M6A online analysis platform based on bioinformatics and generation method

Abstract

The invention provides an m6A online analysis platform based on bioinformatics, which comprises a grouping scheme module, a Peak set screening module, a sample relation module, a Peak feature analysis module, a methylation difference analysis module and a target Peak analysis module, and is characterized in that the Peak set screening module is used for screening a sample; according to the grouping scheme, sample grouping names are modified according to experimental design, grouping is eliminated, and the grouping scheme is determined and comprises the function of newly establishing the grouping scheme; peak set screening is to screen the peak according to a certain condition to form a peak set so as to carry out targeted analysis, including generation of a new peak set and combination of peak set functions; the sample relationship comprises PCA, a correlation heat map and a sample clustering map; the peak feature analysis comprises peak distribution analysis, Motif analysis, enrichment of a related gene GO and enrichment of a related gene KEGG; the methylation difference analysis comprises difference peak analysis, Motif analysis, enrichment of a related gene GO and enrichment of a related gene KEGG; the target Peak analysis comprises a target Peak Wehn diagram, a related gene Wehn diagram, Motif analysis, related gene GO enrichment and related gene KEGG enrichment. The online analysis platform and the generation method are user-friendly, powerful in function and visual in result, and have the advantages that the operation process is simplified, the analysis precision is improved, and result display is enriched.

Description

Technical Field

[0001] The present invention relates to the field of bioinformatics analysis technology, and in particular focuses on research related to RNA modification, and specifically relates to an m6A online analysis platform and a generation method based on bioinformatics, which are used for comprehensive analysis of m6A modifications in RNA molecules. Background Art

[0002] m6A (N6-methyladenine) is one of the most common internal modifications in RNA molecules, and is widely present in eukaryotic mRNA and various non-coding RNAs, such as lncRNA, miRNA, etc. m6A modification plays a key role in gene expression regulation and cell function maintenance by affecting RNA stability, translation efficiency, splicing and transport. With the development of high-throughput sequencing technology, more and more studies have revealed the important role of m6A modification in biological processes and diseases.

[0003] In the field of bioinformatics, m6A methylation, as an important RNA modification, has received extensive attention and research in recent years. m6A modification refers to the addition of a methyl group to the 6th N atom of RNA adenine. This modification is common in the CDS region and 3'UTR region of mRNA, and also exists in non-coding RNAs such as lncRNA and micRNA. m6A modification plays an important role in regulating a variety of molecular functions, such as interacting with proteins to regulate the stability, alternative splicing, translation and subcellular localization of mRNA, and is closely related to a variety of diseases including tumors.

[0004] At present, the methods for high-throughput detection of m6A methylation mainly include LC-MS / MS, colorimetry, meRIP-seq (methylated RNA immunoprecipitation sequencing), m6A-seq and miCLIP-seq. LC-MS / MS and colorimetry can detect the m6A level of RNA as a whole, but they cannot meet the requirements of large-scale gene detection and the distribution of m6A research at the transcriptome level, and they cannot accurately locate the methylation sites. As high-throughput sequencing methods, meRIP-seq, m6A-seq and miCLIP-seq can detect m6A modification on a large scale. Among them, miCLIP-seq can be accurate to single-base methylation resolution, but the experiment is difficult and costly; meRIP-seq and m6A-seq can detect m6A in the whole genome of samples at a relatively reasonable cost, and m6A can be located in the transcript region of about 100nt, so they are widely used in m6A research.

[0005] However, although these technologies provide powerful tools for the study of m6A by enriching m6A-modified RNA fragments with specific antibodies and then performing high-throughput sequencing to obtain the whole-genome distribution map of m6A modification, the large amount of data generated by high-throughput sequencing needs to be processed and analyzed by bioinformatics methods. Most of the existing analysis platforms have problems such as cumbersome operation and complex data interpretation, and lack an online platform that integrates multiple m6A data analysis methods. At the same time, although there are a variety of m6A analysis tools and databases (such as m6A-Atlas, m6AVar, etc.), they still have certain limitations in terms of ease of use, analysis depth, and result display.

[0006] 1. Complex to use: Many tools require users to have a certain programming and bioinformatics background, and are complex to operate.

[0007] 2. Insufficient analysis depth: Some tools have incomplete algorithms for peak detection and functional analysis, which may lead to inaccurate or incomplete results.

[0008] 3. Limited result display: Existing tools are insufficient in result visualization and interactivity, making it difficult to meet the diverse needs of users.

[0009] In addition, with the continuous deepening of m6A research, more and more m6A sites have been identified, and the distribution and differences of these sites in different species, different tissues, and different disease states need to be further analyzed and explored.

[0010] Therefore, developing an efficient and accurate m6A online analysis platform is of great significance for bioinformatics research. The m6A online analysis platform and generation method based on bioinformatics can have a variety of m6A data analysis methods, providing a one-stop service from raw data processing to result analysis. Users only need to upload sequencing data to complete key steps such as Peak set screening, sample relationship, Peak feature analysis, methylation difference analysis and target Peak analysis online, and finally output intuitive and easy-to-understand analysis report results, which greatly simplifies the process of m6A research, improves research efficiency, and promotes the application of m6A in disease diagnosis, treatment and drug development.

