Microbial component detection method based on high-throughput sequencing data

Abstract

The invention relates to the technical field of microbial component detection, in particular to a microbial component detection method based on high-throughput sequencing data. The method comprises the following steps: performing deep sequencing on a microorganism test sample based on a high-throughput sequencing technology to obtain microorganism sample sequencing data; performing relative frequency quantitative calculation on the microorganism sample sequencing data, and performing aggregation behavior recognition among different categories to obtain microorganism category aggregation behavior data; constructing a metabolic pathway network among different microbial categories according to the microbial category gathering behavior data to obtain a microbial metabolic pathway network; and constructing a microbial component detection model according to the microbial metabolic pathway network to obtain the microbial component detection model. The microbial component detection technology is optimized, so that the microbial component detection technology is more perfect.

Description

Technical Field

[0001] The present invention relates to the technical field of microbial component detection, and in particular to a microbial component detection method based on high-throughput sequencing data. Background Art

[0002] High-throughput sequencing (HTS) technology has an important application background in the detection of microbial components. Microbial detection methods such as culture and microscopic observation are limited by culture conditions and morphological characteristics of microorganisms, making it difficult to comprehensively and accurately identify microbial populations in complex environments. In contrast, high-throughput sequencing technology has ultra-high sensitivity and parallel processing capabilities, and can sequence millions of DNA or RNA molecules in a single experiment, thus showing significant advantages in the analysis of microbial components. Through high-throughput sequencing of the whole genome or specific regions (such as 16S rRNA gene) of microbial community DNA or RNA, scientists can conduct comprehensive population diversity, abundance and functional analysis of microbial communities related to the environment, host or disease. HTS technology can not only detect uncultivated microbial species, but also analyze the dynamic changes of microbial populations, thereby helping researchers to gain a deeper understanding of the relationship between microbial communities and environmental or host health. However, a traditional method for detecting microbial components based on high-throughput sequencing data has the problems of inaccurate detection of microbial abundance and low precision in quantifying microbial community structure. Summary of the invention

[0003] Based on this, it is necessary to provide a method for detecting microbial components based on high-throughput sequencing data to solve at least one of the above technical problems.

[0004] To achieve the above object, a method for detecting microbial components based on high-throughput sequencing data is provided, the method comprising the following steps:

[0005] Step S1: obtaining a microbial test sample; performing deep sequencing on the microbial test sample based on high-throughput sequencing technology to obtain microbial sample sequencing data;

[0006] Step S2: performing relative frequency quantitative calculation on the microbial sample sequencing data to obtain relative frequency quantitative data of microbial categories; performing clustering behavior identification between different categories on the relative frequency quantitative data of microbial categories to obtain clustering behavior data of microbial categories;

[0007] Step S3: performing functional gene identification between different microbial categories based on the microbial category clustering behavior data to obtain microbial functional gene data; performing metabolic pathway network construction between different microbial categories based on the microbial functional gene data to obtain a microbial metabolic pathway network;

[0008] Step S4: construct a microbial component detection model according to the microbial metabolic pathway network to obtain a microbial component detection model; send the microbial component detection model to the cloud platform to execute the microbial component detection method.

[0009] The present invention first needs to obtain microbial test samples, which can come from different environments, such as soil, water or organisms. After obtaining the samples, high-throughput sequencing technology is applied for deep sequencing. This technology has the characteristics of high sensitivity and high throughput, and can generate a large amount of sequence data in a short time. The results of deep sequencing can reveal the diversity and relative abundance of various microorganisms present in the microbial samples, laying the foundation for subsequent analysis. The sequencing data obtained by analyzing the bioinformatics software can accurately determine the composition of the microorganisms in the sample and provide necessary information for the subsequent relative frequency quantitative calculation. The successful implementation of this stage is the basis of the entire study, ensuring that the subsequent analysis is scientific and effective. After obtaining the sequencing data of the microbial sample, the relative frequency quantitative calculation is required next. This step is mainly to normalize the sequence data of different microorganisms to obtain the proportion of each microorganism in the total sample. This relative frequency quantitative data helps to understand the structure of the microbial community and its abundance distribution. Afterwards, the clustering behavior between different microbial categories is identified by statistical analysis of the relative frequency data. This can be done by cluster analysis or other multidimensional scaling analysis methods, aiming to reveal the relationship and ecological function between microorganisms. The identification of clustering behavior helps to understand the complexity of microbial communities and their role in ecosystems, and can provide important reference information for subsequent functional gene identification. The clustering behavior data of microbial categories obtained in the previous steps can be used to further identify functional genes between different microbial categories. This process aims to identify genes related to specific functions, such as those involved in biological processes such as carbohydrate metabolism, nitrogen cycle, or drug resistance. By comparing known gene databases, the identified functional genes provide key clues for understanding the ecological functions of microorganisms. Then, based on the identified functional gene data, a metabolic pathway network of microorganisms is constructed. The metabolic pathway network shows the metabolic connections between different microorganisms and how they interact in the environment. The construction of this network not only helps to analyze the metabolic mechanism of microbial communities, but also provides a theoretical basis for the application research of microorganisms and promotes the development of microbial ecology. After the metabolic pathway network of microorganisms is constructed, the next step is to establish a microbial component detection model based on this network information. The construction of this model involves the use of statistical and machine learning methods to integrate the functional genes of microorganisms and their roles in metabolic pathways, so as to achieve accurate detection of microbial community components. The design of the model should ensure its applicability and reliability in different samples so that it can play a role in practical applications. Finally, the constructed microbial component detection model is sent to the cloud platform for real-time application of the microbial component detection method. The application of the cloud platform makes data processing and analysis more efficient, and researchers can obtain and analyze sample data at any time. This mechanism not only improves the efficiency of research, but also provides a broader platform for ecological monitoring and application of microorganisms.Therefore, the present invention is an optimization process for a traditional microbial component detection method based on high-throughput sequencing data, which solves the problems of inaccurate microbial abundance detection and low precision in quantification of microbial community structure in a traditional microbial component detection method based on high-throughput sequencing data, improves the accuracy of microbial abundance detection, and enhances the precision of quantification of microbial community structure.

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

[0011] Step S11: obtaining a microbial test sample;

[0012] Step S12: filtering and separating the microorganism test sample to obtain a microorganism filtration test sample;

[0013] Step S13: centrifuging the microbial filtration test sample to obtain a microbial filtration centrifugal test sample;

[0014] Step S14: performing deep sequencing on the microbial filtration centrifugation test sample based on high-throughput sequencing technology to obtain microbial sample sequencing data.

[0015] The acquisition of microbial test samples in the present invention is the primary link of the research. Samples can be obtained from a variety of sources, such as natural water bodies, soil, plants or animals. When obtaining samples, the representativeness of the samples should be considered to ensure that the real microbial community structure and diversity can be reflected. The process of sample collection should avoid contamination as much as possible, usually under sterile conditions, and sealed in appropriate containers. Effective sample acquisition provides a reliable data basis for subsequent experiments, and can provide important information for the study of the types, abundance and potential ecological functions of microorganisms. In addition, the collection and storage conditions of the samples also have an important impact on the results of subsequent analysis. Therefore, the sampling environment and process should be strictly controlled at this stage to ensure the integrity and availability of the samples. After obtaining the microbial test samples, the next step is to perform filtration and separation. The purpose of this process is to separate the microorganisms in the sample from other impurities by filtration, so as to better perform subsequent analysis. Filter membranes of different pore sizes can be used for filtration, and selection is made according to the size of the microorganisms in the sample to ensure that the target microorganisms can be effectively captured. In this process, the sterility of the filter and the environment must be ensured to prevent the sample from being contaminated during the processing. The samples after filtration and separation can enrich the types and quantity of microorganisms, reduce background noise, and improve the sensitivity and accuracy of subsequent experiments. This treatment not only provides a purer sample for subsequent centrifugation and sequencing, but also lays the foundation for in-depth analysis of the diversity and ecological functions of microbial communities. After completing the filtration and separation treatment, the microbial filtration test samples are then centrifuged. This step is to further concentrate the microorganisms in the sample and remove the existing liquid components and other unnecessary impurities. Centrifugation uses the centrifugal force generated by high-speed rotation to precipitate microbial cells at the bottom of the test tube, thereby separating them from the supernatant. This process can effectively increase the concentration of microorganisms in the sample, facilitating subsequent deep sequencing and analysis. At the same time, the samples after centrifugation are purer, which can reduce the background signal that affects the sequencing results and improve the reliability of the data. In addition, the temperature and time need to be strictly controlled during the centrifugation process to avoid damage to the microorganisms, so as to ensure the acquisition of high-quality microbial samples and provide sufficient guarantee for subsequent deep sequencing. After centrifugation, the final step is to perform deep sequencing of the microbial filtration centrifugation test samples based on high-throughput sequencing technology. High-throughput sequencing technology is efficient and fast, and can generate a large amount of sequence data in a short time, which is particularly important for the study of microbial communities. By deep sequencing of microbial samples, rich genetic information can be obtained to identify the types of microorganisms in the samples and their relative abundance. This step can not only reveal the diversity of microorganisms, but also provide basic data for subsequent functional analysis and ecological research. In addition, deep sequencing can also identify some low-abundance microorganisms, which is of great significance for understanding the structure and function of microbial communities.Ultimately, the results of deep sequencing will provide important data support for fields such as microbial ecology research, environmental monitoring, and biotechnology applications.

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

[0017] Step S21: recombining DNA fragments of the microbial sample sequencing data to obtain recombined microbial DNA fragment data;

[0018] Step S22: using a preset microbial DNA identification database to perform sequence comparison on the microbial DNA fragment recombinant data to obtain microbial DNA fragment category data;

[0019] Step S23: performing relative frequency quantitative calculation on the microbial sample sequencing data according to the microbial DNA fragment category data to obtain microbial category relative frequency quantitative data;

[0020] Step S24: performing clustering behavior identification among different categories on the quantitative data of relative frequency of microorganism categories to obtain clustering behavior data of microorganism categories.

