Method and system for generating NGS detection report

Abstract

The invention provides an NGS detection report generation method and system, and the method comprises the steps: determining a target batch identifier, carrying out the integration of a plurality of discrete sub-table data of a signal analysis file based on the target batch identifier, and generating a report information summary table corresponding to the target batch identifier, the report information summary table comprises data of multiple to-be-reported gene mutations; determining the mutation level of each gene mutation to be reported, and recording the mutation level in the report information summary table; determining a target detection sample, determining suggested interpretation information of to-be-reported gene mutation of the target detection sample according to the mutation level, and recording the suggested interpretation information in the report information summary table to generate report data. According to the embodiment of the invention, the generation efficiency and accuracy of the NGS detection report can be improved, and the labor cost is reduced.

Description

Technical Field

[0001] This invention belongs to the field of data processing technology, and in particular relates to a method and system for generating NGS test reports. Background Technology

[0002] With the increasing precision in cancer diagnosis and treatment, next-generation sequencing (NGS) technology plays a crucial guiding role in treatment decision-making. Compared with traditional Sanger sequencing, NGS technology has significant advantages such as high throughput, high sensitivity, high accuracy, and high efficiency, enabling rapid and accurate detection and analysis of large amounts of genetic information.

[0003] An NGS test report is a clinical test report generated by analyzing and interpreting data generated by NGS technology. The report content may include gene mutations (such as point mutations, small fragment deletion and insertion mutations, tandem duplication mutations, etc.) and copy number abnormalities.

[0004] In related technologies, the NGS test report generation process (also known as the report issuance process) mainly includes two core steps: bioinformatics analysis file maintenance and report writing. This technology relies on manual operations such as sample information acquisition, file transfer, abnormal mutation screening, and mutation rating. The highly repetitive and fragmented manual operations result in long processing times (e.g., 20+ minutes for a single sample, 60+ minutes for multiple mutation samples) and cumbersome processes (e.g., adjusting column widths, scrolling through data, switching between multiple tables), which easily leads to copying errors, information misalignment, or omissions (e.g., template misuse, incorrect parameter referencing), resulting in an error rate as high as 5-10%. Summary of the Invention

[0005] In view of this, the present invention provides a method and system for generating NGS test reports, which improves the efficiency and accuracy of NGS test report generation and reduces labor costs.

[0006] A first aspect of the present invention provides a method for generating an NGS test report, the method comprising:

[0007] The target batch identifier is determined, and the data from multiple discrete sub-tables of the bioinformatics analysis file are integrated based on the target batch identifier to generate a report information summary table corresponding to the target batch identifier. The report information summary table includes data on multiple gene mutations to be reported.

[0008] Determine the mutation level of each gene mutation to be reported, and record the mutation level in the report information summary table;

[0009] Identify the target detection sample, and based on the mutation level, determine the suggested explanation information for the gene mutation to be reported in the target detection sample, and record the suggested explanation information in the report information summary table to generate report data.

[0010] A second aspect of the present invention provides a system for generating NGS test reports, the system comprising:

[0011] The report information summary table generation module is used to determine the target batch identifier and integrate multiple discrete sub-table data of the bioinformatics analysis file based on the target batch identifier to generate a report information summary table corresponding to the target batch identifier. The report information summary table includes data on multiple gene mutations to be reported.

[0012] The mutation level determination module is used to determine the mutation level of each gene mutation to be reported and record the mutation level in the report information summary table;

[0013] The report data generation module is used to identify the target detection sample, determine the suggested explanation information of the gene mutation to be reported in the target detection sample according to the mutation level, and record the suggested explanation information in the report information summary table to generate report data.

[0014] A third aspect of the present invention provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement a method for generating an NGS test report as described in the first aspect above.

[0015] A fourth aspect of the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements a method for generating an NGS detection report as described in the first aspect above.

[0016] A fifth aspect of the present invention provides a computer program product that, when run on a computer, causes the computer to perform a method for generating an NGS test report as described in the first aspect above.

[0017] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0018] In this embodiment, by automatically integrating multiple discrete sub-tables of bioinformatics analysis files, automatically pre-grading to determine the mutation level of each gene mutation to be reported, and generating suggested explanations of gene mutations to be reported for batch target detection samples, the pipeline operation greatly shortens the single sample processing time (reducing the single sample processing time by 70%, from 15 minutes to 3 minutes), supports high-concurrency samples (50+ samples in parallel), and is suitable for batch detection scenarios of hematological malignancies.

[0019] Furthermore, compared to manual processing, the aforementioned assembly line method significantly reduces the error rate (in a 100-sample test, the overall error rate decreased from 6% to <1%), and reports show strong consistency. Moreover, by reducing human intervention, it further reduces reliance on specialized operations, shortening the new employee training cycle from 6 months to 3 months and saving 50% on labor costs. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a schematic diagram of an embodiment of a method for generating an NGS test report provided by an embodiment of the present invention;

[0022] Figure 2 This is a schematic diagram of an embodiment of a method for generating an NGS test report provided by another embodiment of the present invention;

[0023] Figure 3 This is a schematic diagram of an embodiment of a method for generating an NGS test report provided by another embodiment of the present invention;

[0024] Figure 4 This is a schematic diagram of a mutation level determination process provided by an embodiment of the present invention;

[0025] Figure 5 This is a schematic diagram of an embodiment of a method for generating an NGS test report provided by another embodiment of the present invention;

[0026] Figure 6 This is a schematic diagram of the structure of an NGS test report generation system provided in an embodiment of the present invention;

[0027] Figure 7 This is a schematic diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0028] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of the present invention. However, those skilled in the art will recognize that the present application may be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted to avoid unnecessary detail that could obscure the description of the present application.

[0029] The technical solution of the present invention will be illustrated below through specific embodiments.

