WO2023030233A1 - 一种拷贝数变异的检测方法及其应用 - Google Patents

一种拷贝数变异的检测方法及其应用 Download PDF

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WO2023030233A1
WO2023030233A1 PCT/CN2022/115447 CN2022115447W WO2023030233A1 WO 2023030233 A1 WO2023030233 A1 WO 2023030233A1 CN 2022115447 W CN2022115447 W CN 2022115447W WO 2023030233 A1 WO2023030233 A1 WO 2023030233A1
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sample
copy number
tested
target
sequencing data
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French (fr)
Inventor
刘成林
赵宇
旷婷
张之宏
张振
张光亮
张周
揣少坤
汉雨生
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Guangzhou Burning Rock DX Co Ltd
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Guangzhou Burning Rock DX Co Ltd
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Priority claimed from CN202111002171.4A external-priority patent/CN113674803B/zh
Priority claimed from CN202111095132.3A external-priority patent/CN113789371B/zh
Application filed by Guangzhou Burning Rock DX Co Ltd filed Critical Guangzhou Burning Rock DX Co Ltd
Priority to EP22863369.9A priority Critical patent/EP4397773A4/en
Priority to JP2024514011A priority patent/JP7745090B2/ja
Publication of WO2023030233A1 publication Critical patent/WO2023030233A1/zh
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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G16B20/30—Detection of binding sites or motifs
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6869—Methods for sequencing
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G16B20/10—Ploidy or copy number detection
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G16B20/20—Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B30/00—ICT specially adapted for sequence analysis involving nucleotides or amino acids
    • G16B30/10—Sequence alignment; Homology search