[0011] In summary, the development of a practical and accurate bioinformatics-based m6A online analysis platform and its generation method is of great significance to promote the development and application of m6A research. By simplifying the operation process, improving the analysis accuracy and enriching the result display, it provides a powerful tool for scientific researchers and helps to achieve more breakthroughs in m6A research. Summary of the invention

[0012] In order to overcome the shortcomings of the prior art, the present invention application provides an m6A online analysis platform and generation method based on bioinformatics, aiming to provide a user-friendly, powerful, and intuitive analysis platform with the advantages of simplifying the operation process, improving the analysis accuracy, and enriching the result display, that is, providing a graphical user interface to lower the usage threshold so that non-professional users can easily get started; using advanced algorithms and models to improve the accuracy and depth of functional analysis; providing a variety of visualization options and interactive functions to help users understand the analysis results more intuitively.

[0013] The present invention proposes an m6A online analysis platform and generation method based on bioinformatics, including: a grouping scheme module, a Peak set screening module, a sample relationship module, a Peak feature analysis module, a methylation difference analysis module and a target Peak analysis module.

[0014] The grouping scheme module is used to modify sample group names, eliminate groups, and determine grouping schemes according to the experimental design. It includes the function of creating a new grouping scheme, which can perform grouping data, sample pairing options, and default comparison scheme operations.

[0015] The Peak set screening module filters peaks according to certain conditions to form peak sets for targeted analysis, including the functions of generating new peak sets and merging peak sets.

[0016] The sample relationship module includes PCA, correlation heat map, and sample clustering diagram; the peak feature analysis includes peak distribution analysis, Motif analysis, related gene GO enrichment, and related gene KEGG enrichment.

[0017] The Peak feature analysis module includes Peak distribution analysis, Motif analysis, GO enrichment of related genes, and KEGG enrichment of related genes. The methylation difference analysis module includes difference peak analysis, Motif analysis, GO enrichment of related genes, and KEGG enrichment of related genes.

[0018] The target peak analysis module includes target peak Venn diagram, related gene Venn diagram, Motif analysis, related gene GO enrichment, and related gene KEGG enrichment.

[0019] Furthermore, the grouping scheme module specifically clicks "New Grouping Scheme" to create grouping analysis information, checks the grouping "Whether to include in analysis" option according to the experimental requirements, sets the "Sample Name" and "Sample Grouping" information, and after the grouping information is established, the grouping information operations can be viewed, modified, and deleted in this interface.

[0020] Furthermore, the Peak set filtering module specifically clicks "Generate new peak set" to create a new peak set, and clicks "Merge peak sets" to merge the peak sets that have been established under the same analysis task. After establishing a new peak set, you can view or delete the filtering conditions and corresponding cell set information in this interface.

[0021] Furthermore, in the sample relationship module, PCA can specifically perform "parameter switching", "modify sample name", and "modify group name and order". Parameter switching can switch the two-dimensional and three-dimensional display forms of the graph; to modify the sample name, you can select the sample name to be modified, modify it at the Modify Name, and click OK; to modify the group name and order, you can select the group name to be modified, modify it at the Modify Name, and click OK. Click "Adjust Order" and a pop-up window will appear listing all group names. Drag the name up and down with the left mouse button to modify the group order. The list from top to bottom corresponds to the legend from top to bottom by default. Click "Confirm" and the graph will change accordingly.

[0022] In the sample relationship module, the correlation heat map can be modified to "modify sample name and order". Select the sample name to be modified, modify it in the name field, and click OK. Click "Adjust Order" and a pop-up window will appear listing all sample names. Drag the name up and down with the left mouse button to modify the sample order. The list from top to bottom corresponds to the legend from top to bottom by default. Click "Confirm" and the graph will change accordingly.

[0023] The sample cluster diagram in the sample relationship module can be used to perform "sample screening" and "sample name modification". For sample screening, you can check the samples to determine the samples displayed in the diagram. For sample name modification, you can select the sample name to be modified, modify it in the Modify Name field, and click OK.

[0024] Furthermore, the Peak distribution analysis in the Peak feature analysis module includes chromosome distribution, gene distribution bar graph, gene reads distribution graph, functional element distribution, functional element enrichment, and relative gene position distribution.

[0025] The chromosome distribution feature is that the peaks at different positions on different chromosomes are marked in the figure to show the distribution of the peaks on the genome; the gene distribution bar graph feature is that the number, length and other information of peaks in different groups are displayed in the form of a bar graph; the gene reads distribution graph feature is that the number of reads of the sample at different gene positions is visualized to intuitively display the signal value distribution of different peaks; the functional element distribution feature is that the gene functional element distribution characteristics of the peaks in the sample are counted in the form of pie charts; the functional element enrichment feature is that the enrichment score of the peaks on different gene functional elements is calculated and the enrichment results are displayed in a bar graph; the relative gene position distribution feature is that the number and percentage of peaks on the 5'UTR, CDS, and 3'UTR of the coding gene are calculated and displayed in the form of a line graph.

