Method and device for evaluating pathogenicity of mutant genes based on Bayesian algorithm
By evaluating the pathogenicity of variant genes through the Bayesian algorithm and using high-throughput sequencing and evidence weight grading tables, the problems of low accuracy and high manpower consumption in variant gene assessment were solved, and a more efficient assessment of the pathogenicity of variant genes was achieved.
Patent Information
- Application Number
- CN202310626233.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2023-05-30
- Publication Date
- 2025-09-30
- Estimated Expiration
- 2043-05-30
AI Technical Summary
The current assessment of the harmfulness of mutant genes has low accuracy and is labor-intensive. Existing technologies mainly rely on ANNOVAR annotations and ACMG guidelines, but the lack of direct evidence leads to inaccurate assessments.
A Bayesian algorithm-based method was used to obtain gene mutation data through high-throughput sequencing, perform gene annotation and evidence weight grading, and use a pre-constructed evidence weight grading table and posterior probability calculation formula to evaluate the pathogenicity of gene mutation sites.
It improves the accuracy of pathogenicity assessment of mutant genes, reduces manpower consumption, and provides a more efficient assessment process.
Smart Images
Figure CN116665773B_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of gene sequencing technology, and in particular to a method, device, electronic device and computer-readable storage medium for evaluating the pathogenicity of mutant genes based on a Bayesian algorithm. Background Art
[0002] A mutant gene refers to a gene with structural changes due to changes in the DNA molecule, such as the addition or deletion of base pairs. High-throughput sequencing technology is an effective means of detecting mutant genes. Because high-throughput sequencing technology typically detects tens of thousands of mutant genes, it is essential to determine the harmfulness of the mutant gene.
[0003] The current assessment of the harmfulness of mutant genes mainly involves ANNOVAR annotation of the mutant genes, and then analysts make judgments based on the logic provided by the ACMG guidelines. However, the annotation table of ANNOVAR annotation does not directly list the evidence points based on the ACMG guidelines. Therefore, the current assessment of the harmfulness of mutant genes has the problems of low accuracy and high labor consumption. Summary of the Invention
[0004] The present invention provides a method, device and computer-readable storage medium for evaluating the pathogenicity of mutant genes based on a Bayesian algorithm. The main purpose of the method is to solve the problems of low accuracy and high labor consumption in the current evaluation of the harmfulness of mutant genes.
[0005] To achieve the above objectives, the present invention provides a method for evaluating the pathogenicity of a variant gene based on a Bayesian algorithm, comprising:
[0006] Obtaining a DNA sample to be tested, and performing high-throughput sequencing on the DNA sample to be tested to obtain high-throughput sequencing data;
[0007] receiving a data quality control instruction, and comparing the high-throughput sequencing data with a pre-constructed human genome sequence according to the data quality control instruction to obtain gene mutation data;
[0008] Extracting gene mutation sites from the gene mutation data in sequence, annotating the gene mutation sites, and obtaining a high-throughput sequencing annotation table;
[0009] Extracting evidence source categories in sequence from a pre-constructed gene evidence weight grading table, and extracting evidence description features from the high-throughput sequencing annotation table according to the evidence source categories;
[0010] Extracting the evidence weight level corresponding to the evidence source category in the gene evidence weight grading table according to the evidence source category and the evidence description characteristics to obtain an evidence weight level set;
[0011] Calculating the pathogenicity posterior probability of the gene mutation site using the evidence weight level set according to a pre-constructed posterior probability calculation formula;
[0012] The pathogenicity posterior probability is used to query the pathogenicity assessment level of the gene mutation site in a pre-constructed gene pathogenicity assessment table to complete the pathogenicity assessment of the variant gene based on the Bayesian algorithm.
[0013] Optionally, performing high-throughput sequencing on the DNA sample to be tested to obtain high-throughput sequencing data includes:
[0014] Fragmenting the DNA sample to be tested to obtain DNA fragments;
[0015] Amplifying the DNA fragments and adding adapters to obtain a sequencing data source;
[0016] constructing a DNA library based on the sequencing data source, and extracting a DNA sequence to be tested from the DNA library;
[0017] High-throughput sequencing is performed on the DNA sequence to be tested to obtain the high-throughput sequencing data.
[0018] Optionally, before annotating the gene mutation site to obtain a high-throughput sequencing annotation table, the method further comprises:
[0019] Obtaining a header category set of the variation list, and constructing a header column of the variation list according to the header category set;
[0020] Obtaining a header description of each header category in the header category set, and constructing a header description column according to the header description;
[0021] A high-throughput sequencing annotation table to be filled is constructed according to the header column and the header description column of the variation list.