Definitions

  • This application relates to the field of biological information, in particular to a method for detecting copy number variation and its application.
  • Copy number variation is one of the common types of variation in the human genome. Copy number variation includes two types of variation, amplification and deletion of gene copy number.
  • the detection of gene copy number variation can be used to monitor the genome status of the subject, and can also be used to discover the association between specific diseases and certain genomic variations.
  • gene copy number variation may lead to a variety of common genetic diseases, such as BRCA1/2 gene deletion may lead to the risk of hereditary breast cancer; gene copy number variation may affect the occurrence and development of tumors, such as HER2 gene amplification is not only associated with It is related to the occurrence and development of tumors, and it is also an important clinical treatment monitoring and prognostic indicator, and is an important target of tumor targeted therapy.
  • the detection of copy number variation can play a vital role in the monitoring of the genome status of subjects, genome-wide association studies, prevention of genetic diseases, and precise treatment of tumors.
  • subjects carrying certain copy number variations may have a higher lifetime risk of developing a disease (eg, tumor) than the general population. Therefore, the copy number variation detection method can be used to screen out subjects with higher risks, who can receive individualized monitoring of the disease, so as to achieve the purpose of early diagnosis and early treatment.
  • the purpose of the present application is to provide a method for abnormal detection of gene copy number to address the above-mentioned deficiencies in the prior art. This method can at least reduce batch effects, errors and/or improve the stability of copy number detection results, which is of great significance for detecting driving events related to copy number abnormalities and interpreting tumor genome evolution information.
  • the present application provides a method for detecting copy number variation and its application.
  • the present application provides a copy number state analysis method, the method includes, dividing the target interval of the sample to be tested into several window areas, obtaining the sequencing data of the control window area in the sample group to be tested, based on the The sequencing data of the control window area is used to determine the copy number status of the target gene of the sample to be tested; optionally, the control window area includes a window area with a low coverage fluctuation level.
  • the present application provides a copy number status analysis device, comprising the following modules: a receiving module, used to obtain the sequencing data of the sample group to be tested; a determination module, used to determine the target gene in the sample to be tested; a judging module, It is used for determining the copy number state of the target gene in the sample to be tested according to the sequencing data of the sample group to be tested.
  • the present application provides a storage medium, which records a program capable of running the method described in the present application.
  • Figures 1A-1B show the detection results based on the construction of the reference baseline method and the method of the present application. Each box plot represents the distribution of BRCA1 gene exon copy values in 30 samples. Groups A and B represent different batches of probe capture.
  • Figure 1A shows the copy number distribution of each exon of the BRCA1 gene calculated based on the method of constructing a reference baseline.
  • Figure 1B shows the distribution of copy numbers of each exon of the BRCA1 gene calculated by the method of the present application.
  • Figures 2A-2B show the results of sample detection based on the difference between the reference baseline method and the method of this application for NGS library construction. Abscissa: chromosome coordinates; ordinate: estimated copy number (CN) values.
  • Figure 2A shows the results of detecting copy number variations based on the construction of a reference baseline method.
  • Figure 2B shows the results of detecting copy number variation by the method of the present application. Boxes indicate called copy number variations.
  • Figures 3A-3B show the detection results of samples based on different thresholds set in the screening stability window. Abscissa: chromosome coordinates; ordinate: estimated copy number (CN) values.
  • Figure 3A shows the results of detecting copy number variation in samples with a threshold set at 0.05.
  • Figure 3B shows the results of detecting copy number variation in samples with a threshold set at 0.15. Boxes indicate called copy number variations.
  • Figures 4A-4J show the detection results of 10 cases of simulated positive copy number variation samples after constructing batch baselines. Abscissa: chromosome coordinates; ordinate: estimated copy number (CN) values. The box in the figure indicates the detected copy number variation.
  • Figures 5A-5F show an example of the copy number distribution diagram of part of the data of the detection results of the simulated samples in this application.
  • Figures 6A-6C show an example of the copy number distribution diagram of part of the data of the test results of the standard sample in this application.
  • Figures 7A-7C show examples of copy number distribution diagrams of part of the data of the test results of real samples in this application.
  • Figures 8A-8F show examples of copy number distribution diagrams using different baseline detection results for standard sample 1 in this application.
  • next-generation gene sequencing high-throughput sequencing
  • next-generation sequencing generally refer to the second-generation high-throughput sequencing technology and the higher-throughput sequencing methods developed thereafter.
  • Next-generation sequencing Platforms include but are not limited to existing sequencing platforms such as Illumina. With the continuous development of sequencing technology, those skilled in the art can understand that the sequencing methods and devices of other methods can also be used for this method. For example, second-generation gene sequencing It can have the advantages of high sensitivity, high throughput, high sequencing depth, or low cost.
  • Massively Parallel Signature Sequencing Massively Parallel Signature Sequencing, MPSS
  • Polony Sequencing 454pyro sequencing
  • Illumina (Solexa) sequencing Illumina (Solexa) sequencing
  • Ion semi conductor sequencing DNA nano-ball sequencing
  • Complete Genomics DNA nanoarrays Complete Genomics DNA nanoarrays and combined probe anchored ligation sequencing, etc.
  • the second-generation gene sequencing can make it possible to analyze the transcriptome and genome of a species in detail, so it is also called deep sequencing (deep sequencing)
  • the method of the present application can also be applied to first-generation gene sequencing, second-generation gene sequencing, third-generation gene sequencing or single-molecule sequencing (SMS).
  • SMS single-molecule sequencing
  • database usually refers to an organized entity of related data, Regardless of the representation of the data or organized entities.
  • said organized entities of related data may take the form of tables, maps, grids, groups, datagrams, files, documents, lists, or any other form.
  • the database may include any data collected and stored in a computer-accessible form.
  • the term "computing module” generally refers to a functional module for computing.
  • the calculation module can calculate the output value or obtain a conclusion or result according to the input value, for example, the calculation module can be mainly used for calculating the output value.
  • a computing module can be tangible, such as a processor of an electronic computer, a computer or electronic device with a processor, or a computer network, or it can be a program, command line or software package stored on an electronic medium.
  • processing module generally refers to a functional module for data processing.
  • the processing module may be based on processing the input value into statistically significant data, for example, it may be a classification of data for the input value.
  • a processing module may be tangible, such as an electronic or magnetic medium for storing data, and a processor of an electronic computer, a computer or electronic device with a processor, or a computer network, or it may be a program stored on an electronic medium, command line or package.
  • the term "judgment module” generally refers to a functional module for obtaining relevant judgment results.
  • the judging module may calculate an output value or obtain a conclusion or a result according to an input value, for example, the judging module may be mainly used to obtain a conclusion or a result.
  • the judging module can be tangible, such as a processor of an electronic computer, a computer with a processor or an electronic device or a computer network, or it can be a program, a command line or a software package stored on an electronic medium.
  • sample obtaining module generally refers to a functional module for obtaining said sample of a subject.
  • the sample obtaining module may include reagents and/or instruments required to obtain the sample (eg, tissue sample, blood sample, saliva, pleural effusion, peritoneal effusion, cerebrospinal fluid, etc.).
  • lancets, blood collection tubes, and/or blood sample transport boxes may be included.
  • the device of the present application may not contain or contain one or more of the sample obtaining modules, and may optionally have the function of outputting the measured value of the sample described in the present application.
  • the term "receiving module” generally refers to a functional module for obtaining said measured values in said sample.
  • the receiving module may input the samples described in this application (such as tissue samples, blood samples, saliva, pleural effusion, peritoneal effusion, cerebrospinal fluid, etc.).
  • the receiving module may input the measured values of the samples described in the present application (such as tissue samples, blood samples, saliva, pleural effusion, peritoneal effusion, cerebrospinal fluid, etc.).
  • the receiving module can detect the state of the sample.
  • the data receiving module may optionally perform the gene sequencing described in this application (eg, next-generation gene sequencing) on the sample.
  • the data receiving module may optionally include reagents and/or instruments required for the gene sequencing.
  • the data receiving module can optionally detect sequencing depth, sequencing read length count or copy number.
  • the term "copy number variation” generally refers to the amplification or deletion of the copy number of a target interval, a target gene or a target interval in a target gene.
  • the copy number variation analysis method provided in this application can be used for therapeutic or diagnostic purposes.
  • the copy number variation analysis method provided in the present application can be used for non-therapeutic or diagnostic purposes, such as determining whether there is a copy number variation through sequencing results.
  • sliding window method generally refers to a method for dividing a window area.
  • a full-length area may be divided into multiple windows according to the same or different window area lengths.
  • the full-length region can be divided into multiple windows according to the same or different step lengths.
  • the full-length region can be divided into multiple windows according to the same window region length and the same step size.
  • a quality-qualified sample may refer to a sample with a qualified average sequencing depth, minimum sequencing depth, and/or uniformity of coverage.
  • a qualified average sequencing depth can refer to samples with an average sequencing depth of about 100x or greater.
  • a minimum sequencing depth qualified sample can refer to a sample with a minimum sequencing depth of about 30x or greater.
  • a qualified sample with coverage uniformity may refer to a sample in which the number of bases greater than or equal to 20% of the average sequencing depth of the sample accounts for about 90% or more of the total number of bases in the sample.
  • the term "unqualified target interval” generally refers to an interval with low sequencing quality.
  • disqualified intervals may not be suitable for analysis of copy number variation.
  • an unqualified interval may not be suitable for use as a reference or for the construction of a baseline.
  • screening out unqualified intervals can improve the accuracy of test results; in other cases, not screening out unqualified intervals can also obtain test results with certain accuracy.
  • an unqualified interval may refer to an interval with a low sequencing depth; for example, an unqualified interval may refer to an interval in which the interval varies greatly among different samples.
  • the term "region with low capture efficiency" generally refers to a region that is not easily captured by probes that are used. For example, when there is a specific sequence combination in a certain range of sequences, it may be difficult to be captured by nucleic acid probes.
  • the interval with low capture efficiency may refer to an interval with low sequencing depth.
  • a low capture efficiency interval can refer to an interval with a sequencing read count of about 5 or less.
  • the term "unstable interval” generally refers to an interval in which the sequencing results of different samples vary greatly. For example, it may be an interval in which multiple sequencing results in the same sample have large differences. For example, it may be an interval in which the sequencing results of different samples in the same batch vary greatly. For example, it may be an interval in which the sequencing results of different batches of different samples vary greatly. For example, it may be an interval in which the sequencing results of different reference samples differ greatly.
  • the method for determining the unstable interval may be to calculate the ratio of the standard deviation to the mean value of the sequencing depth of a certain interval in different samples, and determine whether the ratio is greater than a certain threshold, such as the threshold can be 0.8, or a person skilled in the art Adjust according to the actual sequencing situation.
  • sample to be tested generally refers to a sample that needs to be detected and determined whether there is a copy number variation in one or more gene regions on the sample.
  • the sample to be tested or its data can be pre-stored in the memory before testing.
  • human reference genome generally refers to the human genome that can function as a reference in gene sequencing.
  • the information of the human reference genome can refer to UCSC (University of California, Santa Cruz).
  • the human reference genome can have different versions, for example, it can be hg19, GRCH37 or ensembl 75.
  • GC content generally refers to the ratio of guanine G and cytosine C to all nucleotides of the sequence in a gene sequence (base sequence).
  • targeted sequencing panel or “panel” generally refers to a group/set of detection objects.
  • one or more target intervals are captured and detected by designing one or more probes, and such one or more probes can form a targeted sequencing panel.
  • targeted sequencing panels can be designed arbitrarily for target genes, target intervals or regions of interest, for example, for several exon regions.
  • a probe may refer to an oligonucleotide that is complementary to an oligonucleotide or target nucleic acid in a region of interest under study.
  • the target interval is the interval for which the probe was designed.
  • sequencing depth generally refers to the number of times a specific region (eg, a specific gene, a specific interval, or a specific base) is detected.
  • the sequencing depth may refer to a base sequence detected by sequencing. For example, by comparing the sequencing depth to the human reference genome, and optionally removing duplicates, the number of sequencing reads on a specific gene, a specific interval, or a specific base position can be determined and counted as the sequencing depth.
  • sequencing depth can be correlated to sequencing depth. For example, sequencing depth can be affected by copy number status.
  • sequencing data generally refers to data of short sequences obtained after sequencing.
  • the sequencing data includes the base sequence of a sequenced short sequence (sequencing read), the number of sequencing reads, and the like.
  • sequencing bias generally refers to the sequencing data bias generated in different intervals.
  • the special arrangement or base ratio of the sequence in the interval can affect the sequencing read length count of the interval. For example, when a bin contains higher or lower GC content, the sequencing read counts for that bin may be biased relative to bins with GC content close to 50%.
  • the term “distribution similarity” may refer to the distribution similarity of two sets of data.
  • the distribution similarity in this application may refer to the similarity of the sequencing read length counts between the reference sample group and the sample to be tested in one or more intervals.
  • the term “statistical distance” may refer to the distance between data values of two sets of data.
  • the statistical distance may refer to the statistic of the difference between the sequencing read length counts of the reference sample group and the sample to be tested in one or more intervals.
  • statistical distance can be calculated by Euclidean distance, Chebyshev distance, Mahalanobis distance, etc.
  • the term "statistical value” may refer to an analytical value calculated from the data value of a sample.
  • the statistical value in the present application may refer to mean value, variance, standard deviation, median value, mode value and the like. Those skilled in the art select one or more statistical values for data analysis according to actual conditions.
  • probability distribution generally refers to the distribution law of the values of random variables.
  • probability distributions can take different forms depending on the type of random variable they belong to.
  • the normal distribution can be used as a probability distribution of a random variable.
  • the term “smoothing” generally refers to a method of data processing that reduces the deviation between one or more of the differences described herein. For example, it may refer to a method of fitting scatter data to a smoothed line. For example, it can be analyzed and smoothed by the method of locally weighted regression. For example, after smoothing, the bias caused by a certain variable (such as GC content) to the sample sequencing data can be eliminated or weakened by eliminating the inherent influence of the variable (such as GC content) on the sample sequencing data.
  • the smoothing process may include obtaining an average value of a certain number of difference values described in this application.
  • the smoothing process may include selecting data values corresponding to different lengths according to a certain interval length, and calculating a difference between different data values.
  • the smoothing process may include dividing the accumulated value of the difference within a certain length range by the interval length to obtain a ratio.
  • said ratio may be considered as the average difference of said differences over the length range.
  • regression generally refers to a method of statistical analysis of the relationship between variables.
  • the present application can obtain a linear or non-linear relationship between sample sequencing data and a certain variable (such as GC content) through regression analysis.
  • the relationship between the sequencing data of a sample and a certain variable (such as GC content) can be obtained through locally weighted regression, and the sequencing data of the sample can be adjusted/corrected through this relationship.
  • correction in this application may refer to processing the sequencing data of the sample according to the relationship between the sequencing data of the sample and a certain variable so as to eliminate or reduce the bias caused by the variable to the sequencing data of the sample.
  • the term "locally weighted regression” generally refers to a regression analysis method that locally introduces weights in the regression analysis of input variables and target variables.
  • the local weighted regression can perform local weighted regression analysis and processing on X according to Y through the (loess(X ⁇ Y)) algorithm.
  • noise reduction generally refers to the removal or reduction of noise data in data.
  • noisy data generally appear as high-frequency signals
  • cluster analysis generally refers to dividing similar objects into different groups by means of classification, so that member objects in the same group have similar attributes.
  • K-means clustering generally refers to a method of cluster analysis. For example, through K-means clustering, a group of data can be classified into several (K) cluster analysis methods according to K cluster centers, and the sum of the distance between each data and its nearest cluster center is the smallest.
  • transform analysis generally refers to a method of analyzing data.
  • transformation analysis can analyze and use data for further processing by transforming the original distribution of the data into a distribution in the transformed domain that is easy to solve or process.
  • transform analysis can involve discrete wavelet transforms.
  • discrete wavelet transform generally refers to the discretization of the scale and translation of the underlying wavelet.
  • discrete wavelet transform can be used as a method of denoising.
  • normalization generally refers to a way of transforming data.
  • normalization may refer to the process of transforming different sets of data to some fixed range.
  • standardization may refer to the process of transforming data of different groups to the same median value.
  • standardization in this application may refer to a processing method of transforming sequencing data of different samples into data at a level close to the median value.
  • the term "significance test” generally refers to the way of judging whether the difference between the sample and the hypothesized distribution is significant.
  • the significance test can be used to determine whether the copy number variation of the sample to be tested is a significant difference.
  • normal probability distribution generally refers to the probability distribution of a random variable.
  • the probability of occurrence of a random variable can be determined through the normality probability distribution and the normality probability distribution density function.
  • the probability of the existence of the copy number variation in the target interval of the sample to be tested can be confirmed through a normal probability distribution.
  • the term "Grubbs test” generally refers to a method of judging and/or screening out outliers. For example, by judging whether a certain value conforms to the overall distribution range, it can be determined whether the value belongs to an outlier.
  • T-test generally refers to a form of statistical hypothesis testing with a Student's t distribution.
  • the T-test can be used to confirm that the copy number variation of a certain target gene in the sample to be tested is significant.
  • the term "about” generally refers to a range of 0.5%-10% above or below the specified value, such as 0.5%, 1%, 1.5%, 2%, 2.5%, above or below the specified value. 3%, 3.5%, 4%, 4.5%, 5%, 5.5%, 6%, 6.5%, 7%, 7.5%, 8%, 8.5%, 9%, 9.5%, or 10%.
  • the present application provides a method for analyzing copy number status
  • the present application provides an analysis method of copy number status, which may include obtaining the sequencing data of the sample group to be tested; determining the target gene in the sample group to be tested; determining the sequence data of the sample group to be tested The copy number status of the target gene in the sample.
  • the application provides a method for analyzing copy number status, which may include the following steps:
  • the application provides a method for analyzing copy number status, which may include the following steps:
  • the control window area may include the first 4 or more windows covering the fluctuation level, and the ratio of the absolute deviation median to the median of the sequencing data of the window area of the qualified sample can be determined.
  • the coverage fluctuation level, or the ratio of the absolute deviation median to the median of the sequencing data of all the qualified samples in the control window area may be about 0.15 or less.
  • the application provides a method for analyzing copy number status, which may include the following steps:
  • Step (S1-1) Obtain the sequencing data of the window area of all samples in the sample group to be tested;
  • Step (S1-2) Obtain qualified samples in the sample group to be tested, and the qualified samples Can include average sequencing depth, minimum sequencing depth and/or coverage uniformity qualified samples;
  • the control window area may include the first 4 or more windows covering the fluctuation level, and the ratio of the absolute deviation median to the median of the sequencing data of the window area of the qualified sample can be determined.
  • Covering the fluctuation level, or the ratio of the absolute deviation median to the median of the sequencing data of all the qualified samples in the control window area may be about 0.15 or less; step (S2-1) : based on the sequencing data of the control window area, determine the normalization coefficient; step (S2-2): based on the normalization coefficient, determine the copy number of each window area of the sample to be tested; step (S2-3 ): determining the significance of the copy number variation of the sample to be tested based on the sequencing data of each window area of the sample to be tested and the sequencing data of other samples in the sample group to be tested in the corresponding window area.
  • the sequencing data may include sequencing depth.
  • the copy number status may comprise copy number amplifications and/or deletions.
  • the copy number status may comprise exon copy number status.
  • the set of samples to be tested may comprise about 10 or more samples.
  • the sample set to be tested may comprise about 10 or more, about 12 or more, about 15 or more, about 20 or more, about 25 or more, about 50 or more Many, or about 100 or more samples.
  • the application may not require a larger number of samples in the same batch.
  • the sample set to be tested can comprise about 10 or less, about 12 or less, about 15 or less, about 20 or less, about 25 or less, or about 50 or less Fewer samples.
  • the copy number status analysis method of the present application can have a higher tolerance for the copy number variation level of the sample to be tested. For example, a sample containing about 30% copy number variation can be evaluated by the analysis method of the present application.
  • samples containing 10% or less, 15% or less, 20% or less, 25% or less, or 30% or less copy number variation can be evaluated by the analytical methods of the present application.
  • the source of the sample in this application can be any sample containing nucleic acid, such as tissue, blood, saliva, pleural effusion, peritoneal effusion, cerebrospinal fluid, and the like.
  • the step (S1) of the method of the present application may further include the step (S1-1): obtaining the sequencing data of the window regions of all samples in the sample group to be tested.
  • the gene sequencing of the present application may include an optional high-throughput sequencing method or module or device.
  • the sequencing can be selected from the group consisting of Solexa sequencing technology, 454 sequencing technology, SOLiD sequencing technology, Complete Genomics sequencing method and semiconductor (Ion Torrent) sequencing technology and their corresponding devices.
  • the step (S1-1) of the method of the present application may include dividing the region where the target gene is located into the window region by a sliding window method.
  • the step size of the windowing method can be about 24 bases.
  • the window region can be about 120 bases in length.
  • the step (S1-1) of the method of the present application may include obtaining the average sequencing depth of each window region after removing repeated sequencing fragments.
  • the step (S1) of the method of the present application may also include the step (S1-2): obtaining qualified samples in the sample group to be tested, and the qualified samples may include the average sequencing depth, minimum sequencing depth and / or cover homogeneity acceptable samples.
  • said average sequencing depth qualified samples include samples whose average sequencing depth can be about 100x or greater.
  • the minimum sequencing depth qualified samples include samples for which the minimum sequencing depth can be about 30x or greater.
  • the various thresholds for acceptable quality can be adjusted according to the sequencing situation.
  • the coverage uniformity may be related to the sequencing depth of each base of the sample.
  • the coverage uniformity can be calculated by the percentage of the number of bases greater than or equal to 20% of the average sequencing depth of the sample to the total number of bases in the sample.
  • the samples that pass coverage uniformity can include samples that have a coverage uniformity of about 90% or greater.
  • the samples that pass coverage uniformity can include samples that have a coverage uniformity of about 90% or greater.
  • the samples passing coverage uniformity can include samples having a coverage uniformity of about 90% or greater, about 92% or greater, about 95% or greater, about 97% or greater, or about 99% or greater.
  • the number of qualified samples in the sample group to be tested may be 10 or more.
  • the step (S1) of the method of the present application may further include a step (S1-3): standardizing the sequencing data of the window regions of all samples in the sample group to be tested.
  • the normalization may include normalizing the sequencing data of each window region of the sample based on the average sequencing depth of all window regions of the sample, and/or normalizing the GC content of each window region of the sample to The sequencing data is normalized for each window region of the sample.
  • the normalization may comprise dividing the sequencing data on each window region of the sample by the sum of the sequencing data on all window regions of the sample, and multiplying by a factor.
  • the factor can be set according to the size of all intervals.
  • the factor may optionally be 1E+07.
  • the factor may optionally be 1E+100, 1E+20, 1E+10, 1E+09, 1E+08, 1E+07, 1E+06, 1E+05, 1E+04, 1E+03, or 1E+02.
  • the normalization may include normalizing the sequencing data of each window area of the sample by a regression method based on the GC content.
  • the regression may comprise locally weighted regression.
  • control window region may comprise a window region with a low level of coverage fluctuation.
  • the coverage fluctuation level may be determined based on the statistical values of the sequencing data in the window area of the quality-qualified samples. For example, the coverage fluctuation level may be determined based on the dispersion of the sequencing data in the window area of the qualified samples. For example, the coverage fluctuation level may be determined based on the absolute deviation median and/or median of the sequencing data of the window region of the qualified samples. For example, the coverage fluctuation level may be determined based on the ratio of the absolute deviation median to the median of the sequencing data of the window area of the qualified sample.
  • the window areas of the qualified samples are sorted according to the coverage fluctuation level from low to high, and the control window area may include two or more windows before the coverage fluctuation level.
  • the window areas of the quality-qualified samples are sorted according to the coverage fluctuation level from low to high, and the control window area may include 4 or more windows before the coverage fluctuation level.
  • the ratio of the median-to-median absolute deviation of the sequencing data for all the quality-qualified samples in the control window region may be about 0.15 or less.
  • the ratio of the absolute deviation median to the median of the sequencing data of all the qualified samples in the control window area can be about 0.15 or less, about 0.14 or less, about 0.13 or Less, about 0.12 or less, about 0.11 or less, about 0.10 or less, about 0.09 or less, about 0.08 or less, about 0.07 or less, about 0.06 or less, or about 0.05 or less Small.
  • the ratio of the absolute deviation median to the median of the sequencing data of all the qualified samples in the control window area may be from about 0.05 to about 0.15, from about 0.07 to about 0.15, from about 0.10 to About 0.15, about 0.12 to about 0.15, about 0.05 to about 0.12, about 0.07 to about 0.12, about 0.10 to about 0.12, about 0.05 to about 0.10, about 0.07 to about 0.10, or about 0.05 to about 0.07.
  • the step (S2) of the present application may further include the step (S2-1): determining a normalization coefficient based on the sequencing data in the control window area.
  • the normalization coefficient can be determined by calculating the average value of the sequencing data of all the quality-qualified samples in the control window area.
  • the coverage level values of abnormal samples in the control window area may be screened out.
  • the abnormal coverage level value may be the coverage level value of each of the control window areas judged to be an abnormal sample by an outlier value analysis method.
  • the outlier analysis method may include Grubbs test.
  • each window can contain the coverage level value of qualified samples in the batch in this window, and then the Grubbs test can be used to check whether these coverage level values contain outliers, and if so, the outliers can be removed. Then, for the remaining coverage level values, you can continue to repeat the Grubbs test to determine whether there are any abnormalities until no abnormal values appear.
  • the number of samples remaining after screening out the abnormal samples may be 40% or more, 70% or more, 80% or more, 90% or more, 95% or more of the number of samples before screening out , or 99% or more.
  • the step (S2) of the present application may further include the step (S2-2): determining the copy number of each window region of the sample to be tested based on the normalization coefficient.
  • the step (S2-2) of the present application may include determining the normalization coefficient of the sample to be tested by normalizing the sequencing data of each window region of the sample to be tested based on the normalization coefficient. The copy number for each window region.
  • the normalization method may include dividing the sequencing data of the sample to be tested in the window area by the normalization coefficient of the window area, and multiplying by the ploidy.
  • the ploidy can be 1.
  • the ploidy can be adjusted according to specific circumstances.
  • the ploidy can be 2.
  • step (S2) described in this application may also include step (S2-3): based on the sequencing data of each window area of the sample to be tested and the sequencing data of other samples in the sample group to be tested in the corresponding window area, determine the The significance of the copy number variation of test samples.
  • the step (S2-3) of the present application may include determining a copy number variation candidate region based on the copy number of each window region of the sample to be tested.
  • the copy number variation candidate region can be determined by region segmentation.
  • the region segmentation may include determining the front and rear endpoints of the copy number variation candidate region through a cyclic binary segmentation algorithm.
  • the step (S2-3) described in this application may include determining the copy number variation based on the sequencing data of the window region in the candidate region of the copy number variation of the sample to be tested and the sequencing data of other samples in the sample group to be tested in the corresponding window region. Significance of variance.
  • the significance of the copy number variation can be determined by means of a significance test.
  • the significance test may comprise a T-test.
  • the present application also provides a copy number status analysis device, which may include the following modules: a receiving module, used to obtain the sequencing data of the sample group to be tested; a determination module, used to determine the target gene in the sample to be tested; A judging module, configured to determine the copy number status of the target gene in the sample to be tested according to the sequencing data of the sample group to be tested.
  • the modules therein can be configured to be executed based on the program stored in the storage medium to implement the copy number status analysis method described in the present application.
  • the present application provides a copy number status analysis method, which may include the following steps: (S1) obtaining the sequencing data of the sample to be tested and/or the sequencing data of multiple reference samples; (S2) dividing the reference samples into Two or more reference sample groups; (S3) determine the reference sample group closest to the sample to be tested; (S4) determine the sample to be tested based on the sequencing data of the reference sample group closest to the sample to be tested The copy number status of the target gene of the sample.
  • the present application provides a copy number status analysis device, which may include the following modules: (M1) a receiving module for obtaining sequencing data of a sample to be tested and/or sequencing data of multiple reference samples; (M2) a processing module , for dividing the reference sample into two or more reference sample groups; (M3) calculation module, for determining the reference sample group closest to the sample to be tested; (M4) judging module, for based on the The sequencing data of the reference sample group closest to the sample to be tested is used to determine the copy number status of the target gene of the sample to be tested.
  • M1 a receiving module for obtaining sequencing data of a sample to be tested and/or sequencing data of multiple reference samples
  • M2 a processing module , for dividing the reference sample into two or more reference sample groups
  • (M3) calculation module for determining the reference sample group closest to the sample to be tested
  • (M4) judging module for based on the The sequencing data of the reference sample group closest to the sample to be tested is used to determine the copy number status of the target gene of the sample
  • the application provides a copy number status analysis method, which may include the following steps:
  • step (S1) Acquiring the sequencing data of the sample to be tested and/or the sequencing data of a plurality of reference samples; step (S1-1): obtaining the sequencing data of the sample to be tested and/or the reference sample by gene sequencing; Step (S1-2): correcting the sequencing data of the sample to be tested and/or the reference sample;
  • step (S2) dividing the reference sample into two or more reference sample groups; step (S2-1): grouping the reference samples; step (S2-2): confirming the sequencing data of the reference sample group statistic value;
  • Step (S4) Based on the sequencing data of the reference sample group closest to the sample to be tested, determine the copy number status of the target gene of the sample to be tested Step (S4-1): determine that the target gene of the sample to be tested is in The copy number CN i on the target interval i; step (S4-3): determine the copy number CN g of the sample to be tested on the target gene; step (S4-4): determine the copy number CN g on the target interval Measure the probability of the existence of the copy number variation of the sample; Step (S4-5): determine the ratio sigRatio of the presence of significant copy number amplification or deletion of the sample to be tested on the target gene; Step (S4-6 ): determine the statistical test parameters for the existence of the copy number variation of the sample to be tested on the target gene; determine the copy number status of the target gene of the sample to be tested by the following content: when CN g ⁇ CN thA , sigRatio ⁇ sigRatio th , and p
  • the present application provides a copy number status analysis device, which may include a module implementing the copy number status analysis method of the present application.
  • the application provides a copy number status analysis method, which may include the following steps:
  • step (S1) Acquiring the sequencing data of the sample to be tested and/or the sequencing data of a plurality of reference samples; step (S1-1): obtaining the sequencing data of the sample to be tested and/or the reference sample by gene sequencing; Step (S1-2): correcting the sequencing data of the sample to be tested and/or the reference sample;
  • step (S2) dividing the reference sample into two or more reference sample groups; step (S2-1): grouping the reference samples; step (S2-2): confirming the sequencing data of the reference sample group statistic value;
  • Step (S4) Based on the sequencing data of the reference sample group closest to the sample to be tested, determine the copy number status of the target gene of the sample to be tested Step (S4-1): determine that the target gene of the sample to be tested is in The copy number CN i on the target interval i; step (S4-2): denoise the copy number on the target interval of the sample to be tested; step (S4-3): determine the The copy number CN g of the sample on the target gene; Step (S4-4): Determine the probability of the existence of the copy number variation of the sample to be tested on the target interval; Step (S4-5): Determine the probability of the copy number variation in the target gene The ratio sigRatio of the presence of significant copy number amplification or deletion of the sample to be tested above; step (S4-6): determine the statistical test parameters for the presence of copy number variation of the sample to be tested on the target gene ; Determine the copy number status of the target gene of the sample to be tested by the following content: when CN g ⁇ CN
  • the present application provides a copy number status analysis device, which may include a module implementing the copy number status analysis method of the present application.
  • the sequencing data of the present application may comprise sequencing read counts.
  • the sequencing data of the present application may include the number of sequencing reads (reads) on the target gene or target interval.
  • the step (S1) or module (M1) of the present application may include the step (S1-1) or module (M1-1): obtaining all the samples of the test sample and/or the reference sample by gene sequencing the sequencing data.
  • the gene sequencing may comprise next-generation gene sequencing (NGS).
  • NGS next-generation gene sequencing
  • the gene sequencing of the present application may include an optional high-throughput sequencing method or module or device.
  • the sequencing can be selected from the group consisting of Solexa sequencing technology, 454 sequencing technology, SOLiD sequencing technology, Complete Genomics sequencing method and semiconductor (Ion Torrent) sequencing technology and their corresponding devices.
  • test sample and/or the reference sample may comprise a nucleic acid-containing sample.
  • sample source of the present application can be any sample containing nucleic acid, such as tissue, blood, saliva, pleural effusion, peritoneal effusion, cerebrospinal fluid, etc.
  • the step (S1-1) or module (M1-1) may include obtaining the sequencing data of each base in the target interval of the test sample and/or reference sample.
  • the target interval may include an interval corresponding to the targeted sequencing panel sequence.
  • the target interval may be about 20 to about 500 bases in length.
  • the length of the target interval may be about 20 to about 500 bases, about 50 to about 500 bases, about 100 to about 500 bases, about 200 to about 500 bases, about 20 to about 200 bases, about 50 to about 200 bases, about 100 to about 200 bases, about 20 to about 100 bases, about 50 to about 100 bases, Or about 20 to about 50 bases.
  • the number of target intervals may be at least about 100.
  • the number of target intervals can be at least about 100, at least about 200, at least about 500, at least about 1000, or at least about 10000.
