Microsatellite instability detection method based on single-sample high-throughput sequencing for microsatellite site micro-offset

Abstract

The invention discloses a microsatellite instability detection method based on single-sample high-throughput sequencing for microsatellite site micro-offset, and relates to the technical field of bioinformatics, and the microsatellite instability detection method comprises the following steps: processing a sequencing sequence of a to-be-detected sample obtained by a high-throughput sequencing technology to obtain a comparison file; according to the method, selection and quality control of microsatellite sites are strictly controlled, a microsatellite instability detection method based on single-tumor sample high-throughput sequencing can be used for analyzing the repetition times of candidate microsatellite site repetition units without depending on a control sample, and meanwhile, the method has very good sensitivity to a sample with relatively large offset, so that the accuracy of the microsatellite instability detection is greatly improved. The method can also be applied to other cancer species with MSI characteristics, and can adapt to detection requirements of different cancer species by adjusting candidate microsatellite loci no matter whether a micro-migration condition exists or not, provide visualization, assist in seeing the degree of microsatellite migration, facilitate manual recheck and reduce misjudgment.

Description

Technical Field

[0001] This invention relates to the field of bioinformatics technology, specifically to a method for detecting microsatellite instability based on single-sample high-throughput sequencing for microsatellite locus micro-shifts. Background Technology

[0002] Microsatellite instability (MSI) is a molecular marker of abnormal changes in the length of short tandem repeat sequences (microsatellites) in the genome due to defects in the DNA mismatch repair (MMR) system. It is of great significance in various cancer types, such as endometrial cancer, where it has key clinical significance: On the one hand, MSI is a core screening biomarker for Lynch syndrome (hereditary nonpolyposis colorectal cancer), with approximately 30% of endometrial cancers associated with MSI. MSI testing can accurately identify Lynch syndrome patients and their families, enabling early diagnosis and treatment and intervention for genetic risks. On the other hand, endometrial cancer patients with high MSI (MSI-H) have a significantly higher response rate to PD-1 / PD-L1 and other immune checkpoint inhibitors than microsatellite stable (MSS) patients. MSI testing has become a core molecular marker guiding the selection of immunotherapy regimens for endometrial cancer, directly affecting clinical treatment decisions and patient prognosis. Currently, the commonly used clinical methods for detecting MSI mainly include immunohistochemistry (IHC) and capillary electrophoresis (CE). IHC detection indirectly infers the MSI status by detecting the expression status of MMR proteins (MLH1, MSH2, MSH6, PMS2). It is simple to operate and has a low cost. Capillary electrophoresis (CE) is the gold standard for traditional MSI detection. It determines the MSI status by detecting changes in the length of the Bethesda panel (5 core sites) or extended sites. However, both immunohistochemistry (IHC) and capillary electrophoresis (CE) have significant limitations: IHC testing carries a clear risk of false positives and false negatives. For example, MLH1 protein deficiency may be caused by promoter methylation rather than germline mutations, which can easily lead to misdiagnosis as Lynch syndrome-related MSI. Some patients with normal MMR protein expression may still have MSI, leading to missed diagnoses in immunotherapy guidance. Furthermore, it cannot directly reflect the sequence change characteristics of microsatellite sites. On the other hand, capillary electrophoresis (CE) has a cumbersome procedure, requiring the separation of tumor and normal tissue samples for pairing and control. Moreover, it cannot simultaneously detect other clinically valuable molecular markers such as driver gene mutations and tumor mutational burden (TMB), resulting in low efficiency in clinical application. The development of high-throughput sequencing (NGS) technology has provided a breakthrough solution for MSI detection. Its core advantages are: ① It can simultaneously cover dozens or even hundreds of microsatellite loci, greatly improving the comprehensiveness and sensitivity of detection and reducing misjudgments caused by single or a few loci; ② It can integrate the simultaneous detection of MSI, TMB, and driver gene mutations (such as key mutations in endometrial cancer such as POLE and PTEN), providing multi-dimensional molecular diagnostic information for clinical use and avoiding sample waste and time delays caused by multiple tests; ③ The detection process is high-throughput and automated, adapting to the needs of clinical batch sample processing. However, existing NGS technology still faces a key problem in the detection of MSI in endometrial cancer: the MSI of endometrial cancer has relatively many "micro-offset" features, with microsatellite locus length changes of 1-2 base pairs. Conventional NGS-MSI analysis algorithms are designed based on the MSI features of other tumors such as colorectal cancer, and their sensitivity to these micro-offset signals is insufficient, which can easily lead to false negative results. At the same time, most existing algorithms rely on paired samples of tumor tissue and normal tissue for comparative analysis. When only tumor samples (such as puncture biopsy, pleural effusion, and ascites samples) can be obtained clinically, the detection cannot be carried out, which seriously limits the clinical application scenarios. Therefore, developing a highly accurate NGS analysis technology that can target the micro-shift characteristics of MSI in endometrial cancer and achieve single-sample detection addresses the core pain points of existing methods and has significant clinical value for improving the screening efficiency of Lynch syndrome in endometrial cancer and optimizing immunotherapy guidance. This is the core background and urgent need for the development of this patent. Summary of the Invention

