A method, system, device and medium for predicting an aso target
By using statistical models and multiple base pairing criteria for screening and scoring, the problem of poor ASO target prediction accuracy in existing technologies has been solved, improving prediction accuracy and reducing false positive rates.
Patent Information
- Application Number
- CN202210916547.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2022-08-01
- Publication Date
- 2025-12-05
- Estimated Expiration
- 2042-08-01
AI Technical Summary
Existing technologies have few ASO target prediction schemes and poor accuracy, especially the lack of training data for software based on eskip finder, which leads to low prediction accuracy for other genes.
A statistical model was used to screen the set of ASO targets with high binding affinity in the genes of the species to be predicted, and multiple base pairing criteria were used to match, score and rank the target ASO targets to screen out potential ASO targets.
It improved the accuracy of ASO target prediction and significantly reduced the false positive rate.
Smart Images
Figure CN115440296B_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the technical field of genes, and in particular to an ASO target prediction method, system, device and medium. BACKGROUND
[0002] Antisense oligonucleotides (ASO) are short nucleic acid analogs with various chemical properties, which can recognize target mRNA sequences through base pairing. Once antisense oligonucleotides are hybridized with their targets, they will act as spatial blockers, block splicing factors to change pre-mRNA splicing, or can block mRNA translation by preventing ribosome recruitment, thereby reducing the expression of harmful proteins. The above characteristics of ASO are expected to have a huge impact on the treatment of many nervous system diseases in the near future.
[0003] In related technologies, the application of ASO often needs to predict its corresponding target. However, there are very few existing schemes for predicting ASO targets, and the accuracy is poor. For example, eskip finder is a software based on a machine learning model to predict ASO targets, and the data of its training set is severely insufficient and relatively single, and most of the training data comes from a single gene (DMD), which leads to the problem of low accuracy when predicting ASO targets on other genes based on the software.
[0004] In summary, the problems in related technologies need to be solved urgently. SUMMARY
[0005] The purpose of the present application is to at least solve one of the technical problems existing in the related art.
[0006] To this end, an object of an embodiment of the present application is to provide an ASO target prediction method, system, device and medium.
[0007] In order to achieve the above technical purpose, the technical solution adopted by the embodiments of the present application comprises:
[0008] On the one hand, the present application provides an ASO target prediction method, which comprises:
[0009] obtaining user inputted basic information and ASO information of a target point to be predicted; the basic information includes species information and gene information;
[0010] According to the basic information and the ASO information, an ASO target set with high binding force in the gene of the species to be predicted is screened through a statistical model; the ASO target set includes a plurality of target ASO targets;
[0011] The target ASO target points are matched and scored according to a plurality of base pairing standards, and the matching scores corresponding to each target ASO target point are determined.
[0012] According to the size of the matching score, each target ASO target point is sorted and displayed after sorting.
[0013] In addition, the ASO target point prediction method according to the above embodiment of the application can further have the following additional technical features:
[0014] Further, in an embodiment of the application, the gene information includes chromosome coordinate information of exons in genes of the species to be predicted.
[0015] Further, in an embodiment of the application, the ASO target point set with high binding force in the genes of the species to be predicted is screened by a statistical model, comprising:
[0016] Selecting a plurality of initial ASO target points from the genes of the species to be predicted;
[0017] Calculating the actual binding force value of each initial ASO target point;
[0018] According to the average value of each binding force actual value, the expected value of the binding force of each initial ASO target point is determined;
[0019] According to the actual binding force value and the expected value of the binding force, the binding force score of each initial ASO target point is determined;
[0020] Selecting a preset number of initial ASO target points with larger binding force scores to obtain an ASO target point set.
[0021] Further, in an embodiment of the application, the binding force score of each initial ASO target point is determined according to the actual binding force value and the expected value of the binding force, comprising:
[0022] The binding force reference value of the initial ASO target point is calculated by the following formula:
[0023]
[0024] In the formula, Pi represents the binding force reference value of the i-th initial ASO target point, ki represents the actual binding force value of the i-th initial ASO target point, λ represents the expected value of the binding force, and e represents Euler's constant.
