Multi-strategy metagenome analysis system, method and equipment and storage medium

Abstract

The invention belongs to the technical field of computer medical detection, solves the problems of poor expansibility and low flexibility of a bioinformatics analysis tool in the prior art, and provides a multi-strategy metagenome analysis system, method and device and a storage medium, the system comprises an application layer, an analysis layer, a support layer and a resource layer, the application layer is used as a user interaction interface, and the analysis layer is used as a user interaction interface. The user instruction receiving layer is used for receiving a user instruction, the analysis layer is used for executing a work task corresponding to the user instruction, the support layer is used for constructing a work environment, and the resource layer is used for configuring resources required by the work task; according to the method, each link of the workflow is modularly designed and mapped to an interactive interface of an application layer, a user directly selects a module corresponding to analysis content needing to be executed by a sample file through the interactive interface, and a system completes task environment establishment and task execution and outputs a report to a user interface; complex instructions required by biological information analysis are simplified, the flexibility of the system is improved, and the use threshold of the system is reduced.

Description

Technical Field

[0001] The present invention relates to the field of computer medical detection technology, and in particular to a multi-strategy metagenomic analysis system, method, device and storage medium. Background Art

[0002] Metagenomics is a key discipline for studying microbial communities and their genomes in the environment. It has far-reaching scientific value and broad application prospects in understanding ecosystem health, human diseases, and responding to environmental changes. With the rapid development of high-throughput sequencing technology, metagenomic data has exploded, and various bioinformatics analysis tools have emerged in large numbers. However, these tools have brought new challenges to researchers in terms of usability and accessibility, especially in the uniformity and standardization of data analysis. Therefore, in order to extract valuable information from massive data, it is urgent to develop a comprehensive, reliable, efficient, flexible and user-friendly analysis workflow.

[0003] In the field of biological information analysis, the Nextflow framework is widely used due to its process automation and reproducible analysis features. However, in actual use, the Nextflow framework requires developers to have a high level of computer knowledge to build complex system logic, resulting in a high threshold for use. To solve this problem, developers developed the nf-core framework based on the Nextflow framework. The nf-core framework provides a series of processing and analysis tools for high-throughput sequencing data, and ensures the accuracy and repeatability of bioinformatics analysis through standardized workflows and modules. These standardized workflows and modules simplify the process of building complex bioinformatics analysis processes to a certain extent and lower the threshold for use. However, the available nf-core modules are limited, and are not suitable for specific parameter modifications and specific process formulation for personalized or newly published research tools or methods, resulting in poor scalability and low flexibility. Summary of the invention

[0004] In view of this, the embodiments of the present invention provide a multi-strategy metagenomic analysis system, method, device and storage medium to solve the problems of poor scalability and low flexibility of bioinformatics analysis tools in the prior art.

[0005] The technical solution adopted by the present invention is:

[0006] On the one hand, the present invention provides a multi-strategy metagenomic analysis system, including an application layer, an analysis layer, a support layer and a resource layer, wherein the application layer is used as a user interaction interface to receive user instructions and configure work tasks based on the user instructions, the analysis layer is used to execute the work tasks, the support layer is used to build a work environment required for executing the work tasks, and the resource layer is used to configure the resources required for the work tasks;

[0007] The user selects the workflow of the work task corresponding to the user instruction as needed in the user interaction interface of the application layer, and the support layer constructs the work environment according to the resource requirements for executing the workflow.

[0008] Preferably, the workflow includes at least one bioinformatics analysis process, and each of the workflows is decomposed into multiple independent secondary modules according to subdivided links and configured at the analysis layer.

[0009] Preferably, the user instruction received by the application layer includes a first configuration instruction or a second configuration instruction, wherein the first configuration instruction is to execute the entire process of the bioinformatics analysis process, and the second configuration instruction is to execute a partial process of the bioinformatics analysis.

[0010] Preferably, the partial process includes a single secondary module or a plurality of secondary modules, and the plurality of secondary modules are continuous or discontinuous processes in the entire process.

[0011] Preferably, the workflow includes a gene set construction process and a genome set construction process, wherein the gene set construction process and the genome set construction process share information.

[0012] Preferably, the workflow includes a first type of flow and a second type of flow, the first type of flow is a default execution flow, and the second type of flow is a flexible configuration flow corresponding to the user instruction.