[0026] The Motif analysis in the Peak Feature Analysis module includes known Motif prediction and unknown Motif prediction. Its characteristics are to set weights according to the base ratio at each position, calculate the diversity value of the base at each position to show the possibility of the occurrence of the base at each position.

[0027] The GO enrichment of related genes in the Peak feature analysis module includes classification secondary bar chart, significant enrichment bar chart, GO enrichment bar chart, significant enrichment bubble chart, and enrichment circle chart.

[0028] The characteristics of the classified secondary bar graph and the significant enrichment bar graph are that the peak-related genes are mapped to the GO terms contained in the molecular functions, cellular components, and biological processes in the GO database, the peak-related genes are enriched and classified, and the GO terms that are significantly enriched in the peak-related genes compared with the whole genome background are found with the help of the hypergeometric test; the GO enrichment bar graph can describe the proportion and significance of the peak-related genes in each type of GO term; the significant enrichment bubble graph can describe the number of peak-related genes in each type of GO term, the significance of GO term, Gene Ratio, etc.; the enrichment circle graph can describe the significance of each type of GO term, the number of up- and down-regulated genes, Gene Ratio, and other results.

[0029] The KEGG enrichment of related genes in the Peak feature analysis module includes number statistics chart, significance column chart, KEGG enrichment bar chart, significance bubble chart, enrichment circle chart, network chart and significance pathway chart.

[0030] The characteristics of the number statistics chart, significance bar chart, KEGG enrichment bar chart, significance bubble chart, and enrichment circle chart are that the peak-related genes are mapped to the KEGG database to enrich and classify the peak-related genes and use the hypergeometric test to find the pathways that are significantly enriched in the peak-related genes compared with the entire genome background; the network diagram and significance pathway diagram are characterized by drawing network diagrams based on the pathway information enriched by the peak-related genes and utilizing the interaction relationship between different pathways to assist in finding the core pathway.

[0031] Furthermore, the differential peak analysis in the methylation differential analysis module includes statistical bar graphs and volcano graphs.

[0032] The characteristic of the statistical bar graph is that it counts the number of difference peaks, with the horizontal axis representing the different comparison groups and the vertical axis representing the number of genes; the characteristic of the volcano plot is that it counts the difference characteristics of the peaks, and uses the difference values ​​and p-values ​​of the peaks to draw a graph to display the difference characteristics of the peaks.

[0033] Motif analysis in the methylation difference analysis module includes known motif prediction and unknown motif prediction. It is characterized by setting weights according to the base ratio at each position and calculating the diversity value of the base at each position to show the possibility of the base appearing at each position.

[0034] The GO enrichment of related genes in the methylation difference analysis module includes classification secondary bar chart, significant enrichment bar chart, GO enrichment bar chart, significant enrichment bubble chart, and enrichment circle chart.

[0035] The characteristics of the classified secondary bar graph and the significant enrichment bar graph are that the peak-related genes are mapped to the GO terms contained in the molecular functions, cellular components, and biological processes in the GO database, the peak-related genes are enriched and classified, and the GO terms that are significantly enriched in the peak-related genes compared with the whole genome background are found with the help of the hypergeometric test; the GO enrichment bar graph can describe the proportion and significance of the peak-related genes in each type of GO term; the significant enrichment bubble graph can describe the number of peak-related genes in each type of GO term, the significance of GO term, Gene Ratio, etc.; the enrichment circle graph can describe the significance of each type of GO term, the number of up- and down-regulated genes, Gene Ratio, and other results.

[0036] The KEGG enrichment of related genes in the methylation difference analysis module includes number statistics chart, significance bar chart, KEGG enrichment bar chart, significance bubble chart, enrichment circle chart, network chart and significance pathway chart.

[0037] The characteristics of the number statistics chart, significance bar chart, KEGG enrichment bar chart, significance bubble chart, and enrichment circle chart are that the peak-related genes are mapped to the KEGG database to enrich and classify the peak-related genes and use the hypergeometric test to find the pathways that are significantly enriched in the peak-related genes compared with the entire genome background; the network diagram and significance pathway diagram are characterized by drawing network diagrams based on the pathway information enriched by the peak-related genes and utilizing the interaction relationship between different pathways to assist in finding the core pathway.

[0038] Furthermore, the target Peak Venn diagram in the target Peak analysis module includes a Venn diagram and an Upset diagram.

[0039] The Venn diagram of related genes in the target Peak analysis module includes Venn diagram and Upset diagram.

[0040] The Motif analysis in the target Peak analysis module includes known Motif prediction and unknown Motif prediction. Its characteristics are to set weights according to the base ratio at each position, calculate the diversity value of the base at each position to show the possibility of the base appearing at each position.

[0041] The GO enrichment of related genes in the target Peak analysis module includes classification secondary bar chart, significant enrichment bar chart, GO enrichment bar chart, significant enrichment bubble chart, and enrichment circle chart.