[0025] The binding force score is determined according to the binding force reference value.
[0026] Further, in an embodiment of the present application, the matching score of each of the target ASO target points is determined according to a plurality of base pairing criteria, and the matching score of each of the target ASO target points is determined according to the following steps:
[0027] The matching score of each of the target ASO target points is determined according to a plurality of base pairing criteria, and the initial score is obtained.
[0028] The initial score is normalized to obtain a normalized score.
[0029] The normalized scores of each of the target ASO target points under the base pairing criteria are accumulated to obtain the matching score of the target ASO target point.
[0030] Further, in an embodiment of the present application, the initial score is normalized to obtain a normalized score, and the steps include:
[0031] For each of the base pairing criteria, the maximum value of the initial score obtained by matching score of each of the target ASO target points is determined.
[0032] According to the ratio of the initial score and the maximum value, the initial score obtained by matching score under the base pairing criteria is normalized to obtain a normalized score.
[0033] Further, in an embodiment of the present application, the method further includes:
[0034] Detecting whether the target ASO target point is located at an enhancer or a silencer, and determining the optimization weight according to the detection result; wherein the optimization weight of the target ASO target point located at the enhancer or the silencer is greater than the optimization weight of the target ASO target point not located at the enhancer or the silencer.
[0035] According to the optimization weight, the matching score is updated, and the best ASO target point is determined from the target ASO target point according to the updated matching score.
[0036] On the other hand, an embodiment of the present application provides an ASO target point prediction system, and the system includes:
[0037] An acquisition module is configured to acquire basic information input by a user and ASO information of a target point to be predicted; the basic information includes species information and gene information.
[0038] A screening module is configured to screen an ASO target point set with high binding force in a gene of a target species according to a statistical model based on the basic information and the ASO information; the ASO target point set includes a plurality of target ASO target points.
[0039] a matching module, configured to score each target ASO target point by a plurality of base pairing criteria to determine a matching score corresponding to each target ASO target point;
[0040] a sorting module, configured to sort each target ASO target point according to the size of the matching score, and display the sorted target ASO target points.
[0041] In another aspect, an embodiment of the present application provides a computer device, comprising:
[0042] at least one processor;
[0043] at least one memory configured to store at least one program;
[0044] When the at least one program is executed by the at least one processor, the at least one processor implements the above-mentioned ASO target point prediction method.
[0045] In another aspect, an embodiment of the present application further provides a computer readable storage medium, which stores a processor executable program, and the above-mentioned processor executable program, when executed by a processor, is used to implement the above-mentioned ASO target point prediction method.
[0046] The advantages and beneficial effects of the present application will be partially given in the following description, partially will become obvious from the following description, or will be understood by the practice of the present application:
[0047] An embodiment of the present application discloses an ASO target point prediction method, which comprises: acquiring user inputted basic information and ASO information of a target point to be predicted; the basic information comprises species information and gene information; according to the basic information and the ASO information, screening an ASO target point set with higher binding force in a gene of a species to be predicted by a statistical model; the ASO target point set comprises a plurality of target ASO target points; scoring each target ASO target point by a plurality of base pairing criteria to determine a matching score corresponding to each target ASO target point; sorting each target ASO target point according to the size of the matching score, and displaying the sorted target ASO target points. The method can improve the accuracy of ASO target point prediction. BRIEF DESCRIPTION OF DRAWINGS
[0048] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following introduces the drawings of the related technical solutions in the embodiments of the present application or the prior art. It should be understood that the drawings in the following introduction are only for the convenience of clearly describing part of the embodiments of the technical solutions of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor on the premise that there is no conflict.
[0049] Figure 1 An implementation environment schematic diagram of a prediction method of an ASO target point provided in the embodiments of the present application.
[0050] Figure 2 A flowchart schematic diagram of a prediction method of an ASO target point provided in the embodiments of the present application.
[0051] Figure 3 A structure schematic diagram of a computer device provided in the embodiments of the present application. DETAILED DESCRIPTION
[0052] The present application will be further described below in conjunction with the drawings of the specification and specific embodiments. The described embodiments should not be regarded as limiting the present application, and all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.