[0013] Preferably, the user instruction is one or more instructions selected from a limited number of instructions on the interactive interface mapped to the application layer by each of the secondary modules in the analysis layer.

[0014] On the other hand, the present invention also provides a multi-strategy metagenomic analysis method, the method comprising:

[0015] Obtaining a sample file submitted by a user through an interactive interface and a user instruction specified on the interactive interface;

[0016] According to the user instruction, construct a working environment required for executing the work task corresponding to the user instruction;

[0017] According to the user instruction, performing bioinformatics analysis on the sample file in the working environment, and outputting an analysis report to a user interaction interface;

[0018] The user instruction includes a first configuration instruction and a second configuration instruction, the first configuration instruction is to execute the entire process of the biological information analysis, and the second configuration instruction is to execute a partial process of the biological information analysis.

[0019] On the other hand, the present invention also provides an electronic device, comprising: at least one processor, at least one memory and computer program instructions stored in the memory, wherein a system as described above is deployed on the electronic device, and when the computer program instructions are executed by the processor, the method as described above is implemented.

[0020] On the other hand, the present invention further provides a computer-readable storage medium on which computer program instructions corresponding to the system as described in any one of the above items are stored, and when the computer program instructions are executed by a processor, the method as described above is implemented.

[0021] In summary, the beneficial effects of the present invention are as follows:

[0022] The multi-strategy metagenomic analysis system, method, device and storage medium provided by the present invention include an application layer, an analysis layer, a support layer and a resource layer; the application layer is used as a user's interaction layer, and the user inputs a sample file through an interaction interface of the application layer, and selects the work task to be completed on demand from the interaction interface, and the support layer calls the tools, libraries, databases and environment support resources of the resource layer to build a work environment according to the workflow selected by the user, and the analysis layer uses the multiple algorithms and models deployed by it to complete the bioinformatics analysis of the sample file; the present invention modularizes each link of the workflow and maps it to the interaction interface of the application layer, and the user directly selects the module corresponding to the analysis content to be executed by the sample file and the environment required for execution through the interaction interface, and the system automatically allocates analysis tools and resources according to the instructions and sample files selected by the user, completes the sample file task analysis and outputs the analysis report to the interaction interface of the application layer for the user to use; modularization and customization are combined to meet the complexity characteristics of bioinformatics analysis, simplify the complex instructions required for bioinformatics analysis, improve the flexibility of the system, and reduce the threshold for using the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solution of the embodiment of the present invention, the drawings required for use in the embodiment of the present invention will be briefly introduced below. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work, and these are all within the protection scope of the present invention.

[0024] Figure 1 This is a schematic diagram of the structure of the multi-strategy metagenomic analysis system in Example 1 of the present invention;

[0025] Figure 2 This is a schematic diagram of the structure of the system analysis process module division in Example 1 of the present invention;

[0026] Figure 3 A schematic diagram of the process processing corresponding to the input sample parameter configuration in Embodiment 1 of the present invention;

[0027] Figure 4 Schematic diagram of the process of the multi-strategy metagenomic analysis method in Example 2 of the present invention;

[0028] Figure 5 Schematic diagram of the structure of an electronic device in Embodiment 3 of the present invention. DETAILED DESCRIPTION

[0029] In order to make the purpose, technical solution and advantages of the embodiment of the present invention clearer, the technical solution in the embodiment of the present invention will be clearly and completely described in conjunction with the drawings in the embodiment of the present invention. It should be noted that, in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply that there is any such actual relationship or order between these entities or operations. In the description of the present invention, it should be understood that the orientation or position relationship indicated by the terms "center", "upper", "lower", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc. is based on the orientation or position relationship shown in the drawings, which is only for the convenience of describing the present application and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation of the present invention. Moreover, the term "include", "comprise" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such a process, method, article or device. In the absence of further restrictions, the elements defined by the phrase "comprising..." do not exclude the existence of other identical elements in the process, method, article or device comprising the elements. If there is no conflict, the various features of the present invention and the embodiments can be combined with each other, all within the protection scope of the present invention.