[0042] The characteristics of the classified secondary bar graph and the significant enrichment bar graph are that the peak-related genes are mapped to the GO terms contained in the molecular functions, cellular components, and biological processes in the GO database, the peak-related genes are enriched and classified, and the GO terms that are significantly enriched in the peak-related genes compared with the whole genome background are found with the help of the hypergeometric test; the GO enrichment bar graph can describe the proportion and significance of the peak-related genes in each type of GO term; the significant enrichment bubble graph can describe the number of peak-related genes in each type of GO term, the significance of GO term, Gene Ratio, etc.; the enrichment circle graph can describe the significance of each type of GO term, the number of up- and down-regulated genes, Gene Ratio, and other results.

[0043] The KEGG enrichment of related genes in the target Peak analysis module includes number statistics chart, significance bar chart, KEGG enrichment bar chart, significance bubble chart, enrichment circle chart, network chart and significance pathway chart.

[0044] The characteristics of the number statistics chart, significance bar chart, KEGG enrichment bar chart, significance bubble chart, and enrichment circle chart are that the peak-related genes are mapped to the KEGG database to enrich and classify the peak-related genes and use the hypergeometric test to find the pathways that are significantly enriched in the peak-related genes compared with the entire genome background; the network diagram and significance pathway diagram are characterized by drawing network diagrams based on the pathway information enriched by the peak-related genes and utilizing the interaction relationship between different pathways to assist in finding the core pathway.

[0045] Preferably, the chromosome distribution, functional element distribution, and relative gene position distribution in the Peak distribution analysis can be "parameter switched", specifically, checking different chromosomes to display in the graph, checking different groups to determine the peaks to display in the graph, switching groups to display the peak distribution characteristics of different groups, checking groups to display in the graph, and switching the relative and absolute values ​​of the vertical axis to display in the graph.

[0046] The statistical bar chart in the differential peak analysis can be used for "parameter switching" and "modification of comparison group name and order", specifically, switch the overview or different comparison groups in the parameters, switch the differential peak or related genes in the statistical type; select the comparison group name to be modified in the comparison group name, modify it in the modification name and click OK, click "Adjust Order" and a pop-up window will appear to list all the names. Drag the name up and down with the left mouse button to modify the order. The list from top to bottom corresponds to the legend from top to bottom by default. Click "Confirm" and the graph will change accordingly.

[0047] The Venn diagram and Upset diagram in the target Peak Venn diagram and the related gene Venn diagram can be "modified name and order". Specifically, select the name to be modified in the name, modify it in the Modify Name, and click OK. Click "Adjust Order" to display a pop-up window listing all the names. Drag the name up and down with the left mouse button to modify the order. The list from top to bottom corresponds to the legend from top to bottom by default. Click "Confirm" and the graph will change accordingly.

[0048] Preferably, the known motif prediction and unknown motif prediction in the motif analysis can perform "parameter switching", specifically, the drop-down box switches the frequency logo map and the weight logo map.

[0049] The classification secondary column chart, significant enrichment column chart, GO enrichment bar chart, and significant enrichment bubble chart of related genes in GO enrichment can be "parameter switched" and "name switched". Specifically, "parameter switch" is to select overview or different comparison groups in parameters, and select to view the enrichment results of differential peak related genes in comparison groups in a single ontology in switching to ontology. The options of parameter switching and switching to ontology can be freely combined; "name switch" is to use GO term's "ID" or "NAME" or "ID+NAME" to draw graphs in name switching.

[0050] The number statistics chart, enrichment circle chart, network chart and significant pathway chart of KEGG enrichment of related genes can be "parameter switched", specifically, select a single group in the parameter to view the enrichment results of differentially expressed genes in the pathway.

[0051] The significance column chart, KEGG enrichment bar chart, and significance bubble chart in the KEGG enrichment of related genes can perform "parameter switching" and "name switching". Specifically, "parameter switching" is to select "single grouping" in the parameters to view the enrichment results of differential genes in the pathway; "name switching" is to use the pathway's "ID" or "NAME" or "ID+NAME" to draw graphs in the name switch.

[0052] Preferably, the significant enrichment column chart, GO enrichment bar chart, significant enrichment bubble chart, enrichment circle chart of the relevant genes GO enrichment and the significant column chart, KEGG enrichment bar chart, significant bubble chart, enrichment circle chart, network chart and significant pathway chart of the relevant genes KEGG enrichment are displayed. You can also choose to use "FDR value" or "P value" in "Change threshold drawing" to draw a graph for the top n terms of significant enrichment. Click OK to update the graph.

[0053] The m6A online analysis platform and generation method based on bioinformatics are characterized in that m6A online analysis is performed on bioinformatics data to form a web-based online report, and the modification and download of graphics contained in the web-based online report are supported. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 This is a structural diagram of the m6A online analysis platform and generation method based on bioinformatics of the present invention.

[0055] Figure 2 The following is an analysis flow chart of a specific implementation manner of the present invention.

[0056] Figure 3 Schematic diagram of the m6A online analysis platform based on bioinformatics of the present invention.

[0057] Figure 4 It is a schematic diagram of a correlation heat map in a specific embodiment of the present invention.

[0058] Figure 5 It is a schematic diagram of the gene reads distribution diagram in a specific embodiment of the present invention.

[0059] Figure 6 It is a schematic diagram of known Motif prediction in a specific embodiment of the present invention.