[0022] Optionally, extracting the evidence weight level corresponding to the evidence source category from the gene evidence weight grading table according to the evidence source category and the evidence description characteristics to obtain an evidence weight level set includes:
[0023] identifying a source of evidence category in the genetic evidence weight grading table according to the source of evidence category;
[0024] Extracting a feature classification description set corresponding to the evidence source category;
[0025] Extracting the target feature classification description to which the evidence description feature belongs from the feature description set;
[0026] Extracting the weight of evidence level corresponding to the target feature classification description;
[0027] The evidence weight levels of all evidence source categories are summarized to obtain the evidence weight level set.
[0028] Optionally, the calculating of the pathogenicity posterior probability of the gene mutation site using the evidence weight level set according to a pre-constructed posterior probability calculation formula includes:
[0029] According to the evidence weight level set, the pathogenic joint probability of the gene mutation site is calculated using a pre-constructed joint probability calculation formula;
[0030] The posterior probability of pathogenicity of the gene mutation site is calculated based on the combined pathogenicity probability and the preset prior probability using the posterior probability calculation formula.
[0031] Optionally, the joint probability calculation formula is as follows:
[0032]
[0033] Among them, P 联合 represents the joint probability of pathogenicity, n PVS Indicates the number of pathogenic evidences with a very strong weight level in the evidence source category, n PS Indicates the number of pathogenicity evidences with strong evidence weight in the evidence source category, n PM Indicates the number of pathogenic evidence with medium weight level in the category of evidence source, n PP Indicates the number of pathogenicity evidences with the weight of evidence level as support in the category of evidence source, n BP Indicates the number of benign evidences with supporting evidence weight level in the evidence source category, n BM Indicates the number of benign evidence with medium evidence weight in the evidence source category, n BS Indicates the number of benign evidence with strong evidence weight in the evidence source category, n BA Indicates the number of benign evidences with a very strong evidence weight level in the evidence source category.
[0034] Optionally, the posterior probability calculation formula is as follows:
[0035]
[0036] Among them, P 后验 represents the posterior probability of pathogenicity, P 先验 represents the prior probability of pathogenicity.
[0037] In order to solve the above problems, the present invention also provides a device for evaluating the pathogenicity of a variant gene based on a Bayesian algorithm, the device comprising:
[0038] A high-throughput sequencing module is used to obtain a DNA sample to be tested, perform high-throughput sequencing on the DNA sample to be tested, and obtain high-throughput sequencing data;
[0039] A gene comparison module is used to receive data quality control instructions and compare the high-throughput sequencing data with the pre-constructed human genome sequence according to the data quality control instructions to obtain gene mutation data;
[0040] A gene annotation module is used to sequentially extract gene mutation sites from the gene mutation data, perform gene annotation on the gene mutation sites, and obtain a high-throughput sequencing annotation table;
[0041] An evidence weight level extraction module is used to sequentially extract evidence source categories from a pre-constructed gene evidence weight grading table, extract evidence description features from the high-throughput sequencing annotation table based on the evidence source categories; extract the evidence weight level corresponding to the evidence source category in the gene evidence weight grading table based on the evidence source categories and the evidence description features, and obtain an evidence weight level set;
[0042] The pathogenicity level assessment module is used to calculate the pathogenicity posterior probability of the gene mutation site using the evidence weight level set according to a pre-constructed posterior probability calculation formula; and use the pathogenicity posterior probability to query the pathogenicity assessment level of the gene mutation site in a pre-constructed gene pathogenicity assessment table.
[0043] In order to solve the above problem, the present invention further provides an electronic device, comprising:
[0044] at least one processor; and,
[0045] a memory communicatively connected to the at least one processor; wherein,
[0046] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to implement the above-mentioned method for evaluating the pathogenicity of variant genes based on the Bayesian algorithm.
[0047] In order to solve the above problems, the present invention also provides a computer-readable storage medium, which stores at least one instruction, and the at least one instruction is executed by a processor in an electronic device to implement the above-mentioned Bayesian algorithm-based method for evaluating the pathogenicity of mutant genes.