[0021] After the sequencing data of the microbial sample is obtained, the present invention first needs to perform DNA fragment recombination on these data. DNA fragment recombination is to splice the short fragments (reads) obtained by sequencing into longer sequences in order to more accurately identify the genome information of the microorganism. This step can fill the gaps in the sequencing by aligning and splicing the overlapping fragments, thereby improving the continuity and integrity of the sequence. Fragment recombination not only helps to make up for the missing in the short sequence sequencing, but also improves the quality of the data, reduces errors, and ensures the accuracy of subsequent comparison and analysis. Through this process, the genetic information of the microorganism is effectively integrated, which is helpful for the classification and functional analysis of the microbial community in the subsequent steps, and provides more accurate genome data support for understanding the diversity and ecological role of microorganisms. The recombined microbial DNA fragments need to be sequenced with the preset microbial DNA identification database. This step is a key link in the classification and identification of microorganisms. Through the comparison, the microbial category to which each DNA fragment belongs can be identified, and the classification information of the fragment can be obtained. The preset database usually contains a large amount of known microbial genome information, and similar sequences can be found through an alignment algorithm (such as BLAST) to determine the type of microorganism in the sample. The accuracy of this step directly affects the effect of subsequent microbial classification and ecological research. The microbial DNA fragment category data obtained after comparison provides a basis for the diversity analysis of microbial communities, and can help researchers identify the types of microorganisms contained in the sample and their relative abundance. This provides important genetic information support for the functional analysis, ecological research and environmental monitoring of microorganisms. After obtaining the category data of microbial DNA fragments, it is necessary to perform relative frequency quantitative calculations on these data. By comparing the number of fragments of each microbial category with the total number of fragments, the relative abundance of each microbial category in the sample can be calculated. This calculation process can reveal the distribution of different microorganisms in the sample and show their relative importance. Relative frequency quantitative data is helpful to study the structural characteristics of microbial communities and understand which microorganisms dominate and which microorganisms are minority populations. Through this data analysis, researchers can more clearly grasp the diversity and community characteristics of microorganisms in the sample, and provide data support for subsequent microbial function and niche analysis. This step can also lay the foundation for subsequent statistical analysis and bioinformatics analysis, and provide quantitative analysis for issues such as the succession and environmental adaptability of microbial communities. After obtaining the quantitative data of the relative frequency of microbial categories, the next step is to identify the clustering behavior between different microbial categories. The identification of clustering behavior is mainly carried out through statistical methods such as cluster analysis, correlation analysis or network analysis. Through these methods, the interaction relationship between microbial categories can be identified, revealing whether there is symbiosis, competition or other forms of ecological interaction between microorganisms in the community. The analysis of clustering behavior helps to understand the structure and function of microbial communities and evaluate the interdependence of different categories of microorganisms and their ecological roles.Through this process, researchers can better understand the complexity of microbial communities and predict the performance and adaptation mechanisms of microorganisms under different environmental conditions. The analysis at this stage provides strong data support for the fields of microbial community ecology research, environmental monitoring and microbial engineering applications.

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

[0023] Step S231: performing relative abundance calculations between different categories on the microbial sample sequencing data according to the microbial DNA fragment category data to obtain microbial category sequence relative abundance data;

[0024] Step S232: performing abundance distribution skewness analysis among different categories on the relative abundance data of microbial category sequences to obtain abundance distribution skewness data;

[0025] Step S233: performing a balanced quantitative analysis on the relative abundance data of the microbial category sequence according to the abundance distribution skewness data to obtain balanced quantitative data of the category sequence;

[0026] Step S234: performing relative frequency quantitative calculation on the microbial sample sequencing data according to the abundance distribution skewness data and the category sequence balance quantitative data to obtain microbial category relative frequency quantitative data.

[0027] The present invention calculates the relative abundance of different categories of microbial sample sequencing data based on the category data of microbial DNA fragments. This process involves comparing the number of DNA fragments of each microorganism with the total number of DNA fragments to calculate the relative abundance of each type of microorganism in the sample. This analysis can effectively reveal the structural characteristics of the microbial community and help researchers understand the distribution of different microbial categories. For example, some microorganisms dominate in a specific environment, while other microorganisms are minor components. Relative abundance data not only provides a basis for ecological research, but also lays a data foundation for subsequent functional analysis and community comparison. Through this step, important clues can be provided for the functional diversity and ecological adaptability analysis of microbial communities. The relative abundance data of microbial category sequences are analyzed for abundance distribution skewness. This analysis aims to evaluate the characteristics of the abundance distribution of different microbial categories and identify the skewness of the abundance distribution. Through statistical analysis of abundance data, it can be determined which microbial categories dominate the community and which are in a low abundance state. The skewed distribution of abundance can reveal the ecological characteristics and functional diversity of microbial communities. For example, positive skewness indicates that certain microbial categories are numerous, while negative skewness indicates that a small number of microorganisms occupy most of the resources. Through this analysis, researchers can have a deeper understanding of the ecological niches, interactions, and adaptation mechanisms of microorganisms in the environment, providing an important reference for subsequent ecological research and environmental monitoring. Based on the abundance distribution skewness data, the relative abundance data of microbial category sequences are balanced and quantitatively analyzed. This process aims to eliminate the bias in the sample, so that the abundance data of different categories can be more fair and accurate in statistical analysis. Balanced quantitative analysis can make the analysis results more comparable by adjusting the abundance data of various microorganisms in the sample to meet certain preset standards or distributions. This is very important for evaluating the true structure of the microbial community, especially in the case of environmental changes or experimental interventions, which can help identify the response mechanism and adaptability of microorganisms. Through this analysis, researchers can obtain more accurate balanced quantitative data of microbial category sequences, laying the foundation for further ecological model establishment and community dynamics analysis. Combining the abundance distribution skewness data and the balanced quantitative data of category sequences, the relative frequency of microbial sample sequencing data is quantitatively calculated. This calculation process integrates the data obtained in the previous steps and finally obtains the relative frequency data of each microbial category in the sample. These relative frequency data can reflect the actual existence of different microbial categories in the sample and help researchers identify their roles and importance in the ecosystem. Relative frequency quantitative data not only helps to understand the composition and dynamic changes of microbial communities, but also provides more comprehensive and reliable basic data for ecological research. Ultimately, these data will support the ecological function analysis of microorganisms, environmental adaptability research, and the development and application of related biotechnologies, providing a scientific basis for theoretical research and practical application of microbial ecology.

[0028] Preferably, performing balanced quantitative analysis on the relative abundance data of microbial category sequences according to the skewed abundance distribution data comprises the following steps:

[0029] Using the preset abundance stratification interval to perform abundance stratification processing on the abundance distribution skewness data, to obtain abundance distribution skewness stratification data;

[0030] The extreme value fluctuation analysis in different layers is performed on the skewed stratified data of abundance distribution to obtain the extreme value fluctuation data of skewed stratification;

[0031] The median difference of different strata is calculated for the skewed stratified extreme value fluctuation data to obtain the skewed stratified median difference data;

[0032] According to the skewed stratified median difference data, the skewed stratified extreme value fluctuation data is balanced interpolated to obtain the skewed stratified balanced interpolation data;

[0033] Based on the skewed stratified balanced interpolation data, a balanced quantitative analysis was performed on the relative abundance data of microbial category sequences to obtain balanced quantitative data of category sequences.

[0034] The present invention uses a preset abundance stratification interval to perform abundance stratification processing on the skewed abundance distribution data to obtain skewed abundance distribution stratified data. The core of this process is to divide the microbial abundance data into different levels so as to more clearly reveal the distribution characteristics of microorganisms in different abundance ranges. By stratifying the data, researchers can analyze the diversity and distribution pattern of microorganisms in each abundance interval. This stratification process can help identify which microbial categories dominate in a specific abundance interval and which microorganisms are rare populations. Through clear abundance intervals, the ecological characteristics of microbial communities and their responses to environmental changes can be better understood, providing a good foundation for subsequent analysis. The extreme value fluctuation analysis in different strata of the skewed abundance distribution stratified data is performed to obtain skewed stratified extreme value fluctuation data. The purpose of extreme value fluctuation analysis is to identify extreme values ​​and their changes in each abundance stratification, which usually refer to very high or very low observations in the abundance distribution. By analyzing the fluctuations of these extreme values, researchers can reveal the survival strategies and ecological adaptability of microorganisms under specific environmental conditions. For example, some microorganisms show extremely high abundance when resources are abundant, but their number drops sharply when resources are scarce. Extreme fluctuation data provide important information for the study of the stability, anti-disturbance ability and ecological interaction of microbial communities, and can provide support for assessing the health of ecosystems. Based on the skewed stratified extreme fluctuation data, the median difference calculation within different layers is performed to obtain the skewed stratified median difference data. The calculation of the median difference is to better understand the changing trend and concentration of microbial abundance in each abundance level. By comparing the median within each abundance layer, the significance and trend of abundance changes can be identified, revealing the biological significance of microbial categories in each abundance level. For example, a significant change in the median of a certain layer means a change in environmental conditions or a change in the competitive relationship between microorganisms. Median difference data helps to study the dynamic changes of microbial communities, reveal their ecological adaptability and succession process, and provide a quantitative basis for ecological and microbiological research. Based on the skewed stratified median difference data, the skewed stratified extreme fluctuation data is balanced interpolated to obtain the skewed stratified balanced interpolation data. The purpose of balanced interpolation is to smooth data between different abundance levels to eliminate existing deviations and outliers and ensure the accuracy and comparability of the analysis results. Through interpolation calculations, reasonable estimates of data can be achieved in each abundance layer, making the abundance distribution more uniform and reflecting the true abundance of microorganisms in the sample. This step helps to eliminate the impact of extreme values ​​on the results and provide a more reliable data basis to obtain scientific and accurate conclusions in subsequent analysis. Based on the skewed stratified balanced interpolation data, a balanced quantitative analysis of the relative abundance data of the microbial category sequence was performed to obtain the balanced quantitative data of the category sequence.The key to this step is to integrate the data processed in the previous steps to form a more stable and accurate abundance data set. Balanced quantitative analysis will provide quantitative support for the true distribution of microbial categories in the sample, which will help study the overall structure and diversity of the microbial community. At the same time, this analysis result can also provide strong data support for ecological research, environmental monitoring and microbial engineering applications. Through this comprehensive analysis, researchers can have a deeper understanding of the role and function of microorganisms in the ecosystem, and provide a solid theoretical foundation and data support for future related research.

