An automated method and system for bioinformatic analysis

Abstract

This invention relates to an automated method and system for bioinformatics analysis. The method includes: acquiring project information and sequencing data of the project to be analyzed; matching corresponding truncation parameters, filtering parameters, and sample merging parameters from a preset database based on the project information, and filtering the sequencing data accordingly; generating sequencing quality statistics, filtered sequence statistics, and sequencing position quality statistics based on the filtering results; determining quality control indicators based on the sequencing quality statistics and filtered sequence statistics; judging the qualification of each sample in the sequencing data hierarchically based on the quality control indicators and issuing corresponding reminders; merging multiple qualified samples, and generating an analysis report based on the merged sample(s) and the sequencing position quality statistics. This invention can customize analysis schemes based on various project information, has strong versatility, and supports automatic import of project information, reducing manual intervention and achieving a high degree of automation.

Description

Technical Field

[0001] This invention belongs to the field of bioinformatics technology, specifically relating to an automated method and system for bioinformatics analysis. Background Technology

[0002] Automation is one of the main goals of bioinformatics analysis. In traditional bioinformatics laboratories, the format, quality, experimental methods, and sequencing techniques of sequencing data cannot be completely standardized, often requiring manual intervention during analysis. Existing analysis protocol generation tools cannot meet the needs of different sequencing types, library preparation types, or product types, and the generated results cannot be directly used in production. Summary of the Invention

[0003] To improve the adaptability, efficiency, and interpretability of bioinformatics analysis, a first aspect of the present invention provides an automated method for bioinformatics analysis, comprising: acquiring project information and sequencing data of a project to be analyzed, wherein the project information includes sequencing type, library construction type, product type, sample name, and library number; matching corresponding truncation parameters, filtering parameters, and sample merging parameters from a preset database based on the project information; filtering the sequencing data based on the truncation parameters, filtering parameters, and sample merging parameters, and generating a sequencing quality statistics file, a filtered sequence statistics file, and a sequencing position quality statistics file based on the filtering results; determining quality control indicators based on the sequencing quality statistics file and the filtered sequence statistics file; judging whether each sample in the sequencing data is qualified according to the quality control indicators at different levels and issuing corresponding reminders; merging multiple qualified samples, and generating an analysis report based on the merged sample or more and the sequencing position quality statistics file.

[0004] In some embodiments of the present invention, the step of matching the corresponding truncation parameters, filtering parameters, and sample merging parameters from the preset database according to the project information includes: matching the sequencing type and library construction type with the truncation parameters in the preset database respectively; matching the product type with the filtering parameters or adapter sequences in the preset database respectively; and matching the sample name and library number with the sample merging parameters in the preset database respectively.

[0005] In some embodiments of the present invention, filtering the sequencing data based on the truncation parameters, filtering parameters, and sample merging parameters includes: filtering blocks or fields of view in the sequencing data; filtering read lengths, base content, and nucleotide sequences in the sequencing data; and filtering the sequencing data according to a preset average sequencing quality, a preset paired-end sequencing overlap length, and sequencing adapters.

[0006] Furthermore, the filtering of read length, base content, and nucleotide sequence in the sequencing data includes: removing sequences whose read length is lower than a first preset value or higher than a second preset value; removing sequences whose N bases are greater than a third preset value; removing sequences whose A bases are greater than a fourth preset value; and removing sequences whose polynucleotide length is greater than a fifth preset value.

[0007] In some embodiments of the present invention, the step of determining whether each sample in the sequencing data is qualified and making corresponding reminders based on the hierarchical classification of the quality control indicators includes: dividing the multiple quality control indicators into quality control items, prompt items, and record items; determining whether the quality control items, prompt items, and record items in each sample of the sequencing data are qualified, and making corresponding reminders based on the determination results.

[0008] In the above embodiments, the step of merging multiple qualified samples and generating an analysis report based on the merged samples and sequencing position quality statistics file includes: merging multiple sequencing data with the same sample number according to the correspondence between sample name, sample number and sub-library number in a preset database; drawing a sequencing quality distribution map and a base type distribution map of the sequencing data based on the sequencing position quality statistics file; and generating an analysis report of the sequencing data based on the sequencing quality statistics file, the filtered sequence statistics file, the sequencing quality distribution map and the base type distribution map.