[0030] Example 1

[0031] See also Figure 1 , Figure 1The schematic diagram of the structure of the multi-strategy metagenomic analysis system includes an application layer, an analysis layer, a support layer, and a resource layer; among which:

[0032] Application layer: It is equipped with command line interface, system instructions, workflow execution, parameter configuration and error re-submission units. Users input user instructions through the command line interface. Parameter configuration includes functions such as task type selection and process customization. It should be noted that: the application layer can accept user instructions input by users through the interactive interface. At the same time, the application layer can also accept instructions issued by other modules of the system. The application layer can accept what kind of instructions generated by the system itself, which is set according to system needs. The present invention does not limit the specific instruction type.

[0033] Analysis layer: It is equipped with a variety of algorithms and models, and can process large amounts of data and perform complex computing tasks; including but not limited to task preprocessing, data analysis and output (result output). Specifically, the analysis layer receives task requests from the application layer, completes data processing and analysis based on pre-configured algorithms and models, and feeds back processing results to the application layer for users to view and download. Preprocessing includes input checking and data quality control, data analysis includes assembly (sequence assembly), species identification, functional annotation, binning and abundance calculation, and result output includes analysis reports and result archiving.

[0034] Support layer: It is equipped with software tools including MetaPhlAn, HUMANN, and various databases such as GTDB and EggNOG, various environment management tools such as conda and Docker, and various development tools such as Nextflow, JAVA, Python, and Perl; the support layer is mainly used to build a working environment, version control, continuous integration of other external tools / continuous deployment (CI / CD), and system operation monitoring to ensure stable system operation.

[0035] Resource layer: The resource layer mainly integrates computing resources including local computers, cloud computing (mainstream cloud computing such as Alibaba Cloud, Amazon Cloud and Tencent Cloud), as well as storage resources such as NAS (file storage), OSS (object storage), S3 (object storage), etc., making the system have good scalability and elasticity.

[0036] In one embodiment, the user selects the workflow of the work task corresponding to the user instruction as needed in the user interaction interface of the application layer, and the support layer constructs the work environment according to the resource requirements for executing the workflow.

[0037] In one embodiment, the user instruction is one or more instructions selected from a limited number of instructions on an interaction interface mapped to the application layer by each of the secondary modules in the analysis layer.

[0038] Specifically, the application layer is provided with a custom analysis process unit (that is, by setting the "mode" parameter value hereinafter), and the user submits a user instruction through the interactive interface of the application layer, and specifies the task requirements of one or several work tasks that need to be completed by the user instruction through parameter settings, such as: one or more requirements of species identification, sequence assembly, functional annotation, binning and abundance calculation; at the same time, the format of the input data of the user instruction, the output result type and the saving path, etc. are configured; in order to improve the applicability of the system and lower the usage threshold, the task requirements are integrated into corresponding independent modules, and the user can complete the requirement input of complex tasks by selecting or inputting specific characterization parameters, such as: by setting the "mode = N" parameter mode, the user can input or select different "N" values, N is an integer, in an optional embodiment, "mode = 0" is set as the default setting, and the requirement characterization system needs to execute and complete all analysis processes, that is, covering all modules;

[0039] Set "mode = 1" to perform data quality control analysis only;

[0040] Set "mode=2" to only perform the sequence assembly step;

[0041] Set "mode=3" to perform species identification analysis only;

[0042] Set "mode=4" to perform gene function annotation only;

[0043] Set "mode=5" to perform binning analysis only.

[0044] If the user selects "mode = 3" for species identification analysis in parameter configuration, the support layer will activate the corresponding conda environment during the species identification analysis process, which includes the required software packages, such as MetaPhlAn, HUMANN, etc. In the species identification analysis task, the analysis layer will call MetaPhlAn, HUMANN and other tools to analyze the sequencing data, output the species identification and classification results, and feed them back to the application layer for visual display. It should be noted that users can choose to use local computers or cloud computing to complete the analysis task according to the size of the data involved in the task.

[0045] In one embodiment, the workflow includes at least one biological information analysis process, and each of the workflows is decomposed into multiple independent secondary modules according to subdivided links and configured at the analysis layer.

