Comprehensive RNA sequence structure analysis and local environment influence weighting method

Abstract

The invention particularly relates to a comprehensive RNA (Ribonucleic Acid) sequence structure analysis and local environment influence weighting method, which comprises the following steps: acquiring a to-be-processed RNA sequence, and inputting the RNA sequence into an RNA sequence structure analysis model; based on a preset distance constraint condition, screening a to-be-treated base pair from the RNA sequence; wherein the base pair to be treated comprises a standard base pair and / or a non-standard base pair; carrying out initialization processing on the base pairing parameters; configuring two adjacent basic groups corresponding to a first basic group i and a second basic group j in the to-be-processed basic group pair based on the displacement parameters, and updating the pairing probability according to weight parameters of adjacent basic group pairs corresponding to the two adjacent basic groups when determining that the two adjacent basic groups are paired; and constructing a pairing weight matrix according to the updated pairing probability corresponding to each basic group to be processed. According to the method, the influence of non-standard base pairs is considered, and the accuracy of the pairing weight matrix is improved. When the method is applied to an RNA secondary structure prediction task, a more accurate result is obtained.

Description

Technical Field

[0001] The present invention relates to the technical field of RNA structure analysis, and in particular to a comprehensive RNA sequence structure analysis and a local environment influence weighting method thereof. Background Art

[0002] In the related art, RNA secondary structure is mainly composed of a stem region structure formed by adjacent base pairing and a ring structure formed by unpaired bases, so it can also be called a stem-loop structure. For the stem region structure, the base pairs near the middle position (center) are relatively stable, while the base pairs at both ends of the stem region (edges) are relatively unstable. This is mainly because when forming a secondary structure, the edge bases are more susceptible to the influence of other regions. Based on this theory, the CDPFold model considers the influence of whether the bases on both sides are paired when the current two bases form a standard pairing. If the bases on both sides form a pairing, it proves that the region can form a stem region, so the weight of the current standard base pairing is increased according to the Gaussian function. It should be noted that the premise of the algorithm is that the two bases form Watson-Crick pairing (AU, CG) and non-Watson-Crick pairing (UG), that is, standard base pairing. This also leads to its low accuracy in RNA secondary structure prediction.

[0003] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present invention, and therefore may include information that does not constitute the prior art known to ordinary technicians in the field. Summary of the invention

[0004] The present invention provides a comprehensive RNA sequence structure analysis and a local environment influence weighting method, a storage medium, a computer program product, and an electronic device, which can obtain a pairing weight matrix with higher accuracy, thereby effectively improving the accuracy of RNA secondary structure prediction, thereby overcoming the defects in the prior art to a certain extent.

[0005] Other features and advantages of the present invention will become apparent from the following detailed description, or may be learned in part by practice of the present invention.

[0006] According to a first aspect of the present invention, a method for comprehensive RNA sequence structure analysis and weighted local environmental impact is provided, the method comprising:

[0007] Obtaining an RNA sequence to be processed, and inputting the RNA sequence into an RNA sequence structure analysis model;

[0008] Based on a preset distance constraint condition, the RNA sequence is screened for base pairs to be processed; wherein the base pairs to be processed include: standard base pairs and / or non-standard base pairs;

[0009] Initializing base pairing parameters; wherein the base pairing parameters include: the pairing probability W between the first base i and the second base j in the base pair to be processed, which is used to configure the displacement parameters of the adjacent base pairs;

[0010] Based on the displacement parameter, two adjacent bases corresponding to the first base i and the second base j in the base pair to be processed are configured, and when determining the pairing of the two adjacent bases, the pairing probability is updated according to the weight parameters of the adjacent base pairs corresponding to the two adjacent bases;

[0011] A pairing weight matrix is ​​constructed according to the updated pairing probability corresponding to each base to be processed.

[0012] In some exemplary embodiments, after the RNA sequence is input into the RNA sequence structure analysis model, the method further comprises:

[0013] A pairing weight matrix with a size of L*L is constructed for the RNA sequence based on the RNA sequence length L, and an initial value of the pairing weight matrix is ​​configured to be 0.