[0009] A second aspect of the present invention provides an automated system for bioinformatics analysis, comprising: an acquisition module for acquiring project information and sequencing data of a project to be analyzed, wherein the project information includes sequencing type, library construction type, product type, sample name, and library number; a matching module for matching corresponding truncation parameters, filtering parameters, and sample merging parameters from a preset database based on the project information; a filtering module for filtering the sequencing data based on the truncation parameters, filtering parameters, and sample merging parameters, and generating a sequencing quality statistics file, a filtered sequence statistics file, and a sequencing position quality statistics file based on the filtering results; a quality control module for determining quality control indicators based on the sequencing quality statistics file and the filtered sequence statistics file; judging whether each sample in the sequencing data is qualified according to the quality control indicators at different levels and issuing corresponding reminders; and a reporting module for merging multiple qualified samples and generating an analysis report based on one or more merged samples and the sequencing position quality statistics file.

[0010] A third aspect of the present invention provides an electronic device comprising: one or more processors; and a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the automated bioinformatics analysis method provided in the first aspect of the present invention.

[0011] In a fourth aspect, the present invention provides a computer-readable medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the automated method for bioinformatics analysis provided in the first aspect of the present invention.

[0012] The beneficial effects of this invention are:

[0013] The purpose of this invention is to provide an automated tool for generating bioinformatics analysis schemes, which can effectively adapt to sequencing data of different formats and qualities, and can automatically match production information such as sequencing type, library construction type, and product type to generate analysis schemes unique to the project, thereby improving analysis efficiency and increasing the interpretability of analysis results. Attached Figure Description

[0014] Figure 1 This is a schematic diagram of the basic process of an automated bioinformatics analysis method in some embodiments of the present invention;

[0015] Figure 2 This is a schematic diagram of the structure of an automated bioinformatics analysis system according to some embodiments of the present invention;

[0016] Figure 3 This is a schematic diagram of the structure of an electronic device in some embodiments of the present invention. Detailed Implementation

[0017] The principles and features of the present invention are described below with reference to the accompanying drawings. The examples given are only for explaining the present invention and are not intended to limit the scope of the present invention.

[0018] refer to Figure 1 In a first aspect, the present invention provides an automated method for bioinformatics analysis, comprising: S100. acquiring project information and sequencing data of a project to be analyzed, wherein the project information includes sequencing type, library construction type, product type, sample name, and library number; S200. matching corresponding truncation parameters, filtering parameters, and sample merging parameters from a preset database according to the project information; S300. filtering the sequencing data based on the truncation parameters, filtering parameters, and sample merging parameters, and generating a sequencing quality statistics file, a filtered sequence statistics file, and a sequencing position quality statistics file based on the filtering results; S400. determining quality control indicators based on the sequencing quality statistics file and the filtered sequence statistics file; judging whether each sample in the sequencing data is qualified according to the quality control indicators at different levels and issuing corresponding reminders; S500. merging multiple qualified samples, and generating an analysis report based on one or more merged samples and the sequencing position quality statistics file.

[0019] In step S100 of some embodiments of the present invention, the project information and sequencing data of the project to be analyzed are retrieved. The project information includes sequencing type, library construction type, product type, sample name, and library number. Specifically, the project information is imported using a Python script.

[0020] Project information: such as project number, project leader, information manager, etc.;

[0021] Production information: such as sequencing type, library construction type, product type, sample name, library number, etc.;

[0022] Disconnection information: such as data storage path, sequencing chip number, disconnection completion time, etc.

[0023] In step S200 of some embodiments of the present invention, the step of matching the corresponding truncation parameters, filtering parameters and sample merging parameters from the preset database according to the project information includes: S201. Matching the sequencing type and library construction type with the truncation parameters in the preset database respectively; S202. Matching the product type with the filtering parameters or adapter sequences in the preset database; S203. Matching the sample name and library number with the sample merging parameters in the preset database respectively.

[0024] Specifically, the matching relationships are as follows: sequencing type / library construction type -> truncation parameters; product type -> adapter sequence / filtering parameters; sample name / library number -> sample merging parameters. -> indicates a matching operation.

[0025] In step S300 of some embodiments of the present invention, filtering the sequencing data based on the truncation parameters, filtering parameters, and sample merging parameters includes: S301. Filtering blocks or fields of view in the sequencing data; S302. Filtering read lengths, base content, and nucleotide sequences in the sequencing data; S303. Filtering the sequencing data according to preset average sequencing quality, preset paired-end sequencing overlap length, and sequencing adapters.

[0026] Furthermore, in step S302, filtering the read length, base content, and nucleotide sequence in the sequencing data includes: removing sequences whose read length is lower than a first preset value or higher than a second preset value; removing sequences whose N base content is greater than a third preset value; removing sequences whose N base content is greater than a fourth preset value; and removing sequences whose polynucleotide length is greater than a fifth preset value.

