Data analysis method integrating targeted sequencing and whole genome sequencing

Abstract

The invention discloses a data analysis method integrating targeted sequencing and whole genome sequencing. The data analysis method comprises the following steps: acquiring sequencing data information; judging whether the sequencing data can be split and identified or not, and if yes, splitting the sequencing data into targeted sequencing data and WGS sequencing data; respectively comparing the targeted sequencing data with the WGS sequencing data to obtain a targeted comparison result and a WGS comparison result; combining the target comparison result and the WGS comparison result to obtain a first comparison result, and performing variation detection and quality control on the first comparison result to obtain high-quality variation data; if not, sending the sequencing data to a preset second module to obtain a second comparison result, and performing variation detection and quality control on the second comparison result to obtain high-quality variation data; and annotating the high-quality variation data and sending the annotated data to a preset management end for downstream breeding application. Targeting and whole genome sequencing are combined, so that the variation detection effect of sequencing data is effectively improved.

Description

Technical Field

[0001] The present invention relates to the fields of biomedicine, agricultural breeding, and more specifically, to a data analysis method integrating targeted sequencing and whole genome sequencing. Background Art

[0002] At present, the most commonly used genotype detection methods for animal and plant breeding include targeted sequencing and whole genome sequencing. Targeted sequencing is more targeted and can quickly screen the prospect sites corresponding to breeding materials, but it cannot understand the genetic background of the whole genome; while whole genome sequencing is more comprehensive in variant detection and can be used as a background selection for hybridization and backcrossing breeding processes. Combining the two detection technologies together, one sequencing will generate targeted and whole genome sequencing reads at the same time, and using the complementary characteristics of the two sequencing data, mutual verification and effective supplementation through data analysis can greatly improve the efficiency and accuracy of variant detection and reduce the cost of genetic testing.

[0003] Therefore, the prior art has defects and needs to be improved urgently. Summary of the invention

[0004] In view of the above problems, the purpose of the present invention is to provide a data analysis method that integrates targeted sequencing and whole genome sequencing, which can effectively improve the variation detection effect of sequencing data.

[0005] The present invention provides a data analysis method integrating targeted sequencing and whole genome sequencing, comprising:

[0006] Obtain sequencing data information;

[0007] Determine whether the sequencing data can be split and identified. If so, split the sequencing data into targeted sequencing data and WGS sequencing data;

[0008] Compare the targeted sequencing data and the WGS sequencing data respectively to obtain the targeted comparison results and the WGS comparison results;

[0009] The targeted alignment results and the WGS alignment results are combined to obtain the first alignment result, and the first alignment result is subjected to variation detection and quality control to obtain high-quality variation data;

[0010] If not, the sequencing data is sent to a preset second module to obtain a second comparison result, and the second comparison result is subjected to variation detection and quality control to obtain high-quality variation data;

[0011] Annotate high-quality variant data and send them to the preset management terminal for downstream breeding applications.

[0012] In this solution, after obtaining the targeted comparison results and the WGS comparison results, it also includes:

[0013] Based on the preset first software tool, all variant sites in the targeted comparison results and the WGS comparison results are extracted to obtain the targeted detection results and the WGS detection results;

[0014] The intersection of the targeted detection results and the WGS detection results was taken to obtain the common variant sites;

[0015] Based on the preset second software tool, the common variant sites are tested for consistency of variation at the site level and the preset sample level. If they are consistent, the corresponding WGS test results are accurate;

[0016] If there is any inconsistency, the corresponding WGS test result is wrong, and the inconsistent position is marked for prompting.

[0017] In this solution, the step of splitting the sequencing data into targeted sequencing data and WGS sequencing data specifically includes:

[0018] Extract the adapter sequence of reads in the sequencing data;

[0019] Compare and analyze the adapter sequences and index tags of reads in the sequencing data with the preset targeted sequencing library and WGS sequencing library adapter sequences respectively;

[0020] If the index tag of the adapter sequence of the reads in the sequencing data is the same as the index of the adapter sequence in the targeted sequencing library, the corresponding sequencing reads are set as targeted sequencing data;

[0021] If the index tag of the adapter sequence of the reads in the sequencing data is the same as the index of the adapter sequence in the WGS sequencing library, the corresponding sequencing reads are set as WGS sequencing data.

