Method, device and equipment for processing genome copy number variation analysis data

Abstract

The invention relates to the technical field of biological information, and discloses a method, a device and equipment for processing genome copy number variation analysis data, and the method comprises the following steps: obtaining copy numbers of all partition intervals of genome sequence sample data; combining adjacent partition intervals with similar copy numbers to form a combined interval; screening the combination interval according to the interval size to obtain a target combination interval greater than a preset threshold value; based on the copy number, classifying the partition intervals in the target combination interval, and counting the number of the intervals of each classification; for each target combination interval, screening the partition intervals in the target combination interval based on the corresponding number of intervals to obtain a target partition interval; and performing drawing display based on the data in the target partition interval. According to the method, data screening logic and conditions capable of accurately presenting the whole genome CNV result are provided, interference information is eliminated, dispute sites are reduced, and the accuracy and readability of detection result interpretation are improved.

Description

Technical Field

[0001] The present invention relates to the field of bioinformatics, and in particular to a method, device and equipment for processing genome copy number variation analysis data. Background Art

[0002] Genome copy number variation (CNV) is a genetic test used to identify the parts of an individual's genomic DNA sequence that are increased or decreased relative to a reference sequence. These changes may include gene duplication, deletion, or insertion, which may be associated with certain genetic diseases or cancers. Currently, the main methods for detecting CNVs include array-based comparative genomic hybridization (aCGH), single nucleotide polymorphism array (SNParray), multiplex ligation-dependent probe amplification (MLPA), etc. Among them, the MLPA method is the gold standard for detecting chromosome copy number variation. With the completion of the Human Genome Project, DNA sequencing technology has entered a stage of rapid development. Genome copy number variation detection (CNV) based on next-generation sequencing (NGS) technology and bioinformatics analysis has provided a new means for prenatal diagnosis and genetic disease screening.

[0003] CNV is an important component of human genetic diversity and plays an important role in diagnosing patients with unknown clinical symptoms and identifying established fetal chromosomal disease syndromes. High-depth double-end whole-genome sequencing (>30×) has high sensitivity and resolution for CNV detection, but it is expensive and has a long test cycle. At the same time, it has high requirements for computer hardware conditions and analysts. Recent studies have found that low-depth whole-genome sequencing technology (1×-5×) or even ultra-low-depth whole-genome sequencing technology (0.1×) can be used to detect aneuploidy variations in 23 pairs of chromosomes and microdeletions / microduplications greater than 50kb / 100kb / 500kb, which can be used to exclude the genetic causes of various diseases such as recurrent spontaneous abortion, congenital malformations, and developmental delay. Compared with high-depth sequencing, low-depth whole-genome sequencing is cheaper, which can reduce the cost of detection, and the detection time is short, so genomic information can be obtained quickly. However, the interpretation of low-depth whole-genome CNV analysis results is difficult. Summary of the invention

[0004] In view of this, the present invention provides a method, device and equipment for processing genome copy number variation analysis data to solve the problem of difficulty in interpreting low-depth or even ultra-low-depth whole-genome CNV analysis results.

[0005] In a first aspect, the present invention provides a method for processing genome copy number variation analysis data based on ultra-low depth whole genome high-throughput sequencing, the method comprising: Obtain the copy number of each partition interval of the genome sequence sample data; Merge adjacent partition intervals with similar copy numbers to form combined intervals; The combination intervals are screened according to the interval size to obtain a target combination interval that is greater than a preset threshold; Based on the copy number, the partition intervals within the target combination interval are classified respectively, and the number of intervals in each classification is counted; For each of the target combination intervals, based on the number of intervals corresponding to the target combination interval, the partition intervals in the target combination interval are screened to obtain a target partition interval; A plot display is performed based on the data in the target partition interval.

[0006] In an optional embodiment, before merging adjacent partition intervals with similar copy numbers to form a combined interval, the process further includes: For the first partition interval corresponding to the Y chromosome in the partition interval, if the copy number is less than the first preset threshold, the first partition interval is deleted.

[0007] In an optional implementation, the value range of the first preset threshold is 0.4-0.6.

[0008] In an optional implementation, for each of the target combined intervals, based on the multiple numbers of intervals in the target combined interval, screening the partition intervals in the target combined interval to obtain the target partition interval includes: Determining corresponding partition interval screening rules based on the number of the multiple intervals of the target combination interval; The partition intervals in the target combination interval are screened according to the determined partition interval screening rule.

