Method and apparatus for correcting sequencing data, detecting chromosomal aneuploidy

Abstract

The application relates to a method and device for correcting sequencing data and detecting chromosomal aneuploidy. The method for correcting sequencing data comprises the following steps: obtaining whole genome sequencing data of a detection sample; determining a window length according to a chromosome to be analyzed, performing window division on each target chromosome of the detection sample according to the window length, and obtaining the number of uniquely aligned sequences of each window falling on each target chromosome according to the whole genome sequencing data; obtaining the GC content of each window, performing GC correction on the number of uniquely aligned sequences of each window according to the GC content and the number of uniquely aligned sequences of each window, and obtaining the number of uniquely aligned sequences of each window after correction; and determining the standard sequence number ratio of the chromosome to be analyzed of the detection sample according to the number of uniquely aligned sequences of each window after correction. The application creatively selects a window of a corresponding length according to the GC content to divide the chromosome into windows, and reduces the influence of different GC content regions of the genome on the correction result at the window level.

Description

Technical Field

[0001] This application relates to the field of bioinformatics, and more specifically, to a method and apparatus for correcting sequencing data and detecting chromosomal aneuploidy. Background Technology

[0002] Sequencing data obtained by PCR amplification on a next-generation sequencer typically exhibit a correlation between sequencing depth and GC content, known as GC bias. GC bias refers to the fact that regions of the genome with a GC content of around 50% are more easily amplified by PCR, producing more reads and higher coverage; while regions with high or low GC content are less easily sequenced, producing fewer reads and less coverage.

[0003] Most regions of the human genome are low-GC (low-GC content), resulting in lower sequencing depths. Conversely, a smaller portion of genomic regions with higher GC bias exhibits higher sequencing depths, leading to uneven sequencing coverage across the genome. This uneven coverage causes various problems in subsequent bioinformatics analyses, such as errors in genome copy number analysis affecting the accuracy of results. To ensure more accurate bioinformatics analyses, GC bias correction is typically performed.

[0004] Therefore, how to perform GC correction on sequencing data is a challenge in improving the accuracy of subsequent bioinformatics analysis results. Summary of the Invention

[0005] To address the aforementioned issues, and to reduce the uneven coverage of genomic regions caused by GC bias in sequencing depth for chromosomes with different GC contents, and to improve the accuracy of subsequent bioinformatics analyses, the primary objective of this application is to provide a method for correcting gene sequencing data, including:

[0006] Obtain whole-genome sequencing data of the test samples;

[0007] The window length is determined based on the chromosome to be analyzed. The target chromosomes of the test sample are divided into windows according to the window length. The number of unique alignment sequences falling into each window on each target chromosome is obtained based on the whole genome sequencing data. When the chromosome to be analyzed is a sex chromosome, each target chromosome includes all autosomes and the chromosome to be analyzed. When the chromosome to be analyzed is an autosome, each target chromosome includes all autosomes.

[0008] Obtain the GC content of each window, and perform GC correction on the number of unique alignment sequences of each window based on the GC content and the number of unique alignment sequences to obtain the corrected number of unique alignment sequences for each window;

[0009] The ratio of the number of standard sequences to be analyzed in the test sample is determined based on the number of uniquely aligned sequences after correction for each window.

[0010] This application divides all chromosomes in the test sample into windows of different lengths based on the different GC contents of the chromosomes to be analyzed. Then, based on the GC content and unique alignment sequence number of each window on each chromosome, GC correction is performed on the number of unique alignment sequences in each window on each chromosome. For low-depth sequencing data of the human genome, this can reduce the problems of excessive fluctuation in the number of unique alignment sequences and excessive noise caused by excessively small windows, as well as the problems of reduced resolution and weakened correction ability caused by excessively large windows. This eliminates the influence of different GC content regions on the correction results and achieves better sequencing data correction effect.

[0011] In one embodiment, determining the window length based on the chromosome to be analyzed, and dividing the target chromosome of the detection sample into windows according to the window length specifically includes:

[0012] Obtain the GC content of the chromosome to be analyzed;

[0013] If the GC content of the chromosome to be analyzed is not less than 0.38 and less than 0.39, the window length is determined to be 90kb to 110kb, and the window of 90kb to 110kb is used to divide the target chromosome of the sample to be tested.

[0014] If the GC content of the chromosome to be analyzed is not less than 0.39 and less than 0.40, the window length is determined to be 40kb to 110kb, and the window of 40kb to 110kb is used to divide the target chromosome of the sample to be tested.

[0015] If the GC content of the chromosome to be analyzed is not less than 0.40 and less than 0.41, the window length is determined to be 10kb to 60kb, and the target chromosomes of the sample to be tested are divided into windows using a window of 10kb to 60kb.

[0016] In one embodiment, the GC content of each window is obtained, and the number of unique alignment sequences in each window is GC corrected based on the GC content and the number of unique alignment sequences, resulting in the corrected number of unique alignment sequences for each window, including:

[0017] Obtain the GC content of each window, and obtain the GC correction coefficient of each chromosome based on the GC content of each window on each chromosome and the number of unique aligned sequences;

[0018] Based on the GC correction coefficient of each target chromosome, the number of unique aligned sequences in each window of the corresponding chromosome is GC corrected to obtain the number of unique aligned sequences in each window after GC correction.

[0019] In one embodiment, determining the standard sequence number ratio of the chromosome to be analyzed in the test sample based on the number of uniquely aligned sequences after correction for each window includes:

[0020] The number of uniquely aligned sequences after GC correction for each window is used to determine the number of uniquely aligned sequences after correction for the chromosome to be analyzed and the total number of uniquely aligned sequences after correction for all autosomes.

[0021] The ratio of the number of standard sequences of the chromosome to be analyzed in the test sample is determined based on the number of uniquely aligned sequences after correction of the chromosome to be analyzed and the total number of uniquely aligned sequences after correction of all autosomes.

