Microbial functional factor evaluation system and method based on data analysis

Abstract

The present invention relates to the field of biological big data analysis technology, and in particular to a microbial functional factor evaluation system and method based on data analysis, the steps of which include: extracting microbial community DNA and obtaining first sequencing data through high-throughput sequencing technology, preprocessing the first sequencing data to obtain second sequencing data; evaluating the functional characteristic index of the microbial community based on the second sequencing data; determining the functional factors of the microbial community based on the functional characteristic index; constructing a functional factor prediction model, and predicting the impact of gastrointestinal environmental changes on the functional characteristics of the microbial community by evaluating the changing trends of the functional factors under different gastrointestinal environmental conditions. The present invention determines the functional factors of the microbial community through the functional characteristics of the microbial community and constructs a functional factor prediction model, thereby improving the reliability and accuracy of the prediction of the functional characteristics of the microbial community by gastrointestinal environmental changes.

Description

Technical Field

[0001] The present invention relates to the technical field of biological big data analysis, and in particular to a microbial functional factor evaluation system and method based on data analysis. Background Art

[0002] The human gastrointestinal microbiome is composed of a large number of complex microorganisms, which play an important role in improving host immunity, food digestion, gastrointestinal endocrine function, neural signal regulation, drug function and metabolism, and endotoxin clearance.

[0003] With the development of high-throughput sequencing technology, omics technology and biological big data analysis technology, the use of markers such as microbial population DNA or RNA to study the construction and evolution of gastrointestinal microbial communities, distribution characteristics, and the mechanism of executing population functions has become a key area in gastrointestinal microbial research.

[0004] When evaluating microbial functional factors through high-throughput sequencing technology, existing technologies ignore the important value of microbial community functional characteristics in determining microbial functional factors, as well as the important significance of the changing trends of microbial functional factors in different gastrointestinal environments in predicting the impact of gastrointestinal environmental changes on the functional characteristics of microbial communities, resulting in the problem of low accuracy in the prediction of microbial community functional characteristics. Summary of the invention

[0005] In order to overcome the defects and shortcomings of the prior art, the present invention provides a microbial functional factor evaluation system and method based on data analysis, which determines the functional factors of the microbial community through the functional characteristics of the microbial community and constructs a functional factor prediction model, thereby improving the reliability and accuracy of predicting the functional characteristics of the microbial community due to changes in the gastrointestinal environment.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] In a first aspect, an embodiment of the present invention provides a method for evaluating microbial functional factors based on data analysis, comprising the following steps:

[0008] Extracting DNA from microbial communities and obtaining first sequencing data using a high-throughput sequencing technology, and preprocessing the first sequencing data to obtain second sequencing data;

[0009] Evaluating a functional characteristic index of a microbial community based on the second sequencing data;

[0010] Determining a functional factor of a microbial community based on the functional characteristic index;

[0011] A functional factor prediction model was constructed to predict the impact of gastrointestinal environmental changes on the functional characteristics of the microbial community by evaluating the changing trends of the functional factors under different gastrointestinal environmental conditions.

[0012] As a preferred technical solution, the step of evaluating the functional characteristic index of the microbial community further includes:

[0013] Obtaining microbial information in the microbial community based on the second sequencing data, wherein the microbial information includes microbial species information and relative abundance information;

[0014] The functional diversity coefficient and functional redundancy coefficient of the microbial community are calculated based on the microbial information. The calculation formula of the functional redundancy coefficient is as follows:

[0015] ;

[0016] In the formula Indicates the microbial community species and The functional similarity of species Indicates the microbial community The relative abundance of species, Indicates the microbial community The relative abundance of species, represents the number of species in the microbial community, represents the functional redundancy coefficient of the microbial community;

[0017] The functional characteristic index of the biological community is calculated based on the functional diversity coefficient and the functional redundancy coefficient. The calculation formula of the functional characteristic index is as follows:

[0018] ;

[0019] In the formula represents the functional diversity coefficient of the microbial community, represents the functional redundancy coefficient of the microbial community, represents the first adjustment factor, Represents the functional characteristic index of the microbial community.

