Report checking method and system capable of automatically feeding back checking result based on snkemake language and application

Abstract

The invention discloses a report checking method capable of automatically feeding back a checking result based on a snapmake language. The method specifically comprises the following steps: step 1, file preparation: preparing a question settlement report and an analysis confirmation sheet excel table; 2, performing report basic information checking, quality control data checking, intra-group sample difference checking, report sample number and display sequence checking, report integrity checking and / or report picture checking in parallel and / or in series on the basis of the question settlement report obtained in the step 1 and an analysis confirmation single excel table; and step 3, generating a webpage version checking report according to one or more pieces of checking log information obtained in the step 2, and automatically sending feedback through a mail. The invention further discloses a system and application for implementing the report checking method, and the system and the application have wide application value.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of high-throughput sequencing scientific research services, and relates to a report checking method and system based on snakemake language and capable of automatically feeding back checking results and application. BACKGROUND

[0002] High-throughput sequencing, also known as "next-generation sequencing", is a revolution of traditional sequencing. Compared with traditional Sanger sequencing, the throughput of new-generation sequencing technology is improved by one to two orders of magnitude, and can economically cover the genome with high sequence coverage. With the gradual stabilization of the performance of high-throughput sequencing instruments and the continuous decline in price, its application is becoming more and more widespread, and therefore research based on high-throughput sequencing data will present an explosive rapid development trend in quantity and application.

[0003] In the field of scientific research services, with the continuous development of high-throughput sequencing services, various sequencing service products will be derived, and each product will send the corresponding customer in the form of a final report. Under the condition of a large number of projects and data, it is necessary to ensure the accuracy and timeliness of each project final report.

[0004] The existing report checking process has the following defects: (1) low efficiency: each report needs to be manually checked, which consumes labor, and manual checking of each report takes about 30 minutes on average; (2) prone to errors: manual checking is difficult to avoid human error checking, such as missing files and visually judging whether the pictures are abnormal; (3) incomplete checking: it is difficult for manual checking to check the completeness of the entire report and each picture, especially for final reports with a large number of files and pictures; (4) no display of checking results: there is no visual display of a complete checking report for the entire report; (5) no email feedback mechanism: manual checking of final reports cannot provide timely email feedback. SUMMARY

[0005] In order to solve the problems in the prior art, the purpose of the present application is to provide a report checking method, system and application based on snakemake language and capable of automatically feeding back checking results.

[0006] The technical implementation difficulty of the present application is as follows:

[0007] 1. Set standards: formulate complete and accurate standards that need to be met by final reports, write the standards into codes, and check the reports in the form of automatic code running, and then feed back the checking results by email.

[0008] The specific standards include the following:

[0009] ① The basic information of the report is correct, such as order number, customer name, contact person, sample quantity, sequencing region, etc.

[0010] 2. The number of samples and the order of samples in the picture must meet the requirements of the analysis confirmation sheet.

[0011] 3. The difference between the samples in the group must be within a controllable range, otherwise it is considered an abnormal sample.

[0012] 4. The number of analysis samples and the order of samples in the picture must meet the requirements of the analysis confirmation sheet.

[0013] 5. The analysis content and the number of files in the report must be complete and cannot be missing.

[0014] 6. The pictures in the report must be clear and complete, and cannot have overlapping, covering, overflowing, etc.

[0015] 2. Write judgment: The number of report files generated by different sample quantities and grouping schemes will be different, such as some samples without grouping information, which will miss the multivariate statistical analysis; some samples are grouped into 3 groups, which can be analyzed by ternary phase diagram, which needs to adjust the number of report files in time.

[0016] 3. Picture abnormality judgment: Due to different character lengths of sample name, grouping name, species name, etc., or too many samples, which may cause picture display abnormalities, such as overlapping between text blocks, intersection between text blocks and graphics, overlapping between graphics and graphics, and text block exceeding page, etc. This needs to write code to judge the pdf picture and mark the abnormal picture.

