Biogeochemical element cyclic analysis method and system for metagenome data set

Abstract

The invention discloses a biogeochemical element cyclic analysis method and system for a metagenome dataset, and the method comprises the steps: obtaining original experimental data, and obtaining an annotation file, a sample grouping file and a non-redundant genome profile file based on the original experimental data; preprocessing the annotation file, the sample grouping file and the non-redundant genome profile file to obtain a sample data set; constructing a chemical element circulation analysis model, and obtaining a profile file of the chemical elements based on the sample data set and the chemical element circulation analysis model; and classifying the profile files of the chemical elements according to the corresponding experimental processing conditions, performing statistical analysis on each class, performing visualization processing, and storing statistical analysis results and visualization processing results. When a user needs to analyze a huge metagenome data set, the user can comprehensively, quickly and flexibly obtain visual related information of microorganisms.

Description

Technical Field

[0001] The present invention belongs to the field of bioinformatics, and in particular relates to a method and system for analyzing biogeochemical element cycles of a metagenomic data set. Background Art

[0002] Detecting biogeochemical cycle-related genes and related microorganisms is a key step in studying soil element cycles. Researchers often spend a lot of time analyzing metagenomic data sets for soil element cycles. The cumbersome process leads to an increase in the error rate of analysis. Although the previously developed gene chip method can simultaneously and quantitatively detect 72 genes of carbon, nitrogen, phosphorus, and sulfur cycles, it is still insufficient in distinguishing element cycle pathways and related microorganisms. At the same time, users still need to manually construct complex element cycle pathway maps and identify microorganisms with biogeochemical cycle-related genes from tens of thousands of NR (Non-Redundant Protein Database) species annotations. In addition, most software used for metagenomic sequences is customized for Linux systems, with high requirements for computing performance, and faces problems such as high cost and complex software installation dependencies. Summary of the invention

[0003] In order to solve the above technical problems, the present invention proposes a biogeochemical element cycle analysis method and system for a metagenomic dataset to solve the problems existing in the above-mentioned prior art.

[0004] To achieve the above object, the present invention provides a method for analyzing biogeochemical element cycles of a metagenomic dataset, comprising:

[0005] Acquire original experimental data, and obtain an annotation file, a sample grouping file, and a non-redundant genome profile file based on the original experimental data; pre-process the annotation file, the sample grouping file, and the non-redundant genome profile file to obtain a sample data set;

[0006] Constructing a chemical element cycle analysis model, and obtaining a profile file of chemical elements based on the sample data set and the chemical element cycle analysis model;

[0007] The profile files of chemical elements are classified and sorted according to the corresponding experimental processing conditions, and each classification is statistically analyzed and then visualized, and the statistical analysis results and visualization results are stored.

[0008] Optionally, the process of obtaining the annotation file includes:

[0009] The original experimental data are assembled, gene prediction is performed on the assembled long sequence fragments, and the amino acid sequence is translated; the amino acid sequence is compared with the KEGG database and the NR database to obtain the corresponding KO entries, and the amino acid sequence is functionally annotated and classified based on the KO entries to obtain an annotation file.

[0010] Optionally, the annotation file includes: a KO entry profile file, a mapping between a non-redundant genome and KO and KEGG PATHWAY, and a BLAST result between a non-redundant genome and NR.

[0011] Optionally, the process of preprocessing the annotation file, the sample grouping file and the non-redundant genome profile file to obtain a sample data set includes:

[0012] Delete the description column in the KO entry profile file and set the first column as the row name; extract the first two columns in the mapping between the non-redundant genome and KO and KEGG PATHWAY and delete duplicate rows; delete the domain classification in the BLAST results of the non-redundant genome and NR; set the first column in the non-redundant genome profile file as the row name to obtain a sample data set.

[0013] Optionally, the chemical element cycle analysis model includes four cycle analysis branches, corresponding to the four chemical elements of carbon, nitrogen, phosphorus and sulfur.