[0046] For details, see Figure 2The workflow is divided into independent secondary modules such as input check (INPUT_HECK), data cleaning (QC), sequence assembly (Assembly), species identification (Marker), gene set construction (Geneset), genome set construction (Binning), and result collation (Polish). At the same time, each secondary module is equipped with corresponding execution tools. The data cleaning module is equipped with Fastp or Trimmomatic tools, the sequence assembly module is equipped with MEGAHIT and SPAdes tools, and the species identification module is equipped with MetaPhlAn, Kraken2, HUMAnN3 and other tools; the gene set construction module is equipped with Prodigal, CD-HIT, bowtie2, eggNog, RGI, VFDB, antiSMASH, Custom DB, BIG-MAP, custom scripts and other tools to complete the prediction, deduplication, annotation, toxicity factor identification, antibiotic resistance gene inspection, biosynthesis gene family identification, custom nucleic acid database search, gene abundance calculation, etc. of protein coding of gene set construction; the genome set construction is equipped with MaxBin2, metaBAT2, CONCOCT, SemiBin, MetaBinner, binny, DAS Tool, CheckM2, dRep, GTDB-Tk, Bowtie2, Salmon, samtools, custom scripts and other tools; the result arrangement module is configured with report, multiQC, postpipeline custom scripts and other tools to produce different types of task results, including the system's standard format and the format of third-party systems, such as the format recognizable by Nextflow.

[0047] In one embodiment, the user instruction received by the application layer includes a first configuration instruction or a second configuration instruction, wherein the first configuration instruction is to execute the entire process of the bioinformatics analysis, and the second configuration instruction is to execute a partial process of the bioinformatics analysis.

[0048] For details, see Figure 3 , Figure 3Configure the corresponding workflow for different parameters. The user submits the sample file in sample.csv format to the system. The system starts the input check (INPUT_CHECK) program and completes the specification check. When the input check passes, enter the parameter configuration module to determine the task type through the "mode" value. If the "mode" value is not specified, or the "mode" value is "0", it defaults to the first configuration instruction and enters the entire workflow; at this time, the user specifies to skip one or more specific processes "M", and the system will ignore the process and directly execute the next process. If it is raw reads, quality control (QC) and assembly (Assembly) are required. If it is clean reads, quality control (QC) can be skipped and directly enter assembly (Assembly); then enter the marker decision, execute the marker (marker) or skip marker (skip_marker) link, and then enter the gene set construction (Genest), and then enter the binning decision point, execute the binning (binning) or no binning (skip_binning) link, and finally enter the polishing process. If the "mode" value represents the execution of a specific process, the user instruction is the second configuration instruction. At this time, the system will directly execute the specific process after completing the input data check.

[0049] In one embodiment, the partial process includes a single secondary module or a plurality of secondary modules, and the plurality of secondary modules are continuous or non-continuous processes in the entire process.

[0050] For details, see Figure 3 In order to improve the flexibility of user-defined processes, the present invention adds multiple screening decision points skip_qc, --skip_marker and --skip_binning to improve the flexibility of process customization. Each decision point can be used alone or in combination. The number and type of screening decision points are not limited to the above three categories. According to the needs of bioinformatics analysis, the number and type of screening decision points can be increased or decreased by iterative versions. It should be noted that the user selects the screening decision points while customizing the workflow in the interactive interface, and / or the system obtains the execution instruction of the screening decision point at the corresponding node when executing the work task. In a preferred embodiment, during the execution of the task, if the execution instruction of the screening decision point is not received within the first threshold time, it is executed according to the preset plan, such as: if the input sample data is contigs, the preset plan is not to execute assembly (Assembly). The preset plans corresponding to different task settings are all within the protection scope of this plan and are not listed here one by one.

[0051] like Figure 3 In the example, if the sample file is raw reads, and the configuration parameter "mode" value is set to "1", it means that the task corresponding to the second configuration instruction selected for this task is to perform data quality control analysis only. After completing data quality control (QC), it directly enters the polishing process (Polish) corresponding to the first configuration instruction; if the input sample file is cleaned reads, the user can choose not to perform data quality control, and directly choose to set the configuration parameter "mode" value to "2" to directly perform assembly (Assembly) and polishing (Polish). Similarly, the user can configure the value of the configuration parameter "mode" to other values ​​according to the needs of the input sample data to complete a specific task. It should be noted that the input sample data can be "reads" and / or "contigs", reads: sequencing read length (small fragment test sequence), contigs: multiple small fragment test sequences spliced; by integrating various tasks into independent modules, users can adjust the tasks and workflows by configuring different conditions and modes, realize flexible processing and analysis of the input sample data, and reduce the usage threshold.

