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WO2024140880A1 - Procédé et appareil d'analyse de variant de nombre de copies, et support de stockage - Google Patents

Procédé et appareil d'analyse de variant de nombre de copies, et support de stockage Download PDF

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Publication number
WO2024140880A1
WO2024140880A1 PCT/CN2023/142605 CN2023142605W WO2024140880A1 WO 2024140880 A1 WO2024140880 A1 WO 2024140880A1 CN 2023142605 W CN2023142605 W CN 2023142605W WO 2024140880 A1 WO2024140880 A1 WO 2024140880A1
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copy
value
number variation
copy number
fragment
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陈燕香
曾立董
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Genemind Biosciences Co Ltd
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Genemind Biosciences Co Ltd
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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
    • G16B20/10—Ploidy or copy number detection
    • 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
    • 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
    • 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
    • G16B5/00—ICT specially adapted for modelling or simulations in systems biology, e.g. gene-regulatory networks, protein interaction networks or metabolic networks

Definitions

  • the present application relates to the technical field of copy number variation detection, and in particular to a method, device and storage medium for copy number variation analysis.
  • Genome copy number variation is a manifestation of genetic diversity between two individuals. It is well known that abnormalities in genome copy number may be the root cause of certain diseases. Genome copy number variation, also known as copy number polymorphism, is a copy number abnormality of DNA fragments longer than 1kb. It is widely distributed in the human genome, and the mutation rate of CNV sites is much higher than SNP (Single nucleotide polymorphism).
  • CMA chromosome microarray analysis
  • Array CGH microarray comparative genomic hybridization technology
  • single nucleotide polymorphism chip analysis has been widely used as the gold standard for detection.
  • Array CGH Since Array CGH is performed under the premise of known probes, it is impossible to detect unknown CNVs; in addition, the cost is high and the burden on the subjects is heavy.
  • the core issue of CNV detection based on data obtained from low-depth whole-genome sequencing is how to use low-depth sequencing data to quickly and accurately locate CNV boundaries and length and other related information.
  • whole-genome sequencing data there are three main core steps for genome CNV analysis: 1. Data processing, including quality control, alignment of sequencing data with reference sequences, genome sequence window division, window alignment read segment (reads) information statistics, window data smoothing, etc. 2. Breakpoint identification and length division. 3. Further removal of biologically meaningless breakpoints and merged segments (segments) and CNV filtering and classification based on CNV length, reads statistical coverage information, etc.
  • the corresponding processing solutions are relatively mature and standardized, and there are relatively many related processing software and algorithm implementations.
  • different software and algorithms have different definitions of this part of the solution and corresponding standards.
  • the copy number variation analysis method and device of the present application can avoid large variance values due to large fluctuations in sequencing data, reduce false positive or false negative judgments caused by this, increase the number of true copy number variation detections, and thus improve the overall detection rate.
  • the preset variance variance ⁇ n, where n is a natural number less than or equal to 3.
  • the copy number variation analysis device of the present application actually implements the method of genome copy number variation analysis of the present application through various modules.
  • the copy number variation fragment acquisition module to be analyzed is used to implement the copy number variation fragment acquisition step 11 of the genome copy number variation analysis method of the present application
  • the breakpoint sorting module is used to implement the breakpoint sorting step 12 of the genome copy number variation analysis method of the present application
  • the breakpoint deletion module is used to implement the breakpoint deletion step 13 of the genome copy number variation analysis method of the present application
  • the candidate copy number variation fragment screening module is used to implement the candidate copy number variation fragment screening step 14 of the genome copy number variation analysis method of the present application; therefore, the specific implementation method or parameter conditions of each module in the copy number variation analysis device of the present application refer to the genome copy number variation analysis method of the present application, and will not be repeated here.
  • 16 clinical sample data with different cv values were selected, as shown in Table 1.
  • the cv values ranged from 0.084 to 0.309.
  • the 16 samples were provided and stored by Zhenmai Biotechnology. No CNVs were detected in the 16 samples using the DNA-copy package standard process.
  • This example uses the above genome copy number variation analysis method to simulate and generate CNVs with a length ranging from 0.5M to 10M based on the 16 clinical sample data, as shown in Table 2.
  • Test 1 The breakpoint filtering method included in the DNAcopy package was used to merge all initial breakpoints and filter CNVs.
  • the filtering threshold sd was set to 2.5 to obtain the final CNV results.
  • Test 2 The above genome copy number variation analysis method was used, and the breakpoint deletion step used method 1 for breakpoint deletion and merging, and the candidate copy number variation fragment screening step used method 1 for CNV filtering to obtain the final CNV result.
  • Test 2 The above genome copy number variation analysis method is used, and the breakpoint deletion step uses method 1 for breakpoint deletion and merging, and the candidate copy number variation fragment screening step uses method 1 for CNV filtering to obtain the final CNV result.
  • the details are as follows:
