An RNA similarity analysis graph calculation method, device, equipment and medium

Abstract

The application relates to an RNA similarity analysis graph calculation method, device, equipment and medium, wherein the RNA similarity analysis graph calculation method comprises the following steps: converting sequence data of a searched RNA into a searched RNA structure graph; performing similarity analysis on the searched RNA structure graph and a target RNA structure graph to obtain a first similarity; determining the number of basic component structures in the searched RNA structure graph, and obtaining a second similarity based on the number of basic component structures in the searched RNA structure graph and the number of basic component structures in the target RNA structure graph; reconstructing the searched RNA structure graph based on the basic component structures in the searched RNA structure graph to generate a searched RNA high-order graph; performing similarity analysis on the searched RNA high-order graph and a target RNA high-order graph to obtain a third similarity; and obtaining the final similarity between the searched RNA and the target RNA based on the first similarity, the second similarity and the third similarity, thereby improving the calculation accuracy.

Description

Technical Field

[0001] This application relates to the field of bioinformatics, and in particular to a method, apparatus, device, and medium for calculating RNA similarity analysis graphs. Background Technology

[0002] Ribonucleic acid (RNA) is the carrier of genetic information found in biological cells and some viruses and viroids. RNA is a long chain molecule formed by the condensation of ribonucleotides through phosphodiester bonds. A ribonucleotide molecule consists of a phosphate group, a sugar sugar, and a base. There are four main types of bases in RNA: A (adenine), G (guanine), C (cytosine), and U (uracil).

[0003] Ribonucleic acid (RNA) plays a vital role in various biological activities. Exploring the composition and structure of RNA is one of the main research directions for biologists today. RNA molecules contain many stem-loop structures formed by complementary base pairs, which are the secondary structures of RNA. These secondary structures can be further subdivided into substructures such as 3' segments, 5' segments, hairpin loops, stems, inner loops, and multi-loop segments.

[0004] Based on the principle that similar RNA structures are likely to have similar functions, biologists mainly analyze the similarity of RNA secondary structures to discover other RNAs with similar functions to the target RNA, thus providing new discoveries for RNA function discovery, virus treatment, and other fields. By comparing the similarity of RNA secondary structures, other types of RNA with different similarities can be discovered, thereby enabling RNA classification and the discovery of new RNAs.

[0005] Currently, biologists mainly rely on visual inspection of stem-loop structures to assess the similarity of RNA secondary structures, which is subjective and inefficient. Other methods, such as tree analysis, wavelet analysis, or other biological algorithms, also exist, but these methods cannot conveniently, intelligently, efficiently, quickly, or intuitively calculate the similarity between the RNA being searched and the target RNA. Summary of the Invention

[0006] Therefore, it is necessary to provide a method, apparatus, device, and medium for calculating RNA similarity analysis graphs to address the aforementioned technical problems.

[0007] In a first aspect, embodiments of this application provide a method for calculating an RNA similarity analysis map, the method comprising:

[0008] The sequence data of the RNA to be searched is converted into a structural diagram of the RNA to be searched;

[0009] A similarity analysis is performed between the RNA structure diagram being searched and the target RNA structure diagram to obtain the first similarity score;

[0010] The number of basic components in the RNA structure diagram being searched is determined, and a second similarity is obtained based on the number of basic components in the RNA structure diagram being searched and the number of basic components in the target RNA structure diagram.

[0011] Based on the basic structural components of the RNA structure diagram being searched, the RNA structure diagram is reconstructed to generate a higher-order diagram of the RNA being searched; a similarity analysis is performed between the higher-order diagram of the RNA being searched and the higher-order diagram of the target RNA to obtain a third similarity score.

[0012] Based on the first similarity, the second similarity, and the third similarity, the final similarity between the RNA being searched and the target RNA is obtained.

[0013] In one embodiment, the step of performing a similarity analysis between the RNA structure map being searched and the target RNA structure map to obtain a first similarity includes:

[0014] The graph kernel decomposition method is used to decompose the RNA structure map to be searched into multiple RNA sub-maps, and to decompose the target RNA structure map into multiple target RNA sub-maps.

[0015] Based on the multiple sub-maps of RNA being searched and the multiple sub-maps of target RNA, a first similarity is obtained.

[0016] In one embodiment, obtaining the first similarity based on the plurality of searched RNA submaps and the plurality of target RNA submaps of the submaps includes:

[0017] The plurality of RNA sub-maps being searched are encoded to obtain a first coding sequence, and the plurality of target RNA sub-maps are encoded to obtain a second coding sequence;

[0018] A first similarity is calculated based on the first encoded sequence and the second encoded sequence.

