Ultrahigh-sensitivity sample component evaluation and traceability method and device

Abstract

The invention discloses an ultrahigh-sensitivity sample component evaluation and traceability method and device. The method comprises the following steps: acquiring a sample, and carrying out nucleic acid extraction, library construction, capture enrichment and sequencing to generate sequencing data; preprocessing the sequencing data to obtain high-quality sequencing data; identifying SNP loci and genotypes thereof in the high-quality sequencing data, and constructing an SNP combination database; carrying out quantitative statistics on the double allelic genotypes of the SNP sites, and calculating the variation abundance of secondary alleles; constructing an SNP site variation abundance background distribution model according to the genotype information and allele variation abundance of the SNP sites; and performing cross contamination assessment on the sample by using the constructed SNP combination database and the variation abundance background distribution model. According to the method, qualitative evaluation of multi-source components in the sample, quantitative calculation of the cross contamination proportion and traceability analysis of non-self sources are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of sample component evaluation and traceability, and in particular to a method and device for sample component evaluation and traceability with ultra-high sensitivity. Background Art

[0002] At present, dual indexing technology is used in the wet experiment stage, so that each sample has a unique dual index combination, and the probability of crosstalk occurring on two independent indexes at the same time will be significantly reduced, thereby reducing the contamination caused by label crosstalk and mismatch. Non-template control (NTC, No Template Control) is a commonly used control sample in molecular diagnostic experiments. It is a reaction performed without adding template DNA to test whether there are false positive results during the reaction process. It is used to evaluate the contamination of reagents, operating procedures or experimental environments.

[0003] Bioinformatics methods are used to identify genetic variations in samples that have the ability to distinguish between different populations. When a genotype that does not match the sample or does not meet the expected characteristics is detected in the sample, it can be used as a basis for evaluating the sample components. There are usually two types of molecular tags, one is short tandem repeats (STR). Depending on the number of tandem repeats, each STR site has a variety of combinations of repeats; the other is single nucleotide polymorphism (SNP) sites. According to the combination of alleles A / B, SNP sites usually have three genotypes (AA, AB, BB). Due to the molecular genetic mechanism of tumor samples, the lack of genomic mismatch repair function will lead to a high level of biological background noise at the STR site, and chromosomal microdeletions will also cause the failure of STR information. SNP sites are ubiquitous and easy to screen, and have better applicability in ctDNA detection.

[0004] Existing wet lab techniques are limited to specific links and scenarios, and cannot specifically identify samples that are not of their own origin, cannot accurately quantify the proportion of components in the sample, and cannot trace the source. Existing bioinformatics methods include some methods and software for contamination assessment based on SNPs, such as GATK CalculateContamination. However, the GATK method requires paired control sample information, and there are deviations in the assessment under single-sample scenarios. At the same time, its method for estimating the contamination ratio still lacks sufficient sensitivity, making it difficult to determine low-frequency (<1%) contamination signals.

[0005] Therefore, it is an urgent problem for those skilled in the art to propose an ultra-high sensitivity method and device for sample component evaluation and traceability to solve the difficulties existing in the prior art. Summary of the invention

[0006] The purpose of the present invention is to provide a method and device for ultra-high sensitivity sample component evaluation and traceability, which can realize qualitative evaluation of multi-source components in a sample, quantitative calculation of cross-contamination ratio and traceability analysis of non-self-source components, and construct a set of sample component evaluation indicators and calculation framework that are independent of control samples.

[0007] To achieve the above object, the present invention provides the following solutions: A method for ultra-high sensitivity sample component assessment and traceability, comprising the following steps: S1. Obtain samples, enter sample patient information and assign sample numbers through the laboratory management system, then perform nucleic acid extraction, library construction, capture enrichment and sequencing to generate sequencing data; S2. Preprocess the sequencing data, including removing adapter sequences, low-quality sequences, and single-base repeat sequences, to obtain high-quality sequencing data, and post the sequencing reads back to the reference genome; S3, identify the SNP sites and their genotypes in the high-quality sequencing data, calculate the population frequency of the minor allele of the SNP site in combination with the population data, select appropriate SNP sites to build a SNP combination database, and record the SNP combination genotype information of each test sample; S4. Quantitatively count the double allele genotypes of the SNP loci, record the number of supporting A allele genotypes and the number of supporting B allele genotypes respectively, divide the allele types into major alleles and minor alleles according to the supporting number, and calculate the variation abundance of the minor allele; S5. Construct a SNP site variation abundance background distribution model based on the genotype information and allele variation abundance of the SNP site; S6. Use the constructed SNP combination database and SNP site variation abundance background distribution model to conduct cross-contamination assessment on samples, achieve qualitative evaluation of multi-source components in samples, quantitative calculation of cross-contamination ratios, and traceability analysis of non-self sources.

[0008] Preferably, in S2, preprocessing the sequencing data to obtain high-quality sequencing data, and posting the sequencing reads back to the reference genome specifically includes: The sequencing data is split into connectors. The split sequencing data is usually in fastq format, including the read name, base sequence, identification symbol and sequencing quality value corresponding to the base sequence; the connector sequence, low-quality sequence and single base repeat sequence are removed from the split sequencing data to obtain high-quality sequencing data; the processed sequencing reads are linked back to the reference genome through sequence alignment to form a bam format file.