[0020] As a preferred technical solution, the calculation formula of the functional diversity coefficient is as follows:

[0021] ;

[0022] In the formula Indicates the microbial community The relative abundance of species, represents the number of species in the microbial community, represents the number of monomorphic species, represents the number of dimorphic species, represents the second adjustment factor, Represents the functional diversity coefficient of the microbial community.

[0023] As a preferred technical solution, the step of determining the functional factors of the microbial community further includes:

[0024] Obtaining the functional characteristic index, and calculating the functional characteristic matching index between microbial communities based on the functional characteristic index;

[0025] Classifying the microbial communities whose functional feature matching index is greater than a preset functional feature matching threshold into microbial functional groups;

[0026] Functional gene annotation is performed based on the microbial functional group and the relative abundance of functional genes is calculated, and the functional gene with the largest relative abundance is taken as the functional factor of the microbial community.

[0027] As a preferred technical solution, the calculation formula of the functional feature matching index is as follows:

[0028] ;

[0029] In the formula Represents microbial community The functional characteristic index, Represents microbial community The functional characteristic index, Indicates taking and The maximum value in Represents the functional feature matching index.

[0030] As a preferred technical solution, the step of constructing a functional factor prediction model further includes:

[0031] Collecting historical data, wherein the historical data includes historical gastrointestinal environmental data and change trend data of the functional factors under corresponding gastrointestinal environmental conditions;

[0032] Performing data preprocessing on the historical data, and dividing the preprocessed historical data into a training set, a validation set, and a test set;

[0033] Building a functional factor prediction model based on an LSTM neural network, inputting the training set into the functional factor prediction model for iterative training, and monitoring the iterative training process through the validation set;

[0034] When the mean square error of the functional factor prediction model on the test set reaches a preset error threshold, the construction of the functional factor prediction model is completed.

[0035] It should be noted here that the first adjustment factor, the second adjustment factor, the preset functional characteristic matching threshold and the preset error threshold are determined as follows: 10,000 sets of sequencing data and historical gastrointestinal environmental data are collected to distinguish whether changes in the gastrointestinal environment affect the functional characteristics of the microbial community, the functional characteristic index of the microbial community is evaluated through sequencing data and the functional factors of the microbial community are determined, the historical gastrointestinal environmental data are substituted into the functional factor prediction model for prediction, the predicted functional factor change trend and the distinction results are simultaneously imported into the fitting software, and the optimal first adjustment factor, the second adjustment factor, the preset functional characteristic matching threshold and the preset error threshold that meet the distinction accuracy of the distinction results are output.

[0036] In a second aspect, an embodiment of the present invention provides a microbial functional factor evaluation system based on data analysis, comprising:

[0037] A data acquisition module, used to extract DNA of microbial communities and obtain first sequencing data through high-throughput sequencing technology, and pre-process the first sequencing data to obtain second sequencing data;

[0038] A functional characteristic index evaluation module, used to evaluate the functional characteristic index of the microbial community based on the second sequencing data;

[0039] A functional factor determination module, used to determine the functional factor of the microbial community based on the functional characteristic index;

[0040] The prediction model building module is used to build a functional factor prediction model, and predict the impact of gastrointestinal environmental changes on the functional characteristics of the microbial community by evaluating the changing trends of the functional factors under different gastrointestinal environmental conditions.

[0041] In a third aspect, an embodiment of the present invention provides an electronic device, comprising: a processor and a memory, wherein the memory stores a computer program that can be called by the processor, and the processor executes a microbial functional factor evaluation method based on data analysis by calling the computer program stored in the memory.

[0042] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium storing instructions, which, when executed on a computer, enable the computer to execute a method for evaluating microbial functional factors based on data analysis.