[0053] In the following description, "some embodiments" are related to a subset of all possible embodiments, but it can be understood that "some embodiments" can be the same subset or different subsets of all possible embodiments, and can be combined with each other without conflict.
[0054] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs. The terms used herein are only for the purpose of describing the embodiments of the present application and are not intended to limit the present application.
[0055] Antisense oligonucleotides (ASO) are short nucleic acid analogues with various chemical properties, which can recognize target mRNA sequences through base pairing. Once antisense oligonucleotides are hybridized with their targets, they will act as spatial blockers, block splicing factors to change pre-mRNA splicing, or can block mRNA translation by preventing ribosome recruitment, thereby reducing the expression of harmful proteins. The above characteristics of ASO are expected to have a huge impact on the treatment of many nervous system diseases in the near future.
[0056] In the related art, the application of ASO often needs to predict its corresponding target. However, there are very few existing schemes for predicting ASO targets, and the accuracy is poor. For example, eskip finder is a software based on a machine learning model to predict ASO targets. The data of its training set is seriously insufficient and relatively single, and most of the training data comes from a single gene (DMD), which leads to the problem of low accuracy when predicting ASO targets on other genes based on the software.
[0057] Therefore, in the embodiments of the present application, a method for predicting ASO targets is provided. The method preliminarily screens ASO targets with higher binding force through a statistical model to obtain an ASO target set. Then, a variety of base pairing standards are used to score the base pairing of the target ASO target, and the matching score can be sorted according to the size and the sorting result is displayed, which facilitates the user to determine the potential ASO target. The method can improve the accuracy of ASO target prediction and greatly reduce the false positive rate.
[0058] Figure 1 is a schematic diagram of an implementation environment of an ASO target prediction method provided by the embodiments of the present application. Referring to Figure 1 , the hardware and software of the implementation environment mainly include a user terminal 101 and a server 102, and the user terminal 101 and the server 102 are in communication connection. The ASO target prediction method can be executed based on the interaction between the user terminal 101 and the server 102, and can be appropriately selected according to actual application, which is not limited in the embodiments.
[0059] The server 102 can be a stand-alone physical server, a server cluster or a distributed system composed of multiple physical servers, or a cloud server providing cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN (Content Delivery Network), and basic cloud computing services such as big data and artificial intelligence platforms. The user terminal 101 and the server 102 can establish a communication connection through a wireless network or a wired network, and the wireless network or the wired network uses standard communication technology and / or protocol. The network can be set as the Internet, or any other network, for example, including but not limited to a local area network (LAN), a metropolitan area network (MAN), a wide area network (WAN), a mobile, wired or wireless network, a private network or any combination of a virtual private network.
[0060] Please refer toFigure 2 , Figure 2 is a flowchart of a prediction method of an ASO target provided by an embodiment of the present application, referring to Figure 2 The prediction method of an ASO target includes but is not limited to:
[0061] Step 110, obtaining basic information input by a user and ASO information of a target position to be predicted; the basic information includes species information and gene information;
[0062] In this step, when it is necessary to predict an ASO target, the user can input the basic information and the ASO information of the target position to be predicted, so that the terminal or the server receives the basic information and the ASO information of the target position to be predicted input by the user, thereby performing a subsequent prediction task. Specifically, in the present application, the basic information input by the user can include the species information of the organism whose ASO target needs to be predicted, for example, whether it is a human or other animals, which is not specifically limited in the present application. The basic information can also include gene information of the species to be predicted. Here, the gene information can be part of the gene information of the species, such as a pre-set range of ASO targets; or it can be all the gene information of the species to be predicted. Generally, the gene information can include the chromosomal coordinate information of the exons in the gene of the species to be predicted. The specific number of exons is not limited in the present application.