[0060] Figure 7 It is a schematic diagram of a Venn diagram in a specific implementation manner of the present invention.

[0061] Figure 8 It is a schematic diagram of a significantly enriched bubble diagram in a specific embodiment of the present invention.

[0062] Fig. 9 It is a schematic diagram of the enrichment circle diagram in a specific implementation manner of the present invention.

[0063] Fig.10 It is a schematic diagram of a network diagram in a specific implementation manner of the present invention.

[0064] Fig.11 Schematic diagram of a significant pathway diagram in a specific embodiment of the present invention. DETAILED DESCRIPTION

[0065] The embodiments of the present invention are described by specific embodiments and in combination with the accompanying drawings. A person skilled in the art can easily understand the advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified and changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention.

[0066] In order to more clearly illustrate the technical solutions in the specific implementation modes of the present invention, a brief introduction is given to the drawings required for use in the description of the embodiments. Obviously, the drawings in the description 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 work.

[0067] In order to achieve the above-mentioned purpose, the present invention application adopts the following technical solutions.

[0068] The present invention provides a specific implementation of an m6A online analysis platform and a generation method based on bioinformatics. The actual operation and application effect of the present invention are described in detail below through specific technical details and implementation steps.

[0069] The m6A online analysis platform and generation method based on bioinformatics applied in the present invention mainly include: a grouping scheme module, a Peak set screening module, a sample relationship module, a Peak feature analysis module, a methylation difference analysis module and a target Peak analysis module. The structure diagram of the m6A online analysis platform and generation method based on bioinformatics ( Figure 1 ), which clearly presents the logical relationship and data flow between the various functional modules of the platform, and demonstrates the systematicness and integrity of the platform from the overall architecture.

[0070] The user enters the username and password to log in to Omicsmart (website: https: / / www.omicsmart.com / home.html# / ) to enter the Omicsmart account overview, selects "m6A analysis platform" and clicks to enter the platform, then clicks "Enter interactive analysis of results" (https: / / www.omicsmart.com / M6A / home.html# / navigation) to enter the bioinformatics-based m6A online analysis platform, which can perform online analysis of bioinformatics data.

[0071] Data input and quality control: users input data, the system performs quality control, unqualified data is re-checked or quality controlled, and qualified data enters subsequent analysis. Analysis flow chart ( Figure 2 ) describes in detail the entire process from data input to the final generation of analysis reports, including key steps such as data quality control, comparison, and analysis of each module, providing users with clear operating guidelines and an overview of the data analysis process.

[0072] Grouping scheme module: Users can modify sample group names, remove groups, and determine grouping schemes according to the experimental design. Users can create group analysis information by clicking "Create a new grouping scheme", check whether to include the group in the analysis according to the experimental requirements, and set the "sample name" and "sample group" information. After establishing the group information, users can view, modify, and delete the group information in this interface.

[0073] Peak set screening (refers to selecting a Peak set that meets specific standards from a large amount of detected Peak data) module: users filter peaks (Peak refers to the local maximum peak of a certain signal in the data) according to certain conditions to form a peak set for targeted analysis. Users click "Generate new peak set" to create a new peak set, or click "Merge peak sets" to merge the peak sets that have been established under the same analysis task. In addition, users can also generate a new peak set directly from "below the table of analysis results". After establishing a new peak set, users can view or delete the filtering conditions and corresponding peak set information in this interface.

[0074] Sample relationship analysis module: including PCA, correlation heat map and sample clustering diagram, which are used to show the relationship and distribution characteristics between samples. PCA (Principal Component Analysis) summarizes the abundance characteristics of data through principal components, so as to reflect the similarity characteristics between different samples in spatial distance; correlation heat map performs correlation analysis between different samples to determine the similarity between different samples; sample clustering diagram draws a tree classification diagram based on the correlation of different samples, and the distance between different samples represents the similarity characteristics between samples. Sample relationship analysis is based on PCA diagram, correlation heat map and sample clustering diagram. Figure 3 This type of graph is used to visualize the data results in the analysis report.

[0075] In PCA, users can perform the "parameter switching", "modify sample name" and "modify group name and order" functions. "Parameter switching" can switch the two-dimensional and three-dimensional display forms of the graph.

[0076] In the correlation heat map, users can modify the sample name and order to better show the correlation between samples. Figure 3 ) visually displays the degree of correlation between samples through color depth and block size, helping users quickly identify the correlation patterns between samples.

[0077] In the sample clustering diagram, users can perform "sample screening" and "modify sample name" operations to display the clustering results of the samples.

[0078] Peak feature analysis module: users can conduct in-depth analysis of peak features, including peak distribution analysis, motif analysis, related gene GO enrichment and related gene KEGG enrichment. Peak feature analysis visualizes the data results of the analysis report in the form of chromosome distribution map, gene distribution bar map, gene reads distribution map, functional element distribution, functional element enrichment, relative gene position distribution map, known motif prediction map, unknown motif prediction map, classification secondary bar map, significant enrichment bar map, GO enrichment bar map, significant enrichment bubble map, enrichment circle map, number statistics map, significance bar map, KEGG enrichment bar map, significance bubble map, enrichment circle map, network map and significant pathway map.