[0014] Optionally, the process of obtaining a profile file of a chemical element based on the sample data set and the chemical element cycle analysis model includes:

[0015] Arranging KO entries related to the corresponding chemical element cycles in the sample data set, extracting abundance information based on the KO entries, and merging the abundance information according to the chemical element cycle process to obtain a cycle abundance data set;

[0016] After performing a normality test on the cycle abundance data set, a significance analysis is performed and the analysis results are stored in a folder named Heatmap; wherein the significance analysis method is selected according to the number of types of experimental conditions corresponding to the original experimental data and the normality test results;

[0017] Check the carbon cycle gene IDs of predefined categories to determine the existing categories; create carbon cycle pathway folders based on the existing categories, count the relevant microorganisms at different taxonomic levels and save them in the corresponding carbon cycle pathway folders;

[0018] β-diversity analysis was performed on the circulating abundance dataset to assess the similarities and differences of the microbial communities, and the analysis results were visualized and stored in a folder named the same as the visualization method.

[0019] Optionally, statistical analysis of each category may include:

[0020] A normality test was performed on each classified data set. If it conformed to the normal distribution, a parametric test was selected for significance analysis. If it did not conform to the normal distribution, a non-parametric test was selected for significance analysis. Based on the results of the normality test and significance analysis, the differential changes in chemical element cycle genes were calculated. Envelope multivariate analysis of variance, similarity analysis and multiple reaction envelope procedure were used to analyze the interactions between multiple chemical element cycle processes.

[0021] Optionally, element-related sub-files are constructed under the root folder according to the number of elements, each sub-file has the same structure, and stores statistical analysis results and visualization processing results.

[0022] The present invention also provides a biogeochemical element cycle analysis system for a metagenomic data set, comprising:

[0023] A data preparation module is used to obtain the original experimental data, and obtain the annotation file sample grouping file and the non-redundant genome profile file based on the original experimental data;

[0024] A data processing module, used for preprocessing the annotation file, the sample grouping file and the non-redundant genome profile file to obtain a sample data set;

[0025] A cycle analysis module, used to construct a chemical element cycle analysis model, and obtain a profile file of chemical elements based on the sample data set and the chemical element cycle analysis model;

[0026] The statistical analysis module is used to classify and sort the profile files of chemical elements according to the corresponding experimental processing conditions, and perform visualization after statistical analysis on each classification;

[0027] The storage module is used to store statistical analysis results and visualization processing results according to preset rules.

[0028] The present invention also provides a computer-readable storage medium having a computer program stored thereon, characterized in that when the program is executed by a processor, the program implements corresponding steps involved in a method for analyzing the biogeochemical element cycle of a metagenomic data set.

[0029] Compared with the prior art, the present invention has the following advantages and technical effects:

[0030] The present invention provides a biogeochemical element cycle analysis method for a metagenomic data set, firstly, the original experimental data is obtained, and an annotation file, a sample grouping file and a non-redundant genome profile file are obtained based on the original experimental data; then, the annotation file, the sample grouping file and the non-redundant genome profile file are preprocessed to obtain a sample data set; again, a chemical element cycle analysis model is constructed, and a profile file of chemical elements is obtained based on the sample data set and the chemical element cycle analysis model; finally, the profile file of chemical elements is classified and sorted according to the corresponding experimental processing conditions, and each classification is statistically analyzed and then visualized, and the statistical analysis results and the visualization results are stored. When a user needs to analyze a large metagenomic data set, the user can obtain intuitive microbial related information comprehensively, quickly and flexibly. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The drawings constituting a part of the present application are used to provide a further understanding of the present application. The illustrative embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0032] Figure 1 is a flow chart of a method according to an embodiment of the present invention;

[0033] Figure 2 4 is a system structure diagram of an embodiment of the present invention. DETAILED DESCRIPTION

[0034] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0035] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0036] Embodiment 1

[0037] like Figure 1 As shown, this embodiment provides a method for analyzing the biogeochemical element cycle of a metagenomic dataset, comprising:

[0038] Acquire original experimental data, and obtain an annotation file, a sample grouping file, and a non-redundant genome profile file based on the original experimental data; pre-process the annotation file, the sample grouping file, and the non-redundant genome profile file to obtain a sample data set;

[0039] In some embodiments, the process of obtaining the annotation file includes:

[0040] The raw sequencing data were assembled, the open reading frame (ORF) was predicted for the assembled contig sequence (long sequence fragment), and the ORF was translated into an amino acid sequence according to 11 sets of genetic codons (NCBI Genetic Code 11); the amino acid sequence was compared with the KEGG database and the NR database to obtain the corresponding KO entries, and the amino acid sequence was functionally annotated and classified based on the KO entries to obtain an annotation file.

