such as the International Human Genome Project and the Global Pathogen Genome Project. These databases provide important data support for global disease monitoring, diagnosis, and vaccine development. For example, by organizing and analyzing pathogen genome information, researchers can discover drug resistance genes of pathogenic microorganisms, identify new pathogenic factors, and even predict the evolutionary trend of pathogenic microorganisms, providing strong support for clinical treatment. Especially in the context of the increasingly serious problem of drug resistance, the application value of pathogenic microorganism genome databases has become increasingly prominent. However, the traditional method of constructing a pathogenic microorganism genome database is not accurate in analyzing the evolutionary relationship of pathogenic microorganism genomes, resulting in a problem of chaotic database construction. Summary of the invention

[0003] Based on this, it is necessary to provide a method for constructing a pathogenic microorganism genome database to solve at least one of the above technical problems.

[0004] To achieve the above object, a method for constructing a pathogenic microorganism genome database comprises the following steps:

[0005] Step S1: obtaining a pathogenic microorganism genome sequence; performing pathogen characteristic point analysis on the pathogenic microorganism genome sequence to obtain pathogen characteristic point data;

[0006] Step S2: Analyze the physiological and biochemical characteristics of the pathogenic microorganism genome sequence according to the pathogen characteristic point data to obtain the pathogen genome physiological and biochemical characteristic data; simulate the genome evolution relationship according to the pathogen genome physiological and biochemical characteristic data to obtain the genome evolution relationship data; perform pre-relation linkage identification on the genome evolution relationship data to obtain pre-relation linkage data;

[0007] Step S3: performing a relational nested structure analysis on the pre-relation linkage data to obtain pre-relation linkage relation nested structure data; performing a relational coding index construction on the pre-relation linkage data according to the pre-relation linkage relation nested structure data to obtain a linkage relation coding index;

[0008] Step S4: Dynamically match the linkage relationship coding index with search conditions to obtain the linkage relationship dynamic search conditions; optimize the cache architecture according to the linkage relationship dynamic search conditions to obtain the linkage relationship cache optimization architecture; construct a genome database for the pathogenic microorganism genome sequence using the linkage relationship cache optimization architecture to obtain the pathogenic microorganism genome database.

[0009] The present invention first obtains the complete genome sequence of pathogenic microorganisms through high-throughput sequencing technology, which provides basic data for subsequent analysis. Then, the genome sequence is analyzed for pathogenic characteristic points to identify important characteristic points related to pathogenicity in the genome, such as pathogenic genes, virulence factors, etc. These characteristic point data will reveal the basic biological characteristics of pathogenic microorganisms, such as pathogenicity, immune escape mechanism, etc., and provide important basis for subsequent physiological and biochemical characteristic analysis and genome evolution relationship simulation. At this stage, the physiological and biochemical characteristics of the pathogenic microorganism genome are deeply analyzed based on the pathogenic characteristic point data, and information such as metabolic pathways and drug resistance genes are extracted. These physiological and biochemical characteristic data help scientists understand the biological functions of pathogenic microorganisms and their interactions with the host. Subsequently, the genome evolution model is used to perform evolutionary analysis on the genome data to reveal the evolutionary relationship, kinship and mutation characteristics between different microbial populations. Then, the pre-relationship linkage identification is carried out to explore the interdependence between different genes, characteristic points and biological functions, and provide dynamic evolutionary paths and key nodes for subsequent research. After nested structure analysis, the pre-relation linkage data can reveal the hierarchical and complex relationships between different feature points, genes and functions in the microbial genome. By deeply mining these relationships, a structured nested relationship data is formed to help researchers understand how various biological processes interact with each other and identify key linkage links. Then, based on these nested structure data, a relationship coding index is constructed. This coding index achieves efficient data access and query by marking and classifying different relationships, laying an organizational framework for the construction of the genome database. After matching dynamic retrieval conditions, the linkage relationship coding index can flexibly extract relevant data according to actual needs, thereby realizing an efficient retrieval mechanism. According to the matching results, the linkage relationship cache architecture is optimized, and the data query speed and resource utilization are improved through intelligent cache management. Finally, a complete pathogenic microorganism genome database is constructed using the optimized cache architecture. This database can not only store pathogenic genome data, but also support complex data analysis and query, providing strong support for the diagnosis, treatment and research of pathogenic microorganisms. Therefore, the present invention is an improvement on a traditional method for constructing a pathogenic microorganism genome database, which solves the problem that the traditional method for constructing a pathogenic microorganism genome database is inaccurate in analyzing the evolutionary relationship of the pathogenic microorganism genome, thereby causing confusion in the database construction, improves the accuracy of the analysis of the evolutionary relationship of the pathogenic microorganism genome, and improves the rationality of the database construction.

[0010] Preferably, step S1 comprises the following steps:

[0011] Step S11: obtaining the genome sequence of pathogenic microorganisms;

[0012] Step S12: filtering low-quality sequences of the pathogenic microorganism genome sequence to obtain a pathogenic microorganism genome filtered sequence;

[0013] Step S13: functionally classify and label the pathogenic microorganism genome filtered sequence to obtain the microorganism genome functional classification sequence;

[0014] Step S14: Analyze the pathogenic characteristic sites of the functional classification sequence of the microbial genome to obtain pathogenic characteristic site data.

[0015] The present invention obtains the complete genome sequence of pathogenic microorganisms through modern high-throughput genome sequencing technology (such as Illumina sequencing, PacBio sequencing, etc.). The genome sequence is the basis for subsequent research, covering the genetic information, gene expression and potential pathogenic mechanism of microorganisms. By sequencing the genome of pathogenic microorganisms, scientists can obtain the data of their whole genome, which provides an important basis for identifying the pathogenicity, drug resistance, tolerance and other characteristics of microorganisms, and is the starting point for disease prevention and treatment research. After obtaining the original genome sequence, there are usually some low-quality sequencing data, such as sequencing errors, sequence duplications or contaminated sequences. If these low-quality data are not filtered, they will affect the accuracy and reliability of subsequent analysis. Therefore, this step filters out low-quality sequences by applying quality control technology to ensure that the obtained genome sequence has high accuracy and integrity. The filtered genome data will provide a high-quality basis for subsequent analysis. Functional classification identification is performed on the filtered genome sequence to identify functional regions and genes related to pathogenicity in the genome. Through gene prediction and functional annotation, genes and functional elements related to cell metabolism, toxin production, drug resistance, immune escape, etc. are identified. This process is usually combined with public gene libraries (such as NCBI, KEGG, etc.) for comparison and annotation, with the aim of helping researchers understand the biological functions of pathogenic microorganisms, identify their potential pathogenic mechanisms, and provide important clues for subsequent feature site analysis and the formulation of disease prevention and control strategies. Based on the functional classification sequence of the microbial genome, further in-depth exploration of feature sites related to pathogenicity, such as specific pathogenic genes, virulence factors, drug resistance genes, and key genes that interact with the host. The analysis of these feature sites can reveal biological characteristics such as pathogenic mechanisms, virulence mechanisms, and adaptive evolution of pathogenic microorganisms. For example, certain feature sites are related to toxin production, immune escape, or drug resistance of microorganisms. Through the analysis of pathogenic feature sites, researchers can provide theoretical support for early diagnosis, drug target discovery, and vaccine design, and provide a scientific basis for controlling diseases caused by pathogenic microorganisms.

[0016] Preferably, step S2 comprises the following steps:

[0017] Step S21: Analyze the physiological and biochemical characteristics of the pathogenic microorganism genome sequence according to the pathogen characteristic point data to obtain the physiological and biochemical characteristic data of the pathogen genome;

[0018] Step S22: Identify the environmental weight impact of the pathogen genome physiological and biochemical characteristic data to obtain physiological and biochemical environmental weight impact data;

[0019] Step S23: simulating the genome evolution relationship of the pathogenic microorganism genome sequence according to the physiological and biochemical environment weight influence data to obtain genome evolution relationship data;

[0020] Step S24: performing preceding relationship linkage identification on the genome evolution relationship data to obtain preceding relationship linkage data.

[0021] The present invention analyzes the physiological and biochemical characteristics of pathogenic microorganism genomes based on previously obtained pathogenic characteristic point data. This process involves a detailed analysis of proteins, enzymes, metabolic pathways, etc. encoded in the genome, so as to identify the physiological activities and metabolic characteristics of microorganisms in the host environment. By analyzing the characteristics of metabolic pathways, energy generation, toxin synthesis, drug resistance, etc., the pathogenic mechanism and biological characteristics of pathogenic microorganisms can be revealed. The obtained physiological and biochemical characteristic data provide a scientific basis for in-depth research on the interaction between pathogenic microorganisms and hosts, pathogenesis, and drug intervention. By analyzing the physiological and biochemical characteristic data of the pathogen genome, the effects of different environmental factors on the characteristics of pathogenic microorganisms are identified. For example, external environmental factors such as temperature, pH value, changes in nutrients, and host immune responses affect the metabolic activity, virulence, or drug resistance of microorganisms. Through environmental weight analysis, the intensity of the influence of different environmental factors on the physiological functions of microorganisms is evaluated, thereby obtaining physiological and biochemical environmental weight influence data. These data help understand how pathogenic microorganisms regulate their biological activities in different environments, reveal their adaptive mechanisms, and provide theoretical support for the development of environmental control strategies. Based on the physiological and biochemical environmental weighted impact data, the evolutionary relationship of the genome of pathogenic microorganisms is simulated. By integrating the impact of environmental factors on physiological and biochemical characteristics, how the microbial genome evolves under different environmental conditions to adapt to new survival challenges is simulated. This process helps researchers understand the adaptive evolution and genetic variation of pathogenic microorganisms, and reveals how the genome adjusts to cope with environmental changes or host immune pressure during long-term evolution. The genome evolutionary relationship data provides important clues for further studying the evolutionary dynamics, mutation hotspots and resistance evolution of microorganisms. In-depth pre-relationship linkage identification of genome evolutionary relationship data, by analyzing the relationship between different genes and between genes and physiological and biochemical characteristics, it reveals how different functional modules, genes and environmental factors in the microbial genome influence each other. For example, mutations in certain genes are closely related to enhanced toxicity, development of drug resistance or adaptation to environmental changes. Through pre-relationship linkage identification, researchers can construct complex causal chains and interaction networks within the microbial genome, providing theoretical support for a better understanding of the evolutionary path, drug resistance mechanism and interaction between pathogenic microorganisms and hosts, and providing data support for the development of new drugs and vaccine design.

