Metagenome-based intestinal flora and brain function relevance evaluation method and system

Abstract

The invention relates to the technical field of microbiology, in particular to a metagenome-based intestinal flora and brain function relevance assessment method and system, which comprises the following steps: extracting microbial DNA of a sample to be assessed, constructing a sequencing library of the microbial DNA, and identifying a short read segment of the microbial DNA by using a preset Illumina sequencer; analyzing genome information of the microbial DNA, respectively identifying proteomics information and metabonomics information of the microbial DNA, and analyzing a microbial view of the to-be-evaluated sample; constructing a multi-modal nerve imaging combination of the to-be-evaluated target, establishing an experimental normal form of the multi-modal nerve imaging combination, and obtaining brain function data of the to-be-evaluated target; and explaining a brain function result of the brain function data, analyzing a visual association view of the brain function result and the microorganism view by using a preset MultiCorr tool, and constructing an association assessment report of the intestinal flora and the brain function corresponding to the target to be assessed. According to the method, the relevance evaluation effect of the intestinal flora and the brain function can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of microbiology, and in particular to a method and system for evaluating the correlation between intestinal flora and brain function based on metagenome. Background Art

[0002] The assessment of the association between intestinal flora and brain function refers to the study and analysis of the interaction between intestinal microbial communities (intestinal flora) and brain function to explore how intestinal microorganisms affect brain health and disease risk. This assessment usually involves technologies from multiple fields such as intestinal microbiome, metabolomics, neuroscience, and bioinformatics.

[0003] At present, the assessment of the correlation between intestinal flora and brain function mainly analyzes the species information of intestinal microorganisms through microbial culture, molecular biology technology and other technologies, and performs correlation analysis between species information and brain function. This method can only provide classification information of microorganisms in the analysis of intestinal flora, rather than complete gene function information, which leads to poor assessment of the correlation between intestinal flora and brain function. Summary of the invention

[0004] The present invention provides a method and system for evaluating the correlation between intestinal flora and brain function based on metagenome, the main purpose of which is to improve the evaluation effect of the correlation between intestinal flora and brain function.

[0005] To achieve the above purpose, the present invention provides a method for evaluating the correlation between intestinal flora and brain function based on metagenomics, comprising:

[0006] Optionally, calculating the microbial activity of the stool sample comprises:

[0007] diluting the stool sample to obtain a diluted stool sample;

[0008] Extracting microorganisms from the diluted stool sample in a preset sterile environment;

[0009] determining a specific enzyme of the microorganism and calculating a specific enzyme activity of the specific enzyme;

[0010] The metabolites of the microorganisms are identified, and the metabolite concentrations of the metabolites are calculated using the following formula:

[0011] ρ=(c×n) / v

[0012] Among them, ρ represents the metabolite concentration of the metabolite, c represents the metabolite amount of the metabolite, n represents the dilution multiple of the fecal sample corresponding to the metabolite, and v represents the volume of the fecal sample corresponding to the metabolite;

[0013] analyzing the gene expression level of the microorganism;

[0014] The microbial activity of the stool sample is calculated based on the specific enzyme activity, the metabolite concentration, and the gene expression level.

[0015] Optionally, the analyzing the gene expression level of the microorganism comprises:

[0016] extracting microbial RNA from the microorganism;

[0017] diluting the microbial RNA to obtain diluted microbial RNA;

[0018] Constructing a PCR reaction system for diluting the microbial RNA;

[0019] Based on the PCR reaction system, using a preset fluorescence quantitative PCR instrument to collect the fluorescence signal of the diluted microbial RNA;

[0020] Based on the fluorescent signal, identifying the target gene CT value and the reference gene CT value of the diluted microbial RNA;

[0021] Based on the target gene CT value and the reference gene CT value, the gene expression level of the diluted microbial RNA was calculated using the following formula:

[0022]

[0023] Among them, τ a represents the gene expression level of the ath target gene of the diluted microbial RNA, E a represents the target gene CT value of the a-th target gene of the diluted microbial RNA, R a represents the reference gene CT value of the a-th target gene of the diluted microbial RNA corresponding to the reference gene, D a represents the target gene standard CT value corresponding to the a-th target gene of the diluted microbial RNA, Q a represents the reference gene standard CT value of the a-th target gene of the diluted microbial RNA corresponding to the reference gene, θ represents the efficiency correction factor, and α represents the dilution correction factor.

[0024] Optionally, when the integrity coefficient of the microbial DNA meets a preset integrity threshold, constructing a sequencing library of the microbial DNA comprises:

[0025] When the integrity coefficient of the microbial DNA meets a preset integrity threshold, configuring ultrasonic parameters of the microbial DNA, wherein the ultrasonic parameters include power, pulse duration, and pause time;

[0026] The microbial DNA is fragmented based on the ultrasonic parameters to obtain DNA fragments of the microbial DNA;

[0027] Purifying the DNA fragments to obtain purified DNA fragments, and connecting the linker sequence of the purified DNA fragments;

[0028] Based on the linker sequence, amplifying the purified fragment DNA to obtain an amplified DNA sequence;

[0029] Purifying the amplified DNA sequence to obtain an initial sequencing library of the microbial DNA;

[0030] The initial sequencing library is quantified to obtain a sequencing library of the microbial DNA.

[0031] Optionally, analyzing the genomic information of the microbial DNA based on the short read segments comprises:

[0032] Calculating the short read quality of the short read;

[0033] Based on the quality of the short read segments, screening target short read segments among the short read segments;

[0034] Assembling the target short read segments to obtain assembled short read segments;

[0035] Analyzing the open reading frames of the assembled short reads, and determining the coding genes of the assembled short reads based on the open reading frames;

[0036] annotating the coding gene to obtain an annotated coding gene;

[0037] Based on the annotated coding genes, the genomic information of the microbial DNA is analyzed.

[0038] Optionally, assembling the target short read segments to obtain assembled short read segments comprises:

[0039] Marking the sequence position of the target short read segment;

[0040] Calculating the matching coefficient of the sequence position;

[0041] Based on the matching coefficient, the optimal assembly score of the target short read segment is calculated using the following formula:

[0042] Asmy(r,s)=max(Asmy(r,s-1),Asmy(r-1,s))+Match(g r ,f s )

[0043] Among them, Asmy(r,s) represents the best assembly score from the sequence position r of the g-th target short read to the sequence position s of the f-th target short read, Asmy(r,s-1) represents the best assembly score from the sequence position r of the g-th target short read to the sequence position s-1 of the f-th target short read, Asmy(r-1,s) represents the best assembly score from the sequence position r-1 of the g-th target short read to the sequence position s of the f-th target short read, Match(g r ,f s ) represents the matching coefficient between the sequence position r of the g-th target short read segment and the sequence position s of the f-th target short read segment. If they match, the matching coefficient is 1, if they do not match, the matching coefficient is 0, and max represents the maximum value function;

[0044] Based on the optimal assembly score, the target short reads are assembled to obtain the assembled short reads.

