Sequencing data analysis method, apparatus and program product

Abstract

This invention discloses a sequencing data analysis method, device, and program product. The method includes: acquiring a raw sequencing data file that indicates a raw sequencing data file; performing an integrated analysis of library construction and sequencing quality control based on the raw sequencing data file to determine the analysis result data, which includes library construction analysis result data and sequencing analysis result data; and performing correlation analysis based on the analysis result data to determine the analysis report data of the raw sequencing data file, so as to output the analysis report data.

Description

Technical Field

[0001] This invention relates to the field of gene technology, and in particular to a sequencing data analysis method, computing device, and computer program product. Background Technology

[0002] Current bioinformatics analysis software, such as the Galaxy open-source web-based bioinformatics analysis platform, customized web-based bioinformatics analysis systems, and lightweight sequencing data quality control web platforms like FastQ Screen Web and NGS QC Toolkit Web Edition, employ web-based graphical interfaces and integrate hundreds of bioinformatics analysis tools (including sequencing quality control, sequence alignment, variant detection, etc.). They allow users to build simple bioinformatics analysis workflows through drag-and-drop and click-based methods, with backend computation based on Linux server clusters. They utilize a layered web architecture, supporting a degree of private deployment and functional expansion, and can interface with some laboratory systems. However, the operational threshold remains extremely high. The tools are numerous and the parameters are complex; a single tool can have dozens of adjustable parameters. Users need solid bioinformatics expertise to rationally select tools, configure parameters, and interpret results. Non-bioinformatics personnel cannot complete the operation independently and still need guidance from bioinformatics professionals, failing to break down professional barriers. Furthermore, there is no dedicated library construction data evaluation module; library construction-related analyses require manually combining multiple tools and cumbersome parameter configuration, failing to achieve automated correlation analysis between library construction and sequencing data and the localization of anomalies. Summary of the Invention

[0003] To address the existing technical problems, this invention provides a sequencing data analysis method, computing device, and computer program product that can achieve bidirectional correlation analysis between library construction quality control indicators and sequencing quality control indicators, and automatically locate the source of quality anomalies.

[0004] In a first aspect, a sequencing data analysis method is provided, comprising: acquiring an original sequencing data file indicating an original sequencing data file; performing an integrated analysis of library construction and sequencing quality control based on the original sequencing data file to determine analysis result data, the analysis result data including library construction analysis result data and sequencing analysis result data; and performing correlation analysis based on the analysis result data to determine analysis report data of the original sequencing data file, so as to display the analysis report data on a web page.

[0005] In a second aspect, a computing device is provided, including a memory and a processor. The memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the sequencing data analysis method provided in the embodiments of this application.

[0006] Thirdly, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps of the sequencing data analysis method provided in the embodiments of this application.

[0007] Fourthly, a sequencing data analysis system is provided, the system comprising a computing device as described in the second aspect and a terminal device communicating with the computing device, the terminal device being used to provide a web-based user interface for interaction with the user.

[0008] This application achieves integrated analysis of library construction and sequencing quality control simultaneously on raw sequencing data files, obtaining library construction analysis results and sequencing analysis results. It is capable of performing integrated analysis of library construction and sequencing quality control at the same time. Through a core correlation algorithm, it realizes bidirectional correlation analysis between library construction quality control indicators and sequencing quality control indicators, automatically locates the source of quality anomalies, solves the fragmented defects of existing technology assessments, and realizes integrated closed-loop assessment. Attached Figure Description

[0009] Figure 1 This is a diagram illustrating the application environment of a sequencing data analysis method in one embodiment; Figure 2 This is a flowchart of a sequencing data analysis method in one embodiment; Figure 3 This is an architecture diagram of a sequencing data analysis system in one embodiment; Figure 4 This is a functional block diagram of a sequencing data analysis system in one embodiment; Figure 5 This is a schematic diagram of a sequencing data analysis device in one embodiment; Figure 6 This is a schematic diagram of the structure of a computing device in one embodiment. Detailed Implementation

[0010] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0011] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the specification of this invention is for the purpose of describing particular embodiments only and is not intended to limit the scope of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0012] In the following description, the expression “some embodiments” refers to a subset of all possible embodiments. However, it should be understood that “some embodiments” can be the same subset or different subsets of all possible embodiments and can be combined with each other without conflict.

[0013] Existing bioinformatics analysis tools often focus on technical implementation, lacking user-friendly interactive designs for non-bioinformatics personnel. Furthermore, they are mostly single-step evaluation tools, failing to achieve integrated analysis of library preparation and sequencing data, further increasing the barrier to entry for non-bioinformatics users. To address these technical pain points, meet the needs of non-bioinformatics researchers to independently and quickly complete library preparation and sequencing data quality assessments, overcome the constraints imposed by the shortage of professional bioinformatics personnel on experimental work, and improve overall experimental efficiency, developing a self-service bioinformatics analysis web software for non-bioinformatics personnel has become an urgent need in the industry.

[0014] Existing technologies all focus on the quality assessment of library preparation or sequencing data, attempting to lower the threshold for bioinformatics analysis, as follows: Existing technical solution 1: Lightweight sequencing data quality control web platform (represented by FastQ Screen Web and NGS QCToolkit Web version).

[0015] These solutions are web-based wrappers around traditional command-line quality control tools (FastQC, FastQ Screen, etc.), employing a B / S architecture. Users can upload sequencing data (FASTQ format) via a browser, while the background calls the command-line tools to complete basic quality control analysis, ultimately returning a quality control report to the user. Some platforms support simple parameter adjustments and provide basic visualization of quality control indicators. Focusing on single-stage quality assessment of sequencing data (primarily in FASTQ format), they can calculate core sequencing quality control indicators such as Q30 ratio, GC content distribution, adapter contamination rate, and sequence repetition rate, generating quality control reports that include basic charts and indicator values. These reports can be viewed online and easily exported.

[0016] Core shortcomings: Limited functionality, supporting only sequencing data quality control and completely ignoring library construction data import and evaluation, making it impossible to achieve integrated analysis of library construction and sequencing data. Researchers still need to process the two types of data separately, making it difficult to quickly pinpoint the root cause of quality abnormalities (such as the correlation between adapter contamination and adapter residue in library construction); lack of personalized adaptation capabilities, with fixed quality control thresholds that cannot be customized based on different sample types (such as rare samples and clinical samples), failing to meet the personalized needs of different laboratories; insufficient response efficiency, lacking batch parallel analysis and breakpoint resume functions, resulting in long waiting times for multi-sample evaluation, and still failing to address the pain point of "untimely evaluation".