[0022] A second objective of this application is to provide a method for detecting chromosomal aneuploidy, comprising:

[0023] The ratio of the number of standard sequences of chromosomes to be analyzed in the test sample and the ratio of the number of standard sequences of chromosomes to be analyzed in the negative sample set were obtained. The ratio of the number of standard sequences of chromosomes to be analyzed was obtained using the method described above for correcting sequencing data.

[0024] The Z score of the chromosome to be analyzed in the test sample is determined based on the ratio of the number of standard sequences of the chromosome to be analyzed in the test sample and the ratio of the number of standard sequences of the chromosome to be analyzed in the negative sample set.

[0025] The aneuploidy of the chromosome to be analyzed in the test sample is determined based on the Z score of the chromosome to be analyzed in the test sample.

[0026] In one embodiment, determining the Z-score of the chromosome to be analyzed in the test sample based on the ratio of the number of standard sequences of the chromosome to be analyzed in the test sample to the ratio of the number of standard sequences of the chromosome to be analyzed in the negative sample set includes:

[0027] The mean and standard deviation of the standard sequence number ratio of the chromosomes to be analyzed in the negative sample set are determined based on the ratio of the number of standard sequences of the chromosomes to be analyzed in the test sample and the ratio of the number of standard sequences of the chromosomes to be analyzed in the negative sample set.

[0028] The Z-score of the chromosome to be analyzed in the test sample is calculated based on the mean and standard deviation of the ratio of the number of standard sequences of the chromosome to be analyzed in the test sample and the ratio of the number of standard sequences of the chromosome to be analyzed in the negative sample set.

[0029] In one embodiment, determining the aneuploidy of the chromosome to be analyzed in the test sample based on the Z score includes:

[0030] Determine whether the Z score of the chromosome to be analyzed in the sample is greater than a preset threshold;

[0031] If the Z score of the chromosome to be analyzed in the test sample is greater than the preset threshold, then the aneuploidy of the chromosome to be analyzed in the test sample is determined to be positive.

[0032] If the Z score of the chromosome to be analyzed in the test sample is less than the preset threshold, then the aneuploidy of the chromosome to be analyzed in the test sample is determined to be negative.

[0033] A third objective of this application is to provide an apparatus for correcting sequencing data, comprising:

[0034] Sequencing data acquisition module: used to acquire whole genome sequencing data of the test sample;

[0035] Window partitioning module: used to partition each target chromosome of the test sample into windows of the corresponding length of the chromosome to be analyzed, and to obtain the number of unique alignment sequences falling into each window on each target chromosome based on whole genome sequencing data;

[0036] GC Correction Module: Used to obtain the GC content of each window, and perform GC correction on the number of unique alignment sequences of each window based on the GC content and the number of unique alignment sequences, to obtain the corrected number of unique alignment sequences for each window;

[0037] Standard sequence number ratio calculation module: used to determine the standard sequence number ratio of the chromosome to be analyzed in the test sample based on the number of unique aligned sequences after correction for each window.

[0038] The fourth objective of this application is to provide an apparatus for detecting chromosomal aneuploidy, comprising:

[0039] Standard sequence number ratio acquisition module: used to acquire the standard sequence number ratio of the chromosomes to be analyzed in the test sample and the standard sequence number ratio of the chromosomes to be analyzed in the negative sample set. The standard sequence number ratio of the chromosomes to be analyzed is obtained using the above-mentioned method for correcting sequencing data.

[0040] Z-score calculation module: used to determine the Z-score of the chromosome to be analyzed in the test sample based on the ratio of the number of standard sequences of the chromosome to be analyzed in the test sample and the ratio of the number of standard sequences of the chromosome to be analyzed in the negative sample set;

[0041] Aneuploidy assessment module: used to determine the aneuploidy of the chromosomes to be analyzed in the test sample based on the Z score of the chromosomes to be analyzed in the test sample.

[0042] The fifth objective of this application is to provide a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the above-described method.

[0043] The sixth objective of this application is to provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method.

[0044] The seventh objective of this application is to provide a computer program product, including a computer program that, when executed by a processor, implements the above-described method. Attached Figure Description

[0045] To more clearly illustrate the technical solutions in the specific embodiments of this application or the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0046] Figure 1 A flowchart illustrating a method for calibrating sequencing data provided in this application embodiment;

[0047] Figure 2 A flowchart of a method for detecting chromosomal aneuploidy is provided in this application.

[0048] Figure 3 A structural block diagram of a device for calibrating sequencing data is provided in this application.

[0049] Figure 4 A structural block diagram of a device for detecting chromosomal aneuploidy provided in this application is provided as an embodiment;

[0050] Figure 5 Internal structure diagram of a computer device provided in an embodiment of this application;

[0051] Figure 6 This is the fitting result of GC correction when dividing chromosomes using different window sizes, as provided in Embodiment 1 of this application;

[0052] Figure 7 This is a schematic diagram illustrating the relationship between the GC correction coefficient and GC content when dividing chromosomes using different window sizes, as provided in Embodiment 1 of this application.

[0053] Figure 8 This is a comparison of the number of unique alignment sequences before and after correction for each window when different window sizes are used to divide 13, 18, and 21 chromosomes in Example 1 of this application. Detailed Implementation

[0054] Reference will now be made to detailed embodiments of this application, one or more of which are described below. Each example is provided for explanation and not for limitation of this application. In fact, it will be apparent to those skilled in the art that various modifications and variations can be made to this application without departing from its scope or spirit. For example, features described or illustrated as part of one embodiment may be used in another embodiment to produce further embodiments.

[0055] Therefore, this application is intended to cover such modifications and variations falling within the scope of the appended claims and their equivalents. Other objects, features, and aspects of this application are disclosed in or will be apparent from the following detailed description. It will be understood by those skilled in the art that this discussion is merely a description of exemplary embodiments and is not intended to limit the broader aspects of this application.

[0056] Traditional methods for correcting sequencing data for GC bias mostly involve setting a large window (e.g., tens of kilobytes to tens of megabytes, such as 20 kb), dividing the entire chromosome into non-overlapping regions of that window size, counting the number of sequencing reads in each window, and calculating the GC content for each window. Based on the window, algorithms such as local multinomial weighted LOESS regression are used to fit the GC content / count data, and correction is performed by multiplying the read count by a correction coefficient.

