Method for filtering false positive mutations of DNA damage in NGS sequencing, and use thereof

Abstract

The present disclosure belongs to the technical field of gene detection. Disclosed in the present disclosure are a method for filtering false positive mutations of DNA damage in NGS sequencing, and the use thereof. The method comprises: S1, on the basis of a mutation subtype distribution of a normal sample, constructing a baseline; S2, performing variant detection on a test sample, determining the difference between a mutation subtype corresponding to the test sample and the baseline by means of a binomial test or a negative binomial test, and on the basis of the difference, determining whether the test sample has DNA damage; S3, for a sample with DNA damage, extracting SNV mutations with VAF ≤ 5% therein, and determining a baseline error rate of the corresponding mutation subtype at a baseline sequencing depth; and S4, calculating the p-value for DNA damage significance at the site, and determining, on the basis of the p-value, whether the corresponding site is a true mutation site or a false positive site. The present invention does not limit the variant allele frequency of DNA damage, can also be applied to more technical platforms, and is not limited by single-end or paired-end sequencing.

Description

Methods and applications of DNA damage false positive mutation filtering in NGS sequencing Technical Field

[0001] This invention belongs to the field of gene detection technology, specifically relating to a method and application for filtering false positive mutations caused by DNA damage in NGS (Next-Generation Sequencing) sequencing. Background Technology

[0002] Formalin-fixed paraffin-embedded (FFPE) samples are widely used in clinical pathology testing, tumor gene testing, and medical research due to their ease of long-term preservation. These tissue samples are also crucial for precision medicine using next-generation sequencing technology. However, some sequence mutations detected in FFPE DNA samples may be due to damage during sample processing, primarily stemming from the following factors:

[0003] 1) Oxidative damage

[0004] Reactive oxygen species (ROS) can oxidize guanine (G) to 8-oxoguanine (8-oxoG). 8-oxoG no longer pairs with C, but instead pairs with A. Therefore, after two replications, G:C pairing can be converted to A:T pairing, resulting in a false positive mutation (G:C / T:AG->T / C->A mutation), as shown in Figure 1.

[0005] 2) DNA damage caused by FFPE treatment

[0006] a. Cross-linking: This refers to the cross-linking between macromolecules (such as proteins and nucleic acids). Formaldehyde causes DNA damage primarily due to the electrophilic nature of its carbonyl group and its relatively low steric hindrance, making it readily cross-linked with nucleic acids and proteins. In vitro, formaldehyde first reacts with free amino groups on proteins or nucleic acids to form unstable hydroxymethyl adducts, which then further react with other nucleic acids or proteins to form stable cross-links. This leads to localized DNA denaturation, affecting the quality of nucleic acid extraction.

[0007] b. Base transitions: Numerous C>U or C>T mutations are present in FFPE DNA samples. In vivo, cytosine spontaneously hydrolyzes and deaminates to uracil, occurring approximately 190 times per cell per day. This process can be repaired by base excision to restore cytosine, but in vitro, this repair is not possible. Therefore, sequencing data will show a large number of false positives for C:G / T:A mutations (C->T / G->A), as shown in Figure 2.

[0008] c. Base loss: This occurs when a DNA molecule loses a purine or pyrimidine base, forming a base-loss site. When tissues are fixed in formalin, the formaldehyde in the formalin is easily oxidized to formic acid in the air, which lowers the pH of the formalin. The N-glycosidic bonds between purine bases and the sugar backbone are easily hydrolyzed at low pH, leading to DNA base loss. Furthermore, the base-loss site can spontaneously cleave through a β-elimination reaction, resulting in DNA strand breaks.

[0009] 3) DNA fragmentation

[0010] The degree of fragmentation in FFPE DNA increases with prolonged storage time and decreasing pH of the formalin used for fixation. PCR success rates are lower for FFPE DNA stored for longer periods compared to fresh FFPE DNA, indicating that DNA fragmentation may continue to occur during sample storage.

[0011] Based on the causes of DNA damage and the principles of NGS sequencing, it can be found that these false positive sites exhibit strand bias, meaning that mutations only exist on the positive or negative strand of the DNA molecule, and the reads obtained during the NGS sequencing process show the characteristics of F1R2 or F2R1.