[0030] Example 1

[0031] Reference Figure 1 This diagram illustrates an embodiment of a method for generating an NGS test report according to an embodiment of the present invention. In practice, NGS sequencing is batch sequencing, with each batch distinguished by a batch identifier (Runname). The sequencer analyzes the batch sample sequencing files (typically 100GB+, containing 50+ samples). This data needs to be converted into human-readable Excel files (each sample in a separate file) through professional bioinformatics analysis. Then, the order distributor interprets the test results for doctors and patients in the form of a test report based on their professional knowledge. Therefore, from testing samples to generating an NGS report, it involves experimental processes based on the sequencer (such as DNA extraction, DNA fragmentation, text control construction, and sequencing reactions), data analysis (i.e., bioinformatics analysis), and report analysis (report writing, review, and publication by the order distributor). Bioinformatics analysis is the process of collecting, processing, analyzing, and interpreting massive amounts of sequencing data using computer science, statistics, and biology methods. In some processing scenarios, mutation information obtained from bioinformatics analysis can be stored in an Excel spreadsheet. This embodiment can be applied to the report analysis stage, and specifically may include the following steps:

[0032] Step 101: Determine the target batch identifier, and integrate the data from multiple discrete sub-tables of the bioinformatics analysis file based on the target batch identifier to generate a report information summary table corresponding to the target batch identifier. The report information summary table includes data on multiple gene mutations to be reported.

[0033] This embodiment can process test samples in batches and generate a report information summary table (also known as a summary table) based on the data of the test samples in that batch.

[0034] In one embodiment, step 101 may involve determining the target batch identifier using the following steps:

[0035] Detect the batch identifier entered by the first user in the first VBA toolbar, and use that batch identifier as the target batch identifier.

[0036] For example, the first user refers to a user with the authority to maintain the summary table of report information. For example, administrators, developers, etc. can all be the first user. This embodiment does not limit this.

[0037] This embodiment does not limit the method by which the first user obtains the target batch identifier. For example, the first user can search for the desired batch identifier (Runname) from a pre-generated list of batch identifiers as the target batch identifier. Each time the VBA program is opened, the latest RunName that has completed bioinformatics analysis is loaded into the batch identifier list. Alternatively, the bioinformatics server will send an email notification after each batch analysis is completed, along with information such as the batch identifier of that batch. In this case, the first user can obtain the target batch identifier from the email.

[0038] In one implementation, after loading the first VBA toolbar generated by the VBA (Visual Basic for Applications, Visual Basic Macro Language) script into Excel, the first VBA toolbar contains a batch identifier input box for the first user to input a batch identifier, which is then used as the target batch identifier.

[0039] The bioinformatics analysis file contains multiple discrete sub-tables of data. Once the target batch identifier is determined, the data from the multiple discrete sub-tables of the bioinformatics analysis file can be integrated based on the target batch identifier to generate a summary table of report information corresponding to the target batch identifier, which makes it convenient for users to summarize and view the data.

[0040] For example, the report information summary table includes data on multiple gene mutations to be reported, such as basic sample information (e.g., test sample number, project name, experiment number, co-number information, gender of the test subject, diagnostic information, sample type, sample reception time, batch identifier, etc.), quality control information, remarks information (e.g., expedited, routine, etc.), mutation classification information, sequencing file address, low-frequency hotspot mutation page information, tandem repeat mutation page information, sample test gender and registered gender, variant naming correction information, CNV (Copy-Number Variant) registration location and preset common CNV abnormality types, etc.

[0041] The bioinformatics analysis file is generated by the bioinformatics server. In one implementation, the first VBA toolbar may also include the following function buttons: "Extract Information," "Transfer File," "Preprocess File," and "Copy to Master Table." When the first user enters the target batch identifier and clicks "Extract Information," the VBA script can extract the data of the test samples corresponding to the target batch identifier from the bioinformatics server to the first data table where the first VBA toolbar is located. When the first user clicks "Transfer File," the VBA script can copy the bioinformatics analysis file corresponding to the target batch identifier from the bioinformatics server to a specified local storage location. When the first user clicks "Preprocess File," the VBA script can generate a report information summary table based on the first data table and preprocess the report information summary table. When the first user clicks "Copy to Master Table," the VBA script can copy the data from the first data table to the full master table.

[0042] Subsequently, you can select the genes to be reported as gene mutations from the report information summary table according to the package. Gene mutations to be reported do not include mutations that do not need to be reported. The key information of the selected gene mutations to be reported can be pasted into the blank space below the report information summary table to fill in the corresponding mutation level, and at the same time, note the database's inclusion information of the mutation.

[0043] In addition, the report information summary table can reserve space for registering CNV abnormalities and common chromosomal abnormality types, which will facilitate the subsequent determination of recommended interpretation information.

[0044] In this embodiment, an Excel add-in is embedded in VBA code, offering strong versatility (no program installation required, ready to use immediately). It also enhances ease of use by optimizing human-computer interaction through hyperlinks and toolbars, reducing manual steps. Furthermore, it boasts high portability; based on the general VBA framework, it can be extended to other NGS testing fields and disciplines (such as WES, solid tumors), improving the efficiency of panel order processing for different batches.

[0045] Step 102: Determine the mutation level of each gene mutation to be reported and record the mutation level in the report information summary table.

[0046] In this step, the mutation grade is used to describe the clinical significance of the variant. In practice, it can be pre-graded according to the CAP / ACMG rules using a pre-grading library based on guidelines such as NCCN, and the mutation grade is recorded in the corresponding position in the report information summary table. The pre-grading results can be manually verified.

[0047] Specifically, in the fields of medicine and precision medicine, the accurate description and classification of variant information is crucial. According to the standards of authoritative academic organizations, variant information is typically described using the HGVS (Human Genome Variation Society) standard nomenclature to ensure consistent, standardized, and easily understandable naming of variants. Regarding somatic variants, the "Interpretation and Reporting of Cancer Sequence Variations" (2017 edition) jointly published by ASCO (American Society of Clinical Oncology), AMP (American Society of Pathologists), and CAP (College of Clinical Pathology) classifies somatic variants into four levels:

[0048] Tier 1 variants are those with clear clinical significance. These variants have clear guiding value for the diagnosis, treatment and prognosis of cancer and are an important reference in clinical decision-making.

[0049] Tier 2 variants have potential clinical significance and may have an impact on certain aspects of cancer, but further research is needed to confirm their clinical significance.

[0050] Tier 3: Variants of unknown clinical significance. Current evidence is insufficient to determine whether these variants have a direct impact on cancer, and more clinical data and research are needed to clarify this.