  • the step (S1) or module (M1) may include a step (S1-2) or module (M1-2): correcting the sequencing data of the sample to be tested and/or the reference sample.
  • the method of the present application may not include step (S1-2) or only include part of step (S1-2).
  • the device of the present application may not include the module (M1-2) or only include part of the module (M1-2).
  • the order of the following steps in the method steps (S1-2) of the present application can be arbitrary: standardize the sequencing data of the test sample and/or reference sample, make the test sample and/or reference sample The sequencing data is smoothed and the target interval with abnormal GC content is screened out.
  • module order of the modules (M1-2) of the device of the present application can be arbitrary: normalize the sequencing data of the test sample and/or reference sample, make the test sample and/or reference The sequencing data of the sample is smoothed and the target interval with abnormal GC content is screened out.
  • the step (S1-2) or module (M1-2) may include: standardizing or homogenizing the sequencing data of the test sample and/or reference sample.
  • the standardization or normalization may comprise dividing the sequencing data on the target interval by the sum of the sequencing data on all target intervals of the sample corresponding to the target interval, and multiplying by a factor.
  • the factor can be set according to the size of all intervals.
  • the factor may optionally be 1E+07.
  • the factor may optionally be 1E+100, 1E+20, 1E+10, 1E+09, 1E+08, 1E+07, 1E+06, 1E+05, 1E+04, 1E+03, or 1E+02.
  • the step (S1-2) or module (M1-2) may include: smoothing the sequencing data of the test sample and/or reference sample.
  • the smoothing may include smoothing the sequencing data of the test sample and/or reference sample through a regression method or a device recording the program based on the sequencing bias.
  • the regression may comprise locally weighted regression.
  • the sequencing bias may include the number of probes covered on the target interval.
  • the sequencing bias may include the GC content of the target interval.
  • the step (S1-2) or module (M1-2) may optionally include: screening out the target interval with abnormal GC content.
  • the target interval with an abnormal GC content may include the target interval with a GC content of about 25% or less and/or the target interval with a GC content of about 75% or higher.
  • said step (S2) or module (M2) may comprise a step (S2-1) or module (M2-1): grouping said reference samples.
  • the reference sample may be from the sample to be tested, or from a sample other than the sample to be tested.
  • a part of the samples to be tested can be divided as reference samples.
  • the reference sample can be updated. For example, after analyzing the sequencing data of a new sample each time, the data of the new sample can be added to the existing database, and the database can be re-established.
  • said grouping may comprise grouping said reference samples based on said sequencing data of said target interval.
  • the grouping may include grouping the reference samples by a method of cluster analysis or a device recording the procedure.
  • the method of cluster analysis may include K-means clustering, hierarchical clustering, density clustering, grid clustering, probability model clustering, or neural network model clustering, etc.
  • the cluster analysis method or the device recording the program may include any clustering, classification and grouping method or the device recording the program.
  • the number of reference samples may be about 30 or more.
  • the number of reference samples may be about 50 or more.
  • the number of reference samples can be about 30 or more, about 40 or more, about 50 or more, about 60 or more, about 70 or more, about 80 or more , about 90 or more, about 100 or more, about 200 or more, about 300 or more, about 400 or more, about 500 or more, or about 1000 or more .
  • the grouping can include dividing into groups of about 2 or more.
  • the grouping includes grouping into about 2 or more, about 3 or more, about 4 or more, about 5 or more, about 6 or more, about 7 or more, about 8 or more, about 9 or more, about 10 or more, about 20 or more, about 30 or more, about 40 or more, about 50 or more, about 60 or more, about 70 or more, about 80 or more, about 90 or more, or about 100 or more.
  • the number of reference samples in each group can be about 30 or more.
  • the number of reference samples in each group can be about 30 or more, about 40 or more, about 50 or more, about 60 or more, about 70 or more, about 80 or more, about 90 or more, about 100 or more, about 200 or more, about 300 or more, about 400 or more, about 500 or more, or about 1000 Or more.
  • said step (S2) or module (M2) may comprise a step (S2-2) or module (M2-2): confirming the statistical value of said sequencing data of said reference sample set.
  • the statistical values of the sequencing data of the reference sample group can be used as each candidate baseline.
  • said validation statistics may comprise calculating the mean and/or standard deviation of said reference samples in each group over said target interval.
  • the step (S2) or module (M2) may include a step (S2-3) or module (M2-3): screening out unqualified target intervals in the reference sample.
  • the unqualified target interval may include an interval with low capture efficiency and/or an unstable interval.
  • the disqualified target interval can comprise a target interval with a sequencing read count of about 5 or less.
  • the unqualified target interval can comprise a sequencing read count of about 30 or less, about 20 or less, about 10 or less, about 5 or less, about 4 or less, about 3 or less , a target interval of about 2 or less, about 1 or less, or about 0 or less.
  • the unqualified target interval may comprise a target interval having a coefficient of variation of about 0.8 or higher, the coefficient of variation being the standard deviation and the mean of the sequencing data for the reference samples in each group over the target interval ratio.
  • the unqualified target interval can comprise a coefficient of variation of about 0.8 or higher, about 0.9 or higher, or about 1.0 or higher.
  • the respective thresholds for capturing the low-efficiency interval and/or the unstable interval can be adjusted according to the sequencing situation.
  • the step (S3) or module (M3) may include confirming the similarity between the test sample and the reference sample group.
  • the confirming the similarity may include confirming the distribution similarity between the reference sample group and the test sample based on the sequencing data of the reference sample group and the test sample on the target interval.
  • the similarity may include the degree of similarity between the reference sample group and the sequencing data of the test sample on the target interval.
  • the confirming the similarity may include confirming the distribution similarity between the reference sample group and the test sample by calculating the statistical distance, the method of similarity algorithm or the device recording the program.
  • the statistical distance may include a statistical value of the difference between the sequencing data of the reference sample group and the sample to be tested on the target interval.
  • the statistical distance may include the statistical value of the absolute value of the difference between the sequencing data of the reference sample group and the sample to be tested on the target interval.
  • the statistical distance may include a p-th power statistical value of the absolute value of the difference between the sequence data of the reference sample group and the sample to be tested on the target interval, where p is 1 or more big.
  • the statistical value may comprise a summed value.
  • the high similarity may include that the statistical distance between the reference sample group and the test sample is short on the target interval.
  • the statistical distance may include Minkowski distance.
  • the similarity algorithm may include cosine similarity, Pearson correlation coefficient, Spearman correlation coefficient, log likelihood similarity, cross entropy, and the like.
  • the copy number status of the target gene in the sample to be tested may include the presence and/or amount of variation in the copy number of the target gene in the sample to be tested.
  • the copy number variation may comprise amplification and/or deletion of copy number.
  • the step (S4) or module (M4) may include a step (S4-1) or module (M4-1): determine the copy number CN i of the target gene of the sample to be tested on the target interval i .
  • the determination of the CN i may include dividing the mean value of the sequencing data on the target interval of the target gene of the sample to be tested by the reference sample group closest to the sample to be tested on the corresponding target interval The mean value of the sequencing data is multiplied by the ploidy to obtain the CN i .
  • the ploidy can be 2.
  • the ploidy can be 1.
  • the ploidy can be adjusted according to specific circumstances.
  • the step (S4) or module (M4) may include a step (S4-2) or module (M4-2): denoising the copy number on the target interval of the sample to be tested.
  • the denoising may include reducing the copy number on the target interval of the sample to be tested through transformation analysis, principal component analysis algorithm, singular value decomposition and/or Gaussian filtering method or a device recording the program. noise.
  • the denoising may include denoising the copy number in the target interval of the sample to be tested by using a discrete wavelet transform method or a device recording the program.
  • the denoising may include reducing the copy number on the target interval of the sample to be tested through methods such as transformation analysis, principal component analysis algorithm, singular value decomposition and/or Gaussian filtering, or a device recording the program. noise.
  • the step (S4) or module (M4) may include a step (S4-3) or module (M4-3): determining the copy number CN g of the target gene in the sample to be tested.
  • the target gene may comprise a gene in which the copy number variation to be determined occurs.
  • the target gene may comprise a gene selected from the following group: ABL1, ABL2, ABRAXAS1, ACVR1, ACVR1B, AKT1, AKT2, AKT3, ALK, ALOX12B, AMER1, APC, AR, ARAF, ARFRP1, ARID1A, ARID1B, ARID2, ARID5B, ASXL1, ASXL2, ASXL3, ATG5, ATM, ATR, ATRX, AURKA, AURKB, AXIN1, AXIN2, AXL, B2M, BAP1, BARD1, BBC3, BCL10, BCL2, BCL2L1, BCL2L11, BCL2L2, BCL6, BCOR, BCORL1, BIRC3, BLM, BMPR1A, BRAF, BRCA1, BRCA2, BRD4, BRD7, BRINP3, BRIP1, BTG1, BTG2, BTK, CALR, CARD11, CASP8, CBFB, CBL, CCND1, CCND2, CCND3, CCNE1, CD
  • the target gene may comprise genes selected from the following group: ALK (the transcript number may be NM_004304.4), ERBB2 (the transcript number may be NM_004448.3), EGFR (the transcript number may be NM_005228.3), FGFR1 (transcript number can be NM_023110.2), FGFR2 (transcript number can be NM_000141.4), CDK4 (transcript number can be NM_000075.3) and MET (transcript number can be NM_000245.3).
  • ALK the transcript number may be NM_004304.4
  • ERBB2 the transcript number may be NM_004448.3
  • EGFR the transcript number may be NM_005228.3
  • FGFR1 transcription number can be NM_023110.2
  • FGFR2 transcription number can be NM_000141.4
  • CDK4 transcription number can be NM_000075.3
  • MET transcript number can be NM_000245.3
  • the sample is selected from the group consisting of tissue samples, blood samples, saliva, pleural effusion, peritoneal effusion and cerebrospinal fluid.
  • the step (S4-3) or module (M4-3) may include the length of the exon of the target gene based on the sample to be tested and the length of the target interval i of the sample to be tested.
  • the copy number CN i determines the CN g .
  • said step (S4-3) or module (M4-3) may comprise determining said CN g based on the following formula,
  • i can represent the target interval
  • j can represent the target exon
  • n can represent the number of target intervals on the target exon j
  • m can represent the number of target exons
  • CN i can represent the copy of the target interval i Number
  • Len j can represent the length of the target exon j.
  • the step (S4) or module (M4) may comprise a step (S4-4) or module (M4-4): determining the probability of the existence of the copy number variation of the sample to be tested on the target interval.
  • the probability of the existence of the copy number variation may include the probability of copy number amplification (p a ) and/or the probability of deletion (p d ) of the sample to be tested on the target interval.
  • the step (S4-4) or module (M4-4) may include, based on the sequencing data of the sample to be tested on the target interval i, and the corresponding target interval The mean and standard deviation of the sequencing data of the close reference sample group, the probability of the existence of the copy number variation confirmed by the method of probability distribution or the device recording the program.
  • the probability distribution may comprise a normality probability distribution.
  • the probability distribution may comprise any common probability distribution.
  • the probability distribution may comprise any discrete probability distribution.
  • the probability distribution may comprise any continuous probability distribution.
  • said step (S4) or module (M4) may comprise a step (S4-5) or module (M4-5): determining the significant copy number amplification or deletion of said test sample on said target gene The presence ratio of sigRatio.
  • the step (S4-5) or module (M4-5) may comprise, dividing the number of target intervals with significant copy number variation on the target gene by the number of all target intervals on the target gene to obtain the Describe sigRatio.
  • the target interval in which the significant copy number variation occurs may include the target interval in which the proportion of the copy number variation is about 30% or higher.
  • the target interval with significant copy number variation can contain the proportion of the copy number variation is about 30% or higher, about 40% or higher, about 50% or higher, about 60% or higher , about 70% or higher, about 80% or higher, about 90% or higher, about 95% or higher, or about 95% or higher of the target interval.
  • said step (S4) or module (M4) may comprise a step (S4-6) or module (M4-6): a statistical test for determining the presence of a copy number variation of said test sample on said target gene parameter.
  • the statistical test parameters may comprise a p-value determined by a test of significance.
  • the significance test may comprise a T-test.
  • the significance test may be in any significant test manner, or a modified significance test manner according to the actual situation.
  • the step (S4-6) or module (M4-6) may include, based on the number of the target interval of the sample to be tested on the target gene, the target gene on the target gene
  • the sequencing data of each target interval of the sample, the standard deviation of the sequencing data of each target interval of the sample to be tested on the target gene, and the reference sample closest to the sample to be tested on the corresponding target gene The mean value and standard deviation of the sequencing data in the target interval of the group were confirmed by the method of T test or the device that described the program, and the p value p ttest was confirmed.
  • the step (S4) or module (M4) can determine the copy number status of the target gene of the sample to be tested by the following content:
  • CN thA can be from about 2.25 to about 4.
  • CN thA can be about 2.25, about 2.50, about 2.75, about 3.00, about 3.25, about 3.50, about 3.75, or about 4.00.
  • CN thD can be from about 1.0 to about 1.75.
  • CN thD can be about 0.25, about 0.50, about 0.75, about 1.00, about 1.25, about 1.50, about 1.75.
  • sigRatio th can be about 0.3 to about 1.
  • sigRatio th can be about 0.3, about 0.4, about 0.5, about 0.6, about 0.7, about 0.8, about 0.9, or about 1.0.
  • p th can be from about 0.05 to about 0.00001.
  • p th can be about 0.05, about 0.01, about 0.001, about 0.0001, about 0.00001, about 0.000001, or about 0.0000001.
  • the present application provides a method for establishing a database, which may include obtaining sequencing data of multiple reference samples, and dividing the reference samples into two or more reference sample groups.
  • the database establishment method may include (S1) obtaining sequencing data of a sample to be tested and/or sequencing data of multiple reference samples; (S2) dividing the reference samples into two or more reference sample groups.
  • the present application provides a device for establishing a database, which may include the following modules: a receiving module, configured to acquire sequencing data of a sample to be tested and/or sequencing data of a plurality of reference samples; a processing module, configured to convert the reference samples into into two or more reference sample groups.
  • the database establishment device may include (M1) a receiving module for obtaining the sequence data of the sample to be tested and/or the sequence data of a plurality of reference samples; (M2) a processing module for dividing the reference sample into Two or more reference sample groups.
  • the application provides a method for establishing a database, which may include the following steps:
  • step (S1) Acquiring the sequencing data of the sample to be tested and/or the sequencing data of a plurality of reference samples; step (S1-1): obtaining the sequencing data of the sample to be tested and/or the reference sample by gene sequencing; Step (S1-2): correcting the sequencing data of the sample to be tested and/or the reference sample;
  • step (S2) dividing the reference sample into two or more reference sample groups; step (S2-1): grouping the reference samples; step (S2-2): confirming the sequencing data of the reference sample group statistical value.
  • the present application provides a database establishment device, which may include a module for realizing the database establishment method of the present application.
  • the application provides a method for establishing a database, which may include the following steps:
  • step (S1) Acquiring the sequencing data of the sample to be tested and/or the sequencing data of a plurality of reference samples; step (S1-1): obtaining the sequencing data of the sample to be tested and/or the reference sample by gene sequencing; Step (S1-2): correcting the sequencing data of the sample to be tested and/or the reference sample;
  • step (S2) dividing the reference sample into two or more reference sample groups; step (S2-1): grouping the reference samples; step (S2-2): confirming the sequencing data of the reference sample group statistical value.
  • the present application provides a database establishment device, which may include a module for realizing the database establishment method of the present application.
  • the present application provides a device for establishing a database, which may include the following modules: (M1) a receiving module for obtaining sequencing data of a sample to be tested and/or sequencing data of a plurality of reference samples; (M2) a processing module, Used to divide the reference samples into two or more reference sample groups.
  • M1 a receiving module for obtaining sequencing data of a sample to be tested and/or sequencing data of a plurality of reference samples
  • M2 a processing module, Used to divide the reference samples into two or more reference sample groups.
  • the present application provides a copy number status analysis method according to the information of the existing database, which may include determining the reference sample group closest to the sample to be tested from two or more reference sample groups, and The sequence data of the closest reference sample group is used to determine the copy number status of the target gene of the sample to be tested.
  • the copy number status analysis method may include (S3) determining the reference sample group closest to the sample to be tested; (S4) determining the reference sample group closest to the sample to be tested based on the sequencing data. Describe the copy number status of the target gene in the sample to be tested.
  • the present application provides a copy number status analysis device, which may include the following modules: a calculation module, used to determine the reference sample group closest to the sample to be tested from two or more reference sample groups; a judgment module, using Based on the sequencing data of the reference sample group closest to the sample to be tested, the copy number status of the target gene of the sample to be tested is determined.
  • the copy number status analysis device may include (M3) a calculation module for determining the reference sample group closest to the sample to be tested; (M4) a judgment module for determining the sample group closest to the sample to be tested
  • the sequence data of the reference sample group is used to determine the copy number status of the target gene of the sample to be tested.
  • the application provides a copy number status analysis method, which may include the following steps:
  • Step (S4) Based on the sequencing data of the reference sample group closest to the sample to be tested, determine the copy number status of the target gene of the sample to be tested Step (S4-1): determine that the target gene of the sample to be tested is in The copy number CN i on the target interval i; step (S4-3): determine the copy number CN g of the sample to be tested on the target gene; step (S4-4): determine the copy number CN g on the target interval Measure the probability of the existence of the copy number variation of the sample; Step (S4-5): determine the ratio sigRatio of the presence of significant copy number amplification or deletion of the sample to be tested on the target gene; Step (S4-6 ): determine the statistical test parameters for the existence of the copy number variation of the sample to be tested on the target gene; determine the copy number status of the target gene of the sample to be tested by the following content: when CN g ⁇ CN thA , sigRatio ⁇ sigRatio th , and p
  • the present application provides a copy number status analysis device, which may include a module implementing the copy number status analysis method of the present application.
  • the application provides a copy number status analysis method, which may include the following steps:
  • Step (S4) Based on the sequencing data of the reference sample group closest to the sample to be tested, determine the copy number status of the target gene of the sample to be tested Step (S4-1): determine that the target gene of the sample to be tested is in The copy number CN i on the target interval i; step (S4-2): denoise the copy number on the target interval of the sample to be tested; step (S4-3): determine the The copy number CN g of the sample on the target gene; Step (S4-4): Determine the probability of the existence of the copy number variation of the sample to be tested on the target interval; Step (S4-5): Determine the probability of the copy number variation in the target gene The ratio sigRatio of the presence of significant copy number amplification or deletion of the sample to be tested above; step (S4-6): determine the statistical test parameters for the presence of copy number variation of the sample to be tested on the target gene ; Determine the copy number status of the target gene of the sample to be tested by the following content: when CN g ⁇ CN
  • the present application provides a copy number status analysis device, which may include a module implementing the copy number status analysis method of the present application.
  • the present application provides a database, which is established according to the copy number status analysis method or the database establishment method described in the present application.
  • the present application also provides a storage medium, which records a program capable of running the method described in the present application.
  • the present application also provides a device, which may include the storage medium described in the present application.
  • the non-transitory computer readable storage medium may include a floppy disk, a flexible disk, a hard disk, a solid state storage (SSS) (such as a solid state drive (SSD)), a solid state card (SSC), a solid state module (SSM)), an enterprise high-grade flash drives, tape, or any other non-transitory magnetic media, etc.
  • SSD solid state drive
  • SSC solid state card
  • SSM solid state module
  • Non-transitory computer readable storage media may also include punched cards, paper tape, cursor sheets (or any other physical media having a pattern of holes or other optically identifiable markings), compact disc read only memory (CD-ROM) , Rewritable Disc (CD-RW), Digital Versatile Disc (DVD), Blu-ray Disc (BD) and/or any other non-transitory optical media.
  • CD-ROM compact disc read only memory
  • CD-RW Rewritable Disc
  • DVD Digital Versatile Disc
  • BD Blu-ray Disc
  • the device of the present application may further include a processor coupled to the storage medium, and the processor may be configured to execute based on a program stored in the storage medium to implement the method described in the present application.
  • the present application also provides a method of the present application, which can be applied in disease diagnosis, prevention and/or treatment.
  • the present application also provides a method of the present application, which can be applied in the monitoring of the copy number status of the target gene.
  • the present application also provides a method of the present application, which can be applied in genome-wide association studies.
  • the method can be used to determine whether the subject has a copy number variation.
  • any one or more of the methods of the present application may be for non-diagnostic purposes.
  • any one or more of the methods of the present application may be for diagnostic purposes.
  • the method can be used in clinical practice by detecting the copy number variation (for example, it can be inferred whether certain specific tumor treatment methods are suitable for the subject).
  • the level of copy number variation detected by the method can be used in clinical practice in combination with biomarkers known in the art.
  • the method of the present application is used to detect copy number variation.
  • the copy number variation detection algorithm of this application can select a sufficient number of samples, for example, 15 cases from the same sample type and the same experimental methodology, and try to ensure that the reagent batches, experimental equipment, etc. used in the experiment are consistent sample data .
  • Each participating sample data needs to come from the BAM file after comparison of NGS sequencing data.
  • the repetitive DNA sequence fragments introduced by PCR during NGS library construction can be removed to obtain uniquely compared DNA fragments.
  • the sliding window method is used to slide 24bp each time, and the region is divided into window regions with a probe fixed length of 120bp, and the average coverage of the uniquely aligned DNA fragments in each window is counted level.
  • the average sequencing depth is required to be ⁇ 100X
  • the minimum sequencing depth is ⁇ 30X
  • the detection method of this application can meet the quality requirements
  • the number of samples is at least 10 cases for testing.
  • the coverage level of each window area can be corrected, including preliminary coverage level correction (based on the sample average coverage level), GC correction and batch correction.
  • the preliminary coverage level correction is to correct the coverage levels of all samples in the batch to the same specified coverage level. Specifically, for each window area of the sample in the batch, the average coverage level obtained by sequencing is divided by the sum of the average coverage levels of all window areas in the sample, and then multiplied by a fixed factor (the factor is 1E+07).
  • GC correction is performed by calculating the GC content of each window, and then using the loess regression method to perform GC bias correction on the coverage level of each window region in the sample.
  • MAD median and median absolute deviation
  • peripheral blood samples were selected to detect the exon copy number variation (LGR) of BRCA1 and BRCA2.
  • LGR exon copy number variation
  • the experiment used RNA probes to specifically capture BRCA1 and BRCA2 gene regions, and then performed high-throughput sequencing to compare the sequencing data with the standard sequence of the human genome hg19 for comparison to obtain the compared BAM file. Subsequently, the method based on the construction of the reference baseline and the method of the present application were used to detect the copy number variation. At the same time, all sample copy number variations were confirmed by the BRCA MASTR Plus Dx kit (based on multiplex PCR capture methodology), including a total of 17 LGR positive samples and 679 negative samples.
  • the data of 14 cell line samples after sequencing and comparison were selected to construct batch baselines.
  • the thresholds for describing the coverage fluctuation level of the description window were set to 0.05 and 0.15, respectively, and two batch baselines were constructed. Then, the samples with known LGR copy number variation (BRCA1:exon4-6del) among the 14 samples were corrected in batches with 2 batch baselines, and then the copy number variation was detected.
  • a batch baseline was constructed from 10 simulated positive samples, and then batch correction and copy number variation identification were performed on the 10 simulated samples using the constructed batch baseline.
  • the results of the 10 simulated samples are shown in Figures 4A-4J, and the 10 simulated copy number variations can all be detected accurately, indicating that the present application can achieve accurate detection of copy number variations in any region.
  • the method of this application uses the clustering method to divide a large amount of real sample data into different sample sets according to the trend clustering of the sequencing depth, and constructs the baselines (average depth and depth fluctuation range) respectively.
  • the baselines average depth and depth fluctuation range
  • the discrete wavelet transform method can optionally be used to smooth and denoise the copy number to improve the signal-to-noise ratio of the sequencing data.
  • the method of the present application detects copy number variation based on the specificity of sample coverage characteristics. Specifically, based on the cluster analysis of large-scale samples, this application constructs multiple control group baselines, which can avoid the baseline mismatch problem caused by inconsistent coverage depth characteristics due to differences in experiments and samples in sequencing; and can integrate multiple coverage depths Correction strategies to reduce sample-specific data differences; ultimately, quantitative analysis and statistical difference evaluation can be used to ensure the accuracy and stability of results.
  • the copy number variation detection method of the present application can be applied not only to the targeted capture sequencing data of a specific gene panel, but also to the capture sequencing data of the whole exome.
  • the database establishment method of the present application may include the following steps:
  • Data preparation module including:
  • Coverage depth correction module including 3 independent corrections with optional order:
  • i represents the site on the target interval
  • n represents the total number of sites on all target intervals
  • RD i represents the sequencing depth of site i on the target interval
  • R is a constant that can be set according to the size of all intervals to ensure that The corrected depth of the test sample is at the same level as the corrected depth of the reference sample.
  • b) Smoothing the sequencing data 1 correcting the characteristics of probe laying, specifically, according to the difference in probe laying multipliers in different intervals in the probe design, such as the number of probes covered on the interval, dividing the interval, each The length of the target interval can be about 24 base pairs, and the average coverage depth RD of each target interval is calculated. According to the number of probes covered on each target interval ProbeN, the sequencing depth on the target interval is locally weighted regression (loess(RD ⁇ ProbeN)) correction, obtain the sequencing depth RD normP after probe correction;
  • GC correction specifically, extending the target interval used for coverage depth calculation to a total length greater than 200bp according to the flanks, calculating the average GC ratio, and according to the GC content of each interval GC, Perform local weighted regression (loess(RD ⁇ GC)) correction on the sequencing depth RD to obtain the GC-corrected sequencing depth RD normGC ;
  • Baseline building blocks specifically including the following steps:
  • a) Sample clustering Existing methods generally use all reference samples as a category to construct a baseline. The method of the present application groups the reference samples, specifically based on the consistency of the coverage depth of each reference sample in the target interval, such as the degree of approximation of the sequencing of the reference samples in the target interval, cluster analysis is performed, and the reference samples Divided into different categories of reference sample groups, the clustering method can be such as K-means clustering, hierarchical clustering and other methods;
  • the setting of the number of reference sample groups needs to take into account the characteristics of tumor samples and the quality of sequencing, and determine the number of reference sample groups according to the number of captured features. For example, the number of reference sample groups can be more than 2, such as 2-10.
  • interval screening can be performed: calculate the coefficient of variation cv of the sequencing depth on each interval, and remove the unstable intervals with large fluctuations in the sample, where:
  • this interval is considered to be an unstable region and is filtered out; at the same time, when the corrected sequencing depth is lower than 5, it is considered to be a region with low capture efficiency and is filtered out, and the final reserved interval is regarded as a stable region. interval.
  • the database of the present application can be obtained, including two or more reference sample groups whose changes in the target interval are consistent.
  • the advantage of the database of this application lies in: through the cluster analysis of large-scale samples, the reference samples are divided into different types of reference sample groups, and the sample-specific background baselines are respectively constructed, which greatly reduces the False positives caused by batch effects in high-throughput sequencing data in copy number variation detection increase the stability of results.
  • the method for eliminating the batch effect of the present application does not need to ensure a sufficient number of samples of the same gene panel in the same batch, which greatly reduces the difficulties in practical application.
  • the copy number status analysis method of the present application may include the following steps:
  • a) According to the similarity between the sample to be tested and the reference sample group, determine the reference sample group closest to the sample to be tested, that is, dynamically filter the baseline: by calculating the statistical distance, such as Minkowski distance, etc. , comparing the sequencing depth of the sample to be tested on each target interval with the sequencing depth of each reference sample group on the target interval, and confirming the statistical distance between the reference sample group and the sample to be tested:
  • the L p value represents the statistical distance
  • i represents the target interval
  • n represents the number of target intervals
  • RD sample represents the sequencing depth of each target interval of the sample to be tested, Indicates the sequencing depth of each target interval of the reference sample group closest to the sample to be tested, where the ploidy can be 2.
  • the copy number smoothing and noise reduction of each interval can be performed: the CNi of each interval can be smoothed and denoised using a noise reduction algorithm to improve the signal-to-noise ratio of the data.
  • the noise reduction method can use discrete wavelet transform (Discrete Wavelet Transformation, DWT), principal component analysis algorithm, singular value decomposition and/or Gaussian filtering method for smooth noise reduction.
  • DWT divides the signal into high-frequency signal and low-frequency signal, passes through low-pass filter and high-pass filter respectively, performs discrete wavelet transform on discrete signal, discretizes continuous wavelet and its wavelet transform, and achieves the purpose of data noise reduction. In this way, the CNi after noise reduction can be obtained;
  • Evaluation of the copy number of each target gene calculate the weighted average copy number CN g of each target gene in the sample, and use the length of the target exon to correct CN i , for example:
  • i represents the target interval
  • j represents the target exon
  • n represents the number of target intervals on target exon j
  • m represents the number of target exons
  • CN i represents the copy number of target interval i
  • Len j represents Length of target exon j.
  • the target interval with significant copy number variation includes the target interval with a proportion of copy number variation of about 30% or higher.
  • Each threshold can be obtained by using a large-scale sample training.
  • CN thA represents the threshold of copy number amplification, and the value can be selected from 2.25 to 4
  • CN thD represents the threshold of copy number deletion, and the value can be selected from 1.0 to 1.75
  • sigRatio th represents significant amplification/deletion
  • the threshold of the ratio can be selected from 0.3 to 1
  • p th represents the threshold of the significant T test, and the value can be selected from 0.05 to 0.00001.
  • the copy number status analysis method of this application dynamically screens the reference sample group closest to the sample to be tested according to the similarity between the sample to be tested and the reference sample group as the background baseline, which can eliminate the batch effect and improve the specificity and sensitivity of detection sex.
  • Database establishment 655 reference samples were used to construct the baseline, and the database construction method of this application was adopted, for example, the k-means clustering algorithm was used to divide the reference samples into 5 reference sample groups, and 5 different candidate baselines were constructed as the database.
  • Construct simulation data use varBen tumor mutation data simulation software (github.com/nccl-jmli/VarBen), based on benign tissue samples, by inserting gene reads, insert target gene reads into the sequencing data, The gradient simulates the amplification of different copy numbers of the target gene, and the simulated sample list is shown in Table 4.
  • target gene Number of simulated samples Simulate copy number gradients ALK 20 2.5, 2.75, 3.0, 3.5, 4.0 ERBB2 20 2.5, 2.75, 3.0, 3.5, 4.0 FGFR1 20 2.5, 2.75, 3.0, 3.5, 4.0 FGFR2 20 2.5, 2.75, 3.0, 3.5, 4.0
  • Figures 5A-5F show examples of copy number distribution diagrams of part of the test result data of the present application.
  • Each dot represents an interval of a gene
  • gray dots represent genes with normal copy number
  • black dots represent genes with copy number amplification or deletion
  • the corresponding gene names are marked.
  • the horizontal axis indicates the chromosome position of the gene
  • the vertical axis indicates the copy number calculated based on the method of this application (the middle horizontal line indicates the copy number of the normal gene)
  • the gray background indicates the background baseline (the reference sample group closest to the sample to be tested) The fluctuation range of each target interval in .
  • Figures 5A-5C simulate different degrees of copy number amplification of the ERBB2 gene
  • Figures 5D-5F show different levels of copy number amplification of the FGFR1 gene
  • the simulated copy number gradients are 2.5, 2.75 and 3.0.
  • the results show that the copy number status analysis method of the present application is used in simulated samples, all simulated genes and copy number amplifications of different gradients can be stably detected, and the copy number prediction is accurate.
  • Positive standard samples This application test includes 30 cases of positive standard samples, which are derived from NCI-BL2009 cell line, using plasmid transfection to transfect the corresponding proportion of the target gene into the cell line to obtain CNV positive data, and using microdroplets Gene copy number was quantified by digital PCR (ddPCR).
  • the plasmid numbers are: Life RPCI11.C-433C10BAC-EGFR, Life RPCI11.C-936I7BAC-CDK4, Life RPCI11.C-163C9BAC-MET, Life RPCI11.C-909L6BAC-ERBB2, Life RPCI11.C-957P17BAC-FGFR1.
  • the list of positive standard samples is shown in Table 6.
  • Database establishment 655 reference samples were used to construct the baseline, and the database construction method of this application was adopted, for example, the k-means clustering algorithm was used to divide the reference samples into 5 reference sample groups, and 5 different candidate baselines were constructed as the database.
  • the copy number status of the copy number amplification positive standard sample was detected according to the copy number status analysis method of the present application, and the detection results are shown in Table 7.
  • Figures 6A-6C show examples of copy number distribution diagrams of part of the test result data of the present application.
  • 6A-6C show the detection results of standard samples of CNV-positive cell lines transfected with plasmids, and the ddPCR-marked copy numbers are 3, 5 and 8, respectively.
  • the results show that the method of the present invention is used in the cell line standard for plasmid transfection, all genes and different copy number states can be stably detected, and the copy number prediction is accurate.
  • Real data The real samples tested in this application include 20 cases of ERBB2 amplification positive samples verified by a third-party immunohistochemical method (IHC). The list of real samples is shown in Table 8.
  • Database construction use 443 reference samples to construct baselines, adopt the database construction method of this application, for example, use k-means clustering algorithm, divide reference samples into reference sample groups, and construct different candidate baselines as databases.
  • Figures 7A-7C show examples of copy number distribution diagrams of part of the test result data of the present application.
  • 7A-7C show the detection results of real ERBB2 positive samples. The results showed that the method of the present application was applied to real samples, and all 20 samples whose IHC results were positive for HER2 could be stably detected.
  • Positive standard samples The test of this application includes 3 cases of positive standard samples, which are from the same source as in Example 9, and are used to detect the test results of different baselines. The list of positive standard samples is shown in Table 10.
  • Database establishment 655 reference samples were used to construct the baseline, and the database construction method of this application was adopted, for example, the k-means clustering algorithm was used to divide the reference samples into 5 reference sample groups, and 5 different candidate baselines were constructed as the database. At the same time, without using the clustering method, all reference samples were constructed as a baseline.
  • Figures 8A-8F show examples of copy number distributions of standard sample 1 using different baseline detection results.
  • the results show that the optimal baseline matched by the method of the present application is the closest to the sample to be tested (the distance between the sample and the baseline is the smallest), the fluctuation (SD) of the overall copy number of the sample is the lowest, the copy number distribution map is the most stable, and the noise is the smallest , indicating that the detection results of this method are more stable.
  • the method of this application can stably detect all genes and different copy number states, while other baselines cannot be stably detected when the copy number is 3.