[0017] Existing technical solution two: Galaxy open-source Web bioinformatics analysis platform, which adopts a web-based graphical interface and integrates hundreds of bioinformatics analysis tools (including sequencing quality control, sequence alignment, variant detection, etc.). It has a wide range of functions, including sequencing data quality control, library construction-related auxiliary analysis (requiring manual configuration of multiple tool combinations), and subsequent bioinformatics analysis; it supports multiple tool combinations and can realize relatively complex bioinformatics analysis workflows; it has user permission management, project management, and analysis history traceability functions; some versions support large file uploads and parallel analysis, and have a certain degree of scalability.

[0018] Core shortcomings: The operational threshold remains extremely high, with numerous tools and complex parameters. A single tool can have dozens of adjustable parameters, requiring users to possess solid bioinformatics expertise to rationally select tools, configure parameters, and interpret results. Non-bioinformatics personnel cannot complete the operation independently and still need guidance from bioinformatics professionals, failing to break down professional barriers. There is no dedicated library construction data evaluation module; library construction-related analyses require manually combining multiple tools and cumbersome parameter configuration, failing to achieve automated correlation analysis and anomaly root cause localization between library construction and sequencing data, and still exhibiting the problem of "fragmented evaluation." Response efficiency is insufficient; resource scheduling uses a static strategy (FIFO queue), resulting in long task queuing times during large-scale sample analysis and sluggish web interface response, hindering rapid evaluation. Reports lack experimental guidance, still primarily displaying bioinformatics indicators without simplified interpretations or experimental improvement suggestions, making them unsuitable for direct experimental optimization by researchers. There is no template-based startup function; each analysis requires rebuilding the workflow, making operation cumbersome and further reducing evaluation efficiency, failing to address the core pain point of untimely evaluation.

[0019] Existing technical solution 3: Institutional customized web-based bioinformatics analysis system (such as self-built platforms by universities and hospitals, represented by the multi-omics data bioinformatics analysis and visualization system of Xiangya School of Medicine, Central South University).

[0020] Developed based on a web-layered architecture, the backend uses technologies such as Django and Nginx, while the frontend uses mainstream web frameworks. It supports browser access and private deployment on the intranet, integrates specific bioinformatics analysis tools, and is mainly adapted to the experimental needs of its own institution. It can realize functions such as sequencing data preprocessing, quality control, and simple analysis, and supports a certain degree of high concurrency processing.

[0021] Key shortcomings: The operation threshold is relatively high, requiring users to have a certain level of bioinformatics knowledge to complete the quality control workflow configuration and result interpretation, making it impossible for non-bioinformatics personnel to operate independently; it lacks integrated evaluation functions for library construction and sequencing data, and its library construction-related analysis functions are weak, making it impossible to pinpoint the root cause of quality anomalies; the report interpretation is technically obscure, lacking simplified explanations and experimental improvement suggestions tailored to experimental scenarios, failing to guide experimenters in optimizing operations; the functions are rigid and lack scalability, unable to be updated in a timely manner according to the development of sequencing technology and changes in experimental needs, and it lacks batch parallel analysis and breakpoint resume functions, still resulting in untimely evaluation issues.

[0022] None of the three existing technical solutions mentioned above have solved the core pain points that this invention aims to address: non-bioinformatics personnel cannot conduct independent evaluations, bioinformatics personnel do not conduct timely evaluations, and experimental efficiency is limited. They generally suffer from defects such as high operational thresholds, inability to achieve integrated evaluation of library construction and sequencing, lack of experimental guidance in reports, low data processing efficiency, insufficient safety and compliance, and poor adaptability.

[0023] like Figure 1 As shown, Figure 1 This diagram illustrates the application environment of a sequencing data analysis method in one embodiment. The environment includes a computing device 20 and a terminal device 10. The terminal device 10 is used for displaying and interacting with the sequencing data analysis method on a web page. The computing device 20 executes the sequencing data analysis method. After logging in via the web page, the terminal device 10 can communicate with the computing device 20 to implement the sequencing data analysis method. The computing device 20 analyzes the raw sequencing data file uploaded by the user, determines the analysis report data, transmits it to the terminal device 10, and displays the analysis report data on the web page's user interface. The sequencing data analysis system supports batch uploading, fragmented uploading, and resume upload. In some embodiments, the computing device 20 can be directly connected to a gene sequencer to directly obtain the raw sequencing data file output by the gene sequencer. The computing device 20 performs evaluation analysis based on the raw sequencing data file to determine the quality report data of the raw sequencing data file.

[0024] Please see Figure 2 This is a flowchart of a sequencing data analysis method provided in an embodiment of this application. The sequencing data analysis method is applied in a computing device and includes the following steps: S11. Obtain the raw sequencing data file.

[0025] In this embodiment, a file upload interface is provided on the web-based user interface, allowing users to upload raw sequencing data files or data containing the path to the raw sequencing data files. The raw sequencing data files are in FASTQ format. Users can also upload a sample information table containing the FASTQ path, from which the raw sequencing data files can be obtained. The sample information table includes at least one of the following: sample name, analysis type, and sample FASTQ path.

[0026] Raw sequencing data files serve as the foundation for integrated analysis, including subsequent library construction and quality control. Sample file information includes at least one of the following: sample name, analysis type, sample FASTQ path, or raw sequencing data in FASTQ format. Sample name: A name used to uniquely identify each sample. Multiple samples in the same analysis task will have different sample names. Analysis type: Specifies the specific analysis task to be performed on this sample. Sample FASTQ path: Represents the complete path of the corresponding sample's FASTQ format sequencing data file stored on a local server or in the cloud. Regardless of whether the above-mentioned raw sequencing data files are used to provide key identification information, sequencing data, or data paths, it ensures that the raw sequencing data can be successfully obtained for subsequent analysis, providing a reliable foundation for subsequent data analysis and result interpretation.

[0027] The user interface provides file controls, allowing users to upload raw sequencing data files or input relevant file descriptions. The system also supports fragmented uploads; for larger raw sequencing data files, fragmented uploads and chunked reading techniques are supported during the upload process to improve upload and processing speed.

[0028] S12. Based on the original sequencing data file, perform integrated analysis of library construction and sequencing quality control to determine the analysis results data, which include library construction analysis results data and sequencing analysis results data.