[0051] Tier 4: Benign / potentially benign variants that are considered to have little or no impact on the diagnosis and treatment of cancer and are classified as low-risk.

[0052] Step 103: Identify the target detection sample, and based on the mutation level, determine the suggested explanation information for the gene mutations to be reported in the target detection sample, and record the suggested explanation information in the report information summary table to generate report data.

[0053] The target detection sample can be the detection sample recorded in the detection report. In one embodiment, the step of determining the target detection sample in step 103 specifically includes:

[0054] The system detects the sample identifier entered by the second user in the second VBA toolbar and uses that sample identifier as the target sample identifier.

[0055] In one example, the sample identifier could be an experiment number. The second user could be an order dispatcher. The second VBA toolbar could be a toolbar for an order dispatch tool generated based on a VBA script; once this order dispatch tool is loaded into Excel, the toolbar can be displayed in the current Excel sheet.

[0056] The second user can find the identifier of the pending test sample under their name in the report information summary table, and copy the pending test sample identifier to the second data table where the second VBA toolbar is located, as the target test sample identifier.

[0057] In other embodiments, the second VBA toolbar may also include a distributor identifier input box. Based on the distributor identifier entered in the input box, if the "Find Sample" button is clicked, the script will search for the corresponding unprocessed detection sample identifier from the total table, use it as the target detection sample identifier, and copy the target detection sample identifier to the corresponding position in the second data table.

[0058] Simultaneously, relevant information corresponding to the target detection sample identifier can be copied to the second data table. Additionally, the sample files of each target detection sample corresponding to the target detection sample identifier can be backed up for the order issuer's operation, and the hyperlink to this backup file can be recorded in the second data table. The order issuer can click on the hyperlinks to handle order issuance steps that cannot be automated at this time, such as merging / correcting gene mutation naming, adjusting mutation grading based on diagnostic information, identifying CNV abnormalities, and pasting them to a fixed location.

[0059] After processing each target detection sample sequentially, based on the final mutation classification, suggested explanations for mutations and CNVs are generated in a report format and added to the Summary table. Simultaneously, the necessary information for the sample report (quality control information, anomaly information, anomaly explanations, etc.) is appended to the explanation file. This suggested explanation information is used to interpret and provide relevant recommendations regarding gene variations or mutations.

[0060] In this embodiment, by automatically integrating multiple discrete sub-tables of bioinformatics analysis files, automatically pre-grading to determine the mutation level of each gene mutation to be reported, and generating suggested explanations of gene mutations to be reported for batch target detection samples, the pipeline operation greatly shortens the single sample processing time (reducing the single sample processing time by 70%, from 15 minutes to 3 minutes), supports high-concurrency samples (50+ samples in parallel), and is suitable for batch detection scenarios of hematological malignancies.

[0061] Furthermore, compared to manual processing, the aforementioned assembly line method significantly reduces the error rate (in a 100-sample test, the overall error rate decreased from 6% to <1%), and reports show strong consistency. Moreover, by reducing human intervention, it further reduces reliance on specialized operations, shortening the new employee training cycle from 6 months to 3 months and saving 50% on labor costs.

[0062] Example 2

[0063] Reference Figure 2This illustration shows a schematic diagram of an embodiment of a method for generating an NGS test report according to another embodiment of the present invention. Based on Embodiment 1, this embodiment provides a more detailed description of the process for generating a report information summary table. In this embodiment, the step of integrating multiple discrete sub-tables of bioinformatics analysis files based on a target batch identifier to generate a report information summary table corresponding to the target batch identifier may further include the following: reading the bioinformatics analysis file corresponding to the target batch identifier from the bioinformatics server to a designated storage location; extracting specified key field information from the multiple discrete sub-tables of the bioinformatics analysis file in the designated storage location, and generating a report information summary table based on the specified key field information.

[0064] This embodiment may include the following steps:

[0065] Step 201: Determine the target batch identifier and read the bioinformatics analysis file corresponding to the target batch identifier from the bioinformatics server to the specified storage location.

[0066] In practice, the files generated by bioinformatics analysis are stored on a bioinformatics server. When a test order is issued, these files need to be transferred to a designated storage location for analysis and archiving. Simultaneously, a comprehensive summary table needs to be maintained, recording basic information, experimental information (such as experimental data and its storage path), and quality control data for each sample in each batch. This data originates from the summary file of sample information for each batch. The comprehensive summary table also includes notes on sample assignments and expedited processing. After test order analysis, specific mutations and anomalies in the samples are also recorded.

[0067] For example, when the first user clicks "Transfer Files," the VBA script can copy the bioinformatics analysis files (including quality control files) corresponding to the target batch identifier from the bioinformatics server to a specified local storage location. This specified storage location can include a subject-specific public folder with a specified path, such as "Panel (Batch) / Year / Month / Day / Sample Name" or " / Subject-Specific Public Folder / Package Type / Date / Sample Name." Simultaneously, the VBA script can register this specified path information in the first data table, for example, by registering the specified path information in the sample path column.

[0068] This embodiment automatically transfers the target sample data corresponding to the target batch identifier in the bioinformatics server to the designated storage location. Compared with manually creating folders and manually transferring data, it can save a lot of time. For example, if processed manually, it would take 5 minutes per sample, while using step 201 only takes 0.5 minutes per sample.

[0069] Step 202: Extract specified key field information from multiple discrete sub-tables of the bioinformatics analysis file in the specified storage location, and generate a summary table of report information based on the specified key field information.

[0070] Specifically, bioinformatics analysis files contain multiple discrete sub-tables, most of which contain a lot of disorganized data. For example, the sub-tables that need to be considered when generating reports include: the main mutation information table (snp_indel_freq_1_sub), the mutation table with low mutation rate but hotspot status (snp_indel_hotspot), the analysis table for tandem repeat mutations (FLT3-ITD), the copy number anomaly information table (cnv and ratio), and the sample gender analysis table (Gender), etc. Their contents and functions are described in Table 1 below:

[0071]

[0072] In practice, each sub-table provides annotations and explanations for anomalies, resulting in numerous columns for each sub-table. For example, the `snp_indel_freq_1_sub` table, which focuses on mutation information, has 64 columns. These columns annotate mutations based on gene mutation information, sequencing quality, database alignment, and software prediction. However, not every annotation column needs special attention; too many columns can actually hinder the selection of key information. For instance, the annotation text in the `cosmic` column is too long, and most of the information is unimportant; only the number of cases included in the `cosmic` database needs to be considered. Also, some columns may have mutation formats that do not meet reporting requirements, necessitating manual information extraction. In short, the report information is located in various sub-tables and the overall sample table, and the collection and filtering process alone requires extensive manual operations such as adjustment, screening, copying, and pasting. To address this issue, this step integrates the data from multiple discrete sub-tables in the bioinformatics analysis file to obtain a report information summary table. This summary table contains key data from each discrete sub-table, facilitating the intuitive identification of anomalies during the order processing and preventing the omission of information pages.