[0003] This invention provides a single-sample high-throughput sequencing-based microsatellite instability (MSI) detection method targeting microsatellite locus micro-shifts. This method effectively addresses the limitations of existing MSI detection methods in the clinical application of endometrial cancer, as mentioned in the background. The accuracy and information dimensions of IHC and CE detection are insufficient to meet the needs of precision medicine. While existing NGS detection has technical advantages, it lacks analytical algorithms for MSI micro-shift characteristics in endometrial cancer and cannot achieve accurate detection of single samples.

[0004] To achieve the above objectives, the present invention provides the following technical solution: a method for detecting microsatellite instability based on single-sample high-throughput sequencing for microsatellite locus micro-shifts, comprising the following steps: Step S1: Process the sequencing sequence of the sample to be tested obtained by high-throughput sequencing technology to obtain the alignment file; Step S2: Obtain from the alignment file the sequence number distribution and percentage distribution of the number of repetitions of the repeat units of the candidate microsatellite sites of the sample to be tested, as well as the total number of sequence sequences for candidate microsatellite sites. Step S3: Perform quality control on candidate microsatellite loci, and then compare the proportion distribution of the number of repetition units of microsatellite loci with the proportion distribution of baseline data to detect the MSI status of candidate microsatellite loci that have passed quality control of the test sample. Step S4: Visualize the distribution of the sequencing sequence proportion of the number of repetitions of the candidate microsatellite sites in the test sample and the distribution of the baseline data proportion. Step S5: Calculate the unstable percentage of candidate microsatellite loci that have passed quality control to assess the MSI status of the sample to be tested.

[0005] According to the above technical solution, in step S1, the bwa and samtools tools are used to align the sequencing sequence data to the human hg19 reference genome, and the genome position is sorted to obtain the alignment file; Both the test sample and the baseline sample were derived from human tissue or blood samples.

[0006] According to the above technical solution, in step S2, based on the genomic coordinates of the candidate microsatellite loci, all sequencing sequences aligned to this region are extracted from the alignment file; By iterating through these sequences and calculating the repetition count of each repeating unit, the distribution of repeating units is obtained. Each repeat count corresponds to a frequency n. The total number of sequencing sequences for this microsatellite locus is N = The number of repetitions for each unit corresponds to a percentage r = (n / N). 100%.

[0007] According to the above technical solution, in S2, the candidate microsatellite sites are single nucleotide repeat sites, and are screened according to the following rules: Candidate microsatellite loci have a repeat length of 13 or more in the repeat unit of the reference genome; The highest allele frequency of candidate microsatellite loci is greater than 75%; The percentage of repetitions of the baseline sample data of candidate microsatellite loci can only have one extreme peak; The standard deviation of the percentage of repetitions in the baseline sample data of candidate microsatellite loci that is greater than 7.5%. It must not exceed 1 / 3 of its proportion; if the proportion is less than 7.5%, the standard deviation shall be... It must not exceed 2.5%; The performance of microsatellite instability checks at unit points was verified using the training set, and microsatellite loci with a positive compliance rate ≥60% and a negative compliance rate ≥90% were selected.