[0017] 4. Automation: No human operation is needed, only waiting for the completion of the report, the process automatically enters the report checking stage, and the checking result is automatically mailed to the analysis personnel, with no human operation throughout, fully utilizing non-working days or non-working hours.

[0018] The present application provides a report checking method based on snakemake language automatic feedback checking result, which specifically comprises the following steps:

[0019] Step 1, file preparation: prepare the final report and analysis confirmation sheet excel table;

[0020] In step 1, the final report includes web display report and result analysis; the analysis confirmation sheet excel table includes analysis basic information, sample information, sample grouping information, grouping comparison information and other analysis confirmation contents;

[0021] Step two, based on the report and analysis confirmation single excel table obtained in step one, serial and / or parallel report basic information verification, quality control data verification, group sample difference verification, report sample number and display order verification, report integrity verification and / or report picture verification;

[0022] Each verification step is independent of each other, and the order can be interchanged. Finally, one or more verification results will form a verification report.

[0023] Among them,

[0024] Report basic information verification:

[0025] Use self-compiled python script to read the project information in the "Microbial Diversity Report.html" file in the report and the analysis basic information in the analysis confirmation single excel table, extract the order number, customer name, contact person, sample quantity, sequencing region in the project information and the analysis basic information, and use python judgment statement to judge whether the value information of the two is consistent. The consistency of the information is compared, and the report basic information log information is obtained. If consistent, feedback log file(√), if not consistent, feedback log file(×);

[0026] The project information in the "Microbial Diversity Report.html" file in the report includes order number, customer name, contact person, sample quantity, sequencing region, and the analysis basic information in the analysis confirmation single excel table includes order number, customer name, contact person, sample quantity, sequencing region, sample species, project type. For the information in the analysis confirmation single excel table, only the first five can be judged to ensure that the basic information of the report and the analysis confirmation single is consistent.

[0027] Quality control data verification:

[0028] Each sample is monitored for quality control, that is, the running quality control statistical results in the report results of each sample are checked with the reference quality control indicators to obtain the quality control monitoring result log information. If the running quality control statistical results in the report results of each sample meet the reference quality control indicators, feedback log file(√), if there is a sample that does not meet the reference indicators, feedback log file(×), and list the sample name that does not meet the sample; The reference quality control indicators include raw data volume and data volume after quality control;

[0029] Group sample difference verification:

[0030] Set the difference degree abnormal threshold, compare the difference degree of all sample group abundance, obtain the difference degree comparison result log information; if one of the sample abundance is greater than 1.5 times of the average value of group abundance or less than 0.5 times of the average value of group abundance, it is considered abnormal, feedback log file(warning), and list the abnormal sample name for manual checking; if there is no sample abundance greater than 1.5 times of the average value of group abundance or less than 0.5 times of the average value of group abundance in all samples, it is considered normal, feedback log file(√).

[0031] Report sample number and display order check:

[0032] Based on the sample number and display order in the report result and the sample number and sample order in the analysis confirmation sheet, check whether they are consistent, obtain the consistency comparison log information; if consistent, feedback log file(√), if inconsistent, feedback log file(×), and list the inconsistent information;

[0033] Report integrity check:

[0034] The number of files in each analysis module in the final report is analyzed, the number of files in the analysis module is compared with the consistency of the final report parameter index, and the number consistency comparison log information is obtained; if the number of files is consistent with the reference index of this type of final report, it is considered that the result of this analysis module is complete, feedback log file(√), if not consistent, feedback log file(×);

[0035] The functions of the analysis module include data quality control, species annotation, community structure, alpha diversity analysis, beta diversity analysis, multivariate statistical analysis, correlation and model prediction analysis, and function prediction analysis;

[0036] Report picture check:

[0037] Based on the self-compiled python script, all pdf pictures in the final report are identified to judge whether the pictures are abnormal, the abnormal pictures are displayed and fed back, including feedback log file(warning), and output abnormal png picture display;

[0038] The abnormality of the picture includes overlap between text blocks, intersection between text blocks and graphics, overlap between graphics, and text blocks beyond the page.

