Automatic analysis method and system for high-throughput sequencing data and storage medium

Abstract

The invention provides an automatic analysis method and system for high-throughput sequencing data and a storage medium, and the method is characterized in that the method comprises the following steps: monitoring a computer table mailbox at regular time, and downloading a high-throughput sequencing computer table newly received by the computer table mailbox, the high-throughput sequencing lookup table comprises a first lookup table containing an index sequence and a second lookup table containing sample information; comparing the first on-machine meter with the second on-machine meter; based on the name of the first lookup table, the successfully compared first lookup table is delivered to a corresponding sequencing platform through a delivery module, and the delivery module triggers monitoring of a sequencing process; splitting the original bcl data obtained after sequencing into fastq data based on a splitting module determined by the cluster scheduling system; and analyzing the fastq data based on an analysis module determined by the cluster scheduling system.

Description

Technical Field

[0001] The present application relates to the field of high-throughput sequencing, and in particular, to an automated analysis method and system for high-throughput sequencing data. Background Art

[0002] The widespread application of high-throughput sequencing technology in clinical diagnosis has become a reality. High-throughput sequencing technology is accompanied by the generation of massive sequencing data. However, the traditional manual delivery method can no longer meet the needs of large-scale data analysis. At the same time, the importance of data security protection has become increasingly prominent, and the privacy and confidentiality of data are facing increasing challenges. The security of high-throughput sequencing data has become a crucial issue in the clinical and scientific research fields. Therefore, the market urgently needs to develop a fully automated analysis method and system suitable for large-scale sequencing data generated by multiple sequencing platforms to reduce manual operation errors and costs. Summary of the invention

[0003] The present application proposes an automated analysis method for high-throughput sequencing data, comprising the following steps: regularly monitoring an upload table mailbox and downloading a high-throughput sequencing upload table newly received by the upload table mailbox, wherein the high-throughput sequencing upload table comprises a first upload table containing an index sequence and a second upload table containing sample information; comparing the first upload table and the second upload table; based on the name of the first upload table, delivering the first upload table that has been successfully compared to a corresponding sequencing platform through a delivery module, wherein the delivery module triggers monitoring of the sequencing process; splitting the original bcl data obtained after the sequencing is completed into fastq data based on a splitting module determined by a cluster scheduling system; and analyzing the fastq data based on an analysis module determined by the cluster scheduling system.

[0004] According to the implementation mode of the present application, regularly monitoring the machine list mailbox and downloading the high-throughput sequencing machine list newly received in the machine list mailbox includes: using crontab to regularly monitor the machine list mailbox, and downloading the high-throughput sequencing machine list newly received in the machine list mailbox based on the email protocol.

[0005] According to an implementation manner of the present application, the following steps are also included: in response to an unsuccessful comparison between the first on-board table and the second on-board table, the first on-board table and the second on-board table that failed to be compared are sent to a notification mailbox for manual review.

[0006] According to an implementation manner of the present application, after the successfully aligned first on-machine table is delivered to the corresponding sequencing platform through a delivery module based on the name of the first on-machine table, the method further includes: sending the result of the delivery completion to the notification mailbox.

[0007] According to an embodiment of the present application, the sequencing platform includes an Illumina sequencing platform, an MGI sequencing platform and a T7 sequencing platform, wherein the delivery module of the Illumina sequencing platform is nextseq_auto_main.pl; the delivery module of the MGI sequencing platform is MGI_auto_main.pl; and the delivery module of the T7 sequencing platform is T7_auto_main.pl.

[0008] According to an implementation manner of the present application, the sequencing platforms include an Illumina sequencing platform, an MGI sequencing platform and a T7 sequencing platform, wherein the delivery module triggers monitoring of the sequencing process including: in response to the Illumina sequencing platform generating a CopyComplete.txt marker file, sequencing is completed; in response to the number of files or the file size of the offline directories of the MGI sequencing platform and the T7 sequencing platform no longer changing within a predetermined time, determining that the sequencing process has been completed.