[0041] Further, metagenomic assembly software such as metaSPAdes and MEGAHIT are used to assemble raw reads, which refer to the original experimental data obtained during the sequencing process. These data are usually short fragments of DNA sequences, also known as reads. The length of each read is usually between 50 and 500 base pairs (bp), and then MetaGeneMark, Prodigal or Glimmer-MG are used to predict genes for the assembled long sequence fragments. Finally, the predicted amino acid sequence is compared with the KEGG and NR databases through tools such as KofamScan and DIAMOND to obtain the KO number to understand the functional category, thereby achieving functional and classification annotations to obtain annotation files.

[0042] For example, the user provides a metagenomic dataset file annotated by the KEGG and NR databases, as well as sample data files under different treatments. If the protein sequence obtained after translation is submitted to KofamScan, KofamScan will compare the protein sequence with the KEGG KO database, identify the matching KO entries, and then obtain the corresponding KO number and KEGG pathway information for each protein sequence according to the comparison results, thereby obtaining a dataset.

[0043] In some embodiments, the annotation file includes: a KO entry profile file, a mapping between a non-redundant genome and KO and KEGGPATHWAY, and a BLAST result of a non-redundant genome and NR.

[0044] In some embodiments, the process of preprocessing the annotation file, the sample grouping file, and the non-redundant genome profile file to obtain a sample data set includes:

[0045] Delete the description column in the KO entry profile file and set the first column as the row name; extract the first two columns in the mapping between the non-redundant genome and KO and KEGG PATHWAY and delete duplicate rows; delete the domain classification in the BLAST results of the non-redundant genome and NR; set the first column in the non-redundant genome profile file as the row name to obtain a sample data set.

[0046] Specifically, before performing element cycle analysis, the sample data set was preprocessed as follows:

[0047] 1) Delete the "description" column in "ko" and set the first column as the row name, making sure all columns are of numeric data type to avoid errors or NA values ​​(missing values) caused by character, factor, and logical data types.

[0048] 2) Eliminate duplicate rows based on the first two columns of "Gene" and extract the first two columns of "Gene".

[0049] 3) Delete the field classification in "tax".

[0050] 4) Set the first column of "abundance" as row names, making sure all columns are numeric data types. Keep the row names as part of the data and make this column the first column in the dataset.

[0051] Among them, "ko" refers to the profile file of KO entries, "group" is the sample grouping file, "Gene" involves the mapping between non-redundant genomes and KO and KEGG PATHWAY in scattered metagenomics, which refers to the use of annotation tools to annotate gene sequences to obtain KO numbers, and then by looking up information in the KEGG PATHWAY database, the KO numbers are associated with specific biological pathways. Finally, a database or table containing the mapping relationship between genes, KO numbers and KEGG PATHWAY is established. "Mapping" ultimately refers to the process of establishing a connection between gene information and its functional classification and biological pathway in the KEGG database. "tax" includes BLAST results for comparing non-redundant genomes with NR. First, the sequences in the non-redundant genome are aligned with the sequences in the NR database using the BLAST tool to find similar sequences. According to parameters such as E value (expected value), the significantly similar sequences in the alignment results are screened out. Finally, based on the similar sequences in the alignment results, the sequences in the non-redundant genome are classified into specific species or taxa using classifiers or phylogenetic methods. "abundance" contains the profile file of non-redundant genomes in metagenomics.

[0052] Constructing a chemical element cycle analysis model, and obtaining a profile file of chemical elements based on the sample data set and the chemical element cycle analysis model;

[0053] In some embodiments, the chemical element cycle analysis model includes four cycle analysis branches, corresponding to the four chemical elements of carbon, nitrogen, phosphorus and sulfur.