[0043] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0044] The present invention first evaluates the functional characteristic index of the microbial community through sequencing data, then determines the functional factors of the microbial community through the functional characteristic index of the microbial community, and finally constructs a functional factor prediction model. By evaluating the changing trend of the functional factors under different gastrointestinal environmental conditions, the influence of gastrointestinal environmental changes on the functional characteristics of the microbial community is predicted, thereby improving the reliability and accuracy of the prediction of the functional characteristics of the microbial community due to gastrointestinal environmental changes. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments of the present invention are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0046] Figure 1 A schematic diagram of the overall process of the microbial functional factor evaluation method based on data analysis provided by the present invention;

[0047] Figure 2 A schematic diagram of a process for evaluating the functional characteristic index in the microbial functional factor evaluation method based on data analysis provided by the present invention;

[0048] Figure 3 A schematic diagram of the structure of a microbial functional factor evaluation system based on data analysis provided by the present invention;

[0049] Figure 4 This is a schematic structural diagram of an electronic device provided by the present invention. DETAILED DESCRIPTION

[0050] The technical solution of the present invention is described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations on the technical solution of the present invention. The embodiments of the present invention and the technical features in the embodiments may be combined with each other unless there is a conflict.

[0051] Before introducing the microbial functional factor evaluation method based on data analysis provided in the embodiment of the present invention, some concepts involved in the embodiment of the present invention are first introduced:

[0052] High-throughput sequencing technology, also known as next-generation sequencing (NGS), is a technology that can rapidly sequence a large number of DNA molecules in parallel.

[0053] OTU operational taxonomic unit clustering, OTU (Operational Taxonomic Units) divides all sequences into OTUs according to different similarity levels in microbial diversity analysis. Usually, sequences with a similarity higher than 97% (species level) are defined as an OTU, and each OTU represents a species.

[0054] The LSTM neural network is built by improving the structure of the recurrent neural network. The LSTM neural network effectively solves the problems of gradient disappearance and gradient explosion in the recurrent neural network. The LSTM neural network works well in scenarios such as classification and real-time prediction of time series data. Its advantage lies in its ability to efficiently handle long-term memory dependency problems.

[0055] The Adam algorithm is an optimized extension of the gradient descent method. Its main function is to ensure an efficient adaptive learning rate for each network weight. This algorithm is widely used in deep learning work.

[0056] like Figure 1 As shown, this embodiment provides a method for evaluating microbial functional factors based on data analysis, which specifically includes the following steps:

[0057] S110: extracting DNA of the microbial community and obtaining first sequencing data through high-throughput sequencing technology, preprocessing the first sequencing data to obtain second sequencing data, wherein the high-throughput sequencing technology includes 16s rRNA high-throughput sequencing technology and metagenomic high-throughput sequencing technology, and the preprocessing includes quality control, sequence alignment, sequence splicing, OTU operational classification unit clustering and species annotation.

[0058] S120: evaluating a functional characteristic index of a microbial community based on the second sequencing data;

[0059] The functional characteristic index is of great significance for determining the functional factors of the microbial community. By comprehensively considering the diversity of the microbial community and the degree of functional redundancy within the community, it is helpful to evaluate and quantify the relationship between the structure and function of the microbial community, thereby providing a basis for determining the microbial population that plays a key role in the function of the ecosystem. The step of evaluating the functional characteristic index of the microbial community further includes:

[0060] Obtaining microbial information in the microbial community based on the second sequencing data, wherein the microbial information includes microbial species information and relative abundance information;

[0061] The functional diversity coefficient and functional redundancy coefficient of the microbial community are calculated based on the microbial information. The calculation formula of the functional redundancy coefficient is as follows:

[0062] ;