[0009] According to an implementation manner of the present application, the analysis module determined by the cluster scheduling system triggered by the delivery module analyzes the fastq data, including: performing quality control on the fastq data; comparing the quality-controlled fastq data with the reference genome and removing duplicates; performing at least one of site depth extraction, variation detection, copy number variation detection, MSI detection, fusion detection, and TMB value calculation on the deduplicated fastq data; and generating a result report.

[0010] According to the implementation mode of the present application, analyzing the fastq data based on the analysis module determined by the cluster scheduling system includes: using software based on a GPU cluster to perform comparison, deduplication and variation detection for ctDNA samples; and using software based on a CPU cluster to perform comparison, deduplication and variation detection for non-ctDNA samples.

[0011] According to the implementation mode of the present application, the following steps are also included: recording the results of each step as a log file.

[0012] The present application also provides an automated analysis system for high-throughput sequencing data, comprising: a memory, the memory storing executable instructions; and one or more processors, the one or more processors communicating with the memory to execute the executable instructions to complete the following operations: regularly monitoring an on-board table mailbox and downloading a high-throughput sequencing on-board table newly received by the on-board table mailbox, the high-throughput sequencing on-board table comprising a first on-board table containing an index sequence and a second on-board table containing sample information; comparing the first on-board table and the second on-board table; based on the name of the first on-board table, delivering the first on-board table that has been successfully compared to a corresponding sequencing platform through a delivery module, the delivery module triggering monitoring of the sequencing process; splitting the original bcl data obtained after the sequencing is completed into fastq data based on a splitting module determined by a cluster scheduling system; and analyzing the fastq data based on an analysis module determined by the cluster scheduling system.

[0013] The present application also provides a computer-readable storage medium for automated analysis of high-throughput sequencing data, the computer-readable storage medium storing executable instructions, the executable instructions being executable by one or more processors to perform the following operations: regularly monitoring an on-board table mailbox and downloading a high-throughput sequencing on-board table newly received by the on-board table mailbox, the high-throughput sequencing on-board table comprising a first on-board table containing an index sequence and a second on-board table containing sample information; comparing the first on-board table and the second on-board table; based on the name of the first on-board table, delivering the first on-board table that has been successfully compared to a corresponding sequencing platform through a delivery module, the delivery module triggering monitoring of the sequencing process; splitting the original bcl data obtained after sequencing into fastq data based on a splitting module determined by a cluster scheduling system; and analyzing the fastq data based on an analysis module determined by the cluster scheduling system.

[0014] The present application provides an automated analysis method for high-throughput sequencing data that is applicable to large-scale sequencing data generated by a variety of sequencing platforms, and can realize automated processing of the entire process from sequencing to data analysis results, as well as real-time monitoring of each stage. At the same time, the method can select different software for data analysis for different types of test samples, thereby reducing errors that may be caused by manual labor and improving processing efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Other features, objects and advantages of the present application will become more apparent by reading the detailed description of non-limiting embodiments made with reference to the following drawings:

[0016] Figure 1 is a schematic diagram of an automated analysis method for high-throughput sequencing data according to an embodiment of the present application;

[0017] Figure 2 is a schematic diagram of a first on-board table applicable to an Illumina sequencing platform according to an embodiment of the present application;

[0018] Figure 3 It is a schematic block diagram of an automated analysis system for high-throughput sequencing data according to an embodiment of the present application. DETAILED DESCRIPTION

[0019] In order to better understand the present application, the technical solution of the present application will be described in more detail with reference to the accompanying drawings. It should be understood that these detailed descriptions are only descriptions of exemplary embodiments of the present application and are not intended to limit the scope of the present application in any way. Throughout the specification, the same reference numerals refer to the same elements. The expression "and / or" includes any combination or all combinations of one or more items in the associated listed items.

[0020] It should be noted that in this specification, expressions such as "first", "second", "third", etc. are only used to distinguish one feature from another feature, and do not represent any limitation on the features. Therefore, without departing from the teaching of this application, the first on-board table discussed below may also be referred to as the second on-board table. And vice versa.

[0021] In the accompanying drawings, the size, proportions and shapes of the figures have been slightly adjusted for ease of illustration. The drawings are for illustration only and are not drawn strictly to scale. As used herein, the terms "substantially," "approximately," and similar terms are used as terms of approximation, not degree, and are intended to account for the inherent deviations in measurements or calculations that would be recognized by one of ordinary skill in the art.