[0063] Step 120, screening an ASO target set with higher binding force in the gene of the species to be predicted by a statistical model according to the basic information and the ASO information; the ASO target set includes a plurality of target ASO targets;
[0064] In this step, after obtaining the basic information and the ASO information input by the user, ASO targets with significantly high binding force in a statistical sense can be screened out by a statistical model. Here, the ASO targets with high binding force can be multiple, that is, an ASO target set with higher binding force in the gene of the species to be predicted is determined. In the present application, the ASO targets in the ASO target set are referred to as target ASO targets.
[0065] Specifically, in some embodiments, the screening of the ASO target set with higher binding force in the gene of the species to be predicted by the statistical model includes:
[0066] selecting a plurality of initial ASO targets from the gene of the species to be predicted;
[0067] calculating the actual value of the binding force of each initial ASO target;
[0068] determining the expected value of the binding force of each initial ASO target according to the average value of each actual value of the binding force;
[0069] determine a binding force score of each of the initial ASO target points according to the actual value of the binding force and the expected value of the binding force;
[0070] select a preset number of initial ASO target points with larger binding force scores to obtain an ASO target point set.
[0071] In the embodiments of the present application, when screening the ASO target point set with high binding force from the genes of the to-be-predicted species through the statistical model, first, a plurality of initial ASO target points can be selected from the genes of the to-be-predicted species. Here, the initial ASO target point is a target point that can possibly bind to the ASO, which can be selected by setting conditions related to experience binding. For example, the initial ASO target point that can bind to the ASO of a certain length can be determined according to the length of the ASO, and the specific selection method can be flexibly adjusted according to needs, which is not limited in the present application.
[0072] After obtaining a plurality of initial ASO target points, the actual value of the binding force of each initial ASO target point can be calculated. Specifically, here, the actual value of the binding force of the initial ASO target point can be calculated by applying the viennaRNA package. After calculating the actual value of the binding force of each initial ASO target point, the expected value of the binding force can be calculated according to the average value of the actual values of the binding force. Then, based on the actual value of the binding force and the expected value of the binding force of each initial ASO target point, the binding force score of each initial ASO target point can be determined.
[0073] Specifically, when calculating the binding force score, the binding force reference value can be determined by using the following formula first:
[0074]
[0075] In the formula, Pi represents the binding force reference value of the i th initial ASO target point, ki represents the actual value of the binding force of the i th initial ASO target point, λ represents the expected value of the binding force, e represents Euler's number, and i is the number of the initial ASO target point, which is a positive integer. In the embodiments of the present application, if the binding force parameter value P of an ASO target point is less than 0.05 (a comparison threshold value given in advance, which can be flexibly set), it can be indicated that the actual value of the binding force of the ASO target point is significantly greater than the expected value of the binding force, that is, it has a strong binding force. Therefore, in the embodiments of the present application, the smaller the value of the binding force parameter value, the higher the binding force score of the ASO target point, so the binding force score of the ASO target point can be determined according to the negative correlation relationship, and the specific functional relationship between the two is not limited in the present application.
[0076] In the embodiments of the present application, it can be understood that the binding force score can reflect the possibility of the initial ASO target and the ASO to bind smoothly to a certain extent. When the binding force score is high, the possibility of the initial ASO target and the ASO to bind smoothly is also high. Conversely, when the binding force score is low, the possibility of the initial ASO target and the ASO to bind smoothly is also low. Therefore, in the embodiments of the present application, after obtaining the binding force scores corresponding to each initial ASO target, the binding force scores can be sorted, for example, in descending order, and then the initial ASO targets with the top several rankings are selected to obtain the ASO target set. Alternatively, the binding force scores can also be sorted in ascending order, and then the initial ASO targets with the last several rankings are selected to obtain the ASO target set.
[0077] It should be noted that in the embodiments of the present application, the number of ASO targets in the selected ASO target set can be flexibly set as needed, and the present application does not limit this.
[0078] Step 130, performing matching scoring on each of the target ASO targets according to a plurality of base pairing standards to determine a matching score value corresponding to each of the target ASO targets;
[0079] In this step, after obtaining each target ASO target, a plurality of base pairing standards can be introduced to perform matching scoring on the target ASO target. Here, the base pairing standard can be a standard determined based on the special thermal energy of RNAs, the GC percentage of ASO, the free energy of ASO, or the distance between the ASO target and the cleavage donor point, and the present application does not limit this. Based on these standards, the matching score value of each target ASO target can be determined.