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Abstract

提供了一种拷贝数变异的检测方法及其应用。还提供了一种拷贝数状态分析方法，包含将待测样本的目标区间划分为若干个窗口区域，获取待测样本组中的对照窗口区域的测序数据，基于对照窗口区域的测序数据，确定待测样本的目标基因的拷贝数状态。

Description

一种拷贝数变异的检测方法及其应用 技术领域
本申请涉及生物信息领域，具体的涉及一种拷贝数变异的检测方法及其应用。
背景技术
拷贝数变异(CNV)是人类基因组中常见的变异类型之一。拷贝数变异包括基因拷贝数的扩增和缺失两种变异类型。对于基因拷贝数变异的检测可以用于监测受试者的基因组状态的，也可以用于发现特定疾病与某些基因组变异的关联性关系。例如，基因发生拷贝数变异可能导致多种常见遗传病，如BRCA1/2基因缺失可能导致遗传性乳腺癌的风险；基因发生拷贝数变异可能影响肿瘤的发生和发展，如HER2基因扩增不仅与肿瘤的发生发展相关，还是一个重要的临床治疗监测及预后指标，并且是肿瘤靶向治疗的一个重要靶点。因此，拷贝数变异的检测可以在受试者的基因组状态监测、全基因组关联研究、遗传病的预防以及肿瘤的精准治疗起着至关重要的作用。例如，携带某些特定的拷贝数变异的受试者与普通人群相比，可以具有更高的患有疾病(例如肿瘤)的终生风险。因此，拷贝数变异检测方法可以用于筛选出具有较高风险的受试者，该受试者可以接受疾病的个体化监测，从而可以达到早诊早治的目的。
传统的拷贝数变异检测方法如ddPCR(微滴式数字PCR)、MLPA(多重连接探针扩增技术)、FISH(荧光原位杂交技术)等，一次只能检测一个或少数几个基因的拷贝数状态，或者只能检测特定基因的拷贝数状态，不能对基因组进行全局分析，具有通量低、成本高的特点。目前，基于高通量技术检测拷贝数变异的方法有很多，但是不同的检测方法结果存在着很大的差异，检测的敏感性和特异性具有一定的局限。一方面，高通量测序技术在建库及测序过程中存在着一定的批次效应和技术误差，另一方面，由于肿瘤样本的复杂性，导致拷贝数检测结果的稳定性存在很大的挑战，使得基于高通量测序技术检测拷贝数变异检测在精准医疗领域有很大的难度。本领域急需一种可以降低批次效应、误差和/或提高拷贝数检测结果稳定性的分析方法。
发明内容
本申请的目的就是针对上述现有技术不足，提供一种针对基因拷贝数异常检测的方法。该方法至少可以降低批次效应、误差和/或提高拷贝数检测结果稳定性，对检测拷贝数异常相 关的驱动事件以及解读肿瘤基因组演化信息有着重要的意义。本申请提供了一种拷贝数变异的检测方法及其应用。
一方面，本申请提供了一种拷贝数状态分析方法，所述方法包含，将待测样本的目标区间划分为若干个窗口区域，获取待测样本组中的对照窗口区域的测序数据，基于所述对照窗口区域的测序数据，确定所述待测样本的目标基因的拷贝数状态；任选地，所述对照窗口区域包含覆盖波动水平低的窗口区域。
一方面，本申请提供了一种拷贝数状态分析装置，包含以下模块：接收模块，用于获取待测样本组的测序数据；确定模块，用于确定待测样本中的目标基因；判断模块，用于根据所述待测样本组的测序数据确定所述待测样本中的目标基因的拷贝数状态。
一方面，本申请提供了一种储存介质，其记载可以运行本申请所述的方法的程序。
本领域技术人员能够从下文的详细描述中容易地洞察到本申请的其它方面和优势。下文的详细描述中仅显示和描述了本申请的示例性实施方式。如本领域技术人员将认识到的，本申请的内容使得本领域技术人员能够对所公开的具体实施方式进行改动而不脱离本申请所涉及发明的精神和范围。相应地，本申请的附图和说明书中的描述仅仅是示例性的，而非为限制性的。
附图说明
本申请所涉及的发明的具体特征如所附权利要求书所显示。通过参考下文中详细描述的示例性实施方式和附图能够更好地理解本申请所涉及发明的特点和优势。对附图简要说明如下：
图1A-1B显示的是基于构建参考基线方法和本申请方法的检测结果图。每个箱线图表示30例样本的BRCA1基因外显子拷贝数值分布。A组和B组分别代表不同批次探针捕获。图1A显示基于构建参考基线方法计算BRCA1基因各外显子的拷贝数分布。图1B显示本申请方法计算BRCA1基因各外显子的拷贝数分布。
图2A-2B显示的是基于构建参考基线方法和本申请方法对NGS建库方法差异的样本检测结果图。横坐标：染色体坐标；纵坐标：评估的拷贝数(CN)值。图2A显示基于构建参考基线方法检测拷贝数变异结果。图2B显示本申请方法检测拷贝数变异结果。方框表示检出的拷贝数变异。
图3A-3B显示的是基于筛选稳定窗口不同设定阈值后的样本检测结果。横坐标：染色体坐标；纵坐标：评估的拷贝数(CN)值。图3A显示设定阈值为0.05的样本检测拷贝数变异结果。图3B显示设定阈值为0.15的样本检测拷贝数变异结果。方框表示检出的拷贝数变异。
图4A-4J显示的是10例模拟阳性拷贝数变异样本构建批次基线后的检测结果。横坐标：染色体坐标；纵坐标：评估的拷贝数(CN)值。图中方框表示表示检出的拷贝数变异。
图5A-5F显示的是，本申请对于模拟样本的检测结果部分数据的拷贝数分布图示例。
图6A-6C显示的是，本申请对于标准样本的检测结果部分数据的拷贝数分布图示例。
图7A-7C显示的是，本申请对于真实样本的检测结果部分数据的拷贝数分布图示例。
图8A-8F显示的是，本申请对于标准样本1使用不同的基线检测结果的拷贝数分布图示例。
具体实施方式
以下由特定的具体实施例说明本申请发明的实施方式，熟悉此技术的人士可由本说明书所公开的内容容易地了解本申请发明的其他优点及效果。
在本申请中，术语“二代基因测序”、高通量测序”或“下一代测序”通常是指第二代高通量测序技术及之后发展的更高通量的测序方法。下一代测序平台包括但不限于已有的Illumina等测序平台。随着测序技术的不断发展，本领域技术人员能够理解的是还可以采用其他方法的测序方法和装置用于本方法。例如，二代基因测序可以具有高灵敏度、通量大、测序深度高、或低成本的优势。根据发展历史、影响力、测序原理和技术不同等，主要有以下几种：大规模平行签名测序(Massively Parallel Signature Sequencing，MPSS)、聚合酶克隆(Polony Sequencing)、454焦磷酸测序(454pyro sequencing)、Illumina(Solexa)sequencing、离子半导体测序(Ion semi conductor sequencing)、DNA纳米球测序(DNA nano-ball sequencing)、Complete Genomics的DNA纳米阵列与组合探针锚定连接测序法等。所述二代基因测序可以使对一个物种的转录组和基因组进行细致全貌的分析成为可能，所以又被称为深度测序(deep sequencing)。例如，本申请的方法同样可以应用于一代基因测序、二代基因测序、三代基因测序或单分子测序(SMS)。在本申请中，术语“数据库”通常是指相关数据的有组织实体，而不管数据或有组织实体的表示方式。例如，所述相关数据的有组织实体可以采取表、映射、网格、分组、数据报、文件、文档、列表的形式或任何其他形式。在本申请中，所述数据库可以包括以计算机可存取的方式来收集并保存的任何数据。
在本申请中，术语“计算模块”通常是指用于计算的功能模块。所述计算模块可以根据输入值计算输出值或得到结论或结果，例如计算模块可以主要是用于计算输出值。计算模块可以是有形的，例如电子计算机的处理器、带有处理器的计算机或电子设备或计算机网络，也可以是存储在电子介质上的一段程序、命令行或软件包。
在本申请中，术语“处理模块”通常是指用于数据处理的功能模块。所述处理模块可以 根据将输入值处理为有统计学意义的数据，例如可以是用于输入值的数据的分类。处理模块可以是有形的，例如用于存储数据的电子或磁介质，以及电子计算机的处理器、带有处理器的计算机或电子设备或计算机网络，也可以是存储在电子介质上的一段程序、命令行或软件包。
在本申请中，术语“判断模块”通常是指用于获得相关判断结果的功能模块。在本申请中，所述判断模块可以根据输入值计算输出值或得到结论或结果，例如判断模块可以主要是用于得到结论或结果。判断模块可以是有形的，例如电子计算机的处理器、带有处理器的计算机或电子设备或计算机网络，也可以是存储在电子介质上的一段程序、命令行或软件包。
在本申请中，术语“样品获得模块”通常是指用于获得受试者的所述样本的功能模块。例如，所述样品获得模块可以包括用以获得所述样本(例如组织样本、血液样本、唾液、胸腔积液、腹腔积液、脑脊液等)所需的试剂和/或仪器。例如，可以包括采血针、采血管和/或血液样本运输箱。例如，本申请的装置可以不含或包含1个或以上的所述样品获得模块，并可以可选地具有输出本申请所述的样本的测量值的功能。
在本申请中，术语“接收模块”通常是指用于获得所述样本中所述测量值的功能模块。在本申请中，所述接收模块可以输入本申请所述样本(例如组织样本、血液样本、唾液、胸腔积液、腹腔积液、脑脊液等)。在本申请中，所述接收模块可以输入本申请所述样本(例如组织样本、血液样本、唾液、胸腔积液、腹腔积液、脑脊液等)的测量值。所述接收模块可以对所述样本的状态进行检测。例如，所述数据接收模块可以可选地对所述样本进行本申请所述的基因测序(例如二代基因测序)。例如，所述数据接收模块可以可选地包括用以进行所述基因测序所需的试剂和/或仪器。所述数据接收模块可以可选地检测出测序深度、测序读长计数或拷贝数。
在本申请中，术语“拷贝数变异”通常是指目标区间、目标基因或目标基因中的目标区间的拷贝数的扩增或缺失。例如，本申请提供的拷贝数变异分析方法可以是用于治疗或诊断目的。例如，本申请提供的拷贝数变异分析方法可以是用于非治疗或诊断目的，例如通过测序结果判定是否存在着拷贝数变异现象。
在本申请中，术语“滑窗法”通常是指一种窗口区域的划分方法，例如，可以按照相同或不同的窗口区域长度，将全长区域的划分为多个窗口。例如，可以按照相同或不同步长，将全长区域的划分为多个窗口。例如，可以按照相同的窗口区域长度，并可以按照相同的步长，将全长区域的划分为多个窗口。
在本申请中，术语“质量合格的样品”通常是通过质量控制标准的样本。例如，质量合格的样品可以是指平均测序深度、最小测序深度和/或覆盖均一度合格的样本。例如，平均测序深度合格可以是指平均测序深度为约100x或以上的样本。例如，最小测序深度合格的样本可以是指最小测序深度为约30x或以上的样本。例如，覆盖均一度合格的样本可以是指大于或等于样本平均测序深度的20％的碱基数量占样本中总碱基数量的百分比为约90％或以上的样本。
在本申请中，术语“不合格目标区间”通常是指测序质量低的区间。例如，不合格的区间可以不适合用作拷贝数变异的分析。例如，不合格的区间可以不适合用作参考或基线的构建。在一些情况下，筛除不合格区间可以提高检测结果的准确性；在另一些情况下，不筛除不合格区间也可以得到具有一定准确性的检测结果。例如，不合格的区间可以是指测序深度低的区间；例如，不合格的区间可以是指该区间在不同的样本中变化大的区间。
在本申请中，术语“捕获效率低区间”通常是指不容易被用到的探针捕获的区间。例如，某区间的序列存在特定的序列组合时，可能难以被核酸探针捕获。例如，捕获效率低区间可以是指测序深度低的区间。例如，捕获效率低区间可以是指测序读长计数为约5或更低的区间。
在本申请中，术语“不稳定区间”通常是指不同样本中测序结果差异大的区间。例如，可以是在同一样本中多次测序结果差异大的区间。例如，可以是在不同样本的同一批次中测序结果差异大的区间。例如，可以是在不同样本的不同批次中测序结果差异大的区间。例如，可以是在不同参考样本中测序结果差异大的区间。例如，确定不稳定区间的方法可以是计算某个区间在不同样本的测序深度的标准差与均值的比值，并确定该比值是否大于某阈值的方法，如阈值可以是0.8，或本领域技术人员根据实际测序情况进行调整。
在本申请中，术语“待测样本”通常是指需要进行检测，并判定该样本上的一个或者多个基因区域是否存在有拷贝数变异的样本。例如，待测样本或其数据可以在进行检测之前预先储存在储存器中。
在本申请中，术语“人类参考基因组”通常是指可以在基因测序中发挥参照功能的人类基因组。所述人类参考基因组的信息可以参考UCSC(University of California，Santa Cruz)。所述人类参考基因组可以有不同的版本，例如，可以为hg19、GRCH37或ensembl 75。
在本申请中，术语“GC含量”通常是指在基因序列(碱基序列)中鸟嘌呤G和胞嘧啶C所占该序列汇总所有核苷酸的比率。
在本申请中，术语“靶向测序面板”或“panel”通常是指一组/一套检测对象。例如，在测序的过程中，对一个或多个目标区间通过设计一个或多个探针进行捕获并检测，这样的一个或多个探针可以组成靶向测序面板。例如，靶向测序面板可以针对目标基因、目标区间或感兴趣的区域进行任意设计，例如也可以针对若干个外显子区域。例如，探针可以是指在研究中与目标区间的寡聚核苷酸或靶核酸互补的寡聚核苷酸。例如，目标区间是设计探针时所针对的区间。
在本申请中，术语“测序深度”通常是指特定区域(例如特定基因、特定区间、特定碱基)被检测的次数。测序深度可以是指通过测序检测的一段碱基序列。例如，通过将测序深度比对到人类参考基因组，并可选地去重，可以确定和统计特定基因、特定区间或特定碱基位置上测序读长的数量，作为测序深度。在一些情况下，测序深度可以与测序深度相关。例如，测序深度可以受到拷贝数状态的影响。
在本申请中，术语“测序数据”通常是指测序后获得的短序列的数据。例如，测序数据包含测序短序列(测序读长)的碱基序列、测序读长的数目等。
在本申请中，术语“测序偏差量”通常是指不同区间产生测序数据偏差量。例如，区间的序列的特殊排列方式或者碱基比例可以对该区间的测序读长计数产生影响。例如，当区间含有较高或较低GC含量时，该区间的测序读长计数相对于GC含量接近50％的区间可以有偏差。
在本申请中，术语“分布相似程度”可以是指两组数据的分布相似程度。例如，本申请中分布相似程度可以是指在一个或多个区间上参考样本组与待测样本的测序读长计数的相似程度。
在本申请中，术语“统计距离”可以是指两组数据的数据值的距离。例如，本申请中统计距离可以是指在一个或多个区间上参考样本组与待测样本的测序读长计数的差值的统计量。例如，可以通过欧式距离，切比雪夫距离，马氏距离等计算统计距离。
在本申请中，术语“统计值”可以是指根据样本的数据值计算的分析数值。例如，本申请的统计值可以是指平均值、方差、标准差、中位数值、众数值等。本领域技术人员根据实际情况选择一个或多个统计值用于分析数据。
在本申请中，术语“概率分布”通常是指随机变量取值的分布规律。例如，根据随机变量所属类型的不同，概率分布可以取不同的表现形式。例如，正态分布可以作为一种随机变量的概率分布。
在本申请中，术语“平滑化”通常是指使一个以上的本申请所述的差值之间的偏差减小的数据处理的方法。例如，可以是指将散点数据拟合到平滑的线的方法。例如，可以通过局部加权回归的方法分析和进行平滑化处理。例如，平滑化处理后，可以通过消除某一变量(例如GC含量)对于样本测序数据的固有影响，而消除或减弱该变量(例如GC含量))对于样本测序数据造成的偏差。例如，所述平滑化处理可以包括获得一定数量的本申请所述差值的平均值。例如，所述平滑化处理可以包括根据一定的间隔长度，选择不同长度所对应的数据值，计算不同数据值的差值。例如，所述平滑化处理可以包括将一定长度范围内，所述差值的累加值再除以间隔长度以获得比值。例如，所述比值可以被认为是该长度范围的所述差值的平均差值。
在本申请中，术语“回归”通常是指变量之间关系的统计分析方法。例如，本申请通过回归分析，可以得出样本测序数据与某一变量(例如GC含量)的线性或非线性关系。例如，可以通过局部加权回归得到样本的测序数据与某一变量(例如GC含量)的关系，并通过这一关系调整/矫正该样本的测序数据。例如，本申请的矫正可以是指根据样本的测序数据与某一变量的关系，对该样本的测序数据进行处理以消除或减弱该变量对该样本的测序数据造成的偏差。
在本申请中，术语“局部加权回归”通常是指在输入变量和目标变量的回归分析中，在局部引入权重的回归分析方法。例如，局部加权回归可以通过(loess(X～Y))的算法根据Y对X进行局部加权回归分析和处理。
在本申请中，术语“降噪化”通常是指对于数据中噪声数据的去除或减少。例如，根据噪声数据一般表现为高频信号，可以通过变换分析、主成分分析算法、奇异值分解和/或高斯滤波等方法对含噪音的数据提取有用信号进行降噪化处理。
在本申请中，术语“聚类分析”通常是指把相似的对象通过分类的方法分成不同的组别，使得在同一个组别中的成员对象都有相似的一些属性。
在本申请中，术语“K均值聚类”通常是指一种聚类分析的方法。例如，通过K均值聚类可以根据K个聚类中心将一组数据分类为若干个(K个)类别的聚类分析的方法，每个数据距离其最近聚类中心的距离和最小。
在本申请中，术语“变换分析”通常是指一种分析数据的方法。例如，变换分析可以通过将数据的原始分布变换为容易求解或处理的变换域的分布，对数据进行分析和用于进一步的处理。例如，变换分析可以包含离散小波变换。
在本申请中，术语“离散小波变换”通常是指对基本小波的尺度和平移进行离散化。例如，离散小波变换可以作为一种降噪化的方法。
在本申请中，术语“标准化”或“归一化”通常是指一种对数据变换的方式。例如，标准化可以是指将不同组的数据变换到某一固定范围的处理。例如，标准化可以是指将不同组的数据变换到同一中位值的处理。例如，本申请的标准化可以是指将不同样本的测序数据变换为的中位值相近水平的数据的处理方式。
在本申请中，术语“显著性检验”通常是指判断样本与假设分布之间的差异是否显著的方式。例如，通过显著性检验可以判断待测样本的拷贝数变异是否属于显著的差异。
在本申请中，术语“正态性概率分布”通常是指随机变量的概率分布。例如，通过正态性概率分布以及正态性概率分布密度函数可以确定一个随机变量发生的概率。例如，可以基于参考样本组的测序数据，通过正态性概率分布，确认待测样本的目标区间拷贝数变异的存在的概率。
在本申请中，术语“格拉布斯检验法”通常是指判断和/或筛除异常值的方法。例如，通过判断某一数值是否符合整体的分布范围，可以确定该数值是否属于异常值。
在本申请中，术语“T检验”通常是指一种有学生t分布的统计假设检验的方式。例如，通过T检验可以确认待测样本的某一目标基因的拷贝数变异具有显著性。
在本申请中，术语“包含”通常是指包括明确指定的特征，但不排除其他要素。
在本申请中，术语“约”通常是指在指定数值以上或以下0.5％-10％的范围内变动，例如在指定数值以上或以下0.5％、1％、1.5％、2％、2.5％、3％、3.5％、4％、4.5％、5％、5.5％、6％、6.5％、7％、7.5％、8％、8.5％、9％、9.5％、或10％的范围内变动。
发明详述
一方面，本申请提供一种拷贝数状态的分析方法
一方面，本申请提供一种拷贝数状态的分析方法，可以包含获取待测样本组的测序数据；确定待测样本中的目标基因；根据所述待测样本组的测序数据确定所述待测样本中的目标基因的拷贝数状态。
一方面，本申请提供一种拷贝数状态的分析方法，可以包含以下步骤：
(S1)将所述目标基因所在区域划分为若干个窗口区域，获取所述待测样本组中的对照窗口区域的测序数据；
(S2)基于所述对照窗口区域的测序数据，确定所述待测样本的目标基因的拷贝数状态。
一方面，本申请提供一种拷贝数状态的分析方法，可以包含以下步骤：
(S1)将所述目标基因所在区域划分为若干个窗口区域，获取所述待测样本组中的对照窗口区域的测序数据；
(S2)基于所述对照窗口区域的测序数据，确定所述待测样本的目标基因的拷贝数状态，将所述质量合格的样品的窗口区域按照所述覆盖波动水平从低到高排序，所述对照窗口区域可以包含覆盖波动水平前4个或更多所述窗口，可以基于所述质量合格的样品的窗口区域的测序数据的绝对离差中位数与中位数的比值，确定所述覆盖波动水平，或者所述对照窗口区域的所有所述质量合格的样品的所述测序数据的绝对离差中位数与中位数的比值可以为约0.15或更小。
一方面，本申请提供一种拷贝数状态的分析方法，可以包含以下步骤：
步骤(S1-1)：获取所述待测样本组中所有样本的窗口区域的测序数据；步骤(S1-2)：获取所述待测样本组中质量合格的样本，所述质量合格的样本可以包含平均测序深度、最小测序深度和/或覆盖均一度合格的样本；步骤(S1-3)：使所述待测样本组中所有样本的窗口区域的所述测序数据标准化；
(S2)基于所述对照窗口区域的测序数据，确定所述待测样本的目标基因的拷贝数状态，将所述质量合格的样品的窗口区域按照所述覆盖波动水平从低到高排序，所述对照窗口区域可以包含覆盖波动水平前4个或更多所述窗口，可以基于所述质量合格的样品的窗口区域的测序数据的绝对离差中位数与中位数的比值，确定所述覆盖波动水平，或者所述对照窗口区域的所有所述质量合格的样品的所述测序数据的绝对离差中位数与中位数的比值可以为约0.15或更小；步骤(S2-1)：基于所述对照窗口区域的测序数据，确定归一化系数；步骤(S2-2)：基于所述归一化系数，确定待测样本的每一个窗口区域的拷贝数；步骤(S2-3)：基于待测样本的每一个窗口区域的测序数据以及相应窗口区域的待测样本组中其它样本的测序数据，确定待测样本的拷贝数变异显著性。
例如，所述测序数据可以包含测序深度。例如，所述拷贝数状态可以包含拷贝数扩增和/或缺失。例如，所述拷贝数状态可以包含外显子拷贝数状态。
例如，所述待测样本组可以包含约10个或更多的样本。例如，所述待测样本组可以包含约10个或更多、约12个或更多、约15个或更多、约20个或更多、约25个或更多、约50个或更多、或约100个或更多的样本。例如，本申请可以不要求同一批次有较多的样本数量。例如，所述待测样本组可以包含约10个或更少、约12个或更少、约15个或更少、约20个或更少、约25个或更少、或约50个或更少的样本。例如，本申请的拷贝数状态分析方法，可以对待测样本的拷贝数变异水平具有较高的容忍度。例如，包含约30％拷贝数变异的样本， 可以通过本申请的分析方法进行评估。例如，包含10％或更低、15％或更低、20％或更低、25％或更低、或30％或更低拷贝数变异的样本，可以通过本申请的分析方法进行评估。例如，本申请的样本来源可以是任意的含有核酸的样本，例如组织、血液、唾液、胸腔积液、腹腔积液、脑脊液等。
例如，本申请方法所述步骤(S1)还可以包含步骤(S1-1)：获取所述待测样本组中所有样本的窗口区域的测序数据。例如，本申请的基因测序可以包含任选的高通量测序方法或模块、装置。例如，测序可以选自下组：Solexa测序技术、454测序技术、SOLiD测序技术、Complete Genomics测序方法和半导体(Ion Torrent)测序技术及其对应的装置。
