Sample contamination detection method, device, equipment, storage medium and program product

Abstract

The present application relates to a sample contamination detection method, device, computer equipment, computer-readable storage medium and computer program product. The method comprises: obtaining the number of mutant base fragments to be tested and the total number of base fragments corresponding to the single nucleotide polymorphism (SNP) site in the sample to be tested; obtaining the frequency of occurrence of the mutant base fragments corresponding to the SNP site in the non-contaminated sample; based on the target distribution information corresponding to the predetermined number of mutant base fragments, using the number of mutant base fragments to be tested, the total number of base fragments and the frequency of occurrence to perform a likelihood estimation of the contamination ratio to obtain the contamination ratio of the sample to be tested; and determining the cross-contamination detection result of the sample to be tested according to the contamination ratio. The use of this method can improve the accuracy of cross-contamination detection.

Description

Technical Field

[0001] The present application relates to the field of bioinformatics, and in particular to a sample contamination detection method, apparatus, computer equipment, computer-readable storage medium, and computer program product. Background Art

[0002] ‌Methylation next-generation sequencing is a method that uses next-generation sequencing technology to sequence DNA methylation fragments. This method not only has the advantages of high throughput, high accuracy and low cost, but also can directly sequence any species, with high flexibility.

[0003] In the process of methylation next-generation sequencing, cross-contamination between samples is a common problem in the preparation of libraries. For example, any minor improper operation in the collection, processing, transportation and detection of samples may lead to cross-contamination of samples. At present, when cross-contamination detection of next-generation sequencing data is carried out, the allele frequency of the SNP (single nucleotide polymorphism) site of the sample to be tested is usually analyzed for cross-contamination detection.

[0004] However, when there is cross-contamination in the sample to be tested, the change in allele frequency under genomic abnormalities can easily lead to detection errors. For example, cancer samples are often mistakenly detected as contaminated samples, resulting in reduced accuracy of cross-contamination detection. Summary of the invention

[0005] Based on this, it is necessary to provide a sample contamination detection method, device, computer equipment, computer-readable storage medium and computer program product that can improve the accuracy of cross-contamination detection in response to the above-mentioned technical problems.

[0006] In a first aspect, the present application provides a sample contamination detection method, comprising:

[0007] Obtain the number of mutant base fragments and the total number of base fragments to be tested corresponding to the single nucleotide polymorphism (SNP) site in the sample to be tested;

[0008] Obtain the occurrence frequency of the mutant base fragment corresponding to the SNP site in the non-contaminated sample;

[0009] Based on the target distribution information corresponding to the predetermined number of mutant base fragments, the number of mutant base fragments to be tested, the total number of base fragments and the frequency of occurrence are used to perform a likelihood estimate of the contamination ratio to obtain the contamination ratio of the sample to be tested;

[0010] The cross-contamination test results of the samples to be tested are determined based on the contamination ratio.

[0011] In a second aspect, the present application also provides a sample contamination detection device, comprising:

[0012] A fragment number acquisition module is used to obtain the number of mutant base fragments to be tested and the total number of base fragments corresponding to the single nucleotide polymorphism (SNP) site in the sample to be tested;

[0013] The frequency acquisition module is used to obtain the occurrence frequency of the mutant base fragment corresponding to the SNP site in the non-contaminated sample;

[0014] A contamination estimation module is used to estimate the likelihood of the contamination ratio based on the target distribution information corresponding to the predetermined number of mutant base fragments, using the number of mutant base fragments to be tested, the total number of base fragments and the frequency of occurrence, to obtain the contamination ratio of the sample to be tested;

[0015] The result determination module is used to determine the cross-contamination detection result of the sample to be tested according to the contamination ratio.

[0016] In a third aspect, the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0017] Obtain the number of mutant base fragments and the total number of base fragments to be tested corresponding to the single nucleotide polymorphism (SNP) site in the sample to be tested;

[0018] Obtain the occurrence frequency of the mutant base fragment corresponding to the SNP site in the non-contaminated sample;

[0019] Based on the target distribution information corresponding to the predetermined number of mutant base fragments, the number of mutant base fragments to be tested, the total number of base fragments and the frequency of occurrence are used to perform a likelihood estimate of the contamination ratio to obtain the contamination ratio of the sample to be tested;

[0020] The cross-contamination test results of the samples to be tested are determined based on the contamination ratio.

[0021] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the following steps are implemented:

[0022] Obtain the number of mutant base fragments and the total number of base fragments to be tested corresponding to the single nucleotide polymorphism (SNP) site in the sample to be tested;

[0023] Obtain the occurrence frequency of the mutant base fragment corresponding to the SNP site in the non-contaminated sample;

[0024] Based on the target distribution information corresponding to the predetermined number of mutant base fragments, the number of mutant base fragments to be tested, the total number of base fragments and the frequency of occurrence are used to perform a likelihood estimate of the contamination ratio to obtain the contamination ratio of the sample to be tested;

[0025] The cross-contamination test results of the samples to be tested are determined based on the contamination ratio.

[0026] In a fifth aspect, the present application further provides a computer program product, including a computer program, which implements the following steps when executed by a processor:

[0027] Obtain the number of mutant base fragments and the total number of base fragments to be tested corresponding to the single nucleotide polymorphism (SNP) site in the sample to be tested;

[0028] Obtain the occurrence frequency of the mutant base fragment corresponding to the SNP site in the non-contaminated sample;

[0029] Based on the target distribution information corresponding to the predetermined number of mutant base fragments, the number of mutant base fragments to be tested, the total number of base fragments and the frequency of occurrence are used to perform a likelihood estimate of the contamination ratio to obtain the contamination ratio of the sample to be tested;

[0030] The cross-contamination test results of the samples to be tested are determined based on the contamination ratio.

[0031] The above-mentioned sample contamination detection method, device, computer equipment, computer-readable storage medium and computer program product obtain the number of mutant base fragments to be tested and the total number of base fragments corresponding to the SNP site in the sample to be tested, and obtain the frequency of occurrence of the mutant base fragments corresponding to the SNP site in the non-contaminated sample. Then, based on the target distribution information corresponding to the predetermined number of mutant base fragments, the likelihood of the contamination ratio is estimated using the number of mutant base fragments to be tested, the total number of base fragments and the frequency of occurrence to obtain the contamination ratio of the sample to be tested. That is, the likelihood of the contamination ratio is estimated by the predetermined target distribution information, and then the cross-contamination detection result is determined by the contamination ratio. There is no need to analyze the allele frequency of the SNP site, which can reduce the detection error when there is cross-contamination in the sample to be tested, thereby improving the accuracy of cross-contamination detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0033] Figure 1 A diagram showing an application environment of a sample contamination detection method in an embodiment;

[0034] Figure 2 A schematic diagram of a flow chart of a sample contamination detection method in one embodiment;

[0035] Figure 3 It is a schematic diagram of estimating the contamination ratio of a simulated contaminated sample in a specific embodiment;

[0036] Figure 4 It is a schematic diagram of the detection results of simulated contaminated samples with different gradients in a specific embodiment;

[0037] Figure 5 A schematic diagram of the linear relationship between the detection contamination ratio and the true gradient in a specific embodiment;

[0038] Figure 6 It is a schematic diagram of a flow chart of a sample contamination detection method in a specific embodiment;

[0039] Figure 7 A schematic diagram of a process framework for sample contamination detection in a specific embodiment;

[0040] Figure 8 is a structural block diagram of a sample contamination detection device in one embodiment;

[0041] Fig. 9 is an internal structure diagram of a computer device in one embodiment;

[0042] Fig.10 FIG. 4 is a diagram showing the internal structure of a computer device in another embodiment. DETAILED DESCRIPTION

[0043] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0044] In this application, unless otherwise specified, the scientific and technical terms used herein have the meanings commonly understood by those skilled in the art. In addition, the terms and laboratory procedures related to protein and nucleic acid chemistry, molecular biology, cell and tissue culture, microbiology, and immunology used herein are terms and routine procedures widely used in the corresponding fields. At the same time, in order to better understand this application, the definitions and explanations of the relevant terms are provided below.