[0054] Specifically, after data preprocessing, the data set was classified into element cycles, and profile files were generated through independent functions of four independent element modules: "Ccyc.Abundance", "Ncyc.Abundance", "Pcyc.Abundance" and "Scyc.Abundance". In addition, it also contains some cycle process function sets, including 20 KEGG positive selection (KO) entries related to 7 different carbon cycle processes, 48 ​​KO entries related to 18 nitrogen cycle processes, 5 KO entries related to 2 phosphorus cycle processes, and 48 KO entries related to 15 sulfur cycle processes. Through these, relevant functional genes and microorganisms in the four elements were extracted. The four independent analysis modules include: carbon cycle, nitrogen cycle, phosphorus cycle and sulfur cycle. In each module, four different functional analyses are provided, including the arrangement and difference analysis of genes related to biogeochemical cycles, analysis of microorganisms with genes related to biogeochemical cycles, β-diversity analysis and data visualization.

[0055] In some embodiments, the process of obtaining a profile file of a chemical element based on the sample data set and the chemical element cycle analysis model includes:

[0056] Arranging KO entries related to the corresponding chemical element cycles in the sample data set, extracting abundance information based on the KO entries, and merging the abundance information according to the chemical element cycle process to obtain a cycle abundance data set;

[0057] After performing a normality test on the cycle abundance data set, a significance analysis is performed and the analysis results are stored in a folder named Heatmap; wherein the significance analysis method is selected according to the number of types of experimental conditions corresponding to the original experimental data and the normality test results;

[0058] Check the carbon cycle gene IDs of predefined categories to determine the existing categories; create carbon cycle pathway folders based on the existing categories, count the relevant microorganisms at different taxonomic levels and save them in the corresponding carbon cycle pathway folders;

[0059] β-diversity analysis was performed on the circulating abundance dataset to assess the similarities and differences of the microbial communities, and the analysis results were visualized and stored in a folder named the same as the visualization method.

[0060] For example, first create four geochemical cycle folders Carbon, Nitrogen, Phosphorus, Sulfur, etc. through the dir.create() function, taking the carbon cycle as an example:

[0061] 1. Arrangement of geochemical cycle related genes. First, arrange the sub-data related to C cycle, such as KO number, and then extract the abundance information corresponding to KO number in carbon cycle through the function C.ko<-ko[rownames(ko)%in%C,], and then merge KO abundance information according to the 7 carbon cycle processes collected from the kegg database to obtain the carbon cycle abundance data set. Finally, save it as a txt file of KO abundance through the following function:

[0062] write.table(C.abundance,file="Results / Carbon / Gene / Abundance / C_cycle_gene_abun.txt", sep="\t", row.names=FALSE).

[0063] 2. Analysis of differences in genes related to biogeochemical cycles: Based on the carbon cycle abundance data set, the Shapiro-Wilk normality test was first performed using the shapiro.test() function in the R language, and then parametric or non-parametric tests were performed for significance analysis based on whether the distribution conformed to the normal distribution. Depending on the number of groups, for two groups, the t.test() / wilcox.test() function in the stats basic package of the R language was used to perform the T test / Wilcoxon test, and for more than two groups, the aov() / kruskal.test() function in the stats basic package was used to perform variance analysis / Kruskal-Wallis test, and finally the difference results were saved in the "Heatmap" folder using the following function:

[0064] write.table(result[[1]],"Results / Carbon / Gene / Heatmap / Diff_C_gene_test.txt", sep="\t", row.names=FALSE).

[0065] 3. Analysis of microorganisms with genes related to biogeochemical cycles: Use the if(rowSums(C.abundance[,:ncol(C.abundance)])>0&Ccyc.h[]==1){function to check whether there are carbon cycle gene IDs from predefined categories in the input data, and return a binary vector indicating whether each category exists (1) or does not exist (0). Then use dir.create to create carbon cycle pathway (ACF, AR, ANCF, etc.) folders. The number of folders is determined by the chemical element categories present in the sample. The relevant microorganisms at each level of the kingdom, phylum, class, order, family, genus and species are counted through the corresponding (Gene, tax, abundance, group) of the community and saved in the corresponding subfolder.

