Cloud analysis process for automatically processing 16S or 18S or ITS data

Abstract

The invention discloses a cloud analysis process for automatically processing 16S or 18S or ITS data. The cloud analysis process for automatically processing the 16S or 18S or ITS data comprises the steps that the task delivery module achieves webpage triggering submission of tasks, upload files are prepared, the upload files are received for ASV classification, ASV file data analysis is conducted, alpha and beta diversity analysis is conducted, and function prediction analysis is conducted on the upload data. According to the cloud analysis process for automatically processing the 16S or 18S or ITS data, time and effort are saved, manual errors are reduced and presented in various visual modes, a user is helped to better understand the data, a comprehensive bioinformatics analysis solution is provided, and the cloud analysis process has important significance in the fields of research, medical treatment, environmental monitoring and the like.

Description

Technical Field

[0001] This invention relates to the field of bioinformatics, and more particularly to a cloud analysis process for automating the processing of 16S, 18S, or ITS data. Background Technology

[0002] Microorganisms are a collective term for a class of tiny, simple organisms, mainly including bacteria, archaea, fungi, and viruses. They are widely distributed in natural environments (such as soil and water bodies) and specific habitats (such as the human gut and industrial fermentation systems). Their community structure, function, and dynamic changes have a crucial impact on human health, agricultural production, industrial manufacturing, and environmental protection. For a long time, research on microbial communities has heavily relied on culture techniques. However, most microorganisms in the natural environment are difficult to isolate and culture, which greatly limits the understanding and utilization of microbial resources. With the revolutionary advancements in sequencing technology, especially metagenomic sequencing, it has become possible to perform high-throughput sequencing of the DNA of all microorganisms in a sample directly without culture. This technology has become a core means of elucidating the species composition and functional potential of microbial communities. However, many technologies currently on the market mainly focus on de novo sequence analysis based on raw sequences, and related downstream data association analysis and functional prediction. However, these methods suffer from time and efficiency waste and often cannot simultaneously accommodate 16S, 18S, and ITS data. Furthermore, the visualization capabilities and presentation methods of different analysis tools vary significantly, posing a challenge to a comprehensive analysis of the results. Additionally, the automation and standardization of group analyses are insufficient, often requiring manual intervention, which increases costs and extends analysis time.

[0003] There is an urgent need in this field for a new technical solution that can support ASV analysis, process 16S, 18S and ITS data simultaneously, and provide high-simultaneity, comprehensive predictive visualization, so as to achieve more efficient and economical in-depth analysis of microbial communities and thus accurately explore their application value. Summary of the Invention

[0004] In view of the aforementioned shortcomings of current XX technologies, this invention provides a cloud analysis workflow for automating the processing of 16S, 18S, or ITS data. This workflow can be executed on a cloud platform and can automatically analyze input ASV and 16S / 18S / ITS data. Only abundance tables, representative sequences, and grouping tables are required as input data to perform multi-group comparative analysis. The workflow automatically selects appropriate analysis methods and visualization techniques based on the differences in the input groups, thereby significantly reducing labor and time costs.

[0005] To achieve the above objectives, the embodiments of the present invention adopt the following technical solutions:

[0006] A cloud analytics workflow for automating the processing of 16S, 18S, or ITS data, characterized in that the automated cloud analytics workflow for processing 16S, 18S, or ITS data includes the following steps:

[0007] The task submission module enables webpage-triggered task submission.

[0008] Prepare to upload files;

[0009] Receive uploaded files and select different comparison methods based on data type to perform ASV classification analysis;

[0010] Community structure distribution analysis was performed on ASV file data, including: statistical analysis of sample microbial community distribution, visualization of community structure, Krona analysis of community structure, and phylogenetic evolution analysis.

[0011] Alpha diversity analysis was performed on the uploaded data, including dilution curve analysis, species accumulation curve analysis, RankAbundance analysis, and visualization analysis.

[0012] Perform beta diversity analysis on the uploaded data, including PCA analysis, PCoA analysis, NMDS analysis, sample hierarchical cluster analysis, Adonis analysis, Anosim analysis, multivariate statistical analysis, association and model prediction analysis;

[0013] Perform predictive analysis on uploaded data, including PICRUSt2 analysis and visualization analysis.

[0014] According to one aspect of the present invention, the task submission module specifically includes a view function based on the Django framework, which triggers the submission of tasks via a webpage.

[0015] According to one aspect of the present invention, the step of receiving the uploaded file includes selecting different alignment methods to perform ASV classification analysis, and obtaining an ASV abundance table file, an ASV representative sequence file, and an analysis confirmation form.

[0016] According to one aspect of the present invention, the ASV abundance table file records the abundance data of each ASV in different samples; the ASV representative sequence file contains the nucleic acid or amino acid sequence in FASTA format corresponding to each ASV; the analysis confirmation form includes a grouping information worksheet, a grouping comparison worksheet, and a sample renaming worksheet.