[0057] The method proposed by Fan and Quake to remove GC bias is the inverse weighting method of GC gradient change. First, the entire chromosome is divided into large non-overlapping regions (e.g., tens of kb to tens of megabytes, such as 20 kb). Then, the GC content of each sequencing sequence in each region is calculated. The sequencing sequences in each non-overlapping region on the chromosome are grouped with a GC value difference of 0.1%. The number of sequencing sequences in each GC value group is counted, and the ratio of the number of sequencing sequences in that region to the average number of sequencing sequences in all chromosomes in the category is used as the GC weight of the sequencing sequences in that group. The number and percentage of unique bases of each chromosome are recalculated, thereby achieving GC correction of the percentage of unique bases on each chromosome and eliminating GC bias.

[0058] However, the aforementioned GC correction methods have their limitations. They only consider sequencing bias caused by differences in GC content within each chromosome, without considering the bias between sequences on different chromosomes. For sequencing data with low GC bias, such as chromosome 21 (GC content 0.408325), the correction effect is acceptable. However, for sequencing data with severe GC bias, the correction effect is not ideal, especially for regions with low GC content such as chromosome 18 (GC content 0.39785) and chromosome 13 (GC content 0.385265). This makes it difficult to eliminate biases introduced by sequencing and the characteristics of the genome itself, affecting the accuracy of subsequent bioinformatics analysis.

[0059] To address at least one of the aforementioned technical problems, the first aspect of this application provides a method for detecting and correcting sequencing data, such as... Figure 1As shown, it includes:

[0060] S10: Obtain whole genome sequencing data of the test sample;

[0061] Specifically, the test sample can be a DNA sample from the object being tested. The object being tested can be a human, a non-human animal, or a plant. The test sample can be a sample to be analyzed for bioinformatics, or it can be a reference sample or a control sample. Control samples generally include positive and negative samples.

[0062] In some specific implementations, the test sample is a cell-free DNA sample from mammalian blood.

[0063] For the purpose of detecting fetal chromosomal aneuploidy, the sample to be tested can be a cell-free fetal DNA sample from the pregnant woman's peripheral blood.

[0064] A negative sample can be a peripheral blood sample from a pregnant woman who is at least 12 weeks pregnant and has a normal karyotype.

[0065] Whole-genome sequencing data refers to sequencing data obtained after preprocessing the raw genome sequencing data. Preprocessing includes quality control, alignment, and filtering. Whole-genome sequencing data can be directly used to calculate the ratio of standard sequence numbers to the chromosome to be analyzed.

[0066] In some implementations, obtaining whole-genome sequencing data from the test sample also includes:

[0067] Whole-genome sequencing was performed on the test samples;

[0068] Obtain the raw sequencing data of the test samples and screen for high-quality raw sequencing data;

[0069] The whole genome sequencing data of the test sample is obtained by comparing and quality-controlling the high-quality raw sequencing data.

[0070] Specifically, the detailed steps for preprocessing raw genome sequencing data include:

[0071] 1. Whole genome sequencing: Using a high-throughput sequencing platform, whole genome sequencing was performed on the cell-free DNA of the pregnant woman's blood sample;

[0072] 2. Quality control: Obtain the raw sequencing data FastQ from the second-generation high-throughput sequencing of the test samples, and use FastQC, FastP or similar software for quality control and filtering to obtain high-quality sequencing data FastQ.

[0073] 3. Alignment: Using BWA or similar software, align the high-quality sequencing data (FASTQ) obtained in step (1) with the human reference genome sequence (e.g., hg19 or hg38) to generate an alignment BAM file. This file reveals the exact location of the sequence in the reference genome (location information includes chromosome number and genome coordinates) and the number of times each location was sequenced. Use samtools or similar software to sort the BAM file and remove duplicates.

[0074] 4. Filtering: The sorted and duplicate-removed BAM files are filtered to remove low-quality sequences, including at least one of the following: repetitive sequences introduced by PCR amplification, sequences containing the base N, sequences not aligned to the reference genome, sequences with an average sequencing quality of less than 20 for five consecutive bases, sequences with low alignment quality, and sequences aligned to multiple positions on the reference genome. Mismatches are not allowed, or at most one mismatch is permitted. Only uniquely aligned sequences are retained, and the number of unique reads (UR) for each chromosome is calculated. The amount of data after alignment is unlimited and can be reasonably set according to different sample sources. It is preferred that the processed sequencing data has at least 3.5M sequences.

[0075] S20: Determine the window length based on the chromosome to be analyzed, divide the target chromosome of the test sample into windows based on the window length, obtain the number of unique alignment sequences falling into each window on each target chromosome based on whole genome sequencing data, when the chromosome to be analyzed is a sex chromosome, each target chromosome includes all autosomes and the chromosome to be analyzed, when the chromosome to be analyzed is an autosome, each target chromosome includes all autosomes.

[0076] Specifically, the chromosome to be analyzed refers to the chromosome in the test sample that needs to be subjected to bioinformatics analysis in order to detect information such as the copy number and mutations of the chromosome to be analyzed.

[0077] In this application, the chromosomes to be analyzed include chromosomes with low GC content. Specifically, the chromosomes to be analyzed include chromosomes with a GC content lower than 0.41, such as chromosome 5, chromosome 13, and chromosome 18.

[0078] Dividing chromosomes into windows based on their corresponding lengths can improve the correction of low-GC-content genomic regions, eliminate biases introduced by sequencing and genomic features, and thus improve the accuracy of subsequent bioinformatics analysis.

[0079] In some implementation schemes, the window length is determined based on the chromosome to be analyzed, and the window is divided into windows for each target chromosome in the test sample according to the window length. Specifically, this includes:

[0080] Obtain the GC content of the chromosome to be analyzed;

[0081] If the GC content of the chromosome to be analyzed is not less than 0.38 and less than 0.39, the window length is determined to be 90kb to 110kb, and a window of 90kb to 110kb is selected to divide the target chromosomes of the sample to be tested.