[0014] In some exemplary embodiments, the screening of base pairs to be processed based on a preset distance constraint condition comprises:

[0015] configuring base pairs in the RNA sequence whose distance is greater than or equal to a preset distance threshold as base pairs to be processed; and

[0016] Base pairs whose distance is less than the preset distance threshold are ignored.

[0017] In some exemplary embodiments, the displacement parameters for configuring adjacent base pairs include: a first displacement parameter α, or a first displacement parameter α and a second displacement parameter β; wherein the initial values ​​of the first displacement parameter α and the second displacement parameter β are 1.

[0018] In some exemplary embodiments, configuring two adjacent bases corresponding to a first base i and a second base j in a base pair to be processed based on the displacement parameter, and updating the pairing probability according to weight parameters of adjacent base pairs corresponding to the two adjacent bases when determining pairing of the two adjacent bases, comprises:

[0019] Determine a first adjacent base i-α and a second adjacent base j+α corresponding to the first base i and the second base j based on the first shift parameter α;

[0020] When it is determined that the first adjacent base i-α and the second adjacent base j+α are successfully paired, a first matching weight of the corresponding first adjacent base pair is calculated, and the matching probability W is updated according to the first matching weight;

[0021] The first displacement parameter α is updated to α+1, and the above steps are repeatedly performed based on the updated first displacement parameter α+1.

[0022] In some exemplary embodiments, the method further comprises:

[0023] Determine the third adjacent base i+β and the fourth adjacent base j-α corresponding to the first base i and the second base j according to the first shift parameter α and the second shift parameter β;

[0024] When it is determined that the third adjacent base i+β and the fourth adjacent base j-α are successfully paired, the second matching weight of the corresponding second adjacent base pair is calculated, and the matching probability W is updated according to the second matching weight;

[0025] The second displacement parameter β is updated to β+1, and the above steps are repeatedly performed based on the updated second displacement parameter β+1.

[0026] In some exemplary embodiments, the method further comprises:

[0027] Pre-configure the weight parameters corresponding to standard base pairs and non-standard base pairs.

[0028] According to a second aspect of the present invention, there is provided a storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the above-mentioned comprehensive RNA sequence structure analysis and local environment impact weighting method are implemented.

[0029] According to a third aspect of the present invention, there is provided a computer program product having a computer program stored thereon, which implements the above-mentioned comprehensive RNA sequence structure analysis and local environment impact weighting method when the computer program is executed by a processor.

[0030] According to a fourth aspect of the present invention, there is provided an electronic device, comprising:

[0031] Processor; and

[0032] A memory, configured to store executable instructions of the processor;

[0033] Wherein, the processor is configured to implement the above-mentioned comprehensive RNA sequence structure analysis and its local environment impact weighting method by executing the executable instructions.

[0034] The comprehensive RNA sequence structure analysis and local environment influence weighting method provided by the embodiment of the present invention can combine the influence of non-standard base pairs when judging base pairing by using preset distance constraints to screen the base pairs to be processed, thereby improving the accuracy of the pairing weight matrix. When it is applied to the RNA secondary structure prediction task, a more accurate RNA secondary structure prediction result can be obtained.

[0035] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The accompanying drawings herein are incorporated into and constitute a part of the specification, illustrate embodiments consistent with the present invention, and together with the specification are used to explain the principles of the present invention. Obviously, the accompanying drawings described below are only some embodiments of the present invention, and for those of ordinary skill in the art, other accompanying drawings can be obtained based on these accompanying drawings without creative work.

[0037] Figure 1 A schematic diagram schematically illustrates a method for comprehensive RNA sequence structure analysis and weighting of local environmental impacts according to an exemplary embodiment of the present invention;

[0038] Figure 2 A schematic diagram schematically illustrates a method flow of a pcRNASA model according to an exemplary embodiment of the present invention;

[0039] Figure 3 The figure schematically shows the composition of an electronic device in an exemplary embodiment of the present invention. DETAILED DESCRIPTION

[0040] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that the present invention will be more comprehensive and complete and fully convey the concepts of the example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0041] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor devices and / or microcontroller devices.