[0012] Currently used filtering methods include filtering initial candidate variants based on statistical regularities. For example, Yost et al. used a binomial test to compare the allele frequency of each variant with the global mismatch rate of its corresponding mutation type, thereby controlling formalin-induced deamination and removing insignificant allele frequencies. Similarly, Kerick et al. used a cutoff value based on the sequencing depth of the variant site for filtering. The FIREVAT algorithm uses a series of filtering parameters, including VAF, alignment quality, and the depth of the reference sequence and the allele sequence.

[0013] In addition, several filtering algorithms are used to identify false positive variants in low-quality or FFPE sequencing data. For example, the cisCall algorithm applies a series of filters; for clustered low-VAF mutations, those with error rates higher than the expected value for mutations occurring within that cluster are filtered through internal statistical calculations. There are also Bayesian variant detection algorithms that, in addition to applying common quality filters, incorporate prior information about specific cancer-related mutations. Another algorithm, LoLoPicker, assesses the background error rate at specific sites using mutations within an FFPE sample panel and then incorporates it into hypothesis testing to identify true and false variants. Pisces' mutation detection software includes a model that recalibrates variant quality based on the bias of the average mutation rate for each possible mutation type, thereby minimizing thermal damage and FFPE deamination.

[0014] FFPE-related deamination, like false positives such as DNA oxidative damage, typically occurs only on the single strand of the original DNA template, causing directional bias between reads1 and reads2 during paired-end sequencing. This strand bias can be used to effectively measure DNA damage. This method of calculating strand imbalance has been adopted by several software programs, such as GATK4's LearnReadOrientationModel module, to filter out false positive mutations that occur only on the single strand during sequencing. This tool primarily uses a Bayesian statistical model to calculate the strand bias generated by single nucleotide substitutions within every three bases. Additionally, SOBDetector software also incorporates strand bias parameters, but instead of direct filtering, it calculates a strand bias score for each variant and adds this value to the mutation results file (vcf) for manual screening.

[0015] However, some of the algorithms mentioned above discard variants with a VAF < 5% or only evaluate performance at VAF > 5%. This is because the general consensus is to discard C:G>T:A variants when the variant allele frequency (VAF) is < 5% or < 10%, as FFPE-related false positives typically only exist at low frequencies. However, this limitation is not ideal because it prevents the detection of potentially clinically relevant low-frequency variants and limits the use of samples with low tumor content. Furthermore, FFPE-related false positives have already been observed at frequencies > 10%.

[0016] Because DNA damage mutations are located on the single strand of the original DNA template, current software or algorithms incorporate DNA template bias into calculations or feature training, which is not applicable to NGS data from single-end sequencing. Current literature shows that most test data are from capture methods, with relatively little data on amplicon sequencing. The performance of single-end sequencing amplicon data on DNA-damaged samples has not yet been observed. Summary of the Invention

[0017] The present invention aims to at least partially solve one of the technical problems in the aforementioned related technologies.

[0018] Therefore, the purpose of this invention is to provide a method and application for filtering false positive mutations in DNA damage during NGS sequencing, which can not limit the variant allele frequency (VAF) of DNA damage, and can be applied to more technology platforms, without being limited by single-end or double-end sequencing.

[0019] To solve the above-mentioned technical problems, the present invention is implemented as follows:

[0020] This invention provides a method for filtering false positive mutations caused by DNA damage in NGS sequencing, the method comprising:

[0021] S1. Construct a baseline based on the distribution of mutant subtypes in normal samples;

[0022] S2. Perform mutation detection on the sample to be tested. Based on the detection results, determine the difference between the mutation subtype corresponding to the sample to be tested and the baseline through binomial test or negative binomial test. Determine whether there is DNA damage in the sample to be tested based on the difference.

[0023] S3. For samples with DNA damage, extract SNV mutations with VAF ≤ 5% and determine the baseline error rate of the corresponding mutation subtype at the baseline sequencing depth.

[0024] S4. Calculate the DNA damage significance p-value of the site based on the baseline error rate, and determine whether the corresponding site is a true mutation site or a false positive site based on the DNA damage significance p-value, thereby achieving filtering.

[0025] In addition, the method for filtering false positive mutations in DNA damage during NGS sequencing according to the present invention may also have the following additional technical features:

[0026] In some implementations, the baseline construction based on the mutant subtype distribution of normal samples in step S1 includes:

[0027] Several normal samples without DNA damage were selected for mutation detection. The original SNV results of the mutation detection were statistically analyzed to obtain the distribution of the mutation subtype to be tested in the normal samples. A baseline was constructed based on this distribution.