[0022] This plan also includes:

[0023] Extracting a comparison index and a corresponding comparison index value in the first comparison result or the second comparison result;

[0024] Multiply the comparison index value by the preset weight coefficient of the corresponding comparison index to obtain the corresponding comparison index proportion value;

[0025] Accumulate the proportion values ​​of all comparison indicators to obtain the accuracy index of the first comparison result or the second comparison result;

[0026] If the accuracy index of the first alignment result or the second alignment result is less than the preset accuracy index threshold, the reads that are not aligned or aligned incorrectly are removed, and the accuracy index of the first alignment result or the second alignment result is recalculated until the accuracy index is greater than the preset threshold, thereby obtaining the reads after quality control;

[0027] The quality-controlled reads were used as the final clean data for variant detection.

[0028] This plan also includes:

[0029] Obtain targeted and whole genome alignment results, and calculate alignment rate, depth, and coverage;

[0030] If the alignment rate, depth, and coverage in the targeted alignment results are less than the preset thresholds, reads that are not aligned or aligned incorrectly in the targeted sequencing will be removed for subsequent variant detection;

[0031] If the alignment rate, depth, and coverage in the whole genome alignment results are less than the preset thresholds, the reads that are not aligned or aligned incorrectly in the whole genome sequencing will be removed for subsequent variation detection.

[0032] In this solution, the quality control step also includes:

[0033] Extract reads from sequencing data that are not aligned to the reference genome;

[0034] Locally assemble the reads in the sequencing data that are not aligned to the reference genome, and perform Blast alignment with the preset NT database to determine the source of the unaligned reads;

[0035] After aligning the read sources that are not aligned to the reference genome through the Black NT database and the sequence after local assembly, if non-target species alignment information is obtained, the corresponding sequencing data is contaminated, and an invalid sequencing read warning message is generated, and the invalid sequencing warning message is sent to the preset management terminal for prompting;

[0036] If the unaligned reads cannot obtain the homologous sequences of non-target species, the corresponding sequencing reads will be set as high-quality sequencing data.

[0037] In this solution, the quality control step also includes:

[0038] Extract the reads insertion fragment length recorded in the targeted alignment result bam;

[0039] If the length of the reads insert fragment is greater than the preset target probe sequence length, the corresponding reads will be deleted.

[0040] This plan also includes:

[0041] Based on the preset statistical software, the depth and coverage of the target interval and non-target interval are obtained;

[0042] According to the depth and coverage of the target interval and the non-target interval, the depth ratio and coverage ratio of the two intervals are obtained;

[0043] If the depth ratio of the two intervals is greater than the preset first ratio threshold and the coverage ratio is greater than the preset first ratio threshold, then the corresponding library is built once, that is, the sequencing data of the second module is normal.

[0044] In this solution, after obtaining high-quality variation data, the following steps are also included:

[0045] Extracting biometric values ​​corresponding to high-quality variant data;

[0046] Based on the same biometric feature, the biometric feature value of the high-quality variant data and the corresponding standard biometric feature value are calculated to obtain the corresponding biometric feature difference;

[0047] If the corresponding biometric feature difference is greater than the corresponding biometric feature difference threshold, the corresponding biometric feature is set as a variant feature;

[0048] The mutation features are annotated on all mutation sites in the corresponding high-quality mutation data.

[0049] In this solution, the breeding application step also includes:

[0050] Based on the preset third software tool, the variant sites are genotyped and phased, the target samples are determined, and the corresponding reference panels are matched according to the target samples;

[0051] Based on the preset genotype filling algorithm and the corresponding reference panel, the missing genotypes in the target sample are predicted to obtain high-density genotypes, which are then filled or covered to obtain variant breeding gene data;

[0052] The variant breeding gene data is sent to a preset downstream end for breeding application.