[0082] If the GC content of the chromosome to be analyzed is not less than 0.39 and less than 0.40, the window length is determined to be 40kb to 110kb, and a window of 40kb to 110kb is selected to divide the target chromosome of the sample to be tested.

[0083] If the GC content of the chromosome to be analyzed is not less than 0.40 and less than 0.41, determine the window length to be 10kb to 60kb, and select a window of 10kb to 60kb to divide the target chromosome of the sample to be tested.

[0084] Specifically, for chromosomes with different GC contents, different window lengths are used to divide all chromosomes into windows so as to perform window-level GC correction on the chromosomes to be analyzed in the test samples.

[0085] For chromosomes with low GC content, such as chromosomes 13 and 18, the bin size can be relatively large, such as 100kb; for chromosomes with moderate GC content, such as chromosome 21, the bin size can be relatively small, such as 20kb. Using this method, not only can GC correction be performed on all bins of the sample being tested, but the accuracy of the corrected unique alignment count can also be improved, resulting in a unique alignment sequence count that is close to the actual level.

[0086] Furthermore, for each bin size, the reference genome was segmented into bins, and the number of unique reads (URs) falling into each bin was calculated using bedtools. To improve the accuracy of the sequence count, bins containing the base N, bins with zero reads, or bins with abnormal GC content (e.g., GC content below 20% or GC content below 80%) were filtered out and excluded from the subsequent calculation of the chromosome standard sequence count ratio.

[0087] S30: Obtain the GC content of each window, and perform GC correction on the number of unique alignment sequences of each window based on the GC content and the number of unique alignment sequences, to obtain the corrected number of unique alignment sequences for each window;

[0088] Specifically, the method for GC correction of the number of unique alignment sequences for each window can employ conventional GC correction methods in the art.

[0089] In some specific implementation schemes, the GC correction method can employ Loess regression. Loess regression uses a locally weighted regression scatter smoothing method. By statistically analyzing the number of sequencing reads and GC content within each window, the Loess regression function in R, i.e., a locally weighted polynomial algorithm, is used to fit a polynomial regression curve to the GC content and read count data. This allows observation of the patterns and trends exhibited locally. The local range is then progressively extended from left to right, calculating a continuous curve to obtain a correction coefficient. The number of unique aligned sequences at the window level is corrected by multiplying the read count of each window by the correction coefficient.

[0090] Accordingly, in order to obtain the number of unique alignment sequences after GC correction at the window level, the GC content of each window is obtained, and the number of unique alignment sequences in each window is GC corrected based on the GC content and the number of unique alignment sequences. The corrected number of unique alignment sequences for each window includes:

[0091] Obtain the GC content of each window, and obtain the GC correction coefficient of each chromosome based on the GC content of each window on each chromosome and the number of unique aligned sequences;

[0092] Based on the GC correction coefficient of each target chromosome, the number of unique aligned sequences in each window of the corresponding chromosome is GC corrected to obtain the number of unique aligned sequences in each window after GC correction.

[0093] Understandably, by performing window-level GC correction on the chromosomes to be analyzed on the test sample according to the window division method of this application, the influence of different GC content regions of the genome on the correction results can be eliminated, thereby achieving better sequencing data correction effect and improving the accuracy of subsequent biological analysis results.

[0094] S40: Determine the ratio of the number of standard sequences of the chromosome to be analyzed in the test sample based on the number of uniquely aligned sequences after correction for each window.

[0095] Specifically, the amount of data for each sample varies during sequencing. Calculating the ratio of standard sequence numbers to the chromosome to be analyzed mainly serves a standardization purpose, removing the differences in sample data volume and thus standardizing the data from different samples to a metric level for subsequent bioinformatics analysis.

[0096] In some implementation schemes, the standard sequence number ratio of the chromosomes to be analyzed in the test sample is determined based on the number of uniquely aligned sequences after correction for each window, including:

[0097] The number of uniquely aligned sequences after GC correction for each window is used to determine the number of uniquely aligned sequences after correction for the chromosome to be analyzed and the total number of uniquely aligned sequences after correction for all autosomes.

[0098] The ratio of the number of standard sequences of the chromosome to be analyzed in the test sample is determined based on the number of uniquely aligned sequences after correction of the chromosome to be analyzed and the total number of uniquely aligned sequences after correction of all autosomes.

[0099] Specifically, the ratio of standard sequence numbers of chromosomes is denoted by GR. For a single sample of chromosome m, GR... m This is the ratio of the number of uniquely aligned sequences on chromosome m to the total number of uniquely aligned sequences on autosomes 1-22 in this sample. The calculation formula is shown in Formula 1:

[0100] Formula 1:

[0101]

[0102] Where m is the chromosome number, m∈(1,2,……,22), CR m TR represents the number of uniquely aligned sequences on chromosome m. m TR represents the total number of unique alignments of autosomes. m It is obtained by calculating the sum of the unique alignment numbers of autosomes 1 to 22.

[0103] This application divides all chromosomes in the test sample into windows of different lengths based on the different GC contents of the chromosomes to be analyzed. Then, based on the GC content and unique alignment sequence number of each window on each chromosome, GC correction is performed on the number of unique alignment sequences in each window on each chromosome. For low-depth sequencing data of the human genome, this can reduce the problems of excessive fluctuation in the number of unique alignment sequences and excessive noise caused by excessively small windows, as well as the problems of reduced resolution and weakened correction ability caused by excessively large windows. This eliminates the influence of different GC content regions on the correction results and achieves better sequencing data correction effect.

[0104] Furthermore, based on the above-described method for correcting sequencing data, a second aspect of this application provides a method for detecting chromosomal aneuploidy, such as... Figure 2 As shown, it includes:

[0105] M10: Obtain the ratio of standard sequence numbers of chromosomes to be analyzed in the test sample and the ratio of standard sequence numbers of chromosomes to be analyzed in the negative sample set;

[0106] M20: Determine the Z score of the chromosome to be analyzed in the test sample based on the ratio of the number of standard sequences of the chromosome to be analyzed in the test sample and the ratio of the number of standard sequences of the chromosome to be analyzed in the negative sample set;

[0107] M30: Determine the aneuploidy of the chromosome to be analyzed in the test sample based on the Z score of the chromosome to be analyzed in the test sample.