[0009] Preferably, in S3, selecting appropriate SNP sites to construct a SNP combination database specifically includes: Identify SNPs and their genotypes within the coverage of high-quality sequencing data. Genotype information is divided into pure and wild type AA, heterozygous variant AB, and pure and variant BB. Combined with population data, calculate the population frequency of the minor allele of the SNP site. The calculation formula is as follows: ; in, is the number of pure and variant genotypes in the population, is the number of heterozygous variant genotypes in the population, is the total number of people; Select appropriate SNP sites whose population frequencies have the ability to identify individuals. Candidate sites should meet the following requirements: site depth is not less than 100; the site is not located in the HLA and other repeat unit intervals; the site allele frequency should be between 0.2-0.8; there should not be more than two genotypes with a population frequency greater than 1% at the site; For n SNP candidate sites, calculate their combined heterozygosity to obtain a set of variant combinations with individual identification capabilities. The combined heterozygosity should be between 0.4 and 0.6. The formula for calculating the combined heterozygosity is as follows: in, is the population frequency of the allele at the i-th SNP site; A database is constructed to record the SNP combination genotype information of each test sample, which is called the germline library; the genotype information of the SNP site is encoded as 0, 1, 2, representing AA, AB and BB respectively, and the missing genotype is encoded as -1. The test samples are labeled by PID and SampleID.

[0010] Preferably, in S4-S5, constructing the SNP site variation abundance background distribution model according to the genotype information and allele variation abundance of the SNP site specifically includes: The biallelic genotypes of SNP loci are quantitatively counted, and the support numbers are Count A and Count B , the allele types are divided into major alleles and minor alleles according to the number of supports, and the abundance of minor allele variation is calculated as follows: In the absence of control samples, statistical tests are performed based on the abundance of allele variation to determine the corresponding genotype; genotypes are divided into pure and heterozygous types, represented by 0 and 1 respectively; the minor allele genotype is inferred by the following method: ; The SNP set was further filtered through population data and sub-allele information to remove sites with too high a proportion of NA in the sub-allele. Afterwards, the population samples were counted and samples with too high a proportion of NA were removed. The remaining samples were used to construct the background distribution model. Four sets of baseline data were formed for each site with the corresponding combination of alleles and genotypes: , , , , in, is the sub-allelic variation abundance set of n SNP sites, represents the sample with minor allele A and genotype 0, represents the sample with minor allele A and genotype 1, represents the sample with minor allele B and genotype 0, Represents samples with minor allele B and genotype 1; fit the data distribution on this basis and select the distribution with the best fit as the baseline model.

[0011] Preferably, in S6, performing cross contamination assessment on the sample specifically includes: By calculating the proportion of SNP sites with a minor allele type of 0 in the sample, that is, the pure sum coefficient, it is determined whether the sample has cross-contamination; if the pure sum coefficient is less than 0.2, the sample is a highly contaminated sample; if the pure sum coefficient is greater than or equal to 0.2, and the proportion of SNP sites with a minor allele type of NA and a minor allele frequency less than 5% is greater than or equal to 0.05, the sample is a low-contamination sample; The loci with NA genotype are the loci specific to the pollution source; the expected frequency of pollution is calculated based on the pollution patterns 0 / 1, 0 / 2, and 1 / 0. , , , calculated by The difference between the expected frequency of pollution and the pollution ratio is calculated using the least squares method. ; Pollution ratio The prior population probabilities of the SNP sites are used to obtain the posterior probabilities of the contamination modes 0 / 1, 0 / 2, and 1 / 0, which are then converted into the germ line list of the individual and the germ line list of the contamination source, and matched with the sample germ line library respectively, and traceability verification is performed based on the matching consistency scores.

[0012] An ultra-high sensitivity sample component evaluation and tracing device is used to perform any of the above ultra-high sensitivity sample component evaluation and tracing methods, comprising: The sample preparation and sequencing module is used to obtain samples, enter sample patient information and assign sample numbers through the laboratory management system, and then perform nucleic acid extraction, library construction, capture enrichment and sequencing to generate sequencing data; Off-machine data preprocessing module, used to preprocess sequencing data, including removing adapter sequences, low-quality sequences and single-base repeat sequences, obtaining high-quality sequencing data, and posting sequencing reads back to the reference genome; The SNP combination database construction module is used to identify the SNP sites and their genotypes in high-quality sequencing data, calculate the population frequency of the minor allele of the SNP site in combination with the population data, select appropriate SNP sites to build the SNP combination database, and record the SNP combination genotype information of each test sample; The module for constructing the background distribution model of variation abundance is used to perform quantitative statistics on the double allele genotypes of the SNP site, record the number of supporting A allele genotypes and the number of supporting B allele genotypes respectively, divide the allele type into major allele and minor allele according to the number of support, and calculate the variation abundance of the minor allele. According to the genotype information of the SNP site and the variation abundance of the allele, the variation abundance background distribution model of the SNP site is constructed; The sample cross-contamination assessment module is used to conduct cross-contamination assessment on samples using the constructed SNP combination database and SNP site variation abundance background distribution model, to achieve qualitative assessment of multi-source components within the sample, quantitative calculation of cross-contamination ratio, and traceability analysis of non-self sources.