[0053] The present invention discloses a data analysis method integrating targeted sequencing and whole genome sequencing, which effectively improves the variation detection effect of sequencing data by combining targeted and whole genome sequencing. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 A flow chart of a data analysis method integrating targeted sequencing and whole genome sequencing is shown in the present invention;

[0055] Figure 2 A flowchart showing the steps of splitting sequencing data into targeted sequencing data and WGS sequencing data according to the present invention is shown. DETAILED DESCRIPTION

[0056] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0057] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0058] Figure 1 A flow chart of a data analysis method integrating targeted sequencing and whole genome sequencing is shown in the present invention.

[0059] like Figure 1 As shown, the present invention discloses a data analysis method integrating targeted sequencing and whole genome sequencing, comprising:

[0060] S101, obtaining sequencing data information;

[0061] S102, determining whether the sequencing data can be split and identified, and if so, splitting the sequencing data into targeted sequencing data and WGS sequencing data;

[0062] S103, aligning the targeted sequencing data and the WGS sequencing data respectively to obtain a targeted alignment result and a WGS alignment result;

[0063] S104, merging the targeted alignment result and the WGS alignment result to obtain a first alignment result, and performing variation detection and quality control on the first alignment result to obtain high-quality variation data;

[0064] S105, if not, sending the sequencing data to a preset second module to obtain a second comparison result, performing variation detection and quality control on the second comparison result to obtain high-quality variation data;

[0065] S106, annotate the high-quality variation data and send it to the preset management terminal for downstream breeding applications.

[0066] According to an embodiment of the present invention, the data to be sequenced are divided into one type of data to be sequenced that can distinguish between two sequencing reads and one type of data to be sequenced that cannot distinguish between the two sequencing reads according to whether they can be identified, wherein the data to be sequenced that can distinguish between the two sequencing reads are split into targeted sequencing data and whole genome (WGS) sequencing data; the data to be sequenced that cannot distinguish between the two sequencing reads are sent to a preset second module, and the variation detection range and effect of the data to be sequenced are improved by combining multiple sequencing methods.

[0067] According to an embodiment of the present invention, after obtaining the targeted alignment result and the WGS sequencing alignment result, the method further includes:

[0068] Based on the preset first software tool, all variant sites in the targeted comparison results and the WGS comparison results are extracted to obtain the targeted detection results and the WGS detection results;

[0069] The intersection of the targeted detection results and the WGS detection results was taken to obtain the common variant sites;

[0070] Based on the preset second software tool, the common variant sites are tested for consistency of variation at the site level and the preset sample level. If they are consistent, the corresponding WGS test results are accurate;

[0071] If there is any inconsistency, the corresponding WGS test result is wrong, and the inconsistent position is marked for prompting.

[0072] It should be noted that, in this embodiment, the preset first software tool is bcftoo ls, and the preset second software tool is GATK. Furthermore, when recording the marked inconsistent positions in the WGS detection results, the marked number of times is recorded. If the marked number of times is greater than the preset number threshold, the WGS sequencing data corresponding to the WGS detection result is deleted. If the marked number of times is less than or equal to the preset number threshold, the corresponding inconsistent position will be marked and displayed.

[0073] Figure 2 A flowchart showing the steps of splitting sequencing data into targeted sequencing data and WGS sequencing data according to the present invention is shown.

[0074] like Figure 2 As shown, in this embodiment of the present invention, the step of splitting the sequencing data into targeted sequencing data and WGS sequencing data specifically includes:

[0075] S201, extracting the adapter sequences of reads in the sequencing data;

[0076] S202, comparing and analyzing the adapter sequences and index tags of reads in the sequencing data with the preset targeted sequencing library and WGS sequencing library adapter sequences respectively;

[0077] S203, if the index tag of the adapter sequence of the reads in the sequencing data is the same as the index tag of the adapter sequence in the targeted sequencing library, the corresponding sequencing reads are set as targeted sequencing data;

[0078] S204, if the index tag of the adapter sequence of the reads in the sequencing data is the same as the index tag of the adapter sequence in the WGS sequencing library, the corresponding sequencing reads are set as WGS sequencing data.