[0108] Specifically, the ratio of standard sequence numbers of the chromosomes to be analyzed was obtained using the method described above for correcting sequencing data.

[0109] This application calculates the Z score of the chromosome to be analyzed based on the ratio of the standard sequence number of the chromosome to be analyzed obtained by the above-mentioned method for correcting sequencing data. This Z score can be used to analyze the aneuploidy of the chromosome to be analyzed, thereby improving the accuracy of the aneuploidy analysis of the chromosome to be analyzed.

[0110] Non-invasive prenatal testing (NIPT) utilizes next-generation sequencing (NGS) technology to construct a library of cell-free fetal DNA (cffDNA) in maternal peripheral blood plasma, followed by gene sequencing. Bioinformatics methods are then used to analyze and process the data from the sequencing instrument to obtain the fetus's genetic information, thereby determining whether the fetus has chromosomal aneuploidy disorders, such as trisomy 21 (Down syndrome), trisomy 18 (Edwards syndrome), and trisomy 13 (Patau syndrome). It is an effective method for screening fetal chromosomal aneuploidy with an accuracy rate exceeding 99%.

[0111] NIPT has gained widespread recognition and promotion due to its non-invasive, safe, early, accurate, and rapid characteristics, and has been quickly accepted in clinical practice. It is currently the most successful clinical application of next-generation sequencing technology.

[0112] In some specific implementation schemes, different window sizes (bin sizes) are used to divide the chromosomes according to the different GC contents of chromosomes 21, 18, and 13. GC correction is performed on the test sample at the window level, which can eliminate the influence of different GC content regions of the genome on the correction results, achieve better sequencing data correction effect, and improve the accuracy of NIPT results.

[0113] In some implementation schemes, the Z-score of the chromosome to be analyzed in the test sample is determined based on the ratio of the number of standard sequences of the chromosome to be analyzed in the test sample to the ratio of the number of standard sequences of the chromosome to be analyzed in the negative sample set, including:

[0114] M201: Determine the mean and standard deviation of the standard sequence number ratio of the chromosomes to be analyzed in the negative sample set based on the ratio of the number of standard sequences of the chromosomes to be analyzed in the test sample and the ratio of the number of standard sequences of the chromosomes to be analyzed in the negative sample set;

[0115] M202: Calculate the Z score of the chromosome to be analyzed in the test sample based on the mean and standard deviation of the ratio of the number of standard sequences of the chromosome to be analyzed in the test sample and the ratio of the number of standard sequences of the chromosome to be analyzed in the negative sample set.

[0116] Specifically, the negative sample set refers to a control sample library constructed from peripheral blood samples of n (e.g., n=100) pregnant women with a gestational age of 12 weeks or greater and normal karyotype. For each negative sample, the unique number of GC-corrected alignments for each window of a single negative sample was calculated according to the method described above for correcting sequencing data. Then, the GR of each negative sample was calculated, and finally, the mean and standard deviation of the GR of the negative reference set were calculated.

[0117] The formula for calculating the mean GR value of chromosome m in the negative reference set is shown in Formula 2:

[0118] Formula 2:

[0119]

[0120] Where m is the chromosome number, m∈(1,2,……22), n is the number of negative reference set samples, k represents the negative reference set sample number, and GR km For the m-th autosomal chromosome GR in the k-th sample, GR_mean m This represents the average value of GR on autosome m in the negative reference set.

[0121] The formula for calculating the GR standard deviation of chromosome m in the negative reference set is shown in Formula 3:

[0122] Formula 3:

[0123]

[0124] Where m is the chromosome number, m∈(1,2,……22), n is the number of negative reference set samples, k represents the negative reference set sample number, and GR km For the m-th autosomal chromosome GR in the k-th sample, GR_mean m SD is the average value of GR on autosome m in the negative reference set. m The standard deviation of chromosome m, GR, in the negative reference set.

[0125] The Z score of chromosome m is represented by Z. m This indicates that the Z-score of the chromosomes in the sample to be tested is calculated using the z-test formula, as shown in Formula 4:

[0126] Formula 4:

[0127]

[0128] Where m is the chromosome number, m∈(1,2,……22), GR m For the m-th autosomal GR of the sample to be tested, GR_mean m SD is the average value of GR on autosome m in the negative reference set.m The standard deviation of chromosome m, GR, in the negative reference set.

[0129] In some implementation schemes, determining the aneuploidy of the chromosome to be analyzed in the test sample based on the Z score includes:

[0130] M301: Determine whether the Z score of the chromosome to be analyzed in the sample is greater than the preset threshold;

[0131] M302: If the Z score of the chromosome to be analyzed in the test sample is greater than the preset threshold, then the aneuploidy of the chromosome to be analyzed in the test sample is determined to be positive.

[0132] M303: If the Z score of the chromosome to be analyzed in the test sample is less than the preset threshold, then the aneuploidy of the chromosome to be analyzed in the test sample is determined to be negative.

[0133] Specifically, the Z-score of the chromosome to be analyzed can be used to determine its euploidy. By comparing the Z-score of the chromosome to be analyzed with a preset threshold, it can be determined whether there is an abnormal number of chromosomes in the sample. For the preset threshold, if the distribution of the proportions of chromosomes to be analyzed in multiple control samples conforms to or approximately conforms to a normal distribution, the z-value and corresponding confidence level can be found in a z-table (normal distribution table). For example, if the confidence level is 99.97%, the corresponding z-value is approximately 3, so the preset threshold can be determined to be 3. If the value exceeds this preset threshold, it indicates that there is a 99.97% probability that the sample is not a normal sample and can be judged as abnormal. Of course, as needed, those skilled in the art can set other confidence levels and then use the corresponding z-values ​​as thresholds to determine whether there is an abnormality.

[0134] In some specific implementation schemes, the preset threshold set in this application is 7.