[0027] Specifically, initialization is performed before generating the analysis script, followed by filtering. Filtering is done using a pre-defined filtering program or software to filter the sequencing data. The filtering steps, from highest to lowest priority, are as follows:

[0028] Tile or Field of View (FOV) Filtering: Remove tiles or fields of view with specified numbers based on the signal and specifications of the sequencing chip;

[0029] Minimum read length filtering: Removes sequences whose read length is less than a specified value;

[0030] Maximum read length filtering: Removes sequences with a read length greater than a specified value;

[0031] N content: Remove sequences with an N base content greater than or equal to a specified value;

[0032] High A content: Remove sequences with an A base content greater than or equal to a specified value;

[0033] polyX: Removes sequences containing polynucleotides (poly-X, where X refers to any type of nucleotide) with a length greater than or equal to the specified value;

[0034] Low-quality base percentage: Remove sequences where the percentage of low-quality bases is greater than or equal to a specified value;

[0035] Average quality: Remove sequences with an average sequencing quality lower than a specified value;

[0036] Paired-End Sequencing Overlap Length: For paired-end sequencing (PE), sequences with an overlap length greater than a specified value are removed.

[0037] Sequencing adapter: Removes sequences containing sequencing adapters.

[0038] In step S400 of some embodiments of the present invention, the step of judging whether each sample in the sequencing data is qualified according to the quality control indicators and making corresponding reminders includes: S401. Dividing the multiple quality control indicators into quality control items, prompt items and record items; S402. Judging whether the quality control items, prompt items and record items in each sample in the sequencing data are qualified, and making corresponding reminders according to the judgment results.

[0039] Specifically, the following statistical files are generated after filtering using a preset script or software program:

[0040] The Basic Statistics of Sequencing Quality (BSSQ) file contains the following data before and after filtering: read length, number of sequences, number of filtered sequences, number of bases, number of filtered bases, number of A / T / G / C / N bases and their percentage, number of bases with a sequencing quality exceeding 20 (Q20) and their percentage, and number of bases with a sequencing quality exceeding 30 (Q30) and their percentage.

[0041] Statistics of Filtered Reads (SFR): Includes read1, read2, the sum of read1 and read2, and the union of read1 and read2 (referring to the case where a pair of reads are filtered out simultaneously): total number of filtered reads, number of duplicate reads, number of reads exceeding the preset data truncation amount, number of reads filtered by block, number of reads filtered by field of view, number of excessively short reads, number of excessively long reads, number of reads containing globally contaminated sequences, number of reads containing contaminated sequences, number of reads with excessive N base content, number of reads with excessive A base content, number of reads with excessive polyX content, number of reads with excessively low-quality bases, number of reads with low average quality, number of reads with excessively short insert fragments, and number of reads containing sequencing adapters;

[0042] fqcheck file: contains the average percentage of A / T / G / C / N bases at each sequencing position and the percentage of each sequencing quality value;

[0043] The BSSQ file is used to provide the sequence number, base number, Q20, and Q30 indicators in the quality control process and analysis report. The SFR file is used to provide two indicators in the quality control process: the proportion of low-quality reads and the proportion of sequencing adapters. The fqcheck file is used to draw sequencing quality distribution maps and base type distribution maps.

[0044] Quality control process: The analysis module in the pre-set software or script matches corresponding quality control indicators based on the project product type and checks whether the filtered data of each group meets the indicators. Quality control indicators are divided into quality control items, prompt items, and record items: Samples that fail to meet quality control items will have their analysis terminated and receive an email reminder; samples that fail to meet prompt items will only receive an email reminder; and record items do not participate in the actual quality control process, but are only retained as data records for future statistical purposes. Specific quality control indicators include:

[0045] Read 1 Q20: Quality control item, the average Q20 proportion of the first sequence (read 1) in paired-end sequencing, obtained from BSSQ;

[0046] Read 2 Q20: Quality control item, the average Q20 proportion of the second sequence (read 2) in paired-end sequencing, obtained from BSSQ;

[0047] Read 1 Q30: Quality control item, average Q30 percentage of read 1, obtained from BSSQ;

[0048] Read 2 Q30: Quality control item, average Q30 percentage of read 2, obtained from BSSQ;

[0049] GC ratio: a quality control item, the average G / C base ratio of read 1 and read 2, calculated from BSSQ;

[0050] read 1 AT separation ratio (AT_sep): Prompt item, the absolute value of the difference between the average A / T base content in read 1, i.e. AT_sep = |A% - T%|read1, calculated from BSSQ;

[0051] Read 2 AT Separation Ratio (AT_sep): Prompt item, the absolute value of the difference between the average A / T base content in read 2, i.e., AT_sep = |A% - T%|read2, calculated from BSSQ;

[0052] N ratio: Prompt item, the average N base ratio of read 1 to read 2, calculated from BSSQ;

[0053] Low quality read ratio: This is a prompt indicating the proportion of low-quality reads out of the total number of reads, calculated from the SFR.