[0009] In an optional embodiment, a partition interval screening rule for screening the partition intervals in the target combination interval is: screening out the partition intervals in the target combination interval whose copy numbers are within a preset range, wherein the preset range is a set value or is determined based on the copy number of the target combination interval.

[0010] In an optional embodiment, the partition intervals within the target combination interval are classified based on the copy number, and the number of intervals in each classification is counted, including: Classify the partition intervals whose copy number is less than the second preset threshold into a first category, and obtain the number of first intervals by counting; Classify the partition intervals whose copy number is greater than or equal to the second preset threshold and less than or equal to the third preset threshold into a second category, and obtain the number of second intervals by counting; Classify the partition intervals whose copy number is greater than the third preset threshold into a third category, and obtain the number of third intervals by counting; The second preset threshold is smaller than the third preset threshold.

[0011] In an optional implementation, the second preset threshold value ranges from 1.6 to 1.8, and the third preset threshold value ranges from 2.2 to 2.4.

[0012] In an optional implementation, the determining corresponding partition interval screening rules based on the number of the multiple intervals of the target combined interval includes: If the sum of the first interval quantity and the third interval quantity is less than or equal to the second interval quantity, the corresponding partition interval screening rule is determined as follows: screening out the partition intervals in the target combination interval whose copy number is within the first preset range; If the sum of the second interval quantity and the third interval quantity is less than or equal to the first interval quantity, the corresponding partition interval screening rule is determined as follows: screening out the partition intervals in the target combination interval whose copy number is within the second preset range; If the sum of the first interval quantity and the second interval quantity is less than or equal to the third interval quantity, the corresponding partition interval screening rule is determined as follows: screening out the partition intervals in the target combination interval whose copy number is within the second preset range; The first preset range is a copy number range set based on the second preset threshold and the third preset threshold; the second preset range is a copy number range set based on the copy number of the target combination interval.

[0013] In an optional embodiment, the first preset range is 1.9-2.1; and / or the starting point value of the second preset range is the copy number of the target combination interval minus 0.1, and the end point value of the second preset range is the copy number of the target combination interval plus 0.1.

[0014] In an optional implementation, obtaining the copy number of each partition interval of the genome sequence sample data includes: Obtaining the copy number ratio of each partition interval of the genome sequence sample data; If the genome sequence sample data includes Y chromosome data, then for the second partition interval corresponding to the Y chromosome and the second partition interval corresponding to the X chromosome, the copy number is determined to be For other partition intervals other than the second partition interval, the copy number is determined to be ; If the genome sequence sample data does not include Y chromosome data, the copy number of each partition interval is determined as ; Here, ratio is the copy number ratio.

[0015] In a second aspect, the present invention provides a device for processing genome copy number variation analysis data based on ultra-low depth whole genome high-throughput sequencing, the device comprising: The interval copy number acquisition module is used to obtain the copy number of each partition interval of the genome sequence sample data; A first screening module is used to delete the first partition interval corresponding to the Y chromosome if the copy number is less than a first preset threshold; A merging module, used to merge adjacent partition intervals with similar copy numbers to form a combined interval; A second screening module is used to screen the combination intervals according to the interval size to obtain a target combination interval greater than a preset threshold; A classification and statistics module, used for classifying the partition intervals within the target combination interval based on the copy number, and counting the number of intervals in each classification; A third screening module is used for screening the partition intervals in the target combination interval based on the number of intervals corresponding to the target combination interval for each target combination interval, so as to obtain a target partition interval; A drawing display module is used to perform drawing display based on the data in the target partition interval.

[0016] In a third aspect, the present invention provides a computer device, comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the method for processing genomic copy number variation analysis data of the first aspect or any corresponding embodiment thereof by executing the computer instructions.

[0017] In a fourth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the method for processing genomic copy number variation analysis data of the first aspect or any corresponding embodiment thereof.

[0018] In a fifth aspect, the present invention provides a computer program product, comprising computer instructions, wherein the computer instructions are used to enable a computer to execute the method for processing genomic copy number variation analysis data of the first aspect or any corresponding embodiment thereof.

[0019] In view of the problem that the CNV result graphs of low-depth or even ultra-low-depth (0.1×) whole genome are mainly presented as discrete dot graphs, which brings challenges to identifying the actual situation of CNV, this embodiment proposes a processing method, device and equipment for genome copy number variation analysis data based on ultra-low-depth whole genome high-throughput sequencing, provides data screening logic and conditions that can accurately present the CNV results of the whole genome, eliminates interference information, reduces controversial sites, is conducive to the intuitive presentation of results, reduces disputes in the interpretation of results, facilitates correct interpretation, and improves the accuracy and readability of the test results.