[0045] Optionally, the brain function result of interpreting the brain function data comprises:

[0046] Performing signal correction on the brain function data to obtain corrected brain function data;

[0047] Extracting features of interest from the corrected brain function data, wherein the features of interest include blood oxygen level dependent signals, event-related potentials, and autoregressive model coefficients;

[0048] Analyzing the brain activation area and brain inhibition area of ​​the target to be evaluated corresponding to the brain function data based on the blood oxygen level dependent signal, the event-related potential and the autoregressive model coefficient;

[0049] The brain function results of the corrected brain function data are explained by the blood oxygen level dependent signal, the event-related potential, the autoregressive model coefficients, the brain activation area, and the brain inhibition area.

[0050] Optionally, extracting the interest feature of the corrected brain function data includes:

[0051] dividing the corrected brain function data into fMRI data, EEG data and time series data;

[0052] calculating a blood oxygen level dependent signal of the fMRI data;

[0053] The ERP waveform of the EEG data was calculated using the following formula:

[0054]

[0055] Where ERP(t) represents the ERP waveform at time t, B represents the number of relevant event components corresponding to the EEG data, Event-Related(o,t) represents the value of the oth relevant event component of the EEG data at time t, and Event-Related represents the correlation function;

[0056] Analyzing event-related potentials of the fMRI data based on the ERP waveform;

[0057] Constructing an autoregressive model for the time series data;

[0058] Calculating the autoregressive model coefficients of the time series data by using the autoregressive model;

[0059] Based on the blood oxygen level dependent signal, the event-related potential and the autoregressive model coefficients, a feature of interest of the corrected brain function data is determined.

[0060] Optionally, analyzing the visual correlation view of the brain function results and the microbial view using a preset MultiCorr tool includes:

[0061] Configure the R environment for running the MultiCorr tool;

[0062] Integrate the brain function results and the microbial view into an R running environment to obtain brain function results and microbial view to be analyzed, and convert the brain function results and microbial view to be analyzed into a brain function result data frame and a microbial view data frame;

[0063] In the running R environment, using the mcorr function of the MultiCorr tool to perform correlation analysis on the brain function result data frame and the microbial view data frame to obtain an analysis result object;

[0064] Determining the visual type of the analysis result object;

[0065] Based on the visual type and the analysis result object, a visual association view of the brain function result and the microbial view is constructed.

[0066] To achieve the above objectives, the present invention also provides a metagenome-based intestinal flora and brain function correlation evaluation system, comprising:

[0067] The sample acquisition module to be evaluated is used to acquire a fecal sample of the target to be evaluated, calculate the microbial activity of the fecal sample, and when the microbial activity meets a preset activity threshold, use the fecal sample as a sample to be evaluated;

[0068] A microbial DNA sequencing module is used to extract the microbial DNA of the sample to be evaluated, and when the integrity coefficient of the microbial DNA meets a preset integrity threshold, construct a sequencing library of the microbial DNA, and based on the sequencing library, use a preset Illumina sequencer to identify short read segments of the microbial DNA;

[0069] A microbial view construction module, used to analyze the genomic information of the microbial DNA based on the short read segments, respectively identify the proteomic information and metabolomic information of the microbial DNA, and analyze the microbial view of the sample to be evaluated based on the genomic information, the proteomic information and the metabolomic information;

[0070] A brain function data acquisition module, used to construct a multimodal neuroimaging combination of the target to be evaluated, and establish an experimental paradigm of the multimodal neuroimaging combination through the multimodal neuroimaging combination, wherein the experimental paradigm includes a task type and a task duration, and based on the experimental paradigm, the brain function data of the target to be evaluated is acquired by using the multimodal neuroimaging combination;

[0071] The associated evaluation report construction module is used to interpret the brain function results of the brain function data, use the preset MultiCorr tool to analyze the visual association view of the brain function results and the microbial view, and construct an associated evaluation report of the intestinal flora and brain function corresponding to the target to be evaluated based on the visual association view.

[0072] In order to solve the above problem, the present invention further provides an electronic device, the electronic device comprising:

[0073] a memory storing at least one instruction; and

[0074] A processor executes instructions stored in the memory to implement the above-mentioned method for evaluating the correlation between intestinal flora and brain function based on metagenomics.

[0075] In order to solve the above problems, the present invention also provides a computer-readable storage medium, in which at least one instruction is stored, and the at least one instruction is executed by a processor in an electronic device to implement the above-mentioned metagenomic-based intestinal flora and brain function correlation assessment method.

[0076] The present invention can screen samples with specific microbial activity by calculating the microbial activity of the fecal sample, thereby improving the reliability of the subsequent sample analysis; optionally, when the microbial activity meets the preset activity threshold, the fecal sample can be used as a sample to be evaluated to screen out samples that meet the activity requirements, thereby serving as the basis for the subsequent sample microbial analysis; the present invention is based on the sequencing library, and the identification of microorganisms by using the preset Illumina sequencer to identify the short read segment of the microbial DNA can be used as the basis for the subsequent analysis of the genetic composition and potential functional information of the microorganism; further, the present invention analyzes the genomic information of the microbial DNA based on the short read segment to reveal the genetic composition of the microorganism, and provides a data basis for the subsequent brain function association analysis with the target to be evaluated. The embodiment of the present invention can obtain comprehensive information about brain structure and function through a combination of multimodal neuroimaging. Finally, the present invention interprets the brain function results of the brain function data as the data basis for the association analysis with microorganisms and uses the preset MultiCorr tool to analyze the visual association view of the brain function results and the microbial view to reveal the potential relationship between them. Therefore, the present invention can improve the effect of evaluating the association between intestinal flora and brain function. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] Figure 1 A schematic diagram of a method for evaluating the association between intestinal flora and brain function based on metagenomics provided in one embodiment of the present invention;

[0078] Figure 2 A functional module diagram of a metagenomic-based intestinal flora and brain function correlation assessment system provided in one embodiment of the present invention;

[0079] Figure 3 A schematic diagram of the structure of an electronic device for implementing the method for evaluating the correlation between intestinal flora and brain function based on metagenomics provided in one embodiment of the present invention.

[0080] 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

[0081] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0082] The embodiment of the present application provides a method for evaluating the correlation between intestinal flora and brain function based on metagenome. The execution subject of the method for evaluating the correlation between intestinal flora and brain function based on metagenome includes but is not limited to at least one of the electronic devices such as a server and a terminal that can be configured to execute the method provided in the embodiment of the present application. In other words, the method for evaluating the correlation between intestinal flora and brain function based on metagenome can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc.