[0022] Preferably, step S23 includes the following steps:

[0023] Step S231: performing spatiotemporal dimension action intensity analysis on the physiological and biochemical environment weight impact data to obtain spatiotemporal dimension action intensity data;

[0024] Step S232: performing pressure chemical reaction change analysis on the pathogenic microorganism genome sequence according to the spatiotemporal action intensity data to obtain pathogen spatiotemporal pressure chemical reaction change data;

[0025] Step S233: Calculating the probability of gene segment variation of the pathogenic microorganism genome sequence based on the pathogen spatiotemporal pressure chemical reaction variation data to obtain gene segment variation probability data;

[0026] Step S234: performing quantitative prediction of the variation trend of the gene fragment variation probability data to obtain quantitative data of the variation trend of the gene fragment;

[0027] Step S235: Perform genome evolution relationship simulation on the genome sequence of the pathogenic microorganism according to the gene segment variation probability data and the gene segment variation trend quantitative data to obtain genome evolution relationship data.

[0028] The present invention analyzes the physiological and biochemical environmental weight impact data to deeply explore the degree of influence of environmental factors (such as temperature, humidity, pH value, etc.) on pathogenic microorganisms in different time and space dimensions. The analysis of the intensity of the spatiotemporal dimension can reveal the dynamic changes of environmental factors on the physiological activities of microorganisms, reflecting how the impact of the environment on microorganisms changes over time and space. These data help researchers understand how pathogenic microorganisms undergo adaptive changes or evolution under different environmental conditions, and thus provide a scientific basis for predicting the viability and pathogenicity of microorganisms in different scenarios. Based on the spatiotemporal dimension intensity data, the chemical reaction changes of pathogenic microorganisms under different environmental pressures are studied. These pressures usually include factors such as temperature changes, changes in nutrient concentrations, and fluctuations in oxygen content. These environmental pressures have an important impact on the metabolic reactions and physiological activities of microorganisms. Through the analysis of changes in pressure chemical reactions, researchers can identify changes in the biochemical reaction paths and metabolic pathways of microorganisms under different pressure conditions, thereby revealing how microorganisms regulate their gene expression and functional performance when responding to environmental pressures. This data provides key information for further understanding the physiological adaptability and stress resistance mechanisms of microorganisms. By analyzing the temporal and spatial pressure chemical reaction change data of pathogenic microorganisms under different environmental pressures, the probability of mutation of each gene fragment in the microbial genome can be inferred. This process involves the use of bioinformatics algorithms, combined with environmental pressure, chemical reaction changes and gene mutation frequency, to calculate the possibility of gene fragment mutation under specific conditions. The variation of gene fragments is a key factor in the adaptive evolution of microorganisms. The variation probability data can help researchers predict the genetic variation and new mutations that occur in microorganisms under specific environments, which is crucial for understanding the changes in microbial resistance and virulence. In this step, based on the gene fragment variation probability data obtained in the previous step, mathematical models and prediction algorithms are used to quantitatively analyze the variation trend of gene fragments. This process can help researchers predict the frequency and type of mutations that will occur in specific genes or genomic regions in the future. Through quantitative prediction, researchers can identify which gene fragments are most likely to mutate and further evaluate the potential impact of these mutations on the biological characteristics of microorganisms (such as toxicity, drug resistance, etc.). Quantitative data on variation trends provide valuable information for the evolutionary direction of pathogenic microorganisms, the spread of drug resistance, and disease prediction. In this step, the evolutionary relationship of pathogenic microorganism genomes is simulated by combining gene fragment variation probability data and gene fragment variation trend quantitative data. By simulating the evolution of microbial genomes under specific environmental pressures, researchers can depict the changing trajectory of pathogenic microorganism genomes in the time dimension and reveal how their genomes gradually evolve with the accumulation of environmental pressure and genetic variation. Genome evolution relationship data can provide a scientific basis for predicting the evolutionary trend of microorganisms, changes in pathogenicity, and the expansion of drug resistance.These data will help identify the future evolutionary paths of pathogenic microorganisms and provide important references for adjusting prevention and control strategies, vaccine development, and antibiotic use.

[0029] Preferably, step S3 comprises the following steps:

[0030] Step S31: performing relationship nesting structure analysis on the preceding relationship linkage data to obtain preceding linkage relationship nesting structure data;

[0031] Step S32: performing pathogenic microorganism population genetic structure analysis on pathogen genome physiological and biochemical characteristic data according to the pre-linkage relationship nested structure data to obtain pathogenic microorganism population genetic structure data;

[0032] Step S33: constructing a relationship coding index for the preceding relationship linkage data according to the preceding linkage relationship nested structure data and the pathogenic microorganism population genetic structure data to obtain a linkage relationship coding index.

[0033] The present invention aims to identify and reveal the complex relationships between different genes or physiological characteristics and their hierarchical structures of mutual influence by performing nested structural analysis on the pre-relationship linkage data. By layering and structuring the multidimensional data, it is possible to clearly identify which relationships are intrinsically dependent and which are externally influenced, thereby revealing the multiple correlations of the microbial genome in its evolutionary process. This analysis helps to understand how genes, environmental characteristics and physiological functions interact through complex networks, and provides a structured information basis for subsequent relationship encoding and data modeling. Based on the nested structural data of pre-relation linkage, the population genetic structure analysis of the physiological and biochemical characteristics of the genome of pathogenic microorganisms is performed. Through population genetics analysis, the genetic differences between different groups or subgroups can be evaluated, and how they show different adaptability or pathogenicity characteristics under environmental pressure. This step helps to reveal the genetic structural characteristics such as gene diversity, genetic drift and gene flow in the pathogenic microorganism population, so as to deeply understand the evolutionary pattern of the microbial population and the selection pressure within the population, and how they interact with the environment, host and pathological characteristics. Combining the nested structure data of the pre-linked relationship and the population genetic structure data, a relationship coding index is constructed to provide a clear and efficient indexing mechanism for the entire data system. The relationship coding index is to integrate and classify data of multiple dimensions by encoding and mapping complex relationships, thereby simplifying data query and subsequent analysis processes. In this way, the connection between data can be accessed, understood and interpreted more quickly, improving data availability and parsing efficiency. For the processing of large-scale pathogen genomic data, building an effective relationship coding index can save computing resources in subsequent research and accelerate research progress in the evolution of pathogenic microorganisms, the transmission path of drug resistance, and other aspects.

[0034] Preferably, step S32 includes the following steps:

[0035] Step S321: performing biochemical attribute similarity comparison on the pathogen genome physiological and biochemical characteristic data according to the pre-linkage relationship nested structure data to obtain genome biochemical attribute similarity data;

[0036] Step S322: performing gene abundance calculation on the genome biochemical attribute similarity data to obtain gene similarity abundance data;

[0037] Step S323: performing genetic population differentiation analysis on the genome biochemical attribute similarity data according to the gene similarity abundance data to obtain genetic population differentiation data;

[0038] Step S324: Perform pathogenic microorganism population genetic structure analysis based on the genetic population differentiation data and the gene similarity abundance data to obtain pathogenic microorganism population genetic structure data.

[0039] The present invention analyzes the nested structure data of the pre-linkage relationship and compares the similarity of biochemical properties of the physiological and biochemical properties of the pathogenic microorganism genome, aiming to identify the similarity in biochemical properties between different pathogenic bacteria groups or individuals. This analysis helps to understand the differences in biochemical characteristics of different pathogenic microorganisms at the genome level, as well as the similarities and differences they show in physiological functions. For example, some pathogenic microorganisms respond to survival challenges through similar metabolic pathways under the same environmental pressure. Therefore, the biochemical property similarity data provides a basis for subsequent genetic analysis and population structure research. Based on the results of the biochemical property similarity comparison, the gene abundance of the relevant genomic region is further calculated to obtain gene similarity abundance data. Gene abundance reflects the relative number or expression level of different genes or genomic fragments in the pathogenic microorganism population. This data can help researchers identify genes or gene clusters that are more active under specific biochemical conditions, and reveal which genes or gene combinations play an important role in the adaptability and evolution of microorganisms. Through abundance calculation, genes that are significantly enriched in different groups or subgroups can be distinguished, and the genetic basis and physiological characteristics of microorganisms can be further understood. Combining the gene similarity abundance data and the genome biochemical property similarity data, the genetic population differentiation analysis was performed. By statistically analyzing the differences in gene abundance between different groups (such as microbial groups in different environments or different hosts), the degree of genetic differentiation of the genome within the group was evaluated. This analysis helps to reveal how groups with different genetic backgrounds show differences in biochemical characteristics, and further analyzes how pathogenic microbial groups adapt to different environmental pressures through natural selection and genetic drift. Genetic population differentiation data provides an in-depth perspective for understanding how microbial groups evolve and differentiate, and helps to reveal their potential adaptive mechanisms. Based on the aforementioned genetic population differentiation data and gene similarity abundance data, the genetic structure of pathogenic microbial populations was analyzed. By comprehensively considering the gene diversity, gene abundance distribution and genetic differentiation within the population, researchers can depict the overall genetic structure of the microbial population, reveal the frequency distribution of different genotypes in the population and their adaptive performance in a specific environment. The analysis of the genetic structure of pathogenic microbial populations can provide an in-depth understanding of the genetic diversity of microbial populations, gene flow patterns and genetic connections between populations, and provide an important theoretical basis for the spread of drug resistance, evolutionary trends and pathogenicity differences of pathogenic microorganisms.