[0013] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the method for ultra-high sensitivity sample component evaluation and tracing is implemented as any one of the above methods.

[0014] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects: (1) Based on NGS sequencing data, the present invention selects SNP sites with good coverage and alleles, screens suitable SNP combinations as molecular tags for individual identification, and constructs an individual genotype database for sample matching and traceability. On this basis, the double allele genotype of the SNP site is used to calculate the variation frequency of the minor allele, and the cross-contamination is qualitatively judged through the pure sum coefficient at the sample level. The population frequency of each SNP site is determined through population data, and the background error rate of the variation direction is combined with the prior population frequency to estimate the contamination ratio and infer the genotype of the contamination source.

[0015] (2) The present invention is used for sample component assessment of NGS sequencing data, which can achieve qualitative assessment of multi-source components in the sample, quantitative calculation of cross-contamination ratio, and traceability analysis of non-self-source components, and construct a set of sample component assessment indicators and calculation framework that do not rely on control samples. For ctDNA detection scenarios, accurate cross-contamination assessment can be achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0017] Figure 1 A schematic flow chart of a method for ultra-high sensitivity sample component evaluation and traceability provided by the present invention; Figure 2 This is a diagram of the pure sum coefficient simulation results of the present invention; Figure 3 is a background distribution diagram of the abundance of SNP alleles in Example 1 of the present invention; Figure 4 The figure is a distribution diagram of consistency scores between samples with consistent sources (paired) and samples with inconsistent sources (unpaired) according to the present invention; Figure 5 is a background distribution diagram of the abundance of SNP alleles in Example 3 of the present invention; Figure 6 This is a comparison chart of the contamination ratio results of the method of the present invention (marked as new) and the GATK-Contamination method (marked as pre); Among them, (a) is a comparison chart of the contamination ratio results of all samples under logarithmic coordinates, and (b) is a comparison chart of the results above 1% in any method. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0020] like Figure 1As shown, the present invention provides a method for evaluating and tracing sample components with ultra-high sensitivity, comprising the following steps: S1. Obtain samples, enter sample patient information and assign sample numbers through the laboratory management system, then perform nucleic acid extraction, library construction, capture enrichment and sequencing to generate sequencing data; S2. Preprocess the sequencing data, including removing adapter sequences, low-quality sequences, and single-base repeat sequences, to obtain high-quality sequencing data, and post the sequencing reads back to the reference genome; S3, identify the SNP sites and their genotypes in the high-quality sequencing data, calculate the population frequency of the minor allele of the SNP site in combination with the population data, select appropriate SNP sites to build a SNP combination database, and record the SNP combination genotype information of each test sample; S4. Quantitatively count the double allele genotypes of the SNP loci, record the number of supporting A allele genotypes and the number of supporting B allele genotypes respectively, divide the allele types into major alleles and minor alleles according to the supporting number, and calculate the variation abundance of the minor allele; S5. Construct a SNP site variation abundance background distribution model based on the genotype information and allele variation abundance of the SNP site; S6. Use the constructed SNP combination database and SNP site variation abundance background distribution model to conduct cross-contamination assessment on samples, achieve qualitative evaluation of multi-source components in samples, quantitative calculation of cross-contamination ratios, and traceability analysis of non-self sources.

[0021] The method of the present invention specifically comprises: 1. Sample Preparation and Sequencing After receiving the sample, the patient information (PID) is entered into the laboratory management system (LIMS) and the sample number (SampleID) is assigned. Different samples from the same patient share the same PID, and the sample number is unique within the laboratory. Nucleic acid extraction is performed in different operating rooms for body fluid samples and tissue samples, and products from different operating batches are stored in 96-well plates. After the nucleic acid product is final-trim, it is connected to the sample-specific adapter sequence (barcode index) for library construction. The library products are distributed according to the product reagent (assay), and samples treated with the same assay are mixed and captured and enriched. According to the expected data volume of different samples, sequencing weights are assigned and sequencing is performed on the machine, and a detailed table of sample information is generated based on the above operations.

[0022] 2. Off-line data preprocessing The sample data is obtained by splitting the adapter, usually in fastq format, including the read name, base sequence, identifier, and sequencing quality value corresponding to the base sequence. The original data removes the adapter sequence, low-quality sequence, and single-base repeat sequence to obtain high-quality sequencing data. The processed sequencing reads are linked back to the reference genome through sequence alignment to form a bam format file.