[0017] According to one aspect of the present invention, the community structure distribution analysis of ASV file data includes, based on the uploaded representative ASV sequence files, using the rdp method to annotate and classify 16S bacteria and archaea and 18S using the uploaded ASV representative sequence files, and using the blast method to classify and annotate ITS; and drawing Venn diagrams or petal diagrams based on the sample numbers in the uploaded ASV table.

[0018] According to one aspect of the present invention, the community structure distribution analysis includes statistical analysis of the distribution of microbial communities in the samples, generating abundance tables of phylum, class, order, family, genus and species for the samples in terms of relative abundance and absolute abundance, and sorting the abundance of each classification to create a species bar chart.

[0019] This includes using QIIME1 to plot heatmaps based on the relative abundance at each level;

[0020] This includes using Krona software to analyze and visualize species annotation results;

[0021] It also includes selecting the ASVs of the species with the highest abundance according to the sorting, performing multiple sequence alignment using MUSCLE, constructing a phylogenetic tree using FastTree, and constructing a classification tree using megan software.

[0022] According to one aspect of the present invention, the alpha diversity analysis of the uploaded data includes using QIIME1 software to calculate the alpha diversity index of the relative abundance of all samples, wherein the selected diversity index is the Simpson index, Chao1 index, observed_species index, Shannon index, goods_coverage index, PD_whole_tree, or ACE index.

[0023] According to one aspect of the present invention, performing alpha diversity analysis on the uploaded data includes:

[0024] Dilution curve analysis was performed using QIIME1, with calculations and plotting done at 1000-step intervals for the largest sample.

[0025] Speciation accumulation curve analysis was performed using the Specaccum function in vegan.

[0026] Visualization, including violinplots and boxplots for all samples, using ggplot2 for plotting;

[0027] Rank Abundance analysis was performed using R software to rank the alpha index of all samples and then visualized.

[0028] According to one aspect of the present invention, the beta diversity analysis of the uploaded data includes:

[0029] PCA analysis involves performing PCA dimensionality reduction analysis on all participating groups and samples. If the number of samples is greater than 3 and the number of groups is greater than 3, a boxplot and a circos plot are generated. If the number of samples is greater than 3 and the number of groups is less than 4, a boxplot is generated.

[0030] PCoA analysis is performed on all participating groups and samples based on multiple distance matrices. When each group has 4 or more samples, a confidence ellipse will be drawn on the 2D plot.

[0031] NMDS analysis is a variable-type multidimensional scaling analysis for all participating groups and samples. It reflects the species information in the sample in the form of points in a multidimensional space. The distance between points illustrates the grouping effect and the magnitude of differences between samples. If the number of samples is greater than 3, confidence ellipses and scatter plots are generated.

[0032] The specific content of hierarchical clustering analysis of samples is to build a UPGMA tree based on the distances between samples;

[0033] The Adonis analysis specifically uses QIIME1 for calculation and is used for nonparametric analysis of variance testing.

[0034] The Anosim analysis specifically involves using QIIME1 to calculate and analyze a grouping factor.

[0035] The specific content of multivariate statistical analysis includes ANOVA and Kruskal-Wallis algorithm calculation for multiple groups (more than 2), analysis using MetaStat algorithm for 2 groups, LEfSe analysis when providing inter-group comparisons, LDA analysis for dimensionality reduction and biomarker screening, and boxplot and cluster heatmap plots for differential species, as well as cladogram tree plots and bar plots for differential species.

[0036] The specific content of the association and model prediction analysis includes calculating the correlation between environmental factors and species, drawing correlation heatmaps and correlation network diagrams, and performing indicator analysis and random forest analysis on biomarker species.

[0037] According to one aspect of the present invention, the PICRUSt2 analysis includes using PICRUSt2 software to calculate and obtain annotation results from the COG, KEGG, PFAM and MetaCyc databases, generating heatmaps of the top 100 functionalities, performing Willcon and T-test tests on different groups, and generating boxplots and correlation heatmaps of the difference results.

[0038] According to one aspect of the invention, before performing the process, ensure that an ASV abundance table containing all samples of 16S / 18S / ITS is prepared, ASV representative sequences are prepared, and a grouping file is created. This file should be in Excel format and contain three worksheets: a sample information table (this worksheet should contain two columns: the first column lists sample information, and the second column contains grouping information); a difference comparison information table (this worksheet should contain two columns: the first column lists the groups to be compared, and the second column contains comments, which can be general or extended); and a sample renaming table (this worksheet should contain two columns: the first column lists the sample names to be renamed, and the second column lists the renamed sample names).

[0039] The advantages of this invention are as follows: the above technical solution eliminates the need for manual, complex bioinformatics analysis, saving significant time and effort; automated analysis reduces the chance of human intervention and minimizes potential errors; the automated process ensures the consistency and standardization of analysis results; analysis results can be obtained quickly, accelerating the decision-making and research process; and it provides an efficient, convenient, and accurate analysis method. Attached Figure Description

[0040] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0041] Figure 1 This is a schematic diagram of a cloud analytics process for automating the processing of 16S, 18S, or ITS data, as described in this invention.

