Automatic analysis method for m6A methylation high-throughput sequencing data

Abstract

The invention discloses an automatic analysis method for m6A methylation high-throughput sequencing data, which comprises the following steps: step 1, acquiring off-computer original data to be analyzed, and creating a project basic information file; step 2, creating a comda operation environment variable configuration file environent.yml and a Docker environment construction script Dockerfile, and putting the comda operation environment variable configuration file environent.yml and the Docker environment construction script Dockerfile into a process configuration file nextflow.config; 3, setting a biological information analysis script of the m6A methylation high-throughput sequencing data automatic data analysis basic module; step 4, configuring an execution sequence and a parallel condition of the sequencing data analysis module sub-modules according to a dependency relationship between the m6A methylation high-throughput sequencing data automatic data analysis basic modules; step 5, according to the operation parameter setting of the nextflow.config configuration file, checking the operation queue of the sub-module of the sequencing data analysis module, and judging whether the dependency relationship is met or not; if yes, executing; if not, an error is returned; and step 6, checking result output and running logs. The method is based on a Nextflow process framework, has project transportability, can perform batch project analysis, and is convenient to control analysis content.

Description

Technical Field

[0001] The present invention relates to the technical field of bioinformatics, and in particular to a m 6 A. Automated analysis method for methylation high-throughput sequencing data. Background Art

[0002] Epigenetics is considered to be a discipline that studies genetic changes without changing the DNA sequence. It focuses on how behavior and environment cause and affect changes in gene function, including DNA methylation, histone modification, chromatin structure and remodeling. As an important research field of epigenetics, more than 170 different types of RNA modifications have been found on coding and non-coding RNAs (ncRNAs) to date, most of which are RNA methylation. As a methylation occurring at the sixth carbon atom of adenosine, N 6 -methyladenosine (m 6 A) is considered to be the most abundant internal modification of mRNA and is widely present in lncRNAs, miRNAs, snRNAs, snoRNAs and rRNAs, covering almost the entire transcriptome.

[0003] m 6 A modified RNA is composed of a 6 A dynamic process in which the multi-component methyltransferase complex, the demethylase "Eraser" and the reading protein "Reader" work together. 6 The dynamics of the A methylation modification process can be affected by different biological environments and growth and development stages. 6 The regulatory role of A and the response of cells to the external environment and internal signals are accurately presented, reflecting the 6 With the rapid development of high-throughput sequencing technology, the emergence of MeRIP-seq technology can achieve mRNA methylation at the whole transcriptome level under different conditions. 6 The mapping of A methylation modification has greatly promoted the study of m 6 A Understanding of the role of methylation modification in gene expression regulation, cell function and disease occurrence.

[0004] The huge potential of MeRIP-seq technology has prompted computational biologists to develop a series of analysis tools. Although the field of epitranscriptomics has made great efforts to ensure the availability of individual tools, the complete MeRIP-seq data mining and analysis process involves multiple analysis steps and frequent data conversions. In addition, there are many optional software for each analysis content, and different software principles are different, which can easily affect the accuracy and uniformity of the analysis results. At the same time, when there are many projects and a large amount of data, manual operations can consume a lot of time and may also lead to human errors, causing the analysis results to not match the actual project. Therefore, automating the bioinformatics analysis process of MeRIP-seq data can reduce human resource consumption and efficiently, automatically and reliably obtain the required analysis results.

[0005] At present, the common methods of bioinformatics analysis process are mainly through script organization and process framework construction, but the former does not support paradigm dependency management and fault repair, and has disadvantages such as low development efficiency and difficult maintenance. The MeRIP-seq data automated analysis process currently developed based on the latter, such as MeRIPseqPipe and MeRIP-PE, all have problems such as incomplete analysis modules and lack of analysis functions, which will lead to the 6 The understanding of the A modification map is incomplete, which affects subsequent functional studies or experimental design.