[0019] In one embodiment, determining the number of basic constituent structures in the RNA structure map being searched, and obtaining a second similarity based on the number of basic constituent structures in the RNA structure map being searched and the number of basic constituent structures in the target RNA structure map, includes:

[0020] Determine the number of each basic component structure in the RNA structure diagram being searched, and determine the number of each basic component structure in the target RNA structure diagram;

[0021] The quantities corresponding to each basic component structure in the RNA structure diagram being searched are used to form a first structure vector, and the quantities corresponding to each basic component structure in the target RNA structure diagram are used to form a second structure vector.

[0022] Based on the first structural vector and the second structural vector, the second similarity is obtained by using the Euclidean distance calculation method.

[0023] In one embodiment, a graph matching algorithm is used to determine the number of each basic component structure in the RNA structure graph being searched and the target RNA structure graph.

[0024] In one embodiment, the formula for calculating the final similarity between the searched RNA and the target RNA based on the first similarity, the second similarity, and the third similarity is as follows:

[0025] score=α*score1+β*score2+γ*score3;

[0026] Where α, β, and γ represent constraint parameters, which are between 0 and 1 and satisfy α + β + γ = 1. score1 represents the first similarity, score2 represents the second similarity, score3 represents the third similarity, and score represents the final similarity.

[0027] In one embodiment, reconstructing the RNA structure diagram based on its fundamental constituent structures to generate a higher-order diagram of the RNA being searched includes:

[0028] Each of the basic constituent structures in the RNA structure diagram being searched is treated as a node;

[0029] The length of each of the aforementioned basic structural components is used as an attribute of the corresponding node;

[0030] The edges are connected according to the topological relationships between the basic components to form a higher-order graph of the RNA being searched.

[0031] Secondly, embodiments of this application also provide an RNA similarity analysis graph calculation device, the device comprising:

[0032] The conversion module is used to convert the first sequence data of the RNA to be searched into a structural diagram of the RNA to be searched.

[0033] The first obtaining module is used to perform similarity analysis on the RNA structure diagram being searched and the target RNA structure diagram to obtain a first similarity.

[0034] The second obtaining module is used to determine the number of basic components in the RNA structure diagram being searched, and to obtain a second similarity based on the number of basic components in the RNA structure diagram being searched and the number of basic components in the target RNA structure diagram.

[0035] The third module is used to reconstruct the structure diagram of the RNA being searched based on the basic component structure of the RNA structure diagram to generate a higher-order diagram of the RNA being searched; and to perform a similarity analysis between the higher-order diagram of the RNA being searched and the higher-order diagram of the target RNA to obtain a third similarity.

[0036] The acquisition module is used to obtain the final similarity between the RNA being searched and the target RNA based on the first similarity, the second similarity, and the third similarity.

[0037] Thirdly, embodiments of this application also provide an electronic device, including a cache module, a control module, and multiple computing units;

[0038] The cache module is used to store target RNA data, which includes a target RNA structure diagram, a second structure vector, and a high-order target RNA diagram.

[0039] The control module is used to distribute the sequence data of multiple RNAs to be searched to the multiple computing units;

[0040] The plurality of computing units are configured to calculate, based on the target RNA data and the sequence data of the RNA being searched, the similarity between each RNA being searched and the target RNA as described in the first aspect above, thereby obtaining the similarity between each RNA being searched and the target RNA.

[0041] Fourthly, embodiments of this application also provide a computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the method described in the first aspect above.

[0042] The aforementioned RNA similarity analysis graph calculation method, apparatus, device, and medium involve: converting the sequence data of the RNA to be searched into a structural graph of the RNA to be searched; performing similarity analysis between the structural graph of the RNA to be searched and the structural graph of the target RNA to obtain a first similarity; determining the number of basic constituent structures in the structural graph of the RNA to be searched, and obtaining a second similarity based on the number of basic constituent structures in the structural graph of the RNA to be searched and the number of basic constituent structures in the structural graph of the target RNA; reconstructing the structural graph of the RNA to be searched based on the basic constituent structures in the structural graph of the RNA to be searched to generate a higher-order graph of the RNA to be searched; performing similarity analysis between the higher-order graph of the RNA to be searched and the higher-order graph of the target RNA to obtain a third similarity; and obtaining the final similarity between the RNA to be searched and the target RNA based on the first similarity, the second similarity, and the third similarity. This invention represents RNA secondary structure as a graph structure in the field of computational science, applying graph analysis methods to RNA secondary structure analysis. Using graph data structures from computational science, it intuitively describes RNA secondary structure. It introduces various graph similarity methods, such as graph similarity analysis, graph structure analysis, and higher-order graph analysis, into RNA secondary structure analysis. This comprehensively considers multiple dimensions of information, including graph similarity analysis, RNA secondary structure composition, and higher-order graph information, to calculate the similarity between RNAs. The calculation results are more accurate and reliable, enabling intelligent and automatic RNA similarity calculation. This greatly facilitates biologists in discovering other RNAs with similar functions to the target RNA, as well as discovering other types of RNA with different similarities to the target RNA, thereby enabling RNA classification and the discovery of new RNAs.