[0035] Preferably, performing balanced interpolation calculation on microbial category sequence relative abundance data according to skewed stratified median difference data comprises the following steps:

[0036] The skewed stratified median difference data is solved by high-order difference accumulation to obtain the skewed stratified high-order difference accumulation data;

[0037] According to the skewness stratification high-order difference cumulative data and the skewness stratification median difference data, the skewness stratification extreme value fluctuation data is evaluated for data point continuity and smoothness, and the skewness stratification fluctuation continuous and smooth data is obtained;

[0038] The skewed stratified balanced interpolation data are obtained by performing balanced interpolation calculation on the skewed stratified continuous and gentle fluctuation data.

[0039] The present invention performs high-order difference accumulation on the skewed stratified median difference data to obtain skewed stratified high-order difference accumulation data. High-order difference accumulation is to gradually and deeply analyze the trend of microbial abundance changes by calculating multiple differences of median differences. This method can effectively capture subtle changes in abundance data, especially nonlinear trends that appear in certain abundance levels. Through the calculation of high-order differences, researchers can reveal the complex relationship between each abundance level, so as to more comprehensively understand the dynamic changes and ecological adaptability of microbial communities. This step lays the foundation for subsequent interpolation calculations, so that data analysis can more accurately reflect the actual abundance status and distribution characteristics of microbial categories. According to the skewed stratified high-order difference accumulation data and the skewed stratified median difference data, the skewed stratified extreme value fluctuation data is evaluated for data points continuously and gently, thereby obtaining skewed stratified fluctuation continuous and gentle data. Through continuous and gentle evaluation, researchers can identify and eliminate the instability caused by extreme value fluctuations, so that the data presents a smoother and more coherent trend. This process is particularly important because microbial abundance data are often affected by environmental and other external factors, resulting in sudden fluctuations and irregular changes. By eliminating these interferences, the evaluated data can more realistically reflect the dynamic behavior and abundance change trend of the microbial community, providing a more reliable data basis for subsequent balanced interpolation calculations. Balanced interpolation calculations are performed on the skewed stratified fluctuation continuous smooth data to obtain skewed stratified balanced interpolation data. The purpose of balanced interpolation is to further process the smoothed data so that the data is more balanced and continuous between different abundance levels. This step establishes connections between data points through interpolation methods, fills in blanks or missing data, and ensures the integrity and consistency of the final data set. Balanced interpolation calculations can eliminate noise and outliers in the data, making the display of relative abundance data of microbial category sequences more accurate and reliable. Through this balanced data, researchers can better understand the distribution characteristics and ecological functions of microorganisms in ecosystems, and provide a scientific basis for ecological monitoring, management and protection of microbial communities.

[0040] Preferably, step S3 comprises the following steps:

[0041] Step S31: Calculate the spatial dispersion between different microbial categories on the microbial category aggregation behavior data according to the quantitative data of relative frequency of microbial categories to obtain the spatial dispersion data of microbial aggregation;

[0042] Step S32: performing functional gene identification between different microbial categories on the microbial sample sequencing data according to the microbial cluster spatial dispersion data and the microbial category cluster behavior data to obtain microbial functional gene data;

[0043] Step S33: constructing a metabolic pathway network among different microbial categories based on the microbial functional gene data according to the quantitative data of relative frequency of microbial categories, and obtaining a microbial metabolic pathway network.

[0044] The present invention calculates the spatial dispersion of the microbial category aggregation behavior based on the quantitative data of the relative frequency of microbial categories to obtain the spatial dispersion data of microbial aggregation. The core of this process is to quantify the spatial distribution pattern and dispersion degree of different microbial categories. By calculating the dispersion, researchers can identify the aggregation characteristics of microbial communities in the environment and the interactions between different microorganisms. For example, a higher spatial dispersion indicates that certain microbial categories are aggregated in a specific area, while a lower dispersion indicates a uniform distribution. This analysis not only helps to understand the niche allocation of microbial communities, but also reveals how environmental factors affect the spatial distribution of microorganisms, providing an important basis for microbial ecology research. Based on the spatial dispersion data of microbial aggregation and the aggregation behavior data of microbial categories, the functional gene identification between different microbial categories is performed on the sequencing data of microbial samples to obtain microbial functional gene data. Functional gene identification is an important step in analyzing the functional potential of microbial communities. By comparing and analyzing the genomic information in the samples, researchers can identify genes related to specific biological functions. This process not only helps to reveal the metabolic capacity and ecological functions of microorganisms, but also further understands their adaptability under specific environmental conditions. By combining spatial dispersion with functional gene data, the role and importance of different microbial categories in community functions can be explored, providing a new perspective for the evaluation of the ecological functions of microbial communities. Based on the quantitative data of the relative frequency of microbial categories, the metabolic pathway network between different microbial categories is constructed for the microbial functional gene data to obtain the microbial metabolic pathway network. The purpose of this step is to construct a network that reflects the metabolic interactions between different microbial categories by integrating the functional gene information of microorganisms. The construction of the metabolic pathway network can clearly show the interactions and dependencies between microorganisms in the metabolic process. For example, some microorganisms participate in the carbon or nitrogen cycle through mutual cooperation. By analyzing the metabolic pathway network, researchers can gain a deeper understanding of how microbial communities play a role in ecosystems and the changes in metabolic networks under different environmental conditions. This information has important guiding significance for fields such as ecological restoration, agricultural microbial applications, and environmental protection.

[0045] Preferably, step S33 includes the following steps:

[0046] Step S331: performing gene interaction frequency relationship analysis between different microbial categories on microbial functional gene data according to the quantitative data of relative frequency of microbial categories to obtain gene interaction frequency relationship data;

[0047] Step S332: evaluating the functional impact of gene fragments on the microbial functional gene data according to the gene interaction frequency relationship data to obtain gene fragment functional impact data;

[0048] Step S333: performing effective association analysis of metabolic pathways in different functional regions on the gene fragment functional impact data to obtain effective association data of metabolic pathways;

[0049] Step S334: construct a metabolic pathway network among different microbial categories based on the gene fragment functional impact data and the metabolic pathway effective association data to obtain a microbial metabolic pathway network.

[0050] The present invention analyzes microbial functional gene data through quantitative data of relative frequency of microbial categories to calculate the frequency relationship of gene interactions between different microbial categories. The core of this process is to determine which genes interact with each other and the frequency of such interactions. In microbial ecosystems, genes of different species affect each other's growth and metabolic characteristics through complex interactions, so it is crucial to understand these interactions. By constructing gene interaction frequency relationship data, researchers can identify the contribution of high-frequency interacting genes to community functions, thereby providing a quantitative basis for the synergy between microorganisms. This step lays an important foundation for subsequent functional evaluation and network construction. According to the gene interaction frequency relationship data, the functional impact of gene fragments on microbial functional gene data is evaluated. The purpose of this evaluation is to identify the importance and influence of specific gene fragments in the function of microbial communities. By analyzing gene fragments with higher interaction frequencies, researchers can determine which genes play a key role in the metabolic activities and ecological adaptability of microorganisms. For example, certain gene fragments enhance the viability of microorganisms or promote their metabolic efficiency under specific environmental conditions. The functional impact data of the gene fragments obtained by the evaluation provide detailed information for the subsequent effective association analysis of metabolic pathways, allowing researchers to focus on those gene fragments that significantly affect the function of the community, thereby deepening their understanding of the ecological function of microorganisms. The effective association analysis of metabolic pathways in different functional regions was performed on the functional impact data of gene fragments to obtain effective association data of metabolic pathways. The goal of this step is to identify effective associations in microbial metabolic pathways by analyzing the functional impact of gene fragments. Through this analysis, researchers can reveal which gene fragments play a key role in specific metabolic pathways and how these genes interact to maintain the function and ecological balance of microorganisms. For example, some functional regions contain multiple genes with high interaction frequencies, which together promote specific biotransformation processes. Effective association data of metabolic pathways provide a basis for building more complex metabolic networks, allowing researchers to deeply understand how microorganisms interact in ecosystems through metabolic pathways. Based on the functional impact data of gene fragments and the effective association data of metabolic pathways, metabolic pathway networks between different microbial categories are constructed to obtain microbial metabolic pathway networks. This construction process is a key link in the synthesis of the analysis results of the previous steps. By integrating the functional associations of all relevant genes and metabolic pathways, a comprehensive metabolic pathway network model is formed. The network can not only visually display the metabolic interactions between different microorganisms, but also reveal the functions of key nodes and key genes in metabolic pathways. The construction of this network is crucial for understanding the role of microbial communities in ecosystems, helping researchers predict how microorganisms respond to environmental changes and how they affect overall ecological functions through metabolic activities.In this way, researchers can gain important insights into microbial ecology and provide a scientific basis for future ecological management and conservation strategies.

[0051] Preferably, step S4 comprises the following steps:

[0052] Step S41: screening the key metabolic node functional genes of the microbial metabolic pathway network to obtain key metabolic node functional gene data;

[0053] Step S42: performing expression detection on the key metabolic node functional gene data to obtain node functional gene expression data;

[0054] Step S43: constructing a microbial component detection model for the node functional gene expression data according to the microbial metabolic pathway network to obtain a microbial component detection model;

[0055] Step S44: Send the microbial component detection model to the cloud platform to execute the microbial component detection method.