[0040] Preferably, step S323 includes the following steps:

[0041] According to the gene similarity abundance data, the gene abundance similarity distribution matrix is ​​constructed for the genome biochemical attribute similarity data to obtain the gene abundance similarity distribution matrix;

[0042] Calculate the Fst value of the gene abundance similarity distribution matrix to obtain the gene similarity Fst value;

[0043] The genetic population differentiation data were obtained by analyzing the gene abundance similarity distribution matrix based on the gene similarity Fst value.

[0044] The present invention constructs a gene abundance similarity distribution matrix based on gene similarity abundance data. The matrix presents the abundance distribution of each gene in different microbial populations or samples by calculating the gene abundance similarity between different samples or populations. This matrix not only helps to reveal the enrichment of different genes in different populations, but also helps to identify which genes show similar abundance characteristics under different environments or host conditions. By structuring these similarity data into a matrix form, it is convenient to perform more complex genetic analysis and differentiation calculations later. The gene abundance similarity distribution matrix is ​​used to evaluate the genetic differentiation degree of gene abundance between different populations by calculating the Fst value. The Fst value is an indicator for measuring the degree of differentiation of gene frequency between populations. The larger the value, the more significant the gene difference between populations, and the smaller the value, the smaller the gene difference between populations. By calculating the Fst value, it is possible to further understand which genes have strong genetic isolation in different populations, thereby providing important quantitative data for the genetic structure of pathogenic microorganism populations. This calculation step is the key to population genetics analysis and helps to reveal the adaptive differentiation of microbial populations during evolution. Based on the gene similarity Fst value calculated in the previous step, the gene abundance similarity distribution matrix is ​​subjected to genetic population differentiation analysis. The core of this analysis is to use the Fst value to identify genes or gene clusters in the genome that show significant population differentiation. The difference in the abundance of these genes reflects the genetic isolation between populations due to different selection pressures, environmental adaptation or gene flow. Through the analysis of genetic population differentiation of these genes, we can deeply understand the genetic structure of microbial populations, reveal the distribution of different genetic subtypes within the population and their adaptability in specific ecological niches. This analysis provides important support for further studying the evolutionary process of pathogenic microorganisms, adaptive mechanisms and genetic connections between populations.

[0045] The beneficial effects of the present invention are as follows: first, the complete genome sequence of pathogenic microorganisms is obtained by high-throughput sequencing technology, which provides basic data for subsequent analysis. Then, the genome sequence is analyzed for pathogenic characteristic points to identify important characteristic points related to pathogenicity in the genome, such as pathogenic genes, virulence factors, etc. These characteristic point data will reveal the basic biological characteristics of pathogenic microorganisms, such as pathogenicity, immune escape mechanism, etc., and provide important basis for subsequent physiological and biochemical characteristic analysis and genome evolution relationship simulation. At this stage, the physiological and biochemical characteristics of the pathogenic microorganism genome are deeply analyzed based on the pathogenic characteristic point data, and information such as metabolic pathways and drug resistance genes are extracted. These physiological and biochemical characteristic data help scientists understand the biological functions of pathogenic microorganisms and their interactions with the host. Subsequently, the genome evolution model is used to perform evolutionary analysis on the genome data to reveal the evolutionary relationship, kinship and mutation characteristics between different microbial populations. Then, the pre-relationship linkage identification is carried out to explore the interdependence between different genes, characteristic points and biological functions, and provide dynamic evolutionary paths and key nodes for subsequent research. After nested structure analysis, the pre-relation linkage data can reveal the hierarchical and complex relationships between different feature points, genes and functions in the microbial genome. By deeply mining these relationships, a structured nested relationship data is formed to help researchers understand how various biological processes interact with each other and identify key linkage links. Then, based on these nested structure data, a relationship coding index is constructed. This coding index achieves efficient data access and query by marking and classifying different relationships, laying an organizational framework for the construction of the genome database. After matching dynamic retrieval conditions, the linkage relationship coding index can flexibly extract relevant data according to actual needs, thereby realizing an efficient retrieval mechanism. According to the matching results, the linkage relationship cache architecture is optimized, and the data query speed and resource utilization are improved through intelligent cache management. Finally, a complete pathogenic microorganism genome database is constructed using the optimized cache architecture. This database can not only store pathogenic genome data, but also support complex data analysis and query, providing strong support for the diagnosis, treatment and research of pathogenic microorganisms. Therefore, the present invention is an improvement on a traditional method for constructing a pathogenic microorganism genome database, which solves the problem that the traditional method for constructing a pathogenic microorganism genome database is inaccurate in analyzing the evolutionary relationship of the pathogenic microorganism genome, thereby causing confusion in the database construction, improves the accuracy of the analysis of the evolutionary relationship of the pathogenic microorganism genome, and improves the rationality of the database construction. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 A schematic diagram of the steps of a method for constructing a pathogenic microorganism genome database;

[0047] Figure 2 for Figure 1 Detailed implementation steps of step S2 in FIG.

[0048] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0049] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are 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 technicians in this field without creative work are within the scope of protection of the present invention.

[0050] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0051] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0052] To achieve this, please refer to Figure 1 to Figure 2 , a method for constructing a pathogenic microorganism genome database, the method comprising the following steps:

[0053] Step S1: obtaining a pathogenic microorganism genome sequence; performing pathogen characteristic point analysis on the pathogenic microorganism genome sequence to obtain pathogen characteristic point data;

[0054] Step S2: Analyze the physiological and biochemical characteristics of the pathogenic microorganism genome sequence according to the pathogen characteristic point data to obtain the pathogen genome physiological and biochemical characteristic data; simulate the genome evolution relationship according to the pathogen genome physiological and biochemical characteristic data to obtain the genome evolution relationship data; perform pre-relation linkage identification on the genome evolution relationship data to obtain pre-relation linkage data;

[0055] Step S3: performing a relational nested structure analysis on the pre-relation linkage data to obtain pre-relation linkage relation nested structure data; performing a relational coding index construction on the pre-relation linkage data according to the pre-relation linkage relation nested structure data to obtain a linkage relation coding index;

[0056] Step S4: Dynamically match the linkage relationship coding index with search conditions to obtain the linkage relationship dynamic search conditions; optimize the cache architecture according to the linkage relationship dynamic search conditions to obtain the linkage relationship cache optimization architecture; construct a genome database for the pathogenic microorganism genome sequence using the linkage relationship cache optimization architecture to obtain the pathogenic microorganism genome database.

[0057] In the embodiment of the present invention, reference Figure 1 FIG. 1 is a schematic diagram of a method for constructing a pathogenic microorganism genome database according to the present invention. In this example, the method for constructing a pathogenic microorganism genome database includes the following steps:

[0058] Step S1: obtaining a pathogenic microorganism genome sequence; performing pathogen characteristic point analysis on the pathogenic microorganism genome sequence to obtain pathogen characteristic point data;

[0059] In the embodiment of the present invention, the genome sequence of the pathogenic microorganism is first obtained. The acquisition of the genome sequence depends on gene sequencing technology, such as high-throughput second-generation sequencing technology (NGS) or third-generation sequencing technology. Through these technologies, DNA or RNA in the pathogenic microorganism sample is extracted, and a specific library construction method is used to convert it into a format suitable for sequencing, and then the sequence data is read by a sequencing instrument to obtain complete pathogenic microorganism genome information. After the genome sequence is obtained, the pathogen characteristic point analysis is performed next. This process involves systematic analysis of characteristic regions in the pathogenic microorganism genome sequence. Through gene annotation software or genome analysis tools (for example, BLAST, Glimmer, GeneMark, etc.), key genes or domains in the pathogen genome are identified, and these characteristic points are marked. Characteristic points include genes that affect pathogenicity, such as virulence factors, drug resistance genes, metabolism-related genes, etc. Using genome analysis tools, combined with known databases (such as NCBI Gene, KEGG, etc.), functional annotations are performed on genes in the sequence to identify potential pathogenic characteristic points. Finally, the pathogenic characteristic point data is output for subsequent analysis.

[0060] Step S2: Analyze the physiological and biochemical characteristics of the pathogenic microorganism genome sequence according to the pathogen characteristic point data to obtain the pathogen genome physiological and biochemical characteristic data; simulate the genome evolution relationship according to the pathogen genome physiological and biochemical characteristic data to obtain the genome evolution relationship data; perform pre-relation linkage identification on the genome evolution relationship data to obtain pre-relation linkage data;

[0061] In the embodiment of the present invention, the physiological and biochemical characteristics of the pathogenic microorganism genome are first analyzed according to the pathogenic characteristic point data. This step is based on the pathogenic characteristic points that have been identified, and a multi-dimensional functional evaluation is performed on it by a biochemical analysis tool, mainly analyzing the physiological and biochemical characteristics such as the metabolic pathway, enzyme system, and material metabolism capacity of the genome. Through the gene annotation results, combined with the metabolic network and biochemical reaction database (such as KEGG, BioCyc, etc.), the metabolic function of the pathogenic microorganism is modeled, and its physiological response under different environmental conditions is evaluated. After completing the analysis of the physiological and biochemical characteristics of the genome, these physiological and biochemical data are used to simulate the genome evolution relationship. This process depends on the sequence alignment and phylogenetic analysis method of the gene. By comparing the genomes of different pathogenic species or different strains, the evolutionary tree of the genome is constructed. Software such as MEGA, RAxML, FastTree can be used to calculate the evolutionary tree of the genome by the maximum likelihood method or the Bayesian method. These softwares analyze the similarity of the genomes between different species or strains, infer their common ancestors, and generate genome evolutionary relationship data. Next, the pre-relationship linkage is identified based on the genome evolutionary relationship data. This process involves an in-depth analysis of the evolutionary linkage between different genomes. The topological structure of the genome evolution tree is used in combination with the sequence homology between genes to identify the pre-relationship between genomes. For example, through the symbiotic evolution between genes, horizontal gene transfer and other phenomena, the key linkage points in the evolutionary relationship are identified, and finally the pre-relationship linkage data is obtained.