[0054] Sequencing adapter ratio: This is a prompt indicating the proportion of reads containing sequencing adapters out of the total number of reads, calculated from the SFR.

[0055] Contract data volume ratio: a quality control item, the ratio of filtered data volume to contract data volume, calculated from BSSQ;

[0056] Filtering Ratio: This is a prompt indicating the ratio of the amount of data after filtering to the amount of data before filtering, calculated from BSSQ.

[0057] During quality control, all lanes in a sample must meet the required quality control data; otherwise, the sample is considered unqualified. The quality control indicators may differ for different product types, such as DNA and RNA products.

[0058] Therefore, in step S500 of the above embodiment, merging multiple qualified samples and generating an analysis report based on the merged samples and sequencing position quality statistics file includes: S501. Merging multiple sequencing data with the same sample number according to the correspondence between sample name, sample number and sub-library number in a preset database; S502. Drawing a sequencing quality distribution map and a base type distribution map of the sequencing data based on the sequencing position quality statistics file; S503. Generating an analysis report of the sequencing data based on the sequencing quality statistics file, the filtered sequence statistics file, the sequencing quality distribution map and the base type distribution map.

[0059] Specifically, the steps include the following:

[0060] Plotting: Based on the statistical files generated in the filtering step, plot the sequencing quality distribution and base type distribution of the samples;

[0061] Generate analysis report: Generate an analysis report data package based on information such as statistical files, sequencing quality distribution map, and base type distribution map, upload it to the analysis report server, and return a PDF report;

[0062] End: Update the task status in the database and send a reminder email to the relevant person in charge.

[0063] Example 2

[0064] refer to Figure 2 In a second aspect, the present invention provides an automated system for bioinformatics analysis, comprising: an acquisition module 11 for acquiring project information and sequencing data of a project to be analyzed, wherein the project information includes sequencing type, library construction type, product type, sample name, and library number; a matching module 12 for matching corresponding truncation parameters, filtering parameters, and sample merging parameters from a preset database according to the project information; a filtering module 13 for filtering the sequencing data based on the truncation parameters, filtering parameters, and sample merging parameters, and generating a sequencing quality statistics file, a filtered sequence statistics file, and a sequencing position quality statistics file based on the filtering results; a quality control module 14 for determining quality control indicators based on the sequencing quality statistics file and the filtered sequence statistics file; judging whether each sample in the sequencing data is qualified according to the quality control indicators at different levels and issuing corresponding reminders; and a reporting module 15 for merging multiple qualified samples and generating an analysis report based on one or more merged samples and the sequencing position quality statistics file.

[0065] Furthermore, the matching module 12 includes: a first matching unit for matching the sequencing type and library construction type with the truncation parameters in a preset database, respectively; a second matching unit for matching the product type with the filtering parameters or adapter sequences in the preset database; and a third matching unit for matching the sample name and library number with the sample merging parameters in the preset database, respectively.

[0066] Example 3

[0067] refer to Figure 3 A third aspect of the present invention provides an electronic device comprising: one or more processors; and a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the automated method for bioinformatics analysis of the present invention in the first aspect.

[0068] Electronic device 500 may include a processing unit (e.g., a central processing unit, a graphics processing unit, etc.) 501, which can perform various appropriate actions and processes according to a program stored in read-only memory (ROM) 502 or a program loaded from storage device 508 into random access memory (RAM) 503. The RAM 503 also stores various programs and data required for the operation of electronic device 500. The processing unit 501, ROM 502, and RAM 503 are interconnected via bus 504. An input / output (I / O) interface 505 is also connected to bus 504.

[0069] Typically, the following devices can be connected to I / O interface 505: input devices 506 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 507 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 508 including, for example, hard disks; and communication devices 509. Communication device 509 allows electronic device 500 to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 3 An electronic device 500 with various devices is shown; however, it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed alternatively. Figure 3 Each box shown can represent a device or multiple devices as needed.