[0056] The present invention screens the functional genes of key metabolic nodes in the microbial metabolic pathway network, aiming to identify key genes that play an important role in the metabolic process. This step determines which genes play a core role in regulating metabolic flow and metabolite production by analyzing the connectivity and functional importance in the metabolic pathway network. Key metabolic nodes are usually bottlenecks for microbial growth and reproduction. Understanding the functions of these genes can reveal the adaptation mechanism of microorganisms in specific environments and their metabolic characteristics. This screening result provides basic data for subsequent expression detection and functional analysis, helping researchers focus on the most potential genes for in-depth research. The expression of the screened key metabolic node functional genes is detected to obtain the expression data of the node functional genes. This step measures the transcription levels of these genes under specific conditions by experimental methods (such as qPCR, RNA-seq, etc.), reflecting their actual activity in the physiological process of microorganisms. Changes in gene expression can provide important information about how microorganisms regulate their metabolic activities under environmental pressure. For example, some genes are highly expressed when nutrients are abundant, but downregulated when resources are tight. By systematically recording these changes, researchers can reveal metabolic regulation mechanisms and their relationship with environmental changes, providing an empirical basis for the ecological characteristics and bioengineering applications of microorganisms. Based on the microbial metabolic pathway network, the node functional gene expression data are analyzed to construct a microbial component detection model. The model aims to predict the relative abundance of various components in the microbial community through the expression characteristics of node genes. Using machine learning algorithms and statistical models, researchers can build a model that comprehensively considers multiple factors (such as environmental factors, metabolic pathways, and gene expression). The construction of this model provides a new method for the quantitative analysis of microbial communities, allowing researchers to accurately predict and monitor changes in microbial communities and their role in ecosystems. In addition, the reliability and accuracy of the model will directly affect the results and applications of microbiome research. The constructed microbial component detection model is sent to the cloud platform to execute the microbial component detection method. This step uses the powerful computing power and storage capacity of cloud computing to process and analyze a large amount of microbial data, improving the efficiency and accuracy of data analysis. Through the cloud platform, researchers can not only obtain test results in real time, but also perform more complex calculations and visualization analysis to further optimize the detection method. In addition, the use of cloud platforms has made data sharing and collaboration between different research institutions more convenient, promoting the openness and transparency of microbiome research. The implementation of this method not only accelerates the detection process of microbial components, but also provides a solid data foundation for subsequent ecological research and application development.

[0057] Preferably, step S43 includes the following steps:

[0058] Step S431: performing metabolic flux balance calculation on the node functional gene expression data according to the microbial metabolic pathway network to obtain metabolic flux balance data;

[0059] Step S432: performing cross-influence analysis on the metabolic flux balance data to obtain metabolic flux cross-influence data;

[0060] Step S433: construct a microbial component detection model for the node functional gene expression data according to the metabolic flow cross-influence data to obtain a microbial component detection model.

[0061] The present invention calculates the metabolic flux balance of the expression data of the node functional genes according to the microbial metabolic pathway network. This process determines the equilibrium state between the generation and consumption of metabolites under given conditions by analyzing the flow of each metabolite in a specific metabolic pathway. Through this balance calculation, the main path of the metabolic flux and the bottleneck reaction can be identified, thereby helping to understand how microorganisms use resources and convert energy under different environments. The metabolic flux balance data not only reflects the metabolic capacity of the microbial community, but also provides basic information for subsequent cross-influence analysis, so that researchers can more comprehensively understand the role and function of microorganisms in the ecosystem. Cross-influence analysis is performed on the metabolic flux balance data to obtain metabolic flux cross-influence data. The main purpose of this step is to identify the interactions and influence relationships between different metabolic pathways. For example, some metabolic pathways influence each other through the same metabolic precursors or common regulatory factors. Through cross-influence analysis, researchers can reveal how these relationships affect the overall metabolic efficiency and the adaptability of microorganisms. In addition, this analysis can help identify key metabolic nodes and potential regulatory points of metabolic flux, providing data support for further metabolic engineering or ecological research. This analysis provides an important basis for establishing more complex metabolic models and optimizing the metabolic network of microorganisms. Based on the metabolic flux cross-influence data and node functional gene expression data, the researchers constructed a microbial component detection model. The model aims to predict the composition and relative abundance of microbial communities by quantifying metabolic flux and its cross-influence. By integrating information on metabolic flux balance and cross-influence, the model can reflect the importance of different microbial species in the metabolic process and the dynamic characteristics of their interactions. The establishment of this model not only enhances the understanding of the complexity of microbial communities, but also provides feasible methodological support for environmental monitoring, microbiome research and bioengineering applications. In addition, the constructed model can also be applied to future experimental design and data analysis, providing a reliable tool for microbial ecology research.

[0062] The beneficial effects of the present invention are that firstly, microbial test samples need to be obtained, and the samples can come from different environments, such as soil, water or organisms. After obtaining the samples, high-throughput sequencing technology is applied for deep sequencing. This technology has the characteristics of high sensitivity and high throughput, and can generate a large amount of sequence data in a short time. The results of deep sequencing can reveal the diversity and relative abundance of various microorganisms present in the microbial samples, laying the foundation for subsequent analysis. The sequencing data obtained by analyzing the bioinformatics software can accurately determine the composition of the microorganisms in the sample and provide the necessary information for the subsequent relative frequency quantitative calculation. The successful implementation of this stage is the basis of the entire study, ensuring that the subsequent analysis is scientific and effective. After obtaining the sequencing data of the microbial sample, the relative frequency quantitative calculation is required next. This step is mainly to normalize the sequence data of different microorganisms to obtain the proportion of each microorganism in the total sample. This relative frequency quantitative data helps to understand the structure of the microbial community and its abundance distribution. Afterwards, the clustering behavior between different microbial categories is identified by statistical analysis of the relative frequency data. This can be done by cluster analysis or other multidimensional scaling analysis methods, aiming to reveal the relationship and ecological function between microorganisms. The identification of clustering behavior helps to understand the complexity of microbial communities and their role in ecosystems, and can provide important reference information for subsequent functional gene identification. The clustering behavior data of microbial categories obtained in the previous steps can be used to further identify functional genes between different microbial categories. This process aims to identify genes related to specific functions, such as those involved in biological processes such as carbohydrate metabolism, nitrogen cycle, or drug resistance. By comparing known gene databases, the identified functional genes provide key clues for understanding the ecological functions of microorganisms. Then, based on the identified functional gene data, a metabolic pathway network of microorganisms is constructed. The metabolic pathway network shows the metabolic connections between different microorganisms and how they interact in the environment. The construction of this network not only helps to analyze the metabolic mechanism of microbial communities, but also provides a theoretical basis for the application research of microorganisms and promotes the development of microbial ecology. After the metabolic pathway network of microorganisms is constructed, the next step is to establish a microbial component detection model based on this network information. The construction of this model involves the use of statistical and machine learning methods to integrate the functional genes of microorganisms and their roles in metabolic pathways, so as to achieve accurate detection of microbial community components. The design of the model should ensure its applicability and reliability in different samples so that it can play a role in practical applications. Finally, the constructed microbial component detection model is sent to the cloud platform for real-time application of the microbial component detection method. The application of the cloud platform makes data processing and analysis more efficient, and researchers can obtain and analyze sample data at any time. This mechanism not only improves the efficiency of research, but also provides a broader platform for ecological monitoring and application of microorganisms.Therefore, the present invention is an optimization process for a traditional microbial component detection method based on high-throughput sequencing data, which solves the problems of inaccurate microbial abundance detection and low precision in quantification of microbial community structure in a traditional microbial component detection method based on high-throughput sequencing data, improves the accuracy of microbial abundance detection, and enhances the precision of quantification of microbial community structure. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 A schematic diagram of the steps of a method for detecting microbial components based on high-throughput sequencing data;

[0064] Figure 2 for Figure 1 Detailed implementation steps of step S2 in FIG.

[0065] 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

[0066] 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.

[0067] 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.

[0068] 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.

[0069] To achieve this, please refer to Figure 1 to Figure 2 , a method for detecting microbial components based on high-throughput sequencing data, the method comprising the following steps:

[0070] Step S1: obtaining a microbial test sample; performing deep sequencing on the microbial test sample based on high-throughput sequencing technology to obtain microbial sample sequencing data;

[0071] Step S2: performing relative frequency quantitative calculation on the microbial sample sequencing data to obtain relative frequency quantitative data of microbial categories; performing clustering behavior identification between different categories on the relative frequency quantitative data of microbial categories to obtain clustering behavior data of microbial categories;

[0072] Step S3: performing functional gene identification between different microbial categories based on the microbial category clustering behavior data to obtain microbial functional gene data; performing metabolic pathway network construction between different microbial categories based on the microbial functional gene data to obtain a microbial metabolic pathway network;

[0073] Step S4: construct a microbial component detection model according to the microbial metabolic pathway network to obtain a microbial component detection model; send the microbial component detection model to the cloud platform to execute the microbial component detection method.

[0074] In the embodiment of the present invention, reference Figure 1 The above is a schematic diagram of the steps of a method for detecting microbial components based on high-throughput sequencing data of the present invention. In this example, the method for detecting microbial components based on high-throughput sequencing data includes the following steps:

[0075] Step S1: obtaining a microbial test sample; performing deep sequencing on the microbial test sample based on high-throughput sequencing technology to obtain microbial sample sequencing data;

[0076] In the embodiment of the present invention, the acquisition of microbial test samples needs to be carried out through strict aseptic operation to avoid interference from external contamination sources. Use precise microbial collection tools, such as sterile sampling tubes and filter membranes, to collect samples from the target environment (such as water, soil or human body surface, etc.), and immediately store them at 4 ° C to ensure that the activity and structural integrity of the microorganisms in the sample are not damaged. Subsequently, the sample is sent to the laboratory for processing and deep sequencing is performed by high-throughput sequencing technology. High-throughput sequencing uses a next-generation sequencing platform, such as Illumina HiSeq or Ion Torrent, to first extract DNA from the sample. The extraction step requires the use of a commercially available DNA extraction kit, such as the Qiagen DNeasy kit, combined with centrifugation and buffer washing steps to ensure the high purity and integrity of the DNA in the sample. After the extraction is completed, the 16S rRNA gene (applicable to bacteria) or the 18S rRNA gene (applicable to eukaryotic organisms such as fungi) is amplified using specific primers, and the library is constructed and prepared for sequencing. The amount of data for deep sequencing is often as high as hundreds of millions of sequences, which can cover the genetic information of a large number of microbial populations in the sample. After sequencing is completed, the initial sequence data, i.e., the microbial sample sequencing data, is generated. This data is usually saved in FASTQ format, which contains the sequence itself and the sequencing quality score.