[0020] In addition, in the embodiment of the present invention, by deleting the first partition interval corresponding to the Y chromosome with a smaller copy number, the error interference caused by factors such as reagents and sequencing errors is eliminated, thereby further improving the accuracy and readability of the test results. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the related technologies, the drawings required for use in the specific embodiments or the related technical descriptions will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0022] Figure 1 It is a flowchart of a method for processing genome copy number variation analysis data based on ultra-low depth whole genome high-throughput sequencing according to an embodiment of the present invention; Figure 2 It is a flowchart of another method for processing genome copy number variation analysis data based on ultra-low depth whole genome high-throughput sequencing according to an embodiment of the present invention; Figure 3 is a schematic diagram of male gene CNV analysis results according to an embodiment of the present invention; Figure 4 is a schematic diagram of a combination interval and a corresponding copy number ratio value according to an embodiment of the present invention; Figure 5 A schematic diagram of a screened combination interval and corresponding copy number ratio value according to an embodiment of the present invention; Figure 6 A schematic diagram of a screened high-confidence partition interval and a corresponding copy number ratio value according to an embodiment of the present invention; Figure 7 A schematic diagram of visualization of optimized interval copy number according to an embodiment of the present invention; Figure 8 A schematic diagram of visualization of interval copy number before optimization according to an embodiment of the present invention; Fig. 9 is a schematic diagram of CNV analysis results of female genes according to an embodiment of the present invention; Fig.10 is a schematic diagram of another combination interval and corresponding copy number ratio value according to an embodiment of the present invention; Fig.11 A schematic diagram of another screened combination interval and corresponding copy number ratio value according to an embodiment of the present invention; Fig.12 A schematic diagram of another screened high-confidence partition interval and corresponding copy number ratio value according to an embodiment of the present invention; Fig.13 Another optimized interval copy number visualization schematic diagram according to an embodiment of the present invention; Fig.14 Another schematic diagram of visualization of interval copy number before optimization according to an embodiment of the present invention; Fig.15 It is a structural block diagram of a device for processing genome copy number variation analysis data based on ultra-low depth whole genome high-throughput sequencing according to an embodiment of the present invention; Fig.16 It is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0023] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0024] According to an embodiment of the present invention, an embodiment of a method for processing genome copy number variation analysis data based on ultra-low depth whole genome high-throughput sequencing is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of executable computer instructions, and, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in an order different from that shown here.

[0025] In this embodiment, a method for processing genome copy number variation analysis data based on ultra-low depth whole genome high-throughput sequencing is provided, which can be used in various computer devices, including terminals and servers, etc. Figure 1 is a flow chart of a method for processing genome copy number variation analysis data based on ultra-low depth whole genome high-throughput sequencing according to an embodiment of the present invention, such as Figure 1 As shown, the process includes the following steps: Step S101, obtaining the copy number of each partition interval (bin interval) of genome sequence sample data.

[0026] Specifically, genome sequence sample data, such as human genome sequence sample data, is data obtained through whole genome sequencing, for example, it may be data obtained through ultra-low depth (0.1×) whole genome high-throughput sequencing.

[0027] Taking human genome sequence sample data as an example, the whole genome of the sample can be sequenced first, and then the obtained whole genome sequencing data can be quality controlled and filtered to obtain high-quality genome sequence sample data. The high-quality genome sequence sample data is then compared, sorted, and deduplicated with the human reference genome to obtain the genome sequence data of the person in the sample, that is, the human genome sequence sample data. Finally, the human genome sequence sample data is compared with the detection baseline to obtain chromosome information (X chromosome, Y chromosome, or chromosomes 1-22), partition interval starting position, and copy number ratio value.

[0028] The above detection baseline can be determined by the following method: first, the human genome data in the 1000 Genomes Project is compared with the reference genome and deduplication is performed to obtain a high-quality 1000 Genomes reference sequence; then, the high-quality 1000 Genomes reference sequence obtained in the previous step is divided into multiple continuous intervals of equal length according to a preset size (e.g., 100kb) as the detection baseline. The subsequent CNV analysis is to compare the human genome sequence sample data with the detection baseline. Therefore, the partitioning method of the human genome sequence sample data is consistent with the partitioning method corresponding to the detection baseline.