[0079] It should be noted that the mixed sequencing library is composed of a targeted sequencing library and a whole genome sequencing library. When constructing the targeted sequencing library and the whole genome sequencing library, the same sample is subjected to targeted sequencing library construction and whole genome sequencing library construction respectively, and the adapter sequences use two different index tags respectively, and during the sequencing process, the index sequence information fastq file header line; the sequencing data is sequenced once on the machine The result contains reads of the two library construction results, and the command line, such as seqkit grep, is used to separate the reads of the two index tags, that is, two different sequencing data are obtained: targeted sequencing data and WGS sequencing data.

[0080] Extracting a comparison index and a corresponding comparison index value in the first comparison result or the second comparison result;

[0081] Multiply the comparison index value by the preset weight coefficient of the corresponding comparison index to obtain the corresponding comparison index proportion value;

[0082] Accumulate the proportion values ​​of all comparison indicators to obtain the accuracy index of the first comparison result or the second comparison result;

[0083] If the accuracy index of the first alignment result or the second alignment result is less than the preset accuracy index threshold, the reads that are not aligned or aligned incorrectly are eliminated, and the operation is repeated until the accuracy index is greater than the preset threshold to obtain the reads after quality control;

[0084] The quality-controlled reads were used as the final clean data for variant detection.

[0085] It should be noted that the comparison indicators include comparison rate, sequencing depth, coverage, etc. For example, the comparison variation indicator is sequencing depth, and the indicator parameter corresponding to the targeted sequencing reads is the total number of bases in the targeted sequencing reads, and the indicator parameter corresponding to the first genome to be tested is the size of the first genome to be tested. For example, the size of the first genome to be tested is 1Mb (million bases), and the total number of bases in the targeted sequencing reads is 10Mb, then the corresponding sequencing depth is 10X; the weight coefficient of each indicator is set in advance, and the accuracy index is used as the benchmark. When it is less than the preset accuracy index threshold, the data to be sequenced corresponding to the comparison result is set as variation data, and the comparison result includes the first comparison result and the second comparison result.

[0086] According to an embodiment of the present invention, the invention comprises:

[0087] Obtain targeted and whole genome alignment results, and calculate alignment rate, depth, and coverage;

[0088] If the alignment rate, depth, and coverage in the targeted alignment results are less than the preset thresholds, reads that are not aligned or aligned incorrectly in the targeted sequencing will be removed for subsequent variant detection;

[0089] If the alignment rate, depth, and coverage in the whole genome alignment results are less than the preset thresholds, the reads that are not aligned or aligned incorrectly in the whole genome sequencing will be removed for subsequent variation detection.

[0090] It should be noted that the sequencing accuracy can be further improved by screening the unaligned or incorrectly aligned reads in the targeted sequencing reads or whole genome sequencing reads through the depth, coverage and alignment rate of the targeted and whole genome range.

[0091] According to an embodiment of the present invention, the quality control step further includes:

[0092] Extract reads from sequencing data that are not aligned to the reference genome;

[0093] Locally assemble the reads in the sequencing data that are not aligned to the reference genome, and perform Blast alignment with the preset NT database to determine the source of the unaligned reads;

[0094] After aligning the read sources that are not aligned to the reference genome through the Black NT database and the sequence after local assembly, the non-target species alignment information is obtained, and the corresponding sequencing data is contaminated, and an invalid sequencing read warning message is generated, and the invalid sequencing warning message is sent to the preset management terminal for prompting;

[0095] If the unaligned reads cannot obtain the homologous sequences of non-target species, the corresponding sequencing reads will be set as high-quality sequencing data.

[0096] It should be noted that reads that are not mapped to the reference genome (Unmaped reads) are extracted for local assembly and Blast alignment is performed with the NT database to determine the source of these reads and detect whether the samples are contaminated.

[0097] According to an embodiment of the present invention, the quality control step further includes:

[0098] Extract the reads insertion fragment length recorded in the targeted alignment result bam;

[0099] If the length of the reads insert fragment is greater than the preset target probe sequence length, the corresponding reads will be deleted.