[0077] Step S2: performing relative frequency quantitative calculation on the microbial sample sequencing data to obtain relative frequency quantitative data of microbial categories; performing clustering behavior identification between different categories on the relative frequency quantitative data of microbial categories to obtain clustering behavior data of microbial categories;

[0078] In an embodiment of the present invention, the obtained microbial sample sequencing data is quantitatively calculated for relative frequency. First, the sequencing data needs to be quality controlled. Common tools such as Trimmomatic or FASTQC software packages can remove low-quality sequences, joint contamination and other unreliable sequencing fragments. The data after quality control will be de-redundant to delete repeated sequencing fragments. Then, the sequence classification is performed using platforms such as QIIME2 or Mothur. These tools assign microbial sequences to different taxonomic units (Operational Taxonomic Units, OTUs) by comparing databases such as Greengenes or SILVA, or divide them into different taxonomic groups based on sequence similarity. After the OTU or taxonomic unit is clear, the relative abundance of each microorganism in the sample is calculated based on the sequencing depth of each microorganism in the taxonomic group. The relative abundance is equal to the number of sequencing sequences of each microorganism divided by the total number of valid sequences, and the quantitative data of the relative frequency of each microbial category is obtained. This data characterizes the proportion of different microorganisms in the entire sample community to ensure the accuracy of subsequent analysis. Next, based on the quantitative data of relative frequency of the above microbial categories, cluster analysis methods in statistics, such as hierarchical clustering or K-means clustering, are used to identify the clustering behavior between different microbial categories. This clustering behavior indicates that certain microorganisms are more inclined to interact or coexist under certain conditions, reflecting the ecological relationship or symbiotic mechanism between microorganisms. Through cluster analysis, microbial category clustering behavior data is generated, which can be used for further ecological analysis of microbial community structure and its function.

[0079] Step S3: performing functional gene identification between different microbial categories based on the microbial category clustering behavior data to obtain microbial functional gene data; performing metabolic pathway network construction between different microbial categories based on the microbial functional gene data to obtain a microbial metabolic pathway network;

[0080] In an embodiment of the present invention, on the basis of the clustering behavior data of microorganism categories, a known microorganism functional gene database (such as KEGG or COG database) is used to identify the functional genes between each microorganism category. First, by comparing the known gene sequences in these functional gene libraries, the gene sequence of each microorganism category is matched with the functional genes in the database. The specific operation includes using a gene annotation tool (such as Prokka) to perform functional annotation on the microorganism genome and identify the functional genes contained in each microorganism species. In this process, it is necessary to strictly follow the sequence alignment algorithm, such as BLAST or HMMER, to ensure that the identification result of the functional gene has a high degree of confidence. The functional gene data of each microorganism category will be output respectively to form the microorganism functional gene data. After the functional gene identification is completed, the microorganism functional gene data is further analyzed to construct the metabolic pathway network between different microorganism categories. The specific steps include, according to the functional gene information of each microorganism, in combination with a known metabolic pathway database (such as MetaCyc or KEGG PATHWAY), the metabolic network is reconstructed by a metabolic network reconstruction tool (such as Pathway Tools or MetaboAnalyst). This step involves the precise calculation of metabolic reactions, especially the transfer relationship between different metabolites among microorganisms, the generation of metabolic intermediates and their consumption pathways. The final output is a microbial metabolic pathway network, which shows the complex relationship between microorganisms through metabolite interactions, laying the foundation for subsequent microbial community function prediction and component detection.

[0081] Step S4: construct a microbial component detection model according to the microbial metabolic pathway network to obtain a microbial component detection model; send the microbial component detection model to the cloud platform to execute the microbial component detection method.

[0082] In an embodiment of the present invention, after obtaining the microbial metabolic pathway network, a microbial component detection model is constructed based on the network. The construction of this model relies on the law of microbial interaction in the metabolic pathway. In order to construct the model, the nodes and edges in the metabolic network are first parsed by linear algebra and topological analysis methods to identify the most critical metabolic pathways and microbial components in the microbial community. The selection of key metabolic nodes depends on parameters such as degree distribution and path centrality. Through mathematical tools such as graph theory analysis methods (e.g., using Cytoscape for graph structure analysis), the key nodes in the metabolic network are mapped to the microbial components, and the contribution rate of each microorganism in the community is finally determined. After the model is constructed, the microbial component detection model needs to be sent to the cloud platform to facilitate remote execution of the microbial component detection method. This step first requires the model to be exported in a standard format (such as JSON or XML format) to ensure the integrity and compatibility of the model data. The microbial component detection model is uploaded to a predetermined cloud server via a secure transmission protocol (such as SFTP or HTTPS). After the cloud platform receives the model, it can call the detection parameters in the model and apply it to subsequent large-scale microbial sample component detection processes. The cloud platform uses the model to perform component analysis on newly input microbial samples, and quickly and accurately quantifies the microbial components by calling the metabolic network and microbial contribution rate information in the model.

[0083] Preferably, step S1 comprises the following steps:

[0084] Step S11: obtaining a microbial test sample;

[0085] Step S12: filtering and separating the microorganism test sample to obtain a microorganism filtration test sample;

[0086] Step S13: centrifuging the microbial filtration test sample to obtain a microbial filtration centrifugal test sample;

[0087] Step S14: performing deep sequencing on the microbial filtration centrifugation test sample based on high-throughput sequencing technology to obtain microbial sample sequencing data.

[0088] In an embodiment of the present invention, the acquisition of microbial test samples involves collecting microbial samples to be analyzed from the target environment. The specific operation includes using a sterile collection device (such as a sterile sampling tube or filter) to collect microorganisms from different sources such as water, soil, food or human samples. Strictly follow the aseptic operation procedures during collection to ensure that interference from environmental pollution is avoided, especially for the collection of pathogenic microorganisms, which must be carried out in a sterile laboratory or a clean environment. After the sample is collected, it is immediately placed in a low temperature (4°C) refrigerated condition to prevent the microorganisms in the sample from denaturing or prematurely decomposing. After sampling, the sample needs to be sent to the laboratory in time for subsequent processing to avoid degradation or contamination of the microbial sample caused by external factors. After the microbial test sample arrives at the laboratory, the sample is first filtered and separated to obtain the target microbial population. This step uses filter membranes with different pore sizes (such as 0.22μm or 0.45μm filter membranes) to select a suitable filter according to the size of the microorganisms in the sample. The sample is passed through a vacuum filtration system or a pressure filtration device. When the liquid sample passes through the filter membrane, particles larger than a specific pore size (such as impurities or non-microbial components) will be intercepted, and the microorganisms will be effectively retained on the filter membrane. This process requires controlling the pressure and filtration time to ensure that the microorganisms are completely retained without being destroyed or lost. After filtration, the resulting filter membrane is the microbial filtration test sample, and the subsequent steps will be further processed based on this sample. After the filtration separation is completed, the microbial filtration test sample is centrifuged to remove the residual liquid and further concentrate the microbial population. First, the microbial filtration sample is gently scraped or eluted from the filter membrane into a sterile centrifuge tube. Subsequently, the sample is centrifuged using a high-speed centrifuge (e.g., a speed of more than 12,000 rpm), and the centrifugation time is usually set to 10-15 minutes to ensure that the microorganisms are deposited to the bottom of the centrifuge tube under the action of centrifugal force. During this process, the centrifugal force and time must be closely monitored to ensure that the microbial sample is not excessively destroyed or decomposed. After centrifugation, the supernatant formed will be discarded, and the sediment at the bottom of the centrifuge tube is the concentrated microbial filtration centrifuge test sample. After obtaining the microbial filtration centrifugation test sample, high-throughput sequencing technology is used to perform deep sequencing analysis on it. First, high-quality DNA needs to be extracted from the microbial sample using a commercially available DNA extraction kit (such as Qiagen DNeasy). After DNA extraction, PCR amplification is required. Under normal circumstances, specific primers are used to amplify the 16S rRNA gene fragment (for bacterial analysis) or the 18S rRNA gene (for fungal or other eukaryotic analysis). After the amplified product is confirmed by electrophoresis to confirm the fragment size, a high-throughput sequencing library is constructed. After the library is constructed, the sample is loaded onto a next-generation sequencing platform (such as Illumina HiSeq or Ion Torrent) for sequencing. The sequencing platform generates a large amount of microbial sequence data, covering the complete genetic information of the microbial sample.Finally, the output microbial sample sequencing data is saved in FASTQ format, which contains each sequence and its sequencing quality information for subsequent analysis.

[0089] Preferably, step S2 comprises the following steps:

[0090] Step S21: recombining DNA fragments of the microbial sample sequencing data to obtain recombined microbial DNA fragment data;

[0091] Step S22: using a preset microbial DNA identification database to perform sequence comparison on the microbial DNA fragment recombinant data to obtain microbial DNA fragment category data;

[0092] Step S23: performing relative frequency quantitative calculation on the microbial sample sequencing data according to the microbial DNA fragment category data to obtain microbial category relative frequency quantitative data;

[0093] Step S24: performing clustering behavior identification among different categories on the quantitative data of relative frequency of microorganism categories to obtain clustering behavior data of microorganism categories.

[0094] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes:

[0095] Step S21: recombining DNA fragments of the microbial sample sequencing data to obtain recombined microbial DNA fragment data;

[0096] In an embodiment of the present invention, first, a large number of short DNA fragments are contained in the sequencing data of the microbial sample, which are usually directly output by a high-throughput sequencing platform, but the order is disordered. To facilitate subsequent analysis, the DNA fragments must be recombined to ensure that these fragments can correctly reflect the complete genome information of the microorganism. Using a DNA fragment recombination algorithm, such as the Overlapping Consensus Assembly algorithm, short fragments with overlapping sequences are spliced ​​into longer continuous sequences to form a complete microbial genome or a larger portion of the genome sequence. This step requires precise pairing calculations to ensure that the overlapping regions between the fragments can be fully matched, thereby generating seamless recombinant DNA fragments. Through this process, the recombinant data of the microbial DNA fragments is obtained, and the recombined data can be used as the basis for further analysis.

[0097] Step S22: using a preset microbial DNA identification database to perform sequence comparison on the microbial DNA fragment recombinant data to obtain microbial DNA fragment category data;

[0098] In an embodiment of the present invention, after obtaining the recombined microbial DNA fragment data, a preset microbial DNA recognition database is used to perform sequence alignment. This step uses a sequence alignment algorithm, such as BLAST (Basic Local Alignment Search Tool) or BWA (Burrows-Wheeler Aligner), to align each fragment in the microbial DNA fragment recombinant data with the known sequence in the microbial DNA recognition database. During the alignment process, the best matching microbial category is screened out by calculating the similarity between the fragment sequence and the known microbial gene sequence in the database, and each DNA fragment is classified. The accuracy of the alignment is determined by setting a strict threshold, such as when the sequence similarity is greater than 95%, it is considered to belong to the corresponding microbial category. The microbial DNA fragment category data finally output records the specific microbial category to which each fragment belongs, providing a clear category classification basis for subsequent analysis.