[0043] Details of one or more embodiments of this application are set forth in the following drawings and description to make other features, objects and advantages of this application more readily apparent. Attached Figure Description

[0044] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0045] Figure 1 This is a schematic diagram illustrating the application environment of the RNA similarity analysis graph calculation method in one embodiment;

[0046] Figure 2 This is a flowchart illustrating the RNA similarity analysis graph calculation method in one embodiment;

[0047] Figure 3 This is the sequence data of the RNA being searched in one embodiment;

[0048] Figure 4This is a diagram of the RNA structure being searched in one embodiment;

[0049] Figure 5 This is a schematic diagram of the basic structural components of the RNA structure being searched in one embodiment;

[0050] Figure 6 This is a flowchart illustrating the specific steps of S202 in one embodiment;

[0051] Figure 7 This is a flowchart illustrating the specific steps of S302 in one embodiment;

[0052] Figure 8 This is a flowchart illustrating the specific steps of S203 in one embodiment;

[0053] Figure 9 This is a schematic diagram of the process for generating a high-order graph of the RNA being searched in one embodiment;

[0054] Figure 10 This is a high-order graph of the RNA being searched in one embodiment;

[0055] Figure 11 This is a structural block diagram of the RNA similarity analysis graph calculation device in one embodiment;

[0056] Figure 12 This is a schematic diagram of the electronic device structure in one embodiment;

[0057] Figure 13 This is a schematic diagram of the calculation process of the calculation unit in one embodiment. Detailed Implementation

[0058] To make the objectives, technical solutions, and advantages of this application clearer, the application is described and illustrated below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application. All other embodiments obtained by those skilled in the art based on the embodiments provided in this application without inventive effort are within the scope of protection of this application.

[0059] Obviously, the accompanying drawings described below are merely some examples or embodiments of this application. Those skilled in the art can apply this application to other similar scenarios based on these drawings without any inventive effort. Furthermore, it is understood that although the efforts made in this development process may be complex and lengthy, for those skilled in the art related to the content disclosed in this application, any changes to design, manufacturing, or production based on the technical content disclosed in this application are merely conventional technical means and should not be construed as insufficient disclosure of the content of this application.

[0060] In this application, the reference to "embodiment" means that a specific feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment that is mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described in this application may be combined with other embodiments without conflict.

[0061] Unless otherwise defined, the technical or scientific terms used in this application shall have the ordinary meaning understood by one of ordinary skill in the art to which this application pertains. The terms “a,” “an,” “an,” “the,” and similar words used in this application do not indicate quantity limitation and may indicate singular or plural. The terms “comprising,” “including,” “having,” and any variations thereof used in this application are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or device that includes a series of steps or modules (units) is not limited to the listed steps or units, but may also include steps or units not listed, or may include other steps or units inherent to these processes, methods, products, or devices. The terms “connected,” “linked,” “coupled,” and similar words used in this application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. “Multiple” used in this application refers to two or more. “And / or” describes the relationship between related objects, indicating that three relationships may exist; for example, “A and / or B” can represent: A alone, A and B simultaneously, and B alone. The character " / " generally indicates that the preceding and following objects are in an "or" relationship. The terms "first," "second," and "third" used in this application are merely to distinguish similar objects and do not represent a specific ordering of the objects.

[0062] The method embodiments provided in this example can be executed on a terminal, computer, or similar computing device. For example, it can run on a terminal. Figure 1 This is a schematic diagram illustrating the application environment of the RNA similarity analysis graph calculation method in one embodiment. For example... Figure 1 As shown, a terminal may include one or more ( Figure 1 Only one is shown in the diagram. A processor 102 and a memory 104 for storing data are also included. The processor 102 may be, but is not limited to, a microprocessor (MCU) or a programmable logic device (FPGA). The terminal may also include a transmission device 106 for communication functions and an input / output device 108. Those skilled in the art will understand that… Figure 1The structure shown is for illustrative purposes only and does not limit the structure of the terminal described above. For example, the terminal may also include components that are larger than... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown are illustrated.