[0028] In some implementations, step S2 includes:

[0029] The frequency of different mutation subtypes in the mutation detection results of the test sample is statistically analyzed, and a one-sided binomial test or negative binomial test is performed based on the baseline to calculate the p-value. The obtained p-value is compared with a preset threshold. When the p-value is not greater than the preset threshold and the number of corresponding mutation subtypes in the test sample is not less than the preset reference value, it is determined that there is DNA damage in the sample; otherwise, it is determined that there is no DNA damage.

[0030] In some implementations, the preset threshold corresponding to the p-value is 0.01.

[0031] In some implementations, the baseline sequencing depth is obtained by taking the median sequencing depth of all SNVs in the corresponding sample as the baseline sequencing depth.

[0032] In some implementations, step S3 involves statistically analyzing the sequencing depth, supporting sequencing base sequences, and VAF for all mutant subtypes to be tested. Based on the statistical information, SNV mutations with VAF ≤ 5% are extracted, and the 95th percentile is selected as the baseline error rate for the mutant subtype to be tested at the baseline sequencing depth in that sample.

[0033] In some of these implementations, the baseline error rate is negatively correlated with a power of log(sequencing depth).

[0034] In some implementations, the specific relationship between the baseline error rate and sequencing depth is as follows:

[0035] In some of these implementations, a preset ultra-high depth cutoff value is also required:

[0036] The sequencing depth is compared with the ultra-high depth cutoff value. When the sequencing depth is greater than the ultra-high depth cutoff value, the error rate no longer decreases with the increase of sequencing depth. The error rate of sites with a depth higher than the cutoff value is used and remains unchanged.

[0037] This invention also provides an application of the method for filtering false positive mutations in DNA damage during NGS sequencing as described in any of the preceding embodiments, wherein the method is applied in single-end sequencing (SE) or paired-end sequencing (PE).

[0038] Compared with the prior art, the present invention has at least the following beneficial effects:

[0039] In this embodiment of the invention, the method for filtering false positive mutations of DNA damage in NGS sequencing can solve the current problems of identification and filtering of false positive mutations of DNA damage that are dependent on strand preference. It is also applicable to mutation sites of different depths and different VAFs, as well as data from different sequencing platforms and sequencing methods, thus providing support for the accurate detection of tumor genes.

[0040] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0041] Figure 1 shows an example of DNA oxidative damage caused by 8-oxoG in the prior art;

[0042] Figure 2 shows an example of DNA damage caused by FFPE treatment in the prior art; specifically, DNA cytosine deamination leads to the conversion of cytosine to uracil, resulting in C:G / T:A (G->A / C->T mutation);

[0043] Figure 3 is a flowchart of a method for filtering false positive mutations in DNA damage during NGS sequencing, as disclosed in an embodiment of the present invention. Detailed Implementation

[0044] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0045] The embodiments of the present invention will be described in detail below with reference to the accompanying drawings and specific examples and application scenarios.

[0046] Please refer to Figure 1. In some embodiments of the present invention, a method for filtering false positive mutations in DNA damage during NGS sequencing is provided. This method can overcome the current technical drawbacks, does not limit the variant allele frequency (VAF) of DNA damage, and is applicable to more technical platforms. It is not limited by single-end or double-end sequencing and has broad application prospects in the precise detection of tumors.

[0047] In some embodiments of the present invention, the method for filtering false positive mutations caused by DNA damage in NGS sequencing specifically includes:

[0048] First, a baseline is constructed using the mutation distribution of 12 subtypes in normal samples. A binomial test or a negative binomial test is used to determine whether the subtype in the test sample differs significantly from the baseline. The cutoff for DNA-damaged samples is determined using a binomial p-value ≤ 0.01. In conventional DNA damage assessment or filtering software, the TOTAL_QSCORE values ​​of the 12 subtypes in the sample are typically calculated using GATK CollectOxoGMetrics or the FilterByOrientationBias module. Lower scores indicate a higher probability of artificial changes. Simultaneously, the F1R2 and F2R1 values ​​are used to determine whether the mutation is a false positive caused by DNA damage. Therefore, this method does not directly determine DNA damage in the sample; subsequent site determination relies on strand bias (whether there is a bias in the F1R2 or F2R1 values ​​of the mutation's supporting reads) for filtering. GATK refers to Genome Analysis Toolkit; CollectOxoGMetrics refers to the module for collecting oxidized guanine (OxoG) metrics; and FilterByOrientationBias refers to the module for filtering by orientation bias.