[0042] In the related art, the CDPFold model takes into account the influence of whether the bases on both sides are paired when the current two bases form a standard pairing. If the bases on both sides form a pairing, it proves that the region can form a stem region, so the weight of the current standard base pairing is increased according to the Gaussian function. It should be noted that the premise of this algorithm is that the two bases form a Watson-Crick pairing (AU, CG) and a non-Watson-Crick pairing (UG), that is, a standard base pairing.

[0043] In view of the shortcomings and deficiencies of the prior art, this exemplary embodiment provides a comprehensive RNA sequence structure analysis and a weighted method for its local environmental impact. Figure 1 As shown, the method may specifically include the following steps:

[0044] Step S11, obtaining an RNA sequence to be processed, and inputting the RNA sequence into an RNA sequence structure analysis model;

[0045] Step S12, based on a preset distance constraint condition, screening the RNA sequence for base pairs to be processed; wherein the base pairs to be processed include: standard base pairs and / or non-standard base pairs;

[0046] Step S13, initializing base pairing parameters; wherein the base pairing parameters include: a pairing probability W between a first base i and a second base j in a base pair to be processed, which is used to configure a displacement parameter of an adjacent base pair;

[0047] Step S14, configuring two adjacent bases corresponding to the first base i and the second base j in the base pair to be processed based on the displacement parameter, and when determining that the two adjacent bases are paired, updating the pairing probability according to the weight parameters of the adjacent base pairs corresponding to the two adjacent bases;

[0048] Step S15, constructing a pairing weight matrix according to the updated pairing probabilities corresponding to each base to be processed.

[0049] Hereinafter, each step of the comprehensive RNA sequence structure analysis and the local environment impact weighting method in this exemplary implementation will be described in more detail with reference to the accompanying drawings and examples.

[0050] In step S11, the RNA sequence to be processed is obtained, and the RNA sequence is input into the RNA sequence structure analysis model.

[0051] Exemplarily, a user can initiate an analysis task for one or more RNA sequences on a terminal device. The terminal device can send the analysis task to the server, which analyzes the RNA sequence and generates corresponding analysis results, and then feeds the analysis results back to the terminal device for display, so that the terminal device and the server collaborate to perform comprehensive RNA sequence structure analysis and its local environmental impact weighting method. Alternatively, the corresponding analysis task can be performed locally on the terminal device, and the RNA sequence structure analysis model (pcRNASA model) can be called for data analysis, and the data analysis results can be output.

[0052] Exemplarily, after the RNA sequence is input into the RNA sequence structure analysis model, the method further includes: constructing a pairing weight matrix of size L*L for the RNA sequence based on the RNA sequence length L, and configuring the initial value of the pairing weight matrix to be 0.

[0053] Specifically, for a RNA sequence of length L currently input, where the RNA sequence may be composed of four bases, A, U, G, and C. First, an L*L square matrix with a value of 0 may be initialized.

[0054] Exemplarily, the method includes: pre-configuring weight parameters corresponding to standard base pairs and non-standard base pairs.

[0055] Specifically, based on biological knowledge, base C and base G are the most stable pairing, so their initial pairing weight is set to 3; base A and base U are relatively stable pairing, so their initial pairing weight is set to 2; base U and base G are relatively unstable pairing, so their initial pairing weight is set to 1; non-standard base pairing is very unstable, so the initial weight is considered to be 0. The formula may include:

[0056]

[0057] In step S12, based on a preset distance constraint condition, the RNA sequence is screened for base pairs to be processed; wherein the base pairs to be processed include: standard base pairs and / or non-standard base pairs.

[0058] Exemplarily, the screening of base pairs to be processed based on a preset distance constraint condition includes:

[0059] configuring base pairs in the RNA sequence whose distance is greater than or equal to a preset distance threshold as base pairs to be processed; and

[0060] Base pairs whose distance is less than the preset distance threshold are ignored.

[0061] Specifically, the distance constraint condition may be preconfigured, for example, the distance threshold between two bases may be configured to be 3 or 4.

[0062] For example, considering the pairing distance constraint, pairing is considered only when the distance between two base pairs is greater than 3. If the distance between base pairs is less than 4, no processing is done and pairing is not considered.