[0029] In this embodiment, the integrated analysis of library construction and sequencing quality control involves simultaneously performing library construction quality control analysis and sequencing quality control analysis. Library construction analysis evaluates the quality of the constructed sequencing library based on multiple library construction quality control indicators. This analysis determines the evaluation data for each indicator and provides a comprehensive assessment of library construction quality. These indicators include, but are not limited to, library concentration, fragment size distribution, adapter residue ratio, amplification cycle number, insert fragment, capture rate, PCR repeatability, and GC preference. Data evaluation methods are used to quantify each library construction quality control indicator, yielding evaluation data for each indicator. This evaluation data includes, but is not limited to, at least one of the following: scores and grades. Grades include, but are not limited to, at least one of the following: acceptable, warning, unacceptable, etc. It is understood that grades can be increased or decreased.

[0030] Sequencing quality control analysis is the quality inspection of raw sequencing data. It is based on sequencing quality control indicators, which include, but are not limited to, at least one of the following: Q30 ratio, GC content distribution, sequence repetition rate, adapter contamination rate, base quality distribution, number of reads in the raw sequencing data file, Q20, number of valid reads, number of bases, number of low-quality reads, adapter-removed reads, total number of reads, insert fragments, sequencing coverage, mutations, alignment rate, number of variant sites, and capture rate. Data evaluation methods are used to quantitatively evaluate each sequencing quality control indicator, obtaining evaluation data for each indicator. Evaluation data includes, but is not limited to, at least one of the following: scores and grades. Grades include, but are not limited to, at least one of the following: acceptable, warning, unacceptable, etc. It is understood that grades can be increased or decreased.

[0031] When a user triggers the start button for the integrated library construction and sequencing quality control analysis on the web-based user interface provided by the terminal device, the terminal device sends the trigger data for the integrated analysis to the computing device. Upon receiving the trigger data, the computing device simultaneously executes the integrated analysis of library construction and sequencing quality control. The integrated analysis of library construction and sequencing quality control means that both library construction analysis and sequencing quality control analysis are performed concurrently.

[0032] S13. Based on the analysis results, perform correlation analysis to determine the analysis report data of the original sequencing data file, so as to output the analysis report data.

[0033] In this embodiment, the analysis report data is a dataset determined based on the original sequencing data files, after integrated analysis and correlation analysis of library construction and sequencing quality control, and is ultimately used for subsequent result interpretation and analysis. Through a core correlation algorithm, relevant indicators in the library construction analysis results and sequencing analysis results are jointly analyzed to establish the correlation between the two types of data, accurately locating the source and type of data anomalies. On the one hand, correlation analysis can determine whether sequencing data anomalies (such as low base quality or excessively high sequencing repetition rate) are caused by library construction quality defects (such as abnormal library fragments or severe adapter contamination), thus clarifying the root cause of the anomaly.

[0034] After analysis, the system automatically generates a comprehensive, easy-to-understand report with experimental guidance, eliminating bioinformatics jargon and explaining the meaning of indicators in experimental language to obtain the final analysis report data. It supports online report preview and export to multiple formats including PDF, Word, and Excel, as well as report sharing and archiving, facilitating researchers' retention, use, and future reference.

[0035] Optionally, after determining the evaluation data for each library construction quality control indicator and each sequencing quality control indicator, the overall quality of library construction and sequencing can be comprehensively judged, and a final quality level can be given.

[0036] After the computing device determines the analysis report data of the raw sequencing data file, it can display the analysis report data on the user interface of the terminal device, or provide a download interface on the user interface to download the analysis report data.

[0037] It is understood that the sequencing data analysis method provided in one embodiment of this application can be implemented based on a webpage, a client, or a mini-program.

[0038] In the above embodiments, by simultaneously performing integrated analysis of library construction and sequencing quality control on the original sequencing data file, library construction analysis results and sequencing analysis results are obtained, enabling simultaneous integrated analysis of library construction and sequencing quality control; through the core correlation algorithm, bidirectional correlation analysis between library construction quality control indicators and sequencing quality control indicators is achieved, automatically locating the source of quality anomalies, solving the fragmented defects of existing technology assessments, and realizing integrated closed-loop assessment.

[0039] In some embodiments, based on the raw sequencing data file, an integrated analysis of library construction and sequencing quality control is performed to determine the analysis results data, including: Selected target analysis tasks are obtained based on the user interface; Based on the original sequencing data files and the target analysis task, perform integrated analysis of library construction and sequencing quality control to determine the library construction analysis results and sequencing analysis results corresponding to the target analysis task.

[0040] In this embodiment, the user interface on the web page of the terminal device provides an analysis task selection control. Users can select the desired analysis task from a variety of analysis modules provided by the user interface, based on their specific needs. Each analysis task corresponds to one analysis module. The sequencing data analysis system has multiple built-in analysis modules, including but not limited to: Quality Control (QC), Sequence Alignment, Genome in a Bottle (GIAB), Non-Invasive Prenatal Testing (NIPT), Variant Allele Frequency (VAF), and Methylation analysis modules. The user interface allows users to select and access the analysis functions and applicable scenarios for each task, adapting to different experimental needs. It also supports user-defined analysis parameters for each module, simplifying the operation process and improving evaluation efficiency. For example, if a user needs to perform quality control, alignment, and genomic position-based base statistics on Ecoli sequencing WGS data, specifying the number of 2 / 3 / 4 alleles within specified coverage depth and mutation frequency thresholds, and assembling and evaluating the sequencing data, they can select the VAF analysis module. If alignment is required, they can select Alignment. The Alignment process will first perform quality control, and then perform alignment. The user selects the analysis module according to their actual needs and submits the task. The target analysis task represents the analysis task corresponding to the selected analysis module. After selecting the target analysis task, the integrated analysis of library preparation and sequencing quality control corresponding to that target analysis task can be started. After the terminal device obtains the target task, it sends it to the computing device.

[0041] Optionally, users can access the system through a browser, entering their username and password to log in; after logging in, they enter the sequencing data analysis system. Users can select data analysis function modules. The data analysis function module workbench provides users with a unified operation entry point, facilitating centralized management of data, tasks, and reports. It implements functions such as user registration / login and role assignment (e.g., super administrator / group administrator / regular user / browser); it supports fine-grained access permission settings by module, role, and parameter, ensuring that users with different roles can only access resources within their corresponding permission scope, guaranteeing data security and operational compliance.