[0063] The memory 104 can be used to store computer programs, such as application software programs and modules, like the computer program corresponding to the RNA similarity analysis graph calculation method in this embodiment. The processor 102 executes various functional applications and data processing by running the computer programs stored in the memory 104, thereby implementing the above-described method. The memory 104 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor 102, and these remote memories can be connected to the terminal via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0064] The transmission device 106 is used to receive or send data via a network. This network includes a wireless network provided by the terminal's communication provider. In one example, the transmission device 106 includes a Network Interface Controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, the transmission device 106 can be a Radio Frequency (RF) module for wireless communication with the Internet.

[0065] Firstly, embodiments of this application provide a method for calculating RNA similarity analysis graphs, such as... Figure 2 As shown, the method includes the following steps:

[0066] S201 converts the sequence data of the RNA to be searched into a structural diagram of the RNA.

[0067] Specifically, an RNA sequence consists of four bases: A (adenine), G (guanine), C (cytosine), and U (uracil). RNA sequence data includes the RNA sequence and a dotted-bracket sequence. The dotted-bracket sequence uses dots and paired brackets to represent the secondary structure information of RNA. Free bases in the RNA sequence that do not form complementary base pairs are represented by a dot ".", while the two bases that form complementary base pairs are represented by a pair of parentheses "()". For example, Figure 3 The sequence data of the RNA being searched is displayed.

[0068] The specific steps for converting the sequence data of the RNA to be searched into a structural graph of the RNA are as follows: Based on the RNA secondary structure information contained in the sequence data of the RNA to be searched, it is converted into a graph structure representation in the field of computational science. Each base in the sequence data of the RNA to be searched is treated as a graph node, and the bases have four characteristics, A, G, C, and U, as attributes of the nodes. The base nodes are connected by edges based on the relationships between secondary structures. For example, Figure 4 This paper presents a structural diagram of the RNA being searched, derived from the sequence data of the RNA being searched.

[0069] S202, A similarity analysis is performed on the RNA structure diagram being searched and the target RNA structure diagram to obtain the first similarity.

[0070] Specifically, the target RNA structure map described in this embodiment is obtained using the same method as in S201, based on the sequence data conversion of the target RNA. This embodiment employs graph similarity analysis to perform similarity analysis between the RNA structure map being searched and the target RNA structure map, obtaining a first similarity score.

[0071] S203, determine the number of basic components in the RNA structure diagram being searched, and obtain a second similarity based on the number of basic components in the RNA structure diagram being searched and the number of basic components in the target RNA structure diagram.

[0072] The basic structural components of an RNA structure diagram include: a 3' segment, a 5' segment, a hairpin loop, a stem, an inner loop, and multiple loop segments. For example, Figure 5 This is a schematic diagram of the basic building blocks of an RNA structure being searched.

[0073] S204, Based on the basic structural components of the RNA structure diagram being searched, the RNA structure diagram being searched is reconstructed to generate a higher-order diagram of the RNA being searched; a similarity analysis is performed between the higher-order diagram of the RNA being searched and the higher-order diagram of the target RNA to obtain a third similarity.

[0074] Specifically, in this embodiment, the RNA structure map to be searched is reconstructed based on its basic structural components to generate a higher-order map of the RNA being searched. Prior to this, the target RNA structure map is also reconstructed based on its basic structural components to generate a higher-order map of the target RNA. This step also employs graph similarity analysis to perform similarity analysis between the higher-order map of the RNA being searched and the higher-order map of the target RNA, obtaining a third similarity score.

[0075] S205, based on the first similarity, the second similarity, and the third similarity, obtain the final similarity between the RNA being searched and the target RNA.

[0076] Steps S201 to S205 above involve: converting the sequence data of the RNA to be searched into a structural diagram of the RNA; performing a similarity analysis between the structural diagram of the RNA to be searched and the structural diagram of the target RNA to obtain a first similarity; determining the number of basic constituent structures in the structural diagram of the RNA to be searched, and obtaining a second similarity based on the number of basic constituent structures in the structural diagram of the RNA to be searched and the number of basic constituent structures in the structural diagram of the target RNA; reconstructing the structural diagram of the RNA to be searched based on the basic constituent structures in the structural diagram of the RNA to be searched to generate a higher-order diagram of the RNA to be searched; performing a similarity analysis between the higher-order diagram of the RNA to be searched and the higher-order diagram of the target RNA to obtain a third similarity; and obtaining the final similarity between the RNA to be searched and the target RNA based on the first similarity, the second similarity, and the third similarity. This invention represents RNA secondary structure as a graph structure in the field of computational science, applying graph analysis methods to RNA secondary structure analysis. Using graph data structures from computational science, it intuitively describes RNA secondary structure. It introduces various graph similarity methods, such as graph similarity analysis, graph structure analysis, and higher-order graph analysis, into RNA secondary structure analysis. This comprehensively considers multiple dimensions of information, including graph similarity analysis, RNA secondary structure composition, and higher-order graph information, to calculate the similarity between RNAs. The calculation results are more reliable, enabling intelligent and automatic RNA similarity calculation. This greatly facilitates biologists in discovering other RNAs with similar functions to the target RNA, as well as discovering other types of RNA with different similarities to the target RNA, thereby enabling RNA classification and the discovery of new RNAs.