[0063] This constraint can be expressed as:

[0064] W ij =0, if |ij|<4

[0065] In step S13, the base pairing parameters are initialized; wherein the base pairing parameters include: the pairing probability W between the first base i and the second base j in the base pair to be processed, which is used to configure the displacement parameters of the adjacent base pairs.

[0066] Exemplarily, the displacement parameters for configuring adjacent base pairs include: a first displacement parameter α, or a first displacement parameter α and a second displacement parameter β; wherein the initial values ​​of the first displacement parameter α and the second displacement parameter β are 1.

[0067] For example, the pairing probability W of base i and base j can be initialized, and the values ​​of α and β can be initialized to 1.

[0068] In step S14, two adjacent bases corresponding to the first base i and the second base j in the base pair to be processed are configured based on the displacement parameter, and when determining the pairing of two adjacent bases, the pairing probability is updated according to the weight parameters of the adjacent base pairs corresponding to the two adjacent bases.

[0069] Exemplarily, step S14 may include:

[0070] Step 141, determining a first adjacent base i-α and a second adjacent base j+α corresponding to a first base i and a second base j based on a first shift parameter α;

[0071] Step 142, when it is determined that the first adjacent base i-α and the second adjacent base j+α are successfully paired, a first matching weight of the corresponding first adjacent base pair is calculated, and the matching probability W is updated according to the first matching weight;

[0072] Step 143: update the first displacement parameter α to α+1, and repeat the above steps based on the updated first displacement parameter α+1.

[0073] Specifically, it can be judged whether adjacent bases form a base pair. For example, consider whether base i-α and base j+α are paired; if they are paired, calculate the weight and add it to W, update α to α+1 and continue to perform this step; if they are not paired, end.

[0074] Furthermore, the above step S14 may also include:

[0075] Step S144, determining the third adjacent base i+β and the fourth adjacent base j-α corresponding to the first base i and the second base j according to the first shift parameter α and the second shift parameter β;

[0076] Step S145, when it is determined that the third adjacent base i+β and the fourth adjacent base j-α are successfully paired, a second matching weight of the corresponding second adjacent base pair is calculated, and the matching probability W is updated according to the second matching weight;

[0077] Step S146, updating the second displacement parameter β to β+1, and repeating the above steps based on the updated second displacement parameter β+1.

[0078] Specifically, it is also possible to further consider whether the adjacent bases in other directions form a base pair. For example, consider whether base i+β and base j-α are paired. If they are paired, the weight is calculated and accumulated to W, and β is updated to β+1 to continue this step; if they are not paired, the process ends.

[0079] Specifically, in addition to individual base pairs, the method also considers the effect of the local environment within the RNA secondary structure on pairing propensity. For example, the pairing propensity of a base is theoretically affected by the pairing state of adjacent bases, suggesting that pre-paired neighbors may promote further pairing by favorably altering the spatial structure of the RNA. To quantify this, the method improves the calculation of the pairing probability between base i and base j and incorporates the effect of adjacent pairing. To quantify this, the method improves the calculation of the pairing probability between base i and base j and incorporates the effect of adjacent pairing. This improvement can be expressed by the formula:

[0080] W (i,j) =P left +P (i,j) +P right

[0081] Among them, P left and P right They represent the pairing probability of base i and base j. Since the base pairs close to the center of the stem are more stable, the Gaussian function is selected as the weight, that is, the closer to the center, the greater the Gaussian weight.

[0082] The formula for calculating the influence weight of neighboring bases using the Gaussian function can be expressed as:

[0083] W (i,j) =W (i,j) +P (i,j) +Caussian(α)

[0084] Here, α represents the distance from base i and base j.

[0085] In step S15, a pairing weight matrix is ​​constructed according to the updated pairing probabilities corresponding to each base to be processed.

[0086] Specifically, after determining whether the adjacent bases of each base pair to be processed in the RNA sequence are paired, the corresponding pairing weight matrix can be generated based on the obtained configuration probability. After repeated calculations, the weight matrix L*L corresponding to the RNA sequence of length L is finally determined. This weight matrix can be applied to the prediction of RNA secondary structure.