[0073] In one embodiment, the specified key field information includes the detection sample identifier; step 202, which generates a report information summary table based on the specified key field information, may further include the following steps:

[0074] Step 202-1: Copy the specified key field information to the first data table where the current VBA toolbar is located, and assign a dispatcher to each test sample identifier in the first data table.

[0075] For example, when the first user clicks "Extract Information," the VBA script can extract specified key field information from each discrete sub-table and copy it to the first data table where the current VBA toolbar is located. The specified key field information includes the specified key field and its corresponding data; the specified key field can be a field determined from the configuration file. For instance, it extracts key information from the "snp_indel_hotspot" sub-table (mutation rate > 1%), the "FLT3-ITD" sub-table (Summary information), and the "Gender" sub-table (sex detection) and adds them to the first data table; it filters out rows from the "cnv" sub-table that do not meet the requirements (BF < 10, Conrad.hg19 != NA), and extracts suspected copy number anomalies in bold (BF > 50, CNV_length > 1kb); and it pre-screens the "ratio" sub-table, focusing on gene copy number information (such as IKZF1, KMT2A, TP53, etc.).

[0076] For example, the specified key fields may include, but are not limited to: barcode, sample identifier, project name, sample type, batch identifier, submitting unit name, sample receipt date, etc.

[0077] In one implementation, tasks can be assigned to each test sample based on pre-configured task allocation information to determine the order dispatcher corresponding to each test sample identifier. For example, orders can be dispatched sequentially based on a given list to determine the order dispatcher for each test sample.

[0078] Step 202-2: Match the specified key field information in the pre-generated remarks registration configuration to determine the corresponding remarks information, and fill the remarks information into the first data table.

[0079] In practice, sample data transfer typically occurs 4-5 days after bioinformatics analysis is completed. During this period, clinical needs often arise, such as expedited processing or changes in project assignments. The remarks registration configuration allows for the recording of these special remarks, and these remarks are matched with the sample number during sample data transfer.

[0080] For example, the remarks information can include expedited remarks (such as regular, market expedited, etc.). The remarks information can be determined by matching the specified key field information (especially the identifier of each test sample) in the pre-generated remarks registration configuration, and then the remarks information can be filled into the specified position in the first data table.

[0081] Step 202-3: Create a new report information summary table and fill it into the report information summary table after processing the first data table. This processing includes: creating new fields, adjusting the sorting of each field, adjusting the column width of each field, and adding filter conditions to one or more fields.

[0082] For example, extract the key columns from the first data table and sort them logically to display as much information as possible in the first data table, so as to avoid having to scroll through columns that are not displayed.

[0083] At the same time, adjust the width of the key columns to the most appropriate width to avoid the columns being too narrow, which would result in incomplete information, and narrow the width of the first but not key columns.

[0084] Create new fields such as Mutalyzer for rapid mutation nomenclature verification, GeneInfo for gene information, and Haematopoietic Count for inclusion in the COMSIC database to facilitate quick information verification. Alternatively, extract information such as NM number, amino acid changes, nucleotide changes, and exon number from the "AAchange.refgene Add Exons" column as new fields, saving on manual extraction errors and reducing the extraction error rate from 10% to below 1%, thus facilitating subsequent verification and import.

[0085] Add filtering conditions to one or more fields, and set multi-level filtering conditions to achieve dynamic risk labeling. For example, Level 1 rules (highlighted in yellow): hard filtering (e.g., population frequency > 0.002, BF < 10); Level 2 rules (highlighted in green): scenarios requiring review (e.g., mutation rate < 2%, chain deviation < -20); Level 3 rules (highlighted in red): high-risk mutation identification (e.g., Haematopoietic Count > 5, Tier 1 gene mutation). Decision derivation: Based on the above labels, determine the CAP / ACMG pre-classification results (e.g., "Tier 1" matches the clinically significant template). Specifically, abnormal information is highlighted in yellow to exclude mutations that do not need to be reported later: such as quality control failure, NM version mismatch, inclusion in the Benign database (benign), and population frequency >0.002 in various population databases; information requiring attention is highlighted in green, and the person issuing the alert will manually determine whether the mutation needs to be retained: such as mutation frequency below 2%, mutation occurrences in this batch greater than 10 times, mutation occurrences in historical batches greater than 500 times, strand deviation greater than -20, sequencing quality below 20, etc.; hotpot mutations (Haematopoietic Count) with a value greater than 5 may be Tier 1 / 2 mutation information lines, which can be bolded, and data with a mutation rate greater than 10% is highlighted in red, etc.

[0086] Automatically check sample gender matching (mark in red if mismatched).

[0087] CNV pre-screening, for example, filters rows with BF < 10 or Conrad.hg19 = NA using algorithms, and marks abnormalities with BF > 50 and CNV_length > 1kb (such as the IKZF1 gene), reducing manual analysis time from the original 3 min / sample to 0.5 min / sample.

[0088] Step 203: Determine the mutation level of each gene mutation to be reported and record the mutation level in the report information summary table.

[0089] Step 204: Identify the target detection sample, and based on the mutation level, determine the suggested explanation information for the gene mutations to be reported in the target detection sample, and record the suggested explanation information in the report information summary table to generate report data.

[0090] In this embodiment, unstructured data is transformed into structured data through automatic transfer of bioinformatics analysis files, extraction of key fields, and cross-table joins. This improves the automation level of report information summary table generation.