[0008] According to the above technical solution, S3 includes site quality control and baseline data acquisition; Site quality control involves performing quality control on the number N of all sequencing sequences of the candidate microsatellite sites obtained in step S2. When the number of monosomic repeats of a candidate microsatellite site in the reference genome is L, the threshold is set to 50. L, when N≥50 If L is positive, the quality control is qualified. The number of qualified microsatellite loci P is counted. If P < 20, there are not enough qualified candidate microsatellite loci, and it is necessary to rebuild the library or increase the amount of sequencing data.

[0009] According to the above technical solution, the method for acquiring baseline data in step S3 includes the following: Provide a background set containing multiple samples in a stable microsatellite state, obtain the percentage of repetitions for each repeating unit of the candidate microsatellite locus in the samples, and obtain the average percentage of repetitions for each repeating unit of each candidate microsatellite locus. and standard deviation ; The MSI status of candidate microsatellite loci that have passed quality control of the test sample is detected, and the number of units with the highest average proportion in the baseline data of each candidate microsatellite locus, num, and the number of units with a proportion ≥1% and a number of unit repetitions less than num, M, is obtained. Since the probability of missing microsatellite instability is much greater than that of insertion, it is only necessary to compare the proportion of repeat counts less than num. To identify whether a site shift has occurred, it is necessary to traverse the proportion r of candidate microsatellite site units with repeat counts less than num in the test sample and compare it with the baseline data to obtain the repeat counts of abnormal units for site interpretation. The calculation process is as follows: ; when When ≥1%, the statistics are satisfied. > or >2 The number of y1, and the statistical satisfaction > and > The number of sites y2, when y1≥3 or y1=M, or y2≥1, the candidate site is determined to be a microsatellite unstable site; when When <1%, the statistics satisfy > The number of y3, and the statistical satisfaction > The number of sites y4 is determined to be a microsatellite unstable site when y1+y2≥3 or y4≥1.

[0010] According to the above technical solution, in step S3, the sequencing platform of the background set sample of the baseline data needs to be the same as the sequencing platform of the sample to be tested. If the sequencing platforms of the baseline sample and the sample to be tested are different, a stable sample background set needs to be reconstructed for the new platform, and the baseline data under the platform needs to be calculated.

[0011] According to the above technical solution, in step S4, a bar chart is used for visualization output. The horizontal axis represents the number of repetitions of the candidate microsatellite locus repeat unit, the vertical axis represents the proportion of that number of repetitions, the standard deviation σ is used as the error bar for the baseline data, and the title includes the microsatellite locus name, the total number of sequencing sequences N, and the locus status information.

[0012] According to the above technical solution, step S5, evaluating whether the sample MSI number is qualified, includes the following: In step S3, the number of unstable sites is q, and the total number of qualified quality control sites is P. Then, MSI-score = q / P. If MSI-score ≥ 0.3, the sample is determined to be an MSI sample; otherwise, it is an MSS sample. The threshold for the MSI-score is determined based on the classification performance of the training set samples, and the threshold is set to 0.3.

[0013] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. This invention rigorously controls the selection and quality of microsatellite loci, improving detection sensitivity. The microsatellite instability detection method based on high-throughput sequencing of single tumor samples eliminates the need for control samples, reducing sample volume and costs. Analysis of the repetition count of candidate microsatellite loci improves detection sensitivity for samples with micro-shifts, while maintaining good sensitivity even for samples with significant shifts. It can also be applied to other cancers with MSI characteristics. Regardless of the presence of micro-shifts, adjusting candidate microsatellite loci can adapt to the detection needs of different cancer types. Visualization is provided to help identify the degree of microsatellite shift, and it also effectively displays unstable loci with low purity, facilitating manual review and reducing misjudgments.