[0048] Compared with the background technology, the current harmfulness assessment of mutant genes has the problems of low accuracy and high labor consumption. In the embodiment of the present invention, high-throughput sequencing is performed on the DNA sample to be tested to obtain high-throughput sequencing data. At this time, the human genome sequence can be used to perform mutation comparison on the gene sequence in the high-throughput sequencing data to obtain gene mutation data, and then the gene mutation site is annotated to obtain a high-throughput sequencing annotation table. Since it is necessary to obtain the evidence weight level corresponding to each evidence description feature in the high-throughput sequencing annotation table, it is necessary to first extract the evidence in the pre-constructed gene evidence weight grading table in sequence. According to the source category, the evidence description features are extracted from the high-throughput sequencing annotation table according to the evidence source category, and then the evidence weight level corresponding to the evidence source category is extracted from the gene evidence weight grading table according to the evidence source category and the evidence description features to obtain the evidence weight level set. At this time, the pathogenic posterior probability of the gene mutation site can be calculated according to the pre-constructed posterior probability calculation formula using the evidence weight level set. Finally, the pathogenic posterior probability is used to query the pathogenicity assessment level of the gene mutation site in the pre-constructed gene pathogenicity assessment table, thereby completing the pathogenicity assessment of the variant gene based on the Bayesian algorithm. Therefore, the variant gene pathogenicity assessment method, device, electronic device and computer-readable storage medium based on the Bayesian algorithm proposed in the present invention can solve the current problem of low accuracy and high labor consumption in the harmfulness assessment of mutant genes. BRIEF DESCRIPTION OF THE DRAWINGS
[0049] Figure 1 A schematic diagram of a process for evaluating the pathogenicity of a variant gene based on a Bayesian algorithm according to an embodiment of the present invention;
[0050] Figure 2 for Figure 1 A detailed implementation flow chart of one step in the process;
[0051] Figure 3 A high-throughput sequencing annotation table provided in one embodiment of the present invention;
[0052] Figure 4 A genetic evidence weight grading table provided in one embodiment of the present invention;
[0053] Figure 5 A gene pathogenicity assessment table provided in one embodiment of the present invention;
[0054] Figure 6 A comprehensive evaluation table of target mutation points provided by one embodiment of the present invention;
[0055] Figure 7 A comprehensive evaluation table of control mutation points provided in one embodiment of the present invention;
[0056] Figure 8 A functional module diagram of a device for evaluating pathogenicity of a variant gene based on a Bayesian algorithm according to an embodiment of the present invention;
[0057] Figure 9 A schematic diagram of the structure of an electronic device for implementing the Bayesian algorithm-based variant gene pathogenicity assessment method provided in one embodiment of the present invention.
[0058] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION
[0059] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.
[0060] The embodiment of the present application provides a method for assessing the pathogenicity of a variant gene based on a Bayesian algorithm. The execution subject of the method for assessing the pathogenicity of a variant gene based on the Bayesian algorithm includes but is not limited to at least one of the electronic devices such as a server and a terminal that can be configured to execute the method provided in the embodiment of the present application. In other words, the method for assessing the pathogenicity of a variant gene based on the Bayesian algorithm can be executed by software or hardware installed on a terminal device or a server device. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc.
[0061] Example 1:
[0062] Reference Figure 1 FIG2 is a flow chart of a method for evaluating the pathogenicity of a variant gene based on a Bayesian algorithm according to an embodiment of the present invention. In this embodiment, the method for evaluating the pathogenicity of a variant gene based on a Bayesian algorithm includes:
[0063] S1. Obtain a DNA sample to be tested, and perform high-throughput sequencing on the DNA sample to be tested to obtain high-throughput sequencing data.
[0064] It can be explained that the DNA sample to be tested refers to DNA sample data extracted from a pre-established DNA database according to DNA extraction instructions. High-throughput sequencing technology, also known as "next-generation" sequencing technology, is characterized by its ability to sequence hundreds of thousands to millions of DNA molecules simultaneously and to measure the length and shortness of general reads. High-throughput sequencing enables more detailed and comprehensive analysis of a species' transcriptome and genome, also known as deep sequencing.
[0065] It is understandable that the high-throughput sequencing data refers to sequencing data obtained by sequencing the DNA sample to be tested using high-throughput sequencing technology.
[0066] In an embodiment of the present invention, performing high-throughput sequencing on the DNA sample to be tested to obtain high-throughput sequencing data includes:
[0067] Fragmenting the DNA sample to be tested to obtain DNA fragments;
[0068] Amplifying the DNA fragments and adding adapters to obtain a sequencing data source;
[0069] constructing a DNA library based on the sequencing data source, and extracting a DNA sequence to be tested from the DNA library;
[0070] High-throughput sequencing is performed on the DNA sequence to be tested to obtain the high-throughput sequencing data.
[0071] It can be explained that the commonly used fragmentation methods for the fragmentation include: DNA restriction enzyme digestion method, hydrodynamic shearing method, ultrasonic fragmentation method, spray atomization method, etc.
[0072] S2. Receive a data quality control instruction, and compare the high-throughput sequencing data and a pre-constructed human genome sequence according to the data quality control instruction to obtain gene mutation data.
[0073] It can be explained that the data quality control instruction refers to an instruction to detect gene mutation sites in the high-throughput sequencing data. The human genome sequence refers to a set of normal gene sequences that do not contain mutation sites. By extracting the normal genome sequence corresponding to the high-throughput sequencing data from the human genome sequence, and then performing a difference comparison between the normal genome sequence and the high-throughput sequencing data, the location of the difference is the gene mutation site. The gene mutation data refers to data containing information on all gene mutation sites.
[0074] S3. Extracting gene mutation sites from the gene mutation data in sequence, annotating the gene mutation sites, and obtaining a high-throughput sequencing annotation table.