[0029] In the embodiments of the present invention, the comparison baseline is constructed using the data of 1,000 genomes to prevent individual differences from affecting the results, improve the reliability and detection sensitivity of the statistical analysis, and help to more comprehensively understand the variation of the genome. In addition, the baseline constructed using a large amount of data is more likely to represent the genomic characteristics of the entire population or a specific group. This makes the analysis results more universal, helps to more broadly and comprehensively understand the variation of the genome, and can be applied to a wider range of populations or disease studies.

[0030] In some optional specific implementations, the above step S101, i.e., obtaining the copy number of each partition interval of the genome sequence sample data, includes: Obtaining the copy number ratio of each partition interval of the genome sequence sample data; If the genome sequence sample data includes Y chromosome data, then for the second partition interval corresponding to the Y chromosome and the second partition interval corresponding to the X chromosome, the copy number is determined to be For other partition intervals other than the second partition interval, the copy number is determined to be ; If the genome sequence sample data does not include Y chromosome data, the copy number of each partition interval is determined as ; Here, ratio is the copy number ratio.

[0031] In the embodiment of the present invention, the calculation method of the copy number in different partition intervals may be different, which can be expressed as follows: ; .

[0032] Step S102, merge adjacent partition intervals with similar copy numbers to form a combined interval. Specifically, relevant technologies can be used to complete copy number similarity judgment and interval merging. Copy number similarity, for example, can be determined when the copy number difference is less than a set threshold.

[0033] Specifically, after the combined intervals are formed, the chromosome information (X chromosome, Y chromosome, or chromosomes 1-22), the starting position of the combined interval, and the copy number ratio value of each combined interval are obtained.

[0034] Step S103, screening the combined intervals according to interval sizes to obtain target combined intervals greater than a preset threshold.

[0035] The preset threshold may be, for example, 4.5 Mb or 4.0 Mb.

[0036] Step S104: based on the copy number, classify the partition intervals within the target combination interval respectively, and count the number of intervals in each classification.

[0037] Step S105 : for each of the target combined intervals, based on the number of intervals corresponding to the target combined interval, the partitioned intervals in the target combined interval are screened to obtain a target partitioned interval.

[0038] Step S106: Drawing and displaying based on the data in the target partition interval, for example, using R language to draw and then display.

[0039] In view of the problem that the CNV result graphs of low-depth or even ultra-low-depth (0.1×) whole genome are mainly presented as discrete dot graphs, which brings challenges to identifying the actual situation of CNV, this embodiment proposes a processing method for genome copy number variation analysis data based on ultra-low-depth whole genome high-throughput sequencing, provides data screening logic and conditions that can accurately present the CNV results of the whole genome, excludes interference information, reduces controversial sites, is conducive to the intuitive presentation of results, reduces disputes in the interpretation of results, facilitates correct interpretation, and improves the accuracy and readability of the test results.

[0040] In this embodiment, a method for processing genome copy number variation analysis data based on ultra-low depth whole genome high-throughput sequencing is provided, which can be used in various computer devices. Figure 2 is a flow chart of a method for processing genome copy number variation analysis data based on ultra-low depth whole genome high-throughput sequencing according to an embodiment of the present invention, such as Figure 2 As shown, the process includes the following steps: Step S201, obtain the copy number of each partition interval of the genome sequence sample data. For details, please refer to Figure 1 Step S101 of the illustrated embodiment will not be described in detail here.

[0041] Step S202: For a first partition interval corresponding to the Y chromosome in the partition interval, if the copy number is less than a first preset threshold, the first partition interval is deleted. The first partition interval is a partition interval corresponding to the Y chromosome in a plurality of partition intervals corresponding to the genome sequence sample data. In the embodiment of the present invention, before performing interval division, the X chromosome sequence, the Y chromosome sequence, and other chromosome sequences are first distinguished, and then the interval division is performed for the X chromosome sequence, the Y chromosome sequence, and other chromosome sequences, respectively.

[0042] Of course, if there is no Y chromosome in the genome sequence sample data, such as female genome sequence sample data, there is no need to perform this screening step.

[0043] The value range of the first preset threshold is 0.4-0.6, for example, it can be 0.5. The calculation method of the copy number of the first partition interval corresponding to the Y chromosome is: , ratio is the copy number ratio value.

[0044] In the embodiment of the present invention, by deleting the first partition interval corresponding to the Y chromosome with a smaller copy number, the error interference caused by factors such as reagents and sequencing errors is eliminated, and the accuracy and readability of the test results are further improved.