[0066] 4.β-diversity analysis and data visualization: Bray-Curtis was created by combining the abundance of carbon cycle microbial communities compiled and counted in the previous step with the vegdist function of the vegan package in R. By calculating the Bray-Curtis distance between different samples, the similarities and differences of the microbial communities between samples can be intuitively understood. Based on the results of the Bray-Curtis distance matrix, the Vegan package was used to convert them into PCoA, PCA, and NMDS analysis results. These files are saved in a subfolder named "Distance". In addition, the folder also contains the visualization results of PCoA, PCA, and NMDS, which are stored in subfolders named "PCoA", "PCA", and "NMDS", respectively.

[0067] In terms of data visualization, the element cycle pattern diagram was drawn through the graphics designed by R language code, which vividly showed the differences in the element cycle pathways of each group. In addition, PCA diagrams, PCoA diagrams, and NMDS diagrams of element cycle genes were drawn to graphically represent the β diversity of these genes in each sample. Finally, a heat map of the abundance of microorganisms involved in each element cycle process at each classification level was drawn, allowing users to quickly obtain relevant information about microorganisms.

[0068] The profile files of chemical elements are classified and sorted according to the corresponding experimental processing conditions, and each classification is statistically analyzed and then visualized, and the statistical analysis results and visualization results are stored.

[0069] In some embodiments, the process of performing statistical analysis on each classification includes:

[0070] A normality test was performed on each classified data set. If it conformed to the normal distribution, a parametric test was selected for significance analysis. If it did not conform to the normal distribution, a non-parametric test was selected for significance analysis. Based on the results of the normality test and significance analysis, the differential changes in chemical element cycle genes were calculated. Envelope multivariate analysis of variance, similarity analysis and multiple reaction envelope procedure were used to analyze the interactions between multiple chemical element cycle processes.

[0071] Specifically, after the functional genes and microbial data sets under different treatments (i.e., different experimental conditions) are classified, the Shapiro-Wilk normality test is first performed using the shapiro.test() function in the R package, and the statistic W is calculated based on the difference between the observed value of the data and the expected value of the normal distribution, and then the critical value is calculated based on the significance level (α=0.05). If the W statistic is greater than the critical value, the null hypothesis cannot be rejected, that is, the data may obey the normal distribution; if the W statistic is less than the critical value, the null hypothesis is rejected, that is, the data does not obey the normal distribution.

[0072] Then, according to whether the data conforms to the normal distribution, parametric tests are selected for normal distribution, and non-parametric tests are used for significance analysis if they do not conform to the normal distribution. The appropriate method is automatically selected according to the number of treatment groups. For two groups, the t.test() / wilcox.test() function in the R language stats basic package is used for T test / Wilcoxon test, and for more than two groups, the aov() / kruskal.test() function in the R language stats basic package is used for variance analysis / Kruskal-Wallis test. Next, the differential changes of element cycle genes are calculated by using T test and Wilcoxon test or variance analysis and Kruskal-Wallis test for differential gene expression analysis. In addition, multivariate statistical analysis is supported for all classified data. Considering the interaction between multiple element cycle processes, methods such as envelope multivariate analysis of variance (Adonis), similarity analysis (ANOSIM) and multiple reaction envelope program (MRPP) are used to perform significance analysis based on the distance matrix. All statistical results are output in tabular form. Envelope multivariate analysis of variance (Adonis) is used to test whether the effects of two or more categorical factors on multivariate response variables are significant. The analysis is performed using the Adonis function in the vegan package. The community data matrix is ​​used as input, and the factor vector is used as the sample grouping to analyze the effects of different groups on the community data. Similarity analysis (ANOSIM) focuses on the significance of differences between and within groups. The R value and P value are calculated by calculating the Bray-Curtis distance between two samples. The R value is used to measure the relative size of the differences between and within groups, and the P value is used to judge the significance of the differences. The multiple reaction envelope procedure (MRPP) aims to search for the grouping scheme with the smallest intra-group distance without considering the distance between groups. All microbial communities are uniformly divided into various possible combinations through permutation, and the statistic δ is constructed. Then the value of the statistic under each grouping is calculated and the distribution of the statistic is statistically analyzed.