[0075] Furthermore, the gene annotation refers to the high-throughput annotation of the biological function and related information of the gene at the gene mutation site using existing bioinformatics methods and tools. The high-throughput sequencing annotation table refers to a table that records the relevant gene information of the gene mutation site, see Figure 3 shown.
[0076] In an embodiment of the present invention, before annotating the gene mutation site to obtain a high-throughput sequencing annotation table, the method further includes:
[0077] Obtaining a header category set of the variation list, and constructing a header column of the variation list according to the header category set;
[0078] Obtaining a header description of each header category in the header category set, and constructing a header description column according to the header description;
[0079] A high-throughput sequencing annotation table to be filled is constructed according to the header column and the header description column of the variation list.
[0080] S4. Extract the evidence source categories in sequence from the pre-constructed gene evidence weight grading table, and extract evidence description features from the high-throughput sequencing annotation table according to the evidence source categories.
[0081] It should be understood that the genetic evidence weight grading table refers to a table that records the evidence weight levels of different evidence source categories under different evidence description characteristics, see Figure 4 The weight of evidence level refers to the pathogenicity level of the evidence source categories based on the ACMG standards, which may include: very strong pathogenicity evidence (PVS), strong pathogenicity evidence (PS), moderate pathogenicity evidence (PM), supported pathogenicity evidence (PP), supported benign evidence (BP), moderate benign evidence (BM), strong benign evidence (BS), and very strong benign evidence (BA). BA in ACMG refers to independent benign evidence.
[0082] S5. Extract the evidence weight level corresponding to the evidence source category from the gene evidence weight grading table according to the evidence source category and the evidence description characteristics to obtain an evidence weight level set.
[0083] See Figure 2 As shown, the evidence weight level corresponding to the evidence source category is extracted from the gene evidence weight grading table according to the evidence source category and the evidence description characteristics to obtain an evidence weight level set, including:
[0084] S51, identifying the evidence source category in the genetic evidence weight grading table according to the evidence source category;
[0085] S52, extracting a feature classification description set corresponding to the evidence source category;
[0086] S53, extracting the target feature classification description to which the evidence description feature belongs from the feature description set;
[0087] S54, extracting the evidence weight level corresponding to the target feature classification description;
[0088] S55. Summarize the evidence weight levels of all evidence source categories to obtain the evidence weight level set.
[0089] It is understandable that the evidence source category refers to a collection of the same evidence source category in the gene evidence weight grading table. For example, the evidence source category composed of protein level category can be found in Figure 4 Shown are the three protein level categories.
[0090] Furthermore, the feature classification description set refers to the feature classification description set corresponding to the evidence source category, which can be found in Figure 4 For example, the feature classification description set corresponding to the population frequency evidence source category is: 0; >0 and <=0.00005; >0.00005 and <0.001; >=0.001 and <0.05; >=0.05 and <0.3; >=0.3; >0.8. When the target feature classification description is >0.00005 and <0.001, the corresponding evidence weight level is BP. Because the mutation site corresponds to multiple evidence source categories, the evidence weight levels of all evidence source categories need to be summarized.
[0091] S6. Calculate the pathogenicity posterior probability of the gene mutation site using the evidence weight level set according to a pre-constructed posterior probability calculation formula.
[0092] It can be explained that the posterior probability calculation formula refers to a probability formula for calculating the pathogenicity category of the mutation site constructed based on the Bayesian algorithm.
[0093] In an embodiment of the present invention, the calculation of the pathogenicity posterior probability of the gene mutation site using the evidence weight level set according to the pre-constructed posterior probability calculation formula includes:
[0094] According to the evidence weight level set, the pathogenic joint probability of the gene mutation site is calculated using a pre-constructed joint probability calculation formula;
[0095] The posterior probability of pathogenicity of the gene mutation site is calculated based on the combined pathogenicity probability and the preset prior probability using the posterior probability calculation formula.
[0096] Furthermore, the joint probability calculation formula is as follows:
[0097]
[0098] Among them, P 联合 represents the joint probability of pathogenicity, n PVS Indicates the number of pathogenic evidences with a very strong weight level in the evidence source category, n PS Indicates the number of pathogenicity evidences with strong evidence weight in the evidence source category, n PM Indicates the number of pathogenic evidence with medium weight level in the category of evidence source, nPP Indicates the number of pathogenicity evidences with the weight of evidence level as support in the category of evidence source, n BP Indicates the number of benign evidences with supporting evidence weight level in the evidence source category, n BM Indicates the number of benign evidence with medium evidence weight in the evidence source category, n BS Indicates the number of benign evidence with strong evidence weight in the evidence source category, n BA Indicates the number of benign evidences with a very strong evidence weight level in the evidence source category.