[0045] Step S203: merge adjacent partition intervals with similar copy numbers to form a combined interval. Figure 1 Step S102 of the illustrated embodiment will not be described in detail here.

[0046] Step S204: filter the combined intervals according to the interval size to obtain a target combined interval greater than a preset threshold. Figure 1 Step S103 of the illustrated embodiment will not be described in detail here.

[0047] Step S205 , based on the copy number, classify the partition intervals within the target combination interval respectively, and count the number of intervals in each classification.

[0048] Specifically, the above step S205, i.e., classifying the partition intervals within the target combination interval based on the copy number, and counting the number of intervals in each classification, includes: Step S2051, classify the partition intervals whose copy number is less than the second preset threshold into a first category, and obtain the number of the first intervals by counting.

[0049] Step S2052: classify the partition intervals whose copy number is greater than or equal to the second preset threshold and less than or equal to the third preset threshold into a second category, and obtain the number of second intervals by counting.

[0050] Step S2053: classify the partition intervals whose copy numbers are greater than the third preset threshold into a third category, and obtain the number of third intervals by counting.

[0051] The second preset threshold is smaller than the third preset threshold.

[0052] Specifically, the second preset threshold value ranges from 1.6 to 1.8, and the third preset threshold value ranges from 2.2 to 2.4. For example, the second preset threshold value may be 1.7, and the third preset threshold value may be 2.3.

[0053] Step S206: for each of the target combined intervals, based on the number of intervals corresponding to the target combined interval, the partitioned intervals in the target combined interval are screened to obtain a target partitioned interval.

[0054] In some optional specific implementations, step S206, i.e., for each of the target combined intervals, based on the multiple numbers of intervals in the target combined interval, screening the partition intervals in the target combined interval to obtain the target partition interval, includes: Step S2061: determining a corresponding partition interval screening rule based on the number of intervals of the target combination interval.

[0055] In some optional specific implementations, the partition interval screening rule for screening the partition intervals in the target combination interval is: screening out the partition intervals in the target combination interval whose copy numbers are within a preset range, wherein the preset range is a set value or is determined based on the copy number of the target combination interval.

[0056] In some optional specific implementations, the above step S2061, i.e., determining the corresponding partition interval screening rule based on the number of the multiple intervals of the target combined interval, includes: If the sum of the first interval quantity and the third interval quantity is less than or equal to the second interval quantity, the corresponding partition interval screening rule is determined as follows: screening out the partition intervals in the target combination interval whose copy number is within the first preset range; If the sum of the second interval quantity and the third interval quantity is less than or equal to the first interval quantity, the corresponding partition interval screening rule is determined as follows: screening out the partition intervals in the target combination interval whose copy number is within the second preset range; If the sum of the first interval quantity and the second interval quantity is less than or equal to the third interval quantity, the corresponding partition interval screening rule is determined as follows: screening out the partition intervals in the target combination interval whose copy number is within the second preset range; Among them, the first preset range is a copy number range set based on the second preset threshold and the third preset threshold; the second preset range is a copy number range set based on the copy number of the target combination interval. The starting value of the second preset range can be the copy number of the target combination interval minus the preset value, and the end value can be the copy number of the target combination interval plus the preset value, and the preset value can be in the range of 0.05-0.2. For example, the starting value of the first preset range is greater than the second preset threshold and the end value is less than the third preset threshold, for example, the second preset threshold is 1.7 and the third preset threshold is 2.3, then the first preset range is 1.9-2.1. The second preset range can be a range centered on the copy number of the target combination interval, for example, the copy number of the target combination interval minus 0.1 is the starting value of the second preset range, and the copy number of the target combination interval plus 0.1 is the end value of the second preset range.

[0057] Step S2062: Screen the partition intervals in the target combination interval according to the determined partition interval screening rule.

[0058] Step S207: plotting and displaying the data in the target partition interval, for example, using R language to plot the copy number in the target partition interval and then displaying it.