[0014] 2. The sequence obtained by sequencing was aligned to the human hg19 reference genome using bwa. The genome was sorted using samtools to obtain the alignment file in bam format. The distribution of the number of repetitions of candidate microsatellite repetitive units was obtained from the alignment file and quality control was performed. The microsatellite status of candidate microsatellite sites in the test sample was detected and visualized. The unstable state of the test sample was determined based on the status of candidate microsatellite sites. This method detects the microsatellite status of candidate microsatellite sites by statistically analyzing the distribution of the number of repetitions of repeating units in candidate microsatellite sites and performing quality control, and then comparing it with the proportion distribution of baseline data. The results are visualized and output. The final microsatellite score is obtained based on the status of all qualified candidate microsatellite sites to evaluate the MSI status of the sample to be tested, which can effectively detect sites that have undergone micro-shifts. Attached Figure Description

[0015] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof.

[0016] In the attached diagram: Figure 1 This is a flowchart of the detection method of the present invention; Figure 2 This is a flowchart of the sample detection process according to the present invention; Figure 3 This is an example diagram of a microsatellite site that failed the screening in this invention; Figure 4 This is an example diagram of microsatellite loci that failed the screening in this invention; Figure 5 This is an example diagram of a microsatellite locus selected by the present invention; Figure 6 This is an example diagram of the second microsatellite site selected by the present invention; Figure 7 This is a diagram illustrating the threshold line determined by the national reference training set in this invention. Figure 8 This is a visualization example of the stable site of the present invention; Figure 9 This is a visual example of a microsatellite site that is significantly offset according to the present invention; Figure 10 This is a visualization example of the microsatellite loci with slight offsets according to the present invention; Figure 11 This is a diagram illustrating the unstable sites of low purity in this invention. Detailed Implementation

[0017] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0018] Example 1: As Figure 1As shown, this invention provides a technical solution: a method for detecting microsatellite instability based on single-sample high-throughput sequencing, targeting microsatellite locus micro-shifts. Microsatellite locus micro-shifts refer to abnormal changes in the length of microsatellite repeat units of 1-2 bases, a characteristic commonly seen in endometrial cancer. The method includes the following steps: Step S1: Process the sequencing sequence of the sample to be tested obtained by high-throughput sequencing technology to obtain the alignment file; Step S2: Obtain from the alignment file the sequence number distribution and percentage distribution of the number of repetitions of the repeat units of the candidate microsatellite sites of the sample to be tested, as well as the total number of sequence sequences for candidate microsatellite sites. Step S3: Perform quality control on candidate microsatellite loci, and then compare the proportion distribution of the number of repetition units of microsatellite loci with the proportion distribution of baseline data to detect the MSI status of candidate microsatellite loci that have passed quality control of the test sample. Step S4: Visualize the distribution of the sequencing sequence proportion of the number of repetitions of the candidate microsatellite sites in the test sample and the distribution of the baseline data proportion. Step S5: Calculate the unstable percentage of candidate microsatellite loci that have passed quality control to assess the MSI status of the sample to be tested.

[0019] Based on the above technical solution, S1, the bwa tool is used to align the sequencing sequence to the human hg19 reference genome, and the samtools tool is used to sort the genome position and generate a BAM format alignment file; Both the test samples and the baseline samples were derived from human tissue samples.

[0020] Based on the above technical solution, S2, according to the genomic coordinates of the candidate microsatellite loci, extract all sequencing sequences aligned to this region from the alignment file; By iterating through these sequences and calculating the repetition count of each repeating unit, the distribution of repeating units is obtained. Each repeat count corresponds to a frequency n. The total number of sequencing sequences for this microsatellite locus is N = The number of repetitions for each unit corresponds to a percentage r = (n / N). 100%.