[0049] Secondly, after confirming that the samples to be tested belong to the DNA damage category, the median depth and 95th percentile allele frequency (Vaf) were statistically analyzed for each of the 12 subtypes in the samples. These depths and frequencies were then used as the baseline depth and baseline error rate for the corresponding mutation types. For each potential DNA damage false-positive mutation type (G->A / C->T; G->T / CA) site in the sample, the dynamic error rate ε (see Formula 1) was used as the background error probability for that site, based on its actual sequencing depth, to facilitate the next step of site authenticity assessment. A lower error rate was used when the depth increased to avoid filtering out high-frequency true mutations. Simultaneously, to prevent the error rate from dropping to unrealistically low values ​​at extremely high depths, the median depth of the sample was used as a cutoff value. When the depth exceeded this value, the error rate no longer decreased with increasing depth. The dynamic error rate increased continuously as the depth decreased, which aligns with the fact that the background Vaf of DNA damage sites is higher at lower depths. Dynamic error rate can effectively assess the true performance of DNA damage sites at ultra-low depths (<100X) or ultra-high depths (≥5000X). While current software and algorithms employ binomial distributions or other algorithms for DNA damage site evaluation, the depth of tissue sample test data is relatively low, and the effectiveness for testing DNA damage at ultra-high depths is unknown. The dynamic error rate in this invention addresses this limitation.

[0050] Finally, for each test site in the sample, the p-value (see Formula 2) is obtained using the current dynamic error rate ε and sequencing depth through a binomial test or a negative binomial test. This p-value serves as the significance value of DNA damage at that site. If p-damage > 0.001, it indicates that there is no significant difference between the background error rate of that site and the corresponding mutation type in the sample, and the site will be filtered out. In conventional software or algorithms, strand bias is used as an important condition for determining whether a mutation originates from DNA damage. Therefore, it is incompatible with single-end sequencing data, thus affecting the filtering effect of the algorithm. This invention does not require determining whether the data belongs to single-end or paired-end sequencing; both can be included in the calculation. Furthermore, some software or algorithms limit the number of DNA damage sites to within 5%. This invention can still exclude potential false positive sites of DNA damage even at low frequencies.

[0051] In real-world clinical tumor samples, this invention tested samples stored in FFPE for more than 2 or 5 years. The calculated p-sample value showed a high linear correlation with the degree of DNA degradation detected by experimental methods; the higher the p-sample value, the greater the degree of degradation and the more severe the DNA damage. The p-sample value in this invention effectively reduces experimental steps and is more sensitive than experimental results. Mutations filtered by this invention significantly reduce false positive mutation sites, making it suitable for low-depth or ultra-high-depth sample detection. Furthermore, this invention is simple to operate and applicable to various platforms and sequencing methods (single-end sequencing (SE) or paired-end sequencing (PE)).

[0052] Example 1:

[0053] To accurately filter out base mutations caused by DNA damage, this invention designs a filtering method for false-positive mutations caused by DNA damage. This method first identifies samples with DNA damage, distinguishing them from normal samples and avoiding false positives for low-frequency mutations in normal samples during filtering. Then, the previously identified DNA-damaged samples are filtered, and the p-value of the target site is calculated to determine whether the site was introduced by DNA damage. Specifically:

[0054] a. Statistically analyze the original SNV (single nucleotide variant) results after mutation detection in several normal DNA samples (manually interpreted to be free of DNA damage) to obtain the distribution of the mutation subtype to be tested in normal samples and construct the corresponding baseline.

[0055] b. Perform the same processing on the original SNV results after variant detection of the test sample, and count the frequency of different mutant subtypes to be tested. Perform a one-sided binomial test or a negative binomial test at baseline and calculate the p-value. When the p-value ≤ 0.01, the sample is considered to have DNA damage. If the number of corresponding mutant subtypes in the test sample is less than the reference value, even if the p-value is significant, it is not considered to be DNA damage.

