Cluster mutation detection method and device based on plane distance

Abstract

The invention belongs to the technical field of biological information, and particularly relates to a cluster mutation detection method and device based on a plane distance, and the method comprises the steps: analyzing sample data to obtain a clustering condition boundary value, determining a range parameter and a mutation parameter, processing to-be-detected sample data, merging clusters to obtain a clustering result, and verifying the result; clustering is carried out based on plane distance recognition, completely different cluster mutation recognition algorithms are adopted, compared with an existing multiple sliding window, the algorithm has higher performance and higher recognition precision, webpage online detection can be conveniently carried out only by providing an API on the basis of the algorithm developed based on Java, and the method has the advantages of being high in practicability and easy to popularize and use. And any matched environment or software does not need to be configured or downloaded, so that the convenience degree is greatly improved.

Description

Technical Field

[0001] The present invention belongs to the field of bioinformatics, and in particular relates to a cluster mutation detection method and device based on plane distance. Background Art

[0002] Mutation refers to the phenomenon that multiple mutations occur in a local region of the genome. These mutations may affect different genes and pathways, leading to the acquisition of multiple functional abnormalities in the same tumor cell. The detection of cluster mutations helps to develop more effective treatment strategies, thereby improving the clinical outcomes of patients and having a profound impact on all aspects of tumor biology. Therefore, it is particularly important to systematically obtain cluster mutation data and related genes.

[0003] At present, the existing cluster mutation identification methods are overly dependent on Python and R binding detection, which requires downloading a large number of operating environments and supporting software. The operation is cumbersome and may have large errors. The convenience and effect of detection are difficult to guarantee.

[0004] Therefore, it is of great significance to develop a new cluster mutation detection method and device. Summary of the invention

[0005] Purpose of the invention: The technical problem to be solved by the present invention is to provide a cluster mutation detection method and device based on plane distance in view of the shortcomings of the prior art. The method is based on Java development and only needs to provide an API to conveniently perform online detection of web pages. It does not require the configuration or download of any supporting environment or software, which greatly improves the convenience and detection effect.

[0006] In order to solve the above technical problems, the present invention discloses a cluster mutation detection method based on plane distance, comprising the following steps:

[0007] Step 1: Screen the sample data and analyze the sample data to obtain the cutoff value that meets the clustering conditions;

[0008] Step 2: Define the range parameter 1 within which mutation sites can be clustered, and define the minimum number of mutations required to achieve clustering conditions, parameter 2;

[0009] Step 3: Process the data of the sample to be tested, merge the clusters according to the key nodes and neighbor node sets, and obtain the clustering results;

[0010] Step 4: Calculate the silhouette coefficient of the clustering results to verify the clustering effect, and verify the results based on the cluster mutation characteristics.

[0011] Wherein, step 1 specifically includes:

[0012] Step 1.1: Obtain sample data that has been verified to have a large number of cluster mutations; filter sample data with mutation type SNP; retain gene column, chromosome column and starting mutation site column data as input test data;

[0013] Step 1.2: Convert the sample data to obtain a rainforest map, draw a k-distance curve to obtain the DBSCAN parameter eps, perform DBSCAN clustering, calculate the silhouette coefficient to test the clustering quality, and obtain the demarcation point. Preferably, the silhouette coefficient is used to evaluate the quality of clustering. The following is a brief description of the steps of the algorithm:

[0014] A. Calculate the silhouette coefficient for each sample:

[0015] For each cluster, calculate the silhouette coefficient of each sample in the cluster in turn;

[0016] For each sample, calculate the average distance between it and other samples in the same cluster as the compactness a; for each sample, calculate the minimum value of the average distance between it and other clusters as the separation b; calculate the silhouette coefficient according to the formula: silhouette = (ba) / max (a, b). If a and b are equal, the silhouette coefficient is 0;

[0017] B. Calculate the average silhouette coefficient:

[0018] Add up the silhouette coefficients of all samples and calculate the total number of samples;

[0019] Calculate the average silhouette coefficient: average Silhouette = total Silhouette / total Samples;

[0020] Returns the mean silhouette coefficient;

[0021] For each cluster, if it contains only one sample, its silhouette coefficient is not calculated. The silhouette coefficient range is (-1, 1). If the silhouette coefficient of the clustering result is closer to 1, it means that the clustering effect is better. If it is close to 0, it means that the sample is at the boundary position. If it is a negative number, it means that the clustering effect is poor.

[0022] Preferably, the rainforest map in step 1.2 is obtained by sorting the chromosome column and the mutation site column and performing log transformation on the distance values;

[0023] Preferably, the demarcation point described in step 1.2 is 1000 bp.