[0063] In the formula Indicates the microbial community species and The functional similarity of species Indicates the microbial community The relative abundance of species, Indicates the microbial community The relative abundance of species, Indicates the microbial community species and The joint contribution of each species to the community function. Species with higher relative abundance have higher weights when evaluating the functional redundancy of microbial communities. All species pairs in the community are traversed through double-layer summation, and the contribution of each species pair is calculated only once. represents the number of species in the microbial community, It is used to ensure that the functional redundancy coefficient is not affected by the number of species in the community. It represents the functional redundancy coefficient of the microbial community. The physical meaning of functional redundancy in ecology refers to multiple species performing the same or similar ecological functions. The functional redundancy coefficient is used to measure the functional similarity and redundancy between species in the ecosystem. Among them, the functional similarity is calculated by annotating the functions of species and constructing a functional set, and then the functional similarity of species is calculated by the Jaccard similarity coefficient. The functional redundancy coefficient depends not only on the functional similarity of species, but also on the relative abundance of species in the community. Species with higher relative abundance have a greater contribution to the functional stability of the community. When the species are similar in function and have higher relative abundance, the species contributes more to the redundancy of the pair. The functional redundancy coefficient not only reflects the overlap of species functions, but also takes into account the relative abundance of species, emphasizing the importance of the roles played by different species in the community.

[0064] The functional characteristic index of the biological community is calculated based on the functional diversity coefficient and the functional redundancy coefficient. The calculation formula of the functional characteristic index is as follows:

[0065] ;

[0066] In the formula represents the functional diversity coefficient of the microbial community, represents the functional redundancy coefficient of the microbial community, represents the first adjustment factor, Represents the functional characteristic index of the microbial community.

[0067] The functional diversity coefficient of the microbial community evaluates the diversity level of the microbial community by quantifying the distribution and abundance differences of species in the community. The introduction of monomorphic species and dimorphic species takes into account the distribution uniformity of species in the community. The functional diversity coefficient reflects the potential ability and complementarity of different species in the community in performing ecosystem functions. Its significance for calculating the functional characteristic index is that the introduction of the functional diversity coefficient ensures that the functional characteristic index can comprehensively measure the complexity and diversity within the microbial community, thereby more accurately evaluating the functional stability and anti-interference ability of the microbial community. The calculation formula of the functional diversity coefficient is as follows:

[0068] ;

[0069] In the formula Indicates the microbial community The relative abundance of species, is the calculation formula of Simpson's Diversity Index. The value range of Simpson's Diversity Index is , represents the number of species in the microbial community, represents the number of monomorphic species, represents the number of dimorphic species, The Chao1 richness estimator is a calculation formula for Chao1 diversity index, which is an indicator used to measure the species diversity of a community. represents the second adjustment factor, It represents the functional diversity coefficient of the microbial community. The dominant species have a greater impact on the Simpson diversity index, that is, the Simpson diversity index is more sensitive to species with high abundance, while the Chao1 diversity index is more sensitive to species with low abundance, that is, the low abundance species data have a greater impact on the Chao1 diversity index. Therefore, the functional diversity coefficient combines the Simpson diversity index and the Chao1 diversity index to evaluate the diversity level of the microbial community.

[0070] S130: Determine a functional factor of a microbial community based on the functional characteristic index;

[0071] The functional feature matching index is calculated by the functional feature index to quantify the functional similarity between different microbial communities. Communities with a functional feature matching index greater than a preset threshold are divided into functional groups, which is helpful for identifying microbial collections with similar functional characteristics. Functional genes that play a leading role in a specific functional group can be determined through functional gene annotation and relative abundance calculation, thereby determining the functional factors of the microbial community. The step of determining the functional factors of the microbial community further includes:

[0072] Obtaining the functional characteristic index, and calculating the functional characteristic matching index between microbial communities based on the functional characteristic index;

[0073] Classifying the microbial communities whose functional feature matching index is greater than a preset functional feature matching threshold into microbial functional groups;

[0074] Functional gene annotation is performed based on the microbial functional group and the relative abundance of functional genes is calculated, and the functional gene with the largest relative abundance is taken as the functional factor of the microbial community.