[0077] In one embodiment, such as Figure 6 As shown, the process of performing similarity analysis between the RNA structure diagram being searched and the target RNA structure diagram to obtain a first similarity score includes the following steps:

[0078] S301, the graph kernel decomposition method is used to decompose the RNA structure map to be searched into multiple RNA sub-maps and to decompose the target RNA structure map into multiple target RNA sub-maps.

[0079] S302, based on the plurality of RNA sub-maps being searched and the plurality of target RNA sub-maps, a first similarity is obtained.

[0080] In one embodiment, such as Figure 7 As shown, obtaining the first similarity based on the plurality of searched RNA sub-maps and the plurality of target RNA sub-maps includes the following steps:

[0081] S401, the plurality of searched RNA sub-maps are encoded to obtain a first coding sequence, and the plurality of target RNA sub-maps are encoded to obtain a second coding sequence.

[0082] S402, based on the first encoded sequence and the second encoded sequence, the specific calculation formula for calculating the first similarity score1 using the Jaccard method is as follows:

[0083]

[0084]

[0085] Among them, G t Represents the target RNA structure diagram, G u This represents the RNA structure diagram being searched, K WL This refers to the WL (Weisfeiler-Lehman) kernel method. Indicates the second encoded sequence, This represents the first encoded sequence.

[0086] In this embodiment, the kernel decomposition method employs the WL (Weisfeiler-Lehman) kernel method to decompose the RNA structure map being searched into multiple sub-maps of the RNA being searched, and to decompose the target RNA structure map into multiple sub-maps of the target RNA. Taking the RNA structure map being searched as an example, the specific decomposition method is to decompose each node in the structure map and the nodes adjacent to that node into a sub-map. If the RNA structure map being searched consists of 50 bases, it can be decomposed into 50 sub-maps, each sub-map consisting of that node and its adjacent nodes. Each node is encoded according to its node attributes (A (adenine), G (guanine), C (cytosine), and U (uracil)) (e.g., A: #1000, G: #1001, C: #1002, U: #1003). Based on the node encoding, the encoding of each sub-map can be obtained. Based on the encoding of the RNA sub-map being searched, a first coding sequence is further obtained, and based on the encoding of the target RNA sub-map, a second coding sequence is obtained.

[0087] In one embodiment, such as Figure 8 As shown, determining the number of basic constituent structures in the RNA structure map being searched, and obtaining a second similarity based on the number of basic constituent structures in the RNA structure map being searched and the number of basic constituent structures in the target RNA structure map, includes the following steps:

[0088] S501, determine the number of each basic component structure in the RNA structure diagram being searched, and determine the number of each basic component structure in the target RNA structure diagram.

[0089] S502, the quantities corresponding to each basic component structure in the RNA structure diagram being searched are used to form a first structure vector, and the quantities corresponding to each basic component structure in the target RNA structure diagram are used to form a second structure vector.

[0090] S503, based on the first structural vector and the second structural vector, the second similarity is obtained by using the Euclidean distance calculation method.

[0091] Specifically, the formula for calculating the second similarity is as follows:

[0092] Find(G t )=[T t ,F t H t ,S t ,I t M t ];

[0093] Find(G u )=[T u ,F u H u ,S u ,I u M u ];

[0094] score2=E_distan([T t ,F t H t ,S t ,I t M t ],[T u ,F u H u ,S u ,I u M u ]);

[0095] Where Find represents the graph structure search method, G t Represents the target RNA structure diagram, G u This represents the RNA structure diagram being searched, T t F t H t S t I t M t These represent the number of 3' fragments, 5' fragments, hairpin loops, stems, inner loops, and multi-loop fragments contained in the target RNA structure map obtained using the graph structure search method; T u F u H u S u Iu M u These represent the number of 3' fragments, 5' fragments, hairpin loops, stems, inner loops, and multi-loop fragments contained in the RNA structure map obtained using the graph structure search method, respectively. [T] u ,F u H u ,S u ,I u M u [T] represents the first structure vector consisting of the number of each basic component structure in the RNA structure diagram being searched. t ,F t H t ,S t ,I t M t ] represents the second structure vector composed of the number of each basic component structure in the target RNA structure diagram, E_distan represents the calculation of Euclidean distance, and score2 represents the structural similarity score between the two diagrams, i.e., the second similarity.