[0006] Therefore, a method is needed to locate mRNAs on the whole transcriptome. 6 A methylation site m 6 A method for automated analysis of high-throughput sequencing data of methylation can reveal RNA methylation patterns under different conditions (different tissues, different pathological states, different time points, etc.) while correlating methylation modification with gene expression regulation, further explaining m 6 A. Function of methylation modification in gene expression regulation. Summary of the invention

[0007] Purpose of the invention: The present invention provides a m 6 A automated analysis method for methylation high-throughput sequencing data, based on the Nextflow process framework, has project portability, can perform batch project analysis, and facilitates control of analysis content.

[0008] Technical solution: The present invention discloses a m 6 A method for automatically analyzing methylation high-throughput sequencing data, comprising the following steps:

[0009] Step 1: Get the original data to be analyzed and create a basic information file for the project. Each project contains multiple samples. The user sets the corresponding data analysis basic module parameters in the configuration file according to the specific analysis tasks of the project.

[0010] Step 2: Create the conda running environment variable configuration file environment.yml and the Docker environment build script Dockerfile, put them into the process configuration file nextflow.config, and further modify the m 6 A. The relative or absolute paths of all input files, output files, data files, log records, scripts, and applications during the operation of the automated data analysis process for methylation high-throughput sequencing data, as well as the configuration of the process operation status;

[0011] Step 3: Set m 6 A bioinformatics analysis script for the basic module of automated data analysis of methylation high-throughput sequencing data, m 6 A. The basic module for automated data analysis of methylation high-throughput sequencing data includes inspection submodule, upstream preprocessing module, epitranscriptome analysis module, joint analysis module, and result reporting module;

[0012] Step 4: Follow m 6 A. Dependencies between basic modules for automated data analysis of methylation high-throughput sequencing data, and configuration of the execution order and parallel conditions of submodules of sequencing data analysis modules;

[0013] Step 5: Check the sub-module run queue of the sequencing data analysis module according to the running parameter settings of the nextflow.config configuration file to determine whether the dependency relationship is met; if so, execute; if not, return an error;

[0014] Step 6: View the result output and operation log.

[0015] Furthermore, in step 1, the raw data to be analyzed is obtained, the data type is fq, and the compression format is fq.gz, bam or bed.

[0016] Furthermore, in step 2, create the conda running environment variable configuration file environment.yml and the Docker environment construction script Dockerfile, and place them into the process configuration file nextflow.config. The specific steps include the following:

[0017] Step 21, define global parameters, including the relative or absolute paths of all input files, output files, data files, log records, scripts and applications, as well as the memory, CPU and running time limits of the process;

[0018] Step 22. Provide the environment.yml file path to nextflow.config, and use Nextflow to create and activate the conda environment according to the dependencies specified by each analysis basic unit;

[0019] Step 23. Specify the method for executing the Docker container.

[0020] Step 24: List all m 6 A basic unit for automated data analysis of methylation high-throughput sequencing data, and its execution is defined according to the dependency relationship of the basic unit.

[0021] Furthermore, in step 3, m 6 A The basic module for automated data analysis of methylation high-throughput sequencing data includes a check submodule, an upstream data preprocessing module, an epitranscriptome analysis module, a transcriptome analysis module, a joint analysis module, and a result reporting module; the check submodule is used to check whether the operating environment and the parameters in nextflow.config meet the requirements, the upstream data preprocessing module is used to preprocess the raw sequencing data, and the epitranscriptome analysis module is used to visualize m 6 A methylation modification at the whole transcriptome level. The transcriptome analysis module is used to provide transcriptome information corresponding to the epitranscriptome information. The joint analysis module is used to analyze the condition-specific m 6 A methylation-regulated genes are predicted, thereby providing information on m 6 A clue to the functional mechanism of methylation regulation, and the result reporting module is used to generate and integrate the analysis results.