[0135] If the Z score of a certain chromosome in a maternal peripheral blood sample is >= 7, it can be considered that the fetus of that mother has trisomy of that chromosome, and the aneuploidy of the chromosome to be analyzed is positive.

[0136] If the Z score of a certain chromosome in a maternal peripheral blood sample to be tested is <7, it can be considered that the fetus of that mother does not have trisomy of that chromosome, and the aneuploidy of the chromosome to be analyzed is negative.

[0137] A third aspect of this application provides an apparatus for correcting sequencing data, such as Figure 3 As shown, it includes:

[0138] Sequencing data acquisition module: used to acquire whole genome sequencing data of the test sample;

[0139] Window partitioning module: used to partition each target chromosome of the test sample into windows according to the corresponding length of the chromosome to be analyzed. Based on the whole genome sequencing data, the number of unique alignment sequences falling into each window on each target chromosome is obtained. When the chromosome to be analyzed is a sex chromosome, each target chromosome includes all autosomes and the chromosome to be analyzed. When the chromosome to be analyzed is an autosome, each target chromosome includes all autosomes.

[0140] GC Correction Module: Used to obtain the GC content of each window, and perform GC correction on the number of unique alignment sequences of each window based on the GC content and the number of unique alignment sequences, to obtain the corrected number of unique alignment sequences for each window;

[0141] Standard sequence number ratio calculation module: used to determine the standard sequence number ratio of the chromosome to be analyzed in the test sample based on the number of unique aligned sequences after correction for each window.

[0142] In some implementation schemes, the window partitioning module includes:

[0143] Chromosome GC content acquisition unit: used to acquire the GC content of the chromosome to be analyzed;

[0144] Window partitioning unit: used to partition the target chromosomes of the sample to be analyzed into windows of 90kb to 110kb when the GC content of the chromosome to be analyzed is not less than 0.38 and less than 0.39.

[0145] When the GC content of the chromosome to be analyzed is not less than 0.39 and less than 0.40, a window of 40kb to 110kb is used to divide the target chromosome of the sample to be tested.

[0146] When the GC content of the chromosome to be analyzed is not less than 0.40 and less than 0.41, a window of 10kb to 60kb is used to divide the target chromosome of the sample to be tested.

[0147] In some implementations, the GC correction module includes:

[0148] GC correction coefficient acquisition unit: used to acquire the GC content of each window, and to acquire the GC correction coefficient of each chromosome based on the GC content of each window on each chromosome and the number of unique aligned sequences;

[0149] Window GC Correction Unit: Used to perform GC correction on the number of unique aligned sequences in each window of the corresponding chromosome according to the GC correction coefficient of each target chromosome, so as to obtain the number of unique aligned sequences after GC correction for each window.

[0150] In some implementations, the standard sequence number ratio calculation module includes:

[0151] Chromosome GC Correction Unit: Used to determine the number of uniquely aligned sequences after GC correction of the chromosome to be analyzed and the total number of uniquely aligned sequences after correction of all autosomes based on the number of uniquely aligned sequences after GC correction in each window;

[0152] Standard sequence number ratio calculation unit: used to determine the standard sequence number ratio of the chromosome to be analyzed based on the number of unique aligned sequences after correction of the chromosome to be analyzed and the total number of unique aligned sequences after correction of all autosomes.

[0153] The fourth aspect of this application provides an apparatus for detecting chromosomal aneuploidy, such as... Figure 4 As shown, it includes:

[0154] Standard sequence number ratio acquisition module: used to acquire the standard sequence number ratio of the chromosomes to be analyzed in the test sample and the standard sequence number ratio of the chromosomes to be analyzed in the negative sample set from the output correction sequencing data of the above device;

[0155] Z-score calculation module: used to determine the Z-score of the chromosome to be analyzed in the test sample based on the ratio of the number of standard sequences of the chromosome to be analyzed in the test sample and the ratio of the number of standard sequences of the chromosome to be analyzed in the negative sample set;

[0156] Aneuploidy assessment module: used to determine the aneuploidy of the chromosomes to be analyzed in the test sample based on the Z score of the chromosomes to be analyzed in the test sample.

[0157] In some implementations, the Z-score calculation module includes:

[0158] Chromosome GR mean and GR standard deviation calculation unit: used to determine the mean and standard deviation of the standard sequence number ratio of the chromosomes to be analyzed in the negative sample set based on the ratio of the number of standard sequences of the chromosomes to be analyzed in the test sample and the ratio of the number of standard sequences of the chromosomes to be analyzed in the negative sample set;

[0159] Z-score calculation unit: used to calculate the Z-score of the chromosome to be analyzed in the test sample based on the mean and standard deviation of the ratio of the number of standard sequences of the chromosome to be analyzed in the test sample and the ratio of the number of standard sequences of the chromosome to be analyzed in the negative sample set.

[0160] In some implementations, the Z-score calculation unit includes:

[0161] Judgment subunit: used to determine whether the Z score of the chromosome to be analyzed in the sample is greater than a preset threshold;

[0162] The first aneuploidy determination unit is used to determine the aneuploidy of the chromosome to be analyzed in the test sample as positive when the Z score of the chromosome to be analyzed in the test sample is greater than a preset threshold.

[0163] The second aneuploidy determination unit is used to determine that the aneuploidy of the chromosome to be analyzed in the test sample is negative when the Z score of the chromosome to be analyzed in the test sample is less than a preset threshold.

[0164] Each module in the above-mentioned device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of a computer device in hardware form or independent of it, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0165] In some implementations, a computer device is provided, which can be a server 104 or a terminal 102, and its internal structure diagram can be as follows. Figure 5 As shown. The computer device includes a processor, memory, and communication interface connected via a system bus. When the computer device is a terminal, it also includes a display screen and input devices connected to the system bus. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The network interface of the computer device is used to communicate with external terminals via a network connection. When the computer program is executed by the processor, it performs the steps in the above method. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen. The input devices of the computer device can be a touch layer covering the display screen, buttons, a trackball, or a touchpad mounted on the computer device casing, or an external keyboard, touchpad, or mouse, etc.