[0099] In detail, the posterior probability calculation formula is as follows:
[0100]
[0101] Among them, P 后验 represents the posterior probability of pathogenicity, P 先验 represents the prior probability of pathogenicity.
[0102] Furthermore, the pathogenicity prior probability is artificially defined and may be 0.1.
[0103] S7. Using the posterior pathogenicity probability, query the pathogenicity assessment level of the gene mutation site in a pre-constructed gene pathogenicity assessment table to complete the pathogenicity assessment of the mutant gene based on the Bayesian algorithm.
[0104] In the embodiment of the present invention, the gene pathogenicity assessment table refers to a table that classifies the pathogenicity of gene mutation sites according to the posterior probability of pathogenicity, which can be found in Figure 5 shown.
[0105] Furthermore, the pathogenicity assessment level refers to the pathogenicity level of the gene mutation site, which may include 7 levels: pathogenic (P), suspected pathogenic (LP), variable significance with a tendency to pathogenic (VUS-LP), unknown significance (VUS), unknown significance with a tendency to be benign (VUS-LB), possibly benign (LB) and benign (B).
[0106] In an embodiment of the present invention, after querying the pathogenicity assessment level of the gene mutation site in a pre-constructed gene pathogenicity assessment table using the posterior pathogenicity probability, the method further includes:
[0107] Obtaining a pathogenicity assessment level set, and constructing a pathogenicity assessment level column according to the pathogenicity assessment level set;
[0108] Create a column for the number of assessed variants corresponding to the pathogenicity assessment level;
[0109] Construct an initial mutation point comprehensive evaluation table based on the mutation quantity column evaluated in the pathogenicity assessment level column;
[0110] The pathogenicity assessment levels of all gene mutation sites are summarized and filled into the initial mutation site comprehensive assessment table to obtain the target mutation site comprehensive assessment table.
[0111] Furthermore, the target mutation point comprehensive evaluation table can be found in Figure 6 In order to comprehensively evaluate the number of gene mutation sites in the DNA sample under different disease treatment evaluation levels, a comprehensive evaluation table of target mutation sites was constructed.
[0112] In an embodiment of the present invention, after constructing a comprehensive evaluation table of gene mutation sites by evaluating the mutation quantity column according to the pathogenicity assessment level column, the method further includes:
[0113] Perform a comprehensive variation assessment on the gene mutation data according to the pre-constructed InterVar database to obtain a comprehensive evaluation table of control mutation points;
[0114] The control mutation site comprehensive evaluation table is used to perform a discrimination evaluation on the target mutation site comprehensive evaluation table.
[0115] It is understandable that the control mutation site comprehensive evaluation table can be found in Figure 7 shown.
[0116] Further, Figure 6 The results show 110 variants with a tendency to be deleterious (classified as P, LP, or VUS-LP) and 5,606 variants with a clear tendency to be benign (classified as B, LB, or VUS-LB). In contrast, the control mutation site comprehensive assessment table derived by the control method (InterVar) shows 10 variants with a tendency to be deleterious and 1,870 variants with a clear tendency to be benign. This shows that the embodiments of the present invention clearly differentiate between variants with a clear tendency to be deleterious, greatly improving analysis efficiency.
[0117] Compared with the background technology, the current harmfulness assessment of mutant genes has the problems of low accuracy and high labor consumption. In the embodiment of the present invention, high-throughput sequencing is performed on the DNA sample to be tested to obtain high-throughput sequencing data. At this time, the human genome sequence can be used to perform mutation comparison on the gene sequence in the high-throughput sequencing data to obtain gene mutation data, and then the gene mutation site is annotated to obtain a high-throughput sequencing annotation table. Since it is necessary to obtain the evidence weight level corresponding to each evidence description feature in the high-throughput sequencing annotation table, it is necessary to first extract the evidence in the pre-constructed gene evidence weight grading table in sequence. According to the source category, the evidence description features are extracted from the high-throughput sequencing annotation table according to the evidence source category, and then the evidence weight level corresponding to the evidence source category is extracted from the gene evidence weight grading table according to the evidence source category and the evidence description features to obtain the evidence weight level set. At this time, the pathogenic posterior probability of the gene mutation site can be calculated according to the pre-constructed posterior probability calculation formula using the evidence weight level set. Finally, the pathogenic posterior probability is used to query the pathogenicity assessment level of the gene mutation site in the pre-constructed gene pathogenicity assessment table, thereby completing the pathogenicity assessment of the variant gene based on the Bayesian algorithm. Therefore, the variant gene pathogenicity assessment method, device, electronic device and computer-readable storage medium based on the Bayesian algorithm proposed in the present invention can solve the current problem of low accuracy and high labor consumption in the harmfulness assessment of mutant genes.
[0118] Example 2:
[0119] like Figure 8 , which is a functional module diagram of a variant gene pathogenicity assessment device based on a Bayesian algorithm provided by one embodiment of the present invention.