[0056] c. For samples identified as having DNA damage, the median sequencing depth of all SNVs in the sample is used as the baseline sequencing depth. The sequencing depth, support reads, VAF, and other information of all mutation subtypes to be tested are calculated. SNV mutations with VAF ≤ 5% are extracted, and the 95th percentile is selected as the baseline error rate ε of the mutation subtype to be tested in the sample at the baseline sequencing depth.

[0057] The site depth dynamic error rate ε, unlike the constant error rate of other error types, is different for sites with different DNA damage types; its error rate increases as the depth decreases.

[0058] In the formula, e is the mutation type to be tested, such as the VAf of C->T / G->A at the 90th percentile in the sample to be tested, which is used as the baseline error rate;

[0059] MedianDepth is the median depth of the sample;

[0060] Depth is the depth of the site to be measured.

[0061] Formula (1) has a better effect in some implementation cases. Although other formulas can be used in theory, after a lot of experimental comparison and verification, Formula (1) has the best filtering effect for samples with low-depth DNA damage.

[0062] d. An error rate is assigned for each sequencing depth, as detailed in formula (1). This error rate differs from a fixed error rate; it decreases exponentially with increasing sequencing depth. At lower sequencing depths, a higher error rate is obtained to filter out mutations introduced by DNA damage. At higher sequencing depths, a lower error rate is obtained, thus avoiding the filtering out of high-frequency real mutations. Furthermore, to prevent the error rate from dropping to extremely low levels at ultra-high sequencing depths, this invention sets a cutoff value. When the sequencing depth exceeds this cutoff value, the error rate no longer decreases with increasing sequencing depth. The error rate corresponding to the cutoff value depth is used as the error rate for the current sequencing depth. This error rate corresponding to the cutoff value depth is also calculated using formula (1).

[0063] e. Calculate the p-value of DNA damage at the site using the error rate mentioned above, as detailed in formula (2). Filter the mutation subtypes to be tested based on the damage_p-value. If the damage_p-value ≤ 0.001, the site is considered a true mutation; otherwise, it is considered a false positive site introduced by DNA damage.

[0064] The calculation of the p-value for the significance of site-specific DNA damage involves performing a binomial test (or negative binomial test) on each C->T / G->A mutation site at the current error rate and depth to obtain the p-value.

[0065] SR~Binomial(D,ε) (2)

[0066] In the formula, SR is the number of support reads;

[0067] D stands for Depth, which is the total number of reads at that site (including reads that do not support mutations, i.e., reference alleles).

[0068] ε is the error rate.

[0069] The method for filtering DNA damage false positive mutations in NGS sequencing of the present invention. First, potential DNA damage samples can be clearly determined based on baseline samples and calculated p-samples, and the severity of DNA damage in the sample can be determined according to the magnitude of the p-sample value (as shown in Table 1). In the process of the experiment, the N / Q ratio is generally used as the standard for judging the quality of the DNA sample. When 0 < N / Q ratio ≤ 3.5, the DNA integrity of the sample is good; when 3.5 < N / Q ratio ≤ 6.5, the DNA integrity of the sample is relatively good; when the N / Q ratio > 6.5, the DNA integrity of the sample is poor. As can be seen from the table, as the N and Q ratios continuously increase, the value of p-sample becomes lower and lower, indicating that the sample quality is getting worse. Therefore, the present invention can assist in the experimental determination of sample quality, serve as an important parameter for sample quality control, reduce experimental steps, and save experimental costs. Secondly, it can solve the dependence on strand bias in the identification and filtering of DNA damage false positive mutations. Some FFPE samples have accumulated C>T and G>A damage mutations in some fragments due to long-term storage. At the same time, due to the existence of these mutations, the PCR amplification efficiency of such fragments during the library construction process before sequencing is low, resulting in a low measured depth. Therefore, the sequencing data in these regions show the characteristics of low depth and high variant allele frequency (VAF). In software of the same type such as GATK, for single-end sequencing data in DNA damage samples, due to the software's dependence on strand bias, it is impossible to effectively filter them out. However, through comparative tests, the method of the present invention can effectively filter out such errors. Moreover, the present invention can also be applied to mutation sites with different depths and different VAFs. For low-quality samples, the degradation is relatively severe and the DNA damage is also very serious. The amplification efficiency of this type of sample is relatively low, and it is difficult to distinguish the true and false mutations when the sample depth is insufficient. However, the present invention can perform false positive filtering on this type of sample, increasing the probability of detection and medication for patients. Finally, NGS detection is widely used in precision medicine. In addition to the common capture method, the amplicon amplification method is widely used because of its fast detection speed, usually producing results within 24 hours. However, this detection method is different on the MGI platform and multiple platforms of Illumina due to differences in sequencing error rates. The present invention can be compatible with the sequencing data of multiple platforms and different sequencing methods at the same time, providing support for the wide application of tumor gene precision detection.