[0052] In one embodiment, during genome set construction or gene set construction, deduplication of samples corresponding to classification bin quality assessment includes:

[0053] Obtaining a first quantity corresponding to the samples to be processed and a first threshold corresponding to the quantity limit;

[0054] If the first number is greater than the first threshold, the first number of samples is divided into N parts according to the second number to obtain N+1 first sample groups, where N is an integer;

[0055] The samples in each first sample group are compared pairwise to remove redundancy, and target samples are obtained.

[0056] The redundant sample set is divided into N+1 first sample groups, wherein the N first sample groups contain the second number of samples, and the number of samples in the N+1 first sample group is the first sample group composed of the remaining samples; the first sample groups are used for pairwise comparison to remove redundancy, and the tasks that can only be completed on one computer are split to multiple computers for execution, so as to quickly obtain global non-redundant results and improve the processing efficiency of deduplication; for example: when constructing a gene set, the CD-HIT tool is used for non-redundant gene set construction to deduplicate the predicted genes, and the first threshold is 8 million genes. For data sets with more than 8 million genes, the gene set is automatically divided into multiple gene subsets according to the second number of 300,000 genes, and each gene subset is executed in parallel on different devices. For another example, when constructing a genome set, it is necessary to remove duplicate bins, and the dRep tool is used for deduplication. When the first threshold is a large data set of 800 bins, it is divided into multiple first sample groups according to the second number of 200 bins, and the bins in each first sample group are double-redundant in parallel, and finally merged to obtain a global non-redundant result. It should be noted that the values ​​of the second number and the first threshold are set by the user according to the actual situation of data analysis, and the present invention does not make specific limitations.

[0057] In one embodiment, the workflow includes a gene set construction process and a genome set construction process, wherein the gene set construction process and the genome set construction process share information.

[0058] Specifically, in the gene set construction, contigs were first measured for protein coding sequences using Prodigal (v2.6.3), genes with a length of less than 150 bp were excluded, and genes that met the requirements were obtained. Gene IDs were renamed in the order of gene length, and the naming rule was: specimen ID + contigs number + sequential number. Subsequently, CD-HIT was used to deduplicate all genes to obtain a non-redundant gene set. At this time, a gene set dictionary was constructed, with the renamed ID as the key and the gene ID of the same cluster as one of the values.

[0059] Subsequently, eggNOG-mapper (v2.1.3) was used to perform functional annotation on the non-redundant gene set based on the EggNOG5 (v5.0.2) database, and the functional annotation information was stored in the gene set dictionary.

[0060] In the construction of the genome set, after the contigs are binned and calculated, a high-quality metagenome assembly genome (MAG) is obtained. The gene combination contained in the contigs and other functional annotations are obtained from the gene set dictionary from the contig numbers contained in the MAG, and then all the gene combinations of all the contigs of the MAG are merged to obtain the gene function interpretation of the MAG. The existing method of explaining the gene function of the MAG is avoided by re-annotating the gene prediction, resulting in information asymmetry with the gene set; thereby realizing information sharing between the gene set construction process and the genome set construction process.

[0061] In one embodiment, the workflow includes a first type of flow and a second type of flow, the first type of flow is a default execution flow, and the second type of flow is a flexible configuration flow corresponding to the user instruction.

[0062] Specifically, the present invention divides the tasks corresponding to gene set construction or genome set construction of sample data into multiple independent processes and integrates them into corresponding modules. Sample data can choose to execute only a single module or multiple modules according to task requirements, thereby realizing a customized process for work tasks, saving computing resources while improving the efficiency of complex bioinformatics analysis. In order to reduce task execution errors or the integrity of execution results, the links in the bioinformatics analysis process that do not involve bioinformatics processing are integrated into the first type of process of the default execution process, which ensures that the subsequent second type of process can proceed smoothly. The first type of process includes input checking and result collation.