[0120] The Bayesian algorithm-based variant gene pathogenicity assessment device 100 of the present invention can be installed in an electronic device. Depending on the functions implemented, the Bayesian algorithm-based variant gene pathogenicity assessment device 100 can include high-throughput sequencing 101, gene alignment 102, gene annotation 103, evidence weight level extraction 104, and pathogenicity level assessment 105. The module of the present invention, which can also be referred to as a unit, refers to a series of computer program segments that can be executed by an electronic device processor and can perform fixed functions, which are stored in the memory of the electronic device.
[0121] The high-throughput sequencing 101 is used to obtain a DNA sample to be tested, perform high-throughput sequencing on the DNA sample to be tested, and obtain high-throughput sequencing data;
[0122] The gene comparison 102 is used to receive data quality control instructions and compare the high-throughput sequencing data and the pre-constructed human genome sequence according to the data quality control instructions to obtain gene mutation data;
[0123] The gene annotation 103 is used to sequentially extract gene mutation sites from the gene mutation data, perform gene annotation on the gene mutation sites, and obtain a high-throughput sequencing annotation table;
[0124] The evidence weight level extraction 104 is used to sequentially extract evidence source categories from the pre-constructed gene evidence weight grading table, extract evidence description features from the high-throughput sequencing annotation table based on the evidence source categories; extract the evidence weight level corresponding to the evidence source category from the gene evidence weight grading table based on the evidence source categories and the evidence description features, and obtain an evidence weight level set;
[0125] The pathogenicity level assessment 105 is used to calculate the pathogenicity posterior probability of the gene mutation site using the evidence weight level set according to the pre-constructed posterior probability calculation formula; and use the pathogenicity posterior probability to query the pathogenicity assessment level of the gene mutation site in the pre-constructed gene pathogenicity assessment table.
[0126] In detail, each module in the variant gene pathogenicity assessment device 100 based on the Bayesian algorithm in the embodiment of the present invention adopts the same method as above when used. Figure 1 The same technical means as the Bayesian algorithm-based pathogenicity assessment method of variant genes described in , and can produce the same technical effects, will not be repeated here.
[0127] Example 3:
[0128] like Figure 9 , which is a schematic diagram of the structure of an electronic device for implementing a method for evaluating pathogenicity of a variant gene based on a Bayesian algorithm, provided by one embodiment of the present invention.
[0129] The electronic device 1 may include a processor 10, a memory 11, a bus 12 and a communication interface 13, and may also include a computer program stored in the memory 11 and executable on the processor 10, such as a variant gene pathogenicity assessment program based on a Bayesian algorithm.
[0130] Wherein, the memory 11 includes at least one type of readable storage medium, and the readable storage medium includes a flash memory, a mobile hard disk, a multimedia card, a card-type memory (for example, SD or DX memory, etc.), a magnetic memory, a disk, an optical disk, etc. In some embodiments, the memory 11 can be an internal storage unit of the electronic device 1, such as a mobile hard disk of the electronic device 1. In other embodiments, the memory 11 can also be an external storage device of the electronic device 1, such as a plug-in mobile hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device 1. Furthermore, the memory 11 can also include both an internal storage unit of the electronic device 1 and an external storage device. The memory 11 can not only be used to store application software and various types of data installed in the electronic device 1, such as the code of the variant gene pathogenicity assessment program based on the Bayesian algorithm, but can also be used to temporarily store data that has been output or is to be output.
[0131] In some embodiments, the processor 10 may be composed of an integrated circuit, such as a single packaged integrated circuit, or a plurality of packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and a combination of various control chips. The processor 10 is the control core (Control Unit) of the electronic device, connecting the various components of the entire electronic device using various interfaces and circuits. It executes or executes programs or modules stored in the memory 11 (such as a variant gene pathogenicity assessment program based on the Bayesian algorithm, etc.), and calls data stored in the memory 11 to perform various functions of the electronic device 1 and process data.
[0132] The bus may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. The bus is configured to enable connection and communication between the memory 11 and at least one processor 10, etc.
[0133] Figure 9 Only the electronic device with components is shown, and it can be understood by those skilled in the art that Figure 9The structure shown does not constitute a limitation on the electronic device 1 , and may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.
[0134] For example, although not shown, the electronic device 1 may further include a power source (such as a battery) for powering the various components. Preferably, the power source may be logically connected to the at least one processor 10 via a power management device, thereby implementing functions such as charging management, discharging management, and power consumption management through the power management device. The power source may further include any components such as one or more DC or AC power sources, a recharging device, a power failure detection circuit, a power converter or inverter, a power status indicator, etc. The electronic device 1 may further include various sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be described in detail here.
[0135] Furthermore, the electronic device 1 may also include a network interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device 1 and other electronic devices.