[0087] Exemplary, reference Figure 2 As shown in the figure, the specific workflow of the pcRNASA model can include:

[0088] S1, input RNA sequence of length L, where the RNA sequence consists of four bases: A, U, G, and C;

[0089] S2, initialize an L*L square matrix with a value of 0;

[0090] S3, first consider the distance constraint: if the distance of the base pair is less than 4, no processing is done and jump to step S7;

[0091] S4, initialize the pairing probability W of base i and base j, and initialize the values ​​of α and β to 1.

[0092] S5, consider whether base i-α and base j+α are paired. If they are paired, calculate the weight and add it to W, update α to α+1 and continue to execute this step; if they are not paired, end;

[0093] S6, consider whether base i+β and base j-α are paired. If they are paired, calculate the weight and add it to W, update β to β+1 and continue this step; if they are not paired, end;

[0094] S6, obtaining the pairing weights of base i and base j and ending the calculation;

[0095] S7, repeating steps S3 to S7 for all base pairs, and finally obtaining the pairing weight matrix corresponding to the RNA sequence.

[0096] Specifically, this method does not consider the pairing weight based on whether the base pair is a standard base pair, but comprehensively considers the impact of non-standard base pairs and judges adjacent bases based on preset distance constraints, which is very important for determining the true secondary structure of RNA sequences.

[0097] Exemplarily, in order to further verify the technical effect of the method of the present invention, we applied the pcRNASA model to the RNA secondary structure prediction task to verify whether the performance of RNA secondary structure prediction can be improved after adding the weight matrix. Precision, recall, and F1 value are recognized performance evaluation indicators in RNA secondary structure prediction tasks. The results further show that the present invention can improve the accuracy of RNA secondary structure prediction. The specific steps include:

[0098] 1) Select the benchmark datasets RNAStralign and TR0 in the RNA secondary structure prediction task as training sets, and ArchiveII and TS0 as test sets;

[0099] 2) Select the RNA sequence characterization model and pcRNASA to complete the downstream RNA secondary structure prediction task. Considering the constraints of the characterization model, the training set and test set are divided into 128 groups and 512 groups according to length, respectively, and sequences with a length greater than 512 are filtered out;

[0100] 3) The RNA secondary structure prediction model based on the U-Net architecture was selected to verify the performance of pcRNASA. The U-Net architecture mainly consists of two parts: encoder and decoder. The encoder is used to extract high-level features of the input data, while the decoder is used to map the features to prediction results of the same size as the input data. Each encoder submodule consists of three layers: two-dimensional convolution, Dropout, and normalization. Two-dimensional convolution compresses the feature map, Dropout helps alleviate model overfitting, and normalization helps speed up the convergence of the model. Each decoder submodule consists of three layers: two-dimensional transposed convolution, Dropout, and normalization. Two-dimensional transposed convolution restores the size of the feature map, and the Dropout and normalization layers have the same functions as the corresponding modules in the encoder.

[0101] In the RNA secondary structure prediction task, the input of U-Net is three-dimensional image-like data. In this verification process, the image-like data includes the L*L*D data obtained by full splicing of the results of the representation model and the L*L*1 base pairing weight matrix obtained by the pcRNASA model, where D is the characteristic dimension of the representation model. When the pairing weight matrix obtained by the pcRNASA model is not added, the input of the U-Net module is L*L*D; when the pairing weight matrix obtained by the pcRNASA model is added, the input of the U-Net module is L*L*(D+1).

[0102] Here, two comparison models are trained separately, and other modules remain the same. The output of U-Net is an L*L*1 single-channel matrix, and each pixel represents the pairing probability of the base in the RNA sequence. The pairing probability can be post-processed to obtain the predicted secondary structure;

[0103] 4) Use the comparison model obtained in step 3 to calculate the precision, recall, and F1 value of the test set. After obtaining the precision, recall, and F1 value of 128 and 512 groups of the same data set, calculate their weighted average as the final evaluation result;

[0104] 5) The reliability of the present invention is verified by comparing the precision, recall and F1 value of the two comparison models.