[0062] Step S3: performing a relational nested structure analysis on the pre-relation linkage data to obtain pre-relation linkage relation nested structure data; performing a relational coding index construction on the pre-relation linkage data according to the pre-relation linkage relation nested structure data to obtain a linkage relation coding index;

[0063] In an embodiment of the present invention, the pre-relation linkage data is first subjected to a relational nested structure analysis. This analysis is based on the obtained pre-relation linkage data, and a graph theory method is used to establish a network structure of complex relationships. By topological analysis of the relationship data, the nested hierarchical relationship between each pathogen genome is identified. A graph theory algorithm, such as a shortest path algorithm, a community detection algorithm, etc., is used to extract the nested hierarchical structure in the relationship, and finally obtain the pre-relation linkage relationship nested structure data. According to the pre-relation linkage relationship nested structure data, a relationship coding index is further constructed. This step encodes the nested structure so that each relationship node can obtain a unique identifier. The encoding process can be implemented by a hash algorithm or an encoding method based on a B-tree or a Trie tree to ensure that each linkage relationship can be quickly retrieved. Using these encoding information, an index structure of the linkage relationship is established to facilitate subsequent efficient database retrieval and update.

[0064] Step S4: Dynamically match the linkage relationship coding index with search conditions to obtain the linkage relationship dynamic search conditions; optimize the cache architecture according to the linkage relationship dynamic search conditions to obtain the linkage relationship cache optimization architecture; construct a genome database for the pathogenic microorganism genome sequence using the linkage relationship cache optimization architecture to obtain the pathogenic microorganism genome database.

[0065] In the embodiment of the present invention, the linkage relationship coding index is first matched with dynamic search conditions. The goal of this step is to optimize the index query process by setting search conditions. Based on the query engine of the database, dynamic query conditions adapted to the characteristics of the linkage relationship data are designed. By using query optimization algorithms (e.g., query plan generation, index selection algorithms, etc.), the most suitable indexing method is adjusted and matched according to different data requirements to improve query efficiency. Next, the cache architecture is optimized according to the dynamic search conditions of the linkage relationship. In order to speed up the retrieval process, a cache strategy is adopted to preload and store the linkage relationship data. By using the LRU (least recently used) cache replacement algorithm, etc., it is determined which data should be cached and dynamically adjusted based on real-time query requirements. Through data access frequency analysis, the cache content is further optimized to ensure efficient query response. Finally, the optimized linkage relationship cache architecture is used to construct a genome database for the pathogenic microorganism genome sequence. At this stage, the pathogenic microorganism genome sequence is stored in an efficient database structure through a cache mechanism and an index system to ensure that the database can quickly respond to various query requests. A relational database (such as MySQL, PostgreSQL) or a NoSQL database (such as MongoDB) can be used for database management, combined with the aforementioned cache architecture to achieve rapid retrieval, storage, and update of genomic data. Finally, the pathogenic microorganism genome database is built and can provide efficient data support for subsequent research.

[0066] Preferably, step S1 comprises the following steps:

[0067] Step S11: obtaining the genome sequence of pathogenic microorganisms;

[0068] Step S12: filtering low-quality sequences of the pathogenic microorganism genome sequence to obtain a pathogenic microorganism genome filtered sequence;

[0069] Step S13: functionally classify and label the pathogenic microorganism genome filtered sequence to obtain the microorganism genome functional classification sequence;

[0070] Step S14: Analyze the pathogenic characteristic sites of the functional classification sequence of the microbial genome to obtain pathogenic characteristic site data.

[0071] In the embodiment of the present invention, DNA or RNA is first extracted from the pathogenic microorganism sample, and the specific operation is to obtain the genome of the pathogenic microorganism through laboratory molecular biology techniques, such as bacterial culture, virus extraction and other methods. The extracted genome is converted into a library suitable for sequencing by a standardized method. After the library is constructed, the genome is sequenced using a high-throughput sequencing technology (such as Illumina sequencing or PacBio long-read sequencing technology). High-throughput sequencing can read a large number of gene sequences at one time, and has a high sequencing accuracy, and can comprehensively obtain the genome sequence of the pathogenic microorganism. After obtaining the original sequencing data, combined with relevant quality control standards, the original data is preliminarily processed and filtered to ensure that the obtained genome sequence is accurate. The pathogenic microorganism genome sequence obtained in step S11 is filtered for low-quality sequences. First, the sequencing data is preliminarily evaluated by quality control software (such as FastQC) to check the quality indicators of the sequence, including sequencing error rate, GC content, sequence length distribution, etc. Low-quality sequences are usually characterized by high error rate, low quality score (Phred score is less than 20) and abnormal GC content. On this basis, a quality filtering tool (such as Trimmomatic) is used to remove low-quality sequences. This process sets a quality threshold to remove sequences with quality scores lower than the set standard, and removes adapter sequences and low-quality regions by trimming to ensure the quality of the filtered genome sequence. Finally, the filtered sequence will be called the pathogenic microorganism genome filtered sequence, providing basic data for subsequent analysis. Functional classification identification of pathogenic microorganism genome filtered sequences. First, the filtered sequences are compared and analyzed using known gene annotations and functional databases (such as KEGG, Gene Ontology). By using sequence alignment tools (such as BLAST or DIAMOND), each gene fragment is aligned with the known gene sequences in the functional database to identify the functional regions of the genes. The results of the alignment can reveal the functional categories of each gene, including metabolism-related genes, drug resistance genes, virulence factors, etc. According to the alignment results, the gene sequences are classified by functional classification algorithms (such as algorithms based on hierarchical clustering or graph clustering) to form pathogenic microorganism genome functional classification sequences. In this process, further annotations are required based on the function and location of the genes to ensure the accuracy and comprehensiveness of the classification. The pathogenic characteristic point analysis is performed on the functional classification sequence of the microbial genome. According to the functional classification information obtained in step S13, the characteristic genes and sequence regions in the pathogenic microorganism genome are further focused on, and these characteristic points are closely related to the pathogenicity of the pathogenic microorganism. The functional classification sequence is refined and analyzed using a pathogenic characteristic point recognition tool (e.g., a feature extraction method based on a hidden Markov model or sequence alignment).The identified characteristic sites include drug resistance genes, virulence factors, immune escape-related genes, etc. These genes play a vital role in the pathogenic process of pathogenic microorganisms. During analysis, it is also necessary to compare with known pathogen feature databases (such as CARD database, VFDB database, etc.) to ensure the accurate identification of characteristic sites. Finally, the pathogen characteristic site data is obtained as the basic data for the next step of analysis. These characteristic site data provide key biological information for the subsequent biological characteristics analysis and database construction of pathogenic microorganisms.

[0072] Preferably, step S2 comprises the following steps:

[0073] Step S21: Analyze the physiological and biochemical characteristics of the pathogenic microorganism genome sequence according to the pathogen characteristic point data to obtain the physiological and biochemical characteristic data of the pathogen genome;

[0074] Step S22: Identify the environmental weight impact of the pathogen genome physiological and biochemical characteristic data to obtain physiological and biochemical environmental weight impact data;

[0075] Step S23: simulating the genome evolution relationship of the pathogenic microorganism genome sequence according to the physiological and biochemical environment weight influence data to obtain genome evolution relationship data;

[0076] Step S24: performing preceding relationship linkage identification on the genome evolution relationship data to obtain preceding relationship linkage data.

[0077] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes:

[0078] Step S21: Analyze the physiological and biochemical characteristics of the pathogenic microorganism genome sequence according to the pathogen characteristic point data to obtain the physiological and biochemical characteristic data of the pathogen genome;

[0079] In the embodiment of the present invention, the pathogenic microorganism genome sequence is analyzed for physiological and biochemical characteristics according to the pathogenic characteristic point data. First, the identified pathogenic characteristic point data (such as metabolism-related genes, virulence genes, drug resistance genes, etc.) are used for detailed genome annotation. Each characteristic point represents a gene with a specific physiological or biochemical function, and the role of these genes in the physiological function, metabolic pathway and interaction with the host of the microorganism is crucial. Using gene annotation databases (such as KEGG, Reactome, etc.), the characteristic points are compared and analyzed to identify the relevant metabolic pathways, enzymatic reactions and their roles in the physiological activities of microorganisms. For example, genes related to important physiological activities such as amino acid metabolism, carbohydrate metabolism, and lipid synthesis in the pathogenic genome can be identified. The physiological functions of these genes are associated with the relevant gene sets and biochemical networks in the existing literature to obtain the physiological and biochemical characteristics data of pathogenic microorganisms, including information such as metabolic pathways, key enzyme activities, energy metabolism, and environmental adaptability. The physiological and biochemical characteristics data of the pathogenic genome finally formed provide a detailed biological background for subsequent analysis.

[0080] Step S22: Identify the environmental weight impact of the pathogen genome physiological and biochemical characteristic data to obtain physiological and biochemical environmental weight impact data;

[0081] In an embodiment of the present invention, the environmental weight impact identification is performed on the physiological and biochemical characteristics data of the pathogen genome. The goal of this process is to evaluate the degree of influence of environmental factors (such as temperature, pH value, oxygen concentration, salinity, etc.) on the physiological and biochemical characteristics of pathogenic microorganisms. First, through literature review or experimental data, the influence weights of known environmental factors on specific physiological characteristics (such as metabolic rate, enzyme activity, etc.) are collected. The influence of these environmental factors is quantified by laboratory data or historical environmental data. On this basis, a weighted scoring algorithm is used to calculate the influence of each environmental factor on each physiological and biochemical characteristic. For example, some metabolic pathways are more active under high temperature or low oxygen environments, so these environmental factors are given higher weights in the analysis. In addition, based on the influence of changes in environmental factors on physiological responses, an environmental impact model is constructed, and the physiological and biochemical environmental weight impact data are obtained by multiple regression analysis or weighted average method. In this way, it is possible to identify which environmental factors have a significant impact on the physiological processes of pathogenic microorganisms and form an environmental weight data set.