[0136] Optionally, the electronic device 1 may further include a user interface, which may be a display or an input unit (such as a keyboard). Optionally, the user interface may also be a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touch device. The display may also be appropriately referred to as a display screen or a display unit, which is used to display information processed in the electronic device 1 and to display a visual user interface.
[0137] It should be understood that the embodiment is for illustration only and the scope of the patent application is not limited to this structure.
[0138] The variable gene pathogenicity assessment program based on the Bayesian algorithm stored in the memory 11 of the electronic device 1 is a combination of multiple instructions. When running in the processor 10, it can achieve the following:
[0139] Obtaining a DNA sample to be tested, and performing high-throughput sequencing on the DNA sample to be tested to obtain high-throughput sequencing data;
[0140] receiving a data quality control instruction, and comparing the high-throughput sequencing data with a pre-constructed human genome sequence according to the data quality control instruction to obtain gene mutation data;
[0141] Extracting gene mutation sites from the gene mutation data in sequence, annotating the gene mutation sites, and obtaining a high-throughput sequencing annotation table;
[0142] Extracting evidence source categories in sequence from a pre-constructed gene evidence weight grading table, and extracting evidence description features from the high-throughput sequencing annotation table according to the evidence source categories;
[0143] Extracting the evidence weight level corresponding to the evidence source category in the gene evidence weight grading table according to the evidence source category and the evidence description characteristics to obtain an evidence weight level set;
[0144] Calculating the pathogenicity posterior probability of the gene mutation site using the evidence weight level set according to a pre-constructed posterior probability calculation formula;
[0145] The pathogenicity posterior probability is used to query the pathogenicity assessment level of the gene mutation site in a pre-constructed gene pathogenicity assessment table to complete the pathogenicity assessment of the variant gene based on the Bayesian algorithm.
[0146] Specifically, the specific implementation method of the processor 10 for the above instructions can refer to Figures 1 to 8 The description of the relevant steps in the corresponding embodiments will not be repeated here.
[0147] Furthermore, if the modules / units integrated into the electronic device 1 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard drive, a magnetic disk, an optical disk, a computer memory, or a read-only memory (ROM).
[0148] The present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program. When the computer program is executed by a processor of an electronic device, the computer program can implement:
[0149] Obtaining a DNA sample to be tested, and performing high-throughput sequencing on the DNA sample to be tested to obtain high-throughput sequencing data;
[0150] receiving a data quality control instruction, and comparing the high-throughput sequencing data with a pre-constructed human genome sequence according to the data quality control instruction to obtain gene mutation data;
[0151] Extracting gene mutation sites from the gene mutation data in sequence, annotating the gene mutation sites, and obtaining a high-throughput sequencing annotation table;
[0152] Extracting evidence source categories in sequence from a pre-constructed gene evidence weight grading table, and extracting evidence description features from the high-throughput sequencing annotation table according to the evidence source categories;
[0153] Extracting the evidence weight level corresponding to the evidence source category in the gene evidence weight grading table according to the evidence source category and the evidence description characteristics to obtain an evidence weight level set;
[0154] Calculating the pathogenicity posterior probability of the gene mutation site using the evidence weight level set according to a pre-constructed posterior probability calculation formula;
[0155] The pathogenicity posterior probability is used to query the pathogenicity assessment level of the gene mutation site in a pre-constructed gene pathogenicity assessment table to complete the pathogenicity assessment of the variant gene based on the Bayesian algorithm.
[0156] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the module division is merely a logical function division, and other division methods may be used in actual implementation.
[0157] The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed across multiple network elements. Some or all of the modules may be selected to achieve the purpose of the solution of this embodiment according to actual needs.
[0158] In addition, the functional modules in various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or hardware plus software functional modules.
[0159] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.
[0160] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention.