[0039] Pre-use python code to write detection script, in the picture exception checking process, using fitz library to read the original pdf picture, selecting and extracting the text and image in the pdf page, and labeling the graphic elements; Shapely library analyzes and processes the position relationship between the text and / or image, and outputs the detection result information with Logging module, when the detection result is abnormal, generates a picture with highlighted abnormal part, helps users quickly identify and handle problems; and / or,

[0040] According to the actual situation, a plurality of exception ignoring rules are set in advance, and one or more ignoring rules are selected before the exception checking process to ignore part of the abnormal conditions.

[0041] Step three, according to one or more checking log information obtained in step two, generate a web version checking report, and automatically send feedback by email;

[0042] Step 3.1, generate a web version checking report:

[0043] The log information of the report basic information checking, the quality control data checking, the group sample difference checking, the report sample number and display order checking, the report integrity checking and / or the report picture checking in step two is comprehensively arranged, and an html web version report is generated, wherein the error (X) and warning (warning) information is displayed in red;

[0044] Step 3.2, automatically send the checking report by email:

[0045] The python package smtplib is used to automatically feed back the web version checking report by email.

[0046] In one preferred embodiment of the present application, the files contained in the file preparation step include: preparing the results of the closing report and the analysis confirmation single excel table.

[0047] In one preferred embodiment of the present application, all output abnormal pictures, the drawing image is drawn by using the PIL library of python language, and the abnormal position module is marked with red.

[0048] The present application also provides a report checking system for realizing the above method, the system comprises: a file preparation module, an information checking module, a report generation and sending feedback module;

[0049] The file preparation module is used to obtain the closing report and the analysis confirmation single excel table;

[0050] The information checking module performs basic information checking, quality control data checking, sample difference checking within a group, sample number and display order checking, report integrity checking, and report picture checking.

[0051] The report generation and sending feedback module is configured to generate a report according to the checking log generated by the information checking module and automatically feed back through an email.

[0052] The application further discloses a hardware system for implementing the report checking method.

[0053] The application further discloses a computer readable storage medium having a computer program stored thereon, and the computer program is executed by a processor to implement the report checking method.

[0054] The application further discloses an application of the report checking method, the report checking system, the hardware system or the computer readable storage medium in automatic checking of a high-throughput sequencing technology report.

[0055] The application has the following advantages:

[0056] The application greatly improves the checking efficiency of a report, and does not need to manually check the report, but directly checks the report through an automatic process, thereby saving labor cost.

[0057] The application can avoid errors caused by manual checking, and it is difficult to avoid checking errors caused by human factors.

[0058] The application can obtain comprehensive results, and it is difficult for manual checking to check the file integrity of the entire report and each picture, especially for a report with a large number of files and pictures.

[0059] The application can automatically arrange and summarize all checking results, and display the checking results in the form of a webpage report, and mark red or display pictures for errors and abnormal information.

[0060] The application processes related data and information through a self-programmed script, converts the original manual checking work into automatic computer checking, unifies the checking standard, improves the checking efficiency and the readability of a checking report.

[0061] The application automatically sends an email based on a python package smtplib to quickly feed back the checking results.

[0062] All operation steps of the application can be traced, and error query is facilitated. BRIEF DESCRIPTION OF DRAWINGS

[0063] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, 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 the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0064] Figure 1 This is a schematic diagram of the process of the present invention.

[0065] Figure 2 These are abnormal images from the intra-group sample difference verification in an embodiment of the present invention.

[0066] Figure 3 For the present invention Figure 2 Normal images of the comparative embodiment.

[0067] Figure 4 The image verification for this embodiment of the invention shows an abnormal overflow of text blocks, resulting in incomplete text display.

[0068] Figure 5 For the present invention Figure 4 Normal images of the comparative embodiment.

[0069] Figure 6 The image verification for this embodiment of the invention shows that the text block and the image are abnormally overlapping, resulting in incomplete text display.

[0070] Figure 7 For the present invention Figure 6 Normal images of the comparative embodiment.

[0071] Figure 8 The image verification for this embodiment of the invention revealed abnormal image overlap between text blocks, resulting in unclear text display.