[0021] like Figure 3-6 As shown, based on the above technical solution, S2, the candidate microsatellite sites are the single nucleotide repeat sites that have passed the screening. A total of 40 sites were finally screened, as shown in Table 1, and the screening was conducted according to the following rules: Candidate microsatellite loci should have a repeat length of 13 or more in the repeat unit of the reference genome, with longer being better; The highest allele frequency of candidate microsatellite loci needs to be greater than 75%, and the higher the better; The percentage of repetitions of the baseline sample data of candidate microsatellite loci can only have one extreme peak; The standard deviation of the percentage of repetitions in the baseline sample data of candidate microsatellite loci that is greater than 7.5%. It should not exceed 1 / 3 of its proportion, and the smaller the better. If the proportion is less than 7.5%, the standard deviation should be [not specified]. It must not exceed 2.5%; The unit point microsatellite instability check and verification performance was obtained through the training set, which contained 24 unstable samples and 24 stable samples. The above algorithm was used to obtain the unit point microsatellite instability check and verification performance, and microsatellite sites with a positive concordance rate ≥60% and a negative concordance rate ≥90% were selected. Specifically, the positive concordance rate is the ratio of candidate microsatellite loci that are judged as unstable in the unstable samples of the training set by the above algorithm, and the negative concordance rate is the ratio of candidate microsatellite loci that are judged as stable in the stable samples of the training set by the above algorithm.

[0022] Table 1 shows the genomic information of the 40 microsatellite loci that passed the screening, as follows:

[0023]

[0024] like Figure 2 As shown, based on the above technical solution, S3 uses a bar chart for visualization output, including site quality control and baseline data acquisition; Site quality control involves performing quality control on the number N of sequencing sequences for all candidate microsatellite sites obtained in step S2. If the number of monosomic repeats of a candidate microsatellite site in the reference genome is L, then the threshold is set to 50. L, when N≥50 If L is a valid value, then the quality control is qualified. Specifically, a microsatellite locus with a repeat unit length of 20 requires 1000 sequencing sequences to pass the quality control. The number of qualified microsatellite loci P is counted. If P < 20, then there are not enough qualified candidate microsatellite loci, and the library needs to be rebuilt.

[0025] Based on the above technical solution, S3, the method for acquiring baseline data includes the following: Provide a background set containing multiple samples in a stable microsatellite state, obtain the percentage of repetitions for each repeating unit of the candidate microsatellite locus in the samples, and obtain the average percentage of repetitions for each repeating unit of each candidate microsatellite locus. and standard deviation ; The MSI status of candidate microsatellite loci that have passed quality control of the test sample is detected, and the number of units with the highest average proportion in the baseline data of each candidate microsatellite locus, num, and the number of units with a proportion ≥1% and a number of unit repetitions less than num, M, is obtained. Since the probability of missing microsatellite instability is much greater than that of insertion, it is only necessary to compare the proportion of repeat counts less than num. To identify whether a site shift has occurred, including micro-shifts, it is necessary to traverse the proportion r of candidate microsatellite site units with repeat counts less than num in the test sample and compare it with the baseline data to obtain the abnormal unit repeat counts for site interpretation. The calculation process is as follows: ; when When ≥1%, the statistics are satisfied. > or >2 The number of y1, and the statistical satisfaction > and > The number of sites y2, when y1≥3 or y1=M, or y2≥1, the candidate site is determined to be a microsatellite unstable site; when When <1%, the statistics satisfy > The number of y3, and the statistical satisfaction > The number of sites y4 is determined to be a microsatellite unstable site when y1+y2≥3 or y4≥1.

[0026] Based on the above technical solution, S3, the sequencing platform of the background set sample of the baseline data needs to be the same as the sequencing platform of the sample to be tested. If the sequencing platform of the baseline sample is different from that of the sample to be tested, a stable sample background set needs to be reconstructed for the new platform, and the baseline data under the platform needs to be calculated, specifically μ and σ. Forty-eight microsatellite-stable tissue samples from the same platform as the test sample were provided as a background set. The average percentage μ and standard deviation σ of the number of repetitions of each repeat unit of the microsatellite locus were calculated and stored in the MSI_baseline.txt file.

[0027] Based on the above technical solution, in step S4, a bar chart is used for visualization output. The horizontal axis represents the number of repetitions of the candidate microsatellite locus repeat unit, the vertical axis represents the proportion of that repetition, and the standard deviation σ serves as the error bar for the baseline data. The title includes the microsatellite locus name, the total number of sequencing sequences N, and locus status information, such as... Figure 8-11 As shown.