[0105] The results in Table 1 are comparisons of experimental results on the benchmark datasets ArchiveII (corresponding training set is RNAStralign) and TS0 (corresponding training set is TR0) using the RNA secondary structure prediction model based on the U-Net architecture with and without the present invention. It can be seen from the results that the F1 values ​​on both datasets have been improved to a certain extent by adding the weight matrix of the present invention. Among them, the improvement on ArchiveII is 3.75%, and the improvement on TS0 is 25%.

[0106] The above results show that: the comprehensive RNA sequence structure analysis and local environmental influence weighting method constructed by the method of the present invention can serve the RNA secondary structure prediction task well and improve the performance of the RNA secondary structure prediction model.

[0107] Table 1

[0108]

[0109]

[0110] The pcRNASA model provided by the method provided in the embodiment of the present invention takes into account the influence and importance of non-standard base pairs in the formation process of RNA secondary structure, and does not eliminate non-standard base pairs, but uses distance constraints to select adjacent base pairs and construct the final weight matrix. It can better play a role in the RNA secondary structure prediction task, so that a more realistic secondary structure can be obtained and serve in practical applications such as RNA vaccine design.

[0111] It should be noted that the above figures are only schematic illustrations of the processes included in the method according to an exemplary embodiment of the present invention, and are not intended to be limiting. It is easy to understand that the processes shown in the above figures do not indicate or limit the time sequence of these processes. In addition, it is also easy to understand that these processes can be performed synchronously or asynchronously, for example, in multiple modules.

[0112] It should be noted that, although several modules or units of the equipment for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to an embodiment of the present invention, the features and functions of two or more modules or units described above can be embodied in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into being embodied by multiple modules or units.

[0113] Figure 3 A schematic diagram of an electronic device suitable for implementing an embodiment of the present invention is shown.

[0114] It should be noted that Figure 3 The electronic device 1000 shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0115] like Figure 3 As shown, the electronic device 1000 includes a central processing unit (CPU) 1001, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 1002 or the program loaded from the storage part 1008 to the random access memory (RAM) 1003. In the RAM 1003, various programs and data required for system operation are also stored. The CPU 1001, ROM 1002 and RAM 1003 are connected to each other through a bus 1004. An input / output (I / O) interface 1005 is also connected to the bus 1004.

[0116] The following components are connected to the I / O interface 1005: an input section 1006 including a keyboard, a mouse, etc.; an output section 1007 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 1008 including a hard disk, etc.; and a communication section 1009 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 1009 performs communication processing via a network such as the Internet. A drive 1010 is also connected to the I / O interface 1005 as needed. A removable medium 1011, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 1010 as needed, so that a computer program read therefrom is installed into the storage section 1008 as needed.

[0117] In particular, according to an embodiment of the present invention, the process described below with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present invention includes a computer program product, which includes a computer program carried on a storage medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication part 1009, and / or installed from a removable medium 1011. When the computer program is executed by a central processing unit (CPU) 1001, various functions defined in the system of the present application are executed.

[0118] It should be noted that the storage medium shown in the embodiment of the present invention may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, device or device. In the present invention, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any storage medium other than computer-readable storage media, which may send, propagate, or transmit programs for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the storage medium may be transmitted using any appropriate medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0119] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present invention. In this regard, each box in the flow chart or block diagram can represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flow chart, and the combination of the boxes in the block diagram or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0120] The units involved in the embodiments of the present invention may be implemented by software or hardware, and the units described may also be arranged in a processor. The names of these units do not, in some cases, limit the units themselves.

[0121] It should be noted that, as another aspect, the present application also provides a storage medium, which can be included in an electronic device; or it can exist independently without being installed in the electronic device. The above storage medium carries one or more programs, and when the above one or more programs are executed by an electronic device, the electronic device implements the method described in the following embodiments. For example, the electronic device can implement the following Figure 1 The individual steps of the method are shown.

[0122] In one embodiment, the present application provides a computer program product, including a computer program, which implements the steps in the above-mentioned method embodiments when executed by a processor.

[0123] In addition, the above-mentioned figures are only schematic illustrations of the processes included in the method according to an exemplary embodiment of the present invention, and are not intended to be limiting. It is easy to understand that the processes shown in the above-mentioned figures do not indicate or limit the time sequence of these processes. In addition, it is also easy to understand that these processes can be performed synchronously or asynchronously, for example, in multiple modules.