[0080] Step 140, sorting each of the target ASO targets according to the size of the matching score value, and displaying the sorted target ASO targets.
[0081] In this step, since the aforementioned matching scoring on the target ASO target is performed according to the base pairing standard, the matching score can better reflect the degree to which the target ASO target meets the base pairing standard, and it can be understood that the more the target ASO target meets the base pairing standard, the higher the matching score, and the more likely it is to make the ASO and the target ASO target bind. Conversely, the less the target ASO target meets the base pairing standard, the lower the matching score, and the more difficult it is to make the ASO and the target ASO target bind.
[0082] Therefore, in the embodiments of the present application, after the matching scores of the target ASO target points are determined, the target ASO target points can be sorted according to the matching scores, that is, the larger the matching score, the higher the ranking of the target ASO target point, and the smaller the matching score, the lower the ranking of the target ASO target point. Then, the sorted target ASO target points can be displayed to the user, so that the user can determine the potential ASO target point.
[0083] In some embodiments, the matching score of each target ASO target point is determined by a plurality of base pairing criteria, and the matching score corresponding to each target ASO target point is determined.
[0084] The initial score of each target ASO target point is determined by matching the target ASO target point with the plurality of base pairing criteria.
[0085] The initial score of each target ASO target point is determined by matching the target ASO target point with the plurality of base pairing criteria.
[0086] The initial score of each target ASO target point is determined by matching the target ASO target point with the plurality of base pairing criteria.
[0087] In the embodiments of the present application, when determining the matching score corresponding to the target ASO target point, first, the initial score of each target ASO target point can be determined by matching the target ASO target point with each base pairing criterion, and the size of the initial score reflects the degree to which the target ASO target point and the ASO meet the base pairing criterion. Then, the initial score of each target ASO target point can be normalized. Specifically, when normalizing, for each base pairing criterion, the maximum value of the initial score obtained by matching each target ASO target point with the base pairing criterion can be determined, and then the initial score of each target ASO target point obtained under the base pairing criterion can be divided by the maximum value. The initial score under each base pairing criterion can be normalized to between 0 and 1. In the embodiments of the present application, the value obtained by normalizing the initial score of the target ASO target point under each base pairing criterion is referred to as the normalized score. It can be understood that each target ASO target point includes the same number of normalized scores as the number of base pairing criteria, and these normalized scores can be accumulated to obtain the matching score corresponding to the target ASO target point.
[0088] In some embodiments, the method further comprises:
[0089] detecting whether the target ASO target point is located at an enhancer or a silencer, and determining the optimization weight according to the detection result; wherein the optimization weight of the target ASO target point located at the enhancer or the silencer is greater than the optimization weight of the target ASO target point not located at the enhancer or the silencer;
[0090] According to the optimization weight, the matching score is updated, and the optimal ASO target is determined from the target ASO targets according to the updated matching score.
[0091] In the embodiments of the present application, the enhancer or silencer optimization mechanism can also be introduced. If the ASO target and the enhancer or silencer are located at the same position, the ASO target has a higher weight to become the optimal ASO target. Therefore, in the embodiments of the present application, it can be detected whether the target ASO target is located at the position of the enhancer or silencer, and the optimization weight is determined according to the detection result. If the target ASO target is located at the position of the enhancer or silencer, the corresponding optimization weight can be larger, for example, it can be 1.1. If the target ASO target is not located at the position of the enhancer or silencer, the corresponding optimization weight can be larger, for example, it can be 0.9. Then, the matching score can be updated by the optimization weight, and the product of the optimization weight and the matching score is taken as the updated matching score, so that the maximum of the updated matching score in the target ASO target can be determined as the optimal ASO target.
[0092] It should be noted that in the embodiments of the present application, only the optimization weight of the target ASO target located at the position of the enhancer or silencer needs to be greater than the optimization weight of the target ASO target not located at the position of the enhancer or silencer, and the specific optimization weight value is not limited.