[0022] It should also be understood that expressions such as "including", "comprising", "having", "containing" and / or "comprising" are open rather than closed expressions in this specification, which indicate the presence of the stated features, elements and / or components, but do not exclude the presence of one or more other features, elements, components and / or combinations thereof. In addition, when expressions such as "at least one of..." appear after a list of listed features, they modify the entire list of features rather than just a single feature in the list. In addition, when describing embodiments of the present application, "may" is used to mean "one or more embodiments of the present application". And, the wording "exemplary" is intended to refer to an example or illustration.

[0023] Unless otherwise specified, all words (including engineering terms and scientific and technological terms) used in this article have the same meaning as those commonly understood by ordinary technicians in the field to which this application belongs. It should also be understood that, unless clearly stated in this application, words defined in common dictionaries should be interpreted as having the same meaning as their meaning in the context of the relevant technology, and should not be interpreted in an idealized or overly formal sense.

[0024] It should be noted that, in the absence of conflict, the features in the embodiments and examples of the present application can be combined with each other. In addition, unless explicitly limited or contradictory to the context, the specific steps included in the method described in the present application are not necessarily limited to the order described, but can be performed in any order or in parallel. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0025] Figure 1 It is a schematic diagram of an automated analysis method for high-throughput sequencing data according to an embodiment of the present application.

[0026] refer to Figure 1 , in step S1010, the machine table mailbox is regularly monitored and the high-throughput sequencing machine table newly received by the machine table mailbox is downloaded. The machine table may contain information of multiple test samples. For example, the high-throughput sequencing machine table includes a first machine table containing an index sequence and a second machine table containing sample information, wherein the first machine table containing the index sequence is used for obtaining and analyzing sequencing data; the second machine table containing sample information such as name, receipt date, report number, barcode, etc. is used to compare and check with the first machine table to avoid errors such as missing samples. In order to effectively check the first machine table and the second machine table, the file names of the first machine table and the second machine table must contain the same content. In addition, in order to accurately identify the corresponding sequencing platform through the file name of the machine table, the file names of the first machine table and the second machine table should contain relevant information about the sequencing platform. According to one embodiment, the file names of the first machine table and the second machine table may contain content such as <machine date>_<machine platform>_<chip number>.

[0027] In step S1020, the first computer table and the second computer table are compared. When the computer table is downloaded, the analysis system will automatically check whether the sample information in the first computer table and the second computer table corresponding to the file name can correspond one to one. If the sample information in the first computer table and the second computer table can correspond one to one, the first computer table is considered to be successfully compared, and the next step can be entered.

[0028] In step S1030, based on the name of the first on-machine table, the first on-machine table that has been successfully compared is delivered to the corresponding sequencing platform through the delivery module, and the delivery module triggers the monitoring of the sequencing process. Different sequencing platforms have different delivery modules. The delivery module will automatically identify the file name of the first on-machine table after successful review, and determine the delivery module corresponding to the first on-machine table based on the information of the sequencing platform in the file name, and then deliver the first on-machine table to the corresponding sequencing platform for sequencing. While delivering the first on-machine table, the delivery module will also trigger the system's monitoring process for the sequencing process.

[0029] In step S1040, the original bcl data obtained after the sequencing is completed is split into fastq data based on the splitting module determined by the cluster scheduling system. The cluster scheduling system is used to manage and schedule cluster resources. The cluster scheduling system can be any scheduling system on the market. According to one embodiment of the present application, the cluster scheduling system can be LSF. LSF (LoadSharing Facility) is a distributed cluster management system software responsible for the management of computing resources and the scheduling of batch jobs. LSF provides a resource management framework for meeting job requirements, finding the best resources to run the job, and monitoring its progress. When the analysis system detects that the sequencing is completed, the cluster scheduling system will automatically assign a splitting module. The splitting module will automatically split the original bcl data into fastq data based on the index sequence in the first on-board table.