Claims
1. A method for evaluating the pathogenicity of a variant gene based on a Bayesian algorithm, characterized in that: The method comprises: Obtaining a DNA sample to be tested, and performing high-throughput sequencing on the DNA sample to be tested to obtain high-throughput sequencing data; receiving a data quality control instruction, and comparing the high-throughput sequencing data with a pre-constructed human genome sequence according to the data quality control instruction to obtain gene mutation data; Extracting gene mutation sites from the gene mutation data in sequence, annotating the gene mutation sites, and obtaining a high-throughput sequencing annotation table; Extracting evidence source categories in sequence from a pre-constructed gene evidence weight grading table, and extracting evidence description features from the high-throughput sequencing annotation table according to the evidence source categories; Extracting the evidence weight level corresponding to the evidence source category in the gene evidence weight grading table according to the evidence source category and the evidence description characteristics to obtain an evidence weight level set; Calculating the pathogenicity posterior probability of the gene mutation site using the evidence weight level set according to a pre-constructed posterior probability calculation formula; Using the posterior pathogenicity probability, query the pathogenicity assessment level of the gene mutation site in a pre-constructed gene pathogenicity assessment table to complete the pathogenicity assessment of the variant gene based on the Bayesian algorithm; The step of extracting the evidence weight level corresponding to the evidence source category from the gene evidence weight grading table according to the evidence source category and the evidence description characteristics to obtain the evidence weight level set includes: identifying a source of evidence category in the genetic evidence weight grading table according to the source of evidence category; Extracting a feature classification description set corresponding to the evidence source category; Extracting the target feature classification description to which the evidence description feature belongs from the feature description set; Extracting the weight of evidence level corresponding to the target feature classification description; Summarizing the weight of evidence levels of all evidence source categories to obtain the weight of evidence level set; The method of calculating the pathogenicity posterior probability of the gene mutation site using the evidence weight level set according to the pre-constructed posterior probability calculation formula includes: According to the evidence weight level set, the pathogenic joint probability of the gene mutation site is calculated using a pre-constructed joint probability calculation formula; Calculate the pathogenicity posterior probability of the gene mutation site using the posterior probability calculation formula based on the pathogenicity joint probability and the preset prior probability; The joint probability calculation formula is as follows: Among them, P 联合 represents the joint probability of pathogenicity, n PVS Indicates the number of pathogenic evidences with a very strong weight level in the evidence source category, n PS Indicates the number of pathogenicity evidences with strong evidence weight in the evidence source category, n PM Indicates the number of pathogenic evidence with medium weight level in the category of evidence source, n PP Indicates the number of pathogenicity evidences with the weight of evidence level as support in the category of evidence source, n BP Indicates the number of benign evidences with supporting evidence weight level in the evidence source category, n BM Indicates the number of benign evidence with medium evidence weight in the evidence source category, n BS Indicates the number of benign evidence with strong evidence weight in the evidence source category, n BA Indicates the number of benign evidences with a very strong evidence weight level in the evidence source category.
2. The method for evaluating pathogenicity of a variant gene based on a Bayesian algorithm according to claim 1, wherein: The high-throughput sequencing of the DNA sample to be tested to obtain high-throughput sequencing data includes: Fragmenting the DNA sample to be tested to obtain DNA fragments; Amplifying the DNA fragments and adding adapters to obtain a sequencing data source; constructing a DNA library based on the sequencing data source, and extracting a DNA sequence to be tested from the DNA library; High-throughput sequencing is performed on the DNA sequence to be tested to obtain the high-throughput sequencing data.
3. The method for evaluating pathogenicity of a variant gene based on a Bayesian algorithm according to claim 2, wherein: Before annotating the gene mutation site to obtain a high-throughput sequencing annotation table, the method further comprises: Obtaining a header category set of the variation list, and constructing a header column of the variation list according to the header category set; Obtaining a header description of each header category in the header category set, and constructing a header description column according to the header description; A high-throughput sequencing annotation table to be filled is constructed according to the header column and the header description column of the variation list.
4. The method for evaluating pathogenicity of a variant gene based on a Bayesian algorithm according to claim 1, wherein: The posterior probability calculation formula is as follows: Among them, P 后验 represents the posterior probability of pathogenicity, P 先验 represents the prior probability of pathogenicity.
5. A device for evaluating the pathogenicity of mutant genes based on a Bayesian algorithm, characterized in that: The method for evaluating pathogenicity of a variant gene based on a Bayesian algorithm according to any one of claims 1 to 4, the device comprising: A high-throughput sequencing module is used to obtain a DNA sample to be tested, perform high-throughput sequencing on the DNA sample to be tested, and obtain high-throughput sequencing data; A gene comparison module is used to receive data quality control instructions and compare the high-throughput sequencing data with the pre-constructed human genome sequence according to the data quality control instructions to obtain gene mutation data; A gene annotation module is used to sequentially extract gene mutation sites from the gene mutation data, perform gene annotation on the gene mutation sites, and obtain a high-throughput sequencing annotation table; An evidence weight level extraction module is used to sequentially extract evidence source categories from a pre-constructed gene evidence weight grading table, extract evidence description features from the high-throughput sequencing annotation table based on the evidence source categories; extract the evidence weight level corresponding to the evidence source category in the gene evidence weight grading table based on the evidence source categories and the evidence description features, and obtain an evidence weight level set; The pathogenicity level assessment module is used to calculate the pathogenicity posterior probability of the gene mutation site using the evidence weight level set according to a pre-constructed posterior probability calculation formula; and use the pathogenicity posterior probability to query the pathogenicity assessment level of the gene mutation site in a pre-constructed gene pathogenicity assessment table.
6. An electronic device, characterized in that: The electronic device comprises: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method for assessing the pathogenicity of a variant gene based on a Bayesian algorithm as described in any one of claims 1 to 4.
7. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method for evaluating the pathogenicity of a variant gene based on a Bayesian algorithm as described in any one of claims 1 to 4 is implemented.