[0093] It can be understood that the method for predicting an ASO target provided in the embodiments of the present application can preliminarily screen the ASO targets with high binding force through a statistical model to obtain an ASO target set. Then, a plurality of base pairing standards are used to score the base pairing of the target ASO target, and the matching score is sorted according to the size and the sorting result is displayed, so as to facilitate the user to determine the potential ASO target. The method can improve the accuracy of ASO target prediction and greatly reduce the false positive rate.
[0094] The embodiments of the present application also provide a system for predicting an ASO target, and the system comprises:
[0095] The acquisition module is configured to acquire the basic information input by the user and the ASO information of the target point position to be predicted. The basic information includes species information and gene information.
[0096] The screening module is configured to screen, according to the basic information and the ASO information, an ASO target set with high binding force in the gene of the species to be predicted through a statistical model. The ASO target set includes a plurality of target ASO targets.
[0097] The matching module is configured to match each target ASO target point with a plurality of base pairing criteria to determine a matching score corresponding to each target ASO target point.
[0098] The sorting module is configured to sort each target ASO target point according to the size of the matching score, and display the sorted target ASO target point.
[0099] It can be understood that, Figure 2 The content of the ASO target point prediction method embodiment shown in the ASO target point prediction system embodiment is applicable to the ASO target point prediction system embodiment, and the ASO target point prediction system embodiment specifically realizes the same functions as the ASO target point prediction method embodiment shown in Figure 2 The ASO target point prediction system embodiment achieves the same beneficial effects as the ASO target point prediction method embodiment shown in Figure 2 The ASO target point prediction system embodiment achieves the same beneficial effects as the ASO target point prediction method embodiment shown in
[0100] Referring to Figure 3 , the embodiment of the present application further discloses a computer device comprising:
[0101] at least one processor 301;
[0102] at least one memory 302 for storing at least one program;
[0103] When the at least one program is executed by the at least one processor 301, the at least one processor 301 implements the ASO target point prediction method embodiment shown in Figure 2 It can be understood that the content of the ASO target point prediction method embodiment shown in
[0104] is applicable to the present computer device embodiment, and the present computer device embodiment specifically realizes the same functions as the ASO target point prediction method embodiment shown in Figure 2 and achieves the same beneficial effects as the ASO target point prediction method embodiment shown in Figure 2 and achieves the same beneficial effects as the ASO target point prediction method embodiment shown in Figure 2
[0105] The embodiment of the present application further discloses a computer readable storage medium, wherein the processor executable program is stored, and the processor executable program is used to implement the ASO target point prediction method embodiment shown in Figure 2
[0106] It can be understood that the content of the ASO target point prediction method embodiment shown in Figure 2 is applicable to the present computer readable storage medium embodiment, and the present computer readable storage medium embodiment specifically realizes the same functions as the ASO target point prediction method embodiment shown inFigure 2 The embodiments of the method for predicting an ASO target shown are the same, and the beneficial effects achieved are the same as those described above for the embodiments of the method for predicting an ASO target shown. Figure 2 The embodiments of the method for predicting an ASO target shown are the same, and the beneficial effects achieved are the same as those described above for the embodiments of the method for predicting an ASO target shown.
[0107] In some alternative embodiments, the functions / operations referred to in the block diagrams can not occur in the order shown in the operational illustrations. For example, two blocks shown in succession can in fact be executed substantially concurrently or the blocks can sometimes be executed in the reverse order, depending upon the functionality / operations involved. Also, although the embodiments presented in the flow diagrams are shown as a sequence of operations, it is to be understood that the logical flow is merely illustrative of alternative embodiments. The disclosed methods are not limited to the order of operations or the specific operations for implementing the embodiments. Alternative embodiments are contemplated in which the order of operations is changed and where sub operations of one operational step are performed in parallel with other operational steps.