[0045] As used herein, the term "sample contamination" refers to a situation where the sample to be tested is mixed with samples from other individuals during preparation or other processing, for example, a situation where nucleic acids from other individuals are mixed during the preparation of the sample to be sequenced.

[0046] As used herein, the term "sequencing" refers to DNA sequencing, which refers to the analysis of the base sequence of a specific DNA fragment, that is, the arrangement of adenine (A), thymine (T), cytosine (C) and guanine (G). Methylation sequencing refers to the sequencing of methylated fragments. It is highly flexible and can directly sequence highly methylated fragments of any species without the need for known genomic sequence information. Methylation targeted sequencing technology is a high-throughput sequencing technology that can detect methylation sites on the genome with high precision and conduct quantitative analysis on them.

[0047] As used herein, the term "mutation" refers to a process in which the structure of a gene changes, resulting in a stable, heritable change in the genotype of a cell, virus or microorganism.

[0048] The term "SNP" (Single Nucleotide Polymorphism) mainly refers to the DNA (Deoxyribo Nucleic Acid) sequence polymorphism caused by the variation of a single nucleotide at the genome level. The polymorphism exhibited by the SNP site only involves the variation of a single base, which can be caused by the conversion or transversion of a single base, or by the insertion or deletion of a base.

[0049] The term "homozygous SNP site" refers to a SNP site at which the same base is displayed at the site on all sequences aligned with the reference genome, and the base is different from the base at the site on the reference genome sequence. For example, if the base at a SNP site on the reference genome sequence is G, and the base at the SNP site on all sequences aligned with the reference genome is A, then the SNP site is called a homozygous SNP site. As used herein, the term "occurrence frequency of mutant base fragments": also known as VAF (variant allelic frequency), is used to characterize the ratio of the number of fragments containing mutant SNPs at the target site to the number of all fragments covering the SNP, that is, the frequency of mutant base fragments appearing at the SNP site. When the wild type is T, VAF is close to 1. For example: the SNP site can be a site where base A mutates to base T, or a site where base T mutates to base A.

[0050] As used herein, the term "target distribution information" refers to the distribution information that the number of mutant base fragments in each SNP site obeys. For example, the distribution that the number of mutant base fragments obeys can be Poisson distribution or normal distribution.

[0051] As used herein, the term "contamination ratio" is used to characterize the ratio of contamination fragments to the total number of base fragments, and the contamination at each SNP site is independent of each other.

[0052] As used in this article, the term "real degree of the initial contamination ratio" is used to characterize the possibility that the initial contamination ratio is the actual contamination ratio. The higher the real degree, the higher the possibility that the initial contamination ratio is the actual contamination ratio, and the lower the real degree, the lower the possibility that the initial contamination ratio is the actual contamination ratio.

[0053] As used herein, the term "uncontaminated sample" refers to a clean and healthy sample without cross contamination, that is, the uncontaminated sample is not mixed with samples from other individuals.

[0054] The sample contamination detection method provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be integrated on the server 104, or it can be placed on the cloud or other network servers. The server 104 can obtain the sample to be tested from the terminal 102. At this time, the server 104 obtains the number of mutant base fragments to be tested and the total number of base fragments corresponding to the single nucleotide polymorphism (SNP) site in the sample to be tested; the server 104 can obtain the frequency of occurrence of the mutant base fragment corresponding to the SNP site in the non-contaminated sample from the data storage system; the server 104 uses the number of mutant base fragments to be tested, the total number of base fragments and the frequency of occurrence to perform a likelihood estimate of the contamination ratio based on the target distribution information corresponding to the predetermined number of mutant base fragments, and obtains the contamination ratio of the sample to be tested; the server 104 determines the cross-contamination detection result of the sample to be tested according to the contamination ratio. Finally, the server 104 can return the cross-contamination detection result of the sample to be tested to the terminal 102 for display. The terminal 102 may be, but is not limited to, various personal computers, laptops, smart phones, tablet computers, IoT devices, and portable wearable devices. The IoT devices may be smart speakers, smart TVs, smart air conditioners, smart car devices, projection devices, etc. Portable wearable devices may be smart watches, smart bracelets, head-mounted devices, etc. Head-mounted devices may be virtual reality (VR) devices, augmented reality (AR) devices, smart glasses, etc. The server 104 may be an independent physical server, a server cluster or distributed system consisting of multiple physical servers, or a cloud server that provides cloud computing services.

[0055] In an exemplary embodiment, Figure 2 As shown, a sample contamination detection method is provided, which is applied to Figure 1 The terminal in the example is used for explanation. It can be understood that the method can also be applied to a server, and can also be applied to a system including a terminal and a server, and is implemented through the interaction between the terminal and the server. In this embodiment, the method includes the following steps 302 to 308. Among them:

[0056] S302, obtaining the number of mutant base fragments to be tested and the total number of base fragments corresponding to the single nucleotide polymorphism (SNP) site in the sample to be tested.

[0057] Among them, the sample to be tested refers to a sample that needs to be tested for cross-contamination. The sample can be selected from samples used for liquid biopsy, including circulating tumor cells, circulating tumor DNA, exosomes, circulating free RNA, cell-free DNA or a combination thereof, most preferably cell-free DNA. In a feasible embodiment, the sample to be tested can be a cfDNA (circulating free DNA) sample. SNP sites refer to single base change points in a gene sequence. The number of mutant base fragments to be tested refers to the number of mutant base fragments in the SNP site of the sample to be tested. The mutant base fragment may refer to a fragment in which base A mutates to base T or base T mutates to base A. The total number of base fragments refers to the total number of base fragments in the SNP site of the sample to be tested.

[0058] In some embodiments, the server obtains the sample to be tested, and then can detect the sample to be tested through methylation targeted sequencing technology (ELSA-seq) to obtain sequencing data, and then use the sequencing data for comparison to obtain the number of mutant base fragments to be tested and the total number of base fragments corresponding to each SNP site in the sample to be tested.

[0059] In some embodiments, the server can obtain the number of mutant base fragments and the total number of base fragments to be tested corresponding to the target SNP site in the sample to be tested. The target SNP site is obtained by screening using pre-set screening conditions. The server can select SNP sites with a preset population frequency between 0.3 and 0.7. For example, SNP sites with a frequency of 0.3 to 0.7 in the oriental population in the hg19 gnomAD (a large-scale human genetic variant database) database can be preferentially selected. The population involved in the site is homozygous with different genotypes, and the probability of avoiding selection frequencies that are too low or too high. Since it is methylation sequencing data, the server can select SNP sites where base A mutates to base T, or base T mutates to base A. The server can also filter out SNP sites in base repeat regions, and SNP sites in repeat regions are prone to errors during sequencing. The server can also select SNP sites whose physical distance between SNP sites is greater than the preset distance, for example, SNP sites whose physical distance is greater than 0.4M can be selected. In a specific embodiment, 1018 SNP sites are obtained by screening together through the above screening conditions.

[0060] In some embodiments, the server can further screen the SNP sites according to the sequencing depth of the SNP sites to obtain the number of mutant base fragments to be tested and the total number of base fragments of the screened SNP sites. For example, SNP sites with a sequencing depth of less than 50X can be filtered out, so that the coverage of the genome can be effectively guaranteed.