[0024] Among them, the range parameter 1 and the minimum mutation number parameter 2 described in step 2 are obtained through geometric principles and rainforest map display principles; the range parameter 1 is 1000√2; and the minimum mutation number parameter 2 is 1.

[0025] Wherein, step 3 specifically includes:

[0026] Step 3.1: Process the input mutation data of the sample to be tested, classify the data by sequence, and record the starting mutation position x of each SNP on the sequence and the distance y from the previous SNP into a node object instance;

[0027] Step 3.2: Map all node object instances into a rainforest graph, and calculate the nearest adjacent nodes based on the positions in the graph;

[0028] Step 3.3: Store the nodes within the range of parameter 1 (range radius is less than or equal to 1000√2) and satisfying the condition of parameter 2 (excluding the node itself, the number of nodes in the range is at least 1) into the node type set and mark them as the key node set;

[0029] Step 3.4: Traverse the key nodes in the above set, calculate all nodes within the range of 1000√2 and record them as the neighbor node set of the current key node;

[0030] Step 3.5: If the neighbor node set of a key node contains key nodes other than itself, the neighbor node sets of the two key nodes are merged into one cluster (the neighbor node set contains the key node), and the above structure is traversed in depth-first order to obtain the clustering result.

[0031] The specific method for verifying the result described in step 4 is as follows:

[0032] 4.1: Verify the mutation type, the mutation types are mainly C>T and C>G;

[0033] 4.2: Verify TCW sequences and enrich the TCW sequence context, where W represents base A or T;

[0034] 4.3: To determine whether the protein has processivity, the consecutive mutations in the cluster should be on the same chain, i.e., 6 consecutive C>T mutations or 6 consecutive G>A mutations;

[0035] 4.4: Determine whether it is in cis. Mutations that occur at adjacent positions on the same chromosome are often on the same reads.

[0036] Preferably, the screening results are verified according to the above-mentioned known cluster mutation conclusions (due to sample data limitations, at least the above three conditions are met), and then compared with the literature data of the existing verification cluster mutation results to determine whether the detection results meet the cluster mutation characteristics.

[0037] The present invention also provides a device for cluster mutation detection based on plane distance, comprising:

[0038] Input unit, input parameter 1, a SNP domain range of 1000√2bp, defines the range in which mutation sites in the graph can be clustered; input parameter 2, the minimum number of SNPs that the domain can tolerate is 1, defining the minimum number of mutations required to achieve clustering conditions within the clustering range; input data, including gene column, chromosome column and starting mutation site column data as input data;

[0039] Output unit, classified cluster mutation data, including gene name, starting mutation site data and cluster mutation category data.

[0040] The present invention also provides a computer-readable storage medium on which a computer program is stored, and when the program is executed by a processor, the above-mentioned cluster mutation detection method is implemented.

[0041] The present invention also provides a device, comprising: a processor and a memory, wherein the memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory, so that the device performs the above-mentioned cluster mutation detection method.

[0042] Beneficial effects: The Kataegis and Omikli cluster mutation detection method developed by the Java algorithm based on two-dimensional space distance provided by the present invention performs clustering based on plane distance recognition and adopts a completely different cluster mutation recognition algorithm. Compared with the existing multiple sliding windows, this algorithm has higher performance and higher recognition accuracy. Moreover, the algorithm developed based on Java only needs to provide an API to conveniently perform online detection of web pages, without the need to configure or download any supporting environment or software, which greatly improves the convenience. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments, and the above and / or other advantages of the present invention will become more clear.

[0044] Figure 1 is a flow chart of the method of the present invention;

[0045] Figure 2 It is a schematic diagram of defining two major parameters of the clustering algorithm in an embodiment of the present invention;

[0046] Figure 3This is a visualization diagram of the cluster mutation online detection sample chromosome 12 obtained by using the interface developed by the algorithm in the embodiment of the present invention;

[0047] Figure 4 It is the silhouette coefficient and the average number of cluster mutations calculated for the final clustering result in the embodiment of the present invention. DETAILED DESCRIPTION

[0048] The present invention will be further described below in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and cannot be used to limit the protection scope of the present invention.

[0049] Embodiment 1:

[0050] The comparison data comes from the article "Signatures of mutational processes in human cancer". The sample is BRCA PD4103a (GRCh37) from the Cosmic database.

[0051] like Figure 1 As shown, the Kataegis and Omikli cluster mutation detection method developed by Java algorithm based on plane distance mainly includes the following four steps:

[0052] 1. Obtain sample data that has been verified to have a large number of cluster mutations; screen sample data with mutation type SNP; retain gene column, chromosome column and starting mutation site column data as input test data, Table 1 is the input test data of this embodiment. Sort the sample data by chromosome and mutation site, draw a k-distance curve with the distance value after log transformation of the rainforest map to obtain the DBSCAN parameter eps, calculate the silhouette coefficient after DBSCAN clustering to be 0.96, and calculate the minimum average value of each chromosome region to be 1000bp.