[0079] Peak distribution analysis is to display the distribution of peaks on the genome, including chromosome distribution, gene distribution bar graph, gene reads distribution graph, functional element distribution, functional element enrichment and relative gene position distribution. In Peak distribution analysis, the chromosome distribution, functional element distribution and relative gene position distribution can be operated by the "parameter switching" function, specifically, check different chromosomes to display in the graph, check different groups to determine the peak to display in the graph, switch groups to display the peak distribution characteristics of different groups, check groups to display in the graph, and switch the relative and absolute values ​​of the vertical axis to display in the graph. Gene reads distribution graph (shows the distribution of reads in specific areas of the genome, Figure 4 ) presents the distribution of the number of reads (the number of short sequence fragments generated when the sequencer sequences the gene sample) at different gene locations in an intuitive graphical manner, providing an important reference for researchers to analyze the distribution of peak signal values.

[0080] Motif analysis is to show the possibility of bases appearing in the peak, including known motif prediction and unknown motif prediction. In Motif analysis, users can perform the "parameter switching" function, specifically switching the frequency logo map and weight logo map in the drop-down box. Motif prediction ( Figure 5 ) shows the prediction results of known motifs in motif analysis, and presents the characteristics of motifs through specific graphic elements (such as base ratio, weight, etc.), which helps to deeply understand the sequence characteristics of peaks.

[0081] Related gene GO enrichment is to map the peak related genes to the GO terms contained in the molecular functions, cellular components, and biological processes in the GO database, and perform enrichment classification and significance test. Related gene GO enrichment includes classification secondary bar chart, significant enrichment bar chart, GO enrichment bar chart, significant enrichment bubble chart, and enrichment circle chart. In the classification secondary bar chart, significant enrichment bar chart, GO enrichment bar chart, and significant enrichment bubble chart, users can perform "parameter switching" and "name switching" functions. Specifically, "parameter switching" is to select an overview or different comparison groups in the parameters, and select to view the enrichment results of the difference peak related genes in the comparison group in a single ontology in the switch to ontology. The options of parameter switching and switching to ontology can be freely matched; "name switching" is to use the GO term's "ID" or "NAME" or "ID+NAME" to draw a graph in the name switch. In the significant enrichment column chart, GO enrichment bar chart, significant enrichment bubble chart, and enrichment circle chart, users can also choose to use "FDR value" or "P value" in "Change threshold drawing" to draw a graph for the top n terms of significant enrichment. Click OK to update the graph. Figure 7 ) Through the visual elements such as bubble size, color and position, it comprehensively displays the multi-dimensional information such as the number of peak-related genes in each GO term, GO term significance, Gene Ratio, etc., which is convenient for users to quickly screen key GO terms and enrich the circle diagram ( Figure 8 ) comprehensively displays the significance of each type of GOterm, the number of up- and down-regulated genes, Gene Ratio and other results in a circular layout, providing users with a global view of gene function enrichment.

[0082] KEGG enrichment of related genes is to map peak related genes to the KEGG database for pathway enrichment classification and significance test, and draw a network diagram to assist in finding core pathways. KEGG enrichment of related genes includes number statistics, significance bar chart, KEGG enrichment bar chart, significance bubble chart, enrichment circle chart, network chart and significance pathway chart. In the number statistics, enrichment circle chart, network chart and significance pathway chart, users can perform the "parameter switching" function operation, specifically, select a single group in the parameters to view the enrichment results of differential genes in the pathway. In the significance bar chart, KEGG enrichment bar chart and significance bubble chart, users can perform the "parameter switching" and "name switching" function operations, specifically, "parameter switching" is to select "single group" in the parameters to view the enrichment results of differential genes in the pathway; "name switching" is to use the "ID" or "NAME" or "ID+NAME" of the pathway in the name switching to draw a graph. In the significant bar graph, KEGG enrichment bar graph, significant bubble graph, enrichment circle graph, network graph and significant pathway graph, users can also choose to use "FDR value" or "P value" in "Change threshold drawing" to draw a graph for the top n terms of significant enrichment. Click OK to update the graph. Network graph ( Fig. 9 ) is drawn based on the pathway information of peak-related gene enrichment, showing the interaction between different pathways through nodes and lines, helping researchers to find core pathways and deeply understand the regulatory network of m6A modification in cell biological processes. Fig.10 ) highlights the significant pathways related to m6A modification, helping researchers focus on key biological pathways and further explore the potential mechanisms of m6A modification in biological processes such as disease occurrence and development.

[0083] Methylation difference analysis module: users analyze methylation differences, including difference peak analysis, motif analysis, related gene GO enrichment and related gene KEGG enrichment. Methylation difference analysis is visualized in the data results of the analysis report with statistical bar graphs, volcano graphs, known motif prediction graphs, unknown motif prediction graphs, classification secondary bar graphs, significant enrichment bar graphs, GO enrichment bar graphs, significant enrichment bubble graphs, enrichment circle graphs, number statistics graphs, significance bar graphs, KEGG enrichment bar graphs, significance bubble graphs, enrichment circle graphs, network graphs and significant pathway graphs.