[0061] In some embodiments, the server can obtain multiple samples to be tested, and perform sample contamination detection on the multiple samples to be tested at the same time to improve detection efficiency.

[0062] S304, obtaining the occurrence frequency of the mutant base fragment corresponding to the SNP site in the non-contaminated sample.

[0063] Among them, the non-contaminated sample refers to a clean and healthy sample without cross contamination. The frequency of occurrence of the mutant base fragment refers to the proportion of the mutant base fragment in all base fragments in the SNP site. For example, the frequency of occurrence of the mutant base fragment can be obtained by obtaining the VAF (variant allelic frequency) value corresponding to the SNP site in the non-contaminated sample.

[0064] In this embodiment, the server can directly obtain the occurrence frequency of the mutant base fragment corresponding to each SNP site in the non-contaminated sample from the database. The server can also obtain the occurrence frequency of the mutant base fragment corresponding to each SNP site in the non-contaminated sample uploaded by the terminal.

[0065] In some embodiments, the server can also obtain the number of mutant base fragments and the number of all base fragments at each SNP site in the uncontaminated sample, and then count the ratio of the number of mutant base fragments to the number of all base fragments to obtain the frequency of occurrence of mutant base fragments at each SNP site in the uncontaminated sample.

[0066] In some embodiments, the server may also sequence the non-contaminated sample to obtain sequencing data, and then compare the sequencing data with the human genome to determine the frequency of occurrence of the mutant base fragment corresponding to the SNP site in the non-contaminated sample.

[0067] In some embodiments, the SNP site includes a homozygous SNP site. In order to retain more SNP sites while ensuring the accuracy of the detection, the homozygous SNP site may be a SNP site in which the frequency of occurrence of the mutant base fragment in the SNP site is less than a target value, and the target value may be set according to the test results. Preferably, the target value may be 0.1.

[0068] S306, based on the target distribution information corresponding to the predetermined number of mutant base fragments, the number of mutant base fragments to be tested, the total number of base fragments and the frequency of occurrence are used to perform likelihood estimation of the contamination ratio to obtain the contamination ratio of the sample to be tested.

[0069] Among them, the target distribution information corresponding to the predetermined number of mutant base fragments refers to the distribution information obeyed by the number of mutant base fragments. The target distribution information is used to characterize the target distribution relationship between the expected number of mutant base fragments of the sample to be tested and the observed number of mutant base fragments to be tested of the sample to be tested. For example, it can be a Poisson distribution relationship or a normal distribution relationship. Preferably, it can be a Poisson distribution relationship. The contamination ratio is used to characterize the ratio of the contaminated fragments to the total number of base fragments. In this embodiment, the contamination of each SNP site is independent of each other.

[0070] It can be understood that the VAF of the homozygous SNP site in the sample without cross-contamination is close to 0. When cross-contamination occurs, the VAF value corresponding to the contaminated sample with the mutated SNP base fragment will increase. Therefore, the contamination ratio of the sample to be tested can be estimated based on the distribution of the mutated fragments at the homozygous SNP site in the sample to be tested.

[0071] In some embodiments, the server may obtain target distribution information corresponding to a predetermined number of mutant base fragments, and then calculate target distribution parameters using the total number of base fragments, the frequency of occurrence, and the contamination ratio according to the target distribution information, and the target distribution parameter may be the expected number of mutant base fragments obtained by estimating the initial contamination ratio. Then, according to the target distribution relationship between the expected number of mutant base fragments and the number of mutant base fragments to be tested, the contamination ratio in the target distribution parameter is estimated by the number of mutant base fragments to be tested, and the contamination ratio of the sample to be tested is obtained.

[0072] In some embodiments, the server can obtain the maximum likelihood estimation algorithm corresponding to the target distribution information, and then use the number of mutant base fragments to be tested, the total number of base fragments, and the frequency of occurrence of mutant base fragments to perform maximum likelihood estimation on the contamination ratio through the maximum likelihood estimation algorithm to obtain the contamination ratio when the joint probability is maximum, that is, to obtain the contamination ratio of the sample to be tested. The goal of maximum likelihood estimation is to find the contamination ratio value that maximizes the probability of occurrence of the observed number of mutant base fragments to be tested, that is, the contamination ratio value that is most likely to explain the observed data.

[0073] S308, determining the cross-contamination detection result of the sample to be tested according to the contamination ratio.

[0074] The cross-contamination detection result is a result used to characterize whether the sample to be tested is a cross-contaminated sample, and may include a detection result characterizing that the sample to be tested is a cross-contaminated sample or a detection result characterizing that the sample to be tested is a non-contaminated sample. A cross-contaminated sample refers to a cross-contamination between the sample to be tested and other samples.

[0075] In some embodiments, the purpose of cross-contamination detection is to maintain high specificity (samples that have not been cross-contaminated are not identified as contaminated samples) while maintaining high sensitivity (the correct proportion of different cross-contamination gradients identified under a certain specificity threshold). The server can then determine the cross-contamination detection result of the sample to be tested by obtaining the contamination ratio threshold at high specificity. If the sample to be tested is an uncontaminated sample, at this time, the sample to be tested can be used for subsequent biomarker research and clinical diagnosis and treatment, etc. For example, using uncontaminated samples to perform liquid biopsy for early tumor screening, recurrence monitoring, etc., thereby ensuring the accuracy of subsequent processing on the basis of ensuring high sensitivity and high specificity. If the sample to be tested is a cross-contaminated sample, at this time, it is necessary to filter out the cross-contaminated sample to prevent errors in subsequent processing.

[0076] The above-mentioned sample contamination detection method obtains the number of mutant base fragments to be tested and the total number of base fragments corresponding to the SNP site in the sample to be tested, and obtains the frequency of occurrence of the mutant base fragments corresponding to the SNP site in the non-contaminated sample. Then, based on the target distribution information corresponding to the predetermined number of mutant base fragments, the likelihood of the contamination ratio is estimated using the number of mutant base fragments to be tested, the total number of base fragments and the frequency of occurrence to obtain the contamination ratio of the sample to be tested. That is, the likelihood of the contamination ratio is estimated by the predetermined target distribution information, and then the cross-contamination detection result is determined by the contamination ratio. There is no need to analyze the allele frequency of the SNP site, which can reduce the detection error when there is cross-contamination in the sample to be tested, thereby improving the accuracy of cross-contamination detection.

[0077] When the target distribution information is pre-determined, the Poisson distribution can simulate rare events that occur within a fixed interval or volume. In the context of DNA sequencing, the contamination fragments from other samples are relatively rare relative to the total number of fragments being sequenced, and this rarity conforms to the Poisson distribution. In addition, the addition of each contamination fragment in the Poisson distribution is independent of the addition of other contamination fragments, because the addition process from the contamination source to the fragment in the sequencing library is independent from one fragment to another. In addition, the selected SNP sites maintain a certain length of base interval, thereby further ensuring the independence of events. Furthermore, the Poisson distribution is applicable to situations where events occur at a constant average rate in a fixed space or time interval, and when the contamination source remains unchanged, the actual contamination ratio is expected to be constant during the sequencing process of a specific sample. Furthermore, the number of contamination fragments in the sample to be tested is the count of discrete events, which conforms to the discrete nature of the Poisson distribution. Furthermore, the Poisson distribution is characterized by its mean (λ), which characterizes the expected number of contamination fragments. And the mean can be estimated from the SNP data, for example, using uncontaminated samples to establish a baseline level of background noise or inferring the contaminated fractions observed based on samples with known contamination proportions.