[0096] In one embodiment, the graph structure lookup method uses the graph matching algorithm in the field of graph computing to perform a retrieval query on the RNA structure graph to be searched and the target specific RNA structure graph. This application uses the graph matching algorithm to determine the number of each basic component structure corresponding to the RNA structure graph to be searched and the target RNA structure graph.

[0097] In one embodiment, the formula for calculating the final similarity between the searched RNA and the target RNA based on the first similarity, the second similarity, and the third similarity is as follows:

[0098] score=α*score1+β*score2+γ*score3;

[0099] Wherein, α, β, and γ represent constraint parameters, which are between 0 and 1 and satisfy α+β+γ=1, score1 represents the first similarity, score2 represents the second similarity, score3 represents the third similarity, and score represents the final similarity between the RNA being searched and the target RNA.

[0100] In one embodiment, such as Figure 9 As shown, the process of reconstructing the structure of the RNA being searched based on its fundamental constituent structures to generate a higher-order diagram of the RNA being searched includes the following steps:

[0101] S601, each of the basic constituent structures in the RNA structure diagram being searched is treated as a node;

[0102] S602, the length of each of the basic components is used as an attribute of the corresponding node;

[0103] S603, connect edges according to the topological relationships between the basic components to form a high-order graph of the RNA being searched.

[0104] Specifically, the 3' fragment, 5' fragment, hairpin loop, stem, inner loop, and multi-loop fragment in the RNA structure diagram to be searched are each treated as a node in the graph structure. The length of the basic constituent structure is the number of bases that make up that basic constituent structure. For example, if the basic constituent structure is composed of 5 bases, then the attribute of the node is 5. Edges are then connected according to the topological relationships between the basic constituent structures, which can be determined from the RNA structure diagram to be searched. Prior to this, the target RNA structure diagram is also reconstructed based on the basic constituent structures in the target RNA structure diagram to generate a higher-order graph of the target RNA.

[0105] For example, Figure 10 This diagram presents a high-order diagram of the RNA being searched, generated from the structure diagram of the RNA being searched. In the diagram, H represents a hairpin loop, S represents a stem, I represents an inner loop, M represents a multi-loop segment, F represents a 5' segment, and T represents a 3' segment.

[0106] This application also provides an RNA similarity analysis graph calculation device, such as... Figure 11 As shown, the device includes:

[0107] The conversion module 710 is used to convert the first sequence data of the RNA to be searched into a structural diagram of the RNA to be searched.

[0108] The first obtaining module 720 is used to perform similarity analysis on the RNA structure diagram being searched and the target RNA structure diagram to obtain a first similarity.

[0109] The second obtaining module 730 is used to determine the number of basic components in the RNA structure map being searched, and to obtain a second similarity based on the number of basic components in the RNA structure map being searched and the number of basic components in the target RNA structure map.

[0110] The third module 740 is used to reconstruct the structure diagram of the RNA being searched based on the basic component structure of the structure diagram of the RNA being searched, and generate a higher-order diagram of the RNA being searched; and to perform a similarity analysis between the higher-order diagram of the RNA being searched and the higher-order diagram of the target RNA to obtain a third similarity.

[0111] The module 750 is used to obtain the final similarity between the RNA being searched and the target RNA based on the first similarity, the second similarity, and the third similarity.

[0112] In one embodiment, the first obtaining module 720 is further configured to decompose the searched RNA structure map into multiple searched RNA sub-maps using a graph kernel decomposition method, and to decompose the target RNA structure map into multiple target RNA sub-maps; and to obtain a first similarity based on the multiple searched RNA sub-maps and the multiple target RNA sub-maps.

[0113] In one embodiment, the first obtaining module 720 is further configured to encode the plurality of searched RNA sub-maps to obtain a first coding sequence, and to encode the plurality of target RNA sub-maps to obtain a second coding sequence; and to calculate a first similarity based on the first coding sequence and the second coding sequence.

[0114] In one embodiment, the second obtaining module 730 is further configured to determine the number of each basic component structure in the RNA structure diagram being searched, and to determine the number of each basic component structure in the target RNA structure diagram; to form a first structure vector by combining the number of each basic component structure in the RNA structure diagram being searched, and to form a second structure vector by combining the number of each basic component structure in the target RNA structure diagram; and to obtain a second similarity based on the first structure vector and the second structure vector using a Euclidean distance calculation method.