[0082] Step S23: simulating the genome evolution relationship of the pathogenic microorganism genome sequence according to the physiological and biochemical environment weight influence data to obtain genome evolution relationship data;

[0083] In the embodiment of the present invention, the genome evolution relationship simulation is performed on the pathogenic microorganism genome sequence according to the physiological and biochemical environment weight influence data. First, by analyzing the function of each gene in the pathogenic microorganism genome and its interaction relationship, combined with the environmental weight influence data, the evolution process of the genome under different environmental conditions is simulated. The genome evolution relationship simulation is based on the mechanisms such as gene mutation, recombination, gene loss and acquisition, considering the selection pressure of the external environment, and inferring how the genome is adaptively evolved in environmental changes. By using an evolutionary tree construction method (such as maximum likelihood method, neighbor joining method or Bayesian inference method), the genomes of different pathogenic microorganisms are compared and analyzed to evaluate their evolutionary branches and evolutionary paths. In the simulation process, the environmental weight influence data is used as a key factor to adjust the fitness of the gene and predict the evolutionary trend of the genome under different environmental conditions. Finally, the genome evolution relationship data obtained includes the evolutionary history, adaptive changes and evolutionary paths of each gene in the genome in a specific environment, providing a detailed evolutionary background for subsequent database construction.

[0084] Step S24: performing preceding relationship linkage identification on the genome evolution relationship data to obtain preceding relationship linkage data.

[0085] In an embodiment of the present invention, the genome evolution relationship data is identified by pre-relation linkage. The core purpose of this step is to reveal the relationship linkage between different genes or genome regions in the genome, particularly the gene pairs with covariance in the evolution process. First, by conducting an in-depth analysis of the genome evolution relationship data, the dependency or interaction between genes is identified. These relationships are manifested as the co-evolution of genes, that is, under specific environments or specific adaptation conditions, there is a close functional linkage between certain genes. In order to identify these pre-relation linkages, the close association between genes or genome fragments is detected by using association rule mining technology (such as Apriori algorithm) or connectivity analysis in graph theory. Based on the function of the gene, the expression pattern and its adaptability to the environment, the gene pairs or genome regions that are interdependent in the genome evolution process are identified. By this method, it can be revealed which genes are "pre-positioned" in the evolution process, that is, their changes directly affect the functions of other genes. Finally, the pre-relation linkage data obtained provide detailed information about the interaction of genes in the genome and the evolution mechanism for subsequent analysis.

[0086] Preferably, step S23 includes the following steps:

[0087] Step S231: performing spatiotemporal dimension action intensity analysis on the physiological and biochemical environment weight impact data to obtain spatiotemporal dimension action intensity data;

[0088] Step S232: performing pressure chemical reaction change analysis on the pathogenic microorganism genome sequence according to the spatiotemporal action intensity data to obtain pathogen spatiotemporal pressure chemical reaction change data;

[0089] Step S233: Calculating the probability of gene segment variation of the pathogenic microorganism genome sequence based on the pathogen spatiotemporal pressure chemical reaction variation data to obtain gene segment variation probability data;

[0090] Step S234: performing quantitative prediction of the variation trend of the gene fragment variation probability data to obtain quantitative data of the variation trend of the gene fragment;

[0091] Step S235: Perform genome evolution relationship simulation on the genome sequence of the pathogenic microorganism according to the gene segment variation probability data and the gene segment variation trend quantitative data to obtain genome evolution relationship data.

[0092] In the embodiment of the present invention, the physiological and biochemical environmental weight impact data is first converted into the action intensity of the spatiotemporal dimension, and the spatiotemporal analysis is performed. This process requires the spatiotemporal distribution analysis of environmental factors (such as temperature, humidity, light, oxygen concentration, etc.). Specifically, by collecting environmental data within a period of time and the physiological and biochemical response data of pathogenic microorganisms under different environmental conditions, a multidimensional data model containing time and space factors is constructed. Using a spatiotemporal analysis method, for example, based on the Kriging interpolation method (Kriging interpolation), the spatial data is smoothed, and at the same time, combined with time series analysis (such as moving average method or exponential smoothing method), the influence intensity of each environmental factor on physiological and biochemical characteristics at different time points and spatial positions is calculated. In this way, the action intensity of environmental factors in different time and space dimensions can be obtained to form spatiotemporal dimension action intensity data. These data reflect the spatiotemporal distribution characteristics of environmental changes on microbial physiological responses, and provide detailed environmental background for subsequent genome evolution simulations. Pressure chemical reaction change analysis is performed based on spatiotemporal dimension action intensity data. First, the spatiotemporal dimension action intensity data is combined with the physiological response data of the pathogenic microorganism genome to identify the changes in the chemical reaction path of the microbial genome under different environmental pressures. Through high-throughput experimental data or literature review, the changing rules of metabolic reactions or enzymatic reactions of pathogens under specific environmental pressures (such as changes in pH, temperature fluctuations, changes in ion concentration, etc.) are collected. Then, kinetic models (such as the Michaelis-Menten kinetic model or the Hill equation) are used to simulate the changes in the chemical reaction rates and reaction pathways of pathogenic microorganisms under different environmental pressure conditions. Combined with the spatiotemporal action intensity data, multifactor analysis (such as variance analysis or principal component analysis) is used to evaluate the intensity of the impact of environmental pressure on the changes in chemical reaction pathways, and further clarify the effect of each pressure condition on the changes in the chemical reactions of pathogenic microorganisms. Finally, the obtained pathogen spatiotemporal pressure chemical reaction change data can describe the adaptive changes of the microbial genome under different environmental pressures and reveal the dynamic adjustment of its metabolic pathways. The mutation probability of gene fragments is calculated using the pathogen spatiotemporal pressure chemical reaction change data. First, based on the pathogen spatiotemporal pressure chemical reaction change data obtained in the previous stage, the gene fragments that undergo reaction changes under specific environmental pressures are identified. These gene fragments are key genes or metabolic regulatory genes related to environmental adaptability. By performing mutation analysis on the sequences of these genes and combining stress conditions, we use gene mutation probability calculation methods to quantify the mutation risk of specific gene fragments under different environmental pressures. For example, we use gene mutation models (such as neutral mutation models or selective mutation models) to calculate the probability of gene mutation under stress, taking into account the selective effect of environmental stress on gene mutation. In the calculation process, we combine environmental stress data (such as temperature, pH, oxygen concentration, etc.) with gene mutation data to evaluate how specific environmental factors affect the mutation frequency and mutation type of genes.This process uses the Monte Carlo simulation method to simulate the gene mutation under different environmental conditions, so as to obtain the mutation probability data of each gene fragment under different environmental conditions. The mutation trend is quantitatively predicted based on the gene fragment mutation probability data. First, the gene fragment mutation probability data obtained in step S233 is used to construct a trend model of gene mutation. This process uses statistical methods, such as linear regression analysis, logistic regression or time series analysis, to predict the long-term trend of gene fragment mutation under given environmental pressure. According to the collected historical data or experimental data, the impact of different environmental pressures on the gene mutation trend in different time periods is evaluated. For example, environmental stress factors such as temperature fluctuations and changes in oxygen concentration are used to analyze the cumulative effect and long-term evolution trend of gene mutation. The dynamic process of gene mutation is considered in the model, such as the change of mutation rate over time, the increasing or decreasing trend of mutation frequency, etc. Finally, the quantitative data of gene fragment mutation trend obtained will provide a basis for the evolution prediction of pathogenic microorganism genome in long-term environmental adaptation, and reveal the mutation pattern of gene fragment under different environmental pressure conditions. Combined with the gene fragment mutation probability data and the quantitative data of gene fragment mutation trend, the evolutionary relationship of pathogenic microorganism genome is simulated. First, the probability data and trend quantitative data of gene fragment variation are used as input parameters, and the evolutionary process of the entire genome is simulated using an evolutionary model (such as Darwin evolution algorithm or gene drift model). This simulation takes into account the variation probability, mutation frequency, variation type of gene fragments and their long-term evolutionary trends under different environmental pressures. By constructing a genome evolution model, the adaptive changes of each gene fragment under different environmental pressures are evaluated, and the evolutionary path of the genome under long-term environmental influences is simulated. Specifically, simulation tools for genome evolution (such as genetic algorithms, Monte Carlo simulations, etc.) are used to predict the adaptive changes of the genome under different conditions and its evolutionary trajectory. During the simulation process, the dynamic process of genome evolution is quantitatively predicted by combining the probability of gene fragment variation and trend quantitative data, and finally the evolutionary relationship data of the pathogenic microorganism genome is formed, providing a detailed map of the adaptive evolution of the pathogenic microorganism genome. These data will provide the necessary genome evolution background for the subsequent database construction.

[0093] Preferably, step S3 comprises the following steps:

[0094] Step S31: performing relationship nesting structure analysis on the preceding relationship linkage data to obtain preceding linkage relationship nesting structure data;

[0095] Step S32: performing pathogenic microorganism population genetic structure analysis on pathogen genome physiological and biochemical characteristic data according to the pre-linkage relationship nested structure data to obtain pathogenic microorganism population genetic structure data;

[0096] Step S33: constructing a relationship coding index for the preceding relationship linkage data according to the preceding linkage relationship nested structure data and the pathogenic microorganism population genetic structure data to obtain a linkage relationship coding index.