[0042] Optionally, when starting the target analysis task, the format and integrity of the raw sequencing data will be checked. Only after the check passes will the integrated analysis of library construction and sequencing quality control be performed.

[0043] In the above embodiments, each analysis module is provided to the user for selection through the user interface, which facilitates the unified scheduling of each module by the system. There is no need for manual switching and configuration of each module's parameters, reducing manual operation steps and operation time. It also reduces compatibility issues when multiple modules run independently, reduces process retries and data rework caused by module incompatibility, and improves the smoothness and efficiency of the overall sequencing data analysis process.

[0044] In some embodiments, the analysis report data includes the causes of abnormal sequencing quality control indicators. Based on the analysis results data, a correlation analysis is performed to determine the analysis report data of the original sequencing data file, including: For a target analysis task, abnormal sequencing quality control indicators are determined based on the evaluation data of each sequencing quality control indicator in the sequencing analysis results data corresponding to the target analysis task. Based on the abnormal sequencing quality control indicators and the pre-established set of indicator causal relationships, the target cause indicators corresponding to the abnormal sequencing quality control indicators are determined. Each causal relationship in the set of indicator causal relationships indicates that the abnormality of the cause indicator is the cause of the abnormality of the sequencing result indicator. The cause indicator indicates either the library construction quality control indicator or the sequencing quality control indicator, and the sequencing result indicator indicates a sequencing quality control indicator that is different from the cause indicator. Based on the evaluation data of the target cause indicators, determine whether the target cause indicators are the cause of abnormal sequencing quality control indicators.

[0045] In this embodiment, abnormal sequencing quality control indicators (QCSIs) refer to sequencing QCSIs that indicate abnormalities in the evaluation data. Sequencing QCSIs are metrics used to evaluate raw sequencing data and reflect the quality of a specific dimension of sequencing. For example, sequencing QCSIs with a warning level or a score below the minimum sequencing QCSI score are identified as abnormal sequencing QCSIs. An abnormality in a sequencing QCSI could be caused by an abnormality in library preparation QCSIs or by an abnormality in a sequencing QCSI during the sequencing process.

[0046] The indicator causal relationship set is a pre-constructed and stored set used to characterize the abnormal transmission relationships between various indicators in the field of sequencing quality control. The indicator causal relationship set contains multiple causal relationships. Each causal relationship corresponds to a set of causal associations between a causal indicator and a sequencing result indicator. When a causal indicator is abnormal, it will directly or indirectly lead to an abnormality in a certain sequencing result indicator. For example: Library construction quality control indicator: high adapter residue → causing sequencing quality control indicator: increased sequencing adapter contamination; Library construction quality control indicator: abnormal fragment distribution → library construction quality control indicator: GC content deviation; Library construction quality control indicator: excessive amplification cycles → causing sequencing quality control indicator: high sequence repetition rate; Sequencing quality control indicator: sequencing optical repeatability → causing sequencing quality control indicator: high sequence repetition rate.

[0047] In the causal relationship set of indicators, sequencing result indicators are sequencing quality control indicators. Causal indicators may be library construction quality control indicators or sequencing quality control indicators. For one sequencing result indicator, there may be one or more causal indicators. Sequencing result indicators that are identical to abnormal quality control indicators are identified from the causal relationship set, and the causal indicators in the corresponding causal relationships of the sequencing result indicators are taken as target causal indicators.

[0048] In the above embodiments, by pre-establishing and maintaining a set of causal relationships for indicators, the system can directly match and obtain the target cause indicators that may cause the abnormality from the set after detecting abnormal sequencing quality control indicators, thereby achieving rapid root cause localization of sequencing quality control abnormalities, avoiding blindly traversing all indicators, and improving analysis efficiency and localization accuracy.

[0049] Optionally, based on the evaluation data of the target cause indicator, determining whether the target cause indicator is the cause of the abnormal sequencing quality control indicator includes at least one of the following: When there is only one target cause indicator, and the evaluation data of the target cause indicator indicates that the target cause indicator is abnormal, then the target cause indicator is determined to be the cause of the abnormal sequencing quality control indicator; or When there are multiple target cause indicators, obtain the characteristic data of the associated indicators of the target cause indicators; determine the target cause indicators corresponding to the characteristic data of the associated indicators included in the analysis results as the indicators that cause the abnormal sequencing quality control indicators.

[0050] In this embodiment, there is one target cause indicator, and when the evaluation data of the target cause indicator indicates that the target cause indicator is abnormal, it means that the target cause indicator has caused the abnormal quality control indicator to be abnormal.

[0051] When there are multiple target cause indicators, it is necessary to investigate based on the characteristic data of the related indicators to determine the abnormal indicator that is causing the abnormal quality control indicator. The characteristic data of the related indicators are auxiliary characteristic data that have a strong correlation with the corresponding target cause indicator and can be used to corroborate or verify whether the target cause indicator is truly abnormal.

[0052] For example, a high sequence repetition rate in Alignment quality control analysis could be due to excessive amplification cycles during library preparation or optical repeats during sequencing. Characteristics associated with excessive amplification cycles include concentrated library fragments, shorter insert fragment distribution, no significant shift in GC distribution, and normal sequencing image quality. Characteristics associated with optical repeats include excessively high sequencing cluster density, overlapping image signals, and repetitive sequences concentrated at the sequencing ends. If the analysis results show concentrated library fragments, shorter insert fragment distribution, no significant shift in GC distribution, and normal sequencing image quality, the target cause is likely excessive library preparation and amplification cycles. If the analysis results show excessively high sequencing cluster density, overlapping image signals, and repetitive sequences concentrated at the sequencing ends, the target cause is likely optical repeats.

[0053] The causal relationships involved may differ depending on the analytical task. For example, in GIAB analysis, low recall reflects insufficient sequencing depth, poor coverage uniformity, low capture rate, large GC bias, poor alignment quality, or poor library fragmentation; low precision reflects errors in library construction PCR amplification, high library repetition rate, adapter residue, or a high sequencing error rate. Therefore, performing different analytical tasks for different scenario modules allows for a comprehensive analysis of library construction and sequencing quality.

[0054] Since the target cause indicator may be a library construction quality control indicator or a sequencing quality control indicator, different analysis results data need to be used for comparison depending on the target cause indicator.