[0073] Specifically, the data were visualized by calculating the above statistical results. Heat maps were used to display the abundance and fold-change value of the element cycle process in each independent group. The element cycle pattern diagram was used to display the differences in element cycle pathways between different treatments and the changes in pathway abundance. The β diversity results were output as PCA, PCoA, and NMDS diagrams of element cycle genes. Microbial abundance heat maps were used to represent the processes of each taxonomic level participating in each element cycle.

[0074] In some embodiments, element-related sub-files are constructed under the root folder according to the number of elements, each sub-file has the same structure, and stores statistical analysis results and visualization processing results.

[0075] The generated folders and files are in a specific structure in the final output directory:

[0076] "Result": This is the root folder, which contains four subfolders: "Carbon", "Nitrogen", "Phosphorus" and "Sulfur".

[0077] Each subfolder contains results related to the element cycle process and is divided into three main categories:

[0078] 1) "Beta diversity": In this folder, you can find the Bray-Curtis distance matrix files of the element cycle genes and the results of the multivariate analysis of variance based on the Bray-Curtis distance matrix. These files are saved in a subfolder named "distance". In addition, this folder also contains the visualization results of PCoA, PCA and NMDS, which are stored in subfolders named "PCoA", "PCA" and "NMDS", respectively.

[0079] 2) "Gene": This folder contains the profile of elemental cycling genes, which is located in a subfolder named "Abundance". It includes the results of the line change analysis and the carbon cycle model diagram, which are stored in a subfolder named "Cycle image". In addition, this folder also includes heat map display and variance analysis results, which are located in a subfolder named "Heatmap".

[0080] 3) Host_relative_Group: There are 7 subfolders under this folder, each of which is named after a specific element cycle process. Each subfolder contains a relative abundance table and heat map display of the microorganisms involved in the corresponding element cycle process at different taxonomic levels (phylum, class, order, family, genus, species).

[0081] like Figure 2 As shown, this embodiment provides a biogeochemical element cycle analysis system for a metagenomic dataset, comprising:

[0082] A data preparation module is used to obtain the original experimental data, and obtain the annotation file sample grouping file and the non-redundant genome profile file based on the original experimental data;

[0083] A data processing module, used for preprocessing the annotation file, the sample grouping file and the non-redundant genome profile file to obtain a sample data set;

[0084] A cycle analysis module, used to construct a chemical element cycle analysis model, and obtain a profile file of chemical elements based on the sample data set and the chemical element cycle analysis model;

[0085] The statistical analysis module is used to classify and sort the profile files of chemical elements according to the corresponding experimental processing conditions, and perform visualization after statistical analysis on each classification;

[0086] The storage module is used to store statistical analysis results and visualization processing results according to preset rules.

[0087] This embodiment also provides a computer-readable storage medium having a computer program stored thereon, characterized in that when the program is executed by a processor, corresponding steps involved in a method for analyzing the biogeochemical element cycle of a metagenomic data set are implemented.

[0088] The above are only preferred specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for analyzing biogeochemical element cycles of a metagenomic dataset, characterized in that: include: Obtaining original experimental data, and obtaining annotation files, sample grouping files, and non-redundant genome profile files based on the original experimental data; Preprocessing the annotation file, the sample grouping file and the non-redundant genome profile file to obtain a sample data set; Constructing a chemical element cycle analysis model, and obtaining a profile file of chemical elements based on the sample data set and the chemical element cycle analysis model; The profile files of chemical elements are classified and sorted according to the corresponding experimental processing conditions, and each classification is statistically analyzed and then visualized, and the statistical analysis results and visualization results are stored.