[0059] In a specific embodiment, for a male gene sample, by comparing with the detection baseline, a total of 30,894 partition intervals and corresponding copy number ratio values ​​were obtained ( Figure 3 On this basis, adjacent partition intervals with similar copy numbers were merged to obtain a total of 73 combined intervals (also called segment intervals) representing the copy number level of the fragment and the corresponding copy number ratio values ​​( Figure 4 ). The copy number ratio values ​​were then converted to corresponding copy number values, empty partition intervals without sequencing reads were filtered, and partition intervals with copy numbers less than 0.5 on chromosome Y were filtered to exclude contamination. Combination intervals below 4 Mb were filtered, and a total of 61 combination intervals above 4 Mb and corresponding copy numbers were obtained ( Figure 5 ). The copy number statistics and screening were performed on the partition intervals in each combined interval, and a total of 6071 high-confidence partition intervals and corresponding copy number values ​​were obtained ( Figure 6 ). Visualize the copy number values ​​of the partition intervals, and you can see the optimized copy number graph ( Figure 7 , Figure 7 The horizontal axis chr1-chr22, and chrX and chrY represent 23 pairs of chromosomes) are compared with the unfiltered copy number map ( Figure 8 The horizontal axis chr1-chr22, and chrX and chrY in the figure represent 23 pairs of chromosomes) are more intuitive and easy to judge.

[0060] In another specific embodiment, for a female gene sample, by comparing with the detection baseline, a total of 30321 partition intervals and corresponding copy number ratios were obtained ( Fig. 9 On this basis, adjacent partition intervals with similar copy numbers were merged to obtain a total of 88 combined intervals representing the copy number level of the fragment and the corresponding copy number ratio values ​​( Fig.10 ). The copy number ratio values ​​were then converted to corresponding copy number values, and empty partition intervals without sequencing reads were filtered. Combination intervals below 4 Mb were filtered to obtain 74 combination intervals above 4 Mb and their corresponding copy number values ​​( Fig.11 ). The copy number statistics and screening were performed on the partition intervals in each combined interval, and a total of 6113 high-confidence partition intervals and corresponding copy number values ​​were obtained ( Fig.12 ). Visualize the copy number values ​​of the partition intervals, and you can also see the optimized copy number graph ( Fig.13 , the horizontal axis chr1-chr22, and chrX and chrY represent 23 pairs of chromosomes) are compared with the unfiltered copy number map ( Fig.14 The horizontal axis chr1-chr22, and chrX and chrY in the figure represent 23 pairs of chromosomes) are more intuitive and easy to judge.

[0061] In this embodiment, a processing device for genome copy number variation analysis data is also provided, which is used to implement the above-mentioned embodiments and preferred embodiments, and will not be repeated here. As used below, the term "module" can implement a combination of software and / or hardware for a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0062] This embodiment provides a processing device for genome copy number variation analysis data based on ultra-low depth whole genome high-throughput sequencing, such as Fig.15 As shown, including: The interval copy number acquisition module 1501 is used to obtain the copy number of each partition interval of the genome sequence sample data; A first screening module 1502 is configured to delete a first partition interval corresponding to the Y chromosome in the partition interval if the copy number is less than a first preset threshold; A merging module 1503 is used to merge adjacent partition intervals with similar copy numbers to form a combined interval; The second screening module 1504 is used to screen the combination intervals according to the interval size to obtain a target combination interval greater than a preset threshold; A classification and statistics module 1505 is used to classify the partition intervals within the target combination interval based on the copy number, and count the number of intervals in each classification; A third screening module 1506 is used for screening the partition intervals in the target combination interval based on the number of intervals corresponding to the target combination interval for each target combination interval, to obtain a target partition interval; The drawing and display module 1507 is used to perform drawing and display based on the data in the target partition interval.

[0063] In some optional implementations, the third screening module 1506 includes: A screening rule determination unit, configured to determine a corresponding partition interval screening rule based on the plurality of interval numbers of the target combined interval; A screening unit is used to screen the partition intervals in the target combination interval according to the determined partition interval screening rule.

[0064] In some optional embodiments, the partition interval screening rule for screening the partition intervals in the target combination interval is: screening out the partition intervals in the target combination interval whose copy numbers are within a preset range, wherein the preset range is a set value or is determined based on the copy number of the target combination interval.

[0065] In some optional implementations, the classification statistics module 1505 includes: A first classification and statistical unit, used for classifying the partition intervals whose copy numbers are less than a second preset threshold into a first category, and obtaining the number of first intervals by statistics; A second classification and statistics unit is used to classify the partition intervals whose copy number is greater than or equal to the second preset threshold and less than or equal to the third preset threshold into a second category, and obtain the number of second intervals by statistics; A third classification and statistical unit, used to classify the partition intervals whose copy numbers are greater than the third preset threshold into a third category, and obtain the number of third intervals by statistics; The second preset threshold is smaller than the third preset threshold.