[0100] It should be noted that quality control of sequencing data includes quality assessment, removal of adapter sequences, filtering of low-quality sequences, removal of N-containing sequences, removal of low-complexity sequences, removal of repetitive sequences, etc. When the length of the reads inserted is greater than the preset target probe sequence length, the corresponding reads will be deleted. This can eliminate interference caused by sites with high similarity to the target probe sequence, thereby improving the accuracy of the sequencing data.

[0101] According to an embodiment of the present invention, it also includes:

[0102] Based on the preset statistical software, the depth and coverage of the target interval and non-target interval are obtained;

[0103] According to the depth and coverage of the target interval and the non-target interval, the depth ratio and coverage ratio of the two intervals are obtained;

[0104] If the depth ratio of the two intervals is greater than the preset first ratio threshold and the coverage ratio is greater than the preset first ratio threshold, then the corresponding library is built once, that is, the sequencing data of the second module is normal.

[0105] It should be noted that the target interval and non-target interval were counted using depth and coverage statistical software, and the depth and coverage ratio of the two intervals were calculated to confirm that the sequencing depth of the target interval was much higher than that of the non-target interval; when the depth ratio was the target interval divided by the depth of the non-target interval, the coverage ratio was the coverage of the target interval divided by the coverage of the non-target interval, and the preset first ratio threshold was greater than 1; when the depth ratio was the non-target interval divided by the depth of the target interval, the coverage ratio was the coverage of the non-target interval divided by the coverage of the target interval, and the preset first ratio threshold was less than 1.

[0106] According to an embodiment of the present invention, after obtaining high-quality variation data, the method further includes:

[0107] Extracting biometric values ​​corresponding to high-quality variant data;

[0108] Based on the same biometric feature, the biometric feature value of the high-quality variant data and the corresponding standard biometric feature value are calculated to obtain the corresponding biometric feature difference;

[0109] If the corresponding biometric feature difference is greater than the corresponding biometric feature difference threshold, the corresponding biometric feature is set as a variant feature;

[0110] The mutation features are annotated on all mutation sites in the corresponding high-quality mutation data.

[0111] It should be noted that the biological characteristics include the stress resistance and tolerance of the target sample corresponding to the sequencing data, and the corresponding biological characteristic value is the value range of the corresponding stress resistance, such as the adapted temperature value range, light intensity range, etc.

[0112] According to an embodiment of the present invention, the breeding application step further includes:

[0113] Based on the preset third software tool, the variant sites are genotyped and phased, the target samples are determined, and the corresponding reference panels are matched according to the target samples;

[0114] Based on the preset genotype filling algorithm and the corresponding reference panel, the missing genotypes in the target sample are predicted to obtain high-density genotypes, which are then filled or covered to obtain variant breeding gene data;

[0115] The variant breeding gene data is sent to a preset downstream end for breeding application.

[0116] It should be noted that by marking the variant sites through annotations and then filling or covering the annotated variant sites respectively through the preset genotype filling algorithm, it is possible to carry out foreground and background selection of breeding materials, molecular marker-assisted backcrossing, population structure, genetic diversity and evolution analysis, gene discovery, whole genome selection analysis, etc.

[0117] According to an embodiment of the present invention, it also includes:

[0118] Based on multiple sets of sequencing data of the same target sample, high-quality variant data of any sequencing data is extracted as reference data, and then compared and analyzed with high-quality variant data of other sequencing data in turn;

[0119] If the variation sites of the reference data and the high-quality variation data of other sequencing data are the same, the annotations of redundant variation features in the high-quality variation data of the reference data or other sequencing data are deleted;

[0120] If the mutation sites of the high-quality mutation data of the reference data and other sequencing data are different, when the mutation features are the same, the different mutation sites and the annotations on the corresponding mutation sites are deleted; when the mutation features are different, the different mutation features are extracted, and the corresponding different mutation features are annotated to the corresponding different mutation sites respectively;

[0121] Traverse all groups of sequencing data until there is no annotation change, obtain the variation features annotated on the variation site, and superimpose the same variation features to obtain the number of times the same variation feature annotated on the variation site is present;

[0122] The number of times the same variant feature is annotated on the variant site is divided by the total number of times all variant features are annotated on the corresponding variant site to obtain the probability value of the same variant feature being annotated on the corresponding variant site;

[0123] If the probability value of the same variant feature annotated at the corresponding variant site is less than the preset probability threshold, the same variant feature is deleted at the corresponding variant site;

[0124] If the probability value of annotating the same mutation feature at the corresponding mutation site is greater than or equal to a preset probability threshold, the probability value of the mutation feature is annotated on the corresponding mutation site.