[0063] Among them, the input check includes sample ID format, basic configuration (such as source file storage path and target file storage path), authenticity verification of files to be processed, sequence file matching verification of double-end data, etc. The result collation includes the first type of report and the second type of report. The first type of report is a report in a format specified by the user, and the second type of report is a report automatically generated by the system with details of this task. The index bookmarks corresponding to the first and second types of reports are directly fed back to the user interaction interface. The content of the second type of report is stored in the system-specified location, and the first type of report is stored in the target storage location configured by the user.

[0064] Example of basic configuration: Perform the following configuration on the command line of the user interaction layer:

[0065] 1) Specify the file path containing the input sample data, such as: "--input sample.csv";

[0066] 2) Specify the storage directory for the output results, such as: "--outdir <outdir>”;

[0067] 3) Set the project number, such as "--Account XXXXXX", to ensure that the work tasks instructed by the user are associated with the project to avoid project confusion and confusing results.

[0068] It should be noted that: when the user is configuring the above parameters, the visual interactive interface of the application layer performs a visual exemplary display. The screen display content is consistent with the parameter configuration items, and the key information in the user configuration information is collected in real time for comparison with the screen display information. If the key information is inconsistent, a reminder will be given. The key information includes file format, project number, etc. For example, the file path containing sample data (D:-XXX-xxx-sample.csv) is checked by comparing the file format of the last file in the extraction path (".csv") with the file format prompted on the screen. If they are consistent, the next parameter configuration is performed. Through real-time display and comparison, the use efficiency can be greatly improved, the use threshold can be lowered, and the correctness of the configuration parameters can be ensured.

[0069] In a preferred embodiment, taking the sample configuration file sample.csv as an example, it supports the following column names, each of which has its specific meaning and function, as described below:

[0070] 1)id: The unique identifier of the sample, used to track and identify the sample throughout the analysis process;

[0071] 2) raw_reads1: the path to the first fastq file of the original double-end sequencing, usually containing the raw data obtained by sequencing;

[0072] 3) raw_reads2: the path of the second fastq file of the original double-end sequencing, paired with raw_reads1;

[0073] 4) clean_reads1: the path of the first double-end sequence fastq file after quality control processing;

[0074] 5) clean_reads2: The path of the second double-end sequence fastq file after quality control processing, paired with clean_reads1;

[0075] 6)contig: The path of the contig file generated during the assembly process, usually a fasta format file containing the assembled continuous sequence;

[0076] Different column name configurations are suitable for different analysis scenarios and requirements. The specific configurations are shown in the following table.

[0077]

[0078] Example 1: test1.csv

[0079] id,raw_reads1,raw_reads2

[0080] sample_0,sample_0_reads_1.fq.gz,sample_0_reads_2.fq.gz

[0081] sample_10,sample_10_reads_1.fq.gz,sample_10_reads_2.fq.gz

[0082] Example 2: test2.csv

[0083] id,clean_reads1,clean_reads2

[0084] sample_0,sample_0_clean_1.fq.gz,sample_0_clean_2.fq.gz

[0085] sample_10,sample_10_clean_1.fq.gz,sample_10_clean_2.fq.gz

[0086] Example 3: test3.csv

[0087] id,clean_reads1,clean_reads2,contig

[0088] sample_0,sample_0_clean_1.fq.gz,sample_0_clean_2.fq.gz,sample_0.contig.fasample_10,sample_10_clean_1.fq.gz,sample_10_clean_2.fq.gz,sample_10.contig.fa

[0089] In one embodiment, the double-ended data check includes:

[0090] Obtain the first file and the second file corresponding to the dual-end data;

[0091] Calculate the file size difference between the first file and the second file to obtain a first difference file;

[0092] Calculate the file size difference between the first difference file and the third file to obtain a second difference file;

[0093] Compare the file sizes of the second difference file and the third file. If the second difference file is smaller than the third file, it is qualified; otherwise, it is unqualified.

[0094] If the first file is larger than the second file, the third file is the second file; if the first file is smaller than the second file, the third file is the first file.