[0066] In some optional implementations, the screening rule determination unit is specifically used to determine the corresponding partition interval screening rule as follows: if the sum of the first interval number and the third interval number is less than or equal to the second interval number, the partition interval screening rule is as follows: the partition intervals with copy numbers within a first preset range in the target combination interval are screened out; if the sum of the second interval number and the third interval number is less than or equal to the first interval number, the partition interval screening rule is as follows: the partition intervals with copy numbers within a second preset range in the target combination interval are screened out; if the sum of the first interval number and the second interval number is less than or equal to the third interval number, the partition interval screening rule is as follows: the partition intervals with copy numbers within a second preset range in the target combination interval are screened out; The first preset range is a copy number range set based on the second preset threshold and the third preset threshold; the second preset range is a copy number range set based on the copy number of the target combination interval.

[0067] In some optional implementations, the interval copy number acquisition module 1501 includes: A copy number ratio acquisition unit, used to acquire the copy number ratio of each partition interval of the genome sequence sample data; The first interval copy number acquisition unit is used to determine the copy number for the second partition interval corresponding to the Y chromosome and the second partition interval corresponding to the X chromosome if the genome sequence sample data includes Y chromosome data. For other partition intervals other than the second partition interval, the copy number is determined to be ; The second interval copy number acquisition unit is used to determine the copy number of each partition interval if the genome sequence sample data does not include Y chromosome data. ; Here, ratio is the copy number ratio.

[0068] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0069] The processing device for genomic copy number variation analysis data in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0070] The embodiment of the present invention also provides a computer device having the above Fig.15 The apparatus for processing genomic copy number variation analysis data is shown.

[0071] See also Fig.16 , Fig.16 is a schematic diagram of the structure of a computer device provided by an optional embodiment of the present invention, such as Fig.16 As shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components are connected to each other using different buses for communication, and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in or on the memory to display the graphical information of the GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Fig.16 A processor 10 is taken as an example.

[0072] The processor 10 may be a central processing unit, a network processor or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be a dedicated integrated circuit, a programmable logic device or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic or any combination thereof.

[0073] The memory 20 stores instructions executable by at least one processor 10, so that the at least one processor 10 executes the method shown in the above embodiment.

[0074] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely arranged relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0075] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid state drive; the memory 20 may also include a combination of the above types of memory.

[0076] The computer device also includes an input device 30 and an output device 40. The processor 10, the memory 20, the input device 30 and the output device 40 may be connected via a bus or other means. Fig.16 The example of connecting through bus is taken in the following.

[0077] The input device 30 can receive input digital or character information, and generate key signal input related to the user settings and function control of the computer device, such as a touch screen, a keypad, a mouse, a track pad, a touch pad, an indicator bar, one or more mouse buttons, a trackball, a joystick, etc. The output device 40 may include a display device, an auxiliary lighting device (e.g., an LED) and a tactile feedback device (e.g., a vibration motor), etc. The above-mentioned display device includes but is not limited to a liquid crystal display, a light emitting diode, a display and a plasma display. In some optional embodiments, the display device may be a touch screen.

[0078] The computer device also includes a communication interface, which is used for the computer device to communicate with other devices or a communication network.

[0079] The embodiment of the present invention also provides a computer-readable storage medium. The method according to the embodiment of the present invention can be implemented in hardware, firmware, or can be implemented as a computer code that can be recorded in a storage medium, or can be implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and will be stored in a local storage medium through a network download, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state hard disk, etc.; further, the storage medium can also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor, or hardware, the method shown in the above embodiment is implemented.

[0080] A part of the present invention may be applied as a computer program product, such as a computer program instruction, which, when executed by a computer, can call or provide the method and / or technical solution according to the present invention through the operation of the computer. Those skilled in the art should understand that the existence of the computer program instruction in a computer-readable medium includes, but is not limited to, a source file, an executable file, an installation package file, etc., and accordingly, the way in which the computer program instruction is executed by the computer includes, but is not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Here, the computer-readable medium may be any available computer-readable storage medium or communication medium accessible to the computer.

[0081] Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A method for processing genome copy number variation analysis data based on ultra-low depth whole genome high-throughput sequencing, characterized in that: The method comprises: Obtain the copy number of each partition interval of the genome sequence sample data; For a first partition interval corresponding to the Y chromosome in the partition interval, if the copy number is less than a first preset threshold, deleting the first partition interval; Merge adjacent partition intervals with similar copy numbers to form combined intervals; The combination intervals are screened according to the interval size to obtain a target combination interval that is greater than a preset threshold; Based on the copy number, the partition intervals within the target combination interval are classified respectively, and the number of intervals in each classification is counted; For each of the target combination intervals, based on the number of intervals corresponding to the target combination interval, the partition intervals in the target combination interval are screened to obtain a target partition interval; A plot display is performed based on the data in the target partition interval.