[0125] It should be noted that the mutation characteristics of the detected high-quality mutations are annotated to facilitate the subsequent evaluation of their possible pathogenicity or biological significance. The high-quality mutation data of any sequencing data is used as reference data, and is compared and analyzed with the high-quality mutation data of other sequencing data in turn. If the annotation of the mutation characteristics in the mutation site is modified, the modified high-quality mutation data are compared pairwise for annotation modification until there is no annotation change.

[0126] According to an embodiment of the present invention, after annotating the probability value of the variation feature on the corresponding variation site, the method further includes:

[0127] Extract the variant features annotated above the variant site;

[0128] Send the mutation features annotated on the mutation site to the preset evaluation end for evaluation, and obtain the evaluation score of the corresponding mutation site;

[0129] The evaluation score of the corresponding variant site is multiplied by the probability value of the variant feature annotated on the corresponding variant site to obtain the evaluation value index of the corresponding variant site;

[0130] The evaluation value indexes of the variant sites are marked in order from large to small, and the corresponding marks are sent to the preset genotype filling algorithm for orderly filling or covering.

[0131] It should be noted that in the same batch of sequencing data, there may be multiple variant sites and the variant characteristics are inconsistent. Therefore, it is necessary to select some higher-value variant sites to prioritize downstream breeding applications.

[0132] The present invention discloses a data analysis method integrating targeted sequencing and whole genome sequencing, which effectively improves the variation detection effect of sequencing data by combining targeted and whole genome sequencing.

[0133] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored, or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0134] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0135] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0136] Those skilled in the art can understand that all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiments; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), disks or optical disks, and other media that can store program codes.

[0137] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

Claims

1. A data analysis method integrating targeted sequencing and whole genome sequencing, characterized in that: include: Obtain sequencing data information; Determine whether the sequencing data can be split and identified. If so, split the sequencing data into targeted sequencing data and WGS sequencing data; Compare the targeted sequencing data and the WGS sequencing data respectively to obtain the targeted comparison results and the WGS comparison results; The targeted alignment results and the WGS alignment results are combined to obtain a first alignment result, and the first alignment result is subjected to variation detection and quality control to obtain high-quality variation data; If not, the sequencing data is sent to a preset second module to obtain a second comparison result, and the second comparison result is subjected to variation detection and quality control to obtain high-quality variation data; Annotate high-quality variant data and send them to the preset management end for downstream breeding applications.

2. A data analysis method integrating targeted sequencing and whole genome sequencing according to claim 1, characterized in that: After obtaining the targeted alignment results and the WGS alignment results, the method further includes: Based on the preset first software tool, all variant sites in the targeted comparison results and the WGS comparison results are extracted to obtain the targeted detection results and the WGS detection results; The intersection of the targeted detection results and the WGS detection results was taken to obtain the common variant sites; Based on the preset second software tool, the common variant sites are tested for consistency of variation at the site level and the preset sample level. If they are consistent, the corresponding WGS test results are accurate; If there is any inconsistency, the corresponding WGS test result is wrong, and the inconsistent position is marked for prompting.

3. A data analysis method integrating targeted sequencing and whole genome sequencing according to claim 1, characterized in that: The step of splitting the sequencing data into targeted sequencing data and WGS sequencing data specifically includes: Extract the adapter sequence of reads in the sequencing data; Compare and analyze the adapter sequences and index tags of reads in the sequencing data with the preset targeted sequencing library and WGS sequencing library adapter sequences respectively; If the index tag of the adapter sequence of the reads in the sequencing data is the same as the index of the adapter sequence in the targeted sequencing library, the corresponding sequencing reads are set as targeted sequencing data; If the index tag of the adapter sequence of the reads in the sequencing data is the same as the index of the adapter sequence in the WGS sequencing library, the corresponding sequencing reads are set as WGS sequencing data.