[0108] Moreover, while the present application has been described in the context of functional modules, it is to be understood that one or more of the functions and / or features can be integrated in a single physical system and / or software module, or one or more functions and / or features can be implemented in separate physical systems or software modules. It is also to be understood that detailed discussion of the actual implementation of each module is not necessary to an understanding of the present application. Rather, the actual implementation is within the routine of an engineer in the art, given the property, function and internal relationships of the various functional modules disclosed herein. Therefore, the scope of the present application is not to be determined by the specific implementations illustrated herein, but only by the claims that follow, along with their full scope of equivalents.
[0109] If the functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application essentially or the part that makes a contribution to the prior art or the part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a number of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the embodiments of the method of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various media that can store program codes.
[0110] The logic and / or steps represented in the flow diagrams or otherwise described herein, for example, can be embodied in non-transitory computer- readable media, executed by an instruction execution system, system, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, system, or device and execute the instructions. In this regard, the term "computer-readable medium" can be used in this specification to generally refer to any media that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, system, or device. The computer-readable medium can be, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium include the following: an electrical connection (electronic) having one or more wires, a portable computer diskette (magnetic), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber (optical), and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium can even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, for example, via an optical scanner, then compiled, interpreted, or otherwise processed, and stored in a computer memory in a form that can be later executed by the instruction execution system, system, or device. The computer-readable medium can be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, for example, via an optical scanner, then compiled, interpreted, or otherwise processed, and stored in a computer memory in a form that can be later executed by the instruction execution system, system, or device.
[0111] More specific examples (a non-exhaustive list) of the computer-readable medium include the following: an electrical connection (electronic) having one or more wires, a portable computer diskette (magnetic), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber (optical), and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium can even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, for example, via an optical scanner, then compiled, interpreted, or otherwise processed, and stored in a computer memory in a form that can be later executed by the instruction execution system, system, or device.
[0112] It should be understood that aspects of the present application can be implemented in hardware, software, firmware, or combinations thereof. In the above-described embodiments, various steps or methods can be implemented in software or firmware that is stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any of the following technologies, known in the art, or combinations thereof, can be used: a discrete logic circuit having logic gates for implementing logic functions upon an application of data signals; an application specific integrated circuit having appropriate combinational logic gates; a programmable gate array (PGA), a field programmable gate array (FPGA), and / or the like.
[0113] In the above-described description of the present specification, the description referring to the terms "one embodiment," "another embodiment," or "some embodiments," etc., means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In the present specification, the illustrative expressions of the above-described terms do not necessarily refer to the same embodiment or example. Also, the specific features, structures, materials, or characteristics described can be combined in any one or more embodiments or examples in an appropriate manner.
[0114] While the embodiments of the present application have been shown and described, it is to be understood that the embodiments of the present application are capable of many variations, modifications, substitutions and changes without departing from the principles and scope of the present application, which are defined by the appended claims and their equivalents.
[0115] The above is a specific description of the preferred embodiments of the present application, but the present application is not limited to the embodiments, and those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present application, and these equivalent modifications or substitutions are included in the scope defined by the claims of the present application
[0116] In the description of the present specification, the description of the terms "one embodiment", "another embodiment" or "certain embodiments" or the like means that the specific features, structures, materials or characteristics described in connection with the embodiments or examples are included in at least one embodiment or example of the present application. In the present specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Also, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.
[0117] While the embodiments of the present application have been shown and described, it is to be understood that the embodiments of the present application are capable of many variations, modifications, substitutions and changes without departing from the principles and scope of the present application, which are defined by the appended claims and their equivalents.