[0075] The calculation formula of the functional feature matching index is as follows:

[0076] ;

[0077] In the formula Represents microbial community The functional characteristic index, Represents microbial community The functional characteristic index, Indicates taking and The maximum value in Represents the functional feature matching index.

[0078] S140: Construct a functional factor prediction model, and predict the impact of gastrointestinal environmental changes on the functional characteristics of microbial communities by evaluating the changing trends of the functional factors under different gastrointestinal environmental conditions. Functional factors are indicators that describe the functional characteristics of microbial communities. The functional characteristics of microbial communities are directly or indirectly regulated by functional factors. Therefore, by evaluating the changing trends of the functional factors under different gastrointestinal environmental conditions, it is possible to predict the impact of gastrointestinal environmental changes on the functional characteristics of microbial communities. For example, a continuous increase in a certain functional factor (such as metabolic function-related genes) may reflect an increase in the metabolic activity of the microbial community, thereby improving the metabolic potential of the community. A certain functional factor (such as nitrogen fixation-related functional genes) is in a downward trend, which may indicate that the nitrogen fixation function is impaired, thereby reducing the conversion and utilization efficiency of nutrients in the community.

[0079] Functional factors reveal which genes in the microbial community drive specific gastrointestinal ecosystem functions, which helps to understand the mechanism of action of microorganisms in different gastrointestinal environments. By predicting the changing trend of functional factors, we can better understand the functional structure of the microbial community and its response mechanism to changes in the gastrointestinal environment. The steps of constructing the functional factor prediction model further include:

[0080] Collecting historical data, wherein the historical data includes historical gastrointestinal environmental data and change trend data of the functional factors under corresponding gastrointestinal environmental conditions;

[0081] Performing data preprocessing on the historical data, and dividing the preprocessed historical data into a training set, a validation set, and a test set;

[0082] Building a functional factor prediction model based on an LSTM neural network, inputting the training set into the functional factor prediction model for iterative training, and monitoring the iterative training process through the validation set;

[0083] When the mean square error of the functional factor prediction model on the test set reaches a preset error threshold, the construction of the functional factor prediction model is completed;

[0084] The functional factor prediction model includes an input layer, an output layer, a hidden layer and a model training layer, wherein the main function of the input layer is to process missing values, data format, data normalization, data set division and other processes on the original historical data to meet the model requirements; the main function of the hidden layer is to build a recurrent neural network layer through LSTM Cell units, and each LSTM Cell unit includes a forget gate, an input gate and an output gate; the main function of the output layer is to output the prediction results; the Adam algorithm is introduced in the model training layer to optimize the training efficiency;

[0085] In the training process of the functional factor prediction model, the weights of each part of the network are first initialized, and the processed data set is input into the model. After the preparatory work is completed, the calculation needs to be started. The model calculates the output value of each neuron of the LSTM neural network in chronological order. After the output value calculation is completed, the time series back propagation is started, and the error value caused by the setting of each parameter in the network to the model is calculated in reverse chronological order. The gradient value corresponding to the network weight can be calculated. At the same time, the network weight is adjusted by the parameter optimization algorithm. After the model is optimized as above, the training set needs to be input into the model again, and the optimized model is continuously trained again. The training is circulated until the loss value of the model is almost no longer changed, and the model reaches a convergence state. At this time, the network weight with the smallest loss error has been trained, and the model training process ends here.

[0086] like Figure 3 As shown, an embodiment of the present invention provides a microbial functional factor evaluation system based on data analysis, comprising:

[0087] A data acquisition module 210 is used to extract DNA from a microbial community and obtain first sequencing data through a high-throughput sequencing technology, and pre-process the first sequencing data to obtain second sequencing data;

[0088] A functional characteristic index evaluation module 220, for evaluating the functional characteristic index of the microbial community based on the second sequencing data;

[0089] A functional factor determination module 230 is used to determine the functional factor of the microbial community based on the functional characteristic index;

[0090] The prediction model building module 240 is used to build a functional factor prediction model, and predict the impact of gastrointestinal environmental changes on the functional characteristics of the microbial community by evaluating the change trend of the functional factors under different gastrointestinal environmental conditions.