[0083] Reference Figure 1 FIG. 1 is a flow chart of a method for evaluating the association between intestinal flora and brain function based on a metagenome according to an embodiment of the present invention. In this embodiment, the method for evaluating the association between intestinal flora and brain function based on a metagenome includes:

[0084] S1. Obtain a stool sample of a target to be evaluated, calculate the microbial activity of the stool sample, and when the microbial activity meets a preset activity threshold, use the stool sample as a sample to be evaluated.

[0085] It should be explained that the target to be evaluated refers to an individual or animal that needs to undergo intestinal microbial evaluation, and the fecal sample refers to feces collected from the target to be evaluated for analyzing its intestinal microbial composition and function.

[0086] The present invention can screen samples with specific microbial activity by calculating the microbial activity of the stool sample, thereby improving the reliability of subsequent sample analysis. The microbial activity refers to the activity of the microorganisms in the stool sample.

[0087] In detail, the calculating the microbial activity of the stool sample comprises:

[0088] diluting the stool sample to obtain a diluted stool sample;

[0089] Extracting microorganisms from the diluted stool sample in a preset sterile environment;

[0090] determining a specific enzyme of the microorganism and calculating a specific enzyme activity of the specific enzyme;

[0091] The metabolites of the microorganisms are identified, and the metabolite concentrations of the metabolites are calculated using the following formula:

[0092] ρ=(c×n) / v

[0093] Among them, ρ represents the metabolite concentration of the metabolite, c represents the metabolite amount of the metabolite, n represents the dilution multiple of the fecal sample corresponding to the metabolite, and v represents the volume of the fecal sample corresponding to the metabolite;

[0094] analyzing the gene expression level of the microorganism;

[0095] The microbial activity of the stool sample is calculated based on the specific enzyme activity, the metabolite concentration, and the gene expression level.

[0096] Among them, the diluted stool sample refers to a sample obtained by diluting the original stool sample by a certain multiple, which is usually to reduce the number of microorganisms in the sample for subsequent analysis and measurement. The sterile environment refers to an environment without any living microorganisms or foreign contaminants. Extracting microorganisms in a sterile environment can avoid the interference of foreign microorganisms and ensure the accuracy of the experimental results. The microorganisms refer to various bacteria, fungi, viruses and other microorganisms present in stool samples. The specific enzymes refer to specific enzymes present in microorganisms. These enzymes can reflect the metabolic activity of microorganisms. The specific enzyme activity refers to the catalytic ability of specific enzymes under specific conditions. The metabolite concentration refers to the concentration of specific metabolites in stool samples. Metabolites are compounds produced by microbial metabolism, and their concentrations can reflect the metabolic activity of microorganisms. The gene expression level refers to the transcriptional activity of specific genes in microorganisms. By measuring the expression level of genes, the metabolic state and function of microorganisms can be understood.

[0097] Furthermore, the analyzing the gene expression level of the microorganism comprises:

[0098] extracting microbial RNA from the microorganism;

[0099] diluting the microbial RNA to obtain diluted microbial RNA;

[0100] Constructing a PCR reaction system for diluting the microbial RNA;

[0101] Based on the PCR reaction system, using a preset fluorescence quantitative PCR instrument to collect the fluorescence signal of the diluted microbial RNA;

[0102] Based on the fluorescent signal, identifying the target gene CT value and the reference gene CT value of the diluted microbial RNA;

[0103] Based on the target gene CT value and the reference gene CT value, the gene expression level of the diluted microbial RNA was calculated using the following formula:

[0104]

[0105] Among them, τ a represents the gene expression level of the ath target gene of the diluted microbial RNA, E a represents the target gene CT value of the a-th target gene of the diluted microbial RNA, R a represents the reference gene CT value of the a-th target gene of the diluted microbial RNA corresponding to the reference gene, D a represents the target gene standard CT value corresponding to the a-th target gene of the diluted microbial RNA, Q a represents the reference gene standard CT value of the a-th target gene of the diluted microbial RNA corresponding to the reference gene, θ represents the efficiency correction factor, and α represents the dilution correction factor.

[0106] Among them, the microbial RNA refers to the RNA molecule extracted from the microorganism, the diluted microbial RNA refers to the sample obtained by diluting the original microbial RNA by a certain multiple, the PCR reaction system refers to the set of all reagents and conditions used for PCR reaction, including primers, templates, dNTPs, buffer and Taq enzyme, etc., the fluorescent signal refers to the signal generated by the amplification of DNA during the PCR reaction, the fluorescent quantitative PCR instrument is an instrument specially used for fluorescent real-time quantitative PCR, which can monitor the fluorescent signal in the PCR reaction in real time and calculate the Ct value of the gene, and the target gene CT value refers to the threshold cycle number (Ct value) of the target gene in the PCR reaction, which reflects The initial copy number of the target gene, the reference gene CT value refers to the threshold cycle number (Ct value) of the reference gene in the PCR reaction, which reflects the initial copy number of the reference gene, the target gene refers to the specific gene of interest in the study, and its expression level is related to the research purpose, the reference gene refers to the gene used as a reference in qPCR analysis, and its expression level is relatively stable under different experimental conditions, the target gene standard CT value refers to the CT value of the target gene under standard conditions, the reference gene standard CT value refers to the CT value of the reference gene under standard conditions, the efficiency correction factor refers to a factor used to correct differences in PCR reaction efficiency, and the dilution correction factor refers to a factor used to correct differences in sample dilution multiples.

[0107] Furthermore, the calculation of the microbial activity of the stool sample based on the specific enzyme activity, the metabolite concentration and the gene expression level can be achieved by statistically analyzing the correlation and related activity weights among the specific enzyme activity, the metabolite concentration and the gene expression level.

[0108] In the present invention, when the microbial activity meets the preset activity threshold, the stool sample can be used as the sample to be evaluated to screen out samples that meet the activity requirements, thereby serving as a basis for subsequent sample microbial analysis. Wherein, the activity threshold refers to the threshold for evaluating whether the microbial activity meets the requirements, and the sample to be evaluated refers to a sample that meets the microbial activity requirements.

[0109] S2. Extract the microbial DNA of the sample to be evaluated. When the integrity coefficient of the microbial DNA meets a preset integrity threshold, construct a sequencing library of the microbial DNA. Based on the sequencing library, use a preset Illumina sequencer to identify short read segments of the microbial DNA.

[0110] It should be explained that the microbial DNA refers to the genetic material in the microbial cells, which is a part of the microbial genome and contains all the genetic information required to encode the life activities of the microorganism. In detail, the microbial DNA can be extracted from the microbial cells using a DNA extraction kit or a laboratory-made extraction method.

[0111] In the present invention, when the integrity coefficient of the microbial DNA meets the preset integrity threshold, a sequencing library of the microbial DNA is constructed for subsequent Illumina sequencing analysis. The sequencing library refers to a DNA library suitable for sequencing on a machine after library construction, purification, quantification and other steps.