[0042] Figure 2 This is a logical diagram of a cloud analytics process for automating the processing of 16S, 18S, or ITS data, as described in this invention.

[0043] Figure 3 ASV classification statistical petal plot generated for a cloud analytics workflow that automates the processing of 16S, 18S, or ITS data as described in this invention.

[0044] Figure 4 This is a community structure species cumulative bar chart of a cloud analysis process for automatically processing 16S, 18S, or ITS data as described in this invention.

[0045] Figure 5 This is a heatmap diagram of the community structure of a cloud analysis process for automating the processing of 16S, 18S, or ITS data as described in this invention.

[0046] Figure 6 This is a Krona diagram illustrating the community structure of a cloud analytics workflow for automating the processing of 16S, 18S, or ITS data, as described in this invention.

[0047] Figure 7 This is a Circos diagram illustrating the community structure of a cloud analytics workflow for automating the processing of 16S, 18S, or ITS data, as described in this invention.

[0048] Figure 8 This is a ternary diagram of the community structure of a cloud analysis process for automating the processing of 16S, 18S, or ITS data as described in this invention.

[0049] Figure 9 An alpha difference boxplot of a cloud analytics workflow for automating the processing of 16S, 18S, or ITS data as described in this invention.

[0050] Figure 10 This is a dilution curve diagram of a cloud analytics process for automatically processing 16S, 18S, or ITS data as described in this invention.

[0051] Figure 11 This is a RankAbundance curve of a cloud analytics process for automating the processing of 16S, 18S, or ITS data as described in this invention.

[0052] Figure 12 This is an NMDS analysis result diagram of a cloud analysis process for automatically processing 16S, 18S, or ITS data as described in this invention.

[0053] Figure 13 This is a PCA analysis result diagram of a cloud analytics workflow for automatically processing 16S, 18S, or ITS data as described in this invention.

[0054] Figure 14 This is a PCoA principal coordinate analysis result diagram of a cloud analytics workflow for automatically processing 16S, 18S, or ITS data as described in this invention.

[0055] Figure 15 This is a multivariate statistical differential species boxplot of a cloud analysis process for automating the processing of 16S, 18S, or ITS data as described in this invention.

[0056] Figure 16 This invention provides a cloud analysis workflow for automating the processing of 16S, 18S, or ITS data, including an interspecies correlation heatmap.

[0057] Figure 17 This is an indicator diagram of a cloud analytics process for automating the processing of 16S, 18S, or ITS data as described in this invention.

[0058] Figure 18 This is a network diagram of a cloud analytics process for automating the processing of 16S, 18S, or ITS data, as described in this invention. Detailed Implementation

[0059] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0060] Example 1

[0061] Before executing this automated cloud analytics workflow for processing 16S, 18S, or ITS data, prepare an ASV abundance table containing all 16S / 18S / ITS samples. Also, prepare representative ASV sequences. Create a grouping file in Excel format containing three worksheets: a sample information table, a difference comparison information table, and a sample renaming table. Specifically: the sample information table should contain two columns: the first column lists sample information, and the second column contains grouping information; the difference comparison information table should contain two columns: the first column lists the groups to be compared, and the second column contains comments, which can be general or extended; the sample renaming table should contain two columns: the first column lists the sample names to be renamed, and the second column lists the renamed sample names.

[0062] This process uses the Django framework in Python to build the front-end and back-end task initiation logic to start the task. The specific operation logic is as follows: Figure 1 As shown.

[0063] The uploaded ASV abundance table and representative sequence data are flattened, and taxonomic abundance tables at the phylum, class, order, family, genus and species levels are generated based on taxonomic relationships.

[0064] Use the plotrix package in R software to draw Venn plots or petal plots for visualization, showing the relationship between unique and shared ASVs of different samples.

[0065] Using the uploaded ASV abundance table, all samples were sorted according to the relative abundance of each species, and the top 15 and top 30 species were selected. Stacked bar charts of species were plotted using the ggplot2 package in R software to display the relative abundance of species of each taxonomic class in different samples, allowing for comparison of the relative abundance distribution of species of each taxonomic class in different samples.

[0066] Heatmaps were calculated and plotted using the pheatmap package in R software. Heatmaps can be used to visualize the similarities and differences between different samples, as well as the relationships between different species or ASVs. Heatmaps help identify patterns and trends between samples or species.

[0067] Use Krona software to create a Krona diagram. Krona diagrams are used to display information between different taxa levels, allowing for a better understanding of the relative proportions of different taxa within a sample. Krona diagrams help visualize species distribution and taxonomic structure.

[0068] Use the circlize package in R software to create a circos diagram. Circos diagrams are used to illustrate the complex relationships and interactions between different categories. They help explore the mutual influences between different categories and their relative distributions.