[0091] Example 3

[0092] Reference Figure 3 This illustration shows a schematic diagram of an embodiment of a method for generating an NGS test report according to another embodiment of the present invention. Based on Embodiment 1 or Embodiment 2, this embodiment provides a more detailed description of the process for determining the mutation level of each gene mutation to be reported. In this embodiment, the step of determining the mutation level of each gene mutation to be reported may further include the following steps: determining the mutation type of the gene mutation to be reported; determining whether the mutation type is included in a specified guideline, and determining the mutation level of the gene to be reported based on the result of this determination and the number of COSMIC cases of the gene mutation to be reported.

[0093] This embodiment may include the following steps:

[0094] Step 301: Determine the target batch identifier, and integrate the data from multiple discrete sub-tables of the bioinformatics analysis file based on the target batch identifier to generate a report information summary table corresponding to the target batch identifier. The report information summary table includes data on multiple gene mutations to be reported.

[0095] Step 302: Determine the mutation type of the gene mutation to be reported.

[0096] For example, mutation types may include virulent mutation types such as frameshift mutations, truncated mutations, or splicing mutations, or point mutations, in-frame mutations, etc.

[0097] Step 303: Determine whether the mutation type is included in the specified guidelines, and based on the result of this determination and the number of COSMIC cases of the gene mutation to be reported, determine the mutation level of the gene to be reported, and record the mutation level in the report information summary table.

[0098] In this step, the selected mutations are pre-classified according to the pre-classification criteria. If they meet the ACMG classification criteria, each mutation is pre-classified according to preset rules using auxiliary rating column information. The mutation level obtained from the pre-classification is noted in the mutation information to facilitate the generation of suggested explanation information later.

[0099] In one embodiment, reference is made to... Figure 4 If the mutation type includes frameshift mutation, truncation mutation, or splicing mutation, then step 303 may further include the following steps:

[0100] Step 303-1: Determine whether the mutation type is included in a designated authoritative guideline.

[0101] The specified authoritative guide can include one or more specified authoritative guides. If there are multiple authoritative guides, matching needs to be performed among them.

[0102] Step 303-3: If the mutation type is included in the designated authoritative guidelines, determine whether the mutation type is included in the designated clinical diagnostic matching guidelines; if yes, determine the mutation level as the first mutation level; if no, determine the mutation level as the second mutation level. The first mutation level indicates that the mutation has clear clinical significance, and the second mutation level indicates that the mutation has potential clinical significance.

[0103] Specifically, if the current mutation type is included in any designated authoritative guideline, it can be further determined whether the mutation type is included in a designated clinical diagnostic matching guideline; if so, the mutation level is determined to be Tier 1; if not, the mutation level is determined to be Tier 2.

[0104] Step 303-5: If the mutation type is not included in the designated authoritative guidelines, determine whether the mutation of the gene to be reported is a tumor suppressor gene; if yes, determine the mutation level as the second mutation level; if no, determine whether the number of COSMIC cases of the mutation of the gene to be reported is greater than or equal to the first value; if it is greater than or equal to the first value, determine the mutation level as the second mutation level; if it is less than the first value, determine the mutation level as the third mutation level. The third mutation level indicates that the clinical significance of the mutation is unclear.

[0105] Specifically, if the current mutation type is not included in any designated authoritative guideline, it can be further determined whether the mutation to be reported is a tumor suppressor gene. If so, the mutation level is determined to be Tier 2. If not, it is further determined whether the number of COSMIC cases of the mutation to be reported is greater than or equal to the first value. If it is greater than or equal to the first value, the mutation level is determined to be Tier 2; if it is less than the first value, the mutation level is determined to be Tier 3. The first value can be an empirical value, for example, 5.

[0106] In another embodiment, reference Figure 4 If the mutation type includes point mutations or in-frame mutations, then step 303 may further include the following steps:

[0107] Step 303-2: Determine whether the mutation type is included in the specified authoritative guidelines.

[0108] The specified authoritative guide can include one or more specified authoritative guides. If there are multiple authoritative guides, matching needs to be performed among them.

[0109] Step 303-4: If the mutation type is included in a designated authoritative guideline, determine whether the gene mutation to be reported is a hotspot mutation. If it is a hotspot mutation and its COSMIC case count is less than the second value, then the mutation level is determined to be the second mutation level. If it is a hotspot mutation and its COSMIC case count is greater than or equal to the second value, further determine whether the gene mutation to be reported is included in a designated clinical diagnostic matching guideline. If yes, then the mutation level is determined to be the first mutation level. If no, then the mutation level is determined to be the second mutation level. Here, the first mutation level indicates that the mutation has clear clinical significance, and the second mutation level indicates that the mutation has potential clinical significance.

[0110] Specifically, if the current mutation type is included in any designated authoritative guideline, it can be further determined whether the gene mutation to be reported is a hotspot mutation. If it is a hotspot mutation, the COSMIC case count is further determined to be less than a second value. If it is less than the second value, the mutation level is determined to be the second mutation level. If the COSMIC case count is greater than or equal to the second value, or if the gene mutation to be reported is not a hotspot mutation, it is determined whether the gene mutation to be reported is included in a designated clinical diagnostic matching guideline. If so, the mutation level is determined to be the first mutation level; otherwise, the mutation level is determined to be the second mutation level. The second value can be an empirical value, for example, the first value is 40.

[0111] Step 303-6: If the mutation type is not included in the designated authoritative guidelines, determine whether the gene mutation to be reported is included in the Oncokb database; if yes, determine the mutation level as the second mutation level; if no, determine whether the number of COSMIC cases of the gene mutation to be reported is greater than or equal to the third value; if it is greater than or equal to the third value, determine the mutation level as the second mutation level; if it is less than the third value, determine the mutation level as the third mutation level. The third mutation level indicates that the clinical significance of the mutation is unclear.

[0112] Specifically, if the current mutation type is not included in any designated authoritative guideline, it is determined whether the gene mutation to be reported is matched with loss-of-function (LOF) or gain-of-function (GOF) mutation information in the Oncokb database; if so, the mutation level is determined to be the second mutation level; if not, it is determined whether the number of COSMIC cases of the gene mutation to be reported is greater than or equal to a third value; if it is greater than or equal to the third value, the mutation level is determined to be the second mutation level; if it is less than the third value, the mutation level is determined to be the third mutation level. The third value can be an empirical value, such as the first value being 5.