[0112] In detail, when the integrity coefficient of the microbial DNA meets a preset integrity threshold, constructing a sequencing library of the microbial DNA comprises:

[0113] When the integrity coefficient of the microbial DNA meets a preset integrity threshold, configuring ultrasonic parameters of the microbial DNA, wherein the ultrasonic parameters include power, pulse duration, and pause time;

[0114] The microbial DNA is fragmented based on the ultrasonic parameters to obtain DNA fragments of the microbial DNA;

[0115] Purifying the DNA fragments to obtain purified DNA fragments, and connecting the linker sequence of the purified DNA fragments;

[0116] Based on the linker sequence, amplifying the purified fragment DNA to obtain an amplified DNA sequence;

[0117] Purifying the amplified DNA sequence to obtain an initial sequencing library of the microbial DNA;

[0118] The initial sequencing library is quantified to obtain a sequencing library of the microbial DNA.

[0119] Among them, the integrity threshold refers to the standard for maintaining the integrity of DNA molecules during ultrasonic fragmentation, the power refers to the intensity of sound waves generated by the ultrasonic crusher, which is an important parameter for controlling the effect of DNA fragmentation, the pulse duration refers to the length of time that the ultrasonic crusher continues to generate sound waves in each treatment, and a shorter pulse duration can reduce the damage to DNA molecules and avoid excessive fragmentation, and the interval time refers to the length of time that the ultrasonic crusher pauses after each treatment. Appropriate interval time helps the recovery of DNA molecules and reduces the risk of excessive fragmentation, the fragmented DNA refers to the DNA molecules obtained after ultrasonic fragmentation treatment, the purified fragmented DNA refers to the DNA after the product of the fragmented DNA is purified, the amplified DNA sequence refers to the DNA sequence obtained by PCR amplification, and its quantity and length have been increased to meet the requirements of the sequencer, and the initial sequencing library refers to the DNA library obtained after the library construction step, which contains DNA fragments with specific connectors and can be used for subsequent sequencing.

[0120] Furthermore, the product purification of the fragmented DNA to obtain the purified fragmented DNA can be performed by purifying the broken products using methods such as column chromatography, centrifugation or magnetic beads to remove impurities; the quantification of the initial sequencing library to obtain the sequencing library of the microbial DNA can be performed by quantifying the constructed library using methods such as a fluorometer or quantitative PCR to ensure that the concentration of the library is suitable for sequencing.

[0121] The present invention is based on the sequencing library, and the microorganism can be identified by using a preset Illumina sequencer to identify the short read segment of the microbial DNA, which can be used as a basis for later analysis of the genetic composition and potential functional information of the microorganism. Wherein, the Illumina sequencer refers to a series of high-throughput sequencing platforms for DNA sequencing, and these sequencers include MiSeq, NextSeq, HiSeq, NovaSeq, etc. The short read segment refers to the short sequence read generated by the sequencer during the sequencing process, and the length of each short read segment is usually between 100-500 base pairs.

[0122] S 3. Based on the short read segments, analyze the genomic information of the microbial DNA, identify the proteomic information and metabolomic information of the microbial DNA respectively, and analyze the microbial view of the sample to be evaluated based on the genomic information, the proteomic information and the metabolomic information.

[0123] Based on the short reads, the present invention analyzes the genomic information of the microbial DNA to reveal the genetic composition of the microorganism, providing a data basis for the subsequent brain function association analysis with the target to be evaluated. The genomic information refers to the genetic composition and potential functional information of the microbial DNA obtained through steps such as short read assembly, ORF prediction, and coding gene annotation.

[0124] In detail, analyzing the genomic information of the microbial DNA based on the short read segments includes:

[0125] Calculating the short read quality of the short read;

[0126] Based on the quality of the short read segments, screening target short read segments among the short read segments;

[0127] Assembling the target short read segments to obtain assembled short read segments;

[0128] Analyzing the open reading frames of the assembled short reads, and determining the coding genes of the assembled short reads based on the open reading frames;

[0129] annotating the coding gene to obtain an annotated coding gene;

[0130] Based on the annotated coding genes, the genomic information of the microbial DNA is analyzed.

[0131] Among them, the short read quality refers to the quality of the bases in each short read and the performance of the sequencer, the target short reads refer to those short reads retained through the quality control and screening process, the assembled short reads refer to the target short reads combined into longer contigs (contigs) or superreads (scaffolds) through assembly software, the open reading frame refers to the region of possible coding genes predicted in the assembled contigs, the coding gene refers to the ORF with potential coding ability predicted in the assembled short reads, these genes may be functional genes in the microbial genome, the annotated coding gene refers to the process of functional annotation of predicted coding genes, and the annotation is usually completed by comparing the gene sequence with public databases (such as NR, KEGG, COG, etc.).

[0132] Furthermore, assembling the target short read segments to obtain assembled short read segments includes:

[0133] Marking the sequence position of the target short read segment;

[0134] Calculating the matching coefficient of the sequence position;

[0135] Based on the matching coefficient, the optimal assembly score of the target short read segment is calculated using the following formula:

[0136] Asmy(r,s)=max(Asmy(r,s-1),Asmy(r-1,s))+Match(g r ,f s )

[0137] Among them, Asmy(r,s) represents the best assembly score from the sequence position r of the g-th target short read to the sequence position s of the f-th target short read, Asmy(r,s-1) represents the best assembly score from the sequence position r of the g-th target short read to the sequence position s-1 of the f-th target short read, Asmy(r-1,s) represents the best assembly score from the sequence position r-1 of the g-th target short read to the sequence position s of the f-th target short read, Match(g r ,f s ) represents the matching coefficient between the sequence position r of the g-th target short read segment and the sequence position s of the f-th target short read segment. If they match, the matching coefficient is 1, if they do not match, the matching coefficient is 0, and max represents the maximum value function;

[0138] Based on the optimal assembly score, the target short reads are assembled to obtain the assembled short reads.

[0139] Among them, the sequence position refers to the position of each base in the target short read segment, the matching coefficient refers to the degree of base matching between two sequences at a specific position during the sequence alignment process, and the best assembly score refers to the score of the best match or assembly of two sequences from a specific position to a specific position during the sequence assembly process. This score reflects the degree of matching between the two sequences at this position and the reliability of the assembly.

[0140] It should be explained that the proteomic information and metabolomic information for respectively identifying the microbial DNA refers to the analytical information obtained after separation, identification and quantitative analysis of all proteins in microbial cells, which can be identified by mass spectrometry technology, chromatography technology and bioinformatics; while the metabolomic information refers to the analytical information obtained after qualitative and quantitative analysis of the metabolites of microorganisms under specific conditions, which can be obtained by gas chromatography-mass spectrometry (GC-MS), liquid chromatography-mass spectrometry (LC-MS) and other technical means.