  • the average copy-ratio information of each copy number variation fragment to be analyzed is judged whether the absolute value of the difference between it and the overall mean is within the standard deviation. If it is greater than the standard deviation, the fragment mean is corrected to the overall mean, and all window values contained in the fragment are replaced by the values calculated by the correction formula. After this processing scheme, the standard deviation of the copy-ratio values of all windows in the sample genome will decrease, thereby obtaining a sample information set with relatively small deviations. The corrected data is used as an internal reference in the subsequent fragment difference judgment step.
  • the difference value is the absolute value of the difference between the average copy-ratio values of the two fragments after taking log2, that is, the first difference value.
  • (11) Calculate the z-score value based on the copy-ratio value of each window of the first copy number variation fragment and the mean and standard deviation of the normal distribution model of the corresponding similar window information set, and judge whether the window is significantly different from the normal window based on the z-score threshold set by the user, and judge the window greater than the z-score threshold as a significant difference window.
  • the set z-score threshold is the first z-score threshold, and the default value is 2.
  • the difference value is the absolute value of the difference between the average copy-ratio values of the two fragments after taking the log2, that is, the first difference value.
  • a window size is slid across the entire genome of the sample genome to capture the same number of windows corresponding to the candidate CNV, forming a certain number of fragment sets containing the same number of windows.
  • step (3) In the case of deleting the breakpoint, merge the two copy number variation fragments to be analyzed before and after, recalculate the average copy-ratio value of the merged fragment, and return to step (3). For the retained breakpoint, determine that its copy number variation fragment to be analyzed is a candidate copy number variation fragment.
  • the fragments with copy-ratio values between 0.9 and 1.1 are directly filtered.
  • the method of steps (5)-(8) is used to re-determine whether the fragment is an abnormal fragment.
  • the z-score value of the fragment is calculated based on the constructed normal distribution model elements, and the z-score value is compared with the threshold set by the user. The default value is 3.0. If the z-score value is greater than the threshold, the current fragment is judged to be an abnormal fragment and is retained. Otherwise, it is filtered out. Specifically including:
  • a window size is slid across the entire genome of the sample genome to capture the same number of windows corresponding to the candidate copy number variation fragments, forming a certain number of fragment sets containing the same number of windows.
  • the normal distribution model elements of the fragment information set corresponding to each fragment of the two candidate copy number variation fragments before and after the breakpoint are calculated respectively: mean (mean), variance (sd); that is, the fourth normal distribution model.
  • the z-score value corresponding to each fragment is calculated based on the normal distribution model elements and the average copy-ratio value of the candidate copy number variation fragments.
  • the z-score value is compared with the threshold set by the user. If the z-score value is greater than the threshold, the copy number variation fragment to be analyzed is judged to be an abnormal fragment, otherwise it is a normal fragment.
  • Test 4 The above genome copy number variation analysis method was used, and the breakpoint deletion step adopted method 1 and adopted the strategy of setting different z-score thresholds according to different cv values of samples to perform breakpoint deletion and merging.
  • the candidate copy number variation fragment screening step adopted method 2 for CNV filtering to obtain the final CNV result. The details are as follows:
  • the average copy-ratio information of each copy number variation fragment to be analyzed is judged whether the absolute value of the difference between it and the overall mean is within the standard deviation. If it is greater than the standard deviation, the fragment mean is corrected to the overall mean, and all window values contained in the fragment are replaced by the values calculated by the correction formula. After this processing scheme, the standard deviation of the copy-ratio values of all windows in the sample genome will decrease, thereby obtaining a sample information set with relatively small deviations. The corrected data is used as an internal reference in the subsequent fragment difference judgment step.
  • the difference value is the absolute value of the difference between the average copy-ratio values of the two fragments after taking the log2, that is, the first difference value.
  • the candidate CNV fragment is judged as a fragment with significant difference from the normal fragment, wherein 95% is a set ratio threshold.
  • the fragment with a copy-ratio value of 0.9-1.1 is directly filtered.
  • the method of steps (8)-(12) is used to re-determine whether the fragment is an abnormal fragment.
  • the z-score value of the fragment is calculated based on the constructed normal distribution model elements, and the z-score value is compared with the user-set The default value is 3.0. If the z-score value is greater than the threshold, the current segment is considered an abnormal segment and is retained, otherwise it is filtered out. Specifically:
  • the z-score value corresponding to each fragment is calculated based on the normal distribution model elements and the average copy-ratio value of the candidate copy number variation fragments.
  • This example uses the same 16 clinical sample data with different cv values as in Example 1, and uses a method similar to Example 1 to simulate and generate CNVs with lengths ranging from 10.5M to 20M, as shown in Table 7.
  • Simulated CNV length range 10.5M-20M, a total of 20 gradients
  • Test 3 Same as Test 3 of Example 1.
  • the total number of CNVs analyzed in the three tests is the same, all 14080, but the number of true CNVs detected in Test 1 is 7575, the number of false CNVs detected is 819, and the overall detection rate is only 0.5380; the number of true CNVs detected in Test 2 is 12991, the number of false CNVs detected is 469, and the overall detection rate is 0.9227; the number of true CNVs detected in Test 3 is 13482, the number of false CNVs detected is 393, and the overall detection rate is 0.9575; the number of true CNVs detected in Test 4 is 13878, the number of false CNVs detected is 465, and the overall detection rate is 0.9857. Comparing the above tests, it can be seen that the genome copy number variation analysis method of the present application can greatly increase the number of true CNV detections and improve the overall detection rate.