[0055] Optionally, the target cause indicators corresponding to the correlation indicator feature data included in the analysis results data are identified as the indicators causing the abnormal sequencing quality control indicators, including at least one of the following: When the target cause indicator is a library construction quality control indicator, the library construction analysis results data are compared with the associated indicator feature data of each target cause indicator. The target cause indicator corresponding to the associated indicator feature data included in the library construction analysis results data is determined as the indicator that causes the abnormal sequencing quality control indicator. When the target cause indicator is a sequencing quality control indicator, the sequencing analysis results data are compared with the associated indicator feature data of each target cause indicator. The target cause indicator corresponding to the associated indicator feature data included in the sequencing analysis results data is determined as the indicator that causes the abnormal sequencing quality control indicator.

[0056] In the above embodiments, by comparing and verifying the associated indicator feature data corresponding to each target cause indicator with the analysis result data, the target cause indicators that are consistent with the abnormal performance and can reasonably explain the abnormal sequencing quality control indicators are selected. In this way, the indicator that truly causes the abnormality is accurately located from multiple target cause indicators, avoiding misjudgment in the scenario of multiple causes and one effect, and improving the accuracy and reliability of abnormal cause location.

[0057] In some embodiments, there may be multiple target analysis tasks, and the method further includes: Based on the original sequencing data file, library preparation analysis and sequencing quality control analysis corresponding to each target analysis task are performed in parallel to determine the analysis result data corresponding to each target analysis task.

[0058] In this embodiment, each target analysis task is executed independently, with no data dependencies or execution order constraints between tasks. During the analysis of a target analysis task, one or more of the currently available computing resources, storage resources, and sequencing data processing resources of the system are monitored in real time. When the system has remaining available resources, it is allowed to perform parallel analysis of another target analysis task while executing the target analysis task, thereby realizing multi-task concurrent processing and improving the overall analysis efficiency and resource utilization of the system.

[0059] If the current system resources are insufficient, the target analysis task to be executed will be set to a queued state, and the system resource release status will be continuously monitored. When the existing analysis task is completed, the system resources are released and restored to a sufficient state, the corresponding target analysis task will be automatically selected from the queued tasks, switched from the queued state to the analysis state and started to execute. No manual intervention is required, realizing the automated scheduling and orderly execution of tasks.

[0060] In the above embodiments, multiple tasks can be processed independently and in parallel, avoiding mutual blocking between tasks; they can automatically run concurrently when resources are sufficient and automatically queue up when resources are insufficient; and queued tasks can be automatically woken up after resources are released, thereby improving the system's automation level and processing throughput.

[0061] In some embodiments, the analysis report data further includes: improvement suggestions corresponding to abnormal sequencing quality control indicators, and the method further includes: After identifying the causes of abnormal sequencing quality control indicators, improvement suggestion data corresponding to the abnormal sequencing quality control indicators are obtained from a pre-established suggestion association library.

[0062] In this embodiment, after determining the target cause index that causes the abnormal sequencing quality control index, improvement suggestion data corresponding to the abnormal sequencing quality control index is matched and obtained from a pre-established and stored suggestion association library based on the abnormal sequencing quality control index and / or the corresponding target cause index.

[0063] The association library is recommended to pre-store multiple sets of associations. Each set of associations is used to link at least one of the following: anomaly type, anomaly sequencing quality control indicators, and target cause indicators, with corresponding improvement suggestion data. Improvement suggestion data includes, but is not limited to, one or more of the following: library construction process adjustment suggestions, reagent replacement suggestions, instrument parameter optimization suggestions, sample processing method improvement suggestions, quality control threshold correction suggestions, and experimental operation specification reminders.

[0064] In the above embodiments, by calling and outputting improvement suggestion data, targeted and executable anomaly handling solutions can be directly provided to operators without the need for manual review of materials or reliance on experience. This achieves fully automated quality control analysis from anomaly identification and root cause localization to solution output.

[0065] Optionally, the method also includes: The task monitoring interface controls at least one of the following during the execution of each target analysis task: task pause, task continuation, task termination, task resume from breakpoint, and task priority adjustment.

[0066] In this embodiment, a task monitoring interface is provided at the system front end, that is, the task monitoring interface is displayed through the web interface of the terminal device. The task monitoring interface receives control commands input by the user to control and manage the execution status of each target analysis task in real time. The control operations include at least one of the following: task pause, task resume, task termination, task breakpoint resume, and task priority adjustment.

[0067] The task pause function temporarily halts the execution of the current target analysis task without exiting the task or clearing intermediate data, putting the task into a paused state and releasing the corresponding computing resources for other tasks. The task resume function resumes the execution of a paused target analysis task from its current progress, without restarting from the beginning. The task termination function directly stops the execution of the target analysis task and, depending on the configuration, releases the system resources and intermediate data occupied by the task. The task breakpoint resume function records and saves the task execution breakpoint information after the target analysis task is abnormally interrupted, manually paused, or the system restarts, allowing execution to resume directly from the breakpoint upon resumption, avoiding duplicate execution of completed steps. The task priority adjustment function modifies the scheduling priority of different target analysis tasks according to user needs or task urgency, ensuring that high-priority tasks are allocated system resources first and enter the analysis state first.

[0068] In the above embodiments, through the task monitoring interface, users can visualize, centrally and finely manage multiple target analysis tasks, and realize flexible intervention and dynamic scheduling of the sequencing analysis process.

[0069] like Figure 3 As shown, Figure 3 This is an architecture diagram of a sequencing data analysis system in one embodiment. The system adopts a privately deployed B / S architecture on an intranet, divided into a user layer, a front-end interaction layer, an application service layer, a business logic layer, and a data storage and security layer. Each layer has clearly defined responsibilities and low coupling, allowing for independent development and maintenance. This architecture integrates the distributed task queue Celery with the supercomputing / high-performance computing cluster job scheduling system (Simple Linux Utility for Resource Management, Slurm) to solve the pain points of traditional bioinformatics software, such as cumbersome interaction, data insecurity, and inefficient scheduling. It efficiently completes the entire bioinformatics analysis process, ensures system stability and scalability, and is suitable for various web-based bioinformatics analysis scenarios, demonstrating high practicality and innovation. Detailed descriptions of each layer are as follows: User Layer: Serving as the human-computer interaction entry point, users access the system, authenticate their identity (including account and password verification, and permission checks), and input operation commands through various mainstream browsers. It supports compatibility across multiple terminals such as computers and tablets. This layer eliminates the need for users to install additional clients, effectively lowering the barrier to entry. Simultaneously, a strict identity verification mechanism clearly defines user permission boundaries, ensuring operational security and providing fundamental entry support for the orderly operation of all levels of the system.