例如，本申请方法所述步骤(S1-1)可以包含通过滑窗法将所述目标基因所在区域划分为所述窗口区域。例如，所述划窗法的步长可以为约24个碱基。例如，所述窗口区域的长度可以为约120个碱基。
例如，本申请方法所述步骤(S1-1)可以包含获取每一个所述窗口区域去除重复测序片段后的平均测序深度。
例如，本申请方法所述步骤(S1)还可以包含步骤(S1-2)：获取所述待测样本组中质量合格的样本，所述质量合格的样本可以包含平均测序深度、最小测序深度和/或覆盖均一度合格的样本。例如，所述平均测序深度合格的样本包含平均测序深度可以为约100x或以上的样本。例如，所述最小测序深度合格的样本包含最小测序深度可以为约30x或以上的样本。例如，质量合格的各个阈值可以根据测序情况进行调整。
例如，所述覆盖均一度可以与所述样本每个碱基的测序深度有关。例如，所述覆盖均一度可以通过大于或等于样本平均测序深度的20％的碱基数量占样本中总碱基数量的百分比计算。例如，所述覆盖均一度合格的样本可以包含覆盖均一度为约90％或以上的样本。例如，所述覆盖均一度合格的样本可以包含覆盖均一度为约90％或以上的样本。例如，所述覆盖均一度合格的样本可以包含覆盖均一度为约90％或以上、约92％或以上、约95％或以上、约97％或以上、或约99％或以上的样本。
例如，所述待测样本组中质量合格的样本数量可以为10个或更多。
例如，本申请方法所述步骤(S1)还可以包含步骤(S1-3)：使所述待测样本组中所有样本的窗口区域的所述测序数据标准化。
例如，所述标准化可以包含基于所述样本所有窗口区域的平均测序深度对所述样本的每一个窗口区域的所述测序数据标准化、和/或基于所述样本的每一个窗口区域的GC含量对所述样本的每一个窗口区域的所述测序数据标准化。
例如，所述标准化可以包含使所述样本的每一个窗口区域上所述测序数据除以所述样本的所有窗口区域上所述测序数据的总和，再乘以因子。例如，所述因子可以根据所有区间的大小进行设置。例如，所述因子可以任选为1E+07。例如，所述因子可以任选为1E+100、1E+20、1E+10、1E+09、1E+08、1E+07、1E+06、1E+05、1E+04、1E+03、或1E+02。
例如，所述标准化可以包含基于GC含量，通过回归的方法使所述样本的每一个窗口区域的所述测序数据标准化。例如，所述回归可以包含局部加权回归。
例如，所述对照窗口区域可以包含覆盖波动水平低的窗口区域。
例如，可以基于所述质量合格的样品的窗口区域的测序数据统计值，确定所述覆盖波动水平。例如，可以基于所述质量合格的样品的窗口区域的测序数据的离差，确定所述覆盖波动水平。例如，可以基于所述质量合格的样品的窗口区域的测序数据的绝对离差中位数和/或中位数，确定所述覆盖波动水平。例如，可以基于所述质量合格的样品的窗口区域的测序数据的绝对离差中位数与中位数的比值，确定所述覆盖波动水平。
例如，将所述质量合格的样品的窗口区域按照所述覆盖波动水平从低到高排序，所述对照窗口区域可以包含覆盖波动水平前2个或更多所述窗口。
例如，将所述质量合格的样品的窗口区域按照所述覆盖波动水平从低到高排序，所述对照窗口区域可以包含覆盖波动水平前4个或更多所述窗口。
例如，所述对照窗口区域的所有所述质量合格的样品的所述测序数据的绝对离差中位数与中位数的比值可以为约0.15或更小。例如，所述对照窗口区域的所有所述质量合格的样品的所述测序数据的绝对离差中位数与中位数的比值可以为约0.15或更小、约0.14或更小、约0.13或更小、约0.12或更小、约0.11或更小、约0.10或更小、约0.09或更小、约0.08或更小、约0.07或更小、约0.06或更小、或约0.05或更小。例如，所述对照窗口区域的所有所述质量合格的样品的所述测序数据的绝对离差中位数与中位数的比值可以为约0.05至约0.15、约0.07至约0.15、约0.10至约0.15、约0.12至约0.15、约0.05至约0.12、约0.07至约0.12、约0.10至约0.12、约0.05至约0.10、约0.07至约0.10、或约0.05至约0.07。
例如，本申请所述步骤(S2)还可以包含步骤(S2-1)：基于所述对照窗口区域的测序数据，确定归一化系数。
例如，可以通过计算所述对照窗口区域的所有所述质量合格的样品的测序数据平均值，确定所述归一化系数。
例如，在确定所述归一化系数前，可以筛除对照窗口区域的异常样本的覆盖水平值。例如，所述异常覆盖水平值可以为每个所述对照窗口区域通过异常值分析方法判断为异常样本 的覆盖水平值。例如，所述异常值分析方法可以包含格拉布斯检验法(Grubbs test)。例如，每个窗口可以包含了批次内质量合格样本在该窗口的覆盖水平值，然后可以采用格拉布斯检验法检验判断这些覆盖水平值是否含有异常值，如果含有，可以将异常值去除。然后对剩余的覆盖水平值，可以继续重复采用格拉布斯检验法检验判断是否有异常，直至无异常值出现。例如，也可以当剩余覆盖水平值数量少于质量合格样本数的60％或更少、50％或更少、或40％或更少时，停止去除异常值，可以将剩余数值都用于确定所述归一化系数。
例如，筛除所述异常样本后剩余的样本数量可以为筛除前样本数量的40％或更多、70％或更多、80％或更多、90％或更多、95％或更多、或99％或更多。
例如，本申请所述步骤(S2)还可以包含步骤(S2-2)：基于所述归一化系数，确定待测样本的每一个窗口区域的拷贝数。
例如，本申请所述步骤(S2-2)可以包含基于所述归一化系数，通过使所述待测样本的每一个窗口区域的所述测序数据归一化的方法，确定待测样本的每一个窗口区域的所述拷贝数。
例如，所述归一化方法可以包含将所述窗口区域的待测样本的测序数据除以所述窗口区域的归一化系数，乘以倍性。例如，对于男性X染色体来说，所述倍性可以为1。当受试者为多倍体是，所述倍性可以根据具体情况进行调整。例如，所述倍性可以为2。
例如，本申请所述步骤(S2)还可以包含步骤(S2-3)：基于待测样本的每一个窗口区域的测序数据以及相应窗口区域的待测样本组中其它样本的测序数据，确定待测样本的拷贝数变异显著性。
例如，本申请所述步骤(S2-3)可以包含基于待测样本的每一个窗口区域的所述拷贝数，确定拷贝数变异候选区域。
例如，可以通过区域分割的方法，确定所述拷贝数变异候选区域。例如，所述区域分割可以包含通过循环二元分割算法，确定所述拷贝数变异候选区域的前后端点。
例如，本申请所述步骤(S2-3)可以包含基于待测样本的所述拷贝数变异候选区域中窗口区域的测序数据以及相应窗口区域的待测样本组中其它样本的测序数据，确定拷贝数变异的显著性。例如，可以通过显著性检验的方法，确定所述拷贝数变异的显著性。例如，所述显著性检验可以包含T检验。
另一方面，本申请还提供了一种拷贝数状态分析装置，可以包含以下模块：接收模块，用于获取待测样本组的测序数据；确定模块，用于确定待测样本中的目标基因；判断模块，用于根据所述待测样本组的测序数据确定所述待测样本中的目标基因的拷贝数状态。
例如，本申请的拷贝数状态分析装置，其中的模块可以被配置为基于存储在所述储存介 质中的程序执行以实现本申请所述的拷贝数状态分析方法。
拷贝数状态分析方法
一方面，本申请提供一种拷贝数状态分析方法，可以包含以下步骤：(S1)获取待测样本的测序数据和/或多个参考样本的测序数据；(S2)将所述参考样本分为两个或以上参考样本组；(S3)确定与所述待测样本最接近的参考样本组；(S4)基于所述与待测样本最接近的参考样本组的测序数据，确定所述待测样本的目标基因的拷贝数状态。
一方面，本申请提供一种拷贝数状态分析装置，可以包含以下模块：(M1)接收模块，用于获取待测样本的测序数据和/或多个参考样本的测序数据；(M2)处理模块，用于将所述参考样本分为两个或以上参考样本组；(M3)计算模块，用于确定与所述待测样本最接近的参考样本组；(M4)判断模块，用于基于所述与待测样本最接近的参考样本组的测序数据，确定所述待测样本的目标基因的拷贝数状态。
一方面，本申请提供一种拷贝数状态分析方法，可以包含以下步骤：
(S1)获取待测样本的测序数据和/或多个参考样本的测序数据；步骤(S1-1)：通过基因测序获取所述待测样本和/或所述参考样本的所述测序数据；步骤(S1-2)：矫正所述待测样本和/或参考样本的所述测序数据；
(S2)将所述参考样本分为两个或以上参考样本组；步骤(S2-1)：使所述参考样本分组；步骤(S2-2)：确认所述参考样本组的所述测序数据的统计值；
(S3)确定与所述待测样本最接近的参考样本组；
(S4)基于所述与待测样本最接近的参考样本组的测序数据，确定所述待测样本的目标基因的拷贝数状态步骤(S4-1)：确定所述待测样本的目标基因在所述目标区间i上的拷贝数CN i；步骤(S4-3)：确定所述待测样本在目标基因上的拷贝数CN g；步骤(S4-4)：确定在所述目标区间上待测样本的拷贝数变异的存在的概率；步骤(S4-5)：确定在所述目标基因上所述待测样本的显著性拷贝数扩增或缺失的存在的比例sigRatio；步骤(S4-6)：确定在所述目标基因上所述待测样本的拷贝数变异的存在的统计检验参数；通过以下内容确定所述待测样本的目标基因的拷贝数状态：当CN g≥CN thA，sigRatio≥sigRatio th，且p ttest≤p th时，确认所述待测样本的目标基因发生拷贝数扩增；当CN g≤CN thD，sigRatio≥sigRatio th，且p ttest≤p th时，确认所述待测样本的目标基因发生拷贝数缺失；当CN thA＜CN g＜CN thD，或sigRatio＜sigRatio th，或p ttest＞p th时，确认所述待测样本的目标基因拷贝数正常，其中CN thA，CN thD，sigRatio th，和p th各自独立地为阈值。
一方面，本申请提供一种拷贝数状态分析装置，可以包含实现本申请拷贝数状态分析方 法的模块。
一方面，本申请提供一种拷贝数状态分析方法，可以包含以下步骤：
(S1)获取待测样本的测序数据和/或多个参考样本的测序数据；步骤(S1-1)：通过基因测序获取所述待测样本和/或所述参考样本的所述测序数据；步骤(S1-2)：矫正所述待测样本和/或参考样本的所述测序数据；
(S2)将所述参考样本分为两个或以上参考样本组；步骤(S2-1)：使所述参考样本分组；步骤(S2-2)：确认所述参考样本组的所述测序数据的统计值；
(S3)确定与所述待测样本最接近的参考样本组；
(S4)基于所述与待测样本最接近的参考样本组的测序数据，确定所述待测样本的目标基因的拷贝数状态步骤(S4-1)：确定所述待测样本的目标基因在所述目标区间i上的拷贝数CN i；步骤(S4-2)：使所述待测样本的所述目标区间上的拷贝数降噪化；步骤(S4-3)：确定所述待测样本在目标基因上的拷贝数CN g；步骤(S4-4)：确定在所述目标区间上待测样本的拷贝数变异的存在的概率；步骤(S4-5)：确定在所述目标基因上所述待测样本的显著性拷贝数扩增或缺失的存在的比例sigRatio；步骤(S4-6)：确定在所述目标基因上所述待测样本的拷贝数变异的存在的统计检验参数；通过以下内容确定所述待测样本的目标基因的拷贝数状态：当CN g≥CN thA，sigRatio≥sigRatio th，且p ttest≤p th时，确认所述待测样本的目标基因发生拷贝数扩增；当CN g≤CN thD，sigRatio≥sigRatio th，且p ttest≤p th时，确认所述待测样本的目标基因发生拷贝数缺失；当CN thA＜CN g＜CN thD，或sigRatio＜sigRatio th，或p ttest＞p th时，确认所述待测样本的目标基因拷贝数正常，其中CN thA，CN thD，sigRatio th，和p th各自独立地为阈值。
一方面，本申请提供一种拷贝数状态分析装置，可以包含实现本申请拷贝数状态分析方法的模块。
例如，本申请的测序数据可以包含测序读长计数。例如，本申请的测序数据可以包含目标基因或目标区间上测序读长(read)的数量。
例如，本申请的所述步骤(S1)或模块(M1)可以包含步骤(S1-1)或模块(M1-1)：通过基因测序获取所述待测样本和/或所述参考样本的所述测序数据。例如，所述基因测序可以包含二代基因测序(NGS)。例如，本申请的基因测序可以包含任选的高通量测序方法或模块、装置。例如，测序可以选自下组：Solexa测序技术、454测序技术、SOLiD测序技术、Complete Genomics测序方法和半导体(Ion Torrent)测序技术及其对应的装置。
例如，所述待测样本和/或所述参考样本可以包含含有核酸的样本。例如，本申请的样 本来源可以是任意的含有核酸的样本，例如组织、血液、唾液、胸腔积液、腹腔积液、脑脊液等。
例如，所述步骤(S1-1)或模块(M1-1)可以包含获取所述待测样本和/或参考样本在目标区间中每个碱基的所述测序数据。
例如，所述目标区间可以包含靶向测序面板序列对应的区间。例如，所述目标区间的长度可以为约20至约500个碱基量。例如，所述目标区间的长度可以为约20至约500个碱基量、约50至约500个碱基量、约100至约500个碱基量、约200至约500个碱基量、约20至约200个碱基量、约50至约200个碱基量、约100至约200个碱基量、约20至约100个碱基量、约50至约100个碱基量、或约20至约50个碱基量。
例如，所述目标区间的数量可以至少为约100个。例如，所述目标区间的数量可以至少为约100个、至少为约200个、至少为约500个、至少为约1000个、或至少为约10000个。
例如，所述步骤(S1)或模块(M1)可以包含步骤(S1-2)或模块(M1-2)：矫正所述待测样本和/或参考样本的所述测序数据。例如，本申请的方法也可以不包含步骤(S1-2)或仅包含步骤(S1-2)的部分步骤。例如，本申请的装置也可以不包含模块(M1-2)或仅包含模块(M1-2)的部分模块。例如，本申请的方法步骤(S1-2)的以下步骤顺序可以是任意的：使所述待测样本和/或参考样本的所述测序数据标准化、使所述待测样本和/或参考样本的所述测序数据平滑化和筛除GC含量异常的所述目标区间。例如，本申请的装置的模块(M1-2)的以下模块顺序可以是任意的：使所述待测样本和/或参考样本的所述测序数据标准化、使所述待测样本和/或参考样本的所述测序数据平滑化和筛除GC含量异常的所述目标区间。
例如，所述步骤(S1-2)或模块(M1-2)可以包含：使所述待测样本和/或参考样本的所述测序数据标准化或均一化。例如，所述标准化或均一化可以包含使所述目标区间上所述测序数据除以所述目标区间对应的样本的所有目标区间上所述测序数据的总和，再乘以因子。例如，所述因子可以根据所有区间的大小进行设置。例如，所述因子可以任选为1E+07。例如，所述因子可以任选为1E+100、1E+20、1E+10、1E+09、1E+08、1E+07、1E+06、1E+05、1E+04、1E+03、或1E+02。
例如，所述步骤(S1-2)或模块(M1-2)可以包含：使所述待测样本和/或参考样本的所述测序数据平滑化。例如，所述平滑化可以包含基于测序偏差量，通过回归的方法或记载该程序的装置使所述待测样本和/或参考样本的所述测序数据平滑化。例如，所述回归可以 包含局部加权回归。
例如，所述测序偏差量可以包含所述目标区间上覆盖的探针数。
例如，所述测序偏差量可以包含所述目标区间的GC含量。
例如，所述步骤(S1-2)或模块(M1-2)可以任选地包含：筛除GC含量异常的所述目标区间。
例如，所述GC含量异常的所述目标区间可以包含GC含量为约25％或更低的所述目标区间和/或GC含量为约75％或更高的所述目标区间。
例如，所述步骤(S2)或模块(M2)可以包含步骤(S2-1)或模块(M2-1)：使所述参考样本分组。例如，所述参考样本可以源自所述待测样本，也可以源自所述待测样本以外的样本。例如，可以将待测样本中的一部分划分出去作为参考样本。例如，所述参考样本可以进行更新，例如每次分析新样品的测序数据后，可以将该新样品的数据添加到已有的数据库中，并进行数据库再次建立处理。
例如，所述分组可以包含基于所述目标区间的所述测序数据使所述参考样本分组。
例如，所述分组可以包含通过聚类分析的方法或记载该程序的装置使所述参考样本分组。
例如，所述聚类分析的方法可以包含K均值聚类、层次聚类、密度聚类、网格聚类、概率模型聚类、或神经网络模型聚类等。例如，所述聚类分析的方法或记载该程序的装置可以包含任意的聚类、分类和分组的方法或记载该程序的装置。
例如，所述参考样本的数量可以为约30个或更多。例如，所述参考样本的数量可以为约50个或更多。例如所述参考样本的数量可以为约30个或更多、约40个或更多、约50个或更多、约60个或更多、约70个或更多、约80个或更多、约90个或更多、约100个或更多、约200个或更多、约300个或更多、约400个或更多、约500个或更多、或约1000个或更多。
例如，所述分组可以包含分为约2个组或更多。例如，当全部参考样本的测序数据较为近似时，可以仅分为1组。例如，所述分组包含分为约2个或更多、约3个或更多、约4个或更多、约5个或更多、约6或更多、约7个或更多、约8个或更多、约9个或更多、约10个或更多、约20个或更多、约30个或更多、约40个或更多、约50个或更多、约60个或更多、约70个或更多、约80个或更多、约90个或更多、或约100个或更多。
例如，每组中所述参考样本的数量可以为约30个或更多。例如，每组中的参考样本的数量可以为约30个或更多、约40个或更多、约50个或更多、约60个或更多、约70个或 更多、约80个或更多、约90个或更多、约100个或更多、约200个或更多、约300个或更多、约400个或更多、约500个或更多、或约1000个或更多。
例如，所述步骤(S2)或模块(M2)可以包含步骤(S2-2)或模块(M2-2)：确认所述参考样本组的所述测序数据的统计值。例如，所述参考样本组的测序数据的统计值可以作为各个候选基线。例如，所述确认统计值可以包含计算在所述目标区间上每组中所述参考样本的均值和/或标准差。
例如，所述步骤(S2)或模块(M2)可以包含步骤(S2-3)或模块(M2-3)：筛除所述参考样本中不合格目标区间。例如，所述不合格目标区间可以包含捕获效率低区间和/或不稳定区间。
例如，所述不合格目标区间可以包含测序读长计数为约5或更低的目标区间。例如，所述不合格目标区间可以包含测序读长计数为约30或更低、约20或更低、约10或更低、约5或更低、约4或更低、约3或更低、约2或更低、约1或更低、或约0或更低的目标区间。
例如，所述不合格目标区间可以包含变异系数为约0.8或更高的目标区间，所述变异系数为所述目标区间上每组中所述参考样本的所述测序数据的标准差与均值的比值。例如，所述不合格目标区间可以包含变异系数为约0.8或更高、约0.9或更高或约1.0或更高。例如，捕获效率低区间和/或不稳定区间的各个阈值可以根据测序情况进行调整。
例如，所述步骤(S3)或模块(M3)可以包含确认所述待测样本与所述参考样本组的相似度。
例如，所述确认相似度可以包含基于在所述目标区间上所述参考样本组与所述待测样本的所述测序数据，确认所述参考样本组与所述待测样本的分布相似程度。
例如，所述相似度可以包含在所述目标区间上所述参考样本组与所述待测样本的所述测序数据的近似程度。
例如，所述确认相似度可以包含通过计算统计距离、相似度算法的方法或记载该程序的装置，确认所述参考样本组与所述待测样本的分布相似程度。例如，所述统计距离可以包含所述目标区间上所述参考样本组与所述待测样本的所述测序数据的差值的统计值。例如，所述统计距离可以包含所述目标区间上所述参考样本组与所述待测样本的所述测序数据的差值的绝对值的统计值。例如，所述统计距离可以包含所述目标区间上所述参考样本组与所述待测样本的所述测序数据的差值的绝对值的p次方的统计值，所述p为1或更大。例如，所述统计值可以包含求和值。
例如，所述相似度高可以包含在所述目标区间上所述参考样本组与所述待测样本的所述统计距离短。
例如，所述统计距离可以包含闵可夫斯基距离。例如，所述统计距离可以包含欧式距离、曼哈顿距离、切比雪夫距离、闵可夫斯基距离(当p＝1时，为曼哈顿距离；当p＝2时，为欧式距离；当p接近于无穷大是，为切比雪夫距离)等。例如，所述相似度算法可以包含余弦相似度、皮尔森相关系数、斯皮尔曼相关系数、对数似然相似度、交叉熵等。
例如，所述待测样本的目标基因的拷贝数状态可以包含述待测样本的目标基因的拷贝数变异的存在和/或数量。
例如，所述拷贝数变异可以包含拷贝数的扩增和/或缺失。
例如，所述步骤(S4)或模块(M4)可以包含步骤(S4-1)或模块(M4-1)：确定所述待测样本的目标基因在所述目标区间i上的拷贝数CN i。
例如，所述确定所述CN i可以包含，使所述待测样本的目标基因所述目标区间上的测序数据的均值除以所述与待测样本最接近的参考样本组在相应目标区间上的测序数据的均值，再乘以倍性，得到所述CN i。
例如，所述倍性可以为2。例如，对于男性X染色体来说，所述倍性可以为1。当受试者为多倍体是，所述倍性可以根据具体情况进行调整。
例如，所述步骤(S4)或模块(M4)可以包含步骤(S4-2)或模块(M4-2)：使所述待测样本的所述目标区间上的拷贝数降噪化。
例如，所述降噪化可以包含通过变换分析、主成分分析算法、奇异值分解和/或高斯滤波的方法或记载该程序的装置使所述待测样本的所述目标区间上的拷贝数降噪化。
例如，所述降噪化可以包含通过离散小波变换的方法或记载该程序的装置使所述待测样本的所述目标区间上的拷贝数降噪化。例如，所述降噪化可以包含通过变换分析、主成分分析算法、奇异值分解和/或高斯滤波等方法或记载该程序的装置使所述待测样本的所述目标区间上的拷贝数降噪化。
例如，所述步骤(S4)或模块(M4)可以包含步骤(S4-3)或模块(M4-3)：确定所述待测样本在目标基因上的拷贝数CN g。
例如，所述目标基因可以包含待确定的拷贝数变异发生的基因。