Claims
1. A method of predicting ASO target, characterized in that, The method comprises: obtaining user inputted basic information and ASO information of a target position to be predicted; the basic information comprises species information and gene information; screening, according to the basic information and the ASO information, an ASO target point set with higher binding force in a gene of a species to be predicted through a statistical model; the ASO target point set comprises a plurality of target ASO target points; determining a matching score corresponding to each target ASO target point by matching each target ASO target point with a plurality of base pairing standards; ranking each target ASO target point according to the matching score, and displaying the ranked target ASO target points; the screening of the ASO target point set with higher binding force in the gene of the species to be predicted through the statistical model comprises: selecting a plurality of initial ASO target points from the gene of the species to be predicted; calculating an actual value of the binding force of each initial ASO target point; determining an expected value of the binding force of each initial ASO target point according to an average value of each actual value of the binding force; determining a binding force score of each initial ASO target point according to the actual value of the binding force and the expected value of the binding force; selecting a preset number of initial ASO target points with larger binding force scores to obtain an ASO target point set; the determination of the binding force score of each initial ASO target point according to the actual value of the binding force and the expected value of the binding force comprises: calculating a reference value of the binding force of the initial ASO target point through the following formula: wherein Pi represents the reference value of the binding force of the i-th initial ASO target point, ki represents the actual value of the binding force of the i-th initial ASO target point, λ represents the expected value of the binding force, and e represents Euler's number; determining the binding force score according to the reference value of the binding force.
2. The method for predicting ASO targets according to claim 1, characterized in that, The gene information comprises chromosome coordinate information of exons in the gene of the species to be predicted.
3. The method for predicting ASO targets according to claim 1, characterized in that, the determination of the matching score corresponding to each target ASO target point by matching each target ASO target point with a plurality of base pairing standards comprises: matching each target ASO target point with a plurality of base pairing standards to obtain an initial score; normalizing each initial score to obtain a normalized score; accumulating the normalized scores of each base pairing standard corresponding to the target ASO target point to obtain a matching score corresponding to the target ASO target point.
4. The method for predicting ASO targets according to claim 3, characterized in that, the normalization of each initial score to obtain a normalized score comprises: for each base pairing standard, determining a maximum value in the initial scores obtained by matching each target ASO target point; normalizing the initial score obtained by matching with the base pairing standard according to the ratio of the initial score to the maximum value to obtain a normalized score.
5. The method for predicting ASO targets according to claim 1, characterized in that, The method further comprises: detecting whether the target ASO target point is at a position of an enhancer or a silencer, and determining an optimization weight according to a detection result; wherein the optimization weight of the target ASO target point at the position of the enhancer or the silencer is greater than the optimization weight of the target ASO target point not at the position of the enhancer or the silencer; updating the matching score according to the optimization weight, and determining an optimal ASO target point from the target ASO target points according to the updated matching score.
6. A prediction system for ASO targets, characterized in that, The system comprises: an acquisition module configured to acquire basic information input by a user and ASO information of a target point to be predicted; the basic information comprises species information and gene information; a screening module configured to screen, according to the basic information and the ASO information, an ASO target point set with high binding force from genes of a species to be predicted by using a statistical model; the ASO target point set comprises a plurality of target ASO target points; a matching module configured to score each target ASO target point by using a plurality of base pairing standards, and determine a matching score corresponding to each target ASO target point; a sorting module configured to sort each target ASO target point according to the size of the matching score, and display the sorted target ASO target points; The screening module is specifically configured to: select a plurality of initial ASO target points from the genes of the species to be predicted; calculate an actual value of the binding force of each initial ASO target point; determine an expected value of the binding force of each initial ASO target point according to an average value of each actual value of the binding force; determine a binding force score of each initial ASO target point according to the actual value of the binding force and the expected value of the binding force; select a preset number of initial ASO target points with a larger binding force score to obtain an ASO target point set; The determination of the binding force score of each initial ASO target point according to the actual value of the binding force and the expected value of the binding force comprises: calculating a reference value of the binding force of the initial ASO target point by using the following formula: In the formula, Pi represents the reference value of the binding force of the i th initial ASO target point, ki represents the actual value of the binding force of the i th initial ASO target point, λ represents the expected value of the binding force, and e represents Euler's number; determine the binding force score according to the reference value of the binding force.
7. A computer device, comprising: comprise: at least one processor; at least one memory for storing at least one program; when the at least one program is executed by the at least one processor, the at least one processor implements the ASO target point prediction method according to any one of claims 1-5.
8. A computer readable storage medium having stored therein a program which is executable by a processor, characterized in that: The program executable by the processor, when executed by the processor, is used to implement the ASO target point prediction method according to any one of claims 1-5.
Citation Information
Patent Citations
ASO target spot prediction system
CN220753084U