[0030] Finally, in step S1050, the fastq data is analyzed based on the analysis module determined by the cluster scheduling system. After the splitting module splits the original bcl data into fastq data, the cluster scheduling system will automatically assign the analysis module. In this application, the cluster scheduling system selects and assigns the shortest and best analysis path according to the type of sample, that is, selects software based on a GPU cluster or software based on a CPU cluster. The analysis module will automatically analyze the fastq data obtained after the splitting.

[0031] According to this application, in order to minimize the risk of data leakage and unauthorized access, this application is always processed on the local server. According to this application, in order to promptly notify relevant personnel of the progress of the analysis and ensure the real-time and traceability of data processing, the results of each process can be pushed through corporate WeChat or email.

[0032] According to the present application, after the library building and mixing are completed at the experimental end, the first machine table containing the index sequence and the second machine table containing the sample information are sent as email attachments to the machine table mailbox. In order to identify the corresponding machine email, the email will be sent with a subject with the words "machine table". According to an embodiment of the present application, the system uses crontab to monitor the machine table mailbox every hour or other time intervals, and downloads the newly received high-throughput sequencing machine table of the machine table mailbox through IMAP or SMTP mail protocol. In order to ensure the validity of the machine table and avoid the situation of machine table errors caused by email withdrawal or modification, the system monitors whether the MD5 of the downloaded machine table changes at certain time intervals. According to an embodiment of the present application, if the MD5 of the downloaded machine table does not change within 10 minutes, it means that the downloaded machine table is valid.

[0033] According to the present application, it will be automatically checked whether the sample information in the newly downloaded first and second computer tables can correspond one to one. When the comparison between the first and second computer tables fails, the first and second computer tables that fail to compare will be sent to the notification mailbox for manual review. The first computer table that has been manually reviewed and corrected and the first computer table that successfully compares with the second computer table will enter the delivery procedure.

[0034] According to the present application, the analysis system will determine whether the first table has been delivered based on the chip number and sequencing platform information in the name of the first table. If the table has not been delivered, the corresponding delivery module will be triggered.

[0035] According to this application, different sequencing platforms correspond to different delivery modules. Specifically, the delivery module of the Illumina sequencing platform is nextseq_auto_main.pl; the delivery module of the MGI sequencing platform is MGI_auto_main.pl; the delivery module of the T7 sequencing platform is T7_auto_main.pl. After the delivery module delivers the first table to the corresponding sequencing platform, the system sends the delivery completion result to the notification mailbox to remind relevant personnel that the batch of data has been successfully delivered.

[0036] The delivery module can not only deliver the first table to the sequencing platform determined by the name of the table, but also trigger the system to monitor the sequencing process. Specifically, the system monitors the offline directory of each sequencing platform. The system determines whether the sequencing is completed by monitoring whether the Illumina sequencing platform generates the CopyComplete.txt marker file. When the marker file is detected, the system determines that the sequencing process is completed; the system determines whether the sequencing is completed by monitoring whether the number of files or the file size in the target folder of the MGI sequencing platform and the T7 sequencing platform changes. When the number of files or the file size in the target folder does not change, the system determines that the sequencing process is completed.

[0037] According to this application, after the sequencing process is completed, the system determines the splitting module based on the cluster scheduling system. The splitting module will use the index sequence information in the first machine table to split the original bcl data obtained by sequencing into fastq data for subsequent analysis of the data. Sequencing data from different sequencing platforms use different splitting modules. Specifically, the Illumina platform uses the bcl2fastq module for splitting; the MGI platform uses the splitbarcode module for splitting; and the DNBSEQ-T7 platform uses the T7_split_GPU module for splitting.

[0038] According to the present application, after sequencing is completed, the fastq data obtained after splitting will be standardizedly named. According to one embodiment of the present application, the data name may include information such as sample type, project number, sample collection date, serial number and product type. The sample type determines the source of the sample; the project number determines whether the test project is a small / medium / large panel, and the product type determines whether the product is DNA or RNA. For example, the fastq data name can be "T032111150001D", where T represents the sample type as cancer tissue / pleural effusion; 03 is the project number, representing that the test project is a large panel; 211115 represents the sample collection time; 0001 represents the serial number; D represents that the product type is DNA. The sample type, project number, sample collection date, and serial number can be determined when the sample is collected, and whether the product type is DNA or RNA needs to be determined and added after extraction.