2. The method for analyzing biogeochemical element cycles of a metagenomic dataset according to claim 1, characterized in that: The process of obtaining an annotation file includes: The original experimental data are assembled, gene prediction is performed on the assembled long sequence fragments, and the amino acid sequence is translated; the amino acid sequence is compared with the KEGG database and the NR database to obtain the corresponding KO entries, and the amino acid sequence is functionally annotated and classified based on the KO entries to obtain an annotation file.

3. The method for analyzing biogeochemical element cycles of a metagenomic dataset according to claim 1, characterized in that: The annotation files include: KO entry profile file, mapping between non-redundant genome and KO and KEGG PATHWAY, and BLAST results between non-redundant genome and NR.

4. The method for analyzing biogeochemical element cycles of a metagenomic dataset according to claim 3, characterized in that: The process of preprocessing the annotation file, the sample grouping file and the non-redundant genome profile file to obtain a sample data set includes: Delete the description column in the KO entry profile file and set the first column as the row name; extract the first two columns in the mapping between the non-redundant genome and KO and KEGG PATHWAY and delete duplicate rows; delete the domain classification in the BLAST results of the non-redundant genome and NR; set the first column in the non-redundant genome profile file as the row name to obtain a sample data set.

5. The method for analyzing biogeochemical element cycles of a metagenomic dataset according to claim 1, characterized in that: The chemical element cycle analysis model includes four cycle analysis branches, corresponding to the four chemical elements of carbon, nitrogen, phosphorus and sulfur respectively.

6. The method for analyzing biogeochemical element cycles of a metagenomic dataset according to claim 5, characterized in that: The process of obtaining a profile file of a chemical element based on the sample data set and the chemical element cycle analysis model includes: Arranging KO entries related to the corresponding chemical element cycles in the sample data set, extracting abundance information based on the KO entries, and merging the abundance information according to the chemical element cycle process to obtain a cycle abundance data set; After performing a normality test on the cycle abundance data set, a significance analysis is performed and the analysis results are stored in a folder named Heatmap; wherein the significance analysis method is selected according to the number of types of experimental conditions corresponding to the original experimental data and the normality test results; Check the carbon cycle gene IDs of predefined categories to determine the existing categories; create carbon cycle pathway folders based on the existing categories, count the relevant microorganisms at different taxonomic levels and save them in the corresponding carbon cycle pathway folders; β-diversity analysis was performed on the circulating abundance dataset to assess the similarities and differences of the microbial communities, and the analysis results were visualized and stored in a folder named the same as the visualization method.

7. The method for analyzing biogeochemical element cycles of a metagenomic dataset according to claim 1, characterized in that: The process of statistical analysis for each category includes: A normality test was performed on each classified data set. If it conformed to the normal distribution, a parametric test was selected for significance analysis. If it did not conform to the normal distribution, a non-parametric test was selected for significance analysis. Based on the results of the normality test and significance analysis, the differential changes in chemical element cycle genes were calculated. Envelope multivariate analysis of variance, similarity analysis and multiple reaction envelope procedure were used to analyze the interactions between multiple chemical element cycle processes.

8. The method for analyzing biogeochemical element cycles of a metagenomic dataset according to claim 1, characterized in that: According to the number of elements, element-related sub-files are constructed under the root folder. Each sub-file has the same structure and stores statistical analysis results and visualization processing results.

9. A biogeochemical element cycle analysis system for a metagenomic dataset, characterized in that: A data preparation module is used to obtain the original experimental data, and obtain the annotation file sample grouping file and the non-redundant genome profile file based on the original experimental data; A data processing module, used for preprocessing the annotation file, the sample grouping file and the non-redundant genome profile file to obtain a sample data set; A cycle analysis module, used to construct a chemical element cycle analysis model, and obtain a profile file of chemical elements based on the sample data set and the chemical element cycle analysis model; The statistical analysis module is used to classify and sort the profile files of chemical elements according to the corresponding experimental processing conditions, and perform visualization after statistical analysis on each classification; The storage module is used to store statistical analysis results and visualization processing results according to preset rules.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the corresponding steps involved in the method according to any one of claims 1 to 8 are implemented.

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