[0095] Specifically, by comparing the difference file between the first file and the second file of the double-end data with the third file corresponding to the reference file, the comparison result is directly used as the detection result. Only the judgment logic needs to be specified, and there is no need to add an additional multiple relationship between the first file and the second file. The sequence file size check of the double-end data can be completed, which can avoid data extraction errors caused by using a fixed preset value library and release computer resources. In a preferred embodiment, the third difference file corresponding to the second difference file and the third file is further compared with the preset file for file size. If the second difference file is larger than the third file, or the third file is larger than the second difference file, and the third difference file is smaller than the preset file, the detection is qualified, otherwise it is unqualified. The preset file can be a file of fixed size or a third file.

[0096] The multi-strategy metagenomic analysis system provided by the present invention comprises an application layer, an analysis layer, a support layer and a resource layer; the application layer serves as an interactive layer for users, and users input sample files through an interactive interface of the application layer, and select work tasks to be completed on demand from the interactive interface; the support layer calls up tools, libraries, databases and environment support resources of the resource layer to build a work environment according to the workflow selected by the user, and the analysis layer uses the multiple algorithms and models deployed by the analysis layer to complete the bioinformatics analysis of the sample files; the present invention modularizes each link of the workflow and maps it to the interactive interface of the application layer, and users directly select modules corresponding to the analysis content to be executed by the sample file and the environment required for execution through the interactive interface; the system automatically allocates analysis tools and resources according to the instructions and sample files selected by the user, completes the task analysis of the sample file and outputs the analysis report to the interactive interface of the application layer for the user to use; modularization and customization are combined to meet the complexity characteristics of bioinformatics analysis, simplify the complex instructions required for bioinformatics analysis, improve the flexibility of the system, and reduce the threshold for using the system.

[0097] Example 2

[0098] See also Figure 4 The present invention further provides a multi-strategy metagenomic analysis method based on the multi-strategy metagenomic analysis system of Example 1, the method comprising:

[0099] S1: Obtaining a sample file submitted by a user through an interactive interface and a user instruction specified on the interactive interface;

[0100] S2: According to the user instruction, construct a working environment required for executing the work task corresponding to the user instruction;

[0101] S3: performing bioinformatics analysis on the sample file in the working environment according to the user instruction, and outputting an analysis report to a user interaction interface;

[0102] The user instruction includes a first configuration instruction and a second configuration instruction, the first configuration instruction is to execute the entire process of the biological information analysis, and the second configuration instruction is to execute a partial process of the biological information analysis.

[0103] Specifically, the user submits a sample file (sample.csv) through the interactive interface of the application layer, and selects the workflow to be executed on the interactive interface. The system calls the corresponding tools and databases to build a working environment according to the workflow to be executed by the sample file. The user instructions include the first configuration instruction and the second configuration instruction. The process that the user specifies the system to complete is also different. The task that the system needs to complete can be a single module or a task composed of multiple modules. For example, the parameter value corresponding to the workflow is determined by the "mode" value: If the input check (INPUT_CHECK) is the default execution program, whether it is the first configuration instruction or the second configuration instruction, Both configuration instructions will perform input checks. When the input check passes, the parameter configuration module will be entered to determine the task type through the "mode" value. If the "mode" value is not specified, or the "mode" value is "0", the first configuration instruction will be used by default, and the entire workflow will be entered; the "mode" value represents the execution of a specific process or the skipping of a specific process "M", and the user instruction is the second configuration instruction. At this time, after completing the input data check, the system will directly execute the specific process, or directly execute the next process when encountering a process that needs to be skipped; "mode = 0" is set as the default setting, which requires the system to complete all analysis processes, that is, covering all modules;

[0104] Set "mode=1" to perform data quality control analysis only;

[0105] Set "mode=2" to only perform the sequence assembly step;

[0106] Set "mode=3" to perform species identification analysis only;

[0107] Set "mode=4" to perform gene function annotation only;

[0108] Set "mode=5" to perform binning analysis only.

[0109] The multi-strategy metagenomic analysis method provided by the present invention is that the user inputs the sample file through the interactive interface of the application layer, and selects the work tasks to be completed on demand from the interactive interface. The support layer calls the tools, libraries, databases and environmental support and other resources of the resource layer to build a work environment according to the workflow selected by the user, and the analysis layer uses the multiple algorithms and models deployed by it to complete the bioinformatics analysis of the sample file; the present invention modularizes the various links of the workflow and maps them to the interactive interface of the application layer. The user directly selects the module corresponding to the analysis content to be executed by the sample file and the environment required for execution through the interactive interface. The system automatically allocates analysis tools and resources according to the instructions and sample files selected by the user, completes the sample file task analysis and outputs the analysis report to the interactive interface of the application layer for the user to use; modularization and customization are combined to meet the complexity characteristics of bioinformatics analysis, simplify the complex instructions required for bioinformatics analysis, improve the flexibility of the system, and reduce the threshold for using the system.