[0078] Therefore, in some embodiments, the target distribution information includes Poisson distribution information; based on the target distribution information of the number of mutant base fragments, the number of mutant base fragments to be tested, the total number of base fragments and the frequency of occurrence are used to perform a likelihood estimation of the contamination ratio to obtain the contamination ratio of the sample to be tested, including the steps of:

[0079] The initial contamination ratio is obtained, and the expected number is calculated based on the initial contamination ratio, the total number of base fragments and the frequency of occurrence to obtain the expected number of mutant base fragments corresponding to the SNP site in the sample to be tested; based on the Poisson distribution information, the number of mutant base fragments to be tested and the expected number of mutant base fragments are used to perform likelihood estimation to obtain the true degree of the initial contamination ratio; the contamination ratio of the sample to be tested is determined based on the comparison result of the true degree with the preset likelihood estimation completion condition.

[0080] The expected number of mutant base fragments is used to characterize the number of mutant base fragments in the SNP site in the sample to be tested under the initial contamination ratio. The initial contamination ratio can be a pre-set initial value or a generated initial value of the contamination ratio.

[0081] In some embodiments, the server can determine the expected number of mutant base fragments corresponding to the SNP site in the sample according to the expected number of mutant base fragments of contamination and the number of mutant base fragments of noise caused by normal sequencing. Among them, the expected number of mutant base fragments of contamination can be calculated by the initial contamination ratio and the total number of base fragments. Then, the number of mutant base fragments of noise can be calculated using the frequency of occurrence of mutant base fragments corresponding to the SNP site in the uncontaminated sample and the total number of base fragments, and then the sum of the number of mutant base fragments of expected contamination and the number of mutant base fragments of noise is calculated to obtain the expected number of mutant base fragments corresponding to the SNP site in the sample to be tested. At this time, the server can use the number of mutant base fragments to be tested and the expected number of mutant base fragments to perform likelihood estimation according to the likelihood estimation function corresponding to the predetermined Poisson distribution to obtain the true degree of the initial contamination ratio. For example, the pollution ratio when maximizing the likelihood estimation function can be used as the pollution ratio of the sample to be tested.

[0082] The preset likelihood estimation completion condition refers to the pre-set condition for completing the likelihood estimation, including finding the optimal solution or reaching the convergence condition. For example, the preset likelihood estimation completion condition may include that the estimated degree of truth exceeds a preset degree of truth threshold or the number of iterations reaches the maximum number of iterations or the calculated degree of truth does not change, etc.

[0083] In some embodiments, the server may compare the degree of truth with a preset degree of truth threshold. When the degree of truth exceeds the preset degree of truth threshold, the contamination ratio corresponding to the degree of truth is used as the contamination ratio of the sample to be tested. When the degree of truth does not exceed the preset degree of truth threshold, it means that the contamination ratio corresponding to the degree of truth is not the contamination ratio when the likelihood estimation function is maximized. At this time, the contamination ratio may be iterated to obtain the iterated contamination ratio. Then, the degree of truth of the iterated contamination ratio is calculated and iterated continuously until the degree of truth exceeds the preset degree of truth threshold.

[0084] In some embodiments, the server can also determine whether the convergence condition is met. When the convergence condition is met, for example, the number of iterations reaches the maximum number of iterations, or the change in the calculated true degree is less than a preset threshold, the convergence condition is met. At this time, the server uses the corresponding contamination ratio as the contamination ratio of the sample to be tested.

[0085] Illustratively, the number of detected mutant base fragments and the expected number of mutant base fragments have a Poisson distribution relationship as shown in the following formula (1).

[0086] Formula (1)

[0087] in, It refers to the number of fragments with mutant bases observed at the i-th SNP site of the sample to be tested, that is, the number of mutant base fragments to be tested. It refers to the Poisson distribution parameter, which is used to characterize the expected number of mutant base fragments corresponding to the i-th SNP site of the sample to be tested. It refers to the Bosphorus distribution.

[0088] Then the likelihood function can be established based on the probability density function of the Poisson distribution as shown in the following formula (2).

[0089] Formula (2)

[0090] Where n refers to the number of homozygous SNP sites in the sample to be tested. To facilitate the calculation of the maximum joint probability, the logarithm of the likelihood function can be taken to obtain the following formula (3).

[0091] Formula (3)

[0092] in, Used to characterize the true degree of the contamination ratio. Then the likelihood function after taking the logarithm can be taken as a negative number, at this time, the minimum value of the likelihood function can be calculated. For example, the Nelder-Mead (an algorithm for calculating the minimum value of a multivariate function) algorithm can be used to calculate the minimum value. In some specific embodiments, the minimize function in the scipy.optimize module of python (a module in the scipy library for solving optimization problems) can also be used to minimize the objective function. For example, the input of the objective function can be the number of mutant base fragments of the SNP in the sample to be tested, the total number of base fragments, the frequency of occurrence of mutant base fragments in the non-contaminated sample, and the expression of the negative log-likelihood function, and then by iterating the initial values ​​of different contamination ratios, the approximate minimum value of the negative log-likelihood function and its corresponding variable contamination ratio value are output, that is, the contamination ratio value when the joint probability is maximum is obtained.

[0093] In the above embodiment, the contamination ratio is estimated by maximizing the likelihood through Poisson distribution information, and there is no need to analyze the allele frequency of the SNP site, thereby reducing the detection errors when there is cross contamination in the sample to be tested and improving the accuracy of cross contamination detection.

[0094] It is understandable that for contaminated samples, the contaminated fragments (SNP fragments containing bases different from the wild-type SNP site) mainly come from two sources: one is the cross-contamination of SNP fragments different from the wild type in the contaminated sample, and the other is a certain proportion of noise in the sequencing error itself. In some embodiments, the expected number calculation is performed based on the initial contamination ratio, the total number of base fragments and the frequency of occurrence to obtain the expected number of mutant base fragments corresponding to the SNP site in the sample to be tested, including the steps of:

[0095] The expected number of contaminated base fragments and the expected number of non-contaminated base fragments corresponding to the SNP site in the sample to be tested are calculated according to the initial contamination ratio and the total number of base fragments; the mutation noise is calculated according to the occurrence frequency and the expected number of non-contaminated base fragments to obtain the number of noise base fragments corresponding to the SNP site in the sample to be tested; the sum of the expected number of contaminated base fragments and the number of noise base fragments is calculated to obtain the expected number of mutant base fragments corresponding to the SNP site in the sample to be tested.

[0096] In this embodiment, the server can calculate the product of the initial contamination ratio and the total number of base fragments to obtain the expected number of contaminated base fragments corresponding to the SNP site in the sample to be tested. Then the difference between the total number of base fragments and the expected number of contaminated base fragments is calculated to obtain the expected number of non-contaminated base fragments. Then the product of the expected number of non-contaminated base fragments and the frequency of occurrence is calculated to obtain the number of noise base fragments corresponding to the SNP site in the sample to be tested. Finally, the sum of the expected number of contaminated base fragments and the number of noise base fragments is calculated to obtain the expected number of mutant base fragments corresponding to the SNP site in the sample to be tested.

[0097] For example, the server may use the formula (4) shown below to calculate the expected number of mutated base fragments corresponding to the SNP site in the sample to be tested.

[0098] Formula (4)

[0099] in, It refers to the Poisson distribution parameter, that is, the expected number of mutant base fragments corresponding to the i-th SNP site in the sample to be tested. It refers to the pollution ratio. It refers to the frequency of occurrence of the same SNP site in uncontaminated samples. It refers to the total number of base fragments corresponding to the i-th SNP site in the sample to be tested.