[0166] Those skilled in the art will understand that Figure 5 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0167] This application also provides a computer device. The computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps in the above-described method.

[0168] This application also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program thereon, which, when executed by a processor, implements the steps of the above-described method.

[0169] This application also provides a computer program product. The computer program product includes a computer program that, when executed by a processor, implements the steps in the method for identifying false-positive gene variants described above.

[0170] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0171] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0172] The implementation scheme of this application will now be described in detail with reference to the embodiments.

[0173] Example 1: Differences in GC correction at different bin sizes

[0174] Sample Collection: Peripheral blood was collected from eligible pregnant women into EDTA tubes according to peripheral blood collection standards. Plasma separation and library construction were performed, followed by sequencing using a high-throughput sequencer (e.g., Illumina, BGI), yielding sequencing files in FASTQ format. The bioinformatics analysis workflow described above was used, employing three different bin sizes: 20kb, 50kb, and 100kb. GC correction was performed using the loess regression function in R.

[0175] In this embodiment, the GC correction method used is the locally weighted regression scatter smoothing method. The Loess regression function is used to perform regression fitting on the total number of raw reads and the GC content of the window. When the bin size is 20kb, 50kb, and 100kb, the fitted values ​​for the reads are as follows: Figure 6 As shown, Figure 6 Plot a graph using the GC content in bin as the x-axis and the fitted value as the y-axis to observe the relationship between read coverage and GC content in the sample genome.

[0176] Depend on Figure 6 It can be seen that this sample is more easily sequenced in regions with a GC content of around 50%, producing more reads and higher coverage. In regions with high or low GC, it is not easy to sequence, producing fewer reads and lower coverage.

[0177] The correction factor weight = median / fit, where median is the median number of reads, or the average number of reads can be used. The correction factor weight is calculated as follows for bin sizes of 20kb, 50kb, and 100kb: Figure 7 As shown.

[0178] The corrected number of reads, corReads = rawReads * weight, is obtained by multiplying readcount by the correction coefficient to get the number of unique alignment sequences after GC correction.

[0179] The three chromosomes, 13, 18, and 21, were corrected using different bin sizes. The reads before and after correction are as follows. Figure 8 As shown.

[0180] Example 2: Detection limit after calibration using different bin sizes

[0181] Sample Collection: Peripheral blood was collected from eligible pregnant women into EDTA tubes according to peripheral blood collection standards. Plasma separation and library construction were performed, followed by sequencing using a high-throughput sequencer (e.g., Illumina, BGI), yielding sequencing files in FASTQ format. The entire analysis process was completed using the aforementioned bioinformatics analysis workflow. The Z-score was calculated, and fetal chromosomal aneuploidy was determined. FF represents fetal concentration. The results are as follows:

[0182] A T13 sample is a positive sample for trisomy 13. 13_100kb represents the Z-value of chromosome 13 when the bin size is 100kb, 13_50kb represents the Z-value when the bin size is 50kb, and 13_20kb represents the Z-value when the bin size is 20kb. A Z-value greater than 7 indicates a positive sample. When the bin size is 100kb, a fetal concentration of at least 3.37% for trisomy 13 can be detected. However, when the bin size is 20 or 50kb, a fetal concentration of 4.0% for trisomy 13 cannot be successfully detected. This indicates that for chromosome 13, using a bin size of 100kb can detect positive samples with even lower fetal concentrations.

[0183] T18 samples are positive for trisomy 18. 18_100kb represents the Z-value of chromosome 18 when bin size is 100kb, 18_50kb represents the Z-value when bin size is 50kb, and 18_20kb represents the Z-value when bin size is 20kb. A Z-value greater than 7 indicates a positive sample. When bin size is 100kb, a fetal concentration of at least 2.42% of trisomy 18 can be detected. When bin size is 50kb, a fetal concentration of at least 2.42% of trisomy 18 can be detected. However, when bin size is 20kb, a fetal concentration of at least 3.36% of trisomy 18 cannot be detected. This indicates that for chromosome 18, using bin sizes of 100kb or 50kb allows for the detection of positive samples with even lower fetal concentrations.

[0184] A T21 sample is a positive sample for trisomy 21. 21_100kb represents the Z-value of chromosome 21 at a bin size of 100kb, 21_50kb represents the Z-value at a bin size of 50kb, and 21_20kb represents the Z-value at a bin size of 20kb. A Z-value greater than 7 indicates a positive sample. When the bin size is 100kb, a fetal concentration of at least 3.21% of trisomy 21 can be detected. When the bin size is 50kb, a fetal concentration of at least 2.88% can be detected. However, when the bin size is 20kb, a fetal concentration of at least 2.88% of trisomy 21 cannot be successfully detected. This indicates that for chromosome 21, using bin sizes of 50kb or 20kb can detect positive samples with even lower fetal concentrations.

[0185] In summary, when the Z threshold is 7, the detection limits for T13 and T18 are lower when the bin size is 100kb; the detection limits for T18 and T21 are lower when the bin size is 50kb; and the detection limit for T21 is lower when the bin size is 20kb. Therefore, to achieve the lowest detection limit for the three chromosomes, the following bin size combinations can be set:

[0186] bin sizes of 100kb, 100kb, and 50kb were used to detect abnormalities in chromosomes 13, 18, and 21, respectively.

[0187] bin sizes of 100kb, 100kb, and 20kb were used to detect abnormalities in chromosomes 13, 18, and 21, respectively.

[0188] bin sizes of 100kb, 50kb, and 50kb were used to detect abnormalities in chromosomes 13, 18, and 21, respectively.

[0189] bin sizes of 100kb, 50kb, and 20kb were used to detect abnormalities in chromosomes 13, 18, and 21, respectively.