4. The data analysis method integrating targeted sequencing and whole genome sequencing according to claim 1, characterized in that: Also includes: Extracting a comparison index and a corresponding comparison index value in the first comparison result or the second comparison result; Multiply the comparison index value by the preset weight coefficient of the corresponding comparison index to obtain the corresponding comparison index proportion value; Accumulate the proportion values ​​of all comparison indicators to obtain the accuracy index of the first comparison result or the second comparison result; If the accuracy index of the first alignment result or the second alignment result is less than the preset accuracy index threshold, the reads that are not aligned or aligned incorrectly are removed, and the accuracy index of the first alignment result or the second alignment result is recalculated until the accuracy index is greater than the preset threshold, thereby obtaining the reads after quality control; The reads after quality control were used as the final clean data for variant detection.

5. The data analysis method integrating targeted sequencing and whole genome sequencing according to claim 1, characterized in that: Also includes: Obtain targeted and whole genome alignment results, and calculate alignment rate, depth, and coverage; If the alignment rate, depth, and coverage in the targeted alignment results are less than the preset thresholds, reads that are not aligned or aligned incorrectly in the targeted sequencing will be removed for subsequent variant detection; If the alignment rate, depth, and coverage in the whole genome alignment results are less than the preset thresholds, the reads that are not aligned or aligned incorrectly in the whole genome sequencing will be removed for subsequent variation detection.

6. The data analysis method integrating targeted sequencing and whole genome sequencing according to claim 1, characterized in that: The quality control step also includes: Extract reads from sequencing data that are not aligned to the reference genome; Locally assemble the reads in the sequencing data that are not aligned to the reference genome, and perform Blast alignment with the preset NT database to determine the source of the unaligned reads; After aligning the read sources that are not aligned to the reference genome through the Blast NT database and the sequence after local assembly, if non-target species alignment information is obtained, the corresponding sequencing data is contaminated, and an invalid sequencing read warning message is generated, and the invalid sequencing warning message is sent to the preset management terminal for prompting; If the unaligned reads cannot obtain homologous sequences of non-target species, the corresponding sequencing reads will be set as high-quality sequencing data.

7. The data analysis method integrating targeted sequencing and whole genome sequencing according to claim 1, characterized in that: The quality control step also includes: Extract the reads insertion fragment length recorded in the targeted alignment result bam; If the length of the reads insert fragment is greater than the preset target probe sequence length, the corresponding reads will be deleted.

8. The data analysis method integrating targeted sequencing and whole genome sequencing according to claim 1, characterized in that: Also includes: Based on the preset statistical software, the depth and coverage of the target interval and non-target interval are obtained; According to the depth and coverage of the target interval and the non-target interval, the depth ratio and coverage ratio of the two intervals are obtained; If the depth ratio of the two intervals is greater than the preset first ratio threshold and the coverage ratio is greater than the preset first ratio threshold, then the corresponding library is built once, that is, the sequencing data of the second module is normal.

9. The data analysis method integrating targeted sequencing and whole genome sequencing according to claim 1, characterized in that: After obtaining high-quality variation data, the method further includes: Extracting biometric values ​​corresponding to high-quality variant data; Based on the same biometric feature, the biometric feature value of the high-quality variant data and the corresponding standard biometric feature value are calculated to obtain the corresponding biometric feature difference; If the corresponding biometric feature difference is greater than the corresponding biometric feature difference threshold, the corresponding biometric feature is set as a variant feature; The mutation features are annotated on all mutation sites in the corresponding high-quality mutation data.

10. The data analysis method integrating targeted sequencing and whole genome sequencing according to claim 1, characterized in that: The breeding application step also includes: Based on the preset third software tool, the variant sites are phased to determine the target samples, and the corresponding reference panels are matched according to the target samples; Based on the preset genotype filling algorithm and the corresponding reference panel, the missing genotypes in the target sample are predicted to obtain high-density genotypes, which are then filled or covered to obtain variant breeding gene data; The variant breeding gene data is sent to a preset downstream end for breeding application.

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