[0070] Specifically, according to embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 509, or installed from a storage device 508, or installed from a ROM 502. When the computer program is executed by a processing device 501, it performs the functions defined in the methods of embodiments of this disclosure. It should be noted that the computer-readable medium described in embodiments of this disclosure can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In embodiments of this disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in connection with an instruction execution system, apparatus, or device. In embodiments of this disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. Program code contained on a computer-readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0071] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device. The aforementioned computer-readable medium carries one or more computer programs, which, when executed by the electronic device, cause the electronic device to:

[0072] Computer program code for performing the operations of embodiments of this disclosure can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages—such as Java, Smalltalk, C++, and Python—and conventional procedural programming languages—such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0073] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0074] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An automated method of bioinformatic analysis, characterized in that, include: Obtain the project information and sequencing data of the project to be analyzed. The project information includes sequencing type, library construction type, product type, sample name, and library number. Based on the project information, the corresponding extraction parameters, filtering parameters, and sample merging parameters are matched from the preset database. The sequencing data is filtered based on the truncation parameters, filtering parameters, and sample merging parameters, and sequencing quality statistics files, filtered sequence statistics files, and sequencing position quality statistics files are generated based on the filtering results. The filtering of the sequencing data based on the truncation parameters, filtering parameters, and sample merging parameters includes: Filter the blocks or fields of view in the sequencing data; The read lengths, base content, and nucleotide sequences in the sequencing data were filtered. The sequencing data is filtered based on preset average sequencing quality, preset paired-end sequencing overlap length, and sequencing adapters; The filtering of read lengths, base content, and nucleotide sequences in the sequencing data includes: Remove sequences from sequencing data whose read length is lower than a first preset value or higher than a second preset value; Remove sequences from sequencing data where the N base content is greater than a third preset value; Remove sequences from sequencing data where the A base content is greater than the fourth preset value; Remove sequences whose polynucleotide length exceeds the fifth preset value; Quality control indicators are determined based on sequencing quality statistics files and filtered sequence statistics files; based on the quality control indicators, each sample in the sequencing data is judged at different levels to determine whether it is qualified and corresponding reminders are given; The step of determining whether each sample in the sequencing data is qualified based on the quality control indicators and issuing corresponding reminders includes: Multiple quality control indicators are divided into quality control items, prompt items, and record items; it is determined whether the quality control items, prompt items, and record items in each sample of the sequencing data are qualified, and corresponding reminders are given based on the judgment results; Multiple qualified samples are merged, and an analysis report is generated based on one or more merged samples and sequencing location quality statistics files.

2. The method of Claim 1, wherein, The step of matching the corresponding truncation parameters, filtering parameters, and sample merging parameters from the preset database based on the project information includes: The sequencing type and library construction type are matched with the truncation parameters in the preset database, respectively; Match product types with filter parameters or connector sequences in a preset database; The sample name and library number are matched with the sample merging parameters in the preset database.

3. The method of automation of biological information analysis according to any one of claims 1 to 2, characterized in that, The process of merging multiple qualified samples and generating an analysis report based on one or more merged samples and sequencing location quality statistics includes: Based on the correspondence between sample name, sample number and sub-library number in the preset database, multiple sequencing data with the same sample number are merged; Based on the sequencing location quality statistics file, plot the sequencing quality distribution map and the base type distribution map of the sequencing data; An analysis report of the sequencing data is generated based on the sequencing quality statistics file, the filtered sequence statistics file, the sequencing quality distribution map, and the base type distribution map.

4. A bioinformation analysis automation system applying the bioinformation analysis automation method according to any one of claims 1 to 3, characterized by, include: The acquisition module is used to acquire project information and sequencing data of the project to be analyzed. The project information includes sequencing type, library construction type, product type, sample name, and library number. The matching module is used to match the corresponding truncation parameters, filtering parameters, and sample merging parameters from a preset database based on the project information. The filtering module is used to filter the sequencing data based on the truncation parameters, filtering parameters, and sample merging parameters, and generate sequencing quality statistics files, filtered sequence statistics files, and sequencing position quality statistics files based on the filtering results. The quality control module is used to determine quality control indicators based on sequencing quality statistics files and filtered sequence statistics files; Based on the quality control indicators, the system determines whether each sample in the sequencing data is qualified and issues corresponding reminders. The reporting module is used to merge multiple qualified samples and generate an analysis report based on one or more merged samples and sequencing location quality statistics files.

5. An electronic device comprising: One or more processors; A storage device for storing one or more programs, which, when executed by one or more processors, cause the one or more processors to implement the automated method of bioinformatics analysis as described in any one of claims 1 to 3.

6. A computer-readable medium having a computer program stored thereon, wherein, When the computer program is executed by a processor, it implements the automated method for bioinformatics analysis as described in any one of claims 1 to 3.

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