[0069] Use the ggtern package in R software to draw a ternary phase diagram. A ternary phase diagram can be used to illustrate the relationships and interactions between three different factors. It helps in understanding the mutual influences and co-occurrence relationships among different factors.

[0070] Using the QIIME1 software, diversity indices were calculated, including the Simpson index, Chao1 index, Observed species index, Shannon index, Goods coverage index, PD whole tree index, and ACE index. These indices provide information about different aspects of biodiversity in the sample.

[0071] Simpson index: Measures species diversity and evenness. A higher value indicates lower species diversity.

[0072] Chao1 index: estimates the number of unobserved species in a sample.

[0073] Observed_species index: measures the number of species actually observed in a sample.

[0074] Shannon Index: Measures species diversity and evenness, taking into account both species abundance and evenness.

[0075] Goods_coverage index: estimates the number of species already covered in the sample.

[0076] PD_whole_tree: Measures the evolutionary relationships among species in a sample.

[0077] ACE index: estimates the number of unobserved species in a sample.

[0078] Wilcoxon test and Kruskal-Wallis test: Depending on the number of groups, the Wilcoxon test is performed when there are 2 or more but less than 6 groups; the Kruskal-Wallis test is performed when there are 6 or more groups. These tests are used to determine whether there are significant differences in the diversity index between different groups and to create boxplots based on the samples from different groups.

[0079] Using QIIME1 software, dilution curve analysis was performed on different diversity indices for all samples. The default step size was 1000 steps for the largest sample. Dilution curve analysis is used to assess the changing trends of diversity indices at different sampling depths to help determine the appropriate sampling depth.

[0080] If the sample size is greater than 1, use R software to plot the Rank Abundance analysis curve. This curve is used to display the species abundance ranking and help understand the relative abundance of different species.

[0081] The uploaded ASV abundance table and grouping table were analyzed using the QIIME1 software to perform grouped beta diversity analysis.

[0082] When the sample size is greater than 4 and the number of groups is less than 4, scatter plots are generated using R software. When the sample size is greater than 4 and the number of groups is greater than 3, 2D and 3D scatter plots are generated using the ggplot2 and grid packages in R software for NMDS analysis, and confidence ellipses are also plotted. NMDS analysis can be used to show the similarity and differences between different samples. When the sample or grouping conditions are not met, scatter plots and confidence ellipses cannot be generated.

[0083] When the sample size is greater than 4 and the number of groups is less than 4, scatter plots are created using R software. When the sample size is greater than 4 and the number of groups is greater than 3, 2D and 3D scatter plots are created using the ggplot2, grid, and plot3D packages in R software for PCA analysis, and confidence ellipses are plotted. PCA analysis helps to explore principal components and relationships between samples. When sample or grouping conditions are not met, scatter plots and confidence ellipses cannot be created.

[0084] First, distance matrices are calculated based on various distance methods (e.g., binary_jaccard, bray_curtis, euclidean, unweighted_unifrac, weighted_unifrac). When the number of samples is greater than 2 and the number of groups is less than 4, 2D and 3D scatter plots are created using R software. When the number of samples is greater than 2 and the number of groups is greater than 3, 2D and 3D scatter plots are created using the ggplot2, grid, and plot3D packages in R software for PCoA analysis, and confidence ellipses are plotted. PCoA analysis is used to visualize the distance and similarity between samples; scatter plots and confidence ellipses cannot be created when sample or grouping conditions are not met.

[0085] Adonis analysis is used for nonparametric ANOVA testing with 999 permutations. Adonis analysis helps determine whether differences between different groups are significant.

[0086] Anosim analysis is used to compare differences between two or more groups, testing whether the difference between groups is greater than the difference within groups, with a permutation count of 999. Anosim analysis helps assess the statistical significance of differences between groups.

[0087] When the number of samples is greater than 2, hierarchical clustering analysis is performed, and tree diagrams are drawn using the ggplot2 and ggtree packages in R software. Tree diagrams are used to illustrate the hierarchical relationships between samples.

[0088] The purpose of these analyses and charts is to provide visual information on the differences and similarities between different samples, so as to support researchers in better understanding the relationships between samples and groups.

[0089] The number of groups affects the choice of statistical analysis method. When there are two groups, the T-test and Wilcoxon test can be used to compare the differences between the two groups. If there are more than two groups, ANOVA and the Kruskal-Wallis test can be used to compare the differences between multiple groups. These methods are used to determine whether there are significant differences between different groups.

[0090] Based on differential groups or differential samples, cluster heatmaps and boxplots are created using the pheatmap package in R software. Heatmaps can be used to visualize the clustering relationships of differential species or features, while boxplots can be used to show the distribution of differences.