[0023] 3. Screening SNPs and building a SNP combination database Identify the SNPs and their genotypes within the assay (product reagent) coverage. The genotype information is divided into pure wild type AA, heterozygous variant AB, and pure variant BB. Combined with population data, calculate the population frequency of the minor allele of the SNP site. The calculation formula is as follows: ; in, is the number of pure and variant genotypes in the population, is the number of heterozygous variant genotypes in the population, is the total number of people; To select appropriate SNP sites, their population frequency must have the ability to identify individuals, and the candidate sites should meet the following requirements: the site depth is not less than 100; the site is not located in the HLA and other repeat unit intervals; the site allele frequency should be between 0.2-0.8; there are no more than two genotypes with a population frequency greater than 1% at the site.

[0024] For n SNP candidate sites, calculate their combined heterozygosity (H) to obtain a set of variant combinations that need to have individual recognition capabilities. The combined heterozygosity should be between 0.4 and 0.6. The calculation formula is as follows: in, is the population frequency of the allele at the ith SNP site; a database is constructed to record the SNP combination genotype information of each test sample, called the germline library. The genotype information of the SNP site is encoded as 0, 1, 2, representing AA, AB and BB respectively, and the missing genotype is encoded as -1. The test samples are annotated by PID and SampleID. An example is shown in Table 1 below:

[0025] Construct a matching score calculation program for evaluating the consistency between samples. Through matching scores, it is possible to confirm the pairing of multiple samples from the same patient, deny the pairing of samples from different patients, and trace the source of contaminated samples. There are two calculation methods for consistency evaluation, as follows: Calculation method 1 is to include all SNP sites for pairwise comparison. The same coding is the consistent site, and the different coding is the differential site. The proportion of consistent sites is the consistency score, and the highest score is the best match.

[0026] Calculation method 2 is to include only heterozygous sites for pairwise comparison, 0-1, 1-0, 1-2, 2-1 are heterozygous inconsistent sites, and the heterozygous inconsistent ratio is the LOH score.

[0027] 4. Constructing a SNP site variation abundance background distribution model The double allele genotypes of SNP loci are quantitatively counted, and their support numbers are CountA and CountB respectively. The allele types are divided into major alleles and minor alleles according to the support numbers. The variation abundance of minor alleles is calculated as follows: In the absence of control samples, statistical tests are performed based on the abundance of allele variation to determine the corresponding genotype. Genotypes can be divided into pure and heterozygous types, represented by 0 and 1 respectively. The sub-allele genotype is inferred by the following method: ; The SNP set is further filtered through the population data and this allele type information to remove sites where the minor allele type is too high for NA. After that, the population samples are counted and the samples with too high NA ratio are removed. The remaining samples are used to build the background distribution model. Each site can form four sets of baseline data with the corresponding allele and genotype combination: , , , , in, is the sub-allelic variation abundance set of n SNP sites, represents the sample with minor allele A and genotype 0, represents the sample with minor allele A and genotype 1, represents the sample with minor allele B and genotype 0, Represents samples with minor allele B and genotype 1.

[0028] On this basis, the data distribution is fitted, the parameters of different distributions such as normal distribution, t distribution, Weibull distribution, etc. are estimated, and the distribution with the best fit is selected as the baseline model.

[0029] 5. Sample cross contamination assessment Based on the assumption that the heterozygosity rate of the SNP combination is 0.5 and the contamination ratio is 20%, the changes in the minor allele type and allele frequency in the sample are shown by simulating changes. There are 4*4=16 combinations of contaminated genotypes, namely 0 / 0, 0 / 1, 0 / 1, 0 / 2, 1 / 0, 1 / 0, 1 / 1, 1 / 1, 1 / 1, 1 / 1, 1 / 2, 1 / 2, 2 / 0, 2 / 1, 2 / 1, 2 / 2. After contamination, the proportion of minor allele type 0 decreased from 50% to 12.5%, and the proportion of minor allele type 1 decreased from 50% to 25%. Since genomic copy number variation and loss of heterozygosity are common in tumor samples, heterozygous SNPs may be affected, resulting in a decrease in the proportion of minor allele type 1, while the proportion of 0 remains unchanged. Therefore, the proportion of pure and site under the SNP combination is selected as a qualitative assessment of cross-contamination in the sample, which is called the pure and coefficient.

[0030] The above allele type background distribution model and genotype inference model are used for contaminated samples to determine the potential contaminated sites and record their allele types and allele variation abundance. According to the possible contamination modes 0 / 1, 0 / 2, 1 / 0, the expected frequency of contamination is calculated. , , , by calculating the observed The difference between the expected frequency of pollution and the pollution proportion is estimated using the least squares method. The specific calculation is as follows: in, is the actual value of the minor allele variation abundance at the site, is the expected value of the variable abundance of components from other sources, is the variation abundance when other source components are heterozygous, is the variation abundance when other source components are pure and type; according to the pollution ratio The prior population probabilities of the SNP sites are used to obtain the posterior probabilities of the contamination modes 0 / 1, 0 / 2, and 1 / 0, which are then converted into the germ line list of the individual and the germ line list of the contamination source, and matched with the sample germ line library respectively, and traceability verification is performed based on the matching consistency scores.