[0072] Figure 9 For the present invention Figure 8 Normal images of the comparative embodiment.

[0073] Figure 10 This is an image of an anomaly report from the completion report of this invention, where errors (×) and warnings are displayed in red.

[0074] Figure 11 For the present invention Figure 10 Comparative examples of project completion reports and normal verification reports are shown in the images. Detailed Implementation

[0075] The application will be further described in detail in combination with the following specific examples and drawings. The process, conditions, experimental methods, etc. for implementing the application are the general knowledge and common sense in the art, and the application does not have special restrictions.

[0076] The application discloses a report checking method capable of automatically feeding back checking results based on a snakemake language, and specifically comprises the following steps: Step 1, file preparation: preparing a conclusion report and an analysis confirmation single excel table; Step 2, based on the conclusion report and the analysis confirmation single excel table obtained in Step 1, performing report basic information checking, quality control data checking, group sample difference checking, report sample number and display order checking, report integrity checking and / or report picture checking in parallel and / or in series; Step 3, generating a web version checking report according to one or more checking log information obtained in Step 2, and automatically sending feedback through an email. The application further discloses a system for implementing the above report checking method and an application. The analysis method can automatically check and process the conclusion report, and can automatically generate a html web version checking report for visual display. The feedback checking report result is comprehensive, the checking report is automatically sent to the email of an analyst, and meanwhile, all operation steps can be traced, so that error query is facilitated.

[0077] The application provides a report checking system capable of automatically feeding back checking results based on a snakemake language and an application thereof, and specifically comprises the following steps:

[0078] Step 1, file preparation: preparing a conclusion report and an analysis confirmation single excel table;

[0079] Step 2, based on the conclusion report and the analysis confirmation single excel table obtained in Step 1, performing report basic information checking, quality control data checking, group sample difference checking, report sample number and display order checking, report integrity checking and / or report picture checking in parallel and / or in series;

[0080] Report basic information checking:

[0081] A self-programmed python script is used to read project information in the conclusion report and analysis basic information in the analysis confirmation single excel table, and then whether the two are consistent is compared, if yes, a log file ( ) is fed back, and if not, a log file ( ) is fed back;

[0082] Quality control data checking:

[0083] Quality control monitoring is performed for each sample, i.e. the quality control statistical results of the final report are checked against the reference quality control indicators. If all the reference indicators are met, the log file is fed back ( √ ); if there are samples that do not meet the reference indicators, the log file is fed back ( × ), and the sample names that do not meet the reference indicators are listed;

[0084] Intra-group sample difference check:

[0085] The intra-group abundance of all samples is compared for difference. If the abundance of a sample is greater than 1.5 times the average intra-group abundance or less than 0.5 times the average intra-group abundance, it is considered abnormal, the log file is fed back ( warning ), and the names of the abnormal samples are listed for manual checking;

[0086] Report sample number and display order check:

[0087] The sample number and display order in the abundance table in the report result are checked against the sample number and sample order in the analysis confirmation sheet. If they are consistent, the log file is fed back ( √ ); if they are not consistent, the log file is fed back ( × ), and the inconsistent information is listed;

[0088] Report integrity check:

[0089] The number of files in each analysis module in the final report is analyzed. If the number of files is consistent with the reference indicators for this type of final report, the results of this analysis module are considered complete, and the log file is fed back ( √ ); if they are not consistent, the log file is fed back ( × );

[0090] Report picture check:

[0091] Based on the self-compiled python script, all pdf pictures in the final report are identified to determine whether the pictures are abnormal. For abnormal pictures, the log file is fed back ( warning ), and the abnormal png pictures are output for display;

[0092] The relevant description of the image abnormality identification method in the picture check includes the following:

[0093] Scheme design: check whether there are any abnormalities in the display of pdf pictures, which can be divided into the following cases: 1. Text block overlap; 2. Text block outside the page; 3. Text block intersecting with graphics. If an abnormal picture is detected, the WARNING information is output, and the picture with overlapping text blocks highlighted in red boxes is output.