[0028] Based on the above technical solution, step S5, evaluating whether the sample MSI number is qualified, includes the following: In step S3, the number of unstable sites is q, and the total number of qualified quality control sites is P. Then, MSI-score = q / P. If MSI-score ≥ 0.3, the sample is determined to be an MSI sample; otherwise, it is an MSS sample. The threshold for the MSI-score is determined based on the classification performance of 48 training set samples, and the threshold is set to 0.3, or 30%.

[0029] like Figure 7 As shown in the figure, 62 national reference samples were used as the test set. It can be seen from the figure that the detection method of the present invention has a classification accuracy of 100% for the test set.

[0030] Example 2: Twenty unstable endometrial cancer samples and 20 stable endometrial cancer samples were selected by capillary electrophoresis, and the results of this algorithm were compared with the results of the msisensor-pro software. The same 48 background samples were analyzed using the msisensor-pro software to obtain a baseline file containing the threshold p for each site, which was then used to analyze the results of these 40 cases of endometrial cancer. The comparison results are shown in Table 2, which presents a comparison of the results from 40 clinical samples of endometrial cancer. Details are as follows:

[0031]

[0032] As can be seen from the above, the score in positive samples is higher than that of msisensor-pro, while the scores of both are relatively low in negative samples. Therefore, the present invention can effectively improve sensitivity without sacrificing specificity, and the consistency with capillary electrophoresis can reach 100% at a threshold of 0.3.

[0033] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for detecting microsatellite instability based on single-sample high-throughput sequencing for microsatellite locus micro-shifts, characterized in that: Includes the following steps: Step S1: Process the sequencing sequence of the sample to be tested obtained by high-throughput sequencing technology to obtain the alignment file; Step S2: Obtain from the alignment file the sequence number distribution and percentage distribution of the number of repetitions of the repeat units of the candidate microsatellite sites of the sample to be tested, as well as the total number of sequence sequences for candidate microsatellite sites. Step S3: Perform quality control on candidate microsatellite loci, and then compare the proportion distribution of the number of repetition units of microsatellite loci with the proportion distribution of baseline data to detect the MSI status of candidate microsatellite loci that have passed quality control of the test sample. Step S4: Visualize the distribution of the sequencing sequence proportion of the number of repetitions of the candidate microsatellite loci in the test sample and the distribution of the baseline data proportion. Step S5: Calculate the unstable percentage of candidate microsatellite loci that have passed quality control to assess the MSI status of the sample to be tested.

2. The microsatellite instability detection method based on single-sample high-throughput sequencing for microsatellite locus micro-shifts according to claim 1, characterized in that: In step S1, the bwa and samtools tools are used to align the sequencing data to the human hg19 reference genome, and the genome position is sorted to obtain the alignment file; Both the test sample and the baseline sample were derived from human tissue or blood samples.

3. The method for detecting microsatellite instability based on single-sample high-throughput sequencing for microsatellite locus micro-shifts according to claim 1, characterized in that: S2 involves extracting all sequencing sequences aligned to this region from the alignment file based on the genomic coordinates of the candidate microsatellite loci. By iterating through these sequences and calculating the repetition count of each repeating unit, the distribution of repeating units is obtained. Each repeat count corresponds to a frequency n. The total number of sequencing sequences for this microsatellite locus is N = The number of repetitions for each unit corresponds to a percentage r = (n / N). 100%.

4. The microsatellite instability detection method based on single-sample high-throughput sequencing for microsatellite locus micro-shifts according to claim 3, characterized in that: The candidate microsatellite sites for S2 are single nucleotide repeat sites, and are screened according to the following rules: Candidate microsatellite loci have a repeat length of 13 or more in the repeat unit of the reference genome; The highest allele frequency of candidate microsatellite loci is greater than 75%; The percentage of repetitions of the baseline sample data of candidate microsatellite loci can only have one extreme peak; The standard deviation of the percentage of repetitions in the baseline sample data of candidate microsatellite loci that is greater than 7.5%. It must not exceed 1 / 3 of its proportion; if the proportion is less than 7.5%, the standard deviation shall be... It must not exceed 2.5%; The performance of microsatellite instability checks at unit points was verified using the training set, and microsatellite loci with a positive compliance rate ≥60% and a negative compliance rate ≥90% were selected.