[0091] In an embodiment of the present invention, the data acquisition module 210 is used to extract microbial community DNA and obtain first sequencing data through high-throughput sequencing technology, preprocess the first sequencing data to obtain second sequencing data, the high-throughput sequencing technology includes 16s rRNA high-throughput sequencing technology and metagenomic high-throughput sequencing technology, and the preprocessing includes quality control, sequence alignment, sequence splicing, OTU operational classification unit clustering and species annotation.

[0092] In an embodiment of the present invention, the functional characteristic index evaluation module 220 is used to evaluate the functional characteristic index of the microbial community based on the second sequencing data, and the step of evaluating the functional characteristic index of the microbial community further includes:

[0093] Obtaining microbial information in the microbial community based on the second sequencing data, wherein the microbial information includes microbial species information and relative abundance information;

[0094] The functional diversity coefficient and functional redundancy coefficient of the microbial community are calculated based on the microbial information. The calculation formula of the functional redundancy coefficient is as follows:

[0095] ;

[0096] In the formula Indicates the microbial community species and The functional similarity of species Indicates the microbial community The relative abundance of species, Indicates the microbial community The relative abundance of species, represents the number of species in the microbial community, It represents the functional redundancy coefficient of the microbial community, where the functional similarity is calculated by annotating the species and constructing a functional set, and then using the Jaccard similarity coefficient to calculate the functional similarity of species;

[0097] The functional characteristic index of the biological community is calculated based on the functional diversity coefficient and the functional redundancy coefficient. The calculation formula of the functional characteristic index is as follows:

[0098] ;

[0099] In the formula represents the functional diversity coefficient of the microbial community, represents the functional redundancy coefficient of the microbial community, represents the first adjustment factor, Represents the functional characteristic index of the microbial community.

[0100] The calculation formula of the functional diversity coefficient is as follows:

[0101] ;

[0102] In the formula Indicates the microbial community The relative abundance of species, represents the number of species in the microbial community, represents the number of monomorphic species, represents the number of dimorphic species, represents the second adjustment factor, Represents the functional diversity coefficient of the microbial community.

[0103] In an embodiment of the present invention, the functional factor determination module 230 is used to determine the functional factor of the microbial community based on the functional characteristic index, and the step of determining the functional factor of the microbial community further includes:

[0104] Obtaining the functional characteristic index, and calculating the functional characteristic matching index between microbial communities based on the functional characteristic index;

[0105] Classifying the microbial communities whose functional feature matching index is greater than a preset functional feature matching threshold into microbial functional groups;

[0106] Functional gene annotation is performed based on the microbial functional group and the relative abundance of functional genes is calculated, and the functional gene with the largest relative abundance is taken as the functional factor of the microbial community.

[0107] The calculation formula of the functional feature matching index is as follows:

[0108] ;

[0109] In the formula Represents microbial community The functional characteristic index, Represents microbial community The functional characteristic index, Indicates taking and The maximum value in Represents the functional feature matching index.

[0110] In an embodiment of the present invention, the prediction model building module 240 is used to build a functional factor prediction model, by evaluating the change trend of the functional factor under different gastrointestinal environmental conditions, predicting the impact of gastrointestinal environmental changes on the functional characteristics of the microbial community, and the step of building a functional factor prediction model further includes:

[0111] Collecting historical data, wherein the historical data includes historical gastrointestinal environmental data and change trend data of the functional factors under corresponding gastrointestinal environmental conditions;

[0112] Performing data preprocessing on the historical data, and dividing the preprocessed historical data into a training set, a validation set, and a test set;

[0113] Building a functional factor prediction model based on an LSTM neural network, inputting the training set into the functional factor prediction model for iterative training, and monitoring the iterative training process through the validation set;

[0114] When the mean square error of the functional factor prediction model on the test set reaches a preset error threshold, the construction of the functional factor prediction model is completed.