[0094] Techniques applied: The detection script is written using python code, which mainly calls python libraries including: fitz library to read original pdf images, accurately select and extract text or images on PDF pages, draw annotations and highlight graphical elements on PDF; PIL library to create image objects for saving; Shapely library for processing and analyzing geometric objects, providing powerful spatial data processing functions, supporting various geometric operations such as intersection, union, difference, etc.; logging module to track program runtime events, including errors, warnings, informational messages, etc.

[0095] Specific operation:

[0096] Usage: pdf_overlap_checker.py [OPTIONS] [FILES]...

[0097] Options:

[0098]

[0099] In the specific implementation process, if it is considered that some overlapping situations in the image are reasonable, one or more running options can be selected, such as --ignore_outside, --ignore_text_overlap, --ignore_shape_overlap, etc. Options to ignore these exceptions;

[0100] If there are many graphical elements in the image, it may cause the time-consuming of the check to be very long. Similarly, if it is considered that the overlap of text blocks with certain graphics is reasonable, or it is confirmed that the text blocks will not overlap, one or more running options can be selected, such as --ignore_curve, --ignore_line, --ignore_rect, --ignore_quad, etc. Options to skip the check of this graphics to speed up the check;

[0101] Then output the abnormal image to the. / pdf-overlap.warning.* directory through the --draw option, and highlight the overlapping part.

[0102] In one specific embodiment, the example is as follows:

[0103] Single image detection:

[0104] python pdf_overlap_checker.py plot.pdf

[0105] Single image ignores text blocks overlapping with graphics:

[0106] python pdf_overlap_checker.py --ignore_shape_overlap plot.pdf

[0107] Batch processing and ignoring the overlap check of curves:

[0108] find ${plot_path} -type f -name "*pdf" | python pdf_overlap_checker.py --ignore_curve

[0109] Step three, generate a web version of the verification report, and automatically send feedback by email

[0110] The log information fed back in step two is comprehensively arranged, and an html web version of the report is generated, wherein the (x) and (warning) information is displayed in red; the web version of the verification report is automatically fed back by email using the python package smtplib.

[0111] Example (detecting abnormality) and comparative example (detecting normality)

[0112] Take the 20-sample analysis conclusion report as an example to illustrate the process of the present application:

[0113] 1. Accept the user's high-throughput sequencing 20-sample analysis conclusion report and analysis confirmation single excel table;

[0114] 2. Use the self-programmed python script to read the project information in the "microbial diversity conclusion report.html" file in the conclusion report and the analysis basic information in the analysis confirmation single excel table, extract the order number, customer name, contact person, sample quantity, sequencing region 5 basic information and their corresponding value values, use the python judgment statement to judge whether the value information of the two is consistent, and perform basic information verification on the above analysis conclusion report;

[0115] 3. Use the self-programmed python script to read the quality control table in the conclusion report, judge whether the number of original sequences and the number of sequences after removing the chimera meet the reference quality control indicators, and perform quality control verification on each sample of the analysis conclusion report;

[0116]

[0117] The results of the above table can show whether the number of original data sequences meets the contract requirement for sequencing amount. If the data amount is insufficient, the percentage of sequences after quality filtering over the original sequences can roughly indicate the sequencing quality. The higher the percentage, the better the sequencing quality. The percentage of sequences after removing chimeras over the sequences after assembly can indicate the chimeric contamination in PCR amplification. The higher the ratio, the better the sequencing quality.

[0118] 4. Use the self-programmed python script to check the sample difference in the final report group. If the sample abundance is greater than 1.5 times the average abundance in the group or less than 0.5 times the average abundance in the group, it is considered abnormal. See Figure 2 , and the comparative example is shown in Figure 3 ;

[0119] Figure 2 The abnormal picture for sample difference in the group is shown. The vertical coordinate is the number of species, the horizontal coordinate is the sample name, and different colors represent the number of species detected at different classification levels. The number of species in sample A9 is abnormal in group A, and the number of species detected in the sample is significantly greater than 1.5 times the average value of the samples in the group.