[0115] In one embodiment, the second obtaining module 730 is further configured to use a graph matching algorithm to determine the number of each basic component structure in the searched RNA structure graph and the target RNA structure graph.

[0116] In one embodiment, the formula for calculating the final similarity between the searched RNA and the target RNA based on the first similarity, the second similarity, and the third similarity is as follows:

[0117] score=α*score1+β*score2+γ*score3;

[0118] Where α, β, and γ represent constraint parameters, which are between 0 and 1 and satisfy α + β + γ = 1. score1 represents the first similarity, score2 represents the second similarity, score3 represents the third similarity, and score represents the final similarity.

[0119] In one embodiment, the third obtaining module 740 is further configured to treat each of the basic components in the RNA structure graph to be searched as a node; use the length of each basic component as an attribute of the corresponding node; and connect edges according to the topological relationship between the basic components to form a high-order graph of the RNA to be searched.

[0120] It should be noted that the above modules can be functional modules or program modules, and can be implemented through software or hardware. For modules implemented through hardware, the above modules can reside in the same processor; or the above modules can be located in different processors in any combination.

[0121] Thirdly, embodiments of this application also provide an electronic device, which is an electronic device based on FPGA (Field Programmable Gate Array) hardware, such as... Figure 12 As shown, it includes a cache module 810, a control module 820, and multiple computing units 830;

[0122] The cache module 810 is used to store target RNA data, which includes a target RNA structure diagram, a second structure vector, and a high-order target RNA diagram.

[0123] Specifically, in this embodiment, the caching module 810 is an on-chip cache. First, the sequence data of the target RNA is input into the computing unit 830. After transformation calculation, graph similarity calculation, search calculation, and higher-order calculation, the decomposed sub-graphs of the secondary structure of the target RNA are obtained, and the second structure vector [T] is found. t ,F t H t ,S t ,I t M t The decomposition subgraphs of the target RNA high-order graph are stored in the on-chip cache.

[0124] The control module 820 is used to distribute the sequence data of multiple RNAs to be searched to the multiple computing units 830;

[0125] Specifically, the large-scale RNA sequence data to be analyzed and searched input from outside the chip is distributed to N computing units 830 through the control module 820, and the corresponding calculations are performed in the computing units 830 in a streamlined manner.

[0126] The calculation unit 830 is used to calculate the similarity between each RNA being searched and the target RNA based on the target RNA data and the sequence data of the RNA being searched.

[0127] like Figure 13 As shown, the specific calculation process of the calculation unit 830 is as follows: First, a transformation calculation is performed to convert the sequence data of the RNA to be searched into a structural diagram of the RNA to be searched. Then, the structural diagram is simultaneously distributed to graph similarity calculation and search calculation, and subgraph decomposition and structural vector solving are performed in parallel. The results are then passed to similarity calculation and higher-order calculation. Next, the decomposed subgraphs of the higher-order graph are solved in the higher-order calculation, and the first similarity score1 and the second similarity score2 are solved in the similarity calculation. Finally, the third similarity score3 and the final similarity score are calculated and output. The decomposed subgraphs of the secondary structure, structural vectors, and the decomposed subgraphs of the higher-order graph are solved in the similarity calculation unit to obtain the final similarity.

[0128] The electronic device in this embodiment is equipped with multiple parallel computing units 830 for pipelined calculation of the similarity between the searched RNA and the target RAN on a large scale, and performs similarity analysis on the large-scale RNA structure to achieve accelerated calculation. In the entire calculation process, multiple computing units 830 use parallel and pipelined computing, making full use of the computing units 830, resulting in high calculation efficiency and accelerated calculation.

[0129] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps in any of the above-described RNA similarity analysis graph calculation method embodiments.

[0130] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical storage, etc. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0131] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0132] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for calculating RNA similarity analysis graphs, characterized in that, The method includes: The sequence data of the RNA to be searched is converted into a structural diagram of the RNA to be searched; wherein, the sequence data of the RNA to be searched includes RNA secondary structure information, and the RNA secondary structure information in the sequence data of the RNA to be searched is converted into a structural diagram of the RNA to be searched; A similarity analysis is performed between the RNA structure diagram being searched and the target RNA structure diagram to obtain the first similarity score; The number of basic constituent structures in the RNA structure diagram being searched is determined, and a second similarity is obtained based on the number of basic constituent structures in the RNA structure diagram being searched and the number of basic constituent structures in the target RNA structure diagram. Based on the basic structural components of the RNA structure diagram being searched, the RNA structure diagram is reconstructed to generate a higher-order diagram of the RNA being searched; a similarity analysis is performed between the higher-order diagram of the RNA being searched and the higher-order diagram of the target RNA to obtain a third similarity score. Based on the first similarity, the second similarity, and the third similarity, the final similarity between the RNA being searched and the target RNA is obtained.