[0091] If the number of groups is greater than or equal to 2 and less than 7, LEfSe analysis can be performed using LEfSe software. LEfSe analysis is used to identify specific species or traits that exist among multiple groups. Results visualization includes bar charts and cladograms of differentially expressed species to show the relative abundance and evolutionary relationships of these specific species.

[0092] The purpose of these analyses and charts is to reveal the differences between different groups, especially in the field of microbiology, where they help to identify species or traits with differential abundance.

[0093] We selected data from the top 30 and top 15 to explore the correlations and importance among the top species in the data.

[0094] A heatmap of interspecies correlations was created using the pheatmap package in R software. This plot illustrates the interactions and relationships between different species.

[0095] Indicator analysis was performed using the ggplot2 and patchwork packages, generating bar charts and bubble charts. Indicator analysis helps determine the importance of different species to specific conditions or groups, while bar charts and bubble charts are used to visually represent the results.

[0096] Use ggplot2, igraph, and network to perform network analysis and draw network diagrams. Network analysis can be used to reveal complex interactions between different species and visualize these relationships using network diagrams.

[0097] Random forest analysis was performed using the randomForest package, and then scatter plots of important species were plotted using the ggplot2 package. Random forest analysis can be used to determine the importance of species, and scatter plots are used to visualize these important species.

[0098] The purpose of these analyses and charts is to reveal the correlations, importance, and interrelationships among different species, and to be used for model predictive analysis.

[0099] The uploaded ASV abundance table, representative sequence, and grouping table were analyzed using PICRUSt2 software for functional prediction. The purpose of this step is to infer the functional characteristics present in the microbial samples.

[0100] We used a Python script to standardize and statistically analyze the KEGG function prediction results. This helps to organize and analyze the prediction results for subsequent analysis.

[0101] The number of groups affects the chosen statistical analysis method. If there are three or more groups, ANOVA and Kruskal-Wallis analysis can be performed to determine differences between the groups. If there are only two groups, Wilcoxon's test can be used. These analytical methods are used to assess the significance of functional characteristics between different groups.

[0102] When there are two or more differentially expressed species, create a boxplot and a heatmap. The boxplot is used to visualize the distribution differences of functional traits between different groups, while the heatmap is used to show the relative abundance of different functional traits.

[0103] The purpose of these analyses and charts is to provide visualizations of the differences in functional characteristics between different groups.

[0104] After completing the above analysis, the present invention stores the analysis results in a specific location for the individual user on the cloud platform. The user can download the results via the download interface on the web page, and can also view the results on the web page.

[0105] Example 2

[0106] 1.1 Document Preparation

[0107] Prepare an ASV abundance table for all samples of 16S / 18S / ITS, prepare representative ASV sequences, and prepare grouping files using three tables, Table 1, Table 2, and Table 3, respectively:

[0108] Table 1

[0109] Sample grouping information

[0110] group samples Group_OEA OEA1, OEA2 Group_OEB OEB1, OEB2 Group_OEC OEC1, OEC2 Group_OED OED1, OED2, OED3, OED4

[0111] "group" represents the group name, and "samples" represents the sample name.

[0112] Table 2

[0113] Comparison information

[0114] groups Group_OEA, Group_OEB Group_OEC, Group_OED

[0115] groups indicates the content of a comparison group.

[0116] Table 3

[0117] Sample renaming

[0118] Before the name change After the name change

[0119] The name before the name change refers to the original sample name, and the name after the name change refers to the sample name that is to be changed to.

[0120] The ASV abundance table for the first 5 rows of data in one instance is shown in Table 4:

[0121] Table 4

[0122] ASV_ID OEA1 OEA2 OEB1 OEB2 OEC1 OEC2 OED1 OED2 OED3 OED4 1 10712 31 190 133 214 407 266 492 0 66 2 4304 6655 0 0 0 0 0 0 0 0 3 974 2141 0 0 432 2963 1176 147 1158 407 4 277 1103 769 656 1516 808 988 686 632 915

[0123] ASV stands for sequence:

[0124] >1

[0125] TGAGGAATATTGGTCAATGGGCGCGAGCCTGAACCAGCC

[0126] >2

[0127] TGGGGGATATTGCACAATGGGGGAAACCCTGATGCAGCG

[0128] 1.2 Task Initiation

[0129] This is a cloud platform built using the Django package in Python, with a front-end and back-end. The task initiation logic is illustrated below. Figure 1 As shown.

[0130] Example 3

[0131] 2.1 Data Processing

[0132] The uploaded ASV abundance table and representative sequences were flattened, and taxonomic abundance tables at the phylum, class, order, family, genus, and species levels were generated based on taxonomic relationships. See Table 5 for details.

[0133] Table 5

[0134]

[0135]

[0136] 2.2 Visualization

[0137] Use the plotrix package in R software to create Venn plots or petal plots to show the relationship between unique and shared ASVs of different samples. ASV classification statistics petal plots are shown below. Figure 3 As shown.