[0120] 5. Use the self-programmed python script to check the number of samples and the display order in the final report. Whether the number of samples and the display order in the abundance table in the final report are consistent with the number of samples and the sample order in the analysis confirmation sheet;

[0121] 6. Use the self-programmed python script to check the integrity of the final report. Analyze the number of files in each analysis module in the final report to determine whether the number of files is consistent with the reference index for this type of final report;

[0122] 7. Use the self-programmed python script to identify all pdf images in the final report. Determine whether the images are abnormal, display and feedback the abnormal images, and output the abnormal png images. See Figure 4 , 6 ,8

[0123] Figure 4 The cumulative column chart for species is shown. The horizontal coordinate is the sample name, the vertical coordinate is the relative abundance, and different colors represent different species. The abnormality is shown in the picture of the text block overflow, which makes the picture display information incomplete. The abnormal information position is marked with a red box. See the right side text part. The comparative example is shown in Figure 5 ;

[0124] Figure 6For species difference heat map: the horizontal coordinate is the sample name, the vertical coordinate is the species name, different colors represent the abundance of different species and sample grouping, the abnormality is that the text block overlaps with the figure, which makes the picture display information incomplete, the abnormal information position is marked with a red box, see the right side text part, the comparative example is shown in Figure 7 ;

[0125] Figure 8 For correlation network diagram: the connection line represents the significant correlation between species and species, different colors represent the corresponding door level of the species, the abnormality is that the text block overlaps with the text block, which makes the picture display information fuzzy, the abnormal information position is marked with a red box, see the red box overlapping area in the upper left corner, the comparative example is shown in Figure 9 ;

[0126] 8. Generate a web version of the verification report, summarize all verification information and send feedback by email;

[0127] As shown in Figure 10 and Figure 11 , wherein the abnormal report picture of the final report verification of the embodiment of the present application is shown, wherein the error (X) and warning (warning) information are displayed in red; Figure 11 is the normal report picture of the final report verification of the comparative example of Figure 10 .

[0128] Compared with the existing manual inspection, the report checking method in the present application is obviously superior to the existing manual inspection in terms of inspection time, whether it can be processed at non-working time, verification accuracy, cost, etc. As shown in the following table:

[0129] Gain effect comparison table / portion

[0130]

[0131] The various aspects of the present application are described with reference to flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each block of the flowchart and / or block diagram, as well as the combination of blocks in the flowchart and / or block diagram, can be implemented by computer readable program instructions.

[0132] These computer readable program instructions can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks. These computer readable program instructions can also be stored in a computer readable storage medium that can include a non-transitory computer readable storage medium that can be a computer readable storage medium having no data signals on it. The instructions can be executed by one or more processors to produce a computer-implemented process such that the instructions, which execute via one or more processors of the computer or other programmable data processing apparatus, create means for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks. The computer readable program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus or other device to produce a computer implemented process such that the instructions which execute on the computer or other programmable data processing apparatus implement the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0133] The computer readable program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus or other device to produce a computer implemented process such that the instructions which execute on the computer or other programmable data processing apparatus implement the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0134] The computer readable program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus or other device to produce a computer implemented process such that the instructions which execute on the computer or other programmable data processing apparatus implement the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0135] The present application is not limited to the above embodiments. Changes and modifications can be made by those skilled in the art to the above embodiments without departing from the spirit and scope of the application.

Claims

1. A report checking method based on snakemake language automatic feedback verification result, characterized in that, The method specifically comprises the following steps: Step one, file preparation: prepare the final report and analysis confirmation single excel table; Step two, based on the final report and analysis confirmation single excel table obtained in step one, perform report basic information verification, quality control data verification, group sample difference degree verification, report sample number and display order verification, report integrity verification and / or report picture verification in parallel and / or series; Step three, generate a web version verification report according to one or more verification log information obtained in step two, and automatically send feedback through email.

2. The reporting check method of claim 1, wherein, In step one, the final report includes a web display report and result analysis result; the analysis confirmation single excel table includes analysis basic information, sample information, sample grouping information, and grouping comparison information analysis confirmation content; In the analysis confirmation single excel table, the analysis basic information includes order number, customer name, contact person, sample type, sequencing amount requirement, and sample order quantity.