[0141] Optionally, the present invention analyzes the microbial view of the sample to be evaluated based on the genomic information, the proteomic information and the metabolomic information to reveal the complex relationship between the microorganism and the host, and provide data basis for the subsequent analysis of association with brain function. Among them, the microbial view refers to a description of the structure and function of the microbial community, which reflects the types, quantities, proportions of various microorganisms in the microbial community and the complex network of interactions between them. The microbial view is usually based on a comprehensive analysis of multi-omics data such as microbial genomes, proteomes and metabolomes. In detail, the microbial view can use tools such as Cytoscape or Gephi for network analysis to visualize the associations between different omics data.

[0142] S4. Construct a multimodal neural imaging combination of the target to be evaluated, and establish an experimental paradigm of the multimodal neural imaging combination through the multimodal neural imaging combination, wherein the experimental paradigm includes a task type and a task duration, and based on the experimental paradigm, use the multimodal neural imaging combination to obtain brain function data of the target to be evaluated.

[0143] It should be explained that the multimodal neuroimaging combination refers to a method that combines multiple neuroimaging techniques, such as functional magnetic resonance imaging (fMRI), structural magnetic resonance imaging (sMRI), diffusion tensor imaging (DTI), positron emission tomography (PET) and electroencephalography (EEG). Through the multimodal neuroimaging combination, comprehensive information about brain structure and function can be obtained.

[0144] The experimental paradigm refers to a combination of experimental conditions, tasks or stimuli designed for a specific purpose, wherein the task type may be a cognitive task, an emotional task or other types of tasks, and the task duration refers to the duration of performing the task.

[0145] Under the experimental paradigm, the brain function data refers to data about brain activity patterns obtained through neuroimaging technology. In the multimodal neuroimaging combination, the brain function data may include the output of fMRI, EEG and other technologies, which are used to display the activation patterns of the brain in different tasks or states.

[0146] S5. Interpret the brain function results of the brain function data, use the preset MultiCorr tool to analyze the visual association view of the brain function results and the microbial view, and construct an association evaluation report of the intestinal flora and brain function corresponding to the target to be evaluated based on the visual association view.

[0147] The present invention interprets the brain function results of the brain function data as the data basis for correlation analysis with microorganisms. The brain function results refer to the results of activation patterns and inhibition patterns of the brain under different tasks or states obtained by analyzing and correcting the brain function data.

[0148] In detail, the brain function result of interpreting the brain function data includes:

[0149] Performing signal correction on the brain function data to obtain corrected brain function data;

[0150] Extracting features of interest from the corrected brain function data, wherein the features of interest include blood oxygen level dependent signals, event-related potentials, and autoregressive model coefficients;

[0151] Analyzing the brain activation area and brain inhibition area of ​​the target to be evaluated corresponding to the brain function data based on the blood oxygen level dependent signal, the event-related potential and the autoregressive model coefficient;

[0152] The brain function results of the corrected brain function data are explained by the blood oxygen level dependent signal, the event-related potential, the autoregressive model coefficients, the brain activation area, and the brain inhibition area.

[0153] Among them, the corrected brain function data refers to the brain function data after signal correction. Signal correction usually includes steps such as removing motion artifacts, correcting time series drift, and head movement correction to improve the quality and reliability of the data. The blood oxygen level dependent signal refers to the signal change caused by the change of blood oxygenation level in functional magnetic resonance imaging (fMRI). The event-related potential refers to the potential change associated with a specific event in the electroencephalogram (EEG). The autoregressive model coefficient refers to the coefficient used to describe the dynamic characteristics of the data sequence in time series analysis. The autoregressive model coefficient can reflect the stability, trend, periodicity, etc. of the time series. The brain activation area refers to the area in the brain where the activity is enhanced when performing a specific task or processing a specific stimulus. These areas are usually related to cognitive functions. The brain inhibition area refers to the area in the brain where the activity is weakened when performing a specific task or processing a specific stimulus. These areas may be related to the inhibitory control of the task.

[0154] Furthermore, the extracting of interest features of the corrected brain function data includes:

[0155] dividing the corrected brain function data into fMRI data, EEG data and time series data;

[0156] calculating a blood oxygen level dependent signal of the fMRI data;

[0157] The ERP waveform of the EEG data was calculated using the following formula:

[0158]

[0159] Where ERP(t) represents the ERP waveform at time t, B represents the number of relevant event components corresponding to the EEG data, Event-Related(o,t) represents the value of the oth relevant event component of the EEG data at time t, and Event-Related represents the correlation function;

[0160] Analyzing event-related potentials of the fMRI data based on the ERP waveform;

[0161] Constructing an autoregressive model for the time series data;

[0162] Calculating the autoregressive model coefficients of the time series data by using the autoregressive model;

[0163] Based on the blood oxygen level dependent signal, the event-related potential and the autoregressive model coefficients, a feature of interest of the corrected brain function data is determined.

[0164] Among them, the fMRI data refers to brain function data obtained by functional magnetic resonance imaging technology, the EEG data refers to brain electrical activity data obtained by electroencephalography technology, the time series data refers to continuous data recorded within a certain time range, the ERP waveform refers to the waveform obtained by analyzing the potential changes related to specific events in the EEG data, these waveforms reflect the brain's response to events at different time points, the related event components of the EEG data refer to the potential change parts related to specific events in the EEG data, these components can be extracted by time window selection, filtering and other methods, the value of the related event component at time t refers to the potential value of the related event component at a specific time point, the autoregressive model refers to a statistical model used to describe the dynamic characteristics of time series data, and the related function refers to a function that calculates the corresponding values ​​of the related event components at different times.

[0165] Furthermore, the calculation of the blood oxygen level dependent signal of the fMRI data may be achieved by functional magnetic resonance imaging technology.

[0166] Furthermore, the brain function results of the corrected brain function data explained by the blood oxygen level dependent signal, the event-related potential, the autoregressive model coefficients, the brain activation area and the brain inhibition area can be presented in the form of charts through activation maps, connection matrices, etc.

[0167] The present invention uses the preset MultiCorr tool to analyze the visual association view of the brain function results and the microbial view to reveal the potential relationship between them. The visual association view refers to displaying the analysis results in a graphical form to intuitively show the correlation between different omics data.

[0168] In detail, the use of the preset MultiCorr tool to analyze the visual correlation view of the brain function results and the microbial view includes:

[0169] Configure the R environment for running the MultiCorr tool;

[0170] Integrate the brain function results and the microbial view into an R running environment to obtain brain function results and microbial view to be analyzed, and convert the brain function results and microbial view to be analyzed into a brain function result data frame and a microbial view data frame;

[0171] In the running R environment, using the mcorr function of the MultiCorr tool to perform correlation analysis on the brain function result data frame and the microbial view data frame to obtain an analysis result object;

[0172] Determining the visual type of the analysis result object;

[0173] Based on the visual type and the analysis result object, a visual association view of the brain function result and the microbial view is constructed.