[0138] Example 4

[0139] Using the uploaded ASV abundance table, all samples were sorted according to the relative abundance of each species, and the top 15 and top 30 species were selected. The ggplot2 package in R software was used to create stacked bar charts of species to display the relative abundance of species from different taxa in different samples, allowing for comparison of the relative abundance distribution of species from different taxa in different samples. The community structure species cumulative bar chart is shown below. Figure 4 As shown.

[0140] Heatmaps were calculated and plotted using the pheatmap package in R software. Heatmaps can be used to show the similarities and differences between different samples, as well as the relationships between different species or ASVs. This plot helps identify patterns and trends between samples or species; a community structure heatmap is shown below. Figure 5 As shown.

[0141] Using Krona software, Krona diagrams were created. Krona diagrams are used to display information between different taxonomic levels, allowing for a better understanding of the relative proportions of different taxa within a sample. This diagram helps visualize species distribution and taxonomic structure; a community structure Krona diagram is shown below. Figure 6 As shown.

[0142] Use the `circlize` package in R software to create a circos diagram. Circos diagrams are used to illustrate the complex relationships and interactions between different taxa. This diagram helps explore the mutual influences between different taxa and their relative distribution. A community structure circos diagram is shown below. Figure 7 As shown.

[0143] Using the ggtern package in R software, a ternary phase diagram is drawn. A ternary phase diagram can be used to illustrate the relationships and interactions between three different factors. This diagram helps to understand the mutual influences and co-occurrence relationships between different factors; a community structure ternary diagram is shown below. Figure 8 As shown.

[0144] Example 5

[0145] Using the QIIME1 (v1.9.1) software, diversity indices were calculated, including the Simpson index, Chao1 index, Observed_species index, Shannon index, Goods_coverage index, PD_whole_tree index, and ACE index. These indices provide information about different aspects of biodiversity in the sample.

[0146] Simpson index: Measures species diversity and evenness. A higher value indicates lower species diversity.

[0147] Chao1 index: estimates the number of unobserved species in a sample.

[0148] Observed_species index: measures the number of species actually observed in a sample.

[0149] Shannon Index: Measures species diversity and evenness, taking into account both species abundance and evenness.

[0150] Goods_coverage index: estimates the number of species already covered in the sample.

[0151] PD_whole_tree: Measures the evolutionary relationships among species in a sample.

[0152] ACE index: estimates the number of unobserved species in a sample.

[0153] Wilcoxon and Kruskal-Wallis tests: Depending on the number of groups, the Wilcoxon test (when there are 2 or more groups but less than 6 groups) and the Kruskal-Wallis test (when there are 6 or more groups) are performed. These tests are used to determine whether there are significant differences in the diversity index between different groups, and boxplots are created for samples from different groups. An alpha difference boxplot is shown below. Figure 9 As shown.

[0154] Using QIIME1 software, dilution curve analysis was performed on different diversity indices for all samples. The default step size was 1000 steps for the largest sample. Dilution curve analysis is used to assess the changing trends of diversity indices at different sampling depths to help determine the appropriate sampling depth.

[0155] If the sample size is greater than 1, use R software to plot the Rank Abundance analysis curve. This curve is used to display the species abundance ranking and help understand the relative abundance of different species.

[0156] Example 6

[0157] The uploaded ASV abundance table and grouping table were analyzed using the QIIME1 software.

[0158] When the sample size is greater than 4 and the number of groups is less than 4, scatter plots are created using R software. When the sample size is greater than 4 and the number of groups is greater than 3, 2D and 3D scatter plots are created using the ggplot2 and grid packages in R software, and confidence ellipses are also plotted. NMDS analysis can be used to show the similarity and differences between different samples. When the sample or grouping conditions are not met, scatter plots and confidence ellipses cannot be created.

[0159] When the sample size is greater than 4 and the number of groups is less than 4, use R software to create scatter plots. When the sample size is greater than 4 and the number of groups is greater than 3, use the ggplot2, grid, and plot3D packages in R software to create 2D and 3D scatter plots and draw confidence ellipses. PCA analysis helps to explore principal components and relationships between samples; however, scatter plots and confidence ellipses cannot be created when sample or grouping conditions are not met.

[0160] Distance matrices are calculated based on various distance metrics (e.g., binary_jaccard, bray_curtis, euclidean, unweighted_unifrac, weighted_unifrac). When the number of samples is greater than 2 and the number of groups is less than 4, 2D and 3D scatter plots are created using R software. When the number of samples is greater than 2 and the number of groups is greater than 3, 2D and 3D scatter plots are created using the ggplot2, grid, and plot3D packages in R software, and confidence ellipses are plotted. PCoA analysis is used to visualize the distance and similarity between samples; scatter plots and confidence ellipses cannot be created when sample or grouping conditions are not met.

[0161] Adonis analysis was used for nonparametric ANOVA with a permutation count of 999. Adonis analysis helps determine whether differences between different groups are significant. The results of the Adonis analysis are shown in Table 6.