[0022] Furthermore, building the upstream data preprocessing module specifically includes the following steps:

[0023] (1) Constructing a data filtering unit, modularizing the filtering code of raw sequencing data by the data filtering software fastp and Trim Galore, with the default parameters of automatically detecting and trimming the sequencing adapter sequence, removing sequences with an average base quality score (Q-score) ≤ 20, reads less than 36 bases in length, and low-complexity sequences;

[0024] (2) constructing a ribosome removal unit to modularize the code used by the alignment software Hisat2 to filter data aligned to ribosomal RNA;

[0025] (3) Construct a reference genome alignment unit, modularize the code of the alignment software Hisat2 / Bowtie2 / STAR to align the sequence to the reference genome, and set the default tool to Hisat2 in the process configuration file nextflow.config;

[0026] (4) Construct a statistical unit for alignment results, and modularize the codes and scripts for the alignment results, including the coverage of reads on the genome (transcript), gene saturation analysis, length distribution of reads fragments, and distribution statistics of the number of reads in some regions of the genome, such as CDS exon and 5'UTR exon.

[0027] Furthermore, building an epitranscriptome analysis module specifically includes the following steps:

[0028] (1) Construct a single sample m 6 A modification enrichment region (Peak) detection unit will use MACS2 / exomePeak2 / MeTPeak (the process configuration file nextflow.config sets the default tool to MACS2) to detect m in the transcriptome range. 6 The code for identifying methylation-modified regions in A is modularized; at the same time, in order to ensure the high accuracy of Peak Calling results, the code for merging Peaks identified by different methods (MACS2 / exomePeak2 / MeTPeak) to form "Consensus Peak" is modularized by MSPC (Multiple Sample Peak Calling) / BEDTools (the default tool in the process configuration file nextflow.config is set to BEDTools). Users need to modify the relevant parameters in the configuration file nextflow.config to ensure that at least two or more tools are used for Peak Calling;

[0029] (2) Construct the Peaks merging unit between repeated samples within the group. When there are two or more biological replicates within the group, the R package ChIPseeker is used to merge the Peaks with an enrichment factor ≥ 2 and an overlap ≥ 50% that appear in at least two biological replicates.

[0030] (3) Construct a Peak-related gene analysis unit, which will find the Peak's distribution on the genome features and the genes in a specific range around it based on the Peak's location on the genome and the information of known elements on the annotated genome (such as genes, transcription start sites, stop codons, 3'UTR, etc.), including the distribution of Peaks on gene functional elements, Peak distribution on chromosomes, and Peak enrichment distribution analysis codes, and modularize them;

[0031] (4) Construct the inter-group Peak merging and multi-sample clustering analysis unit, merge the inter-group Peaks, perform hierarchical clustering analysis on the signal intensity matrix of different samples on the merged Peak set, and draw the code modularization of the heat map;

[0032] (5) Construct a peak difference analysis unit between groups, modularize the statistical annotation and visualization codes of common or unique peaks between groups, and modularize the difference analysis codes of the comparison groups according to the relative methylation level of each peak;

[0033] (6) Construct a motif analysis unit to modularize the code for using Homer to identify significant motif sequences in Peak;

[0034] (7) Construct gene function enrichment analysis unit: modularize the obtained Peak-related gene GO / KEGG functional enrichment analysis code.

[0035] Furthermore, a transcriptome analysis module is constructed, which specifically includes the following steps:

[0036] (1) Construct an expression level quantification unit and modularize the gene expression level quantification code of the quantitative software FeatureCounts;

[0037] (2) constructing an inter-group expression difference analysis unit and modularizing the difference analysis code of the comparison groups according to the gene expression level;

[0038] (3) Construct a gene function enrichment analysis unit and modularize the obtained GO / KEGG functional enrichment analysis code of differentially expressed genes.