[0031] An ultra-high sensitivity sample component evaluation and tracing device, using any of the above ultra-high sensitivity sample component evaluation and tracing methods, comprising: The sample preparation and sequencing module is used to obtain samples, enter sample patient information and assign sample numbers through the laboratory management system, and then perform nucleic acid extraction, library construction, capture enrichment and sequencing to generate sequencing data; Off-machine data preprocessing module, used to preprocess sequencing data, including removing adapter sequences, low-quality sequences and single-base repeat sequences, obtaining high-quality sequencing data, and posting sequencing reads back to the reference genome; The SNP combination database construction module is used to identify the SNP sites and their genotypes in high-quality sequencing data, calculate the population frequency of the minor allele of the SNP site in combination with the population data, select appropriate SNP sites to build the SNP combination database, and record the SNP combination genotype information of each test sample; The module for constructing the background distribution model of variation abundance is used to perform quantitative statistics on the double allele genotypes of the SNP site, record the number of supporting A allele genotypes and the number of supporting B allele genotypes respectively, divide the allele type into major allele and minor allele according to the number of support, and calculate the variation abundance of the minor allele. According to the genotype information of the SNP site and the variation abundance of the allele, the variation abundance background distribution model of the SNP site is constructed; The sample cross-contamination assessment module is used to conduct cross-contamination assessment on samples using the constructed SNP combination database and SNP site variation abundance background distribution model, to achieve qualitative assessment of multi-source components within the sample, quantitative calculation of cross-contamination ratio, and traceability analysis of non-self sources.

[0032] Specifically, the data preprocessing module uses fastp software to remove adapter sequences and low-quality sequences from the sample raw data, and uses bwa software to align the sequencing data to the human reference genome (hg19) to obtain a bam file.

[0033] The SNP combination database construction module uses varscan software to identify variants in bam files and obtain vcf files. Candidate SNPs are screened from the vcf files according to the following conditions: The site depth is not less than 100; The locus is not located in the HLA and other repeat unit intervals; The locus allele frequency should be between 0.2-0.8; There are no more than two genotypes with a population frequency greater than 1% at the locus.

[0034] The variant abundance background distribution model building module uses pysam pileup to identify the SNP alleles and corresponding support numbers in the test samples. The SNP site coverage reads must meet the following conditions: The base quality value of the site sequencing is not less than 30; The site coverage reads are not unmapped, secondary mapped, or soft-clip mapped; The site is located in the overlap interval of pair-end reads, and the base sequences of the overlap are consistent.

[0035] Thus, we can get the number of genotypes supporting A allele Count. A and the number of supporting B alleles Count B , and calculate the genotype information in two ways: One is the ref-alt allele method, which is divided into pure wild-type AA, heterozygous variant AA, and pure variant BB according to the consistency with the reference genome. The three genotypes are coded as 0, 1, and 2. This information is used for recording and matching of the germline library. Each SNP site is fixedly recorded as chrom-pos-ref-alt as an identifier; One is the minor allele method, where the genotype is divided into pure type 0 and heterozygous type 1. This information is used for contamination ratio assessment and genotype inference, and each SNP site is recorded as chrom-pos-minor_allele.

[0036] The sample cross-contamination assessment module uses the above method to screen the SNP combination to identify each site in the form of ref-alt allele, and randomly generates genotype information 0, 1, and 2 according to the heterozygosity rate of 0.5 to initialize the germline database.

[0037] The germline database has writing and searching functions. When a new sample enters the germline database, the sample number information and genotype information are recorded. At the same time, the database is traversed, and the consistency is compared with the recorded historical samples one by one. The optimal matching score is calculated and the matching sample number information is obtained.

[0038] The new sample identifies the variant allele information of each site in the SNP combination in a pileup manner, and determines the genotype sequence according to the variant abundance of the SNP site. The variant abundance of the site is less than 20% and is marked as 0, the variant abundance is between [20%, 80%] and is marked as 1, and the variant abundance is greater than 80% and is marked as 2.

[0039] The consistency is determined based on the best matching score and the matching sample number information. The specific rules for consistency determination are as follows: If it is the first record, the consistency score is less than 0.75, then the sample consistency level is B, if the score is greater than or equal to 0.75, then the consistency level is C; If there are different samples with the same PID and the consistency score is greater than or equal to 0.75, the consistency grade is A, and if the score is less than 0.75, the consistency grade is D.

[0040] For grades A and B, the consistency evaluation result is PASS; for grades C and D, the consistency evaluation result is FAIL. In particular, for samples with a high tumor ratio, if the LOH score is greater than 0.2, the result is LOH.

[0041] Component evaluation is performed by simulating SNP sites. The data simulation method is as follows: (1) Randomly distribute and combine [0, 1, 1, 2] to form a SNP set sequence of 100 samples; (2) The variation abundance of the loci with genotype 0 is randomly generated according to a normal distribution with a mean of 0 and a variance of 0.01. The variation abundance of the loci with genotype 1 is randomly generated according to a normal distribution with a mean of 0.5 and a variance of 0.01. The variation abundance of the loci with genotype 2 is randomly generated according to a normal distribution with a mean of 1 and a variance of 0.01. (3) Generate simulated data of mixed components in different proportions. The simulated data are given in the form of minor alleles, providing information on minor allele genotypes and minor allele variation abundance; (4) Calculate the sample pure sum coefficient.