[0039] According to the present application, different sequencing platforms correspond to different data splitting paths. The analysis system determines whether the data splitting is completed by detecting whether the number of files and the total size of the folders under the data splitting path change within a period of time. When the number of files and the total size of the folders no longer change, the analysis system considers that the data splitting is completed, and the analysis module then starts to analyze the data.

[0040] According to the present application, the analysis of the fastq data by the analysis module determined based on the cluster scheduling system includes: performing quality control on the data; aligning with a reference genome and removing duplicates; extracting locus depth from the aligned data; variant detection; copy number variation (CNV) detection; MSI detection; fusion detection; calculating the TMB value; and generating a result report.

[0041] The sample table may contain various types of test samples, mainly ctDNA samples and other samples such as FFPE. Among them, the analysis time required for ctDNA samples is relatively long. The GPU has parallel computing capabilities and a high computing speed, and is suitable for some computationally intensive tasks; while the CPU has a wider applicability and generality. In the present application, the software can be adapted to both types of computing nodes at the same time, and according to the specific computing tasks and resource configurations, flexibly select to use the GPU or the CPU for analysis to achieve the best computing efficiency and performance. According to the implementation manner of the present application, in order to improve the efficiency and accuracy of data analysis on the premise of cost savings, the system selects software based on different clusters for data analysis for different types of test samples.

[0042] According to the present application, for ctDNA samples, the system uses software tools suitable for the GPU for alignment, duplicate removal, and variant detection; for non-ctDNA samples, the system uses software tools suitable for the CPU for alignment, duplicate removal, and variant detection. Specifically, the analysis system uses the sla tool in the baseNumber software suitable for the GPU for alignment, and uses the slb tool in the baseNumber software suitable for the GPU for duplicate marking; for non-ctDNA samples, the analysis system uses the bwa-mem software suitable for the CPU for sequence alignment, and configures the picard software for duplicate marking. In the variant detection process of ctDNA, the slc tool in the baseNumber software suitable for the GPU is used for variant detection; for non-ctDNA samples, the analysis system uses software such as Vardict and Mutect2 suitable for the CPU for variant detection.

[0043] According to the present application, when the system monitors that the analysis of the fastq data by the analysis module is completed, it automatically performs result summarization. Specifically, each sample in the same batch will have an independent folder named after the fastq data name. After the analysis task of each sample is completed, an analysis completion marker file named <fastq data name.out> or <fastq data name.check> will be generated in its corresponding folder to mark that the sample has completed the analysis. The system scans all the sample folders in this batch every 1 minute. When the analysis completion marker files exist in all the folders, the system considers that this batch has completed the analysis, and the system automatically performs result summarization.

[0044] The present application also proposes an automated analysis system for high-throughput sequencing data, which can be implemented in the form of a mobile terminal, a personal computer (PC), a tablet computer, a server, etc. Figure 3 , which shows a structural schematic diagram of a system suitable for implementing the embodiments of the present application.

[0045] like Figure 3 As shown, the computer system includes one or more processors, a communication unit, etc. The one or more processors are, for example, one or more central processing units (CPUs) 301, and / or one or more graphics processing units (GPUs) 313, etc. The processors can perform various appropriate actions and processes according to executable instructions stored in a read-only memory (ROM) 302 or executable instructions loaded from a storage unit 308 to a random access memory (RAM) 303. The communication unit 312 may include, but is not limited to, a network card, and the network card may include, but is not limited to, an IB (Infiniband) network card.

[0046] The processor can communicate with ROM 302 and / or RAM 303 to execute the executable instructions, be connected to the communication unit 312 through the bus 304, and communicate with other target devices through the communication unit 312, so as to complete the operation corresponding to any one of the methods proposed in the embodiments of the present application, for example: regularly monitor the on-board table mailbox and download the high-throughput sequencing on-board table newly received by the on-board table mailbox, wherein the high-throughput sequencing on-board table includes a first on-board table containing an index sequence and a second on-board table containing sample information; compare the first on-board table and the second on-board table; based on the name of the first on-board table, deliver the first on-board table that has been successfully compared to the corresponding sequencing platform through a delivery module, and the delivery module triggers the monitoring of the sequencing process; split the original bcl data obtained after the sequencing is completed into fastq data based on the splitting module determined by the cluster scheduling system; analyze the fastq data based on the analysis module determined by the cluster scheduling system.