[0084] Differential peak analysis includes statistical bar graphs and volcano graphs. In the statistical bar graph, users can perform the "parameter switching" and "modify comparison group name and order" functions, specifically switching the overview or different comparison groups in the parameters, and switching differential peaks or related genes in the statistical type; select the comparison group name to be modified in the comparison group name, modify it in the modification name, and click OK. Click "adjust order" to display a pop-up window listing all names. Drag the name up and down with the left mouse button to modify the order. The default from top to bottom in the list corresponds to the legend from top to bottom. Click "confirm" and the graph changes accordingly. The volcano graph is a statistical peak differential feature. The peak differential value and p-value are used to draw a graph to show the differential features of the peak. Users can perform the "parameter switching" function operation, and users can switch different comparison groups.

[0085] Similar to the Motif analysis, related gene GO enrichment and related gene KEGG enrichment in the Peak feature analysis module, methylation differential analysis provides a comprehensive understanding of the peak differential features.

[0086] Target Peak Analysis Module: Users can conduct in-depth analysis of target peaks, including target peak Venn diagram, related gene Venn diagram, Motif analysis, related gene GO enrichment and related gene KEGG enrichment. Target Peak Analysis visualizes the data results of the analysis report with Venn diagram and Upset diagram in target peak Venn diagram, Venn diagram and Upset diagram in related gene Venn diagram, known Motif prediction diagram, unknown Motif prediction diagram, classification secondary bar chart, significant enrichment bar chart, GO enrichment bar chart, significant enrichment bubble chart, enrichment circle chart, number statistics chart, significance bar chart, KEGG enrichment bar chart, significance bubble chart, enrichment circle chart, network chart and significance pathway chart.

[0087] The target peak Venn diagram analyzes the intersection of different peak sets through the Venn diagram, including the Venn diagram and Upset diagram. Users can select the "Change Name and Order" function in "Parameter Switching". Users can modify the name of the peak set and change the position of the peak in the Venn diagram. The special operation is to click the corresponding "Intersection" or "Independent Ownership" area in the Venn diagram to filter the table. Multiple areas can be selected at the same time. In addition, the target peak Venn diagram ( Figure 6 ) presents the intersection and union relationships between different sets in a concise and clear graphic, helping users determine the distribution of m6A signals associated with specific phenotypes or traits under different conditions.

[0088] The Venn diagram of related genes also analyzes the intersection of different peak sets through the Venn diagram, including the Venn diagram and Upset diagram. Users can perform the "Modify Name and Order" function operation, specifically select the name to be modified in the name, modify it in the Modify Name, and click OK. Click "Adjust Order" to display a pop-up window listing all names. Drag the name up and down with the left mouse button to modify the order. The list from top to bottom corresponds to the legend from top to bottom by default. Click "Confirm" and the graph will change accordingly.

[0089] Similar to the Motif Analysis, Related Gene GO Enrichment, and Related Gene KEGG Enrichment in the Peak Feature Analysis module, Targeted Peak Analysis helps users identify m6A signals associated with specific phenotypes or features.

[0090] Results display and utilization: The bioinformatics-based m6A online analysis platform generates a web-based report. Users can modify the color, font type, font size, etc. of the graphics in the web-based online report according to actual needs, and download the required graphics for subsequent research or publication.

[0091] Through the above specific implementation methods, the present invention can screen effective Peak sets, sample relationships, Peak feature analysis, methylation difference analysis and target Peak analysis according to user needs, and can perform peak distribution analysis, Motif analysis and related gene enrichment analysis on peak feature analysis, perform difference peak analysis, Motif analysis and related gene enrichment analysis on methylation difference analysis, and perform target Peak Venn diagram, related gene Venn diagram, Motif analysis and related gene enrichment analysis on target Peak analysis. In addition, the m6A analysis platform also provides rich visual graphics and table interactions, providing a complete data analysis and mining method.

[0092] This technical solution has certain technical effects. First, it has a multi-parameter selection function: multi-parameter selection of classic m6A analysis contents such as effective peak identification, differential peak analysis, and motif analysis, which improves the screening of effective m6A signals; second, interactive analysis function: interactive screening of graphic tables, combined with visual graphics for targeted screening of results, to more quickly determine m6A signals related to sample characteristics and sample phenotypes; third, process-based results: multiple visual results for one analysis task, multiple analysis tasks integrated into one process report, integrated display of task-related results, and convenient for the overall interpretation of the results.

[0093] Compared with the prior art, the present invention has the following advantages.

[0094] 1. Quick start operation

[0095] Simple operation, detailed instructions, and personalized data analysis.

[0096] 2. Dynamic interactive data mining

[0097] Graphs can be drawn according to actual needs, and data can be selected for secondary graph drawing based on the graphical display results; PCA graphs, motif graphs, Venn diagrams and other visual graphics facilitate efficient data mining.

[0098] 3. Personalized and efficient graphic beautification

[0099] Graphics can be modified in online reports in terms of color, font type, font size, etc. There are multiple types of graphics such as peak distribution and difference peak distribution, which facilitates the use of data exploration results to generate graphics required by journals.