2. The method according to claim 1, characterized in that, The similarity analysis of the RNA structure diagram being searched and the target RNA structure diagram to obtain the first similarity includes: The graph kernel decomposition method is used to decompose the RNA structure map to be searched into multiple RNA sub-maps, and to decompose the target RNA structure map into multiple target RNA sub-maps. Based on the multiple sub-maps of RNA being searched and the multiple sub-maps of target RNA, a first similarity is obtained.

3. The method according to claim 2, characterized in that, The process of obtaining the first similarity based on the plurality of searched RNA sub-maps and the plurality of target RNA sub-maps includes: The plurality of RNA sub-maps being searched are encoded to obtain a first coding sequence, and the plurality of target RNA sub-maps are encoded to obtain a second coding sequence; A first similarity is calculated based on the first encoded sequence and the second encoded sequence.

4. The method according to claim 1, characterized in that, The step of determining the number of basic constituent structures in the RNA structure map being searched, and obtaining a second similarity based on the number of basic constituent structures in the RNA structure map being searched and the number of basic constituent structures in the target RNA structure map, includes: Determine the number of each basic component structure in the RNA structure diagram being searched, and determine the number of each basic component structure in the target RNA structure diagram; The quantities corresponding to each basic component structure in the RNA structure diagram being searched are used to form a first structure vector, and the quantities corresponding to each basic component structure in the target RNA structure diagram are used to form a second structure vector. Based on the first structural vector and the second structural vector, the second similarity is obtained by using the Euclidean distance calculation method.

5. The method according to claim 4, characterized in that, A graph matching algorithm is used to determine the number of each basic component structure in the RNA structure graph being searched and the target RNA structure graph.

6. The method according to claim 1, characterized in that, The formula for calculating the final similarity between the searched RNA and the target RNA based on the first similarity, the second similarity, and the third similarity is as follows: ； Where α, β, and γ represent constraint parameters, which are between 0 and 1 and satisfy α+β+γ=1. score1 represents the first similarity, score2 represents the second similarity, score3 represents the third similarity, and score represents the final similarity.

7. The method according to claim 1, characterized in that, The process of reconstructing the structure diagram of the RNA being searched, based on its fundamental structural components, to generate a higher-order diagram of the RNA being searched includes: Each of the basic constituent structures in the RNA structure diagram being searched is treated as a node; The length of each of the aforementioned basic structural components is used as an attribute of the corresponding node; The edges are connected according to the topological relationships between the basic components to form a higher-order graph of the RNA being searched.

8. An RNA similarity analysis graph calculation device, characterized in that, The device includes: A conversion module is used to convert the sequence data of the RNA to be searched into a structural diagram of the RNA to be searched; wherein the sequence data of the RNA to be searched includes RNA secondary structure information, and the RNA secondary structure information in the sequence data of the RNA to be searched is converted into a structural diagram of the RNA to be searched; The first obtaining module is used to perform similarity analysis on the RNA structure diagram being searched and the target RNA structure diagram to obtain a first similarity. The second obtaining module is used to determine the number of basic components in the RNA structure diagram being searched, and to obtain a second similarity based on the number of basic components in the RNA structure diagram being searched and the number of basic components in the target RNA structure diagram. The third module is used to reconstruct the structure diagram of the RNA being searched based on the basic component structure of the RNA structure diagram to generate a higher-order diagram of the RNA being searched; and to perform a similarity analysis between the higher-order diagram of the RNA being searched and the higher-order diagram of the target RNA to obtain a third similarity. The module is configured to obtain the final similarity between the RNA being searched and the target RNA based on the first similarity, the second similarity, and the third similarity.

9. An electronic device, characterized in that, It includes a cache module, a control module, and multiple computing units; The cache module is used to store target RNA data, which includes a target RNA structure diagram, a second structure vector, and a high-order target RNA diagram. The control module is used to distribute the sequence data of multiple RNAs to be searched to the multiple computing units; The computing unit is configured to calculate and execute the method according to any one of claims 1 to 7 based on the target RNA data and the sequence data of the RNA being searched, to obtain the similarity between each RNA being searched and the target RNA.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1 to 7.

Images