[0100] In the above embodiment, the expected number of contaminated base fragments and the number of noise base fragments are calculated, and then the expected number of contaminated base fragments and the number of noise base fragments are summed to obtain the expected number of mutant base fragments corresponding to the SNP site in the sample to be tested. This not only takes into account the situation when contamination occurs, but also takes into account the noise caused by sequencing, thereby improving the accuracy of the calculated expected number of mutant base fragments.

[0101] In some embodiments, S308, determining the cross-contamination detection result of the sample to be tested according to the contamination ratio includes the steps of:

[0102] Obtain the contamination ratio threshold. When the contamination ratio is greater than the contamination ratio threshold, the cross-contamination detection result indicates that the sample to be tested is a cross-contaminated sample; when the contamination ratio is less than or equal to the contamination ratio threshold, the cross-contamination detection result indicates that the sample to be tested is a non-contaminated sample.

[0103] In this embodiment, in order to ensure the accuracy of the cross-contamination detection result, a contamination ratio threshold value can be obtained, and then the contamination ratio obtained by the detection is compared with the contamination ratio threshold value, and then the cross-contamination detection result of the sample to be tested is determined according to the comparison result. Among them, when the obtained detection result is that the sample to be tested is a cross-contaminated sample, it means that the sample to be tested is cross-contaminated by other samples. When the obtained detection result is that the sample to be tested is a non-contaminated sample, it means that the sample to be tested is a sample that has not been cross-contaminated by other samples and is a clean and healthy sample. That is, by determining the cross-contamination detection result through the comparison result between the contamination ratio and the contamination ratio threshold value, the cross-contamination detection result can be quickly obtained, and by setting an accurate contamination ratio threshold value, the accuracy of the cross-contamination detection result can be improved.

[0104] It is understandable that the purpose of cross-contamination detection is to maintain high specificity (samples without cross-contamination are not identified as contaminated samples) while maintaining high sensitivity (the correct ratio of different cross-contamination gradients is identified under a certain specific threshold). In some embodiments, obtaining the contamination ratio threshold includes the steps of:

[0105] Obtain each uncontaminated sample, cross-mix each uncontaminated sample according to the simulated contamination ratio to obtain each simulated contaminated sample corresponding to the simulated contamination ratio; use each uncontaminated sample and each simulated contaminated sample to perform a cross-contamination test to obtain a test result; obtain a contamination ratio detection value under target specificity according to the test result, and use the contamination ratio detection value under target specificity as a contamination ratio threshold.

[0106] In this embodiment, the server can obtain each pollution-free sample, and then each pollution-free sample can be subjected to methylation sequencing to obtain a result file containing sequencing fragments, which is a bam file that has filtered out bases with poor sequencing quality and removed a large number of repeated fragments. The bam file can be a binary file format for storing large-scale sequencing data, or it can be a comparison result for storing sequences and reference genomes. Then, a signal source sample and a background sample are selected from each pollution-free sample, and then a corresponding proportion of fragments are extracted from the result file of the signal source sample according to the simulated pollution ratio, and mixed into the result file of the background sample, wherein the simulated pollution ratio refers to the pollution ratio to be simulated. The simulated pollution ratio can be set with pollution ratios of different gradients, such as 1 / 10,000, 2 / 10,000, 5 / 10,000, 2 / 10,000, 5 / 100 and 1 / 100. Then, by continuously replacing the pollution signal source sample and the background sample, each simulated mixed cross-contamination sample is obtained. At this point, each pollution-free sample and each simulated contamination sample can be used to perform a cross-contamination test to obtain a test result. The test result can include the test accuracy of different simulated pollution ratios under different specificities. Then, the contamination ratio detection value under the target specificity can be determined according to the test accuracy, and the contamination ratio detection value under the target specificity is used as the contamination ratio threshold.

[0107] For example, the clean bam files of 183 healthy samples without cross contamination can be mixed in a gradient, including 1 / 10,000 (123 cases), 2 / 10,000 (124 cases), 5 / 10,000 (127 cases), 1 / 1,000 (126 cases), 2 / 1,000 (130 cases), 5 / 1,000 (127 cases) and 1 / 100 (114 cases), with 3 repetitions each time, and finally 871 mixed samples are randomly drawn. Among them, 183 blood cfDNA samples marked as healthy and uncontaminated can be subjected to methylation sequencing to obtain result files containing sequencing fragments, and then mixed in a gradient to obtain 871 simulated mixed cross-contaminated samples. Then, the contamination ratio of the simulated data was tested using different threshold conditions, including VAF less than 0.00005, VAF less than 0.0001, VAF less than 0.0005, VAF less than 0.001, VAF less than 0.005, VA less than 0.01, VAF less than 0.05, VAF less than 0.1, and VAF less than 0.25 of homozygous SNPs. Figure 3 As shown in the figure, it is a schematic diagram of the maximum likelihood algorithm for estimating the contamination ratio of simulated contaminated samples under different homozygous thresholds. The vertical axis is the estimated contamination ratio score ( ), the horizontal axis is the group actually divided according to the cross-contamination gradient. The subgraph title is the SNP threshold condition. For example, the label "0.00005_0.99995" means that VAF is less than 0.00005 or VAF is greater than 0.99995. VAF is the frequency of mutant base fragments at the SNP site. When the wild type is T, VAF is close to 1. Figure 3 It can be seen that when the VAF threshold is less than 0.25, there are abnormal points in the detection of low-contamination gradient samples. At the same time, when the VAF is less than 0.01 and more stringent threshold conditions, the detection results are poor and deviate from expectations. Therefore, in order to retain more SNP sites while ensuring the effect, VAF less than 0.1 is used as the threshold for homozygous SNPs. Figure 4 As shown, it is a schematic diagram of the detection results of simulated contaminated samples with different gradients. The vertical axis is the contamination ratio score obtained by the likelihood estimation of each simulated contaminated sample ( ), the horizontal axis is the pollution gradient. Further, Figure 5 The figure shows the linear relationship between the detected pollution ratio and the true gradient, where the horizontal axis is the true gradient Expected , the vertical axis is the pollution ratio score obtained by detection ( ). It can be clearly seen from the figure that the linear relationship between the two is significant, the coefficient of the linear relationship is 1.0966, the intercept is 0.0003, and R^2=0.8809.

[0108] Furthermore, by summarizing the detection contamination ratios of 183 healthy samples and 871 samples with different levels of contamination, the results are shown in Table 1 below:

[0109] Table 1 Summary of test results of different cross-contamination gradient data sets at 95% specificity

[0110]

[0111] It is obvious that under 95% specificity (i.e., the contamination ratio when 173 out of 183 samples without cross contamination can be correctly judged as uncontaminated is used as the threshold), the cross-contaminated samples with a true gradient of 2 / 10,000 can reach 56% detection (i.e., 69 out of 124 contaminated samples exceed this threshold and are correctly identified as contaminated samples), and the cross-contaminated samples with a true gradient of 5 / 10,000 can reach 98% detection. At this time, the contamination ratio detection value under 95% specificity can be used as the contamination ratio threshold.

[0112] In the above embodiment, contaminated samples are simulated by cross-mixing each uncontaminated sample according to a simulated contamination ratio, and then a cross-contamination test is performed on the uncontaminated sample and the simulated contaminated sample. Thus, the contamination ratio detection value under target specificity is used as the contamination ratio threshold according to the test result, thereby avoiding the situation where it is directly set based on human experience. The contamination ratio threshold is determined through a simulated test, thereby improving the accuracy of the obtained contamination ratio threshold.