[0174] Among them, the MultiCorr tool refers to a tool for association analysis of multi-omics data sets, the running R environment refers to an environment in which R software is installed, which can be RStudio, RGui or other R environments on the local computer, the brain function result data frame refers to a data frame object created by brain function results in the R environment, the microbial view data frame refers to a data frame object created by microbial view in the R environment, the mcorr function is a function in the MultiCorr package, used to perform association analysis of multi-omics data, it accepts two data frames as input, and returns a result object containing an association matrix, the analysis result object refers to the R object returned by the MultiCorr function after performing the association analysis, it contains information such as association matrix, correlation statistics and p-value, and the visual type refers to the visualization form of the analysis result object, such as heat map, network diagram, scatter plot and other types.

[0175] Furthermore, the operating R environment for configuring the MultiCorr tool may install RStudio or other R interfaces.

[0176] Furthermore, in the running R environment, the mcorr function of the MultiCorr tool is used to perform association analysis on the brain function result data frame and the microbial view data frame to obtain the correlation analysis of the analysis result object, including correlation matrix, correlation statistics, p-value and other analyses.

[0177] Exemplarily, based on the visual type and the analysis result object, a visual association view of the brain function result and the microbial view is constructed. When the visual type is a heat map, the following plot function is used to implement it: plot(mcorr_result), where mcorr_result is an analysis result object.

[0178] Finally, based on the visual association view, the present invention constructs an association assessment report of the intestinal flora and brain function corresponding to the target to be assessed, which helps to reveal the interaction between brain health and microbial communities. The association assessment report includes: the purpose of the association analysis, the target of the association analysis, the results of the association analysis, and the discussion of the results of the association analysis.

[0179] The present invention is to solve the problems described in the background technology. Therefore, the present invention can improve the evaluation effect of the correlation between intestinal flora and brain function and reduce the excessive consumption of time and human resources.

[0180] like Figure 2 , which is a functional module diagram of a metagenomic-based intestinal flora and brain function correlation assessment system provided in one embodiment of the present invention.

[0181] The intestinal flora and brain function correlation evaluation system 100 based on the metagenomics of the present invention can be installed in an electronic device. According to the functions implemented, the intestinal flora and brain function correlation evaluation system 100 based on the metagenomics can include a sample acquisition module 101 to be evaluated, a microbial DNA sequencing module 102, a microbial view construction module 103, a brain function data acquisition module 104 and an associated evaluation report construction module 105. The module of the present invention can also be referred to as a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0182] The sample acquisition module 101 to be evaluated is used to acquire a stool sample of a target to be evaluated, calculate the microbial activity of the stool sample, and when the microbial activity meets a preset activity threshold, use the stool sample as a sample to be evaluated;

[0183] The microbial DNA sequencing module 102 is used to extract the microbial DNA of the sample to be evaluated, and when the integrity coefficient of the microbial DNA meets a preset integrity threshold, construct a sequencing library of the microbial DNA, and based on the sequencing library, use a preset Illumina sequencer to identify short read segments of the microbial DNA;

[0184] The microbial view construction module 103 is used to analyze the genomic information of the microbial DNA based on the short read segments, respectively identify the proteomic information and metabolomic information of the microbial DNA, and analyze the microbial view of the sample to be evaluated based on the genomic information, the proteomic information and the metabolomic information;

[0185] The brain function data acquisition module 104 is used to construct a multimodal neural imaging combination of the target to be evaluated, and establish an experimental paradigm of the multimodal neural imaging combination through the multimodal neural imaging combination, wherein the experimental paradigm includes a task type and a task duration, and based on the experimental paradigm, the brain function data of the target to be evaluated is acquired by using the multimodal neural imaging combination;

[0186] The association evaluation report construction module 105 is used to interpret the brain function results of the brain function data, analyze the visual association view of the brain function results and the microbial view using a preset MultiCorr tool, and construct an association evaluation report of the intestinal flora and brain function corresponding to the target to be evaluated based on the visual association view.

[0187] In detail, each module in the intestinal flora and brain function correlation evaluation system 100 based on the metagenome in the embodiment of the present invention is used in the same manner as described above. Figure 1 The same technical means as the metagenomic-based intestinal flora and brain function correlation assessment method described in , and can produce the same technical effects, will not be repeated here.

[0188] like Figure 3 , is a schematic diagram of the structure of an electronic device for implementing a method for evaluating the correlation between intestinal flora and brain function based on metagenomes, provided in one embodiment of the present invention.

[0189] The electronic device 1 may include a processor 10, a memory 11 and a bus 12, and may also include a computer program stored in the memory 11 and executable on the processor 10, such as a method program for evaluating the correlation between intestinal flora and brain function based on metagenomics.

[0190] Among them, the memory 11 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, mobile hard disk, multimedia card, card-type memory (for example: SD or DX memory, etc.), magnetic memory, disk, optical disk, etc. The memory 11 may be an internal storage unit of the electronic device 1 in some embodiments, such as a mobile hard disk of the electronic device 1. The memory 11 may also be an external storage device of the electronic device 1 in other embodiments, such as a plug-in mobile hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device 1. Further, the memory 11 also includes an internal storage unit of the electronic device 1 and an external storage device. The memory 11 can not only be used to store application software and various types of data installed in the electronic device 1, such as the code of the method program for evaluating the correlation between intestinal flora and brain function based on the metagenomics, but also can be used to temporarily store data that has been output or is to be output.

[0191] The processor 10 may be composed of an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or a plurality of integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and combinations of various control chips, etc. The processor 10 is the control core (Control Unit) of the electronic device, and uses various interfaces and lines to connect the various components of the entire electronic device, and executes or executes the programs or modules stored in the memory 11 (such as the method program for evaluating the correlation between intestinal flora and brain function based on the metagenome, etc.), and calls the data stored in the memory 11 to execute various functions of the electronic device 1 and process data.

[0192] The bus 12 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus 12 may be divided into an address bus, a data bus, a control bus, etc. The bus 12 is configured to realize connection and communication between the memory 11 and at least one processor 10, etc.

[0193] Figure 3 Only an electronic device with components is shown, and those skilled in the art will understand that Figure 3The structure shown does not constitute a limitation on the electronic device 1, and may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0194] For example, although not shown, the electronic device 1 may also include a power source (such as a battery) for supplying power to each component. Preferably, the power source may be logically connected to the at least one processor 10 through a power management device, so that the power management device can realize functions such as charging management, discharging management, and power consumption management. The power source may also include any components such as one or more DC or AC power sources, recharging devices, power failure detection circuits, power converters or inverters, power status indicators, etc. The electronic device 1 may also include a variety of sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be repeated here.

[0195] Furthermore, the electronic device 1 may also include a network interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device 1 and other electronic devices.

[0196] Optionally, the electronic device 1 may further include a user interface, which may be a display, an input unit (such as a keyboard), or a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, and an OLED (Organic Light-Emitting Diode) touch device. The display may also be appropriately referred to as a display screen or a display unit, which is used to display information processed in the electronic device 1 and to display a visual user interface.