[0070] Front-end interaction layer: This layer receives user commands and primarily implements page visualization, analysis task submission, bioinformatics data upload, analysis result visualization, and operation feedback. It supports batch uploads of common bioinformatics data formats such as FASTQ and BED, and performs preliminary format validation on uploaded data to prevent invalid data from entering subsequent analysis processes. Analysis results can be displayed in tables and graphs for quick preview and interpretation, while real-time feedback on data upload and task analysis progress significantly enhances the user experience.

[0071] Application Service Layer: Serving as the intermediary between the front-end interaction layer and the business logic layer, this layer is responsible for unified interface scheduling, hierarchical user permission control, and full-process management of bioinformatics analysis tasks. It collaborates with the Celery and Slurm task scheduling systems to achieve reasonable task allocation, asynchronous processing, and end-to-end control. This layer parses and standardizes user commands transmitted from the front-end, assigns different operation permissions based on user roles, and simultaneously coordinates with Celery to process asynchronous tasks and with Slurm to schedule and provide feedback on task execution status, synchronizing this information to the front-end in real time to ensure the coordinated and orderly operation of all layers of the system.

[0072] Business Logic Layer: As the core functional layer of the system, it is responsible for the flexible orchestration of bioinformatics analysis workflows, the invocation and coordination of commonly used bioinformatics analysis tools (such as FastQC, BWA, STAR, DESeq2, etc.), and the integration of Slurm task scheduling functions to achieve efficient distribution, parallel execution, and progress control of bioinformatics analysis tasks. It also handles the parsing and standardization of intermediate and final results during the analysis process. Based on different bioinformatics analysis needs, various analysis tools can be combined to form standardized workflows, effectively improving analysis efficiency and ensuring accurate and standardized analysis results.

[0073] Data and Storage Layer: As the foundation for stable system operation, this layer is responsible for the categorized storage and secure management of all system data. It employs a hierarchical storage architecture: raw bioinformatics data, intermediate analysis files, and final results are stored in object storage to ensure efficient reading and writing of large volumes of data; structured data such as user information, permission configurations, and task records are stored in a relational database for easy querying and statistics. Simultaneously, through data encryption, regular backups, and access control mechanisms, it effectively prevents data leakage, loss, or tampering, ensuring data integrity, security, and traceability.

[0074] like Figure 4 As shown, Figure 4 This is a functional block diagram of a sequencing data analysis system in one embodiment.

[0075] This system mainly consists of five core functional modules, which work together to complete the entire process of automated quality assessment, from data access and analysis to report output. The specific functions are as follows: The User and Permission Management module enables user registration / login and role assignment (such as super administrator / group administrator / regular user / browser); it supports fine-grained access permission settings by module, role, and parameter to ensure that users with different roles can only access resources within their corresponding permission scope, thus guaranteeing data security and operational compliance.

[0076] The analysis task creation module includes multiple built-in analysis functions, providing selection options and descriptions of applicable scenarios to adapt to different experimental needs. It supports user-defined analysis parameters, simplifying the operation process and improving evaluation efficiency. It supports batch import of library construction information (Excel / CSV format) and also supports FASTQ large file fragment upload and verification. It features automatic identification of paired data, sample ID consistency verification, and MD5 verification of file integrity, ensuring the accuracy, integrity, and consistency of imported data and preventing data errors from affecting analysis results. FASTQ large files refer to files with a storage size less than a preset storage threshold, for example, a preset storage threshold of 2GB. Other large files exceeding the preset storage threshold are uploaded to the server via other tools or directly from the sequencer to the server.

[0077] Integrated Analysis Execution Module: As the core functional module of the system, it is responsible for the unified execution of integrated analysis of library construction and sequencing quality control. Through the core correlation algorithm, it realizes bidirectional correlation analysis between library construction quality control indicators and sequencing quality control indicators, automatically locates the source of quality anomalies, solves the defects of fragmented evaluation in existing technologies, and realizes integrated closed-loop evaluation.

[0078] Task monitoring and management module: Provides real-time progress bars and task status display (such as queued / analyzing / completed / failed) on the web interface, making it convenient for experimenters to keep track of task progress in real time; supports tasks to pause, resume, terminate, resume from breakpoint, and adjust priority, allowing for flexible management of tasks according to experimental needs and avoiding unnecessary waiting.

[0079] Report generation and download module: After analysis, it automatically generates a comprehensive, easy-to-understand report with experimental guidance suggestions, eliminating bioinformatics jargon and explaining the meaning of indicators in experimental language; it supports online report preview, and also supports exporting to multiple formats such as PDF / Word / Excel, as well as report sharing and archiving, making it convenient for researchers to retain, use, and follow up on. In this system, users log in → enter the data analysis workbench → select an analysis task → configure parameters and upload raw sequencing data files or files containing sequencing data paths → the raw sequencing data files are automatically verified → integrated analysis is started → the task is monitored in real time → automatic reminders are given upon completion of the analysis → report preview and export for archiving, forming a complete closed loop. The entire process is simple, efficient, and highly guided, achieving the core goal of enabling non-bioinformatics personnel to complete quality assessments independently and quickly.

[0080] In one or more of the above embodiments, this application has one or more of the following features: This system integrates library preparation and sequencing data evaluation functions into a web-based system, enabling joint analysis and anomaly root cause localization for both types of data. It addresses the limitations of traditional tools that can only evaluate sequencing data and cannot form a closed loop, thus improving the comprehensiveness and accuracy of the evaluation. It achieves integrated evaluation of both library preparation and sequencing data from two dimensions, automatically locating the root cause of anomalies and overcoming the fragmented evaluation and inability to trace the causes of anomalies in existing technologies, thereby improving the comprehensiveness and accuracy of quality assessment.

[0081] It features built-in multi-scenario standard analysis modules to adapt to the personalized needs of different laboratories and sample types. Adopting a front-end / back-end separation and API-standardized architecture, it facilitates system expansion and integration with LIMS and laboratory management systems, enhancing system versatility and adaptability. Its template-based design and scalable architecture adapt to the personalized needs of different sample types and laboratories, supporting integration with LIMS systems, and enabling widespread application in various clinical and research laboratories.