[0097] In the embodiment of the present invention, the pre-relation linkage data is first subjected to structured analysis to reveal the hierarchy and relevance between different data items. To this end, a hierarchical clustering algorithm is used to perform multi-level analysis on the pre-relation linkage data. In the specific operation, the pre-relation linkage data is first normalized so that each type of data item has the same scale to eliminate the influence of dimensional differences. Then, a hierarchical clustering algorithm (such as a single link, full link or average link clustering method) is used to cluster according to the similarity between the data items to construct a relational nested structure. The clustering process calculates the distance metric (such as Euclidean distance, Manhattan distance, etc.) between each data point, and gradually merges the relational data with high similarity according to the distance, and finally obtains a tree structure or a nested hierarchical structure. By analyzing the hierarchical structure of these relational data in detail, the deep dependency between each relational data item can be revealed, and the pre-relationship nested structure data can be obtained, thereby laying the foundation for subsequent data processing. First, the pre-relationship nested structure data is used, combined with the physiological and biochemical characteristics data of the pathogenic genome, to conduct an in-depth analysis of the genetic structure of the pathogenic microorganism population. Specifically, according to the physiological and biochemical characteristics of pathogen genomes and pre-relation linkage data, gene population structure analysis methods (such as Fst analysis or Wright's fixation index) are used to evaluate the genetic differences between different groups. In the step, the genome data of pathogenic microorganisms are grouped according to their physiological and biochemical characteristics, and the microbial genomes with higher similarity are grouped together. Next, using population genetics theory, the genetic variation index (such as Huffman index or gene diversity index) between different groups is calculated. Through this method, it can be revealed how the genetic structure of pathogenic microorganism populations changes under different physiological environments and genetic characteristics, which gene regions contribute the most to the genetic differences of the populations, and the genetic structure data of pathogenic microorganism populations can be obtained. This analysis helps to deeply understand the genetic diversity of pathogenic microorganisms and provide necessary support for subsequent database construction and pathogenic microorganism feature extraction. Combining the nested structure data of pre-relationship linkage relationships with the genetic structure data of pathogenic microorganism populations, a coding index for pre-relationship linkage data is constructed. First, using pre-relationship linkage data and population genetic structure data, a multidimensional coding system is designed to reflect the complex interactions between relationship data. The specific implementation method is based on tree-like nested structure data, using Huffman coding or Lempel-Ziv coding to assign a unique identifier to each relationship item, ensuring that each level of nested relationship can be represented by an accurate code. In the coding process, different codes are assigned to different gene groups according to the results of genetic structure analysis, and the codes are weighted according to factors such as gene diversity within the group or the expression of specific genes, so that the codes not only reflect the relationship level between data, but also reflect the weight of genetic variation. In the coding process, bit operations and tree structure search algorithms are used to ensure that the coding process is efficient and accurate.Ultimately, the resulting linkage relationship encoding index will include the relationships among all relevant data items and their importance in the genetic structure, forming a compact and efficient index structure that will facilitate rapid retrieval and processing of pathogenic microorganism genomic data.

[0098] Preferably, step S32 includes the following steps:

[0099] Step S321: performing biochemical attribute similarity comparison on the pathogen genome physiological and biochemical characteristic data according to the pre-linkage relationship nested structure data to obtain genome biochemical attribute similarity data;

[0100] Step S322: performing gene abundance calculation on the genome biochemical attribute similarity data to obtain gene similarity abundance data;

[0101] Step S323: performing genetic population differentiation analysis on the genome biochemical attribute similarity data according to the gene similarity abundance data to obtain genetic population differentiation data;

[0102] Step S324: Perform pathogenic microorganism population genetic structure analysis based on the genetic population differentiation data and the gene similarity abundance data to obtain pathogenic microorganism population genetic structure data.

[0103] In the embodiment of the present invention, according to the nested structure data of the pre-linkage relationship, the biochemical attribute similarity analysis is performed by comparing the various biochemical attributes in the physiological and biochemical characteristic data of the pathogenic microorganism genome. First, the physiological and biochemical characteristics of the pathogenic microorganism genome are extracted as a number of numerical features, which include but are not limited to the concentration of metabolites, enzyme activity, cell membrane structure components, metabolic pathways, etc. Then, the Euclidean distance or Manhattan distance is used to calculate the similarity between the physiological and biochemical attributes of different genomes. Specifically, for each group of pathogenic genomes, its biochemical attribute data is first standardized to eliminate the deviation caused by different dimensions. Then, the distance between the physiological and biochemical attribute vectors of each genome is calculated, and it is grouped by a cluster analysis method to identify genomes with similar biochemical characteristics. The results of these similarity comparisons will form genome biochemical attribute similarity data for subsequent gene abundance calculation and genetic population analysis. Based on the genome biochemical attribute similarity data obtained in step S321, gene abundance is calculated. Gene abundance calculation quantifies its abundance by analyzing the copy number and expression amount of different genes in each genome. The specific operation steps are: first, use the genome data to calibrate the existence of each gene, and then calculate the abundance of each gene in the genome by the ratio of sequencing depth and gene copy number. A gene abundance calculation method is used, for example, by normalizing gene expression data to calculate the relative abundance of each gene. In the calculation process, the abundance data can be weighted in combination with the adaptive characteristics of each genome under different environments or conditions to ensure that the environmental variation of the genome is taken into account during the analysis. After the calculation is completed, gene similarity abundance data will be obtained, which reflects the differences in biochemical characteristics of different genomes and provides data support for subsequent genetic population differentiation analysis. Based on gene similarity abundance data, genetic population differentiation analysis is performed on genome biochemical attribute similarity data. First, the genetic differences between different genome populations are analyzed using gene abundance data, and the Fst index (fixation index) method is used to evaluate the degree of genetic differentiation between genome populations. The calculation of Fst value depends on the difference in gene frequency between populations. In specific implementation, the gene abundance data is first subjected to statistical analysis of gene frequency, and the gene abundance data is converted into gene frequency. The degree of genetic differentiation between populations is evaluated by comparing the gene frequency differences between different populations. If the Fst value is large, it means that there are large genetic differences between the populations. Then, the genetic structure of the population is further analyzed using methods such as principal component analysis (PCA) or genetic distance matrix (such as neighbor-joining method). This analysis not only reveals the genetic heterogeneity between populations, but also provides the necessary quantitative basis for the genetic structure of the pathogenic microorganism population, and finally generates genetic population differentiation data. Combining genetic population differentiation data and gene similarity abundance data, the genetic structure of the pathogenic microorganism population is analyzed.First, the genetic population differentiation data and the gene similarity abundance data are combined for multivariate analysis to construct a genetic structure model of the pathogenic microorganism population. The specific method is to use the structural equation model (SEM) or cluster analysis method to integrate these two types of data to reveal the intrinsic relationship between gene abundance and population genetic differentiation. By analyzing the interaction between genetic differences and gene abundance between populations, the key genes and genomic characteristics that affect the genetic structure of the population can be identified. At the same time, combined with the geographical distribution and environmental characteristics of the population, the population structure can be further refined to obtain the genetic structure data of the pathogenic microorganism population. This genetic structure data not only reflects the genetic diversity of the pathogenic microorganism population, but also reveals the connection between different genetic populations, providing a key basis for the subsequent construction of the database and the characteristic analysis of pathogenic microorganisms.

[0104] Preferably, step S323 includes the following steps:

[0105] According to the gene similarity abundance data, the gene abundance similarity distribution matrix is ​​constructed for the genome biochemical attribute similarity data to obtain the gene abundance similarity distribution matrix;

[0106] Calculate the Fst value of the gene abundance similarity distribution matrix to obtain the gene similarity Fst value;

[0107] The genetic population differentiation data were obtained by analyzing the gene abundance similarity distribution matrix based on the gene similarity Fst value.

[0108] In an embodiment of the present invention, a gene abundance similarity distribution matrix is ​​constructed based on gene similarity abundance data. First, the abundance data of each gene in the pathogenic microorganism genome is obtained, and these abundance data represent the relative expression of different genes in different pathogenic microorganism genomes. Then, based on these abundance data, the similarity between genes is calculated. When calculating the similarity, methods such as Pearson correlation coefficient or cosine similarity can be used to measure the similarity between gene abundances. For example, the Pearson correlation coefficient can calculate the linear relationship between the abundance data of two genes in different genomes, and the closer the value is to 1, the higher the similarity; while the cosine similarity is to evaluate their similarity by calculating the angle between the two gene abundance vectors, and the closer the value is to 1, the more similar the abundance distribution is. For each pair of genes, the abundance similarity between them is calculated, and finally a gene abundance similarity distribution matrix is ​​obtained, and each element in the matrix represents the degree of similarity of the corresponding gene pair. The Fst value is calculated for the gene abundance similarity distribution matrix. Fst (Fixation Index) is a commonly used indicator for measuring gene frequency differences between populations. In this step, the data in the gene abundance similarity distribution matrix are used to calculate the Fst value to understand the differentiation of gene abundance between different groups. First, the abundance distribution of genes in different groups (such as microbial groups in different environments) is calculated. In this process, the gene abundance value is converted into a relative frequency (or normalized abundance) to eliminate the influence of sequencing depth and other deviations. Then, for each gene, its frequency in different groups is calculated, and the differences between groups are compared. Specifically, the gene frequency of each group is calculated, and these frequencies are used to calculate the degree of genetic differentiation between groups. The range of Fst values ​​is usually between 0 and 1. The higher the Fst value, the greater the difference in gene frequency between groups, indicating that the genetic differentiation between groups is more significant. By calculating the Fst value of each gene, a quantitative basis can be provided for the subsequent analysis of genetic population differentiation. Genetic population differentiation analysis is performed on the gene abundance similarity distribution matrix according to the gene similarity Fst value. The purpose of this step is to analyze the genetic differentiation of different groups through the Fst value, and to identify the genetic structure and evolutionary relationship between groups in combination with the gene abundance similarity distribution matrix. First, the Fst value is used to evaluate the degree of genetic differentiation between populations. Based on the size of the Fst value, it can be determined which genes have significant genetic differentiation between populations. Genes with larger Fst values ​​are further analyzed to examine the abundance differences of these genes in different populations and interpret them in combination with the biological background. Secondly, cluster analysis or principal component analysis (PCA) and other methods are used to combine gene abundance similarity data with Fst values ​​to divide the genetic structure of the population. Cluster analysis can group the genome according to genetic distance by analyzing gene abundance similarity and Fst values, revealing the genetic connections and differences between populations.Ultimately, through genetic population differentiation analysis, genetic population differentiation data can be obtained, which provide a basis for the subsequent construction of genetic maps of pathogenic microbial populations and further database construction.