[0113] Step 304: Identify the target detection sample, and based on the mutation level, determine the suggested explanation information for the gene mutations to be reported in the target detection sample, and record the suggested explanation information in the report information summary table to generate report data.

[0114] In this embodiment, the system can automatically screen for gene mutations to be reported, determine the mutation type of each mutation, and determine the mutation level based on the mutation type, guideline inclusion, and the number of COSMIC cases, thereby improving the efficiency and accuracy of pre-classification. Experimental data show that...

[0115] The automated pre-classification accuracy was 90% when tested on 30 samples, while the accuracy of manual pre-classification by a novice was 80%, reducing the error rate from 20% to 10%.

[0116] Example 4

[0117] Reference Figure 5 This illustration shows a schematic diagram of an embodiment of a method for generating an NGS test report according to another embodiment of the present invention. This embodiment, based on Embodiment 1, Embodiment 2, or Embodiment 3, provides a more detailed description of the process for determining the suggested interpretation information of abnormal mutations. In this embodiment, the step of determining the suggested interpretation information of the gene mutation to be reported in the target test sample according to the mutation level may further include the following steps: obtaining the current project name and determining the cancer type based on the project name; generating a matching tag based on the mutation level, the name of the gene mutation to be reported, and the cancer type; and searching for the suggested interpretation information corresponding to the matching tag in a pre-generated template library.

[0118] This embodiment may include the following steps:

[0119] Step 401: Determine the target batch identifier, and integrate the data from multiple discrete sub-tables of the bioinformatics analysis file based on the target batch identifier to generate a report information summary table corresponding to the target batch identifier. The report information summary table includes data on multiple gene mutations to be reported.

[0120] Step 402: Determine the mutation level of each gene mutation to be reported and record the mutation level in the report information summary table.

[0121] Step 403: Obtain the current project name and determine the cancer type based on the project name.

[0122] In one implementation, the project name may include cancer type keywords, which can be used to query a cancer type database to obtain the corresponding cancer type.

[0123] Step 404: Generate matching tags based on mutation level, name of gene mutation to be reported, and cancer type.

[0124] For example, mutation level, name of the gene mutation to be reported, and cancer type can be combined into a matching tag.

[0125] Step 405: Search for the suggested explanation information corresponding to the matching tag in the pre-generated template library, and record the suggested explanation information in the report information summary table to generate report data.

[0126] Once the matching tag is obtained, it can be matched in the pre-generated template library to obtain the suggested explanation information corresponding to the tag. The suggested explanation information is then recorded in the report information summary table. The data in the report information summary table can be used as report data for subsequent generation of the test report.

[0127] In this embodiment, the following steps may also be included:

[0128] Import the report data in batches into the reporting system, which then fills the data into a predefined report template to generate a test report.

[0129] Specifically, in the reporting system, report data is imported, and multiple reports are imported in batches to generate test reports in batches.

[0130] The generated test reports can also be manually reviewed. The order processor opens the backup file and simultaneously opens the file's data entry interface in the report system. All samples are selected, "Import Molecular Tumor Results," and "Connection File" is chosen. After confirmation, batch import of report data from multiple reports begins. Simultaneously, the order processor will review the reports. If necessary, suggested explanations are modified in the file (and archived). After verification, an explanation file is generated using the "Quick Report" function in the second VBA toolbar. This explanation file is imported into the report system, and after further confirmation, it is submitted. Modifications to the explanation file are then simultaneously recorded in the overall summary table.

[0131] After completing the batch order placement, you can also import the mutation and remarks information temporarily registered in the current VBA into the full master table. At the same time, you can also clear the main interface information of the current VBA and archive these order placement records to the (Signboard) sub-table.

[0132] In this embodiment, by obtaining the current project name and determining the cancer type based on the project name, matching tags are generated according to the mutation level, the name of the gene mutation to be reported, and the cancer type. The suggested explanation information corresponding to the matching tag is searched in the pre-generated template library, thereby realizing the batch determination of suggested explanation information for abnormal mutations and improving the input efficiency of suggested explanation. For example, it only takes 1 minute to import 10 sample reports in batches, while manual processing takes 10 minutes, which is 10 times more efficient.

[0133] It should be noted that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0134] Example 5

[0135] Reference Figure 6 The diagram illustrates the structure of an NGS test report generation system provided by an embodiment of the present invention, which may specifically include the following modules:

[0136] The target batch determination module 501 is used to determine the target batch identifier;

[0137] The report information summary table generation module 502 is used to integrate multiple discrete sub-table data of the bioinformatics analysis file based on the target batch identifier to generate a report information summary table corresponding to the target batch identifier. The report information summary table includes data on multiple gene mutations to be reported.

[0138] The mutation level determination module 503 is used to determine the mutation level of each gene mutation to be reported and record the mutation level in the report information summary table;

[0139] The target sample determination module 504 is used to determine the target detection sample;

[0140] The report data generation module 505 is used to determine the suggested explanation information of the gene mutation to be reported in the target detection sample according to the mutation level, and to record the suggested explanation information in the report information summary table to generate report data.

[0141] In one embodiment, the target batch determination module 501 is specifically used for:

[0142] Detect the batch identifier entered by the first user in the first VBA toolbar, and use that batch identifier as the target batch identifier.

[0143] In one embodiment, the report information summary table generation module 502 specifically includes the following modules:

[0144] The file transfer module is used to read the bioinformatics analysis file corresponding to the target batch identifier from the bioinformatics server to the specified storage location;

[0145] The summary table generation module is used to extract specified key field information from multiple discrete sub-table data of the bioinformatics analysis file in the specified storage location, and generate a report information summary table based on the specified key field information.

[0146] In one embodiment, the summary table generation module is specifically used for:

[0147] Copy the specified key field information to the first data table where the current VBA toolbar is located, and assign a dispatcher to each detection sample identifier in the first data table;

[0148] Match the specified key field information in the pre-generated remarks registration configuration to determine the corresponding remarks information, and fill the remarks information into the first data table;

[0149] Create a new report information summary table, and fill the report information summary table with the processed data from the first data table. The processing includes: creating new fields, adjusting the sorting of each field, adjusting the column width of each field, and adding filter conditions to one or more fields.