[0113] It is understandable that in order to improve the accuracy of the occurrence frequency of the mutant base fragments, the occurrence frequencies of the mutant base fragments corresponding to the SNP sites in multiple non-contaminated samples can be averaged. In some embodiments, the occurrence frequency of the mutant base fragments corresponding to the SNP sites in the non-contaminated samples is obtained, including:

[0114] Obtain each uncontaminated sample, perform methylation targeted sequencing comparison on each uncontaminated sample, and obtain the number of mutant base fragments and the total number of base fragments at the SNP site in each uncontaminated sample; calculate the ratio between the number of mutant base fragments and the total number of base fragments to obtain the proportion of mutant bases corresponding to the SNP site in each uncontaminated sample; calculate the average of the mutant base proportions to obtain the frequency of occurrence of mutant base fragments corresponding to the SNP site in the uncontaminated samples.

[0115] In this embodiment, detection can be performed by methylation targeted sequencing technology, wherein cfDNA can be extracted from uncontaminated samples. Then, a pre-library is constructed, that is, a whole genome pre-library is prepared by bisulfite conversion, end repair, adding adapters, and PCR amplification. Then, fragment sequencing of a specific region is performed, that is, by hybridizing the pre-library with a probe containing a specific sequence, the target methylation region associated with cancer in the human genome is specifically captured. Streptavidin magnetic beads are used to enrich the DNA fragments captured by the probe, and the enriched DNA is amplified as a template to obtain the final library. Then, sequencing analysis is performed, and after quantification and quality control of the final library, the final library is subjected to high-throughput sequencing with a gene sequencer to obtain the measured methylation data. During the sequencing process, a large number of base strings are generated after the nucleic acid fragments of each sample are analyzed by the sequencer, and these strings are called reads. Each read represents the sequence information of a nucleic acid fragment. At this time, the methylation data is compared with the human genome, and SNP sites are identified to obtain a VCF file, which stores gene sequence mutation information, records the fragment information of the SNP sites in the sample, and the number of fragments with different bases.

[0116] In some embodiments, the server performs sequencing comparison on multiple non-contaminated samples simultaneously to obtain the number of mutant base fragments and the total number of base fragments at the SNP site in each non-contaminated sample, thereby improving the efficiency of sequencing comparison.

[0117] In this embodiment, the server calculates the ratio between the number of mutant base fragments and the total number of base fragments at each SNP site to obtain the mutant base ratio corresponding to the SNP site. Then, the sum of the mutant base ratios corresponding to the same SNP site in all non-contaminated samples is calculated, and the ratio of the sum to the number of all SNP sites is calculated to obtain the average value of the mutant base ratio, that is, the occurrence frequency of the mutant base fragments corresponding to the SNP site in the non-contaminated sample is obtained.

[0118] In the above embodiment, each non-contaminated sample is obtained, and then the mutant base ratio corresponding to the SNP site in each non-contaminated sample is calculated. Finally, the average of the mutant base ratio is calculated to obtain the frequency of occurrence of the mutant base fragment corresponding to the SNP site in the non-contaminated sample, that is, the average mutant base ratio is used as the frequency of occurrence of the mutant base fragment corresponding to the SNP site in the non-contaminated sample, thereby improving the accuracy of the obtained frequency of occurrence, thereby reducing the error of sample contamination detection and improving the accuracy of detection.

[0119] Similarly, the sample to be tested can be sequenced and aligned to obtain a VCF file, that is, in some embodiments, the number of mutant base fragments to be tested and the total number of base fragments corresponding to the SNP site in the sample to be tested are obtained, including the steps of:

[0120] Obtain a sample to be tested, perform targeted methylation sequencing comparison on the sample to be tested, and obtain mutation information of a preset SNP site in the sample to be tested; and determine the number of mutant base fragments and the total number of base fragments corresponding to the SNP site in the sample to be tested from the mutation information of the preset SNP site.

[0121] In this embodiment, the sample to be tested can be subjected to targeted methylation sequencing comparison, and then the mutation information of the preset SNP site in the sample to be tested can be obtained, and the mutation information of the preset SNP site is used to characterize the gene sequence mutation information of the corresponding SNP site, and can include the number of fragments including different bases, for example, the number of mutant base fragments and the total number of base fragments can be included. The preset SNP is selected according to the pre-set selection conditions. Preferably, the preset SNP site can be 1018 SNP sites, and the information of the 1018 SNP sites is shown in Table 3 below. Then the preset SNP site can be further filtered, for example, the SNP site with a sequencing depth of less than 50X in the preset SNP site can be filtered out, and then the number of mutant base fragments and the total number of base fragments of each SNP site after filtering are obtained, so as to obtain the number of mutant base fragments and the total number of base fragments corresponding to the SNP site in the sample to be tested.

[0122] In the above embodiment, by filtering the preset SNP sites in the sample to be tested, the number of mutant base fragments and the total number of base fragments corresponding to the SNP sites in the sample to be tested are obtained, thereby improving the accuracy of the data corresponding to the SNP sites obtained, and then using the data corresponding to the SNP sites in the sample to be tested to perform cross-contamination detection, thereby improving the accuracy of cross-contamination detection.

[0123] In a specific embodiment, Figure 6 As shown, a flow chart of a sample contamination detection method is provided, which specifically includes the following steps:

[0124] S602, obtaining a sample to be tested and each non-contaminated sample, performing methylation targeted sequencing comparison on the sample to be tested and each non-contaminated sample, and obtaining mutation information of a preset homozygous SNP site in the sample to be tested and the number of mutant base fragments and the total number of base fragments at the same SNP site in each non-contaminated sample;

[0125] S604, filtering and extracting from the mutation information of the preset homozygous SNP site to obtain the number of mutant base fragments and the total number of base fragments corresponding to the target SNP site in the sample to be tested.

[0126] S606, calculating the ratio between the number of mutant base fragments and the total number of base fragments at the SNP site in each non-contaminated sample, and obtaining the mutant base ratio corresponding to the SNP site in each non-contaminated sample. Calculating the average of the mutant base ratios, and obtaining the occurrence frequency of the mutant base fragments corresponding to the SNP site in the non-contaminated sample.

[0127] S608, obtaining an initial contamination ratio, and calculating the expected number of contaminated base fragments and the expected number of non-contaminated base fragments corresponding to the SNP site in the sample to be tested based on the initial contamination ratio and the total number of base fragments.

[0128] S610, performing mutation noise calculation according to the occurrence frequency and the expected number of non-contaminated base fragments to obtain the number of noise base fragments corresponding to the SNP site in the sample to be tested.

[0129] S612, calculating the sum of the expected number of contaminated base fragments and the number of noise base fragments to obtain the expected number of mutant base fragments corresponding to the SNP site in the sample to be tested.

[0130] S614, based on Poisson distribution information, use the number of mutant base fragments to be tested and the expected number of mutant base fragments to perform likelihood estimation to obtain the true degree of the initial contamination ratio. Determine the contamination ratio of the sample to be tested based on the true degree and the preset likelihood estimation completion condition;

[0131] S616, obtaining a contamination ratio threshold, when the contamination ratio is greater than the contamination ratio threshold, the cross contamination detection result indicates that the sample to be tested is a cross contamination sample. When the contamination ratio is less than or equal to the contamination ratio threshold, the cross contamination detection result indicates that the sample to be tested is a non-contaminated sample.

[0132] In the above embodiment, Poisson distribution information is used to perform likelihood estimation using the number of mutant base fragments to be tested and the expected number of mutant base fragments to obtain the contamination ratio of the sample to be tested, and then the cross-contamination detection result is determined by comparing with the contamination ratio threshold. This can reduce the occurrence of detection errors when cross-contamination exists in the sample to be tested, thereby improving the accuracy of cross-contamination detection.