[0124] Other embodiments of the invention will readily occur to those skilled in the art after considering the specification and practicing the invention herein. This application is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art that are not disclosed by the present invention. The specification and examples are to be considered exemplary only, and the true scope and spirit of the invention are indicated by the claims.

[0125] It should be understood that the present invention is not limited to the exact construction that has been described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. A comprehensive RNA sequence structure analysis and local environmental impact weighting method, characterized in that: The method comprises: Obtaining an RNA sequence to be processed, and inputting the RNA sequence into an RNA sequence structure analysis model; Based on a preset distance constraint condition, the RNA sequence is screened for base pairs to be processed; wherein the base pairs to be processed include: standard base pairs and / or non-standard base pairs; Initializing base pairing parameters; wherein the base pairing parameters include: the pairing probability W between the first base i and the second base j in the base pair to be processed, which is used to configure the displacement parameters of the adjacent base pairs; Based on the displacement parameter, two adjacent bases corresponding to the first base i and the second base j in the base pair to be processed are configured, and when determining the pairing of the two adjacent bases, the pairing probability is updated according to the weight parameters of the adjacent base pairs corresponding to the two adjacent bases; A pairing weight matrix is ​​constructed according to the updated pairing probability corresponding to each base to be processed.

2. The method according to claim 1, characterized in that After the RNA sequence is input into the RNA sequence structure analysis model, the method further comprises: A pairing weight matrix with a size of L*L is constructed for the RNA sequence based on the RNA sequence length L, and an initial value of the pairing weight matrix is ​​configured to be 0.

3. The method according to claim 1, characterized in that The screening of base pairs to be processed based on the preset distance constraint condition includes: configuring base pairs in the RNA sequence whose distance is greater than or equal to a preset distance threshold as base pairs to be processed; and Base pairs whose distance is less than the preset distance threshold are ignored.

4. The method according to claim 1, characterized in that The displacement parameters used to configure adjacent base pairs include: a first displacement parameter α, or a first displacement parameter α and a second displacement parameter β; wherein the initial values ​​of the first displacement parameter α and the second displacement parameter β are 1.

5. The method according to claim 4, characterized in that The configuring two adjacent bases corresponding to the first base i and the second base j in the base pair to be processed based on the displacement parameter, and when determining the pairing of the two adjacent bases, updating the pairing probability according to the weight parameters of the adjacent base pairs corresponding to the two adjacent bases, includes: Determine a first adjacent base i-α and a second adjacent base j+α corresponding to the first base i and the second base j based on the first shift parameter α; When it is determined that the first adjacent base i-α and the second adjacent base j+α are successfully paired, a first matching weight of the corresponding first adjacent base pair is calculated, and the matching probability W is updated according to the first matching weight; The first displacement parameter α is updated to α+1, and the above steps are repeatedly performed based on the updated first displacement parameter α+1.

6. The method according to claim 4 or 5, characterized in that: The method further comprises: Determine the third adjacent base i+β and the fourth adjacent base j-α corresponding to the first base i and the second base j according to the first shift parameter α and the second shift parameter β; When it is determined that the third adjacent base i+β and the fourth adjacent base j-α are successfully paired, the second matching weight of the corresponding second adjacent base pair is calculated, and the matching probability W is updated according to the second matching weight; The second displacement parameter β is updated to β+1, and the above steps are repeatedly performed based on the updated second displacement parameter β+1.

7. The method according to claim 1, characterized in that The method further comprises: Pre-configure the weight parameters corresponding to standard base pairs and non-standard base pairs.

8. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the comprehensive RNA sequence structure analysis and local environment impact weighting method thereof according to any one of claims 1 to 7 are implemented.

9. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the base-integrated RNA sequence structure analysis and the local environment impact weighting method thereof according to any one of claims 1 to 7 are implemented.

10. An electronic device, characterized in that: include: processor; as well as A memory, configured to store executable instructions of the processor; The processor is configured to execute the comprehensive RNA sequence structure analysis and local environment impact weighting method according to any one of claims 1 to 7 by executing the executable instructions.

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