[0042] like Figure 2 The simulation results of the pure sum coefficient are shown, and 0.2 is preliminarily determined as the threshold to distinguish between low-proportion pollution and high-proportion pollution: The sample is piled up to identify the variant allele information of each site in the SNP combination. According to the variation abundance of the minor allele, the minor allele is inferred in the following way: ; in is a statistical test under the SNP background distribution model. The genotype background distribution and The genotype background distribution of The genotype in the CI95 interval of the distribution is determined to be the corresponding genotype, otherwise it is NA.

[0043] By calculating the proportion of SNP sites with a minor allele type of 0 in the sample, that is, the pure sum coefficient, it is determined whether the sample has cross-contamination. If the pure sum coefficient is less than 0.2, the sample is inferred to be a highly contaminated sample; if the pure sum coefficient is greater than or equal to 0.2, and the proportion of SNP sites with a minor allele type of NA and a minor allele frequency less than 5% is greater than or equal to 0.05, the sample is inferred to be a low-contamination sample; otherwise, the sample is inferred to be a single-source sample.

[0044] The loci with NA genotype are the loci unique to the pollution source. Based on the possible pollution patterns 0 / 1, 0 / 2, 1 / 0, calculate the expected frequency of pollution , , , by calculating the observed The difference between the expected frequency of pollution and the pollution proportion is estimated using the least squares method. .

[0045] According to the pollution ratio The prior population probabilities of the SNP sites are used to obtain the posterior probabilities of the contamination modes 0 / 1, 0 / 2, and 1 / 0, which are then converted into the germ line list of the individual and the germ line list of the contamination source, and matched with the sample germ line library respectively, and traceability verification is performed based on the matching consistency scores.

[0046] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the method for ultra-high sensitivity sample component evaluation and tracing is implemented as any one of the above methods.

[0047] In specific example one, SNP combinations were screened based on whole exome tumor population data of 90 cases. Samples were obtained, and the sample patient information was entered into the laboratory management system and the sample number was assigned. Then nucleic acid extraction, library construction, capture enrichment and sequencing were performed to generate sequencing data; the sequencing data was preprocessed, including the removal of adapter sequences, low-quality sequences and single-base repeat sequences to obtain high-quality sequencing data, and the sequencing reads were posted back to the reference genome; the SNP sites and their genotypes in the high-quality sequencing data were identified, and the population frequency of the minor allele of the SNP site was calculated in combination with the population data, and the appropriate SNP sites were selected to construct a SNP combination database, and the SNP combination genotype information of each test sample was recorded; The double allele genotypes of SNP sites were quantitatively counted, and the number of supporting A allele genotypes and the number of supporting B allele genotypes were recorded respectively. The allele types were divided into major alleles and minor alleles according to the number of supports, and the variation abundance of minor alleles was calculated; based on the genotype information and allele variation abundance of SNP sites, a variation abundance background distribution model of SNP sites was constructed; using the constructed SNP combination database and SNP site variation abundance background distribution model, the samples were evaluated for cross-contamination, and qualitative evaluation of multi-source components in the samples, quantitative calculation of cross-contamination ratios, and traceability analysis of non-self sources were achieved. A total of 100 SNP sites were screened for sample component evaluation. Figure 3 The background distribution of SNP allele abundance is shown as follows: Figure 4 The figure shows the distribution of consistency scores between samples with consistent sources (paired) and inconsistent sources (unpaired). The x-axis shows the results of SNP sets with different numbers of sites: In the specific example 2, the traceability evaluation results in the customized panel are introduced. The traceability evaluation is performed based on the whole exome and customized data. The data analysis adopts the analysis method in the above example 1. Different types of samples from 200 patients were collected, and the tumor tissue samples on the day of surgery were captured and sequenced by the whole exome to obtain the somatic variation map and design it as a customized panel. In the customized panel, the SNP combination obtained in Example 1 was added. Plasma free DNA samples at multiple time points after surgery were captured and sequenced by the customized panel with the same PID recorded in the experimental process. The white blood cell control sample was used as verification data and captured and sequenced by the whole exome and personalized panel at the same time. In the traceability results of the personalized panel data, two samples tested for the first time were matched to the germline database records that were not their own, and they were not included in the clinical analysis later. After further checking the records, the two inconsistent matching records happened to cross-match, which was caused by the error of tissue sample records. Other plasma samples tested multiple times were matched to their own corresponding germline database records.

[0048] In the specific example 3, the traceability evaluation results in the customized panel were introduced. Based on the data of more than 1,000 cases of 769-gene tumor population, the components were evaluated and the contamination ratio was calculated. The data analysis was carried out using the analysis method in the above example 1. A total of 229 SNP sites were screened for sample component evaluation. Figure 5 Shown is the background distribution of SNP allele abundance; Figure 6 (a)-(b) show the comparison results of the contamination ratio between the method of the present invention (marked as new) and the GATK-Contamination method (marked as pre); The specific classification statistics are shown in Table 2:

[0049] Among them, GATK calculated that the contamination ratio of the samples was above 0.8. After traceability analysis, it was found that the sample labeling was wrong, resulting in mismatch with the paired normal sample. In fact, no cross-contamination occurred in the samples.