[0150] In one embodiment, the mutation level determination module 503 further includes the following modules:

[0151] A mutation type determination module is used to determine the mutation type of the gene mutation to be reported;

[0152] The mutation level determination module is used to determine whether the mutation type is included in the specified guidelines, and to determine the mutation level of the gene to be reported based on the result of the determination and the number of COSMIC cases of the mutation of the gene to be reported.

[0153] In one embodiment, the mutation type includes frameshift mutation, truncation mutation, or splicing mutation; the mutation level determination module is specifically used for:

[0154] Determine whether the mutation type is included in a designated authoritative guideline;

[0155] If the mutation type is included in the designated authoritative guideline, then it is determined whether the mutation type is included in the designated clinical diagnosis matching guideline; if yes, then the mutation level is determined to be the first mutation level; if no, then the mutation level is determined to be the second mutation level, wherein the first mutation level indicates that the mutation has clear clinical significance, and the second mutation level indicates that the mutation has potential clinical significance.

[0156] If the mutation type is not included in the specified authoritative guidelines, it is determined whether the gene mutation to be reported is a tumor suppressor gene; if so, the mutation level is determined to be the second mutation level; if not, it is determined whether the number of COSMIC cases of the gene mutation to be reported is greater than or equal to a first value; if it is greater than or equal to the first value, the mutation level is determined to be the second mutation level; if it is less than the first value, the mutation level is determined to be the third mutation level, where the third mutation level indicates that the clinical significance of the mutation is unclear.

[0157] In one embodiment, the mutation type includes point mutations or in-frame mutations; the mutation level determination module is specifically used for:

[0158] Determine whether the mutation type is included in a designated authoritative guideline;

[0159] If the mutation type is included in the designated authoritative guideline, it is determined whether the gene mutation to be reported is a hotspot mutation. If it is a hotspot mutation and its COSMIC case count is less than the second value, the mutation level is determined to be the second mutation level. If it is a hotspot mutation and its COSMIC case count is greater than or equal to the second value, it is further determined whether the gene mutation to be reported is included in the designated clinical diagnostic matching guideline. If yes, the mutation level is determined to be the first mutation level; if no, the mutation level is determined to be the second mutation level. The first mutation level indicates that the mutation has clear clinical significance, and the second mutation level indicates that the mutation has potential clinical significance.

[0160] If the mutation type is not included in the specified authoritative guidelines, it is determined whether the gene mutation to be reported is included in the Oncokb database; if yes, the mutation level is determined to be the second mutation level; if no, it is determined whether the number of COSMIC cases of the gene mutation to be reported is greater than or equal to a third value; if it is greater than or equal to the third value, the mutation level is determined to be the second mutation level; if it is less than the third value, the mutation level is determined to be the third mutation level, where the third mutation level indicates that the clinical significance of the mutation is unclear.

[0161] In one embodiment, the report data generation module 505 is specifically used for:

[0162] Obtain the current project name and determine the cancer type based on the project name;

[0163] Generate matching tags based on the mutation level, the name of the gene mutation to be reported, and the cancer type;

[0164] Search the pre-generated template library for suggested explanations of the matching tags.

[0165] In one embodiment, the target sample determination module 504 is specifically used for:

[0166] The system detects the sample identifier entered by the second user in the second VBA toolbar and uses that sample identifier as the target sample identifier.

[0167] In one embodiment, the system may further include the following modules:

[0168] The import module is used to import the report data in batches into the reporting system, whereby the reporting system fills the report data into a pre-defined report template to generate a test report.

[0169] This invention provides a system for generating NGS test reports. By applying this system, the various steps in the aforementioned method embodiments can be implemented.

[0170] It should be noted that the module division in the NGS test report generation system provided in the above embodiments is illustrative and only represents a logical functional division. In actual implementation, other division methods may be used. Furthermore, the functional modules in the various embodiments of this invention can be integrated into a single processor, exist as separate physical entities, or be integrated into a single module. The integrated modules described above can be implemented in hardware or as software functional modules.

[0171] If the integrated module is implemented as a software functional module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the technical solution of the embodiments of the present invention can be embodied in the form of a computer program product, which is stored in a computer storage medium and includes several instructions to cause an electronic device or processor to execute all or part of the steps of the methods in the various embodiments of the present invention. The aforementioned computer storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0172] Furthermore, the system for generating an NGS test report and the method for generating an NGS test report provided in the above embodiments belong to the same concept, and their specific implementation process can be found in the method embodiments, which will not be repeated here.

[0173] Reference Figure 7 The diagram illustrates an electronic device according to an embodiment of the present invention. Figure 7 As shown, the electronic device in this embodiment of the invention includes: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps in the above-described method embodiment for generating an NGS test report. Alternatively, when the processor executes the computer program, it implements the functions of each module in the above-described system embodiment for generating an NGS test report.

[0174] For example, the computer program may be divided into one or more modules, which are stored in the memory and executed by the processor to complete this application. The one or more modules may be a series of computer program instruction segments capable of performing a specific function, which can be used to describe the execution process of the computer program in the electronic device.

[0175] The electronic device may be a desktop computer, a cloud server, or other computing device. The electronic device may include, but is not limited to, a processor and memory. Those skilled in the art will understand that... Figure 4 This is merely one example of an electronic device and does not constitute a limitation on the electronic device. It may include more or fewer components than illustrated, or combine certain components, or different components. For example, the electronic device may also include input / output devices, network access devices, buses, etc.

[0176] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor.

[0177] The memory can be an internal storage unit of the electronic device, such as a hard drive or RAM. Alternatively, it can be an external storage device, such as a plug-in hard drive, Smart Media Card (SMC), Secure Digital (SD) card, Flash Card, etc. Furthermore, the memory can include both internal and external storage units. The memory is used to store the computer program and other programs and data required by the electronic device. The memory can also be used to temporarily store data that has been output or will be output.

[0178] This invention also discloses an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements a method for generating an NGS detection report as described in the foregoing embodiments.

[0179] This invention also discloses a computer-readable storage medium storing a computer program that, when executed by a processor, implements a method for generating an NGS detection report as described in the foregoing embodiments.

[0180] This invention also discloses a computer program product that, when run on a computer, causes the computer to execute a method for generating an NGS detection report as described in the foregoing embodiments.