[0197] It should be understood that the embodiment is for illustration only and the scope of the patent application is not limited to this structure.

[0198] The program of the method for evaluating the correlation between intestinal flora and brain function based on the metagenome stored in the memory 11 of the electronic device 1 is a combination of multiple instructions. When running in the processor 10, the following can be achieved:

[0199] Obtaining a stool sample of a target to be evaluated, calculating the microbial activity of the stool sample, and when the microbial activity meets a preset activity threshold, using the stool sample as a sample to be evaluated;

[0200] Extracting the microbial DNA of the sample to be evaluated, and when the integrity coefficient of the microbial DNA meets a preset integrity threshold, constructing a sequencing library of the microbial DNA, and based on the sequencing library, identifying short read segments of the microbial DNA using a preset Illumina sequencer;

[0201] Based on the short reads, analyzing the genomic information of the microbial DNA, identifying the proteomic information and metabolomic information of the microbial DNA respectively, and analyzing the microbial view of the sample to be evaluated based on the genomic information, the proteomic information and the metabolomic information;

[0202] Constructing a multimodal neuroimaging combination of the target to be evaluated, establishing an experimental paradigm of the multimodal neuroimaging combination through the multimodal neuroimaging combination, wherein the experimental paradigm includes a task type and a task duration, and based on the experimental paradigm, using the multimodal neuroimaging combination to obtain brain function data of the target to be evaluated;

[0203] The brain function results of the brain function data are interpreted, and a visual correlation view of the brain function results and the microbial view is analyzed using a preset MultiCorr tool. Based on the visual correlation view, a correlation evaluation report of the intestinal flora and brain function corresponding to the target to be evaluated is constructed.

[0204] Specifically, the specific implementation method of the processor 10 for the above instructions can refer to Figures 1 to 3 The description of the relevant steps in the corresponding embodiments will not be repeated here.

[0205] Furthermore, if the module / unit integrated in the electronic device 1 is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, and a read-only memory (ROM).

[0206] The present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor of an electronic device, the computer program can implement:

[0207] Obtaining a stool sample of a target to be evaluated, calculating the microbial activity of the stool sample, and when the microbial activity meets a preset activity threshold, using the stool sample as a sample to be evaluated;

[0208] Extracting the microbial DNA of the sample to be evaluated, and when the integrity coefficient of the microbial DNA meets a preset integrity threshold, constructing a sequencing library of the microbial DNA, and based on the sequencing library, identifying short read segments of the microbial DNA using a preset Illumina sequencer;

[0209] Based on the short reads, analyzing the genomic information of the microbial DNA, identifying the proteomic information and metabolomic information of the microbial DNA respectively, and analyzing the microbial view of the sample to be evaluated based on the genomic information, the proteomic information and the metabolomic information;

[0210] Constructing a multimodal neuroimaging combination of the target to be evaluated, establishing an experimental paradigm of the multimodal neuroimaging combination through the multimodal neuroimaging combination, wherein the experimental paradigm includes a task type and a task duration, and based on the experimental paradigm, using the multimodal neuroimaging combination to obtain brain function data of the target to be evaluated;

[0211] The brain function results of the brain function data are interpreted, and a visual correlation view of the brain function results and the microbial view is analyzed using a preset MultiCorr tool. Based on the visual correlation view, a correlation evaluation report of the intestinal flora and brain function corresponding to the target to be evaluated is constructed.

[0212] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, systems and methods can be implemented in other ways. For example, the system embodiments described above are only illustrative, and actual implementation may have other division methods.

[0213] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0214] In addition, each functional module in each embodiment of the present invention may be integrated into one processing unit, each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0215] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0216] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A method for evaluating the association between intestinal flora and brain function based on metagenomics, characterized in that: The method comprises: Obtaining a stool sample of a target to be evaluated, calculating the microbial activity of the stool sample, and when the microbial activity meets a preset activity threshold, using the stool sample as a sample to be evaluated; Extracting the microbial DNA of the sample to be evaluated, and when the integrity coefficient of the microbial DNA meets a preset integrity threshold, constructing a sequencing library of the microbial DNA, and based on the sequencing library, identifying short read segments of the microbial DNA using a preset Illumina sequencer; Based on the short reads, analyzing the genomic information of the microbial DNA, identifying the proteomic information and metabolomic information of the microbial DNA respectively, and analyzing the microbial view of the sample to be evaluated based on the genomic information, the proteomic information and the metabolomic information; Constructing a multimodal neuroimaging combination of the target to be evaluated, establishing an experimental paradigm of the multimodal neuroimaging combination through the multimodal neuroimaging combination, wherein the experimental paradigm includes a task type and a task duration, and based on the experimental paradigm, using the multimodal neuroimaging combination to obtain brain function data of the target to be evaluated; The brain function results of the brain function data are interpreted, and a visual correlation view of the brain function results and the microbial view is analyzed using a preset MultiCorr tool. Based on the visual correlation view, a correlation evaluation report of the intestinal flora and brain function corresponding to the target to be evaluated is constructed.

2. The method for evaluating the association between intestinal flora and brain function based on metagenome according to claim 1, characterized in that: The calculating the microbial activity of the stool sample comprises: diluting the stool sample to obtain a diluted stool sample; Extracting microorganisms from the diluted stool sample in a preset sterile environment; determining a specific enzyme of the microorganism and calculating a specific enzyme activity of the specific enzyme; The metabolites of the microorganisms are identified, and the metabolite concentrations of the metabolites are calculated using the following formula: ρ=(c×n) / v Among them, ρ represents the metabolite concentration of the metabolite, c represents the metabolite amount of the metabolite, n represents the dilution multiple of the fecal sample corresponding to the metabolite, and v represents the volume of the fecal sample corresponding to the metabolite; analyzing the gene expression level of the microorganism; The microbial activity of the stool sample is calculated based on the specific enzyme activity, the metabolite concentration, and the gene expression level.

3. The method for evaluating the association between intestinal flora and brain function based on metagenome according to claim 2, characterized in that: The analyzing the gene expression level of the microorganism comprises: extracting microbial RNA from the microorganism; diluting the microbial RNA to obtain diluted microbial RNA; Constructing a PCR reaction system for diluting the microbial RNA; Based on the PCR reaction system, using a preset fluorescence quantitative PCR instrument to collect the fluorescence signal of the diluted microbial RNA; Based on the fluorescent signal, identifying the target gene CT value and the reference gene CT value of the diluted microbial RNA; Based on the target gene CT value and the reference gene CT value, the gene expression level of the diluted microbial RNA was calculated using the following formula: Among them, τ a represents the gene expression level of the ath target gene of the diluted microbial RNA, E a represents the target gene CT value of the a-th target gene of the diluted microbial RNA, R a represents the reference gene CT value of the a-th target gene of the diluted microbial RNA corresponding to the reference gene, D a represents the target gene standard CT value corresponding to the a-th target gene of the diluted microbial RNA, Q a represents the reference gene standard CT value of the a-th target gene of the diluted microbial RNA corresponding to the reference gene, θ represents the efficiency correction factor, and α represents the dilution correction factor.