3. The reporting check method of claim 1, wherein, In step two, each verification step is independent of each other; Report basic information verification: read the project information in the final report obtained in step one and the analysis basic information in the analysis confirmation single excel table, and perform consistency comparison on the information to obtain report basic information log information; and / or, Quality control data verification: for each sample, compare the running quality control statistical result in the final report result with the reference quality control index to perform quality control monitoring and obtain quality control monitoring result log information; and / or, Group sample difference degree verification: set a difference degree abnormal threshold, compare the abundance within each sample group, and obtain difference degree comparison result log information; and / or, Report sample number and display order verification: compare the sample number and display order in the abundance table in the report result with the sample number and sample order in the analysis confirmation single to obtain consistency comparison log information; and / or, Report integrity verification: analyze the number of files in each analysis module in the final report, and perform consistency comparison on the number of files in the analysis module with the final report parameter index to obtain number consistency comparison log information; and / or, Report picture verification: identify all pdf pictures in the final report, judge whether the pictures are abnormal, display and feedback the abnormal pictures, obtain log file of feedback abnormal information, and output abnormal picture display.

4. The reporting check method of claim 3, wherein, In the report basic information verification, the order number, customer name, contact person, sample quantity, and sequencing region in the project information and the analysis basic information are judged for consistency; and / or, In the quality control data verification, the reference quality control index includes raw data amount and data amount after quality control, the running quality control statistical result conforms to the reference quality control index feedback log file normal, and does not conform to the feedback abnormal and lists the sample names that do not conform; and / or, In the group sample difference degree verification, the difference degree abnormal threshold is set to be 1.5 times of the average sample abundance in the group or 0.5 times of the average sample abundance in the group, and when the difference degree result is not within the difference degree abnormal threshold range, feedback is normal; and / or, In the report integrity verification, the functions of the analysis module include data quality control, species annotation, community structure, alpha diversity analysis, beta diversity analysis, multivariate statistical analysis, correlation and model prediction analysis, and functional prediction analysis; and / or, In the report picture verification, the picture abnormalities include overlapping between text blocks, intersection between text blocks and graphics, overlapping between graphics, and text blocks exceeding the page.

5. The reporting check method of claim 3, wherein, In the picture abnormality verification process, the original pdf picture is read, the text and images in the pdf page are selected and extracted, the graphic elements are labeled, the positional relationship between the text and / or images is analyzed and processed, and the detection result information is output; and / or, A plurality of abnormality ignoring rules are preset, and one or more ignoring rules are selected before the abnormality verification process to ignore part of the abnormal conditions.

6. The reporting check method of claim 1, wherein, In step three, the log information of the report basic information verification, the quality control data verification, the group sample difference degree verification, the report sample number and display order verification, the report integrity verification, and / or the report picture verification fed back in step two are sorted, an html web page version report is generated, errors and warnings on the web page version report are marked with red, and the web page version verification report is automatically fed back by email using the python package smtplib.

7. A report checking system for implementing the report checking method according to any one of claims 1 to 6, characterized by The report checking system comprises a file preparation module, an information verification module, and a report generation and sending feedback module; The file preparation module is used to obtain the final report and the analysis confirmation single excel table; The information verification module performs report basic information verification, quality control data verification, group sample difference degree verification, report sample number and display order verification, report integrity verification, and report picture verification; The report generation and sending feedback module is used to generate a report according to the verification log generated by the information verification module and automatically feed back by email.

8. A hardware system for implementing the method of reporting checks as claimed in any one of claims 1 to 6, characterised in that, The hardware system comprises a memory and a processor; the memory stores a computer program, and when the computer program is executed by the processor, the report checking method of any one of claims 1-6 is realized.

9. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to realize the report checking method of any one of claims 1-6.

10. Use of the report checking method of any one of claims 1-6, the report checking system of claim 7, the hardware system of claim 8, or the computer readable storage medium of claim 9 in automatic verification and picture verification of high-throughput sequencing technology final report.

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