[0099] Step S23: performing relative frequency quantitative calculation on the microbial sample sequencing data according to the microbial DNA fragment category data to obtain microbial category relative frequency quantitative data;

[0100] In an embodiment of the present invention, the relative frequency quantitative calculation of the sequencing data of the microbial sample is performed based on the microbial DNA fragment category data. This step first counts the relative abundance of each microbial category in the sample according to the number of DNA fragments belonging to each microbial category. The relative frequency calculation is performed using the following formula: the ratio of the number of fragments of a certain microbial category to the total number of sequenced fragments. By calculating the relative frequencies of all microbial categories, the relative abundance information of different microbial categories in the sample is obtained. The relative frequency quantitatively reflects the proportion of each microorganism in the sample, and the relative frequency quantitative data of microbial categories are used for subsequent aggregation behavior identification and ecological function analysis.

[0101] Step S24: performing clustering behavior identification among different categories on the quantitative data of relative frequency of microorganism categories to obtain clustering behavior data of microorganism categories.

[0102] In an embodiment of the present invention, the clustering behavior between different microbial categories is identified based on the quantitative data of the relative frequency of microbial categories. By using a clustering analysis algorithm (such as Moran's I index or Ripley's K function), the clustering characteristics of microbial categories in spatial or ecological dimensions are analyzed to explore the distribution pattern of microorganisms in samples. Clustering behavior identification determines which microbial populations have similar abundance distribution characteristics and work together in a certain functional ecological niche by clustering analysis of relative frequency quantitative data. For example, it is analyzed whether there are certain microorganisms that tend to co-occur at high frequencies under specific environmental conditions, or whether they exhibit ecological cooperation phenomena. The identified microbial clustering behavior data will provide new insights into microbial ecology and functional research.

[0103] Preferably, step S23 includes the following steps:

[0104] Step S231: performing relative abundance calculations between different categories on the microbial sample sequencing data according to the microbial DNA fragment category data to obtain microbial category sequence relative abundance data;

[0105] Step S232: performing abundance distribution skewness analysis among different categories on the relative abundance data of microbial category sequences to obtain abundance distribution skewness data;

[0106] Step S233: performing a balanced quantitative analysis on the relative abundance data of the microbial category sequence according to the abundance distribution skewness data to obtain balanced quantitative data of the category sequence;

[0107] Step S234: performing relative frequency quantitative calculation on the microbial sample sequencing data according to the abundance distribution skewness data and the category sequence balance quantitative data to obtain microbial category relative frequency quantitative data.

[0108] In an embodiment of the present invention, the relative abundance calculation between different microbial categories is performed based on the microbial DNA fragment category data. First, it is necessary to count the DNA fragments of each microbial category to determine the number of fragments of each category. Then, the number of these fragments is compared with the total number of fragments in the microbial sample to calculate the relative abundance of each category in the sample. This process uses the relative abundance formula: relative abundance = number of fragments of a certain microbial category / total number of fragments. The relative abundance of each microbial category reflects its proportion in the entire microbial population. Through this calculation process, the relative abundance data of the microbial category sequence is obtained, which provides an accurate basis for the subsequent skewed analysis of the abundance distribution. After obtaining the relative abundance data of the microbial category sequence, it is necessary to perform a skewed analysis on the abundance distribution between different microbial categories. The skewed analysis of the abundance distribution is to evaluate whether the abundance data of each category presents a skewed distribution through a statistical method, that is, whether there are certain microbial categories whose abundance is much higher or lower than other categories. The commonly used method is the calculation of the skewness coefficient, which is based on the skewed abundance distribution data, and the relative abundance data of the microbial category sequence is balanced and quantitatively analyzed. Balanced quantitative analysis aims to explore whether the distribution of each microbial category in the sample tends to be balanced, or whether some categories dominate. By using the Shannon diversity index or the Simpson diversity index, the degree of balance of the population can be quantitatively evaluated. According to the aforementioned abundance distribution skewness data and category sequence balance quantitative data, the final relative frequency quantitative calculation of the microbial sample sequencing data is performed. This step combines the skewness and balance between different microbial categories to ensure that the calculation of relative frequency can more accurately reflect the microbial composition in the sample. In this process, the abundance values ​​of each category will be reweighted so that those categories that show skewness or extreme abundance are appropriately corrected to reduce calculation errors. The relative frequency quantitative formula is adjusted based on the aforementioned steps, and finally the relative frequency quantitative data of microbial categories are obtained, which provides a stable quantitative basis for microbial function analysis and niche research.

[0109] Preferably, performing balanced quantitative analysis on the relative abundance data of microbial category sequences according to the skewed abundance distribution data comprises the following steps:

[0110] Using the preset abundance stratification interval to perform abundance stratification processing on the abundance distribution skewness data, to obtain abundance distribution skewness stratification data;

[0111] The extreme value fluctuation analysis in different layers is performed on the skewed stratified data of abundance distribution to obtain the extreme value fluctuation data of skewed stratification;

[0112] The median difference of different strata is calculated for the skewed stratified extreme value fluctuation data to obtain the skewed stratified median difference data;

[0113] According to the skewed stratified median difference data, the skewed stratified extreme value fluctuation data is balanced interpolated to obtain the skewed stratified balanced interpolation data;

[0114] Based on the skewed stratified balanced interpolation data, a balanced quantitative analysis was performed on the relative abundance data of microbial category sequences to obtain balanced quantitative data of category sequences.

[0115] In an embodiment of the present invention, the abundance distribution skewed data is subjected to abundance stratification processing according to a preset abundance stratification interval. First, an abundance stratification standard is set. For example, the abundance can be divided into four levels: low abundance (0%-25%), medium abundance (26%-50%), high abundance (51%-75%) and extremely high abundance (76%-100%). Next, the abundance distribution skewed data is traversed, and the relative abundance value of each microbial category is corresponded to the above-mentioned levels. For each microbial category, its abundance value is recorded in the corresponding hierarchical data structure, and finally a data set after abundance stratification processing is formed, that is, the abundance distribution skewed stratified data. This process ensures that the distribution characteristics of microorganisms in different abundance levels can be more clearly identified and compared in subsequent analysis. The abundance distribution skewed stratified data is subjected to extreme value fluctuation analysis in different strata. Specifically, it is first necessary to calculate the maximum and minimum values ​​in each abundance layer. On this basis, the extreme value fluctuation of each level is calculated, and for each abundance layer, the extreme value fluctuation amplitude in the layer is recorded. Through this analysis, the stability of microbial abundance in each abundance layer can be identified. The greater the fluctuation, the more drastic the change in abundance, and vice versa. Finally, the skewed stratified extreme value fluctuation data is generated, which provides data support for the subsequent median difference calculation. After obtaining the skewed stratified extreme value fluctuation data, the median difference in different layers is calculated. For each abundance layer, the median of the abundance of all microbial categories in the layer must be calculated first. The median is calculated by sorting, that is, the abundance values ​​are arranged from small to large, and the average of the two middle values ​​is taken for the even number of abundance values, and the middle value is taken for the odd number. After obtaining the median, the median difference between adjacent abundance layers is further calculated, and the median difference between all abundance layers is recorded to form the skewed stratified median difference data, which reflects the differences in abundance distribution characteristics of different abundance layers. According to the skewed stratified median difference data, the skewed stratified extreme value fluctuation data is balanced interpolation calculation. This step aims to reduce the deviation of abundance distribution between different abundance layers through interpolation to make the data more balanced. The linear interpolation method is used to infer the new abundance value based on the extreme value fluctuation data and median difference of adjacent abundance layers. In the specific calculation process, the extreme value fluctuations of adjacent layers are weighted averaged to obtain the interpolated abundance data. After interpolation calculation, skewed stratified balanced interpolation data are finally generated, laying the foundation for the subsequent analysis of the relative abundance data of microbial category sequences. After obtaining the skewed stratified balanced interpolation data, a balanced quantitative analysis is performed on the relative abundance data of microbial category sequences. This step uses the balanced abundance data to adjust the original relative abundance to eliminate the deviation caused by uneven abundance. The specific implementation method is to combine the skewed stratified balanced interpolation data with the relative abundance data of microbial category sequences, and recalculate the abundance of each microbial category by weighted average method or other suitable quantitative methods.This process ensures that the final category sequence balanced quantitative data truly reflects the actual distribution of microorganisms in the sample and provides reliable data support for microbial community analysis.

[0116] Preferably, performing balanced interpolation calculation on microbial category sequence relative abundance data according to skewed stratified median difference data comprises the following steps:

[0117] The skewed stratified median difference data is solved by high-order difference accumulation to obtain the skewed stratified high-order difference accumulation data;

[0118] According to the skewness stratification high-order difference cumulative data and the skewness stratification median difference data, the skewness stratification extreme value fluctuation data is evaluated for data point continuity and smoothness, and the skewness stratification fluctuation continuous and smooth data is obtained;

[0119] The skewed stratified balanced interpolation data are obtained by performing balanced interpolation calculation on the skewed stratified continuous and gentle fluctuation data.