例如，所述目标基因可以包含选自以下组基因：ABL1、ABL2、ABRAXAS1、ACVR1、ACVR1B、AKT1、AKT2、AKT3、ALK、ALOX12B、AMER1、APC、AR、ARAF、ARFRP1、ARID1A、ARID1B、ARID2、ARID5B、ASXL1、ASXL2、ASXL3、 ATG5、ATM、ATR、ATRX、AURKA、AURKB、AXIN1、AXIN2、AXL、B2M、BAP1、BARD1、BBC3、BCL10、BCL2、BCL2L1、BCL2L11、BCL2L2、BCL6、BCOR、BCORL1、BIRC3、BLM、BMPR1A、BRAF、BRCA1、BRCA2、BRD4、BRD7、BRINP3、BRIP1、BTG1、BTG2、BTK、CALR、CARD11、CASP8、CBFB、CBL、CCND1、CCND2、CCND3、CCNE1、CD274、CD28、CD58、CD74、CD79A、CD79B、CDC73、CDH1、CDH18、CDK12、CDK4、CDK6、CDK8、CDKN1A、CDKN1B、CDKN1C、CDKN2A、CDKN2B、CDKN2C、CEBPA、CENPA、CHD1、CHD2、CHD4、CHD8、CHEK1、CHEK2、CIC、CIITA、CREBBP、CRKL、CRLF2、CRYBG1、CSF1R、CSF3R、CSMD1、CSMD3、CTCF、CTLA4、CTNNA1、CTNNB1、CUL3、CUL4A、CXCR4、CYLD、CYP17A1、CYP2D6、DAXX、DCUN1D1、DDR1、DDR2、DDX3X、DICER1、DIS3、DNAJB1、DNMT1、DNMT3A、DNMT3B、DOT1L、DPYD、DTX1、DUSP22、EED、EGFR、EIF1AX、EIF4E、EMSY、EP300、EPCAM、EPHA2、EPHA3、EPHA5、EPHA7、EPHB1、EPHB4、ERBB2、ERBB3、ERBB4、ERCC1、ERCC2、ERCC3、ERCC4、ERCC5、ERG、ERRFI1、ESR1、ETV4、ETV5、ETV6、EWSR1、EZH2、EZR、FANCA、FANCC、FANCD2、FANCE、FANCF、FANCG、FANCI、FANCL、FANCM、FAS、FAT1、FAT3、FBXW7、FGF10、FGF12、FGF14、FGF19、FGF23、FGF3、FGF4、FGF6、FGF7、FGFR1、FGFR2、FGFR3、FGFR4、FH、FLCN、FLT1、FLT3、FLT4、FOXA1、FOXL2、FOXO1、FOXO3、FOXP1、FRS2、FUBP1、FYN、GABRA6、GALNT12、GATA1、GATA2、GATA3、GATA4、GATA6、GEN1、GID4、GLI1、GNA11、GNA13、GNAQ、GNAS、GPS2、GREM1、GRIN2A、GRM3、GSK3B、H3F3A、H3F3B、H3F3C、HDAC1、HDAC2、HGF、HIST1H1C、HIST1H2BD、HIST1H3A、HIST1H3B、HIST1H3C、HIST1H3D、HIST1H3E、HIST1H3G、HIST1H3H、HIST1H3I、HIST1H3J、HIST2H3D、HIST3H3、HLA-A、HLA-B、HLA-C、HNF1A、HOXB13、HRAS、HSD3B1、HSP90AA1、ICOSLG、ID3、IDH1、IDH2、IFNGR1、IGF1、IGF1R、IGF2、IGHD、IGHJ、IGHV、IKBKE、IKZF1、IL10、IL7R、INHA、INHBA、INPP4A、INPP4B、INSR、IRF2、IRF4、IRS1、IRS2、ITK、ITPKB、JAK1、JAK2、JAK3、JUN、KAT6A、KDM5A、KDM5C、KDM6A、KDR、KEAP1、KEL、KIR2DL4、KIR3DL2、KIT、KLF4、KLHL6、KLRC1、KLRC2、KLRK1、KMT2A、KMT2C、KMT2D、KRAS、LATS1、LATS2、LMO1、LRP1B、LTK、LYN、MAF、MAGI2、MALT1、MAP2K1、MAP2K2、MAP2K4、MAP3K1、MAP3K13、MAP3K14、 MAPK1、MAPK3、MAX、MCL1、MDC1、MDM2、MDM4、MED12、MEF2B、MEN1、MERTK、MET、MFHAS1、MGA、MIR21、MITF、MKNK1、MLH1、MLH3、MPL、MRE11、MSH2、MSH3、MSH6、MST1、MST1R、MTAP、MTOR、MUTYH、MYC、MYCL、MYCN、MYD88、MYOD1、NAV3、NBN、NCOA3、NCOR1、NCOR2、NEGR1、NF1、NF2、NFE2L2、NFKBIA、NKX2-1、NKX3-1、NOTCH1、NOTCH2、NOTCH3、NOTCH4、NPM1、NRAS、NRG1、NSD1、NSD2、NSD3、NT5C2、NTHL1、NTRK1、NTRK2、NTRK3、NUP93、NUTM1、P2RY8、PAK1、PAK3、PAK5、PALB2、PALLD、PARP1、PARP2、PARP3、PAX5、PBRM1、PCDH11X、PDCD1、PDCD1LG2、PDGFRA、PDGFRB、PDK1、PGR、PHOX2B、PIK3C2B、PIK3C2G、PIK3C3、PIK3CA、PIK3CB、PIK3CD、PIK3CG、PIK3R1、PIK3R2、PIK3R3、PIM1、PLCG2、PLK2、PMS1、PMS2、PNRC1、POLD1、POLE、POM121L12、PPARG、PPM1D、PPP2R1A、PPP2R2A、PPP6C、PRDM1、PREX2、PRKAR1A、PRKCI、PRKDC、PRKN、PTCH1、PTEN、PTPN11、PTPRD、PTPRO、PTPRS、PTPRT、QKI、RAB35、RAC1、RAD21、RAD50、RAD51、RAD51B、RAD51C、RAD51D、RAD52、RAD54L、RAF1、RARA、RASA1、RB1、RBM10、RECQL4、REL、RET、RHEB、RHOA、RICTOR、RIT1、RNF43、ROS1、RPA1、RPS6KA4、RPS6KB2、RPTOR、RSPO2、RUNX1、RUNX1T1、SDC4、SDHA、SDHAF2、SDHB、SDHC、SDHD、SETD2、SF3B1、SGK1、SH2B3、SH2D1A、SHQ1、SLC34A2、SLIT2、SLX4、SMAD2、SMAD3、SMAD4、SMARCA4、SMARCB1、SMARCD1、SMO、SNCAIP、SOCS1、SOX10、SOX17、SOX2、SOX9、SPEN、SPI1、SPOP、SPTA1、SRC、SRSF2、STAG2、STAT3、STAT4、STAT5A、STAT5B、STAT6、STK11、STK40、SUFU、SYK、TAF1、TBX21、TBX3、TCF3、TCF7L2、TEK、TENT5C、TERC、TERT、TET1、TET2、TGFBR1、TGFBR2、TIPARP、TMEM127、TMPRSS2、TNFAIP3、TNFRSF14、TOP1、TOP2A、TP53、TP63、TP73、TRAF2、TRAF3、TRAF7、TRIM58、TRPC5、TSC1、TSC2、TSHR、TYRO3、U2AF1、UGT1A1、VEGFA、VEGFB、VEGFC、VHL、WISP3、WRN、WT1、XIAP、XPO1、XRCC2、XRCC3、YAP1、YES1、ZAP70、ZBTB16、ZBTB2、ZNF217、ZNF703和ZNRF3。例如，所述目标基因可以包含选自以下组基因：ALK(转录本编号可以是NM_004304.4)、ERBB2(转录本编号可以是NM_004448.3)、EGFR(转录本编号可以是NM_005228.3)、FGFR1(转录本编号可以是NM_023110.2)、FGFR2(转录本编号可以是NM_000141.4)、CDK4(转录本编号可以是NM_000075.3)和MET(转录本编号可以是 NM_000245.3)。
例如，本申请中样本选自以下组：组织样本、血液样本、唾液、胸腔积液、腹膜积液和脑脊液。
例如，所述步骤(S4-3)或模块(M4-3)可以包含基于所述待测样本的所述目标基因的外显子的长度以及所述待测样本的所述目标区间i上的拷贝数CN i，确定所述CN g。
例如，所述步骤(S4-3)或模块(M4-3)可以包含基于下式确定所述CN g，
Figure PCTCN2022115447-appb-000001
其中，i可以表示目标区间，j可以表示目标外显子，n可以表示目标外显子j上的目标区间个数，m可以表示目标外显子个数，CN i可以表示目标区间i的拷贝数，Len j可以表示目标外显子j的长度。
例如，所述步骤(S4)或模块(M4)可以包含步骤(S4-4)或模块(M4-4)：确定在所述目标区间上待测样本的拷贝数变异的存在的概率。
例如，所述拷贝数变异的存在的概率可以包含在所述目标区间上所述待测样本发生拷贝数扩增的概率(p a)和/或缺失的概率(p d)。
例如，所述步骤(S4-4)或模块(M4-4)可以包含，基于所述待测样本在所述目标区间i上的测序数据、以及在相应目标区间上所述与待测样本最接近的参考样本组的测序数据的均值与标准差，通过概率分布的方法或记载该程序的装置确认所述拷贝数变异的存在的概率。
例如，所述概率分布可以包含正态性概率分布。例如，所述概率分布可以包含任意常见的概率分布。例如，所述概率分布可以包含任意的离散的概率分布。例如，所述概率分布可以包含任意的连续的概率分布。
例如，所述步骤(S4)或模块(M4)可以包含步骤(S4-5)或模块(M4-5)：确定在所述目标基因上所述待测样本的显著性拷贝数扩增或缺失的存在的比例sigRatio。
例如，所述步骤(S4-5)或模块(M4-5)可以包含，使所述目标基因上发生显著性拷贝数变异的目标区间数量除以所述目标基因上所有目标区间数量，得到所述sigRatio。
例如，所述发生显著性拷贝数变异的目标区间可以包含所述拷贝数变异的比例为约30％或更高的所述目标区间。例如，所述发生显著性拷贝数变异的目标区间可以包含所述拷贝数变异的比例为约30％或更高、约40％或更高、约50％或更高、约60％或更高、约70％或更高、约80％或更高、约90％或更高、约95％或更高、或约95％或更高的所述目标区间。
例如，所述步骤(S4)或模块(M4)可以包含步骤(S4-6)或模块(M4-6)：确定在所述目标基因上所述待测样本的拷贝数变异的存在的统计检验参数。
例如，所述统计检验参数可以包含通过显著性检验确定的p值。
例如，所述显著性检验可以包含T检验。例如，所述显著性检验可以任意的显著性检验方式，以及根据实际情况进行变形的显著性检验方式。
例如，所述步骤(S4-6)或模块(M4-6)可以包含，基于在所述目标基因上所述待测样本的所述目标区间的数量、在所述目标基因上所述待测样本的各个所述目标区间的测序数据、在所述目标基因上所述待测样本的各个所述目标区间的测序数据的标准差、以及在相应目标基因上述与待测样本最接近的参考样本组的目标区间上的测序数据的均值和标准差，通过T检验的方法或记载该程序的装置确认p值p ttest。
例如，所述步骤(S4)或模块(M4)可以通过以下内容确定所述待测样本的目标基因的拷贝数状态：
当CN g≥CN thA，sigRatio≥sigRatio th，且p ttest≤p th时，确认所述待测样本的目标基因发生拷贝数扩增；
当CN g≤CN thD，sigRatio≥sigRatio th，且p ttest≤p th时，确认所述待测样本的目标基因发生拷贝数缺失；
当CN thA＜CN g＜CN thD，或sigRatio＜sigRatio th，或p ttest＞p th时，确认所述待测样本的目标基因拷贝数正常，其中CN thA，CN thD，sigRatio th，和p th各自独立地为阈值。
例如，其中CN thA可以为约2.25至约4。例如，其中CN thA可以为约2.25、约2.50、约2.75、约3.00、约3.25、约3.50、约3.75、或约4.00。
例如，其中CN thD可以为约1.0至约1.75。例如，其中CN thD可以为约0.25、约0.50、约0.75、约1.00、约1.25、约1.50、约1.75。
例如，其中sigRatio th可以为约0.3至约1。例如，其中sigRatio th可以为约0.3、约0.4、约0.5、约0.6、约0.7、约0.8、约0.9、或约1.0。
例如，其中p th可以为约0.05至约0.00001。例如，其中p th可以为约0.05、约0.01、约0.001、约0.0001、约0.00001、约0.000001、或约0.0000001。
数据库建立
一方面，本申请提供一种数据库建立方法，可以包含获取多个参考样本的测序数据，以及将所述参考样本分为两个或以上参考样本组。
例如，所述数据库建立方法可以包含(S1)获取待测样本的测序数据和/或多个参考样 本的测序数据；(S2)将所述参考样本分为两个或以上参考样本组。
一方面，本申请提供一种数据库建立装置，可以包含以下模块：接收模块，用于获取待测样本的测序数据和/或多个参考样本的测序数据；处理模块，用于将所述参考样本分为两个或以上参考样本组。
例如，所述数据库建立装置可以包含(M1)接收模块，用于获取待测样本的测序数据和/或多个参考样本的测序数据；(M2)处理模块，用于将所述参考样本分为两个或以上参考样本组。
一方面，本申请提供一种数据库建立方法，可以包含以下步骤：
(S1)获取待测样本的测序数据和/或多个参考样本的测序数据；步骤(S1-1)：通过基因测序获取所述待测样本和/或所述参考样本的所述测序数据；步骤(S1-2)：矫正所述待测样本和/或参考样本的所述测序数据；
(S2)将所述参考样本分为两个或以上参考样本组；步骤(S2-1)：使所述参考样本分组；步骤(S2-2)：确认所述参考样本组的所述测序数据的统计值。
一方面，本申请提供一种数据库建立装置，可以包含实现本申请数据库建立方法的模块。
一方面，本申请提供一种数据库建立方法，可以包含以下步骤：
(S1)获取待测样本的测序数据和/或多个参考样本的测序数据；步骤(S1-1)：通过基因测序获取所述待测样本和/或所述参考样本的所述测序数据；步骤(S1-2)：矫正所述待测样本和/或参考样本的所述测序数据；
(S2)将所述参考样本分为两个或以上参考样本组；步骤(S2-1)：使所述参考样本分组；步骤(S2-2)：确认所述参考样本组的所述测序数据的统计值。
一方面，本申请提供一种数据库建立装置，可以包含实现本申请数据库建立方法的模块。
另一方面，本申请提供一种数据库建立装置，可以包含以下模块：(M1)接收模块，用于获取待测样本的测序数据和/或多个参考样本的测序数据；(M2)处理模块，用于将所述参考样本分为两个或以上参考样本组。
拷贝数状态分析方法
一方面，本申请根据已有数据库的信息提供一种拷贝数状态分析方法，可以包含从两个或以上参考样本组确定与待测样本最接近的参考样本组，以及基于所述与待测样本最接近的参考样本组的测序数据，确定所述待测样本的目标基因的拷贝数状态。
例如，所述拷贝数状态分析方法可以包含(S3)确定与所述待测样本最接近的参考样本组；(S4)基于所述与待测样本最接近的参考样本组的测序数据，确定所述待测样本的目标 基因的拷贝数状态。
一方面，本申请提供一种拷贝数状态分析装置，可以包含以下模块：计算模块，用于从两个或以上参考样本组确定与所述待测样本最接近的参考样本组；判断模块，用于基于所述与待测样本最接近的参考样本组的测序数据，确定所述待测样本的目标基因的拷贝数状态。
例如，所述拷贝数状态分析装置可以包含(M3)计算模块，用于确定与所述待测样本最接近的参考样本组；(M4)判断模块，用于基于所述与待测样本最接近的参考样本组的测序数据，确定所述待测样本的目标基因的拷贝数状态。
一方面，本申请提供一种拷贝数状态分析方法，可以包含以下步骤：
(S3)确定与所述待测样本最接近的参考样本组；
(S4)基于所述与待测样本最接近的参考样本组的测序数据，确定所述待测样本的目标基因的拷贝数状态步骤(S4-1)：确定所述待测样本的目标基因在所述目标区间i上的拷贝数CN i；步骤(S4-3)：确定所述待测样本在目标基因上的拷贝数CN g；步骤(S4-4)：确定在所述目标区间上待测样本的拷贝数变异的存在的概率；步骤(S4-5)：确定在所述目标基因上所述待测样本的显著性拷贝数扩增或缺失的存在的比例sigRatio；步骤(S4-6)：确定在所述目标基因上所述待测样本的拷贝数变异的存在的统计检验参数；通过以下内容确定所述待测样本的目标基因的拷贝数状态：当CN g≥CN thA，sigRatio≥sigRatio th，且p ttest≤p th时，确认所述待测样本的目标基因发生拷贝数扩增；当CN g≤CN thD，sigRatio≥sigRatio th，且p ttest≤p th时，确认所述待测样本的目标基因发生拷贝数缺失；当CN thA＜CN g＜CN thD，或sigRatio＜sigRatio th，或p ttest＞p th时，确认所述待测样本的目标基因拷贝数正常，其中CN thA，CN thD，sigRatio th，和p th各自独立地为阈值。
一方面，本申请提供一种拷贝数状态分析装置，可以包含实现本申请拷贝数状态分析方法的模块。
一方面，本申请提供一种拷贝数状态分析方法，可以包含以下步骤：
(S3)确定与所述待测样本最接近的参考样本组；
(S4)基于所述与待测样本最接近的参考样本组的测序数据，确定所述待测样本的目标基因的拷贝数状态步骤(S4-1)：确定所述待测样本的目标基因在所述目标区间i上的拷贝数CN i；步骤(S4-2)：使所述待测样本的所述目标区间上的拷贝数降噪化；步骤(S4-3)：确定所述待测样本在目标基因上的拷贝数CN g；步骤(S4-4)：确定在所述目标区间上待测样本的拷贝数变异的存在的概率；步骤(S4-5)：确定在所述目标基因上所述待测样本的显著性拷贝数扩增或缺失的存在的比例sigRatio；步骤(S4-6)：确定在所述目标基因上所述待 测样本的拷贝数变异的存在的统计检验参数；通过以下内容确定所述待测样本的目标基因的拷贝数状态：当CN g≥CN thA，sigRatio≥sigRatio th，且p ttest≤p th时，确认所述待测样本的目标基因发生拷贝数扩增；当CN g≤CN thD，sigRatio≥sigRatio th，且p ttest≤p th时，确认所述待测样本的目标基因发生拷贝数缺失；当CN thA＜CN g＜CN thD，或sigRatio＜sigRatio th，或p ttest＞p th时，确认所述待测样本的目标基因拷贝数正常，其中CN thA，CN thD，sigRatio th，和p th各自独立地为阈值。
一方面，本申请提供一种拷贝数状态分析装置，可以包含实现本申请拷贝数状态分析方法的模块。
数据库、设备和应用方法
一方面，本申请提供一种数据库，其根据本申请所述的拷贝数状态分析方法或数据库建立方法建立。
另一方面，本申请还提供了一种储存介质，其记载可以运行本申请所述的方法的程序。
另一方面，本申请还提供了一种设备，其可以包含本申请所述的储存介质。例如，所述非易失性计算机可读存储介质可以包括软盘、柔性盘、硬盘、固态存储(SSS)(例如固态驱动(SSD))、固态卡(SSC)、固态模块(SSM))、企业级闪存驱动、磁带或任何其他非临时性磁介质等。非易失性计算机可读存储介质还可以包括打孔卡、纸带、光标片(或任何其他具有孔型图案或其他光学可识别标记的物理介质)、压缩盘只读存储器(CD-ROM)、可重写式光盘(CD-RW)、数字通用光盘(DVD)、蓝光光盘(BD)和/或任何其他非临时性光学介质。
例如，本申请的设备还可以包含耦接至所述储存介质的处理器，所述处理器可以被配置为基于存储在所述储存介质中的程序执行以实现本申请所述的方法。
另一方面，本申请还提供了一种本申请的方法，可以在疾病诊断、预防和/或治疗中的应用。
另一方面，本申请还提供了一种本申请的方法，可以在目标基因的拷贝数状态监测中的应用。
另一方面，本申请还提供了一种本申请的方法，可以在全基因组关联研究中的应用。
在本申请中，所述方法可以用于判断所述受试者是否具有拷贝数变异。例如，本申请的任一个或多个方法可以是非诊断目的的。例如，本申请的任一个或多个方法可以是诊断目的的。
在本申请中，所述方法可以用于通过检测所述拷贝数变异，用于临床实践(例如可以推 测某些特定的肿瘤治疗方式是否适于该受试者)。在某些情况下，所述方法检测出的拷贝数变异水平可以与本领域已知的生物标志物联合使用于临床实践。
不欲被任何理论所限，下文中的实施例仅仅是为了阐释本申请的方法和用途等，而不用于限制本申请发明的范围。
实施例
实施例1
1.1数据准备
选取30例阴性外周血样本，然后采用同一批实验试剂，从外周血中提取DNA，并通过片段化、加接头及PCR扩增等实验步骤制备全基因组预文库。接着，将制备好的预文库分成两份，分别用不同批次的探针，记为A批次和B批次，与预文库杂交，特异性地捕获人类基因组中BRCA1基因，获得终文库A和终文库B。用测序仪对两份终文库进行高通量测序。最后，将测序数据与人类基因组标准序列hg19进行比对，获得比对后的BAM文件。
1.2基于构建参考基线的传统方法检测拷贝数变异
事先，用前期收集的足量(例如：50例及以上)拷贝数正常的阴性样本作为参考集构建参考基线。随后，将两组实验样本采用该参考集构建的基线计算BRCA1基因上各外显子的拷贝数值及检测拷贝数变异。从计算的外显子拷贝数结果看(如图1A所示)，A批次探针捕获的实验数据均一性更好，与理论拷贝数值2更接近，而B批次探针捕获的结果相对更差，特别是BRCA1基因8号外显子，所有样本都有明显偏低的偏好。另一方面，从拷贝数变异检出结果看，采用B批次探针实验组中，30例样本检出2例来自BRCA1的假阳性的拷贝数变异。说明由于可能的探针批次差异，采用传统的基于参考基线方式很容易导致拷贝数变异检出的准确性下降。
1.3基于本申请方法检测拷贝数变异
因此，接下来采用本申请方法检测拷贝数变异。
(1)数据准备
本申请的拷贝数变异检测算法，可以选取足够的样本数量，例如可以是15例来自相同样本类型和相同实验方法学，且尽可能保证实验中使用试剂批次、实验设备等是一致的样本数据。每个参与的样本数据需要来自NGS测序数据比对后的BAM文件。
(2)基于BAM文件去重和DNA序列片段覆盖深度的统计
对每个样本BAM文件，首先，可以去除由于NGS建库中PCR引入的重复DNA序列片 段，获取唯一比对DNA片段。然后，根据要检测的目标DNA区域，采用滑窗法，每次滑动24bp，将该区域划分成探针固定长度为120bp的窗口区域，并统计每个窗口内唯一比对的DNA片段的平均覆盖水平。
(3)样本测序覆盖度质控
任选地，对每个样本进行质量控制，判断平均测序深度、最小测序深度和覆盖均一性是否满足要求。其中，要求平均测序深度≥100X，最小测序深度≥30X，覆盖均一性≥90％(指碱基测序深度≥样本平均测序深度20％的碱基占比，公式如下：覆盖均一度＝(大于或等于样本平均测序深度的20％的碱基数量/样本中总碱基数量)×100％。如果样本数据质量不满足要求，可以不用于矫正基线的构建。本申请的检测方法可以对满足质量合格的样本数至少为10例进行检测。
(4)数据校正及归一化处理
为了减低噪音和系统性偏差对拷贝数变异检测结果的影响，可以对每个窗口区域的覆盖水平进行矫正，包括覆盖水平初步矫正(基于样本平均覆盖水平)、GC矫正和批次矫正。
(5)覆盖水平初步矫正