[0115] The above-mentioned parameters and steps for each unit module to achieve corresponding functions in the microbial functional factor evaluation system based on data analysis of the present invention can refer to the parameters and steps in the embodiment of the microbial functional factor evaluation method based on data analysis above, and will not be repeated here.

[0116] like Figure 4 As shown, an embodiment of the present invention further provides an electronic device 300, including a memory 320 for storing a computer program 322; and a processor 310 for executing the computer program 322 to implement a microbial functional factor evaluation method based on data analysis as in any of the above embodiments.

[0117] It should be noted that Figure 4 It is a structural diagram of an electronic device 300 according to an exemplary embodiment. The content in the diagram cannot be regarded as any limitation on the scope of application of the present invention.

[0118] Specifically, the electronic device 300 may include: at least one processor 310, at least one memory 320, a power supply 330, a communication interface 340, an input / output interface 350, and a communication bus 360. The memory 320 is used to store a computer program 322, which is loaded and executed by the processor 310 to implement the relevant steps in the microbial functional factor evaluation method based on data analysis disclosed in any of the above embodiments. In addition, the electronic device 300 in the embodiment of the present invention may be specifically an electronic computer.

[0119] In an embodiment of the present invention, a power supply 330 is used to provide a working voltage for each hardware device on the electronic device 300; the communication interface 340 can create a data transmission channel between the electronic device 300 and an external device, and the communication protocol it follows is any communication protocol that can be applied to the technical solution of the present invention, and is not specifically limited here; the input and output interface 350 is used to obtain external input data or output data to the outside world, and its specific interface type can be selected according to specific application needs and is not specifically limited here.

[0120] In addition, the memory 320, as a carrier for storing resources, can be a read-only memory, a random access memory, a disk or an optical disk, etc. The resources stored thereon can include an operating system 321, a computer program 322, etc., and the storage method can be temporary storage or permanent storage.

[0121] The operating system 321 is used to manage and control the hardware devices and computer programs on the electronic device 300, and can be Windows Server, Netware, Unix, Linux, etc. In addition to the computer program 322 that can be used to complete the microbial functional factor evaluation method based on data analysis performed by the electronic device 300 disclosed in any of the aforementioned embodiments, the computer program 322 can further include a computer program 322 that can be used to complete other specific tasks.

[0122] An embodiment of the present invention further provides a computer-readable storage medium for storing a computer program 322. When the computer program 322 is executed by the processor 310, the microbial functional factor evaluation method based on data analysis as described in any of the above embodiments is implemented.

[0123] For example, the computer readable storage medium can be a read-only memory (ROM), a random access memory (RAM), a compact disc (CD-ROM), a magnetic tape, a floppy disk, an optical data storage device, etc.

Claims

1. A method for evaluating microbial functional factors based on data analysis, characterized in that: The steps include: Extracting DNA from microbial communities and obtaining first sequencing data through high-throughput sequencing technology, preprocessing the first sequencing data to obtain second sequencing data, wherein the preprocessing includes quality control, sequence alignment, sequence splicing, OTU operational taxonomic unit clustering, and species annotation; Obtaining microbial information in the microbial community based on the second sequencing data, calculating a functional diversity coefficient and a functional redundancy coefficient of the microbial community based on the microbial information, and then evaluating a functional characteristic index of the microbial community; Calculating a functional characteristic matching index between microbial communities based on the functional characteristic index, and determining a functional factor of the microbial community based on the functional characteristic matching index; Construct a functional factor prediction model to predict the impact of gastrointestinal environmental changes on the functional characteristics of the microbial community by evaluating the changing trends of the functional factors under different gastrointestinal environmental conditions; The functional factors of the microbial community are determined, including: Acquiring the functional feature matching index, and classifying the microbial community whose functional feature matching index is greater than a preset functional feature matching threshold into a microbial functional group; Annotating functional genes based on the microbial functional groups and calculating the relative abundance of functional genes, and taking the functional gene with the largest relative abundance as the functional factor of the microbial community; The calculation formula of the functional feature matching index is as follows: ； In the formula Represents microbial community The functional characteristic index, Represents microbial community The functional characteristic index, Indicates taking and The maximum value in Represents the functional feature matching index.