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Abstract

L'invention concerne un procédé et un appareil d'analyse de variant de nombre de copies, ainsi qu'un support de stockage. Le procédé de la présente demande consiste à : acquérir tous les points de rupture de chaque chromosome d'un génome d'échantillon à soumettre à une détection, puis prendre deux segments, dont les points de rupture sont associés, comme segments de variant du nombre de copies à analyser ; prendre, comme première valeur de différence, une valeur de différence entre les valeurs moyennes du rapport de copie des deux segments de variant du nombre de copies à analyser, dont les points de rupture sont associés, puis effectuer un séquençage des point de rupture en fonction de la première valeur de différence ; traiter les points de rupture en séquence, filtrer les points de rupture d'après la première valeur de différence des points de rupture et une valeur seuil minimale prédéfinie et/ou d'après la différence entre les segments de variant du nombre de copies à analyser des points de rupture et un segment normal, puis déterminer des segments de variant du nombre de copies candidats ; et filtrer les segments de variant de nombre de copies candidats d'après les valeurs de rapport de copie, déterminer les segments anormaux et les prendre comme segments de variant du nombre de copies. Selon la présente demande, le procédé permet d'éviter une détermination erronée provoquée par une grande fluctuation des données de séquençage, ainsi que d'augmenter le nombre réel de variants de nombre de copies détectés, ce qui permet d'augmenter le taux de détection global.
PCT/CN2023/142605 2022-12-30 2023-12-28 Procédé et appareil d'analyse de variant de nombre de copies, et support de stockage Ceased WO2024140880A1 (fr)

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CN119864076A (zh) * 2025-03-25 2025-04-22 广州凯普医学检验所有限公司 基因组拷贝数变异分析数据的处理方法、装置及设备

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