为了矫正不同样本测序覆盖深度的差异，覆盖水平初步矫是将批次内所有样本的覆盖水平矫正到同一指定的覆盖水平。具体地，对批次内样本的每一个窗口区域，测序所得平均覆盖水平除以样本内所有窗口区域的平均覆盖水平的总和，再乘以固定因子(因子为1E+07)。
(6)GC矫正
为了矫正由于GC偏好导致测序覆盖深度差异，GC矫正通过计算每个窗口GC含量，然后利用loess回归方法对样本内每一个窗口区域的覆盖水平进行GC偏好矫正。
(7)批次矫正
i.获取批次内所有质控合格样本的GC矫正数据。
ii.计算参与构建批次基线的样本在每个窗口内的覆盖水平的中位值(median)和绝对离差中位值(MAD)。如果MAD/median＞设定阈值(例如，设定阈值可以为约0.05至约0.15)，表明该窗口覆盖水平不稳定，需要被剔除。
iii.保留MAD/median<小于设定阈值的窗口，或者保留MAD/median最小的前4个窗口，作为覆盖水平稳定的窗口区域。
iv.接着，对保留的每个覆盖水平稳定的窗口区域，采用grubbs检验去除该窗口内异常的覆盖水平值，然后计算剩余覆盖水平值的平均水平，作为批次矫正参考系数。
v.最后，对于每个待测样本，基于上述计算的批次矫正参考系数，对每个窗口区域的覆 盖水平进行归一化处理，并计算拷贝数CN值，每个窗口拷贝数CN值计算公式如下：
Figure PCTCN2022115447-appb-000002
(8)拷贝数变异识别
利用CBS算法识别样本目标区域上的断点位置，获取候选的拷贝数变异区域。然后，对每个候选的拷贝数变异区域进行显著性检验，具体地，通过T检验，判断待测样本在候选拷贝数变异区域上的窗口覆盖水平与批次内其他样本在该区域的覆盖水平是否具有显著性差异，从而判断候选拷贝数变异的可靠性。
其中BRCA1基因外显子拷贝数分布如图1B所示，可以看出，相比基于构建参考基线的传统方法，本申请方法获得拷贝数结果均一性更好，特别是对批次差异大的B组探针，效果更明显，并且两组实验数据都没有检出任何假阳性拷贝数变异。
实施例2
选取20例细胞系样本，其中19例为阴性样本，1例为已知的外显子拷贝数变异(LGR)样本(BRCA1:Exon 12amp)。实验采用仪器自动化建库方式获取高通量测序数据。最后，将测序数据与人类基因组标准序列hg19进行比对，获得比对后的BAM文件。将样本BAM文件分别用基于构建参考基线的传统方法和本申请的方法检测拷贝数变异。其中，基于构建参考基线方法采用的基线可以是用早期手动化建库方式的样本数据建立(例如实施例1中所用的参考基线)。
含有拷贝数变异的阳性样本结果如图2A-2B所示，基于构建参考基线传统方法检测结果(图2A所示)数据背景噪音极大，无法检出拷贝数变异，而本申请方法数据背景噪音明显更小，可以检出拷贝数变异(图2B所示)，说明不同实验方法产生的NGS数据可能有很大差异，基于手动化建库方式的数据构建的基线不适用于自动化建库数据。说明当实验方法发生改变时，如果采用参考基线传统方式，需要事先收集足够量使用该实验方法的样本数据，然后人工构建新的基线，这大大增加了实验成本和人力浪费。
实施例3
选取696例外周血样本检测BRCA1和BRCA2的外显子拷贝数变异(LGR)，实验采用RNA探针特异性捕获BRCA1和BRCA2基因区域，然后经高通量测序，将测序数据与人类基因组标准序列hg19进行比对，获得比对后的BAM文件。随后，分别采用基于构建参考基线的方法和本申请方法检测拷贝数变异。同时，所有样本拷贝数变异都经BRCA MASTR Plus  Dx试剂盒(基于多重PCR捕获方法学)确认，一共包含17例LGR阳性样本和679例阴性样本。
以BRCA MASTR Plus Dx试剂盒检出结果为基准，分别得到该696例外周血样本基于构建参考基线传统方法检测结果和本申请方法检测结果的灵敏度和特异性，分别如表1和表2所示。
表1.基于构建参考基线方法的检测结果
Figure PCTCN2022115447-appb-000003
表2.本申请方法的检测结果
Figure PCTCN2022115447-appb-000004
对比表1和表2可以看出，相比传统构建基线方法，通过本申请方法在不损失灵敏度的情况下，可以大大降低样本假阳性，检测准确性可以从75.3％提高到98.9％。
实施例4
选取14例细胞系样本测序比对后的数据构建批次基线，构建批次基线过程中，对于描述窗口覆盖波动水平的阈值，分别设为0.05和0.15，构建2条批次基线。然后分别用2条批次基线对该14例样本中已知LGR拷贝数变异的样本(BRCA1:exon4-6del)进行批次矫正，再检测拷贝数变异。
含有拷贝数变异的阳性样本结果如图3A-3B所示，基于不同窗口覆盖波动水平的阈值构建的批次基线都可以明显地检测到拷贝数变异，说明本申请筛选稳定区间的阈值范围都可以实现对于拷贝数变异的检测。
实施例5
选取10例阴性细胞系样本作为模拟样本本底，然后选择10例已被文献报道的BRCA1和 BRCA2基因的LGR拷贝数变异作为待模拟的突变(表3所示)，包含5种拷贝数扩增变异和5种拷贝数缺失变异。经过模拟将上述拷贝数扩增变异和拷贝数缺失变异人为加入到模拟样本本底数据中，最终得到10例阳性LGR模拟样本数据。
将10例模拟阳性样本构建批次基线，再使用构建的批次基线对该10例模拟样本进行批次矫正和拷贝数变异识别。10例模拟样本的结果如图4A-4J所示，模拟的10例拷贝数变异都能准确检出，说明本申请对于任意区域的拷贝数变异都可以实现准确的检测。
表3：10例模拟的拷贝数变异类型
附图名称 基因 拷贝数变异
BRCA1外显子16-19拷贝数扩增(图4A) BRCA1 外显子16-19_Dup
BRCA1外显子3-7拷贝数扩增(图4B) BRCA1 外显子3-7_Dup
BRCA2外显子14-18拷贝数扩增(图4C) BRCA2 外显子14-18_Dup
BRCA2外显子3拷贝数扩增(图4D) BRCA2 外显子3_Dup
BRCA2外显子25-27拷贝数扩增(图4E) BRCA2 外显子25-27_Dup
BRCA1外显子15-22拷贝数缺失(图4F) BRCA1 外显子15-22_Del
BRCA1外显子2拷贝数缺失(图4G) BRCA1 外显子2_Del
BRCA1外显子7-12拷贝数缺失(图4H) BRCA1 外显子7-12_Del
BRCA2外显子15-16拷贝数缺失(图4I) BRCA2 外显子15-16_Del
BRCA2外显子8-11拷贝数缺失(图4J) BRCA2 外显子8-11_Del
实施例6
本申请的方法，使用聚类方法，将大量的真实样本数据，根据测序深度的趋势聚类情况划分成不同的样本集合，分别构建基线(平均深度及深度波动范围)，根据样本与基线的相似性动态筛选背景基线，消除批次效应的同时，提高检测的特异性和敏感性。同时可以任选地使用离散小波变换方法对拷贝数进行平滑降噪，提高测序数据的信噪比。
为了提供一种高通量测序中可以实现高敏感性和精准度的拷贝数变异检测的方法和介质，本申请的方法基于样本覆盖特征特异性的差异化来检测拷贝数变异。具体地，本申请基于大规模样本的聚类分析，构建多条对照组基线，可以避免了测序中由于实验和样本差异导致覆盖深度特征不一致引起的基线不匹配问题；并且可以综合多种覆盖深度矫正策略，降低样本特异性的数据差异；最终可以通过定量分析、统计学差异评估，保证结果的准确性和稳定性。本申请拷贝数变异检测的方法不仅可以适用于特定基因面板(panel)的靶向捕获测序数据， 也可以适用于全外显子组的捕获测序数据。为了上述问题，本申请的数据库建立方法可以包含如下步骤：
1.数据准备模块，包括：
a)序列比对：将高通量测序原始fastq数据回帖到人类参考基因组，确定待测样本、参考样本的目标区间与人类参考基因组参考序列匹配的序列；
b)重复序列去除：将PCR扩增过程中产生的重复序列去掉；
c)覆盖深度计算：计算目标区间上每个碱基的测序深度RD Base；
2.覆盖深度矫正模块，包括3方面独立的，顺序任选的矫正：
a)使所述测序数据标准化：样本总测序深度矫正，具体地可以是，对每个目标区间各个位点上的覆盖深度根据样本总测序深度进行标准化，以消除不同样本之间的测序数据量差异，获得RD normD；
Figure PCTCN2022115447-appb-000005
其中，i表示目标区间上的位点，n表示所有目标区间上位点的总数，RD i表示目标区间上位点i的测序深度，R为一个常数，可以根据所有区间的大小进行设置，以保证待测样本矫正后的深度和参考样本组矫正后的深度在相同的水平。
b)使所述测序数据平滑化1：探针铺设特征矫正，具体可以是，根据探针设计中不同区间探针铺设乘数差异，例如区间上覆盖的探针数，将区间分割，每个目标区间长度可以为约24个碱基对，并计算各个目标区间平均覆盖深度RD，根据每个目标区间上覆盖的探针数ProbeN，对目标区间上测序深度进行局部加权回归(loess(RD～ProbeN))矫正，得到探针矫正后的测序深度RD normP；
c)使所述测序数据平滑化2：GC矫正，具体可以是，将用于覆盖深度计算的目标区间按照侧翼延伸至总长大于200bp长度，计算平均GC占比，根据各区间的GC含量GC，对测序深度RD进行局部加权回归(loess(RD～GC))矫正，得到GC矫正后的测序深度RD normGC；
d)可选的，可以进行筛选GC含量均衡的区间，将GC极度不均衡的区域(CG含量低于0.25，或GC含量高于0.75的区域)过滤掉，使用矫正后的覆盖深度进行拷贝数变异检测。
3.基线构建模块，具体包含以下步骤：
a)样本聚类：已有方法一般将所有参考样本作为一个类别来构建基线。本申请的方法对于参考样本进行分组，具体是基于各参考样本覆盖深度在目标区间上变化的一致性，例如在目标区间上参考样本的所述测序的近似程度，进行聚类分析，将参考样本分成不同类别的参 考样本组，聚类方法可以例如K均值聚类、层次聚类等方法；
b)基线构建：对于每个参考样本组，分别构建一条基线。具体为，分别计算每个参考样本组中，各个目标区间上所有参考样本的平均测序深度
Figure PCTCN2022115447-appb-000006
和测序深度的标准差
Figure PCTCN2022115447-appb-000007
作为基线(Baseline i)，其中i＝{1,2,3,4,…}。例如，每个参考样本组中需要有足够样本数量才具有统计学意义，每个参考样本组的样本数不低于30例。参考样本组数的设定需要考虑到肿瘤样本特征及测序质量，根据捕捉到的特征数确定参考样本组数，例如参考样本组数量可以为2个以上，如2-10条。
c)可选的，可以进行区间筛选：计算每个区间上测序深度的变异系数cv，去掉在样本中波动程度大的不稳定区间，其中：
Figure PCTCN2022115447-appb-000008
其中，
Figure PCTCN2022115447-appb-000009
和
Figure PCTCN2022115447-appb-000010
分别表示各个目标区间上所有参考样本的平均测序深度和测序深度的标准差。当cv>0.8时，该区间被认为是不稳定区域而被过滤掉；同时矫正后的测序深度低于5时，被认为是捕获效率低的区域而被过滤掉，最终保留的区间作为稳定的区间。
最终，根据本实施例可以得到本申请的数据库，包含在目标区间上变化具有一致性的两个或以上参考样本组。与现有技术相比，本申请的数据库的优势在于：通过大规模样本的聚类分析，将参考样本划分为不同类别的参考样本组，分别构建样本特异性的背景基线，极大的降低了高通量测序数据在拷贝数变异检测中批次效应产生的假阳性，增加结果的稳定性。同时本申请消除批次效应的方法不需要同批次中保证有足够数量的相同基因panel的样本，极大降低了实际应用中的困难。
实施例7
为了解决测序中由于实验和样本差异导致覆盖深度特征不一致引起的基线不匹配问题，本申请还提供了一种拷贝数状态分析方法。本申请的拷贝数状态分析方法可以包含如下步骤：
a)根据所述待测样本与所述参考样本组的相似度，确定与所述待测样本最接近的参考样本组，即动态筛选基线：通过统计距离的计算方法，例如闵可夫斯基距离等，比较各个目标区间上待测样本的测序深度与该目标区间上各个参考样本组的测序深度，确认所述参考样本组与所述待测样本的统计距离：
Figure PCTCN2022115447-appb-000011
其中，L p值表示统计距离，i表示目标区间，n表示目标区间个数，
Figure PCTCN2022115447-appb-000012
表示待测样本的目标区间i的测序深度，
Figure PCTCN2022115447-appb-000013
表示参考样本组的目标区间i的测序深度，其中p任选地大于或等于1。选择与待测样本具有最小统计距离(L p值最小，表示相似度最高)的参考样本组作为该待测样本的背景基线(Baseline x)。
b)基因拷贝数检测，具体如下：
i.各待测样本的目标区间拷贝数评估：计算待测样本各个目标区间的拷贝数CN i，公式如下：
Figure PCTCN2022115447-appb-000014
其中，RD sample表示待测样本的各个目标区间的测序深度，
Figure PCTCN2022115447-appb-000015
表示与待测样本最接近的参考样本组的各个目标区间的测序深度，其中倍性可以为2。
ii.可选的，可以进行各区间拷贝数平滑降噪：使用降噪算法对各个区间的CNi进行平滑降噪，提高数据的信噪比。降噪方法可以使用离散小波变换(Discrete Wavelet Transformation，DWT)、主成分分析算法、奇异值分解和/或高斯滤波的方法进行平滑降噪。DWT是将信号分成高频信号和低频信号，分别通过低通滤波器和高通滤波器，将离散信号作离散小波变换，将连续的小波及其小波变换离散化，达到数据降噪的目的。这样可以得到降噪后的CNi；
iii.各目标基因的拷贝数评估：计算样本各个目标基因的加权平均拷贝数CN g，并使用目标外显子的长度对CN i进行矫正，例如：
Figure PCTCN2022115447-appb-000016
其中，i表示目标区间，j表示目标外显子，n表示目标外显子j上的目标区间个数，m表示目标外显子个数，CN i表示目标区间i的拷贝数，Len j表示目标外显子j的长度。
iv.确定在所述目标区间上待测样本的拷贝数变异的存在的概率，例如可以是多个区间的测序深度与筛选出的背景基线的分布进行正态性检验，公式如下：
Figure PCTCN2022115447-appb-000017
Figure PCTCN2022115447-appb-000018
其中，
Figure PCTCN2022115447-appb-000019
表示待测样本的目标区间i的测序深度，
Figure PCTCN2022115447-appb-000020
表示与待测样本最接近的参考样本组的各个目标区间的测序深度的均值，
Figure PCTCN2022115447-appb-000021
表示与待测样本最接近的参考样本组的各个目标区间的测序深度的标准差，lower.tail＝F计算右尾概率，p a表示该区间发生拷贝数扩增的概率值，lower.tail＝T计算左尾概率，p d表示该区间发生拷贝数缺失的概率值。
v.各目标基因显著性比例评估：分别计算各个目标基因显著性扩增或缺失的比例sigRatio，公式如下：
Figure PCTCN2022115447-appb-000022
其中，所述发生显著性拷贝数变异的目标区间包含拷贝数变异的比例为约30％或更高的所述目标区间。
vi.各目标基因整体水平显著性检验：对每个目标基因上各个目标区间矫正后的测序深度基于与待测样本最接近的参考样本组的各个区间的平均测序深度，进行T检验，判断样本与基线的差异是否显著，得到p ttest。
c)确定所述待测样本的目标基因的拷贝数状态，判断标准为：
Figure PCTCN2022115447-appb-000023
各个阈值可通过使用大规模样本训练得到。其中：CN thA表示拷贝数扩增的阈值，取值可以任选为2.25～4；CN thD表示拷贝数缺失的阈值，取值可以任选为1.0～1.75；sigRatio th表示显著性扩增/缺失比例的阈值，取值可以任选为0.3～1；p th表示显著性T检验的阈值，取值可以任选为0.05～0.00001。
本申请的拷贝数状态分析方法根据待测样本与参考样本组的相似性动态筛选与待测样本最接近的参考样本组作为背景基线，可以消除批次效应，也可以提高检测的特异性和敏感性。
实施例8
数据库建立：使用655例参考样本构建基线，采用本申请的数据库构建方法，例如使用k均值聚类算法，将参考样本划分为5个参考样本组，构建5条不同候选基线作为数据库。
构建模拟数据：使用varBen肿瘤突变数据模拟软件(github.com/nccl-jmli/VarBen)，以良性组织样本为基础，通过插入基因的读段的方式，在测序数据中插入目标基因的读段，梯度模拟目标基因不同拷贝数扩增的情况，模拟样本列表如表4所示。
表4：模拟样本列表
目标基因 模拟样本数 模拟拷贝数梯度
ALK 20 2.5，2.75，3.0，3.5，4.0
ERBB2 20 2.5，2.75，3.0，3.5，4.0
FGFR1 20 2.5，2.75，3.0，3.5，4.0
FGFR2 20 2.5，2.75，3.0，3.5，4.0
根据本申请的拷贝数状态分析方法检测模拟样本，检测的结果如表5所示。
表5：本申请在模拟样本中的检测结果
Figure PCTCN2022115447-appb-000024
Figure PCTCN2022115447-appb-000025
图5A-5F显示的是，本申请的检测结果部分数据的拷贝数分布图示例。每个点表示基因的一个区间，灰色的点表示拷贝数正常的基因，黑色的点表示拷贝数发生扩增或缺失的基因，同时标注出相应的基因名。横轴表示基因所在的染色体位置，纵轴表示基于本申请方法计算得到的拷贝数(中间横线表示正常基因的拷贝数)，灰色背景表示背景基线(与待测样本最接近的参考样本组)中各目标区间的波动范围。图5A–5C分别为模拟ERBB2基因发生不同程度的拷贝数扩增，图5D–5F分别为FGFR1基因发生不同层度的拷贝数扩增，模拟拷贝数梯度为2.5，2.75和3.0。结果表明，本申请的拷贝数状态分析方法用于模拟样本中，所有模拟基因及不同梯度的拷贝数扩增均能稳定检测，且拷贝数预测准确。
实施例9
阳性标准样本：本申请测试包括30例阳性标准样本，来源于NCI-BL2009细胞系，使用质粒转染的方式将相应比例的目标基因转染至细胞系中，获得CNV阳性数据，并使用微滴式数字PCR(ddPCR)定量基因的拷贝数。质粒编号为：Life RPCI11.C-433C10BAC-EGFR，Life RPCI11.C-936I7BAC-CDK4，Life RPCI11.C-163C9BAC-MET，Life RPCI11.C-909L6BAC-ERBB2，Life RPCI11.C-957P17BAC-FGFR1。阳性标准样本列表如表6所示。
表6：阳性标准样本列表
基因 标准样本数 ddPCR标定拷贝数
CDK4 10 3,5,8
ERBB2 10 3,5,8
EGFR 10 3,5,8
FGFR1 10 3,5,8
MET 10 3,5,8
数据库建立：使用655例参考样本构建基线，采用本申请的数据库构建方法，例如使用k均值聚类算法，将参考样本划分为5个参考样本组，构建5条不同候选基线作为数据库。
根据本申请的拷贝数状态分析方法检测拷贝数扩增阳性标准样本的拷贝数状态，检测的结果如表7所示。
表7：本申请在阳性标准样本的检测结果
Figure PCTCN2022115447-appb-000026
图6A-6C显示的是，本申请的检测结果部分数据的拷贝数分布图示例。图6A-6C表示质粒转染的CNV阳性细胞系标准样本的检测结果，ddPCR标定拷贝数分别为3，5和8。结果表明，本发明方法用于质粒转染的细胞系标准品中，所有的基因以及不同的拷贝数状态下，均能稳定检出，且拷贝数预测准确。
实施例10
真实数据：本申请测试的真实样本包括第三方免疫组化法(IHC)验证的20例ERBB2扩增阳性样本，真实样本列表如表8所示。
表8：真实样本列表
基因 样本数 IHC结果
ERBB2 20 拷贝数：3+
数据库建立：使用443例参考样本构建基线，采用本申请的数据库构建方法，例如使用k均值聚类算法，将参考样本划分为参考样本组，构建不同候选基线作为数据库。
根据本申请的拷贝数状态分析方法检测真实样本的拷贝数状态，检测的结果如表9所示。
表9：本申请在真实样本中的检测结果
Figure PCTCN2022115447-appb-000027
图7A-7C显示的是，本申请的检测结果部分数据的拷贝数分布图示例。图7A-7C表示真实ERBB2阳性样本的检测结果。结果表明，本申请方法用于真实样本中，20例IHC结果为HER2阳性的样本，均能稳定检出。
实施例11
阳性标准样本：本申请测试包括3例阳性标准样本，同实施例9中来源，用于检测不同基线的检测结果。阳性标准样本列表如表10所示。
表10：阳性标准样本列表
样本 基因 ddPCR标定拷贝数
标准样本1 CDK4，ERBB2，EGFR，FGFR1，MET 3
标准样本2 CDK4，ERBB2，EGFR，FGFR1，MET 5
标准样本3 CDK4，ERBB2，EGFR，FGFR1，MET 8
数据库建立：使用655例参考样本构建基线，采用本申请的数据库构建方法，例如使用k均值聚类算法，将参考样本划分为5个参考样本组，构建5条不同候选基线作为数据库。同时，不使用聚类方法，将所有的参考样本构建1条基线。
分别使用聚类算法得到的5条基线和不聚类的1条基线，作为参考对照，检测拷贝数扩增标准样本的拷贝数状态。基线的选择和样本的波动情况如表11所示，检测的结果如表12所示。
表11：不同基线检测结果的波动情况
Figure PCTCN2022115447-appb-000028
Figure PCTCN2022115447-appb-000029
表12：不同基线的检测结果
Figure PCTCN2022115447-appb-000030
Figure PCTCN2022115447-appb-000031
图8A-8F显示的是，标准样本1使用不同的基线检测结果的拷贝数分布示例图。
结果表明，使用本申请发明方法匹配到的最优基线与待测样本最相近(样本与基线的距离值最小)，样本整体拷贝数的波动(SD)最低，拷贝数分布图最稳定，噪音最小，说明本方法检测结果更加稳定。本申请方法，在所有基因以及不同的拷贝数状态下，均能稳定检出，其他基线在拷贝数为3时无法稳定检出。
前述详细说明是以解释和举例的方式提供的，并非要限制所附权利要求的范围。目前本申请所列举的实施方式的多种变化对本领域普通技术人员来说是显而易见的，且保留在所附的权利要求和其等同方案的范围内。