[0082] The quality control report is simplified and optimized by removing bioinformatics jargon and explaining the meaning of indicators and the impact of anomalies in experimental language. An anomaly suggestion association library is built in, with each anomaly corresponding to a specific possible cause and a feasible experimental improvement plan, so that the evaluation results can be directly transformed into the basis for experimental optimization. The simplified report and the feasible experimental improvement suggestions make the evaluation results not just a simple display of indicators, but a basis that can be directly used for experimental optimization, thereby improving the practical application value of the evaluation results and reducing experimental costs.

[0083] For large FASTQ files, direct access to internal network server paths or fragmented upload and block reading techniques are employed to improve upload and processing speed. Simultaneously, multi-threaded parallel computing technology optimizes algorithm efficiency, reducing the analysis cycle from days to minutes to hours, meeting rapid evaluation needs. The application of integrated analysis workflows, batch parallel processing, and breakpoint resume capabilities further shortens the evaluation cycle from days to minutes to hours, breaking inefficient cycles and improving the overall efficiency of library preparation, sequencing, and subsequent experimental stages.

[0084] The entire process is accessed via a browser, requiring no client installation or command-line skills. A guided interactive workflow allows lab personnel to simply select an analysis task, configure parameters, and start the analysis, completely breaking down bioinformatics barriers. It enables independent operation by non-bioinformatics personnel, eliminating reliance on specialized bioinformatics staff and requiring no prior knowledge of bioinformatics or command-line operations. Suitable for all lab staff, it addresses the industry pain point of a shortage of bioinformatics talent.

[0085] Employing a private intranet deployment architecture, data is stored entirely on the user's local server / internal network, without being uploaded to any third-party platform. It features AES-256 data encryption, fine-grained access control, and automatic auditing of operation logs, meeting the security and compliance requirements for sensitive research and clinical data. These technologies, including private intranet deployment, end-to-end data encryption, and fine-grained access control, ensure that sensitive data is not leaked, meeting the security and compliance requirements for research and clinical data and protecting data privacy.

[0086] In another aspect, this application provides a computer program product, including a computer program that, when executed by a processor, implements the sequencing data analysis method of any embodiment of this application, or implements the sequencing data analysis method of any embodiment of this application when executed.

[0087] In the computer program product, the optional implementation form of the program module architecture of the computer program that implements the sequencing data analysis method or each step of the sequencing data analysis method can be a sequencing data analysis device.

[0088] Please see Figure 5One embodiment of this application provides a sequencing data analysis device, including: an acquisition module 51 for acquiring raw sequencing data files; a determination module 52 for performing integrated analysis of library construction and sequencing quality control based on the raw sequencing data files, and determining analysis result data, the analysis result data including library construction analysis result data and sequencing analysis result data; the determination module 52 is also used to perform correlation analysis based on the analysis result data, and determine analysis report data of the raw sequencing data files, so as to display the analysis report data on a web page.

[0089] Optionally, the determining module 52 is also used for: Selected target analysis tasks are obtained based on the user interface; Based on the original sequencing data files and the target analysis task, perform integrated analysis of library construction and sequencing quality control to determine the library construction analysis results and sequencing analysis results corresponding to the target analysis task.

[0090] Optionally, the determining module 52 is also used for: For a target analysis task, abnormal sequencing quality control indicators are determined based on the evaluation data of each sequencing quality control indicator in the sequencing analysis results data corresponding to the target analysis task. Based on the abnormal sequencing quality control indicators and the pre-established set of indicator causal relationships, the target cause indicators corresponding to the abnormal sequencing quality control indicators are determined. Each causal relationship in the set of indicator causal relationships indicates that the abnormality of the cause indicator is the cause of the abnormality of the sequencing result indicator. The cause indicator indicates either the library construction quality control indicator or the sequencing quality control indicator, and the sequencing result indicator indicates a sequencing quality control indicator that is different from the cause indicator. Based on the evaluation data of the target cause indicators, determine whether the target cause indicators are the cause of abnormal sequencing quality control indicators.

[0091] Optionally, the determining module 52 is also used for: When there is only one target cause indicator, and the evaluation data of the target cause indicator indicates that the target cause indicator is abnormal, then the target cause indicator is determined to be the cause of the abnormal sequencing quality control indicator; or When there are multiple target cause indicators, obtain the characteristic data of the associated indicators of the target cause indicators; determine the target cause indicators corresponding to the characteristic data of the associated indicators included in the analysis results as the indicators that cause the abnormal sequencing quality control indicators.

[0092] Optionally, the determining module 52 is also used for: When the target cause indicator is a library construction quality control indicator, the library construction analysis results data are compared with the associated indicator feature data of each target cause indicator. The target cause indicator corresponding to the associated indicator feature data included in the library construction analysis results data is determined as the indicator that causes the abnormal sequencing quality control indicator. When the target cause indicator is a sequencing quality control indicator, the sequencing analysis results data are compared with the associated indicator feature data of each target cause indicator. The target cause indicator corresponding to the associated indicator feature data included in the sequencing analysis results data is determined as the indicator that causes the abnormal sequencing quality control indicator.

[0093] Optionally, the determining module 52 is also used for: Based on the original sequencing data file, library preparation analysis and sequencing quality control analysis corresponding to each target analysis task are performed in parallel to determine the analysis result data corresponding to each target analysis task.

[0094] Optionally, the determining module 52 is also used for: After identifying the causes of abnormal sequencing quality control indicators, improvement suggestion data corresponding to the abnormal sequencing quality control indicators are obtained from a pre-established suggestion association library.

[0095] Optionally, a monitoring module 53 is also included, for: The task monitoring interface controls at least one of the following during the execution of each target analysis task: task pause, task continuation, task termination, task resume from breakpoint, and task priority adjustment.

[0096] It will be understood by those skilled in the art that Figure 5 The structure of the sequencing data analysis device does not constitute a limitation on the device itself; each module can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in hardware within or independently of the controller in the computing device, or stored in software in the memory of the computing device, so that the controller can invoke and execute the operations corresponding to each module. In other embodiments, the sequencing data analysis device may include more or fewer modules than illustrated.

[0097] Please see Figure 6 In another aspect of this application, a computing device 20 is also provided, including a memory 3011 and a processor 3012. The memory 3011 stores a computer program. When the computer program is executed by the processor, the processor 3012 performs the sequencing data analysis method provided in any of the above embodiments of this application, or performs the steps of the sequencing data analysis method provided in any of the above embodiments of this application. The computing device may include a desktop computer, a laptop computer, a tablet computer, a handheld computer, a smart speaker, a server, etc., a mobile phone (e.g., a smartphone, a cordless phone, etc.), a wearable device (e.g., a pair of smart glasses or a smartwatch), or similar devices.