4. The method for evaluating the association between intestinal flora and brain function based on metagenome according to claim 3, characterized in that: When the integrity coefficient of the microbial DNA meets a preset integrity threshold, constructing a sequencing library of the microbial DNA comprises: When the integrity coefficient of the microbial DNA meets a preset integrity threshold, configuring ultrasonic parameters of the microbial DNA, wherein the ultrasonic parameters include power, pulse duration, and pause time; The microbial DNA is fragmented based on the ultrasonic parameters to obtain DNA fragments of the microbial DNA; Purifying the DNA fragments to obtain purified DNA fragments, and connecting the linker sequence of the purified DNA fragments; Based on the linker sequence, amplifying the purified fragment DNA to obtain an amplified DNA sequence; Purifying the amplified DNA sequence to obtain an initial sequencing library of the microbial DNA; The initial sequencing library is quantified to obtain a sequencing library of the microbial DNA.

5. The method for evaluating the association between intestinal flora and brain function based on metagenome according to claim 4, characterized in that: The step of analyzing the genomic information of the microbial DNA based on the short read segments comprises: Calculating the short read quality of the short read; Based on the quality of the short read segments, screening target short read segments among the short read segments; Assembling the target short read segments to obtain assembled short read segments; Analyzing the open reading frames of the assembled short reads, and determining the coding genes of the assembled short reads based on the open reading frames; annotating the coding gene to obtain an annotated coding gene; Based on the annotated coding genes, the genomic information of the microbial DNA is analyzed.

6. The method for evaluating the association between intestinal flora and brain function based on metagenome according to claim 5, characterized in that: The step of assembling the target short read segments to obtain assembled short read segments comprises: Marking the sequence position of the target short read segment; Calculating the matching coefficient of the sequence position; Based on the matching coefficient, the optimal assembly score of the target short read segment is calculated using the following formula: Asmy(r,s)=max(Asmy(r,s-1),Asmy(r-1,s))+Match(g r ,f s ) Among them, Asmy(r, s) represents the best assembly score from the sequence position r of the g-th target short read to the sequence position s of the f-th target short read, Asmy(r, s-1) represents the best assembly score from the sequence position r of the g-th target short read to the sequence position s-1 of the f-th target short read, Asmy(r-1, s) represents the best assembly score from the sequence position r-1 of the g-th target short read to the sequence position s of the f-th target short read, Match(g r , f s ) represents the matching coefficient between the sequence position r of the g-th target short read segment and the sequence position s of the f-th target short read segment. If they match, the matching coefficient is 1, if they do not match, the matching coefficient is 0, and max represents the maximum value function; Based on the optimal assembly score, the target short reads are assembled to obtain the assembled short reads.

7. The method for evaluating the association between intestinal flora and brain function based on metagenome according to claim 6, characterized in that: The brain function result of interpreting the brain function data includes: Performing signal correction on the brain function data to obtain corrected brain function data; Extracting features of interest from the corrected brain function data, wherein the features of interest include blood oxygen level dependent signals, event-related potentials, and autoregressive model coefficients; Analyzing the brain activation area and brain inhibition area of ​​the target to be evaluated corresponding to the brain function data based on the blood oxygen level dependent signal, the event-related potential and the autoregressive model coefficient; The brain function results of the corrected brain function data are explained by the blood oxygen level dependent signal, the event-related potential, the autoregressive model coefficients, the brain activation area, and the brain inhibition area.

8. The method for evaluating the association between intestinal flora and brain function based on metagenome according to claim 7, characterized in that: The extracting of interest features of the corrected brain function data comprises: dividing the corrected brain function data into fMRI data, EEG data and time series data; calculating a blood oxygen level dependent signal of the fMRI data; The ERP waveform of the EEG data was calculated using the following formula: Where ERP(t) represents the ERP waveform at time t, B represents the number of relevant event components corresponding to the EEG data, Event-Related(o, t) represents the value of the oth relevant event component of the EEG data at time t, and Event-Related represents the correlation function; Analyzing event-related potentials of the fMRI data based on the ERP waveform; Constructing an autoregressive model for the time series data; Calculating the autoregressive model coefficients of the time series data by using the autoregressive model; Based on the blood oxygen level dependent signal, the event-related potential and the autoregressive model coefficients, a feature of interest of the corrected brain function data is determined.

9. The method for evaluating the association between intestinal flora and brain function based on metagenome according to claim 8, characterized in that: The method of analyzing the visual correlation view of the brain function results and the microbial view using the preset MultiCorr tool includes: Configure the R environment for running the MultiCorr tool; Integrate the brain function results and the microbial view into an R running environment to obtain brain function results and microbial view to be analyzed, and convert the brain function results and microbial view to be analyzed into a brain function result data frame and a microbial view data frame; In the running R environment, using the mcorr function of the MultiCorr tool to perform correlation analysis on the brain function result data frame and the microbial view data frame to obtain an analysis result object; Determining the visual type of the analysis result object; Based on the visual type and the analysis result object, a visual association view of the brain function result and the microbial view is constructed.

10. A metagenomic-based intestinal flora and brain function correlation evaluation system, characterized in that: The system comprises: The sample acquisition module to be evaluated is used to acquire a fecal sample of the target to be evaluated, calculate the microbial activity of the fecal sample, and when the microbial activity meets a preset activity threshold, use the fecal sample as a sample to be evaluated; A microbial DNA sequencing module is used to extract the microbial DNA of the sample to be evaluated, and when the integrity coefficient of the microbial DNA meets a preset integrity threshold, construct a sequencing library of the microbial DNA, and based on the sequencing library, use a preset Illumina sequencer to identify short read segments of the microbial DNA; A microbial view construction module, used to analyze the genomic information of the microbial DNA based on the short read segments, respectively identify the proteomic information and metabolomic information of the microbial DNA, and analyze the microbial view of the sample to be evaluated based on the genomic information, the proteomic information and the metabolomic information; A brain function data acquisition module, used to construct a multimodal neuroimaging combination of the target to be evaluated, and establish an experimental paradigm of the multimodal neuroimaging combination through the multimodal neuroimaging combination, wherein the experimental paradigm includes a task type and a task duration, and based on the experimental paradigm, the brain function data of the target to be evaluated is acquired by using the multimodal neuroimaging combination; The associated evaluation report construction module is used to interpret the brain function results of the brain function data, use the preset MultiCorr tool to analyze the visual association view of the brain function results and the microbial view, and construct an associated evaluation report of the intestinal flora and brain function corresponding to the target to be evaluated based on the visual association view.

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