[0133] In a specific embodiment, Figure 7 As shown, a schematic diagram of the process framework of sample contamination detection is provided. Specifically: the server can use 123 uncontaminated healthy clean samples to perform detection through methylation targeted sequencing technology to obtain information on 1018 SNP sites in each uncontaminated sample, and then perform statistics based on the information on the 1018 SNP sites in each uncontaminated sample to obtain the average noise level N of each SNP site in the uncontaminated sample. The noise level is used to characterize the ratio of the number of reads containing the mutant SNP to the number of all reads covering the SNP, that is, the frequency of occurrence of the mutant base fragment. Then, when the sample to be tested is obtained, the methylation targeted sequencing technology (ELSA-seq) is also used for detection to obtain the information of 1018 SNP sites in the sample to be tested. Then, the SNP sites are filtered to filter out the SNP sites with less than 50X sequencing depth, and the information of each filtered SNP site is obtained, including the number of mutant base fragments to be tested. and total number of base fragments Then, the information of each SNP site after filtering and the frequency of occurrence are used to establish the likelihood estimation function of the Poisson distribution, and then the maximum likelihood estimation is performed through the maximum likelihood estimation algorithm to obtain the unknown number contamination ratio when the joint probability is maximum. Finally, the cross-contamination detection result corresponding to the sample to be tested is determined by comparing the contamination ratio with the contamination ratio threshold. That is, the cross-contamination detection result is determined by the contamination ratio obtained by Poisson distribution likelihood estimation, which can reduce the detection errors when there is cross-contamination in the sample to be tested, thereby improving the accuracy of cross-contamination detection.

[0134] In a specific embodiment, the detection effect of the present application scheme can be verified by experiments. Specifically, a lung cancer (RS20002151PLA) and an ovarian cancer (RS21002966PLA) cfDNA blood sample can be obtained. Both samples are obtained by methylation targeted sequencing technology. Then, different gradients are added and mixed, and the gradient mixing ratio distribution is 1 / 10,000, 5 / 10,000, 1 / 1,000 and 5 / 1,000, of which RS20002151PLA is the contamination source sample and RS21002966PLA is the background sample. It is pre-set that the number of available SNP sites contained in the Panel is 1018, and then due to the probe design and individual heterogeneity factors, the number of SNPs that can be detected is between 400-600. At this point, the server can perform cross-contamination detection on samples of different gradients, and the test results obtained are shown in Table 2 below:

[0135] Table 2 Test results

[0136]

[0137] Among them, at the contamination ratio threshold of 95% specificity (threshold = 0.000504), all negative samples were correctly judged as negative (4 / 4). When the gradient ratio was 1 / 10,000 or above, the model reported all samples as positive (8 / 8), achieving a detection of 1 / 10,000. It can be seen that the contamination ratio obtained according to the maximum likelihood estimation is The value is positively correlated with the mixing ratio gradient. Therefore, this application can improve the accuracy of cross-contamination detection, and the detection limit can be approximately 1 / 10,000. That is, the effect of this application scheme is verified by using 871 samples of simulated contamination and experimental simulated contaminated samples. The evaluation indicators include two aspects: evaluating the calculated contamination ratio Relationship with actual pollution ratio and pollution ratio calculated by evaluation Whether it increases with the increase of the actual contamination ratio, and then ensures that the non-contaminated samples are not misidentified, and the cross-contaminated samples of any gradient can be correctly identified (that is, to ensure high specificity and high sensitivity). Among them, the information of 1018 SNP sites can be shown in Table 3 below. chr represents the chromosome number where the SNP site is located. Start represents the starting position of the site. End represents the ending position of the site. REF represents the wild-type base that is the same as the hg19 reference genome at the SNP site. ALT represents the base after the SNP site mutates.

[0138] Table 3 SNP site information table

[0139]

[0140]

[0141]

[0142]

[0143]

[0144]

[0145]

[0146]

[0147] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0148] Based on the same inventive concept, the embodiment of the present application also provides a sample contamination detection device for implementing the sample contamination detection method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more sample contamination detection device embodiments provided below can refer to the limitations of the sample contamination detection method above, and will not be repeated here.

[0149] In an exemplary embodiment, Figure 8 As shown, a sample contamination detection device 800 is provided, comprising: a fragment number acquisition module 802, a frequency acquisition module 804, a contamination estimation module 806 and a result determination module 808, wherein:

[0150] The fragment number acquisition module 802 is used to obtain the number of mutant base fragments to be tested and the total number of base fragments corresponding to the single nucleotide polymorphism (SNP) site in the sample to be tested;

[0151] The frequency acquisition module 804 is used to obtain the occurrence frequency of the mutant base fragment corresponding to the SNP site in the non-contaminated sample;

[0152] The contamination estimation module 806 is used to perform a likelihood estimation of the contamination ratio based on the target distribution information corresponding to the predetermined number of mutant base fragments, using the number of mutant base fragments to be tested, the total number of base fragments and the frequency of occurrence, to obtain the contamination ratio of the sample to be tested;

[0153] The result determination module 808 is used to determine the cross-contamination detection result of the sample to be tested according to the contamination ratio.

[0154] In one embodiment, the target distribution information includes Poisson distribution information; the contamination estimation module 806 is also used to obtain the initial contamination ratio, perform expected number calculation based on the initial contamination ratio, the total number of base fragments and the frequency of occurrence, and obtain the expected number of mutant base fragments corresponding to the SNP site in the sample to be tested; based on the Poisson distribution information, use the number of mutant base fragments to be tested and the expected number of mutant base fragments to perform likelihood estimation to obtain the true degree of the initial contamination ratio; determine the contamination ratio of the sample to be tested based on the true degree and the preset likelihood estimation completion conditions.

[0155] In one embodiment, the contamination estimation module 806 is also used to calculate the expected number of contaminated base fragments and the expected number of non-contaminated base fragments corresponding to the SNP site in the sample to be tested based on the initial contamination ratio and the total number of base fragments; calculate the mutation noise based on the occurrence frequency and the expected number of non-contaminated base fragments to obtain the number of noise base fragments corresponding to the SNP site in the sample to be tested; calculate the sum of the expected number of contaminated base fragments and the number of noise base fragments to obtain the expected number of mutant base fragments corresponding to the SNP site in the sample to be tested.

[0156] In one embodiment, the result determination module 808 is also used to obtain a contamination ratio threshold. When the contamination ratio is greater than the contamination ratio threshold, the cross-contamination detection result indicates that the sample to be tested is a cross-contaminated sample; when the contamination ratio is less than or equal to the contamination ratio threshold, the cross-contamination detection result indicates that the sample to be tested is a non-contaminated sample.

[0157] In one embodiment, the result determination module 808 is also used to obtain each uncontaminated sample, cross-mix each uncontaminated sample according to the simulated contamination ratio, and obtain each simulated contaminated sample corresponding to the simulated contamination ratio; use each uncontaminated sample and each simulated contaminated sample to perform a cross-contamination test to obtain a test result; obtain a contamination ratio detection value under target specificity based on the test result, and use the contamination ratio detection value under target specificity as a contamination ratio threshold.

[0158] In one embodiment, the frequency acquisition module 804 is also used to obtain each uncontaminated sample, perform methylation targeted sequencing comparison on each uncontaminated sample, and obtain the number of mutant base fragments and the total number of base fragments at the SNP site in each uncontaminated sample; calculate the ratio between the number of mutant base fragments and the total number of base fragments to obtain the ratio of mutant bases corresponding to the SNP site in each uncontaminated sample; calculate the average of the mutant base ratios to obtain the frequency of occurrence of mutant base fragments corresponding to the SNP site in the uncontaminated samples.