[0162] Table 6

[0163] Df SumsOfSqs MeanSqs F.Model R2 p-value (>F) Signif Group_factor 3 1.3231 0.44103 1.0526 0.34482 0.007 ** Residuals 6 2.5139 0.41899 0.65518 Total 9 3.837 1

[0164] Anosim analysis is used to compare differences between two or more groups, testing whether the difference between groups is greater than the difference within groups, with a permutation count of 999. Anosim analysis helps assess the statistical significance of differences between groups. The results of the Anosim analysis are shown in Table 7.

[0165] Table 7

[0166] methodname ANOSIM teststatisticname R samples 10 numberofgroups 4 teststatistic 0.50617283950617287 p-value 0.012 number of permutations 999

[0167] When the number of samples is greater than 2, hierarchical clustering analysis is performed, and tree diagrams are drawn using the ggplot2 and ggtree packages in R software. Tree diagrams are used to illustrate the hierarchical relationships between samples.

[0168] Example 7

[0169] The number of groups affects the choice of statistical analysis method. For a group of two, the T-test and Wilcoxon test can be used to compare differences between the two groups. If the number of groups is greater than two, ANOVA and the Kruskal-Wallis test can be used to compare differences between multiple groups. These methods are used to determine whether there are significant differences between different groups.

[0170] Based on differential groups or differential samples, cluster heatmaps and boxplots are created using the pheatmap package in R software. Heatmaps can be used to visualize the clustering relationships of differential species or features, while boxplots can be used to show the distribution of differences.

[0171] If the number of groups is greater than or equal to 2 and less than 7, LEfSe analysis can be performed using LEfSe software. LEfSe analysis is used to identify specific species or traits that exist among multiple groups. Results visualization includes bar charts and cladograms of differentially expressed species to show the relative abundance and evolutionary relationships of these specific species.

[0172] Example 8

[0173] We selected the top 30 and top 15 data points to explore the correlations and importance among the top species. We used the pheatmap package in R to create a correlation heatmap. The correlation heatmap is used to illustrate the interactions and associations between different species.

[0174] Use ggplot2, igraph, and network to perform network analysis and draw network diagrams. Network analysis can be used to reveal complex interactions between different species and visualize these relationships using network diagrams.

[0175] Random forest analysis was performed using the randomForest package, and then scatter plots of important species were plotted using the ggplot2 package. Random forest analysis can be used to determine the importance of species, and scatter plots are used to visualize these important species.

[0176] Example 9

[0177] The uploaded ASV abundance table, representative sequence, and grouping table were analyzed using PICRUSt2 software to predict the functional characteristics present in the microbial samples.

[0178] We used Python to write scripts to standardize and statistically analyze the KEGG function prediction results, and then organized and analyzed the results for further analysis.

[0179] The number of groups affects the chosen statistical analysis method. If there are three or more groups, ANOVA and Kruskal-Walli's analysis can be performed to determine differences between groups. If there are only two groups, the Wilcoxon test can be used to assess the significance of functional traits between groups. When there are two or more differentially expressed species, boxplots and heatmaps are created. Boxplots visualize the distribution differences of functional traits between groups, while heatmaps show the relative abundance of different functional traits.

[0180] Example 10

[0181] After completing the above analysis, this invention stores the analysis results in a specific location for the individual user on the cloud platform. Users can download the results via a web-based download interface, or view the results on a web-based interface for convenient access and retrieval. Users can download the results or view them online as needed.

[0182] The advantages of this invention are as follows: the above technical solution eliminates the need for manual, complex bioinformatics analysis, saving significant time and effort; automated analysis reduces the chance of human intervention and minimizes potential errors; the automated process ensures the consistency and standardization of analysis results; analysis results can be obtained quickly, accelerating the decision-making and research process; and it provides an efficient, convenient, and accurate analysis method.

[0183] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A cloud analytics workflow for automating the processing of 16S, 18S, or ITS data, characterized in that, The cloud analytics process for automating the processing of 16S, 18S, or ITS data includes the following steps: The task submission module enables webpage-triggered task submission. Prepare to upload files; Receive uploaded files and select different comparison methods based on data type to perform ASV classification analysis; Community structure distribution analysis was performed on ASV file data, including: statistical analysis of sample microbial community distribution, visualization of community structure, Krona analysis of community structure, and phylogenetic evolution analysis. Alpha diversity analysis was performed on the uploaded data, including dilution curve analysis, species accumulation curve analysis, RankAbundance analysis, and visualization analysis. Perform beta diversity analysis on the uploaded data, including PCA analysis, PCoA analysis, NMDS analysis, sample hierarchical cluster analysis, Adonis analysis, Anosim analysis, multivariate statistical analysis, association and model prediction analysis; Perform functional predictive analysis on uploaded data, including PICRUSt2 analysis and visualization analysis.