[0039] Furthermore, building the result report module specifically includes the following steps:

[0040] (1) Construct an upstream data preprocessing result reporting unit, including a data quality control report and a comparison result statistical report;

[0041] (2) Construct epitranscriptome result reporting units, including Peak statistics, Peak-related gene analysis, Peak clustering and differences between groups, Motif sequence enrichment on Peak, and Peak-related gene function enrichment;

[0042] (3) Construct transcriptome result reporting units, including gene expression information, differences in gene expression between groups, and functional enrichment;

[0043] (4) Construct joint analysis reporting units, including conditional m 6 A List of regulated expression genes and related functional enrichment.

[0044] Furthermore, in step 4, the data type provided by the project information is used to determine the analysis modules that can be executed on the data, and the modules are connected according to the dependency relationships of the basic units of the analysis basic modules, so as to maximize the flexibility of the process while ensuring the stability of the system.

[0045] Furthermore, in step 6, check the data analysis process and output error information saved in the log file if there is a problem with the program. Perform fault recovery by checking the log file and correcting the error information so that the program can be run again to quickly recover to the error reporting stage.

[0046] Beneficial effects: Compared with the prior art, the present invention has the following significant advantages: (1) The present invention can realize the automated analysis of RNA methylation high-throughput sequencing data, and has the advantages of portability and fault repair, which helps to reduce the analysis pressure of analysts and facilitates the control of analysis content; (2) The submodules are organized according to the dependency relationship, and the submodules can be automatically connected according to the configuration file parameters, which maximizes the flexibility of process implementation while ensuring the stability of the system; (3) The analysis results can be presented in the form of a web report, and a single project result chart hyperlink can be set to facilitate users to obtain the analysis results. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 It is a schematic diagram of the method flow of the present invention.

[0048] Figure 2 The m of the present invention 6 A Schematic diagram of the basic module structure for automated data analysis of methylation high-throughput sequencing data.

[0049] Figure 3 It is a schematic diagram of the structure of the upstream preprocessing module of the present invention.

[0050] Figure 4 Schematic diagram of the structure of the epitranscriptome analysis module of the present invention.

[0051] Figure 5 Schematic diagram of the structure of the transcriptome analysis module of the present invention. DETAILED DESCRIPTION

[0052] like Figure 1 As shown, a m 6 A method for automatically analyzing methylation high-throughput sequencing data, comprising the following steps:

[0053] Step 1: Obtain the original data to be analyzed and create a basic information file for the project. Each project contains multiple samples. The user sets parameters in the configuration file according to the task information to select the analysis module required for the project.

[0054] Step 2: Create the conda running environment variable configuration file environment.yml and the Docker environment build script Dockerfile, put them into the process configuration file nextflow.config, and further modify the m 6A. The relative or absolute paths of all input files, output files, data files, log records, scripts, and applications during the operation of the automated data analysis process for methylation high-throughput sequencing data, as well as the configuration of the process operation status;

[0055] Step 3: Set m 6 A bioinformatics analysis script for the basic module of automated data analysis of methylation high-throughput sequencing data, m 6 A. The basic module for automated data analysis of methylation high-throughput sequencing data includes inspection submodule, upstream preprocessing module, epitranscriptome analysis module, joint analysis module, and result reporting module;

[0056] Step 4: Follow m 6 A. Dependencies between basic modules for automated data analysis of methylation high-throughput sequencing data, and configuration of the execution order and parallel conditions of submodules of sequencing data analysis modules;

[0057] Step 5: Check the sub-module run queue of the sequencing data analysis module according to the running parameter settings of the nextflow.config configuration file to determine whether the dependency relationship is met; if so, execute; if not, return an error;

[0058] Step 6: View the result output and operation log.

[0059] In step 1, the raw data to be analyzed is obtained. The data type can be fq, compressed format fq.gz, bam or bed.