[0190] Table 1

[0191]

[0192] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0193] The above embodiments merely illustrate several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for calibrating sequencing data, characterized in that, include: Obtain whole-genome sequencing data of the test samples; The window length is determined based on the chromosome to be analyzed. The target chromosomes of the test sample are divided into windows according to the window length. The number of unique alignment sequences falling into each window on each target chromosome is obtained based on the whole genome sequencing data. When the chromosome to be analyzed is a sex chromosome, each target chromosome includes all autosomes and the chromosome to be analyzed. When the chromosome to be analyzed is an autosome, each target chromosome includes all autosomes. Obtain the GC content of each window, and perform GC correction on the number of unique alignment sequences of each window based on the GC content and the number of unique alignment sequences to obtain the corrected number of unique alignment sequences for each window; The ratio of the number of standard sequences to be analyzed in the test sample is determined based on the number of uniquely aligned sequences after correction for each window. The step of determining the window length based on the chromosome to be analyzed, and dividing the target chromosome of the detection sample into windows according to the window length, specifically includes: Obtain the GC content of the chromosome to be analyzed; If the GC content of the chromosome to be analyzed is not less than 0.38 and less than 0.39, the window length is determined to be 90kb~110kb, and the window of 90kb~110kb is used to divide the target chromosome of the sample to be tested into windows. If the GC content of the chromosome to be analyzed is not less than 0.39 and less than 0.40, the window length is determined to be 40kb~110kb, and the window of 40kb~110kb is used to divide the target chromosome of the sample to be tested into windows. If the GC content of the chromosome to be analyzed is not less than 0.40 and less than 0.41, the window length is determined to be 10kb~60kb, and the target chromosomes of the sample to be tested are divided into windows using a window of 10kb~60kb.

2. The method according to claim 1, characterized in that, GC correction methods include using Loess regression.

3. The method according to claim 1, characterized in that, The process involves obtaining the GC content of each window, and then performing GC correction on the number of unique alignment sequences for each window based on the GC content and the number of unique alignment sequences. The corrected number of unique alignment sequences for each window includes: Obtain the GC content of each window, and obtain the GC correction coefficient of each chromosome based on the GC content of each window on each chromosome and the number of unique aligned sequences; Based on the GC correction coefficient of each target chromosome, the number of unique aligned sequences in each window on the corresponding chromosome is GC corrected to obtain the number of unique aligned sequences in each window after GC correction.

4. The method according to claim 1, characterized in that, The method of determining the standard sequence number ratio of the chromosome to be analyzed in the test sample based on the number of uniquely aligned sequences after correction for each window includes: The number of uniquely aligned sequences after correction for each window and the total number of uniquely aligned sequences after correction for all autosomes are determined based on the number of uniquely aligned sequences after correction for each window. The ratio of the number of standard sequences of the chromosome to be analyzed in the test sample is determined based on the number of uniquely aligned sequences after correction of the chromosome to be analyzed and the total number of uniquely aligned sequences after correction of all autosomes.

5. A method for detecting chromosomal aneuploidy, characterized in that, include: The standard sequence number ratio of the chromosomes to be analyzed in the test sample and the standard sequence number ratio of the chromosomes to be analyzed in the negative sample set are obtained, wherein the standard sequence number ratio of the chromosomes to be analyzed is obtained by the method described in any one of claims 1 to 4; The Z score of the chromosome to be analyzed in the test sample is determined based on the ratio of the number of standard sequences of the chromosome to be analyzed in the test sample and the ratio of the number of standard sequences of the chromosome to be analyzed in the negative sample set. The aneuploidy of the chromosome to be analyzed in the test sample is determined based on the Z score of the chromosome to be analyzed in the test sample.

6. An apparatus for calibrating sequencing data, characterized in that, include: Sequencing data acquisition module: used to acquire whole genome sequencing data of the test sample; Window partitioning module: used to partition the target chromosomes of the test sample into windows of corresponding lengths according to the chromosomes to be analyzed. Based on the whole-genome sequencing data, the number of unique alignment sequences falling into each window on each target chromosome is obtained. When the chromosomes to be analyzed are sex chromosomes, each target chromosome includes all autosomes and the chromosome to be analyzed; when the chromosomes to be analyzed are autosomes, each target chromosome includes all autosomes. The specific steps of determining the window length based on the chromosomes to be analyzed and partitioning the target chromosomes of the test sample into windows based on the window length include: Obtain the GC content of the chromosome to be analyzed; If the GC content of the chromosome to be analyzed is not less than 0.38 and less than 0.39, the window length is determined to be 90kb~110kb, and the window of 90kb~110kb is used to divide the target chromosome of the sample to be tested into windows. If the GC content of the chromosome to be analyzed is not less than 0.39 and less than 0.40, the window length is determined to be 40kb~110kb, and the window of 40kb~110kb is used to divide the target chromosome of the sample to be tested into windows. If the GC content of the chromosome to be analyzed is not less than 0.40 and less than 0.41, the window length is determined to be 10kb~60kb, and the target chromosomes of the sample to be tested are divided into windows using a window of 10kb~60kb. GC Correction Module: Used to obtain the GC content of each window, and perform GC correction on the number of unique alignment sequences of each window based on the GC content and the number of unique alignment sequences, to obtain the corrected number of unique alignment sequences for each window; Standard sequence number ratio calculation module: used to determine the standard sequence number ratio of the chromosome to be analyzed in the test sample based on the number of unique aligned sequences after correction for each window.

7. A device for detecting chromosomal aneuploidy, characterized in that, include: Standard sequence number ratio acquisition module: used to acquire the standard sequence number ratio of the chromosome to be analyzed in the test sample and the standard sequence number ratio of the chromosome to be analyzed in the negative sample set, wherein the standard sequence number ratio of the chromosome to be analyzed is obtained by the method described in any one of claims 1 to 4; Z-score calculation module: used to determine the Z-score of the chromosome to be analyzed in the test sample based on the ratio of the number of standard sequences of the chromosome to be analyzed in the test sample and the ratio of the number of standard sequences of the chromosome to be analyzed in the negative sample set; Aneuploidy assessment module: used to determine the aneuploidy of the chromosomes to be analyzed in the test sample based on the Z score of the chromosomes to be analyzed in the test sample.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the method described in any one of claims 1 to 5.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method described in any one of claims 1 to 5.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the method described in any one of claims 1 to 5.

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