[0047] In addition, various programs and data required for the operation of the device may also be stored in RAM 303. CPU 301, ROM 302 and RAM 303 are connected to each other via bus 304. In the case of RAM 303, ROM 302 is an optional module. RAM 303 stores executable instructions, or writes executable instructions into ROM 302 during operation, and the executable instructions enable CPU 301 to perform operations corresponding to the above-mentioned communication method. Input / output interface (I / O interface) 305 is also connected to bus 304. Communication unit 312 can be integrated, or it can be configured to have multiple submodules (e.g., multiple IB network cards) and be linked on the bus.

[0048] The following components are connected to the I / O interface 305: an input section 306 including a keyboard, a mouse, etc.; an output section 307 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 308 including a hard disk, etc.; and a communication section 309 including a network interface card such as a LAN card, a modem, etc. The communication section 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to the I / O interface 305 as needed. A removable medium 311, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 310 as needed.

[0049] If necessary, Figure 3 The architecture shown is only an optional implementation method. In the specific practice, the above can be modified according to actual needs. Figure 3 The number and type of components can be selected, deleted, added or replaced; different functional components can be set up by separate settings or integrated settings, for example, the GPU and the CPU can be set up separately or the GPU can be integrated on the CPU, the communication unit 312 can be set up separately or integrated on the CPU or GPU, etc. These replaceable implementations all fall within the protection scope disclosed in the present invention.

[0050] In particular, according to this application, the reference process Figure 1 The described process can be implemented as a computer program product. For example, the present application proposes a computer program product, including computer-readable instructions, which implement the following operations when executed by a processor: regularly monitoring the on-board table mailbox and downloading the high-throughput sequencing on-board table newly received by the on-board table mailbox, wherein the high-throughput sequencing on-board table includes a first on-board table containing an index sequence and a second on-board table containing sample information; comparing the first on-board table and the second on-board table; based on the name of the first on-board table, delivering the first on-board table that has been successfully compared to the corresponding sequencing platform through a delivery module, wherein the delivery module triggers monitoring of the sequencing process; splitting the original bcl data obtained after sequencing into fastq data based on the splitting module determined by the cluster scheduling system; analyzing the fastq data based on the analysis module determined by the cluster scheduling system.

[0051] In such an embodiment, the computer program product can be downloaded and installed from the network through the communication unit 309, and / or read and installed from the removable medium 311. When the computer program product is executed by the central processing unit (CPU) 301, the above functions defined in the method of the present application are performed.

[0052] The technical solution of the present application may be implemented in many ways. For example, the technical solution of the present application may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The sequence of steps used to illustrate the method is provided only for the purpose of more clearly illustrating the technical solution. Unless otherwise specified, the method steps of the present application are not limited to the sequence specifically described above. In addition, in some embodiments, the present application may also be implemented as a storage medium for storing a computer program product.

[0053] The above description is only an implementation method of the present application and an explanation of the technical principles used. Those skilled in the art should understand that the scope of protection involved in the present application is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the technical concept. For example, the above features are replaced with the technical features with similar functions disclosed in this application (but not limited to) by each other.

Claims

1. An automated analysis method for high-throughput sequencing data, It is characterized in that The following steps are involved: Regularly monitor the machine list mailbox and download the high-throughput sequencing machine list newly received by the machine list mailbox, wherein the high-throughput sequencing machine list includes a first machine list containing an index sequence and a second machine list containing sample information; Comparing the first on-machine table with the second on-machine table; Based on the name of the first on-machine table, the first on-machine table that has been successfully aligned is delivered to the corresponding sequencing platform through a delivery module, and the delivery module triggers monitoring of the sequencing process; The original bcl data obtained after sequencing is split into fastq data based on the splitting module determined by the cluster scheduling system; The fastq data is analyzed based on the analysis module determined by the cluster scheduling system.