Claims (19)

  1. 一种拷贝数状态分析方法，
    所述方法包含，将待测样本的目标区间划分为若干个窗口区域，获取待测样本组中的对照窗口区域的测序数据，基于所述对照窗口区域的测序数据，确定所述待测样本的目标基因的拷贝数状态；任选地，所述对照窗口区域包含覆盖波动水平低的窗口区域。
  2. 如权利要求1所述的方法，所述方法还包含以下步骤：
    (S1)获取所述待测样本的测序数据和/或多个参考样本的测序数据；
    (S2)将所述参考样本分为两个或以上参考样本组；
    (S3)确定与所述待测样本最接近的参考样本组；
    (S4)基于所述与待测样本最接近的参考样本组的测序数据，确定所述待测样本的目标基因的拷贝数状态。
  3. 如权利要求1-2中任一项所述的方法，
    所述方法包含，将质量合格的样品的窗口区域按照覆盖波动水平从低到高排序，所述对照窗口区域包含覆盖波动水平前2个或更多、或前4个或更多所述窗口，或者所述对照窗口区域的所有所述质量合格的样品的所述测序数据的绝对离差中位数与中位数的比值为约0.15或更小。
  4. 如权利要求3所述的方法，
    所述方法包含，基于所述质量合格的样品的窗口区域的测序数据统计值，确定所述覆盖波动水平；任选地，基于所述质量合格的样品的窗口区域的测序数据的绝对离差中位数与中位数的比值，确定所述覆盖波动水平。
  5. 如权利要求3-4中任一项所述的方法，
    所述方法包含，基于所述对照窗口区域的测序数据，确定归一化系数；任选地，通过计算所述对照窗口区域的所有所述质量合格的样品的测序数据平均值，确定所述归一化系数。
  6. 如权利要求5所述的方法，
    所述方法包含，基于所述归一化系数，确定待测样本的每一个窗口区域的拷贝数；任选地，所述归一化包含将所述窗口区域的待测样本的测序数据除以所述窗口区域的归一化系数，乘以倍性。
  7. 如权利要求1-6中任一项所述的方法，
    所述方法包含，基于待测样本的每一个窗口区域的测序数据以及相应窗口区域的待测样本组中其它样本的测序数据，确定待测样本的拷贝数变异显著性；任选地，通过T检验 显著性检验的方法，确定所述拷贝数变异的显著性。
  8. 如权利要求1-7中任一项所述的方法，
    所述待测样本选自以下组：组织样本、血液样本、唾液、胸腔积液、腹膜积液和脑脊液。
  9. 如权利要求2-8中任一项所述的方法，
    所述步骤(S2)包含步骤(S2-1)：使所述参考样本分组，所述分组包含基于目标区间的所述测序数据通过聚类分析的方法使所述参考样本分组；优选地，所述聚类分析的方法包含K均值聚类和/或层次聚类；
    所述步骤(S2)包含步骤(S2-2)：确认所述参考样本组的所述测序数据的统计值；优选地，所述确认统计值包含计算在所述目标区间上每组中所述参考样本的均值和/或标准差。
  10. 如权利要求2-9中任一项所述的方法，
    所述步骤(S3)包含基于在目标区间上所述参考样本组与所述待测样本的所述测序数据，通过计算统计距离的方法，确认所述参考样本组与所述待测样本的分布相似程度；优选地，所述分布相似程度高包含在所述目标区间上所述参考样本组与所述待测样本的所述统计距离短。
  11. 如权利要求10所述的方法，
    所述统计距离包含所述目标区间上所述参考样本组与所述待测样本的所述测序数据的差值的绝对值的p次方的统计值，所述p为1或更大，优选地，所述统计值包含求和值；优选地，所述统计距离包含闵可夫斯基距离。
  12. 如权利要求2-11中任一项所述的方法，
    所述步骤(S4)包含基于所述待测样本的所述目标基因的外显子的长度以及所述待测样本的目标区间i上的拷贝数CN i，确定待测样本在目标基因上的拷贝数CN g；优选地，所述步骤(S4)包含基于下式确定所述CN g，
    Figure PCTCN2022115447-appb-100001
    其中，i表示目标区间，j表示目标外显子，n表示目标外显子j上的目标区间个数，m表示目标外显子个数，CN i表示目标区间i的拷贝数，Len j表示目标外显子j的长度。
  13. 如权利要求2-12中任一项所述的方法，
    所述步骤(S4)包含：确定在目标区间上待测样本的拷贝数变异的存在的概率，所述拷 贝数变异的存在的概率包含在目标区间上所述待测样本发生拷贝数扩增的概率(p a)和/或缺失的概率(p d)；优选地，所述步骤(S4)包含，基于所述待测样本在目标区间i上的测序数据、以及在相应目标区间上所述与待测样本最接近的参考样本组的测序数据的均值与标准差，通过概率分布的方法确认所述拷贝数变异的存在的概率；优选地，所述概率分布包含正态性概率分布。
  14. 如权利要求2-13中任一项所述的方法，
    所述步骤(S4)包含：确定在所述目标基因上所述待测样本的显著性拷贝数扩增或缺失的存在的比例sigRatio；优选地，使所述目标基因上发生显著性拷贝数变异的目标区间数量除以所述目标基因上所有目标区间数量，得到所述sigRatio；所述发生显著性拷贝数变异的目标区间包含所述拷贝数变异的比例为约30％或更高的目标区间；
    所述步骤(S4)还包含：确定在所述目标基因上所述待测样本的拷贝数变异的存在的统计检验参数；优选地，基于在所述目标基因上所述待测样本的所述目标区间的数量、在所述目标基因上所述待测样本的各个目标区间的测序数据、在所述目标基因上所述待测样本的各个目标区间的测序数据的标准差、以及在相应目标基因上述与待测样本最接近的参考样本组的目标区间上的测序数据的均值和标准差，通过T检验的方法确认p值p ttest。
  15. 如权利要求14所述的方法，
    所述步骤(S4)通过以下内容确定所述待测样本的目标基因的拷贝数状态：
    当CN g≥CN thA，sigRatio≥sigRatio th，且p ttest≤p th时，确认所述待测样本的目标基因发生拷贝数扩增；
    当CN g≤CN thD，sigRatio≥sigRatio th，且p ttest≤p th时，确认所述待测样本的目标基因发生拷贝数缺失；
    当CN thA＜CN g＜CN thD，或sigRatio＜sigRatio th，或p ttest＞p th时，确认所述待测样本的目标基因拷贝数正常，其中CN thA，CN thD，sigRatio th，和p th各自独立地为阈值；优选地，其中CN thA为约2.25至约4；优选地，其中CN thD为约1.0至约1.75；优选地，其中sigRatio th为约0.3至约1；优选地，其中p th为约0.05至约0.00001。
  16. 如权利要求2-15中任一项所述的方法，
    所述目标基因包含选自以下组基因：ABL1、ABL2、ABRAXAS1、ACVR1、ACVR1B、AKT1、AKT2、AKT3、ALK、ALOX12B、AMER1、APC、AR、ARAF、ARFRP1、ARID1A、ARID1B、ARID2、ARID5B、ASXL1、ASXL2、ASXL3、 ATG5、ATM、ATR、ATRX、AURKA、AURKB、AXIN1、AXIN2、AXL、B2M、BAP1、BARD1、BBC3、BCL10、BCL2、BCL2L1、BCL2L11、BCL2L2、BCL6、BCOR、BCORL1、BIRC3、BLM、BMPR1A、BRAF、BRCA1、BRCA2、BRD4、BRD7、BRINP3、BRIP1、BTG1、BTG2、BTK、CALR、CARD11、CASP8、CBFB、CBL、CCND1、CCND2、CCND3、CCNE1、CD274、CD28、CD58、CD74、CD79A、CD79B、CDC73、CDH1、CDH18、CDK12、CDK4、CDK6、CDK8、CDKN1A、CDKN1B、CDKN1C、CDKN2A、CDKN2B、CDKN2C、CEBPA、CENPA、CHD1、CHD2、CHD4、CHD8、CHEK1、CHEK2、CIC、CIITA、CREBBP、CRKL、CRLF2、CRYBG1、CSF1R、CSF3R、CSMD1、CSMD3、CTCF、CTLA4、CTNNA1、CTNNB1、CUL3、CUL4A、CXCR4、CYLD、CYP17A1、CYP2D6、DAXX、DCUN1D1、DDR1、DDR2、DDX3X、DICER1、DIS3、DNAJB1、DNMT1、DNMT3A、DNMT3B、DOT1L、DPYD、DTX1、DUSP22、EED、EGFR、EIF1AX、EIF4E、EMSY、EP300、EPCAM、EPHA2、EPHA3、EPHA5、EPHA7、EPHB1、EPHB4、ERBB2、ERBB3、ERBB4、ERCC1、ERCC2、ERCC3、ERCC4、ERCC5、ERG、ERRFI1、ESR1、ETV4、ETV5、ETV6、EWSR1、EZH2、EZR、FANCA、FANCC、FANCD2、FANCE、FANCF、FANCG、FANCI、FANCL、FANCM、FAS、FAT1、FAT3、FBXW7、FGF10、FGF12、FGF14、FGF19、FGF23、FGF3、FGF4、FGF6、FGF7、FGFR1、FGFR2、FGFR3、FGFR4、FH、FLCN、FLT1、FLT3、FLT4、FOXA1、FOXL2、FOXO1、FOXO3、FOXP1、FRS2、FUBP1、FYN、GABRA6、GALNT12、GATA1、GATA2、GATA3、GATA4、GATA6、GEN1、GID4、GLI1、GNA11、GNA13、GNAQ、GNAS、GPS2、GREM1、GRIN2A、GRM3、GSK3B、H3F3A、H3F3B、H3F3C、HDAC1、HDAC2、HGF、HIST1H1C、HIST1H2BD、HIST1H3A、HIST1H3B、HIST1H3C、HIST1H3D、HIST1H3E、HIST1H3G、HIST1H3H、HIST1H3I、HIST1H3J、HIST2H3D、HIST3H3、HLA-A、HLA-B、HLA-C、HNF1A、HOXB13、HRAS、HSD3B1、HSP90AA1、ICOSLG、ID3、IDH1、IDH2、IFNGR1、IGF1、IGF1R、IGF2、IGHD、IGHJ、IGHV、IKBKE、IKZF1、IL10、IL7R、INHA、INHBA、INPP4A、INPP4B、INSR、IRF2、IRF4、IRS1、IRS2、ITK、ITPKB、JAK1、JAK2、JAK3、JUN、KAT6A、KDM5A、KDM5C、KDM6A、KDR、KEAP1、KEL、KIR2DL4、KIR3DL2、KIT、KLF4、KLHL6、KLRC1、KLRC2、KLRK1、KMT2A、KMT2C、KMT2D、KRAS、LATS1、LATS2、LMO1、 LRP1B、LTK、LYN、MAF、MAGI2、MALT1、MAP2K1、MAP2K2、MAP2K4、MAP3K1、MAP3K13、MAP3K14、MAPK1、MAPK3、MAX、MCL1、MDC1、MDM2、MDM4、MED12、MEF2B、MEN1、MERTK、MET、MFHAS1、MGA、MIR21、MITF、MKNK1、MLH1、MLH3、MPL、MRE11、MSH2、MSH3、MSH6、MST1、MST1R、MTAP、MTOR、MUTYH、MYC、MYCL、MYCN、MYD88、MYOD1、NAV3、NBN、NCOA3、NCOR1、NCOR2、NEGR1、NF1、NF2、NFE2L2、NFKBIA、NKX2-1、NKX3-1、NOTCH1、NOTCH2、NOTCH3、NOTCH4、NPM1、NRAS、NRG1、NSD1、NSD2、NSD3、NT5C2、NTHL1、NTRK1、NTRK2、NTRK3、NUP93、NUTM1、P2RY8、PAK1、PAK3、PAK5、PALB2、PALLD、PARP1、PARP2、PARP3、PAX5、PBRM1、PCDH11X、PDCD1、PDCD1LG2、PDGFRA、PDGFRB、PDK1、PGR、PHOX2B、PIK3C2B、PIK3C2G、PIK3C3、PIK3CA、PIK3CB、PIK3CD、PIK3CG、PIK3R1、PIK3R2、PIK3R3、PIM1、PLCG2、PLK2、PMS1、PMS2、PNRC1、POLD1、POLE、POM121L12、PPARG、PPM1D、PPP2R1A、PPP2R2A、PPP6C、PRDM1、PREX2、PRKAR1A、PRKCI、PRKDC、PRKN、PTCH1、PTEN、PTPN11、PTPRD、PTPRO、PTPRS、PTPRT、QKI、RAB35、RAC1、RAD21、RAD50、RAD51、RAD51B、RAD51C、RAD51D、RAD52、RAD54L、RAF1、RARA、RASA1、RB1、RBM10、RECQL4、REL、RET、RHEB、RHOA、RICTOR、RIT1、RNF43、ROS1、RPA1、RPS6KA4、RPS6KB2、RPTOR、RSPO2、RUNX1、RUNX1T1、SDC4、SDHA、SDHAF2、SDHB、SDHC、SDHD、SETD2、SF3B1、SGK1、SH2B3、SH2D1A、SHQ1、SLC34A2、SLIT2、SLX4、SMAD2、SMAD3、SMAD4、SMARCA4、SMARCB1、SMARCD1、SMO、SNCAIP、SOCS1、SOX10、SOX17、SOX2、SOX9、SPEN、SPI1、SPOP、SPTA1、SRC、SRSF2、STAG2、STAT3、STAT4、STAT5A、STAT5B、STAT6、STK11、STK40、SUFU、SYK、TAF1、TBX21、TBX3、TCF3、TCF7L2、TEK、TENT5C、TERC、TERT、TET1、TET2、TGFBR1、TGFBR2、TIPARP、TMEM127、TMPRSS2、TNFAIP3、TNFRSF14、TOP1、TOP2A、TP53、TP63、TP73、TRAF2、TRAF3、TRAF7、TRIM58、TRPC5、TSC1、TSC2、TSHR、TYRO3、U2AF1、UGT1A1、VEGFA、VEGFB、VEGFC、VHL、WISP3、WRN、WT1、XIAP、XPO1、XRCC2、XRCC3、YAP1、YES1、ZAP70、ZBTB16、ZBTB2、ZNF217、ZNF703和ZNRF3。
  17. 一种拷贝数状态分析装置，包含以下模块：接收模块，用于获取待测样本组的测序数据；确定模块，用于确定待测样本中的目标基因；判断模块，用于根据所述待测样本组的测序数据确定所述待测样本中的目标基因的拷贝数状态。
  18. 如权利要求17所述的拷贝数状态分析装置，包含以下模块：(M1)接收模块，用于获取待测样本的测序数据和/或多个参考样本的测序数据；(M2)处理模块，用于将所述参考样本分为两个或以上参考样本组；(M3)计算模块，用于确定与所述待测样本最接近的参考样本组；(M4)判断模块，用于基于所述与待测样本最接近的参考样本组的测序数据，确定所述待测样本的目标基因的拷贝数状态。
  19. 一种储存介质，其记载可以运行权利要求1-16中任一项所述的方法的程序。
PCT/CN2022/115447 2021-08-30 2022-08-29 一种拷贝数变异的检测方法及其应用 Ceased WO2023030233A1 (zh)

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Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117153249A (zh) * 2023-10-26 2023-12-01 北京华宇亿康生物工程技术有限公司 用于检测smn基因拷贝数变异的方法、设备和介质
CN117265069A (zh) * 2023-09-21 2023-12-22 北京安智因生物技术有限公司 基于半导体测序平台检测brca1/2基因拷贝数变异
CN117334249A (zh) * 2023-05-30 2024-01-02 上海品峰医疗科技有限公司 基于扩增子测序数据检测拷贝数变异的方法、设备和介质
CN120748484A (zh) * 2025-08-15 2025-10-03 广州燃石医学检验所有限公司 基因拷贝数变异类型检测方法及相关产品

Citations (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20130316915A1 (en) * 2010-10-13 2013-11-28 Aaron Halpern Methods for determining absolute genome-wide copy number variations of complex tumors
CN104133914A (zh) * 2014-08-12 2014-11-05 厦门万基生物科技有限公司 一种消除高通量测序引入的gc偏差及对染色体拷贝数变异的检测方法
US20160300013A1 (en) * 2015-04-10 2016-10-13 Agilent Technologies, Inc. METHOD FOR SIMULTANEOUS DETECTION OF GENOME-WIDE COPY NUMBER CHANGES, cnLOH, INDELS, AND GENE MUTATIONS
CN106650312A (zh) * 2016-12-29 2017-05-10 安诺优达基因科技(北京)有限公司 一种用于循环肿瘤dna拷贝数变异检测的装置
CN106715711A (zh) * 2014-07-04 2017-05-24 深圳华大基因股份有限公司 确定探针序列的方法和基因组结构变异的检测方法
CN106951737A (zh) * 2016-11-18 2017-07-14 南方医科大学 一种检测流产组织dna拷贝数变异和嵌合体的方法
CN108256292A (zh) * 2016-12-29 2018-07-06 安诺优达基因科技(北京)有限公司 一种拷贝数变异检测装置
CN108427864A (zh) * 2018-02-14 2018-08-21 南京世和基因生物技术有限公司 一种拷贝数变异的检测方法、装置以及计算机可读介质
CN111968701A (zh) * 2020-08-27 2020-11-20 北京吉因加科技有限公司 检测指定基因组区域体细胞拷贝数变异的方法和装置
CN113674803A (zh) * 2021-08-30 2021-11-19 广州燃石医学检验所有限公司 一种拷贝数变异的检测方法及其应用
CN113789371A (zh) * 2021-09-17 2021-12-14 广州燃石医学检验所有限公司 一种基于批次矫正的拷贝数变异的检测方法

Family Cites Families (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20140370504A1 (en) 2011-12-31 2014-12-18 Bgi Diagnosis Co., Ltd. Method for detecting genetic variation
EP2826865B8 (en) 2012-01-20 2017-08-16 BGI Genomics Co., Ltd. Method and system for determining whether copy number variation exists in sample genome, and computer readable medium
US10395759B2 (en) 2015-05-18 2019-08-27 Regeneron Pharmaceuticals, Inc. Methods and systems for copy number variant detection

Patent Citations (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20130316915A1 (en) * 2010-10-13 2013-11-28 Aaron Halpern Methods for determining absolute genome-wide copy number variations of complex tumors
CN106715711A (zh) * 2014-07-04 2017-05-24 深圳华大基因股份有限公司 确定探针序列的方法和基因组结构变异的检测方法
CN104133914A (zh) * 2014-08-12 2014-11-05 厦门万基生物科技有限公司 一种消除高通量测序引入的gc偏差及对染色体拷贝数变异的检测方法
US20160300013A1 (en) * 2015-04-10 2016-10-13 Agilent Technologies, Inc. METHOD FOR SIMULTANEOUS DETECTION OF GENOME-WIDE COPY NUMBER CHANGES, cnLOH, INDELS, AND GENE MUTATIONS
CN106951737A (zh) * 2016-11-18 2017-07-14 南方医科大学 一种检测流产组织dna拷贝数变异和嵌合体的方法
CN106650312A (zh) * 2016-12-29 2017-05-10 安诺优达基因科技(北京)有限公司 一种用于循环肿瘤dna拷贝数变异检测的装置
CN108256292A (zh) * 2016-12-29 2018-07-06 安诺优达基因科技(北京)有限公司 一种拷贝数变异检测装置
CN108427864A (zh) * 2018-02-14 2018-08-21 南京世和基因生物技术有限公司 一种拷贝数变异的检测方法、装置以及计算机可读介质
CN111968701A (zh) * 2020-08-27 2020-11-20 北京吉因加科技有限公司 检测指定基因组区域体细胞拷贝数变异的方法和装置
CN113674803A (zh) * 2021-08-30 2021-11-19 广州燃石医学检验所有限公司 一种拷贝数变异的检测方法及其应用
CN113789371A (zh) * 2021-09-17 2021-12-14 广州燃石医学检验所有限公司 一种基于批次矫正的拷贝数变异的检测方法

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
See also references of EP4397773A4 *

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117334249A (zh) * 2023-05-30 2024-01-02 上海品峰医疗科技有限公司 基于扩增子测序数据检测拷贝数变异的方法、设备和介质
CN117265069A (zh) * 2023-09-21 2023-12-22 北京安智因生物技术有限公司 基于半导体测序平台检测brca1/2基因拷贝数变异
CN117265069B (zh) * 2023-09-21 2024-05-14 北京安智因生物技术有限公司 基于半导体测序平台检测brca1/2基因拷贝数变异
CN117153249A (zh) * 2023-10-26 2023-12-01 北京华宇亿康生物工程技术有限公司 用于检测smn基因拷贝数变异的方法、设备和介质
CN117153249B (zh) * 2023-10-26 2024-02-02 北京华宇亿康生物工程技术有限公司 用于检测smn基因拷贝数变异的方法、设备和介质
CN120748484A (zh) * 2025-08-15 2025-10-03 广州燃石医学检验所有限公司 基因拷贝数变异类型检测方法及相关产品

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