[0109] Preferably, step S33 includes the following steps:

[0110] Step S331: performing a relationship structure coupling analysis on the front-end relationship linkage data according to the front-end linkage relationship nested structure data and the pathogenic microorganism population genetic structure data to obtain front-end relationship structure coupling data;

[0111] Step S332: performing weighted correlation evaluation on the pre-relationship structure coupling data to obtain weighted correlation data of the pre-relationship structure;

[0112] Step S333: encoding the weighted related data of the predecessor relation structure to obtain the predecessor relation structure encoded data;

[0113] Step S334: construct a relation coding index for the pre-relation structure coding data using a B+ tree algorithm to obtain a linkage relation coding index.

[0114] In an embodiment of the present invention, the pre-relationship linkage data is subjected to a relationship structure coupling analysis according to the pre-relationship nested structure data and the pathogenic microorganism population genetic structure data. First, the pre-relationship linkage relationship nested structure data is composed of the interaction information between multiple pathogen genomes, which can be represented by gene interaction networks, co-expression networks, etc. The pathogenic microorganism population genetic structure data contains the genetic differences and population differentiation characteristics of different individuals in the population. The two types of data are combined for coupling analysis, the purpose of which is to identify the potential relationship structure between genomes and between populations. During the specific operation, the gene interaction network of the pathogenic microorganism population is first constructed according to the gene expression similarity or genotype similarity, and the association relationship between genes is identified. Then, the genome genetic structure data is coupled with these association relationships, and the coupling strength between different genes is quantitatively analyzed using the weighted average method. This step can be performed by calculating the coupling coefficient between genes (for example, by calculating the Pearson correlation coefficient or Spearman rank correlation coefficient between genes), so as to obtain the coupling strength of each pair of genes, and finally form the pre-relationship structure coupling data. Weighted correlation evaluation is performed based on the pre-relationship structure coupling data. The purpose of weighting the pre-relationship structure coupling data is to further quantify the strength of the relationship between different genes or groups. First, the coupling strength between each pair of genes is quantitatively evaluated, and the weighted value is calculated according to the coupling coefficient (such as Pearson correlation coefficient or Spearman coefficient) to represent the degree of association between genes. Then, these weighted correlation data are statistically processed to obtain an evaluation matrix of the relationship between genes or groups. Each element in this matrix represents the weighted correlation degree between genes or groups. Through weighted evaluation, genes or groups with strong inter-group relationships can be more accurately identified, and genes that play an important role in group evolution and genetic structure changes can be further screened out. These data will be used to build a more accurate coding index and database query system in subsequent steps. Encoding the pre-relationship structure weighted correlation data. The core of this step is to convert the weighted correlation evaluation results into a data format that is easy to store and query, which is convenient for subsequent database operations. First, the weighted correlation values ​​between each pair of genes or groups in the pre-relationship structure weighted correlation data are mapped to coding values ​​using specific coding rules. For example, integer coding can be used to assign strong correlation relationships to smaller integer values, weak correlation relationships to larger integer values, or vice versa. The specific coding method can be adjusted according to actual needs. The coding process can also be standardized according to a preset standard, for example, the weighted correlation value is normalized so that it falls within a specific numerical range. The final output of this step is the pre-relationship structure coded data, in which the correlation information of each gene pair is converted into a coded value, which is convenient for subsequent retrieval and data operations. The pre-relationship structure coded data is subjected to a relational coding index construction by a B+ tree algorithm.B+ tree is a self-balancing tree data structure, which is widely used in database indexing systems. In this step, the characteristics of B+ tree are used to organize the pre-relation structure encoding data into an efficient index tree structure for fast retrieval and access to relevant information. First, all the encoding data are sorted according to certain rules (such as lexicographic order) and inserted into the B+ tree. Each node contains a key value (i.e., the encoding value of the pre-relation) and a pointer to the child node. When it is necessary to find the relationship of a specific gene, the relevant data can be quickly located through the B+ tree structure. Since the B+ tree has a low search complexity (logarithmic time complexity), it can significantly improve the efficiency of subsequent query operations. Finally, the constructed B+ tree index provides an efficient storage and retrieval method for the pre-relation structure encoding data, supporting fast query and update of large-scale databases.

[0115] Preferably, step S4 comprises the following steps:

[0116] Step S41: performing support range query limitation on the linkage relationship coding index to obtain support range query limitation data;

[0117] Step S42: matching the linkage relationship coding index with dynamic search conditions according to the support range query limiting data to obtain the linkage relationship dynamic search conditions;

[0118] Step S43: Optimizing the cache architecture according to the dynamic search conditions of the linkage relationship and the cache database to obtain a linkage relationship cache optimization architecture;

[0119] Step S44: constructing a genome database for the pathogenic microorganism genome sequence using a relational database and a linkage relationship cache optimization architecture to obtain a pathogenic microorganism genome database.

[0120] In the embodiment of the present invention, the linkage relationship coding index is first limited to support range query. The purpose of this step is to determine the effective query range of each relationship coding value in the database to improve query efficiency and accuracy. First, according to the overall structure of the database, the scope of supported query is defined, which is usually based on the preset business needs or the relationship strength between genomes. For example, some genes are related to the biochemical characteristics of specific pathogens, or have strong expression activity under certain environments, so a threshold can be set to limit the query relationship coding value to fall within this range. This step can be implemented by interval query. Based on the sorting characteristics of the B+ tree index, the coding value that meets the range condition can be efficiently determined by algorithms such as binary search during the retrieval process. In the implementation, the maximum and minimum relationship values ​​of each gene or gene group are first determined by scanning the coding data, and then the query range is further limited according to the preset threshold, thereby forming support range query limit data. This process ensures that only the relationship related to the current demand is considered during the query, avoids the interference of irrelevant data, and optimizes the subsequent data retrieval performance. The cache architecture is optimized according to the dynamic retrieval conditions of the linkage relationship and the cache database, with the purpose of optimizing the access efficiency of the database and reducing repeated calculations and data reading operations. First, the dynamic retrieval conditions will affect the update and use strategy of the cached data. By analyzing the characteristics of each query, some commonly used genomic information can be cached in memory in advance. For example, for frequently queried genes or relational data, the system can cache them in a high-speed cache database to reduce access to the underlying relational database. The core of this step is to adjust the cache architecture based on query frequency, data access mode, and dynamic retrieval conditions so that the system can quickly respond to user query needs. Cache optimization strategies include analyzing query conditions, identifying hot data, and allocating more cache space for it in the system. The heat of data can be evaluated by frequency counting and time windows. Common optimization strategies include LRU (least recently used) algorithm and LFU (least frequently used) algorithm. When optimizing the cache architecture, the system will adjust the size and content of the cache according to specific needs to improve the cache hit rate, thereby improving the system response speed. Based on the linkage relationship cache optimization architecture obtained in the previous step, combined with the storage advantages of relational databases, a genome database is constructed for pathogenic microorganism genome sequences. The core of this step is to integrate the cache optimization architecture with relational database storage to build an efficient and scalable database system. First, using the table structure of a relational database, the genome sequence and pre-relational encoding data are stored in different tables and connected through foreign keys and other relationships. By designing the genome sequence table and the relational index table, efficient association of gene data with its corresponding relational index data can be achieved. During the construction process, the commonly used data in the cache optimization architecture is first loaded into the memory cache to ensure fast response to frequently accessed data.When the database needs to access uncommon genomic data, it will directly access the relational database to ensure the persistence and integrity of data storage. In implementation, transaction management is used to ensure data consistency, and the index mechanism is used to improve query efficiency. For example, the genome sequence data table will be indexed according to the gene ID field for quick search. The related relational index table will also be optimized for storage using efficient indexing methods such as B+ trees or hash tables to ensure that each gene and its associated relational encoding data can be quickly queried. The database can support dynamic updates and expansions of genomic data in subsequent use, maintaining the efficiency and scalability of queries. Ultimately, the genome database will combine the advantages of cache architecture and relational databases to form an efficient and stable pathogenic microorganism genome database to provide support for subsequent genomic research and data mining.

[0121] Preferably, step S43 includes the following steps:

[0122] Step S431: estimating the extreme value of cache access according to the supported range query limiting data, and obtaining the extreme value data of range query cache access;

[0123] Step S432: Designing a cache replacement strategy for the cache database according to the range query cache access extreme value data to obtain an access cache replacement strategy;

[0124] Step S433: Optimize the cache architecture based on the access cache replacement strategy and the range query cache access extreme value data to obtain the linkage relationship cache optimization architecture.