[0053] Table 1

[0054]

[0055]

[0056] 2. Based on the demarcation point of 1000bp obtained in step 1, two key parameters are determined: parameter 1, which defines the range in which the mutation sites in the graph can be clustered, and parameter 2, which defines the minimum number of mutations required to achieve the clustering condition. The above parameters are obtained using the principles of geometry and the display principle of the rainforest map. For details, refer to Figure 2 ;

[0057] Depend on Figure 2It can be seen that, taking the demarcation point of 1000bp as an example, according to the definition of the rainforest diagram, the next SNP must be on the y=x line with the previous SNP position y=0 as the starting point; when the previous SNP transmutation cluster is small (0bp), the allowable range of the next adjacent SNP transmutation distance will be reduced to 1000bp ( Figure 2 When the transmutation distance of the previous SNP is large, this clustering method will allow a larger transmutation distance of the next adjacent SNP - 1000√2 ( Figure 2 It can be seen that this type of algorithm can dynamically limit the maximum distance range of the next mutation site according to the actual mutation site distance while ensuring that the maximum value includes the 1000bp demarcation point, which can relatively reduce the detection error.

[0058] 3. Process the input mutation data of the sample to be tested, classify the data by sequence, record the starting mutation position x of each SNP on the sequence and the distance y from the previous SNP value into a node object instance, and record the corresponding gene name into the current node instance; map all the above node object instances into a rainforest map, and calculate the nearest adjacent nodes according to the positions in the map, and store the nodes that meet the conditions of parameter 2 within the range of parameter 1 (range radius is less than or equal to 1000√2) into the node type set, and mark them as key node sets; traverse the key nodes in the above set, and calculate all nodes within the range of 1000√2 as the neighbor node set of the current key node. If the neighbor node set of a key node contains key nodes other than itself, the neighbor sets of the two key nodes are merged into a cluster (the neighbor node set contains the key node), and the above structure is traversed in depth first to obtain the clustering result; Table 2 is the clustering result of this embodiment taking chromosome 12 as an example.

[0059] Table 2

[0060] Gene Position Cluster_Number Type Strand GRIP1 66847196-66848938 6 Kataegis - PTPRB 70993642-70994342 6 Kataegis - GRIP1 66854364-66856770 6 Kataegis - PTPRB 70985092-70986204 10 Kataegis - TSPAN8 71722858-71723706 7 Kataegis - FICD 108913672-108913942 5 Kataegis + LGR5 71850868-71852522 11 Kataegis + TSPAN8 71773055-71773270 2 Omikli - SRGAP1 64297537-64297812 3 Omikli + MY01H 109884221-109884756 2 Omikli + TSPAN8 71742442-71742459 3 Omikli - LGR5 71862778-71862785 2 Omikli + HELB 66729876-66731227 4 Omikli + LGR5 71864737-71865531 4 Omikli + HELB 66726752-66727147 2 Omikli + LGR5 71867643-71867824 2 Omikli + GRIP1 66850692-66851595 3 Omikli - SRGAP1 64269477-64269683 3 Omikli + TRHD 72798975-72799548 2 Omikli +

[0061] 4. Calculate the silhouette coefficient of the clustering results to test the clustering effect of the algorithm. Figure 4 It is the silhouette coefficient calculated for the final clustering result and the average number of cluster mutations. It can be seen from the figure that the silhouette coefficient is 0.9186.

[0062] 5. Verify the accuracy of the detected cluster mutation results using cluster mutation characteristics:

[0063] (1) From Table 3, we can see that the mutation types are mainly C>T and C>G;

[0064] Table 3

[0065] Mutation type C>G C>T G>C G>A C>A Frequency 29％ 29％ 11％ 10％ 6％

[0066] (2) The results of the Strand column in Table 2 indicate that all cluster mutation regions occur on the same strand, proving the processivity and cis arrangement of cluster mutations.

[0067] Embodiment 2:

[0068] The computer-readable storage medium of this embodiment stores a computer program thereon, and when the program is executed by a processor, the steps in the cluster mutation detection method based on plane distance of Embodiment 1 are implemented.

[0069] The computer-readable storage medium of this embodiment may be an internal storage unit of the terminal, such as a hard disk or memory of the terminal; the computer-readable storage medium of this embodiment may also be an external storage device of the terminal, such as a plug-in hard disk, a smart memory card, a secure digital card, a flash memory card, etc. equipped on the terminal; further, the computer-readable storage medium may also include both an internal storage unit of the terminal and an external storage device.