[0120] In an embodiment of the present invention, first, the skewed stratified median difference data is solved by high-order difference accumulation. This step aims to capture the changing trend of abundance data through the high-order difference method. The specific implementation process is: first determine the order of high-order difference to be calculated, usually taking 1st and 2nd order differences. On this basis, the median difference of each layer of abundance is calculated and accumulated. The skewed stratified high-order difference accumulation data finally formed is the high-order difference and its accumulation, which can reflect the changing rate and trend of microbial abundance data. The acquisition of this data will lay the foundation for the subsequent continuous and gentle evaluation of fluctuations. According to the obtained skewed stratified high-order difference accumulation data and skewed stratified median difference data, the skewed stratified extreme value fluctuation data is evaluated for data point continuous and gentle. This step mainly focuses on how to smooth the data between different abundance layers in order to better reflect the overall trend of microbial abundance. First, the skewed stratified extreme value fluctuation data is divided into multiple data point intervals. In each interval, the average value of continuous data points is calculated by the weighted average method, and it is combined with the median difference data. After obtaining the continuous and gentle data of skewed stratified fluctuations, balanced interpolation calculation is performed. The purpose of this step is to accurately interpolate the relative abundance data of the microbial category sequence based on the continuous and gentle data, so as to ensure the balance of the data. The specific implementation process is: set the interpolation model, for example, use linear interpolation or spline interpolation method to apply the gentle data to the abundance sequence of the microbial category. During the interpolation process, the interpolation nodes are first determined, and these nodes are selected according to the changing trend of the gentle data. For each pair of interpolation nodes, the following formula is used for interpolation calculation: I(x) = (1-t)Y1 + tY2, where I(x) is the interpolation result, Y1 and Y2 are the abundance values ​​of the adjacent nodes, and t is the number of interpolation points. The above formula is used to calculate the abundance value of the new data point and incorporate it into the final relative abundance data of the microbial category sequence. Interpolation calculation can not only eliminate the discontinuity of the data, but also enhance the overall stability of the data. The skewed stratified balanced interpolation data finally obtained will provide a more accurate and reliable basis for the detection of microbial components.

[0121] Preferably, step S3 comprises the following steps:

[0122] Step S31: Calculate the spatial dispersion between different microbial categories on the microbial category aggregation behavior data according to the quantitative data of relative frequency of microbial categories to obtain the spatial dispersion data of microbial aggregation;

[0123] Step S32: performing functional gene identification between different microbial categories on the microbial sample sequencing data according to the microbial cluster spatial dispersion data and the microbial category cluster behavior data to obtain microbial functional gene data;

[0124] Step S33: constructing a metabolic pathway network among different microbial categories based on the microbial functional gene data according to the quantitative data of relative frequency of microbial categories, and obtaining a microbial metabolic pathway network.

[0125] In an embodiment of the present invention, when calculating the spatial discreteness of the microbial category clustering behavior according to the quantitative data of the relative frequency of the microbial category, the spatial distribution data of the microbial sample is first collected. For each microbial category, its position distribution in space is defined, and the coordinates of each sample are represented by a two-dimensional or three-dimensional coordinate system. Next, a method for calculating spatial discreteness is used, and a variance-based discreteness index is commonly used. When it is specifically implemented, the mean position of each microbial category in its spatial position is first calculated, and then the discreteness is calculated according to the difference between the position of each sample and the mean position. After completing the calculation of the spatial discreteness data of the microbial cluster, the functional gene identification of the microbial sample sequencing data is performed based on this data and the microbial category clustering behavior data. This step first requires the establishment of a functional gene database, which contains the genome information and functional annotations of known microorganisms. The microbial sample sequence data obtained by high-throughput sequencing is compared with the functional gene database. Here, an alignment algorithm, such as BLAST (Basic Local Alignment Search Tool), is used to perform a sequence similarity search to identify the sequence corresponding to the functional gene in the database in the sequencing data. In the alignment process, a suitable similarity threshold is set to ensure the accuracy of the identified functional genes. Each successfully identified gene records its corresponding microbial category and its clustering behavior characteristics. The final microbial functional gene data includes the name of the functional gene, functional annotations, and its abundance information in each microbial category, which provides support for further analysis. After obtaining the microbial functional gene data, the microbial metabolic pathway network is further constructed based on the quantitative data of the relative frequency of the microbial category. This step first requires integrating the action information of the microbial functional genes, and mapping the corresponding metabolic pathway for each functional gene with the help of the existing metabolic pathway database (such as the KEGG database). The specific implementation process is: integrating the microbial functional gene data with its corresponding metabolic pathway information to form a basic data set for metabolic pathway construction. Using the network analysis algorithm, a metabolic pathway network is constructed. According to the relationship between the functional gene and its metabolic pathway, a graph structure represented by nodes (functional genes) and edges (interactions between genes) is constructed. Each node represents a functional gene, and each edge represents the role of the gene in the metabolic pathway. The constructed metabolic pathway network is optimized and analyzed using the shortest path algorithm and connected component analysis in graph theory to identify key genes and metabolic pathways that play an important role between different microbial categories. The final microbial metabolic pathway network contains functional genes of different microbial categories and their interactions in the metabolic process, providing a basis for in-depth research on the biological functions of microbial components and their ecological significance.

[0126] Preferably, step S33 includes the following steps:

[0127] Step S331: performing gene interaction frequency relationship analysis between different microbial categories on microbial functional gene data according to the quantitative data of relative frequency of microbial categories to obtain gene interaction frequency relationship data;

[0128] Step S332: evaluating the functional impact of gene fragments on the microbial functional gene data according to the gene interaction frequency relationship data to obtain gene fragment functional impact data;

[0129] Step S333: performing effective association analysis of metabolic pathways in different functional regions on the gene fragment functional impact data to obtain effective association data of metabolic pathways;

[0130] Step S334: construct a metabolic pathway network among different microbial categories based on the gene fragment functional impact data and the metabolic pathway effective association data to obtain a microbial metabolic pathway network.

[0131] In an embodiment of the present invention, when calculating the gene interaction frequency relationship of microbial functional gene data according to the quantitative data of relative frequency of microbial categories, it is first necessary to extract the functional gene information under each microbial category from the previously obtained microbial functional gene data. Subsequently, an interaction matrix is ​​constructed, in which rows and columns represent different microbial categories and functional genes, respectively. Each element of the matrix represents the interaction frequency between functional genes under a specific microbial category. The calculation formula is as follows: "The interaction frequency of functional gene j in microbial category i is equal to the number of times functional gene j co-occurs in category i, divided by the total number of times functional gene j appears in all categories." Through the construction of this interaction matrix, gene interaction frequency relationship data is obtained, which provides a basis for the subsequent evaluation of the functional impact of gene fragments. After obtaining the gene interaction frequency relationship data, the functional impact of gene fragments is evaluated. First, the functional gene is mapped to its corresponding gene fragment, and a mapping relationship between the gene fragment and the functional gene is constructed. For each gene fragment, the interaction frequency relationship data is used to evaluate its functional impact in different microbial categories. The specific implementation process includes the following steps: define functional impact indicators, such as interaction strength, and obtain the influence of each gene fragment by weighted summation of interaction frequency relationship data. According to the calculated functional impact indicators, the gene fragments are divided into different functional impact levels, and specific thresholds are set for classification. Based on the functional impact data of gene fragments, effective association analysis of metabolic pathways in different functional areas is performed. First, the previously constructed functional gene database and metabolic pathway database need to be integrated for subsequent analysis. Functional genes are mapped to metabolic pathways to form a functional gene distribution table of metabolic pathways. This table records the functional genes contained in each metabolic pathway and their impact levels in the functional impact data of gene fragments. Through statistical analysis methods, such as chi-square test or correlation analysis, the relationship between the functional impact of each metabolic pathway and its related gene fragments is evaluated. Set an appropriate statistical significance level to determine the effective association between metabolic pathways and gene fragments with high functional impact. Organize the analysis results into effective association data of metabolic pathways, and clarify the functional genes contained in each metabolic pathway and their functional impact levels. Metabolic pathways are regarded as nodes in the network, and functional genes are used as connecting edges. The weight of each edge can be set according to the functional impact data of the gene fragment, reflecting the importance of the gene in the metabolic pathway. The metabolic pathway network is constructed using graph theory to generate a directed graph in which nodes represent metabolic pathways and edges represent the role of functional genes. The shortest path algorithm can be used to analyze the shortest connection between nodes in the network. Network analysis techniques such as cluster analysis and centrality measurement are used to identify metabolic pathways and functional genes that play a key role among different microbial categories. These analysis results can help reveal the metabolic characteristics of microorganisms and their ecological functions.Finally, the microbial metabolic pathway network data is output, reflecting the metabolic pathway relationships between different microbial categories and their interactions at the functional gene level, providing an important foundation for the research of microbial ecology and bioengineering.

[0132] Preferably, step S4 comprises the following steps:

[0133] Step S41: screening the key metabolic node functional genes of the microbial metabolic pathway network to obtain key metabolic node functional gene data;

[0134] Step S42: performing expression detection on the key metabolic node functional gene data to obtain node functional gene expression data;

[0135] Step S43: constructing a microbial component detection model for the node functional gene expression data according to the microbial metabolic pathway network to obtain a microbial component detection model;

[0136] Step S44: Send the microbial component detection model to the cloud platform to execute the microbial component detection method.

[0137] In the embodiment of the present invention, after the microbial metabolic pathway network is constructed, the key metabolic nodes in the network need to be screened to identify the functional genes therein. The screening process first needs to analyze the importance of each node in the network through a graph theory algorithm (such as PageRank or betweenness centrality) to calculate the weight distribution of each metabolic node. Then, through these weight data, the nodes that play a key metabolic role in the metabolic pathway are screened, especially those nodes with high connectivity or located on important metabolic pathways in the metabolic pathway network. The screened metabolic nodes have significant functional genes in different microbial categories, and the expression of these genes directly affects the efficiency of metabolic function. The output obtained by this step is key metabolic node functional gene data, which specifically includes each metabolic node and its corresponding functional gene set. After the key metabolic node functional gene data is screened, the next step is to detect the expression of these functional genes. Gene expression detection needs to be realized by high-throughput sequencing data, and the specific process includes extracting and screening out the mRNA sequence in the microbial sample related to the key metabolic node. Then, quantitative PCR or transcriptome sequencing technology (RNA-Seq) is used to amplify and sequence mRNA to obtain the expression value of each gene. These values ​​reflect the activity levels of functional genes at key metabolic nodes in different microbial categories. These data are structured and stored as node functional gene expression data for subsequent microbial component detection model construction. After obtaining the node functional gene expression data, these data need to be used to build a microbial component detection model. The model construction process needs to be based on the topological structure of the metabolic pathway network and the gene expression data, and use statistical methods (such as principal component analysis PCA or linear discriminant analysis LDA) to extract features and identify key gene expression patterns that can distinguish different microbial categories. The specific operation steps include: first, extracting several gene sets with the greatest differences from a large amount of gene expression data through principal component analysis; then, combining the node weights of each functional gene in the metabolic pathway network, weighting the extracted gene sets, and constructing a linear equation or classifier for predicting the composition of unknown microbial samples. The result of the model output is a microbial component detection model, which can detect and classify the components of future microbial samples. After the microbial component detection model is built, the model needs to be uploaded to the cloud platform to perform the microbial component detection task. First, the model data is formatted into a portable file (such as JSON or XML format) through a stable network connection and transferred to a dedicated cloud storage server. Then, the cloud platform will call the uploaded model and perform microbial component detection in combination with real-time sequencing data. The process includes inputting the newly acquired high-throughput sequencing data into the model, calculating the component detection results of the microbial sample through the model, and returning the test results to the data terminal for display. The entire process realizes the automation of microbial component detection and improves detection efficiency and accuracy.