2. The analysis method according to claim 1, It is characterized in that Regularly monitoring the machine list mailbox and downloading the high-throughput sequencing machine list newly received in the machine list mailbox includes: Use crontab to regularly monitor the machine list mailbox, and download the high-throughput sequencing machine list newly received in the machine list mailbox based on the email protocol.

3. The analysis method according to claim 1, It is characterized in that The following steps are also included: In response to the first on-board table and the second on-board table being unsuccessfully compared, the first on-board table and the second on-board table that are unsuccessfully compared are sent to a notification mailbox for manual review.

4. The analysis method according to claim 1, It is characterized in that After delivering the successfully aligned first on-machine table to the corresponding sequencing platform through a delivery module based on the name of the first on-machine table, the method further includes: The result of delivery completion is sent to the notification mailbox.

5. The analysis method according to claim 1, It is characterized in that The sequencing platforms include an Illumina sequencing platform, an MGI sequencing platform and a T7 sequencing platform, wherein the delivery module of the Illumina sequencing platform is nextseq_auto_main.pl; the delivery module of the MGI sequencing platform is MGI_auto_main.pl; and the delivery module of the T7 sequencing platform is T7_auto_main.pl.

6. The analysis method according to claim 1, It is characterized in that The sequencing platforms include Illumina sequencing platform, MGI sequencing platform and T7 sequencing platform, wherein the delivery module triggers the monitoring of the sequencing process including: In response to the Illumina sequencing platform generating a CopyComplete.txt marking file, sequencing is completed; In response to the number of files or the size of files in the download directories of the MGI sequencing platform and the T7 sequencing platform no longer changing within a predetermined time, it is determined that the sequencing process has been completed.

7. The analysis method according to claim 1, It is characterized in that The analysis module determined by the cluster scheduling system triggered by the delivery module analyzes the fastq data, including: Performing quality control on the fastq data; The quality-controlled fastq data were compared with the reference genome and duplicates were removed; Perform at least one of site depth extraction, variation detection, copy number variation detection, MSI detection, fusion detection, and TMB value calculation on the deduplicated fastq data; and Generate a report of the results.

8. The analysis method according to claim 7, It is characterized in that The analysis module determined by the cluster scheduling system triggered by the delivery module analyzes the fastq data, including: GPU cluster-based software is used for comparison, duplication removal, and variant detection of ctDNA samples; For non-ctDNA samples, CPU cluster-based software is used for comparison, deduplication and variation detection.

9. The analysis method according to claim 1, It is characterized in that The following steps are also included: Record the results of each step to a log file.

10. An automated analysis system for high-throughput sequencing data, It is characterized in that include: a memory storing executable instructions; as well as One or more processors, the one or more processors communicating with the memory to execute the executable instructions to perform the following operations: Regularly monitor the machine list mailbox and download the high-throughput sequencing machine list newly received by the machine list mailbox, wherein the high-throughput sequencing machine list includes a first machine list containing an index sequence and a second machine list containing sample information; Comparing the first on-machine table with the second on-machine table; Based on the name of the first on-machine table, the first on-machine table that has been successfully aligned is delivered to the corresponding sequencing platform through a delivery module, and the delivery module triggers monitoring of the sequencing process; The original bcl data obtained after sequencing is split into fastq data based on the splitting module determined by the cluster scheduling system; The fastq data is analyzed based on the analysis module determined by the cluster scheduling system.

11. A computer-readable storage medium for automated analysis of high-throughput sequencing data, It is characterized in that The computer-readable storage medium stores executable instructions that can be executed by one or more processors to perform the following operations: Regularly monitor the machine list mailbox and download the high-throughput sequencing machine list newly received by the machine list mailbox, wherein the high-throughput sequencing machine list includes a first machine list containing an index sequence and a second machine list containing sample information; Comparing the first on-machine table with the second on-machine table; Based on the name of the first on-machine table, the first on-machine table that has been successfully aligned is delivered to the corresponding sequencing platform through a delivery module, and the delivery module triggers monitoring of the sequencing process; The original bcl data obtained after sequencing is split into fastq data based on the splitting module determined by the cluster scheduling system; The fastq data is analyzed based on the analysis module determined by the cluster scheduling system.

Images