[0125] In the embodiment of the present invention, firstly, cache access extreme value estimation is performed based on the limited data supporting range query. The core goal of this step is to estimate which data will be frequently accessed by analyzing the data access pattern within the query range, thereby optimizing the use efficiency of the cache. In the specific implementation, the access pattern of the gene or genome region queried within a specific range is first determined by analyzing the previous query history and data access frequency. Through the temporal analysis of the data (for example, using a sliding window algorithm), it can be estimated which data will be frequently accessed, thus becoming the focus of the cache. For some gene sequences, they appear in multiple queries, forming a higher access frequency. Based on this access frequency, extreme value estimation will be performed, that is, predicting which data access frequency will reach the expected extreme value within a given query range. This step can be performed by statistical methods, such as frequency analysis or probability distribution estimation (for example, normal distribution or Poisson distribution). Through these statistical techniques, the access trend of each data block in the future can be inferred, and finally the range query cache access extreme value data is obtained. The cache replacement strategy is designed based on the range query cache access extreme value data. The goal of this step is to ensure that genomic data with high access frequency can be stored under limited cache space through an effective cache management algorithm. First, the cache access extreme value data estimated in step S431 is used to identify which genomic sequences will have a higher access frequency in future queries. In order to optimize the efficiency of cache use, a cache replacement strategy needs to be designed. Common strategies include LRU (least recently used), LFU (least frequently used) and MFU (most frequently used). However, considering the dynamic nature of query data and the imbalance of access frequency, combined with the analysis results of access extreme value data, a dynamic cache replacement algorithm based on data access estimation can be designed. For example, for genomic data with a high predicted access frequency, a preloading strategy can be used to store these data in the cache first and set a longer cache storage cycle. For data with a low predicted access frequency, a shorter cache cycle can be set and replaced using an LRU or LFU strategy. Through this dynamic replacement strategy, the cache content can be adjusted according to actual access requirements, thereby improving the cache hit rate and reducing the system's cache replacement overhead. Cache architecture optimization is performed based on access cache replacement strategy and range query cache access extreme value data. This step aims to form an efficient cache architecture by integrating the previously obtained access patterns and cache replacement strategies. In the specific implementation, firstly, according to the data obtained in steps S431 and S432, combined with the characteristics of the query load, the hierarchical structure of the cache architecture is designed. Assuming that in some cases, the query of genomic data is concentrated in a certain range (for example, the gene sequence of certain specific pathogens), then these data should be stored in the top layer of the cache first. The structure of the cache can be divided into multiple levels, with the top-level cache storing frequently accessed gene sequence data, and the low-frequency data being stored in the bottom-level cache.The bottom cache can adopt a more traditional replacement strategy, such as LRU. Through this hierarchical cache structure, cache resources can be effectively utilized to maintain the stability of the system and the efficiency of data access without sacrificing performance. When optimizing the cache architecture, the cache update mechanism also needs to be considered. The extreme value estimation of data access (step S431) provides the necessary basis for the adjustment of the cache architecture, and the size and content of the cache can be dynamically adjusted. For example, in some cases, certain genomic data will become frequently accessed due to changes in experimental conditions, and the system needs to respond to these changes in a timely manner and adjust the cache content. This can be achieved through a regular cache evaluation mechanism, and the cache storage strategy is dynamically adjusted according to changes in access frequency, thereby further improving the overall performance of the system. Finally, the optimized cache architecture can not only improve the cache hit rate and reduce the number of database accesses, but also effectively alleviate database pressure and improve query response speed.

[0126] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0127] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for constructing a pathogenic microorganism genome database, characterized in that: The following steps are involved: Step S1: obtaining a pathogenic microorganism genome sequence; performing pathogen characteristic point analysis on the pathogenic microorganism genome sequence to obtain pathogen characteristic point data; Step S2: Analyze the physiological and biochemical characteristics of the pathogenic microorganism genome sequence according to the pathogen characteristic point data to obtain the physiological and biochemical characteristic data of the pathogen genome; The genome evolution relationship is simulated based on the physiological and biochemical characteristics of the pathogen genome to obtain the genome evolution relationship data; Performing pre-relation linkage identification on genome evolution relationship data to obtain pre-relation linkage data; Step S3: performing relationship nesting structure analysis on the pre-relationship linkage data to obtain pre-relationship nesting structure data; According to the nested structure data of the preceding linkage relationship, a relation coding index is constructed for the preceding linkage relationship data to obtain a linkage relationship coding index; Step S4: performing dynamic search condition matching on the linkage relationship coding index to obtain the linkage relationship dynamic search condition; optimizing the cache architecture according to the linkage relationship dynamic search condition to obtain the linkage relationship cache optimization architecture; The genome database of pathogenic microorganism genome sequences is constructed using the linkage relationship cache optimization architecture to obtain the pathogenic microorganism genome database.

2. The method for constructing a pathogenic microorganism genome database according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: obtaining the genome sequence of pathogenic microorganisms; Step S12: filtering low-quality sequences of the pathogenic microorganism genome sequence to obtain a pathogenic microorganism genome filtered sequence; Step S13: functionally classify and label the pathogenic microorganism genome filtered sequence to obtain the microorganism genome functional classification sequence; Step S14: Analyze the pathogenic characteristic points of the microbial genome functional classification sequence to obtain pathogenic characteristic point data.

3. The method for constructing a pathogenic microorganism genome database according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: Analyze the physiological and biochemical characteristics of the pathogenic microorganism genome sequence according to the pathogen characteristic point data to obtain the physiological and biochemical characteristic data of the pathogen genome; Step S22: Identify the environmental weight impact of the pathogen genome physiological and biochemical characteristic data to obtain physiological and biochemical environmental weight impact data; Step S23: simulating the genome evolution relationship of the pathogenic microorganism genome sequence according to the physiological and biochemical environment weight influence data to obtain genome evolution relationship data; Step S24: performing pre-relation linkage identification on the genome evolution relationship data to obtain pre-relation linkage data.

4. The method for constructing a pathogenic microorganism genome database according to claim 3, characterized in that: Step S23 includes the following steps: Step S231: performing spatiotemporal dimension action intensity analysis on the physiological and biochemical environment weight impact data to obtain spatiotemporal dimension action intensity data; Step S232: performing pressure chemical reaction change analysis on the pathogenic microorganism genome sequence according to the spatiotemporal action intensity data to obtain pathogen spatiotemporal pressure chemical reaction change data; Step S233: Calculating the probability of gene segment variation of the pathogenic microorganism genome sequence based on the pathogen spatiotemporal pressure chemical reaction variation data to obtain gene segment variation probability data; Step S234: performing quantitative prediction of the variation trend of the gene fragment variation probability data to obtain quantitative data of the variation trend of the gene fragment; Step S235: Perform genome evolution relationship simulation on the genome sequence of the pathogenic microorganism according to the gene segment variation probability data and the gene segment variation trend quantitative data to obtain genome evolution relationship data.

5. The method for constructing a pathogenic microorganism genome database according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: performing relationship nesting structure analysis on the pre-relation linkage data to obtain pre-relation linkage relationship nesting structure data; Step S32: performing pathogenic microorganism population genetic structure analysis on pathogen genome physiological and biochemical characteristic data according to the pre-linkage relationship nested structure data to obtain pathogenic microorganism population genetic structure data; Step S33: constructing a relationship coding index for the preceding relationship linkage data according to the preceding linkage relationship nested structure data and the pathogenic microorganism population genetic structure data to obtain a linkage relationship coding index.

6. The method for constructing a pathogenic microorganism genome database according to claim 5, characterized in that: Step S32 includes the following steps: Step S321: performing biochemical attribute similarity comparison on the pathogen genome physiological and biochemical characteristic data according to the pre-linkage relationship nested structure data to obtain genome biochemical attribute similarity data; Step S322: performing gene abundance calculation on the genome biochemical attribute similarity data to obtain gene similarity abundance data; Step S323: performing genetic population differentiation analysis on the genome biochemical attribute similarity data according to the gene similarity abundance data to obtain genetic population differentiation data; Step S324: Perform pathogenic microorganism population genetic structure analysis based on the genetic population differentiation data and the gene similarity abundance data to obtain pathogenic microorganism population genetic structure data.

7. The method for constructing a pathogenic microorganism genome database according to claim 6, characterized in that: Step S323 includes the following steps: According to the gene similarity abundance data, the gene abundance similarity distribution matrix is ​​constructed for the genome biochemical attribute similarity data to obtain the gene abundance similarity distribution matrix; Calculate the Fst value of the gene abundance similarity distribution matrix to obtain the gene similarity Fst value; The genetic population differentiation data were obtained by analyzing the gene abundance similarity distribution matrix based on the gene similarity Fst value.

8. The method for constructing a pathogenic microorganism genome database according to claim 5, characterized in that: Step S33 includes the following steps: Step S331: performing a relationship structure coupling analysis on the front-end relationship linkage data according to the front-end linkage relationship nested structure data and the pathogenic microorganism population genetic structure data to obtain front-end relationship structure coupling data; Step S332: performing weighted correlation evaluation on the pre-relationship structure coupling data to obtain weighted correlation data of the pre-relationship structure; Step S333: encoding the weighted related data of the predecessor relation structure to obtain the predecessor relation structure encoded data; Step S334: construct a relation coding index for the pre-relation structure coding data using a B+ tree algorithm to obtain a linkage relation coding index.

9. The method for constructing a pathogenic microorganism genome database according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: performing support range query limitation on the linkage relationship coding index to obtain support range query limitation data; Step S42: matching the linkage relationship coding index with dynamic search conditions according to the support range query limiting data to obtain the linkage relationship dynamic search conditions; Step S43: Optimizing the cache architecture according to the dynamic search conditions of the linkage relationship and the cache database to obtain a linkage relationship cache optimization architecture; Step S44: constructing a genome database for the pathogenic microorganism genome sequence using a relational database and a linkage relationship cache optimization architecture to obtain a pathogenic microorganism genome database.

10. The method for constructing a pathogenic microorganism genome database according to claim 9, characterized in that: Step S43 includes the following steps: Step S431: estimating the extreme value of cache access according to the supported range query limiting data, and obtaining the extreme value data of range query cache access; Step S432: Designing a cache replacement strategy for the cache database according to the range query cache access extreme value data to obtain an access cache replacement strategy; Step S433: Optimize the cache architecture based on the access cache replacement strategy and the range query cache access extreme value data to obtain the linkage relationship cache optimization architecture.

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