[0110] Example 3

[0111] The present invention provides an electronic device and a storage medium, see Figure 5 , comprising at least one processor, at least one memory and computer program instructions stored in the memory.

[0112] Specifically, the above-mentioned processor may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of an embodiment of the present invention, and the electronic device includes at least one of the following: a computer, a mobile terminal, a PC, a tablet computer, etc.

[0113] The memory may include a large capacity memory for data or instructions. By way of example and not limitation, the memory may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive or a combination of two or more of these. Where appropriate, the memory may include a removable or non-removable (or fixed) medium. Where appropriate, the memory may be inside or outside the data processing device. In a particular embodiment, the memory is a non-volatile solid-state memory.

[0114] In one example, the electronic device may further include a communication interface and a bus, wherein the processor, the memory, and the communication interface are connected via the bus and communicate with each other.

[0115] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.< / outdir>

Claims

1. A multi-strategy metagenomic analysis system, comprising an application layer, an analysis layer, a support layer and a resource layer, characterized in that: The application layer is used as a user interaction interface to receive user instructions and configure work tasks based on the user instructions. The analysis layer is used to execute the work tasks. The support layer is used to build the work environment required to execute the work tasks. The resource layer is used to configure the resources required for the work tasks. The user selects the workflow of the work task corresponding to the user instruction as needed in the user interaction interface of the application layer, and the support layer constructs the work environment according to the resource requirements for executing the workflow.

2. The multi-strategy genome analysis system according to claim 1, characterized in that: The workflow includes at least one biological information analysis process, and each of the workflows is decomposed into multiple independent secondary modules according to subdivided links and configured in the analysis layer.

3. The multi-strategy metagenomic analysis system according to claim 2, characterized in that: The user instruction received by the application layer includes a first configuration instruction or a second configuration instruction, wherein the first configuration instruction is to execute the entire process of the bioinformatics analysis, and the second configuration instruction is to execute a partial process of the bioinformatics analysis.

4. The multi-strategy metagenomic analysis system according to claim 3, characterized in that: The partial process includes a single secondary module or a plurality of secondary modules, and the plurality of secondary modules are continuous or discontinuous processes in the entire process.

5. The multi-strategy metagenomic analysis system according to claim 3, characterized in that: The workflow includes a gene set construction process and a genome set construction process, wherein the gene set construction process and the genome set construction process share information.

6. The multi-strategy genome analysis system according to any one of claims 2 to 4, characterized in that: The workflow includes a first type of flow and a second type of flow, the first type of flow is a default execution flow, and the second type of flow is a flexible configuration flow corresponding to the user instruction.

7. The multi-strategy genome analysis system according to claim 6, characterized in that: The user instructions are one or more instructions selected from a limited number of instructions on the interactive interface mapped to the application layer by each of the secondary modules in the analysis layer.

8. A multi-strategy metagenomic analysis method, characterized in that: The method comprises: Obtaining a sample file submitted by a user through an interactive interface and a user instruction specified on the interactive interface; According to the user instruction, construct a working environment required for executing the work task corresponding to the user instruction; According to the user instruction, performing bioinformatics analysis on the sample file in the working environment, and outputting an analysis report to a user interaction interface; The user instruction includes a first configuration instruction and a second configuration instruction, the first configuration instruction is to execute the entire process of the biological information analysis, and the second configuration instruction is to execute a partial process of the biological information analysis.

9. An electronic device, characterized in that: include: At least one processor, at least one memory and computer program instructions stored in the memory, the electronic device is deployed with the system as described in any one of claims 1 to 7, and when the computer program instructions are executed by the processor, the method as described in claim 8 is implemented.

10. A computer-readable storage medium storing computer program instructions corresponding to the system according to any one of claims 1 to 7, characterized in that: When the computer program instructions are executed by a processor, the method as claimed in claim 8 is implemented.

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