[0060] In step 2, create the conda running environment variable configuration file environment.yml and the Docker environment construction script Dockerfile, and put them into the process configuration file nextflow.config. The specific steps include the following:

[0061] Step 21, define global parameters, including the relative or absolute paths of all input files, output files, data files, log records, scripts and applications, as well as the memory, CPU and running time limits of the process;

[0062] Step 22. Provide the environment.yml file path to nextflow.config, and use Nextflow to create and activate the conda environment according to the dependencies specified by each analysis basic unit;

[0063] Step 23. Specify the method for executing the Docker container.

[0064] Step 24: List all m 6A basic unit for automated data analysis of methylation high-throughput sequencing data, and its execution is defined according to the dependency relationship of the basic unit.

[0065] like Figure 2 As shown, in step 3, m 6 A The basic module for automated data analysis of methylation high-throughput sequencing data includes a check submodule, an upstream data preprocessing module, an epitranscriptome analysis module, a transcriptome analysis module, a joint analysis module, and a result reporting module; the check submodule is used to check whether the operating environment and the parameters in nextflow.config meet the requirements, the upstream data preprocessing module is used to preprocess the raw sequencing data, and the epitranscriptome analysis module is used to visualize m 6 A methylation modification at the whole transcriptome level. The transcriptome analysis module is used to provide transcriptome information corresponding to the epitranscriptome information. The joint analysis module is used to analyze the condition-specific m 6 A methylation-regulated genes are predicted, thereby providing information on m 6 A clue to the functional mechanism of methylation regulation, and the result reporting module is used to generate and integrate the analysis results.

[0066] Build the inspection submodule to check whether the operating environment and parameters in nextflow.config meet the requirements.

[0067] like Figure 3 As shown in the figure, building the upstream data preprocessing module specifically includes the following steps:

[0068] (1) Constructing a data filtering unit, modularizing the filtering code of raw sequencing data by the data filtering software fastp and Trim Galore, with the default parameters of automatically detecting and trimming the sequencing adapter sequence, removing sequences with an average base quality score (Q-score) ≤ 20, reads less than 36 bases in length, and low-complexity sequences;

[0069] (2) constructing a ribosome removal unit to modularize the code used by the alignment software Hisat2 to filter data aligned to ribosomal RNA;

[0070] (3) Construct a reference genome alignment unit, modularize the code of the alignment software Hisat2 / Bowtie2 / STAR to align the sequence to the reference genome, and set the default tool to Hisat2 in the process configuration file nextflow.config;

[0071] (4) Construct a statistical unit for alignment results, and modularize the codes and scripts for the alignment results, including the coverage of reads on the genome (transcript), gene saturation analysis, length distribution of reads fragments, and distribution statistics of the number of reads in some regions of the genome, such as CDS exon and 5'UTR exon.

[0072] like Figure 4 As shown in the figure, building an epitranscriptome analysis module specifically includes the following steps:

[0073] (1) Construct a single sample m 6 A modification enrichment region (Peak) detection unit will use MACS2 / exomePeak2 / MeTPeak (the process configuration file nextflow.config sets the default tool to MACS2) to detect m in the transcriptome range. 6 The code for identifying methylation-modified regions in A is modularized; at the same time, in order to ensure the high accuracy of Peak Calling results, the code for merging Peaks identified by different methods (MACS2 / exomePeak2 / MeTPeak) to form "Consensus Peak" is modularized by MSPC (Multiple Sample Peak Calling) / BEDTools (the default tool in the process configuration file nextflow.config is set to BEDTools). Users need to modify the relevant parameters in the configuration file nextflow.config to ensure that at least two or more tools are used for Peak Calling;

[0074] (2) Construct the Peaks merging unit between repeated samples within the group. When there are two or more biological replicates within the group, the R package ChIPseeker is used to merge the Peaks with an enrichment factor ≥ 2 and an overlap ≥ 50% that appear in at least two biological replicates.