[0070] Table 1 Corresponding relationship between N / Q value of DNA sample and p-sample

[0071] For the parts not described in detail in the present invention, reference can be made to the existing technologies in the art or the well-known technologies to those skilled in the art.

[0072] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of the present invention.

Claims

1. A method for filtering false-positive mutations caused by DNA damage in NGS sequencing, characterized in that, The method includes: S1. Construct a baseline based on the distribution of mutant subtypes in normal samples; S2. Perform mutation detection on the sample to be tested. Based on the detection results, determine the difference between the mutation subtype corresponding to the sample to be tested and the baseline through binomial test or negative binomial test. Determine whether there is DNA damage in the sample to be tested based on the difference. S3. For samples with DNA damage, extract SNV mutations with VAF ≤ 5% and determine the baseline error rate of the corresponding mutation subtype at the baseline sequencing depth. S4. Calculate the DNA damage significance p-value of the site based on the baseline error rate, and determine whether the corresponding site is a true mutation site or a false positive site based on the DNA damage significance p-value, thereby achieving filtering.

2. The method for filtering false positive mutations caused by DNA damage in NGS sequencing according to claim 1, characterized in that, Step S1, which involves constructing a baseline based on the distribution of mutant subtypes in normal samples, includes: Several normal samples without DNA damage were selected for mutation detection. The original SNV results of the mutation detection were statistically analyzed to obtain the distribution of the mutation subtype to be tested in the normal samples. A baseline was constructed based on this distribution.

3. The method for filtering false positive mutations caused by DNA damage in NGS sequencing according to claim 1, characterized in that, Step S2 includes: The frequency of different mutation subtypes in the mutation detection results of the test sample is statistically analyzed, and a one-sided binomial test or negative binomial test is performed based on the baseline to calculate the p-value. The obtained p-value is compared with a preset threshold. When the p-value is not greater than the preset threshold and the number of corresponding mutation subtypes in the test sample is not less than the preset reference value, it is determined that there is DNA damage in the sample; otherwise, it is determined that there is no DNA damage.

4. The method for filtering false positive mutations caused by DNA damage in NGS sequencing according to claim 3, characterized in that, The preset threshold for the p-value is 0.

01.

5. The method for filtering false positive mutations caused by DNA damage in NGS sequencing according to claim 1, characterized in that, The baseline sequencing depth is obtained by taking the median sequencing depth of all SNVs in the corresponding sample as the baseline sequencing depth.

6. The method for filtering false positive mutations caused by DNA damage in NGS sequencing according to claim 1, characterized in that, In step S3, the sequencing depth, supporting sequencing base sequence, and VAF of all the mutant subtypes to be tested are statistically analyzed. Based on the statistical information, SNV mutations with VAF ≤ 5% are extracted, and the 95th percentile is selected as the baseline error rate of the mutant subtype to be tested in the sample at the baseline sequencing depth.

7. The method for filtering false positive mutations caused by DNA damage in NGS sequencing according to claim 1, characterized in that, The baseline error rate is negatively correlated with log(sequencing depth).

8. The method for filtering false positive mutations caused by DNA damage in NGS sequencing according to claim 1, characterized in that, The relationship between the baseline error rate and sequencing depth is as follows:

9. The method for filtering false positive mutations caused by DNA damage in NGS sequencing according to claim 1, characterized in that, Preset ultra-high depth cutoff value; The sequencing depth is compared with the ultra-high depth cutoff value. When the sequencing depth is greater than the ultra-high depth cutoff value, the error rate corresponding to the cutoff value depth is used as the error rate of the current sequencing depth.

10. The application of a method for filtering false positive mutations in DNA damage during NGS sequencing according to any one of claims 1-9, characterized in that, The method is applied in single-end sequencing (SE) or paired-end sequencing (PE).

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