5. The microsatellite instability detection method based on single-sample high-throughput sequencing for microsatellite locus micro-shifts according to claim 3, characterized in that: S3 includes site quality control and baseline data acquisition; Site quality control involves performing quality control on the number N of all sequencing sequences of the candidate microsatellite sites obtained in step S2. When the number of monosomic repeats of a candidate microsatellite site in the reference genome is L, the threshold is set to 50. L, when N≥50 If L is positive, the quality control is qualified. The number of qualified microsatellite loci P is counted. If P < 20, there are not enough qualified candidate microsatellite loci, and the library needs to be rebuilt or the amount of sequencing data needs to be increased.

6. The microsatellite instability detection method based on single-sample high-throughput sequencing for microsatellite locus micro-shifts according to claim 5, characterized in that: The method for acquiring baseline data in step S3 includes the following: Provide a background set containing multiple samples in a stable microsatellite state, obtain the percentage of repetitions for each repeating unit of the candidate microsatellite locus in the samples, and obtain the average percentage of repetitions for each repeating unit of each candidate microsatellite locus. and standard deviation ; The MSI status of candidate microsatellite loci that have passed quality control of the test sample is detected, and the number of units with the highest average proportion in the baseline data of each candidate microsatellite locus, num, and the number of units with a proportion ≥1% and a number of unit repetitions less than num, M, is obtained. Since the probability of missing microsatellite instability is much greater than that of insertion, it is only necessary to compare the proportion of repeat counts less than num. To identify whether a site shift has occurred, it is necessary to traverse the proportion r of candidate microsatellite site units with repeat counts less than num in the test sample and compare it with the baseline data to obtain the repeat counts of abnormal units for site interpretation. The calculation process is as follows: ； when When ≥1%, the statistics are satisfied. > or >2 The number of y1, and the statistical satisfaction > and > The number of sites y2, when y1≥3 or y1=M, or y2≥1, the candidate site is determined to be a microsatellite unstable site; when When <1%, the statistics satisfy > The number of y3, and the statistical satisfaction > The number of sites, y4, is determined to be a microsatellite unstable site when y1+y2≥3 or y4≥1.

7. The microsatellite instability detection method based on single-sample high-throughput sequencing for microsatellite locus micro-shifts according to claim 5, characterized in that: In S3, the sequencing platform of the background set sample of the baseline data needs to be the same as the sequencing platform of the sample to be tested. If the sequencing platforms of the baseline sample and the sample to be tested are different, a stable sample background set needs to be reconstructed for the new platform, and the baseline data under the platform needs to be calculated.

8. The method for detecting microsatellite instability based on single-sample high-throughput sequencing for microsatellite locus micro-shifts according to claim 1, characterized in that: In step S4, a bar chart is used for visualization output. The horizontal axis represents the number of repetitions of the candidate microsatellite locus repeat unit, the vertical axis represents the proportion of that number of repetitions, the standard deviation σ is used as the error bar for the baseline data, and the title includes the microsatellite locus name, the total number of sequencing sequences N, and the locus status information.

9. A method for detecting microsatellite instability based on single-sample high-throughput sequencing for microsatellite locus micro-shifts according to claim 5, characterized in that: Step S5, evaluating whether the sample MSI number is qualified, includes the following: In step S3, the number of unstable sites is q, and the total number of qualified quality control sites is P. Then, MSI-score = q / P. If MSI-score ≥ 0.3, the sample is determined to be an MSI sample; otherwise, it is an MSS sample. The threshold for the MSI-score is determined based on the classification performance of the training set samples, and the threshold is set to 0.3.

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