[0075] (3) Construct a Peak-related gene analysis unit, which will find the Peak's distribution on the genome features and the genes in a specific range around it based on the Peak's location on the genome and the information of known elements on the annotated genome (such as genes, transcription start sites, stop codons, 3'UTR, etc.), including the distribution of Peaks on gene functional elements, Peak distribution on chromosomes, and Peak enrichment distribution analysis codes, and modularize them;

[0076] (4) Construct the inter-group Peak merging and multi-sample clustering analysis unit, merge the inter-group Peaks, perform hierarchical clustering analysis on the signal intensity matrix of different samples on the merged Peak set, and draw the code modularization of the heat map;

[0077] (5) Construct a peak difference analysis unit between groups, modularize the statistical annotation and visualization codes of common or unique peaks between groups, and modularize the difference analysis codes of the comparison groups according to the relative methylation level of each peak;

[0078] (6) Construct a motif analysis unit to modularize the code for using Homer to identify significant motif sequences in Peak;

[0079] (7) Construct gene function enrichment analysis unit: modularize the obtained Peak-related gene GO / KEGG functional enrichment analysis code.

[0080] like Figure 5 As shown in the figure, the transcriptome analysis module is constructed, which specifically includes the following steps:

[0081] (1) Construct an expression level quantification unit and modularize the gene expression level quantification code of the quantitative software FeatureCounts;

[0082] (2) constructing an inter-group expression difference analysis unit and modularizing the difference analysis code of the comparison groups according to the gene expression level;

[0083] (3) Construct a gene function enrichment analysis unit and modularize the obtained GO / KEGG functional enrichment analysis code of differentially expressed genes.

[0084] Build a joint analysis module and use m6Aexpress based on m 6 A Log-linear relationship between methylation signal intensity and gene expression level for conditional m 6 A Modularity of the regulated expression gene prediction code.

[0085] Building the result report module includes the following steps:

[0086] (1) Construct an upstream data preprocessing result reporting unit, including a data quality control report and a comparison result statistical report;

[0087] (2) Construct epitranscriptome result reporting units, including Peak statistics, Peak-related gene analysis, Peak clustering and differences between groups, Motif sequence enrichment on Peak, and Peak-related gene function enrichment;

[0088] (3) Construct transcriptome result reporting units, including gene expression information, differences in gene expression between groups, and functional enrichment;

[0089] (4) Construct joint analysis reporting units, including conditional m 6 A List of regulated expression genes and related functional enrichment.

[0090] In step 4, determine the analysis modules that can be executed based on the data type provided by the project information, and connect them according to the dependencies of the basic units of the analysis basic modules to maximize the flexibility of the process while ensuring the stability of the system.

[0091] In step 5, it is checked whether the execution queue of the basic unit of the analysis basic module selected by the current user in the configuration file nextflow.config is satisfied. If satisfied, the current queue is executed; if not satisfied, an error is returned.

[0092] In step 6, check the error information saved in the log file if there is a problem with the program during the data analysis process. Perform fault recovery by checking the log file and correcting the error information so that the program can be run again to quickly recover to the error reporting stage.

[0093] In practical applications, the toolkit used in the present invention includes a main program code written in Nextflow, a default process configuration file nextflow.config, 5 submodule nextflow codes and 25 subprogram script codes written in R or shell. The main program is named main.nf. The process is based on Nextflow, Shell and R language and can be used on the Linux system platform.

[0094] During the application process, the configuration file nextflow.config provides detailed descriptions of the corresponding parameters to guide analysts to use the tool correctly. Parameters are divided into two types: required parameters and optional parameters. Analysts need to provide input values ​​for required parameters. Optional parameters have default values, and analysts can also set parameters according to their needs to adjust the process.