2. The cloud analytics workflow for automating the processing of 16S, 18S, or ITS data according to claim 1, characterized in that, The task submission module specifically includes a view function based on the Django framework, which triggers the submission of tasks via a webpage.

3. The cloud analytics workflow for automating the processing of 16S, 18S, or ITS data according to claim 1, characterized in that, The steps for receiving uploaded files include selecting different alignment methods for ASV classification analysis, obtaining ASV abundance table files, ASV representative sequence files, and analysis confirmation forms.

4. The cloud analytics process for automating the processing of 16S, 18S, or ITS data according to claim 3, characterized in that, The ASV abundance table file records the abundance data of each ASV in different samples; the ASV representative sequence file contains the FASTA format nucleic acid or amino acid sequence corresponding to each ASV; the analysis confirmation form includes a grouping information worksheet, a group comparison worksheet, and a sample renaming worksheet.

5. The cloud analytics workflow for automating the processing of 16S, 18S, or ITS data according to claim 1, characterized in that, The community structure distribution analysis of ASV file data includes annotating and classifying 16S bacteria and archaea and 18S using the rdp method based on the uploaded ASV representative sequence files, and classifying and annotating ITS using the blast method; and drawing Venn diagrams or petal diagrams based on the sample number in the uploaded ASV table.

6. The cloud analytics workflow for automating the processing of 16S, 18S, or ITS data according to claim 1, characterized in that, The community structure distribution analysis includes statistical analysis of the distribution of microbial communities in the samples, generating abundance tables for phylum, class, order, family, genus, and species for each sample, and sorting the abundance of each classification to create a species bar chart. This includes using QIIME1 to plot heatmaps based on the relative abundance at each level; This includes using Krona software to analyze and visualize species annotation results; It also includes selecting the ASVs of the species with the highest abundance according to the sorting, performing multiple sequence alignment using MUSCLE, constructing a phylogenetic tree using FastTree, and constructing a taxonomic tree using megan software.

7. The cloud analytics workflow for automating the processing of 16S, 18S, or ITS data according to claim 1, characterized in that, The alpha diversity analysis of the uploaded data includes using the QIIME1 software to calculate the alpha diversity index of the relative abundance of all samples. The selected diversity indices are the Simpson index, Chao1 index, observed_species index, Shannon index, goods_coverage index, PD_whole_tree, and ACE index.

8. The cloud analytics workflow for automating the processing of 16S, 18S, or ITS data according to claim 1, characterized in that, The alpha diversity analysis of the uploaded data includes: Dilution curve analysis was performed using QIIME1, with calculations and plotting done at 1000-step intervals for the largest sample. Speciation accumulation curve analysis was performed using the Specaccum function in vegan. Visualization, including violinplots and boxplots for all samples, using ggplot2 for plotting; Rank Abundance analysis was performed using R software to rank the alpha index of all samples and then visualized.

9. The cloud analytics workflow for automating the processing of 16S, 18S, or ITS data according to claim 1, characterized in that, The beta diversity analysis of the uploaded data includes: PCA analysis involves performing PCA dimensionality reduction analysis on all participating groups and samples. If the number of samples is greater than 3 and the number of groups is greater than 3, a boxplot and a circos plot are generated. If the number of samples is greater than 3 and the number of groups is less than 4, a boxplot is generated. PCoA analysis is performed on all participating groups and samples based on multiple distance matrices. When each group has 4 or more samples, a confidence ellipse will be drawn on the 2D plot. NMDS analysis is a variable-type multidimensional scaling analysis for all participating groups and samples. It reflects the species information in the sample in the form of points in a multidimensional space. The distance between points illustrates the grouping effect and the magnitude of differences between samples. If the number of samples is greater than 3, confidence ellipses and scatter plots are generated. The specific content of hierarchical clustering analysis of samples is to build a UPGMA tree based on the distances between samples; The Adonis analysis specifically uses QIIME1 for calculation and is used for nonparametric analysis of variance testing. The Anosim analysis specifically involves using QIIME1 to calculate and analyze a grouping factor. The specific content of multivariate statistical analysis includes multi-group ANOVA and Kruskal-Wallis algorithm calculation, two groups using MetaStat algorithm analysis, LEfSe analysis when providing inter-group comparisons, LDA analysis for dimensionality reduction and biomarker screening, and boxplot and cluster heatmap plots for differential species, as well as cladogram tree plots and bar plots for differential species. The specific content of the association and model prediction analysis includes calculating the correlation between environmental factors and species, drawing correlation heatmaps and correlation network diagrams, and performing indicator analysis and random forest analysis on biomarker species.

10. The cloud analytics process for automating the processing of 16S, 18S, or ITS data according to claim 1, characterized in that, The PICRUSt2 analysis includes using PICRUSt2 software to calculate and obtain annotation results from the COG, KEGG, PFAM, and MetaCyc databases, generating heatmaps of the top 100 functionalities, performing Willcon and T-test tests on different groups, and generating boxplots and correlation heatmaps of the differences.

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