[0050] In specific Example 4, the quantitative evaluation results of gradient dilution samples are introduced. A mixed sample of the P3 cell line was spiked into the P5 cell line, with a spike gradient of 1%, 0.2%, 0.1%, 0.05%, 0.025%, and 0.0125%, and each gradient was repeated 3 times, as well as the unspiked P5 original sample, which was sequenced twice, for a total of 20 samples. The data analysis used the analysis method in Example 1 above. A set of 229 SNP sites was used to evaluate the contamination ratio. The results were consistent with expectations, and 0.01% of non-self-derived components could be accurately evaluated. The evaluation calculation results are shown in Table 3 below: Table 3 sample concentration contamination suspected_snp_num sample1 0.00% 0.000% 1 sample2 0.00% 0.000% 0 sample3 0.01% 0.004% 5 sample4 0.01% 0.008% 8 sample5 0.01% 0.008% 9 sample6 0.03% 0.012% 12 sample7 0.03% 0.023% 19 sample8 0.03% 0.018% 12 sample9 0.05% 0.044% 37 sample10 0.05% 0.066% 42 sample11 0.05% 0.047% 39 sample12 0.10% 0.076% 39 sample13 0.10% 0.086% 47 sample14 0.10% 0.102% 50 sample15 0.20% 0.220% 64 sample16 0.20% 0.202% 62 sample17 0.20% 0.192% 63 sample18 1.00% 0.966% 71 sample19 1.00% 0.997% 72 sample20 1.00% 0.976% 71 The present invention constructs a complete set of calculation frameworks and evaluation indicators for sample component evaluation under NGS sequencing. Among them, the combined heterozygosity rate is used to evaluate the effectiveness of the SNP combination, the SNP genotype is used to build the germline database, the background distribution model is used for the typing evaluation of the SNP loci, the category of the minor allele is used for genotype estimation in the scenario of no control samples, the pure sum coefficient is used for the qualitative evaluation of non-self-source components, the contamination ratio is the quantitative result of non-self-source components, and the consistency score and LOH score are used to match and identify the source of samples.

[0051] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0052] The principles and implementation methods of the present invention are described in this article using specific examples. The description of the above embodiments is only used to help understand the method and core idea of ​​the present invention. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A method for ultra-high sensitivity sample component assessment and traceability, characterized in that: The following steps are involved: S1. Obtain samples, enter sample patient information and assign sample numbers through the laboratory management system, then perform nucleic acid extraction, library construction, capture enrichment and sequencing to generate sequencing data; S2. Preprocess the sequencing data, including removing adapter sequences, low-quality sequences, and single-base repeat sequences, to obtain high-quality sequencing data, and post the sequencing reads back to the reference genome; S3, identify the SNP sites and their genotypes in the high-quality sequencing data, calculate the population frequency of the minor allele of the SNP site in combination with the population data, select appropriate SNP sites to build a SNP combination database, and record the SNP combination genotype information of each test sample; S4. Quantitatively count the double allele genotypes of the SNP loci, record the number of supporting A allele genotypes and the number of supporting B allele genotypes respectively, divide the allele types into major alleles and minor alleles according to the supporting number, and calculate the variation abundance of the minor allele; S5. Construct a SNP site variation abundance background distribution model based on the genotype information and allele variation abundance of the SNP site; S6. Using the constructed SNP combination database and SNP site variation abundance background distribution model, the samples are evaluated for cross-contamination, and qualitative evaluation of multi-source components within the samples, quantitative calculation of cross-contamination ratios, and traceability analysis of non-self sources are achieved.

2. The method for ultra-high sensitivity sample component evaluation and traceability according to claim 1, characterized in that: In S2, the sequencing data is preprocessed to obtain high-quality sequencing data, and the sequencing reads are posted back to the reference genome, specifically including: The sequencing data is split into connectors. The split sequencing data is usually in fastq format, which contains the read segment name, base sequence, identifier and sequencing quality value corresponding to the base sequence; Remove adapter sequences, low-quality sequences, and single-base repeat sequences from the split sequencing data to obtain high-quality sequencing data; The processed sequencing reads are aligned back to the reference genome to form a bam format file.

3. The method for ultra-high sensitivity sample component evaluation and traceability according to claim 1, characterized in that: In S3, selecting appropriate SNP sites to construct a SNP combination database specifically includes: Identify the SNPs and their genotypes within the coverage of the high-quality sequencing data. The genotype information is divided into pure wild type AA, heterozygous variant AB, and pure variant BB. Combined with the population data, calculate the population frequency of the minor allele of the SNP site. The calculation formula is as follows: ； in, is the number of pure and variant genotypes in the population, is the number of heterozygous variant genotypes in the population, is the total number of people; Select appropriate SNP sites whose population frequencies have the ability to identify individuals. Candidate sites should meet the following requirements: site depth is not less than 100; the site is not located in the HLA and other repeat unit intervals; the site allele frequency should be between 0.2-0.8; there should not be more than two genotypes with a population frequency greater than 1% at the site; For n SNP candidate sites, calculate their combined heterozygosity to obtain a set of variant combinations with individual identification capabilities. The combined heterozygosity should be between 0.4 and 0.