Claims

1. A m 6 A method for automated analysis of methylation high-throughput sequencing data, characterized in that: The steps include: Step 1: Obtain the original data to be analyzed and create a basic information file for the project. Each project contains multiple samples. The user sets parameters in the configuration file according to the task information to select the analysis module required for the project. Step 2: Create the conda running environment variable configuration file environment.yml and the Docker environment build script Dockerfile, put them into the process configuration file nextflow.config, and further modify the m 6 A. The relative or absolute paths of all input files, output files, data files, log records, scripts, and applications during the operation of the automated data analysis process for methylation high-throughput sequencing data, as well as the configuration of the process operation status; Step 3: Set m 6 A bioinformatics analysis script for the basic module of automated data analysis of methylation high-throughput sequencing data, m 6 A. The basic module for automated data analysis of methylation high-throughput sequencing data includes inspection submodule, upstream preprocessing module, epitranscriptome analysis module, joint analysis module, and result reporting module; Step 4: Follow m 6 A. Dependencies between basic modules for automated data analysis of methylation high-throughput sequencing data, and configuration of the execution order and parallel conditions of submodules of sequencing data analysis modules; Step 5: According to the running parameter settings of the nextflow.config configuration file, check the running queue of the submodule of the sequencing data analysis module to determine whether the dependency relationship is met; If satisfied, execute; if not satisfied, return an error; Step 6: View the result output and operation log.

2. The m according to claim 1 6 A method for automated analysis of methylation high-throughput sequencing data, characterized in that: In step 1, the raw data to be analyzed is obtained, the data type is fq, and the compression format is fq.gz, bam, or bed.

3. The m according to claim 1 6 A method for automated analysis of methylation high-throughput sequencing data, characterized in that: In step 2, create the conda running environment variable configuration file environment.yml and the Docker environment construction script Dockerfile, and put them into the process configuration file nextflow.config. The specific steps include the following: Step 21, define global parameters, including the relative or absolute paths of all input files, output files, data files, log records, scripts and applications, as well as the memory, CPU and running time limits of the process; Step 22. Provide the environment.yml file path to nextflow.config, and use Nextflow to create and activate the conda environment according to the dependencies specified by each analysis basic unit; Step 23. Specify the method for executing the Docker container. Step 24: List all m 6 A basic unit for automated data analysis of methylation high-throughput sequencing data, and its execution is defined according to the dependency relationship of the basic unit.

4. The m according to claim 1 6 A method for automated analysis of methylation high-throughput sequencing data, characterized in that: In step 3, m 6 A. The basic module for automated data analysis of methylation high-throughput sequencing data includes inspection submodule, upstream data preprocessing module, epitranscriptome analysis module, transcriptome analysis module, joint analysis module, and result reporting module; The check submodule is used to check whether the operating environment and parameters in nextflow.config meet the requirements. The upstream data preprocessing module is used to preprocess the raw sequencing data. The epitranscriptome analysis module is used to visualize m 6 A methylation modification at the whole transcriptome level. The transcriptome analysis module is used to provide transcriptome information corresponding to the epitranscriptome information. The joint analysis module is used to analyze the condition-specific m 6 A methylation-regulated genes are predicted, thereby providing information on m 6 A clue to the functional mechanism of methylation regulation, and the result reporting module is used to generate and integrate the analysis results.

5. The method according to claim 4 6 A method for automated analysis of methylation high-throughput sequencing data, characterized in that: Building the upstream data preprocessing module specifically includes the following steps: (1) Construct a data filtering unit and modularize the filtering code of raw sequencing data by data filtering software fastp and Trim Galore. The default parameters are to automatically detect and trim the sequencing adapter sequence, and remove sequences with an average base quality score (Q-score) ≤ 20, read segments with a length of less than 36 bases, and low-complexity sequences; (2) constructing a ribosome removal unit to modularize the code used by the alignment software Hisat2 to filter data aligned to ribosomal RNA; (3) Construct a reference genome alignment unit, modularize the code of the alignment software Hisat2 / Bowtie2 / STAR to align the sequence to the reference genome, and set the default tool to Hisat2 in the process configuration file nextflow.config; (4) Construct a statistical unit for alignment results, and modularize the codes and scripts for the alignment results, including the coverage of reads on the genome, gene saturation analysis, length distribution of reads fragments, and the distribution number statistics of reads in some regions of the genome, such as CDS exon and 5'UTRexon.