2. The method according to claim 1, characterized in that The value range of the first preset threshold is 0.4-0.

6.

3. The method according to claim 1, characterized in that The step of screening the partition intervals in the target combined interval based on the multiple numbers of intervals in the target combined interval to obtain the target partition interval includes: Determining corresponding partition interval screening rules based on the number of the multiple intervals of the target combination interval; The partition intervals in the target combination interval are screened according to the determined partition interval screening rule.

4. The method according to claim 3, characterized in that The partition interval screening rule for screening the partition intervals in the target combination interval is: screening out the partition intervals in the target combination interval whose copy numbers are within a preset range, wherein the preset range is a set value or is determined based on the copy number of the target combination interval.

5. The method according to claim 4, characterized in that The step of classifying the partition intervals within the target combination interval based on the copy number, and counting the number of intervals in each classification, includes: Classify the partition intervals whose copy number is less than the second preset threshold into a first category, and obtain the number of first intervals by counting; Classify the partition intervals whose copy number is greater than or equal to the second preset threshold and less than or equal to the third preset threshold into a second category, and obtain the number of second intervals by counting; Classify the partition intervals whose copy number is greater than the third preset threshold into a third category, and obtain the number of third intervals by counting; The second preset threshold has a value range of 1.6-1.8, and the third preset threshold has a value range of 2.2-2.

4.

6. The method according to claim 5, characterized in that The determining of corresponding partition interval screening rules based on the number of the multiple intervals of the target combination interval includes: If the sum of the first interval quantity and the third interval quantity is less than or equal to the second interval quantity, the corresponding partition interval screening rule is determined as follows: screening out the partition intervals in the target combination interval whose copy number is within the first preset range; If the sum of the second interval quantity and the third interval quantity is less than or equal to the first interval quantity, the corresponding partition interval screening rule is determined as follows: screening out the partition intervals in the target combination interval whose copy number is within the second preset range; If the sum of the first interval quantity and the second interval quantity is less than or equal to the third interval quantity, the corresponding partition interval screening rule is determined as follows: screening out the partition intervals in the target combination interval whose copy number is within the second preset range; The first preset range is a copy number range set based on the second preset threshold and the third preset threshold; the second preset range is a copy number range set based on the copy number of the target combination interval.

7. The method according to claim 6, characterized in that The first preset range is 1.9-2.1; and / or the starting point value of the second preset range is the copy number of the target combination interval minus 0.1, and the end point value of the second preset range is the copy number of the target combination interval plus 0.

1.

8. The method according to any one of claims 1 to 3, characterized in that The obtaining of the copy number of each partition interval of the genome sequence sample data includes: Obtaining the copy number ratio of each partition interval of the genome sequence sample data; If the genome sequence sample data includes Y chromosome data, then for the second partition interval corresponding to the Y chromosome and the second partition interval corresponding to the X chromosome, the copy number is determined to be For other partition intervals other than the second partition interval, the copy number is determined to be ; If the genome sequence sample data does not include Y chromosome data, the copy number of each partition interval is determined as ; Here, ratio is the copy number ratio.

9. A processing device for genome copy number variation analysis data based on ultra-low depth whole genome high-throughput sequencing, characterized in that: The device comprises: The interval copy number acquisition module is used to obtain the copy number of each partition interval of the genome sequence sample data; A first screening module, for deleting a first partition interval corresponding to the Y chromosome in the partition interval if the copy number is less than a first preset threshold; A merging module, used to merge adjacent partition intervals with similar copy numbers to form a combined interval; A second screening module is used to screen the combination intervals according to the interval size to obtain a target combination interval greater than a preset threshold; A classification and statistics module, used for classifying the partition intervals within the target combination interval based on the copy number, and counting the number of intervals in each classification; A third screening module is used for screening the partition intervals in the target combination interval based on the number of intervals corresponding to the target combination interval for each target combination interval, so as to obtain a target partition interval; A drawing display module is used to perform drawing display based on the data in the target partition interval.

10. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the method for processing genome copy number variation analysis data based on ultra-low depth whole genome high-throughput sequencing according to any one of claims 1 to 8 by executing the computer instructions.

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