[0138] Preferably, step S43 includes the following steps:

[0139] Step S431: performing metabolic flux balance calculation on the node functional gene expression data according to the microbial metabolic pathway network to obtain metabolic flux balance data;

[0140] Step S432: performing cross-influence analysis on the metabolic flux balance data to obtain metabolic flux cross-influence data;

[0141] Step S433: construct a microbial component detection model for the node functional gene expression data according to the metabolic flow cross-influence data to obtain a microbial component detection model.

[0142] In the embodiment of the present invention, the purpose of metabolic flux balance calculation is to dynamically analyze the node functional gene expression data through the microbial metabolic pathway network, so as to calculate the reaction flow rate in which each key gene participates in the metabolic process. In this process, the steady-state metabolic flux analysis method (FBA, Flux Balance Analysis) is used to solve the material balance of each metabolic reaction in the network. The specific implementation steps include: first, the reaction equation related to metabolism is extracted from the microbial metabolic pathway network, and the stoichiometric relationship of these reactions is defined. Then, using the expression of the node functional gene as a constraint, a linear programming algorithm (such as the Simplex algorithm) is used to solve how the metabolic flux is balanced and distributed in the network. Finally, metabolic flux balance data is obtained, which specifically reflects the material flow direction and relative flow rate of each metabolic reaction in the metabolic network. After obtaining the metabolic flux balance data, the cross-influence between each metabolic flux needs to be further analyzed to explore the interaction between different metabolic reactions. The cross-influence analysis is based on network topology and metabolic flux data, and the influence of each metabolic reaction in the network is calculated using a quantitative influencing factor analysis method (such as a random walk algorithm). The specific operation process includes: firstly, the metabolic flux balance data is associated with each reaction node in the metabolic pathway network, and the interaction between each metabolic flux is quantified by constructing an influence matrix. Then, the weighted nodes and edges in the network are used to determine the conduction path and influence intensity between the metabolic fluxes. Through the comprehensive evaluation of these interactions, the metabolic flux cross-influence data are generated, which shows how different metabolic reactions influence each other in the network and regulate the balance of the entire metabolic system. After completing the metabolic flux cross-influence analysis, the microbial component detection model is constructed based on the metabolic flux cross-influence data and the node functional gene expression data. In the process of model construction, the key correlation patterns between metabolic flux and gene expression need to be identified through regression analysis and feature selection. The specific implementation steps are: first, based on the metabolic flux cross-influence data, the metabolic reactions and corresponding functional genes with the greatest impact on the microbial components are extracted. Then, the detection model for predicting microbial components is constructed by methods such as multivariate linear regression or logistic regression. The construction of the model also needs to be cross-validated to ensure its robustness and accuracy. Finally, the output microbial component detection model can accurately predict the relative proportions of different microbial components in the sample and provide a basis for subsequent microbial detection.

[0143] 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 therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0144] 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 detecting microbial components based on high-throughput sequencing data, characterized in that: The following steps are involved: Step S1: obtaining a microbial test sample; performing deep sequencing on the microbial test sample based on high-throughput sequencing technology to obtain microbial sample sequencing data; Step S2: performing relative frequency quantitative calculation on the microbial sample sequencing data to obtain relative frequency quantitative data of microbial categories; performing clustering behavior identification between different categories on the relative frequency quantitative data of microbial categories to obtain clustering behavior data of microbial categories; Step S3: performing functional gene identification between different microbial categories based on the microbial category clustering behavior data to obtain microbial functional gene data; performing metabolic pathway network construction between different microbial categories based on the microbial functional gene data to obtain a microbial metabolic pathway network; Step S4: construct a microbial component detection model according to the microbial metabolic pathway network to obtain a microbial component detection model; send the microbial component detection model to the cloud platform to execute the microbial component detection method.

2. The method for detecting microbial components based on high-throughput sequencing data according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: obtaining a microbial test sample; Step S12: filtering and separating the microorganism test sample to obtain a microorganism filtration test sample; Step S13: centrifuging the microbial filtration test sample to obtain a microbial filtration centrifugal test sample; Step S14: performing deep sequencing on the microbial filtration centrifugation test sample based on high-throughput sequencing technology to obtain microbial sample sequencing data.

3. The method for detecting microbial components based on high-throughput sequencing data according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: recombining DNA fragments of the microbial sample sequencing data to obtain recombined microbial DNA fragment data; Step S22: using a preset microbial DNA identification database to perform sequence comparison on the microbial DNA fragment recombinant data to obtain microbial DNA fragment category data; Step S23: performing relative frequency quantitative calculation on the microbial sample sequencing data according to the microbial DNA fragment category data to obtain microbial category relative frequency quantitative data; Step S24: performing clustering behavior identification among different categories on the quantitative data of relative frequency of microorganism categories to obtain clustering behavior data of microorganism categories.

4. The method for detecting microbial components based on high-throughput sequencing data according to claim 3, characterized in that: Step S23 includes the following steps: Step S231: Calculate the relative abundance of different categories of microbial sample sequencing data according to the microbial DNA fragment category data to obtain microbial category sequence relative abundance data; Step S232: performing abundance distribution skewness analysis among different categories on the relative abundance data of microbial category sequences to obtain abundance distribution skewness data; Step S233: performing a balanced quantitative analysis on the relative abundance data of the microbial category sequence according to the skewed abundance distribution data to obtain balanced quantitative data of the category sequence; Step S234: performing relative frequency quantitative calculation on the microbial sample sequencing data according to the abundance distribution skewness data and the category sequence balance quantitative data to obtain microbial category relative frequency quantitative data.

5. The method for detecting microbial components based on high-throughput sequencing data according to claim 4, characterized in that: The balanced quantitative analysis of the relative abundance data of microbial class sequences based on the skewed abundance distribution data includes the following steps: Using the preset abundance stratification interval to perform abundance stratification processing on the abundance distribution skewness data, to obtain abundance distribution skewness stratification data; The extreme value fluctuation analysis in different layers is performed on the skewed stratified data of abundance distribution to obtain the extreme value fluctuation data of skewed stratification; The median difference of different strata is calculated for the skewed stratified extreme value fluctuation data to obtain the skewed stratified median difference data; According to the skewed stratified median difference data, the skewed stratified extreme value fluctuation data is balanced interpolated to obtain the skewed stratified balanced interpolation data; Based on the skewed stratified balanced interpolation data, a balanced quantitative analysis was performed on the relative abundance data of microbial category sequences to obtain balanced quantitative data of category sequences.

6. The method for detecting microbial components based on high-throughput sequencing data according to claim 5, characterized in that: The balanced interpolation calculation of the relative abundance data of microbial category sequences based on the skewed stratified median difference data includes the following steps: The skewed stratified median difference data is solved by high-order difference accumulation to obtain the skewed stratified high-order difference accumulation data; According to the skewness stratification high-order difference cumulative data and the skewness stratification median difference data, the skewness stratification extreme value fluctuation data is evaluated for data point continuity and smoothness, and the skewness stratification fluctuation continuous and smooth data is obtained; The skewed stratified balanced interpolation data are obtained by performing balanced interpolation calculation on the skewed stratified continuous and gentle fluctuation data.

7. The method for detecting microbial components based on high-throughput sequencing data according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: Calculate the spatial dispersion between different microbial categories on the microbial category aggregation behavior data according to the quantitative data of relative frequency of microbial categories to obtain the spatial dispersion data of microbial aggregation; Step S32: performing functional gene identification between different microbial categories on the microbial sample sequencing data according to the microbial cluster spatial dispersion data and the microbial category cluster behavior data to obtain microbial functional gene data; Step S33: constructing a metabolic pathway network among different microbial categories based on the microbial functional gene data according to the quantitative data of relative frequency of microbial categories, and obtaining a microbial metabolic pathway network.

8. The method for detecting microbial components based on high-throughput sequencing data according to claim 7, characterized in that: Step S33 includes the following steps: Step S331: performing gene interaction frequency relationship analysis between different microbial categories on microbial functional gene data according to the quantitative data of relative frequency of microbial categories to obtain gene interaction frequency relationship data; Step S332: evaluating the functional impact of gene fragments on the microbial functional gene data according to the gene interaction frequency relationship data to obtain gene fragment functional impact data; Step S333: performing effective association analysis of metabolic pathways in different functional regions on the gene fragment functional impact data to obtain effective association data of metabolic pathways; Step S334: construct a metabolic pathway network among different microbial categories based on the gene fragment functional impact data and the metabolic pathway effective association data to obtain a microbial metabolic pathway network.

9. The method for detecting microbial components based on high-throughput sequencing data according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: screening the key metabolic node functional genes of the microbial metabolic pathway network to obtain key metabolic node functional gene data; Step S42: performing expression detection on the key metabolic node functional gene data to obtain node functional gene expression data; Step S43: constructing a microbial component detection model for the node functional gene expression data according to the microbial metabolic pathway network to obtain a microbial component detection model; Step S44: Send the microbial component detection model to the cloud platform to execute the microbial component detection method.

10. The method for detecting microbial components based on high-throughput sequencing data according to claim 9, characterized in that: Step S43 includes the following steps: Step S431: performing metabolic flux balance calculation on the node functional gene expression data according to the microbial metabolic pathway network to obtain metabolic flux balance data; Step S432: performing cross-influence analysis on the metabolic flux balance data to obtain metabolic flux cross-influence data; Step S433: construct a microbial component detection model for the node functional gene expression data according to the metabolic flow cross-influence data to obtain a microbial component detection model.

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