[0181] The embodiments described above are only used to illustrate the technical solutions of this application, and are not intended to limit it. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for generating an NGS test report, characterized in that, The method includes: The target batch identifier is determined, and the data from multiple discrete sub-tables of the bioinformatics analysis file are integrated based on the target batch identifier to generate a report information summary table corresponding to the target batch identifier. The report information summary table includes data on multiple gene mutations to be reported. Determine the mutation level of each gene mutation to be reported, and record the mutation level in the report information summary table; Identify the target detection sample, and based on the mutation level, determine the suggested explanation information for the gene mutation to be reported in the target detection sample, and record the suggested explanation information in the report information summary table to generate report data.

2. The method according to claim 1, characterized in that, The determination of the target batch identifier includes: Detect the batch identifier entered by the first user in the first VBA toolbar, and use that batch identifier as the target batch identifier.

3. The method according to claim 1 or 2, characterized in that, The process of integrating multiple discrete sub-tables of bioinformatics analysis files based on the target batch identifier to generate a summary table of report information corresponding to the target batch identifier includes: Read the bioinformatics analysis file corresponding to the target batch identifier from the bioinformatics server to the specified storage location; In the specified storage location, specified key field information is extracted from multiple discrete sub-tables of the bioinformatics analysis file, and a summary table of report information is generated based on the specified key field information.

4. The method according to claim 3, characterized in that, The specified key field information includes the test sample identifier; the extraction of the specified key field information from multiple discrete sub-tables of the bioinformatics analysis file, and the generation of a report information summary table based on the specified key field information, includes: Copy the specified key field information to the first data table where the current VBA toolbar is located, and assign a dispatcher to each detection sample identifier in the first data table; Match the specified key field information in the pre-generated remarks registration configuration to determine the corresponding remarks information, and fill the remarks information into the first data table; Create a new report information summary table, and fill the report information summary table with the processed data from the first data table. The processing includes: creating new fields, adjusting the sorting of each field, adjusting the column width of each field, and adding filter conditions to one or more fields.

5. The method according to claim 1, characterized in that, The determination of the mutation level of each gene mutation to be reported includes: Determine the mutation type of the gene mutation to be reported; Determine whether the mutation type is included in the specified guidelines, and based on the result of this determination and the number of COSMIC cases of the mutation of the gene to be reported, determine the mutation level of the gene to be reported.

6. The method according to claim 5, characterized in that, The mutation types include frameshift mutations, truncation mutations, or splicing mutations; the determination of whether the mutation type is included in the specified guidelines, and based on the result of this determination combined with the number of COSMIC cases of the mutation of the gene to be reported, to determine the mutation level of the gene to be reported, includes: Determine whether the mutation type is included in a designated authoritative guideline; If the mutation type is included in the designated authoritative guideline, then it is determined whether the mutation type is included in the designated clinical diagnosis matching guideline; if yes, then the mutation level is determined to be the first mutation level; if no, then the mutation level is determined to be the second mutation level, wherein the first mutation level indicates that the mutation has clear clinical significance, and the second mutation level indicates that the mutation has potential clinical significance. If the mutation type is not included in the specified authoritative guidelines, it is determined whether the gene mutation to be reported is a tumor suppressor gene; if so, the mutation level is determined to be the second mutation level; if not, it is determined whether the number of COSMIC cases of the gene mutation to be reported is greater than or equal to a first value; if it is greater than or equal to the first value, the mutation level is determined to be the second mutation level; if it is less than the first value, the mutation level is determined to be the third mutation level, where the third mutation level indicates that the clinical significance of the mutation is unclear.

7. The method according to claim 5, characterized in that, The mutation types include point mutations or in-frame mutations; the determination of whether the mutation type is included in the specified guidelines, and based on the result of this determination combined with the number of COSMIC cases of the mutation of the gene to be reported, to determine the mutation level of the gene to be reported, includes: Determine whether the mutation type is included in a designated authoritative guideline; If the mutation type is included in the designated authoritative guideline, it is determined whether the gene mutation to be reported is a hotspot mutation. If it is a hotspot mutation and its COSMIC case count is less than the second value, the mutation level is determined to be the second mutation level. If it is a hotspot mutation and its COSMIC case count is greater than or equal to the second value, it is further determined whether the gene mutation to be reported is included in the designated clinical diagnostic matching guideline. If yes, the mutation level is determined to be the first mutation level; if no, the mutation level is determined to be the second mutation level. The first mutation level indicates that the mutation has clear clinical significance, and the second mutation level indicates that the mutation has potential clinical significance. If the mutation type is not included in the specified authoritative guidelines, it is determined whether the gene mutation to be reported is included in the Oncokb database; if yes, the mutation level is determined to be the second mutation level; if no, it is determined whether the number of COSMIC cases of the gene mutation to be reported is greater than or equal to a third value; if it is greater than or equal to the third value, the mutation level is determined to be the second mutation level; if it is less than the third value, the mutation level is determined to be the third mutation level, where the third mutation level indicates that the clinical significance of the mutation is unclear.

8. The method according to claim 1, 2, 5, 6, or 7, characterized in that, The step of determining the suggested interpretation information for the reportable gene mutation in the target detection sample based on the mutation level includes: Obtain the current project name and determine the cancer type based on the project name; Generate matching tags based on the mutation level, the name of the gene mutation to be reported, and the cancer type; Search the pre-generated template library for suggested explanations of the matching tags.

9. The method according to claim 1, characterized in that, The determination of the target detection sample includes: The system detects the sample identifier entered by the second user in the second VBA toolbar and uses that sample identifier as the target sample identifier.

10. A system for generating NGS test reports, characterized in that, The system includes: The report information summary table generation module is used to determine the target batch identifier and integrate multiple discrete sub-table data of the bioinformatics analysis file based on the target batch identifier to generate a report information summary table corresponding to the target batch identifier. The report information summary table includes data on multiple gene mutations to be reported. The mutation level determination module is used to determine the mutation level of each gene mutation to be reported and record the mutation level in the report information summary table; The report data generation module is used to identify the target detection sample, determine the suggested explanation information of the gene mutation to be reported in the target detection sample according to the mutation level, and record the suggested explanation information in the report information summary table to generate report data.

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