2. The microbial functional factor evaluation method based on data analysis according to claim 1, characterized in that: The functional characteristic index for evaluating the microbial community includes: Acquiring the microbial information, wherein the microbial information includes microbial species information and relative abundance information; The functional diversity coefficient and functional redundancy coefficient of the microbial community are calculated based on the microbial information. The calculation formula of the functional redundancy coefficient is as follows: ； In the formula Indicates the microbial community species and The functional similarity of species Indicates the microbial community The relative abundance of species, Indicates the microbial community The relative abundance of species, represents the number of species in the microbial community, represents the functional redundancy coefficient of the microbial community; The functional characteristic index of the biological community is calculated based on the functional diversity coefficient and the functional redundancy coefficient. The calculation formula of the functional characteristic index is as follows: ； In the formula represents the functional diversity coefficient of the microbial community, represents the functional redundancy coefficient of the microbial community, represents the first adjustment factor, Represents the functional characteristic index of the microbial community.

3. The microbial functional factor evaluation method based on data analysis according to claim 2, characterized in that: The calculation formula of the functional diversity coefficient is as follows: ； In the formula Indicates the microbial community The relative abundance of species, represents the number of species in the microbial community, represents the number of monomorphic species, represents the number of dimorphic species, represents the second adjustment factor, Represents the functional diversity coefficient of the microbial community.

4. The microbial functional factor evaluation method based on data analysis according to claim 1, characterized in that: The constructing of the functional factor prediction model comprises: Collecting historical data, wherein the historical data includes historical gastrointestinal environmental data and change trend data of the functional factors under corresponding gastrointestinal environmental conditions; Performing data preprocessing on the historical data, and dividing the preprocessed historical data into a training set, a validation set, and a test set; Building a functional factor prediction model based on an LSTM neural network, inputting the training set into the functional factor prediction model for iterative training, and monitoring the iterative training process through the validation set; When the mean square error of the functional factor prediction model on the test set reaches a preset error threshold, the construction of the functional factor prediction model is completed.

5. A microbial functional factor evaluation system based on data analysis, which is implemented based on the microbial functional factor evaluation method based on data analysis according to any one of claims 1 to 4, characterized in that: The system comprises: A data acquisition module, used to extract DNA of microbial communities and obtain first sequencing data through high-throughput sequencing technology, and pre-process the first sequencing data to obtain second sequencing data; A functional characteristic index evaluation module, used to evaluate the functional characteristic index of the microbial community based on the second sequencing data; A functional factor determination module, used to determine the functional factor of the microbial community based on the functional characteristic index; The prediction model building module is used to build a functional factor prediction model, and predict the impact of gastrointestinal environmental changes on the functional characteristics of the microbial community by evaluating the changing trends of the functional factors under different gastrointestinal environmental conditions.

6. An electronic device comprising: A processor and a memory, wherein the memory stores a computer program that can be called by the processor; characterized in that the processor executes the microbial functional factor evaluation method based on data analysis as described in any one of claims 1 to 4 by calling the computer program stored in the memory.

7. A computer-readable storage medium, characterized in that: Instructions are stored, and when the instructions are executed on a computer, the computer is caused to execute the microbial functional factor evaluation method based on data analysis as described in any one of claims 1 to 4.

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