[0098] The processor 3012 is the control center, connecting various parts of the computing device via various interfaces and lines. It executes software programs and / or modules stored in the memory 3011, and calls data stored in the memory 3011 to perform various functions and process data. Optionally, the processor 3012 may include one or more processing cores; the processor 3012 includes, but is not limited to, one or more combinations of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), Field-Programmable Gate Array (FPGA), etc. Preferably, the processor 3012 may integrate an application processor and a modem processor, wherein the application processor mainly handles the operating system, user page, and applications, and the modem processor mainly handles wireless communication. It is understood that the aforementioned modem processor may not be integrated into the processor 3012.

[0099] The memory 3011 can be used to store software programs and modules. The processor 3012 executes various functional applications and data processing by running the software programs and modules stored in the memory 3011. The memory 3011 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, application programs required for at least one function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the computing device, etc. In addition, the memory 3011 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device. Accordingly, the memory 3011 may also include a memory controller to provide the processor 3012 with access to the memory 3011.

[0100] In another aspect, this application also provides a storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the sequencing data analysis method provided in any of the above embodiments of this application.

[0101] In another aspect, this application provides a sequencing data analysis system. The system includes a computing device as provided in this application embodiment and a terminal device communicating with the computing device. The terminal device provides a web-based user interface for user interaction. After logging into the sequencing data analysis system via a browser, the terminal device displays the web-based user interface of the browser-logged-in sequencing data analysis system, allowing for user interaction.

[0102] Those skilled in the art will understand that all or part of the processes in the methods provided in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0103] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. The scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A sequencing data analysis method, characterized in that, include: Obtain the raw sequencing data file; Based on the original sequencing data file, an integrated analysis of library construction and sequencing quality control is performed to determine the analysis results data, which include library construction analysis results data and sequencing analysis results data. Based on the analysis results, a correlation analysis is performed to determine the analysis report data of the original sequencing data file, so as to output the analysis report data.

2. The sequencing data analysis method as described in claim 1, characterized in that, Based on the original sequencing data file, an integrated analysis of library construction and sequencing quality control is performed to determine the analysis results data, including: Selected target analysis tasks are obtained based on the user interface; Based on the original sequencing data file and the target analysis task, an integrated analysis of library construction and sequencing quality control is performed to determine the library construction analysis results data and the sequencing analysis results data corresponding to the target analysis task.

3. The sequencing data analysis method as described in claim 2, characterized in that, The library construction analysis results data include evaluation data for each library construction quality control indicator; the sequencing analysis results data include evaluation data for each sequencing quality control indicator; the analysis report data includes the reasons for abnormal sequencing quality control indicators; and the step of performing correlation analysis based on the analysis results data to determine the analysis report data of the original sequencing data file includes: For one of the target analysis tasks, abnormal sequencing quality control indicators are determined based on the evaluation data of each sequencing quality control indicator in the sequencing analysis result data corresponding to the target analysis task. Based on the abnormal sequencing quality control indicators and the pre-established set of indicator causal relationships, the target cause indicators corresponding to the abnormal sequencing quality control indicators are determined. Each causal relationship in the set of indicator causal relationships indicates that the abnormality of the cause indicator is the cause of the abnormality of the sequencing result indicator. The cause indicator indicates either the library construction quality control indicator or the sequencing quality control indicator, and the sequencing result indicator indicates a sequencing quality control indicator that is different from the cause indicator. Based on the evaluation data of the target cause indicator, determine whether the target cause indicator is the cause of the abnormal sequencing quality control indicator.

4. The sequencing data analysis method as described in claim 3, characterized in that, The step of determining whether the target cause indicator is the cause of the abnormal sequencing quality control indicator based on the evaluation data of the target cause indicator includes at least one of the following: When there is only one target cause indicator, and the evaluation data of the target cause indicator indicates that the target cause indicator is abnormal, then the target cause indicator is determined to be the cause of the abnormal sequencing quality control indicator; or When there are multiple target cause indicators, obtain the associated indicator feature data of the target cause indicators; determine the target cause indicators corresponding to the associated indicator feature data included in the analysis result data as the indicators that cause the abnormal sequencing quality control indicators.

5. The sequencing data analysis method as described in claim 4, characterized in that, The step of identifying the target cause indicator corresponding to the correlation indicator feature data included in the analysis results data as the cause of the abnormal sequencing quality control indicator includes at least one of the following: When the target cause indicator is a library construction quality control indicator, the library construction analysis result data is compared with the associated indicator feature data of each target cause indicator. The target cause indicator corresponding to the associated indicator feature data included in the library construction analysis result data is determined as the indicator that causes the abnormal sequencing quality control indicator. When the target cause indicator is a sequencing quality control indicator, the sequencing analysis result data is compared with the associated indicator feature data of each target cause indicator. The target cause indicator corresponding to the associated indicator feature data included in the sequencing analysis result data is determined as the indicator that causes the abnormal sequencing quality control indicator.

6. The sequencing data analysis method according to any one of claims 2 to 5, characterized in that, The target analysis task can be multiple, and the method further includes: Based on the original sequencing data file, library construction analysis and sequencing quality control analysis corresponding to each target analysis task are performed in parallel to determine the analysis result data corresponding to each target analysis task.

7. The sequencing data analysis method according to any one of claims 3 to 5, characterized in that, The analysis report data also includes: improvement suggestions corresponding to abnormal sequencing quality control indicators; the method further includes: After determining the cause of the abnormal sequencing quality control indicators, improvement suggestion data corresponding to the abnormal sequencing quality control indicators are obtained from a pre-established suggestion association library.

8. The sequencing data analysis method according to any one of claims 2 to 5, characterized in that, The method further includes: The task monitoring interface controls at least one of the following during the execution of each target analysis task: task pause, task continuation, task termination, task resume from breakpoint, and task priority adjustment.

9. A computing device, characterized in that, The method includes a memory and a processor, the memory storing a computer program that, when executed by the processor, causes the processor to perform the steps of the method as described in any one of claims 1 to 8.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 8.

11. A sequencing data analysis system, characterized in that, The system includes the computing device as described in claim 9 and a terminal device that communicates with the computing device, wherein the terminal device is used to provide a web-based user interface to enable interaction with the user.

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