[0100] In summary, the present invention provides a user-friendly, powerful, and intuitive bioinformatics-based m6A online analysis platform and generation method, which greatly simplifies the process of m6A research, improves research efficiency, and promotes the application of m6A in disease diagnosis, treatment, and drug development. The problems of the prior art are solved through specific technical solutions, and significant effects are achieved during implementation. Therefore, the present invention is innovative, practical, and has promotion value.

[0101] In this specification, some specific implementation details are described in detail, however, this does not mean that they are necessary or unique. Within the scope of the present invention, any similar replacement, modification or change is possible, as long as they do not deviate from the spirit and scope of the present invention. In addition, although some embodiments are described in detail in this specification, these descriptions should not limit the scope of the present invention, because many changes and modifications can be made within the scope of the idea of ​​the present invention. Therefore, the scope of protection of the present invention should be as listed in the claims.

Claims

1. An m6A online analysis platform and generation method based on bioinformatics, characterized in that: The platform includes a grouping scheme module, a Peak set screening module, a sample relationship module, a Peak feature analysis module, a methylation difference analysis module, and a target Peak analysis module, among which: The grouping scheme module is used to modify the sample group name, remove groups, and determine the grouping scheme according to the experimental design. It specifically includes the function of creating a new grouping scheme, which can perform grouping data, sample pairing options, and default comparison scheme operations; Peak set screening module, which is used to screen peaks according to certain conditions to form peak sets for targeted analysis, including generating new peak sets and merging peak sets; The sample relationship module is used to display the relationship between samples, including PCA analysis, correlation heat map and sample clustering diagram; Peak feature analysis module, used to conduct in-depth analysis of peak features, including peak distribution analysis, Motif analysis, GO enrichment of related genes, and KEGG enrichment of related genes; Methylation difference analysis module, used to analyze methylation differences, including difference peak analysis, Motif analysis, related gene GO enrichment and related gene KEGG enrichment; The target peak analysis module is used to perform comprehensive analysis on the target peak, including target peak Venn diagram, related gene Venn diagram, Motif analysis, related gene GO enrichment and related gene KEGG enrichment.

2. The m6A online analysis platform and generation method based on bioinformatics according to claim 1, characterized in that: The grouping scheme module further allows the user to click "New Grouping Scheme" to create grouping analysis information, check whether to include the group in the analysis according to the experimental requirements, and set the "Sample Name" and "Sample Grouping" information; after the grouping information is established, the grouping information can be viewed, modified and deleted in this interface.

3. The m6A online analysis platform and generation method based on bioinformatics according to claim 1, characterized in that: The Peak set filtering module further allows the user to click "Generate new peak set" to create a new peak set, click "Merge peak sets" to merge the peak sets that have been created under the same analysis task, or directly generate a new peak set from the bottom of the analysis result table; after creating a new peak set, the user can view or delete the filtering conditions and corresponding peak set information in this interface.

4. The m6A online analysis platform and generation method based on bioinformatics according to claim 1, characterized in that: The PCA analysis in the sample relationship module allows users to "switch parameters", "modify sample names" and "modify group names and order"; the correlation heat map allows users to "modify sample names and order"; the sample clustering map allows users to "screen samples" and "modify sample names".

5. The m6A online analysis platform and generation method based on bioinformatics according to claim 1, characterized in that: The Peak distribution analysis in the Peak feature analysis module specifically includes chromosome distribution, gene distribution bar graph, gene reads distribution graph, functional element distribution, functional element enrichment and relative gene position distribution; Motif analysis includes known Motif prediction and unknown Motif prediction; GO enrichment of related genes includes classification secondary bar graph, significant enrichment bar graph, GO enrichment bar graph, significant enrichment bubble graph and enrichment circle graph; KEGG enrichment of related genes includes number statistics graph, significance bar graph, KEGG enrichment bar graph, significance bubble graph, enrichment circle graph, network graph and significance pathway graph.

6. The m6A online analysis platform and generation method based on bioinformatics according to claim 1, characterized in that: The difference peak analysis in the methylation difference analysis module includes statistical bar graphs and volcano graphs; the motif analysis includes known motif prediction and unknown motif prediction; the analysis contents of related gene GO enrichment and related gene KEGG enrichment are the same as the corresponding analysis contents in the Peak feature analysis module.

7. The m6A online analysis platform and generation method based on bioinformatics according to claim 1, characterized in that: The target Peak Venn diagram and related gene Venn diagram in the target Peak analysis module include Venn diagram and Upset diagram; the analysis contents of Motif analysis, related gene GO enrichment and related gene KEGG enrichment are the same as the corresponding analysis contents in the Peak feature analysis module and the methylation difference analysis module.

8. The m6A online analysis platform and generation method based on bioinformatics according to claim 5, 6 or 7, characterized in that: The platform also includes parameter switching and graphic modification functions, allowing users to switch different parameter settings according to their needs and modify the color, font type and font size in the generated graphics.

9. The m6A online analysis platform and generation method based on bioinformatics is characterized by: The platform uses the m6A online analysis platform described in any one of claims 1 to 8 to perform m6A online analysis on bioinformatics data, form a web-based report, and support the modification and downloading of graphics contained in the web-based online report.

Images