[0159] In one embodiment, the fragment number acquisition module 802 is also used to obtain the sample to be tested, perform methylation targeted sequencing comparison on the sample to be tested, and obtain the mutation information of the preset SNP site in the sample to be tested; determine the number of mutant base fragments and the total number of base fragments corresponding to the SNP site in the sample to be tested from the mutation information of the preset SNP site.

[0160] In one embodiment, the SNP site includes a homozygous SNP site, and the occurrence frequency of the mutant base fragment in the homozygous SNP site is less than the target value.

[0161] Each module in the above-mentioned sample contamination detection device can be implemented in whole or in part by software, hardware or a combination thereof. Each of the above-mentioned modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0162] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Fig. 9 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store sequencing data of samples to be tested, sequencing data of non-contaminated samples, target distribution information, etc. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a sample contamination detection method is implemented.

[0163] In an exemplary embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as shown in FIG. Fig.10 As shown. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit and an input device. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface, the display unit and the input device are connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a mobile cellular network, near field communication (Near Field Communication, NFC) or other technologies. When the computer program is executed by the processor, a sample contamination detection method is implemented. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device shell, or an external keyboard, touchpad or mouse.

[0164] Those skilled in the art will understand that Fig. 9 or Fig.10 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0165] In one embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiments when executing the computer program.

[0166] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0167] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0168] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0169] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0170] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0171] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A sample contamination detection method, characterized in that: The method comprises: Obtain the number of mutant base fragments and the total number of base fragments to be tested corresponding to the single nucleotide polymorphism (SNP) site in the sample to be tested; Obtain the occurrence frequency of the mutant base fragment corresponding to the SNP site in the non-contaminated sample; Based on the target distribution information corresponding to the predetermined number of mutant base fragments, the number of mutant base fragments to be tested, the total number of base fragments and the occurrence frequency are used to perform a likelihood estimation of the contamination ratio to obtain the contamination ratio of the sample to be tested, the target distribution information is used to characterize the target distribution relationship between the expected number of mutant base fragments of the sample to be tested and the observed number of mutant base fragments to be tested of the sample to be tested, the expected number of mutant base fragments is obtained by obtaining an initial contamination ratio and calculating the expected number based on the initial contamination ratio, the total number of base fragments and the occurrence frequency; The cross-contamination detection result of the sample to be tested is determined according to the contamination ratio.

2. The method according to claim 1, characterized in that: The target distribution information includes Poisson distribution information; the target distribution information corresponding to the predetermined number of mutant base fragments is used to estimate the likelihood of the contamination ratio using the number of mutant base fragments to be tested, the total number of base fragments and the occurrence frequency to obtain the contamination ratio of the sample to be tested, including: Obtaining an initial contamination ratio, and performing an expected number calculation based on the initial contamination ratio, the total number of base fragments, and the occurrence frequency to obtain the expected number of mutated base fragments corresponding to the SNP site in the sample to be tested; Based on Poisson distribution information, likelihood estimation is performed using the number of mutant base fragments to be tested and the expected number of mutant base fragments to obtain the true degree of the initial contamination ratio; The contamination ratio of the sample to be tested is determined according to the degree of truth and a preset likelihood estimation completion condition.

3. The method according to claim 2, characterized in that The expected number calculation based on the initial contamination ratio, the total number of base fragments and the occurrence frequency to obtain the expected number of mutant base fragments corresponding to the SNP site in the sample to be tested includes: Calculating the expected number of contaminated base fragments and the expected number of non-contaminated base fragments corresponding to the SNP site in the sample to be tested according to the initial contamination ratio and the total number of base fragments; Perform mutation noise calculation according to the occurrence frequency and the expected number of non-contaminated base fragments to obtain the number of noise base fragments corresponding to the SNP site in the sample to be tested; The sum of the expected number of contaminated base fragments and the number of noise base fragments is calculated to obtain the expected number of mutant base fragments corresponding to the SNP site in the sample to be tested.

4. The method according to any one of claims 1 to 3, characterized in that: Determining the cross-contamination detection result of the sample to be tested according to the contamination ratio includes: Obtaining a contamination ratio threshold value, when the contamination ratio is greater than the contamination ratio threshold value, the cross-contamination detection result indicates that the sample to be tested is a cross-contaminated sample; When the contamination ratio is less than or equal to the contamination ratio threshold, the cross-contamination detection result indicates that the sample to be tested is a non-contaminated sample.

5. The method according to claim 4, characterized in that The obtaining of the pollution ratio threshold comprises: Obtaining each non-polluted sample, and cross-mixing the each non-polluted sample according to the simulated pollution ratio to obtain each simulated polluted sample corresponding to the simulated pollution ratio; Perform a cross-contamination test using the non-contaminated samples and the simulated contaminated samples to obtain a test result; A contamination ratio detection value under target specificity is acquired according to the test result, and the contamination ratio detection value under target specificity is used as the contamination ratio threshold.

6. The method according to any one of claims 1 to 3, characterized in that: The method of obtaining the occurrence frequency of the mutant base fragment corresponding to the SNP site in the non-contaminated sample includes: Obtain each non-contamination sample, and perform methylation targeted sequencing comparison on each non-contamination sample to obtain the number of mutant base fragments and the total number of base fragments at the SNP site in each non-contamination sample; Calculating the ratio between the number of mutant base fragments and the total number of base fragments to obtain the ratio of mutant bases corresponding to the SNP sites in each of the non-contaminated samples; The average of the mutant base ratios is calculated to obtain the frequency of occurrence of the mutant base fragments corresponding to the SNP site in the non-contaminated sample.

7. The method according to any one of claims 1 to 3, characterized in that: The step of obtaining the number of mutant base fragments to be tested and the total number of base fragments corresponding to the SNP site in the sample to be tested includes: Obtaining a sample to be tested, performing targeted methylation sequencing comparison on the sample to be tested, and obtaining mutation information of a preset SNP site in the sample to be tested; The number of mutated base fragments and the total number of base fragments corresponding to the SNP site in the sample to be tested are determined from the mutation information of the preset SNP site.

8. The method according to any one of claims 1 to 3, characterized in that: The SNP site includes a homozygous SNP site, and the occurrence frequency of the mutant base fragment in the homozygous SNP site is less than the target value.

9. A sample contamination detection device, characterized in that: The device comprises: A fragment number acquisition module is used to obtain the number of mutant base fragments to be tested and the total number of base fragments corresponding to the single nucleotide polymorphism (SNP) site in the sample to be tested; The frequency acquisition module is used to obtain the occurrence frequency of the mutant base fragment corresponding to the SNP site in the non-contaminated sample; A contamination estimation module, for performing a likelihood estimation of the contamination ratio based on the target distribution information corresponding to the predetermined number of mutant base fragments, using the number of mutant base fragments to be tested, the total number of base fragments and the frequency of occurrence, to obtain the contamination ratio of the sample to be tested, wherein the target distribution information is used to characterize the target distribution relationship between the expected number of mutant base fragments of the sample to be tested and the observed number of mutant base fragments to be tested of the sample to be tested, and the expected number of mutant base fragments is obtained by obtaining an initial contamination ratio and calculating the expected number based on the initial contamination ratio, the total number of base fragments and the frequency of occurrence; The result determination module is used to determine the cross-contamination detection result of the sample to be tested according to the contamination ratio.

10. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 8 are implemented.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

12. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

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