6. The formula for calculating the combined heterozygosity is as follows: in, is the population frequency of the allele at the i-th SNP site; A database is constructed to record the SNP combination genotype information of each test sample, which is called the germline library; the genotype information of the SNP site is encoded as 0, 1, 2, representing AA, AB and BB respectively, and the missing genotype is encoded as -1. The test samples are labeled by PID and SampleID.

4. The method for ultra-high sensitivity sample component evaluation and traceability according to claim 1, characterized in that: In S4-S5, based on the genotype information and allele variation abundance of the SNP site, a SNP site variation abundance background distribution model is constructed, which specifically includes: The biallelic genotypes of SNP loci are quantitatively counted, and the support numbers are Count A and Count B , the allele types are divided into major alleles and minor alleles according to the number of supports, and the abundance of minor allele variation is calculated as follows: In the absence of control samples, statistical tests are performed based on the abundance of allele variation to determine the corresponding genotype; genotypes are divided into pure and heterozygous types, represented by 0 and 1 respectively; the minor allele genotype is inferred by the following method: ； The SNP set was further filtered through population data and sub-allele information to remove sites with too high a proportion of NA in the sub-allele. Afterwards, the population samples were counted and samples with too high a proportion of NA were removed. The remaining samples were used to construct the background distribution model. Four sets of baseline data were formed for each site with the corresponding combination of alleles and genotypes: ， ， ， ， in, is the sub-allelic variation abundance set of n SNP sites, represents the sample with minor allele A and genotype 0, represents the sample with minor allele A and genotype 1, represents the sample with minor allele B and genotype 0, Represents samples with minor allele B and genotype 1; fit the data distribution on this basis and select the distribution with the best fit as the baseline model.

5. The method for ultra-high sensitivity sample component evaluation and traceability according to claim 1, characterized in that: In S6, cross contamination assessment is performed on the sample, specifically including: By calculating the proportion of SNP sites with a minor allele type of 0 in the sample, that is, the pure sum coefficient, it is determined whether the sample has cross-contamination; if the pure sum coefficient is less than 0.2, the sample is a highly contaminated sample; if the pure sum coefficient is greater than or equal to 0.2, and the proportion of SNP sites with a minor allele type of NA and a minor allele frequency less than 5% is greater than or equal to 0.05, the sample is a low-contamination sample; The loci with NA genotype are the loci specific to the pollution source; the expected frequency of pollution is calculated based on the pollution patterns 0 / 1, 0 / 2, and 1 / 0. , , , calculated by The difference between the expected frequency of pollution and the pollution ratio is calculated using the least squares method. ; Pollution ratio The prior population probabilities of the SNP sites are used to obtain the posterior probabilities of the contamination modes 0 / 1, 0 / 2, and 1 / 0, which are then converted into the germ line list of the individual and the germ line list of the contamination source, and matched with the sample germ line library respectively, and traceability verification is performed based on the matching consistency scores.

6. An ultra-high sensitivity sample component evaluation and traceability device, characterized in that: A method for performing ultra-high sensitivity sample component assessment and traceability as described in any one of claims 1 to 5, characterized in that it comprises: The sample preparation and sequencing module is used to obtain samples, enter sample patient information and assign sample numbers through the laboratory management system, and then perform nucleic acid extraction, library construction, capture enrichment and sequencing to generate sequencing data; Off-machine data preprocessing module, used to preprocess sequencing data, including removing adapter sequences, low-quality sequences and single-base repeat sequences, obtaining high-quality sequencing data, and posting sequencing reads back to the reference genome; The SNP combination database construction module is used to identify the SNP sites and their genotypes in high-quality sequencing data, calculate the population frequency of the minor allele of the SNP site in combination with the population data, select appropriate SNP sites to build the SNP combination database, and record the SNP combination genotype information of each test sample; The module for constructing the background distribution model of variation abundance is used to perform quantitative statistics on the double allele genotypes of the SNP site, record the number of supporting A allele genotypes and the number of supporting B allele genotypes respectively, divide the allele type into major allele and minor allele according to the number of support, and calculate the variation abundance of the minor allele. According to the genotype information of the SNP site and the variation abundance of the allele, the variation abundance background distribution model of the SNP site is constructed; The sample cross-contamination assessment module is used to use the constructed SNP combination database and SNP site variation abundance background distribution model to perform cross-contamination assessment on samples, realize qualitative assessment of multi-source components in samples, quantitative calculation of cross-contamination ratio and traceability analysis of non-self sources.

7. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for evaluating and tracing sample components with ultra-high sensitivity as claimed in any one of claims 1 to 5 is implemented.

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