6. The method according to claim 4 6 A method for automated analysis of methylation high-throughput sequencing data, characterized in that: The construction of the epitranscriptome analysis module includes the following steps: (1) Construct a single sample m 6 A modification enrichment region Peak detection unit, will use MACS2 / exomePeak2 / MeTPeak to detect m 6 A modularizes the code for identifying methylation modification regions. The default tool in the process configuration file nextflow.config is set to MACS2. At the same time, in order to ensure the high accuracy of the Peak Calling results, the code for merging the peaks identified by different methods in MSPC / bedtools to form a "Consensus Peak" is modularized. Users need to modify the relevant parameters in the configuration file nextflow.config to ensure that at least two or more tools are used for Peak Calling. (2) Construct the Peaks merging unit between repeated samples within the group. When there are two or more biological replicates within the group, the R package ChIPseeker is used to merge the Peaks with an enrichment factor ≥ 2 and an overlap ≥ 50% that appear in at least two biological replicates. (3) Construct a Peak-related gene analysis unit, which will find the distribution of Peaks on genomic features and gene information within a specific range around them based on the location of Peaks on the genome and the known element information on the annotated genome, including the distribution of Peaks on gene functional elements, the distribution of Peaks on chromosomes, and the code for Peak enrichment distribution analysis. (4) Construct the inter-group Peak merging and multi-sample clustering analysis unit, merge the inter-group Peaks, perform hierarchical clustering analysis on the signal intensity matrix of different samples on the merged Peak set, and draw the code modularization of the heat map; (5) Construct a peak difference analysis unit between groups, modularize the statistical annotation and visualization codes of common or unique peaks between groups, and modularize the difference analysis codes of the comparison groups according to the relative methylation level of each peak; (6) Construct a motif analysis unit to modularize the code for using Homer to identify significant motif sequences in Peak; (7) Construct gene function enrichment analysis unit: modularize the obtained Peak-related gene GO / KEGG functional enrichment analysis code.

7. The method according to claim 4 6 A method for automated analysis of methylation high-throughput sequencing data, characterized in that: Building a transcriptome analysis module includes the following steps: (1) Construct an expression level quantification unit and modularize the gene expression level quantification code of the quantitative software FeatureCounts; (2) constructing an inter-group expression difference analysis unit and modularizing the difference analysis code of the comparison groups according to the gene expression level; (3) Construct a gene function enrichment analysis unit and modularize the obtained GO / KEGG functional enrichment analysis code of differentially expressed genes.

8. The method according to claim 4 6 A method for automated analysis of methylation high-throughput sequencing data, characterized in that: Building the result report module includes the following steps: (1) Construct an upstream data preprocessing result reporting unit, including a data quality control report and a comparison result statistical report; (2) Construct epitranscriptome result reporting units, including Peak statistics, Peak-related gene analysis, Peak clustering and differences between groups, Motif sequence enrichment on Peak, and Peak-related gene function enrichment; (3) Construct transcriptome result reporting units, including gene expression information, differences in gene expression between groups, and functional enrichment; (4) Construct joint analysis reporting units, including conditional m 6 A List of regulated expression genes and related functional enrichment.

9. The m according to claim 1 6 A method for automated analysis of methylation high-throughput sequencing data, characterized in that: In step 4, determine the analysis modules that can be executed based on the data type provided by the project information, and connect them according to the dependencies of the basic units of the analysis basic modules to maximize the flexibility of the process while ensuring the stability of the system.

10. The method according to claim 1 6 A method for automated analysis of methylation high-throughput sequencing data, characterized in that: In step 6, check the error information saved in the log file if there is a problem with the program during the data analysis process. Perform fault recovery by checking the log file and correcting the error information so that the program can be run again to quickly recover to the error reporting stage.

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