[0070] The computer-readable storage medium of this embodiment is used to store computer programs and other programs and data required by the terminal. The computer-readable storage medium can also be used to temporarily store data that has been output or is to be output.

[0071] Embodiment 3:

[0072] The computer device of this embodiment includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps in the cluster mutation detection method based on plane distance of Embodiment 1 are implemented.

[0073] In this embodiment, the processor may be a central processing unit, or other general-purpose processors, digital signal processors, application-specific integrated circuits, readily available programmable gate arrays or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The memory may include a read-only memory and a random access memory, and provide instructions and data to the processor. A portion of the memory may also include a non-volatile random access memory. For example, the memory may also store information about the device type.

[0074] Those skilled in the art will appreciate that the disclosed content of the embodiments may be provided as methods, systems, or computer program products. Therefore, the present solution may adopt the form of hardware embodiments, software embodiments, or embodiments combining software and hardware. Moreover, the present solution may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) containing computer-usable program codes.

[0075] The present solution is described with reference to the flowchart and / or block diagram of the method and computer program product according to the embodiment of the present solution. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions; these computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0076] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0077] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0078] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the embodiments of the above-mentioned methods. The storage medium can be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc.

Claims

1. A cluster mutation detection method based on plane distance, characterized in that: The following steps are involved: Step 1: Screen the sample data and analyze to obtain the cutoff value that meets the clustering conditions; Step 2: Define the range parameter 1 within which mutation sites can be clustered, and define the minimum number of mutations required to achieve clustering conditions, parameter 2; Step 3: Process the data of the sample to be tested, merge the clusters according to the key nodes and neighbor node sets, and obtain the clustering results; Step 4: Verify the clustering effect and verify the results based on the cluster mutation characteristics.

2. The detection method according to claim 1, characterized in that: The step 1 specifically includes: Step 1.1: Obtain sample data that has been verified to have a large number of cluster mutations; screen the sample data, and retain the gene column, chromosome column, and starting mutation site column data of the sample data whose mutation type is SNP as input detection data; Step 1.2: Convert the sample data of SNP mutation type in step 1.1 to obtain a rainforest map, draw a k-distance curve to obtain the DBSCAN parameter eps, calculate the silhouette coefficient after DBSCAN clustering to test the clustering quality, and obtain the cutoff point.

3. The detection method according to claim 2, characterized in that: The rainforest map described in step 1.2 is obtained by sorting the chromosome column and the starting mutation site column of the sample data with mutation type SNP in step 1.1, and then performing log transformation on the distance values.

4. The detection method according to claim 2, characterized in that: The demarcation point mentioned in step 1.2 is 1000 bp.

5. The detection method according to claim 1, characterized in that: The range parameter 1 and the minimum mutation number parameter 2 described in step 2 are obtained by geometric principles and rainforest map display principles; the range parameter 1 is 1000√2; The minimum mutation number parameter 2 is 1.

6. The detection method according to claim 1, characterized in that: Step 3 specifically includes: Step 3.1: Process the input mutation data of the sample to be tested, classify the data by sequence, record the starting mutation position x of each SNP on the sequence and the distance y from the previous SNP into a node object instance, and record the corresponding gene name into the current node instance; Step 3.2: Map all node instances into a rainforest graph and calculate the nearest adjacent nodes based on their positions in the graph; Step 3.3: Store the nodes that meet the condition of parameter 2 within the range of parameter 1 into the node type set, and mark the node type set as the key node set; Step 3.4: Traverse the key nodes in the above set, calculate all nodes within the range of parameter 1 and store them in the set, and record this set as the neighbor node set of the current key node; Step 3.5: If the neighbor node set of a key node contains key nodes other than itself, the neighbor node sets of the two key nodes are merged into one cluster, and the above structure is traversed in depth-first order to obtain the clustering result.

7. The detection method according to claim 1, characterized in that: The specific method for verifying the results described in step 4 is: 4.1: Verify mutation type; 4.2: Verify TCW sequence; 4.3: Determine whether it has the ability to continuously synthesize; 4.4: Determine whether it is arranged in cis order.

8. A device for cluster mutation detection based on plane distance, characterized in that: include: Input unit, input parameter 1, a SNP domain range of 1000√2bp, defining the range in which mutation sites in the graph can be clustered; Input parameter 2, the minimum number of SNPs that the domain can tolerate is 1, which defines the minimum number of mutations required to achieve clustering conditions within the clustering range; input data, including gene column, chromosome column and starting mutation site column data as input data; Output unit, classified cluster mutation data, including gene name, starting mutation site data and cluster mutation category data.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the detection method according to any one of claims 1 to 6 is implemented.

10. A device comprising: A processor and a memory, wherein the memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory, so that the device executes the detection method according to any one of claims 1 to 6.

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