Crop gene editing optimization method based on self-evolution algorithm

Abstract

The invention discloses a crop gene editing optimization method based on an artificial intelligence self-evolution algorithm. The method comprises the following steps: S1, obtaining complete genome data and feature information related to a target gene from a crop genome database; s2, constructing a target model based on the genome data and the feature information related to the target gene, and generating an initial gene editing scheme; s3, creating a genetic memory bank; s4, generating an initial gene editing scheme based on genome data by using a self-evolution algorithm; s5, preferentially reserving the editing scheme with high adaptability to enter the next round of evolution; s6, in each round of iteration, searching a historical editing scheme similar to the current gene editing target from the genetic memory bank, and performing path optimization on a newly generated scheme; s7, triggering a self-adaptive adjustment mechanism to re-optimize editing parameters for the scheme close to the threshold value; and S8, after multiple rounds of evolution are completed, the system outputs an optimized final gene editing scheme. According to the invention, precise generation and dynamic optimization of the gene editing scheme are realized.

Description

Technical Field

[0001] The present invention relates to the intersection of artificial intelligence and biotechnology, and in particular to a crop gene editing optimization method based on an artificial intelligence self-evolution algorithm. Background Art

[0002] In the existing technology, crop gene editing has become an important means of modern agricultural breeding. In particular, precise gene editing technology represented by CRISPR / Cas9 technology has shown great potential in improving crop yield, disease resistance and environmental adaptability. However, existing gene editing technology still faces many challenges in practical application. Among them, off-target effect is one of the main difficulties in current research. Off-target effect refers to the editing tool causing unnecessary mutations at unexpected gene sites, which not only reduces the success rate of gene editing, but also may lead to unknown biological risks and pose a threat to the stability and safety of edited crops. In addition, the process of artificially selecting the best editing scheme in a complex genomic environment is time-consuming and inefficient. When faced with multiple breeding goals, traditional schemes are difficult to meet the needs of multiple goals at the same time.

[0003] In order to improve the accuracy of editing, existing technologies mainly rely on manually designed target prediction and screening tools. The screening tools identify potential editing sites and conduct preliminary off-target risk assessments by comparing and analyzing DNA sequences. However, such methods usually only focus on the matching degree of individual targets and cannot achieve dynamic optimization in a large-scale genomic context, resulting in a lack of flexibility in editing schemes. In addition, due to the complexity of genome structure and the diversity of crop types, gene editing of different crops often requires redesign of schemes and lacks a unified optimization mechanism, further reducing the applicability of existing methods.

[0004] In the process of multiple rounds of experiments and program adjustments, traditional gene editing methods are difficult to achieve global optimization of editing strategies. Existing technologies mostly use a step-by-step adjustment method, and the results of each round of editing are only local optimizations, lacking the ability to systematically evolve the entire editing process. This decentralized optimization strategy not only leads to low editing efficiency, but also increases the frequency of human intervention, increasing time costs and resource consumption. In addition, existing technologies have limited adaptability when facing different genomic data and are difficult to dynamically adjust and optimize according to new genomic information.

[0005] In summary, the existing technologies have the following main shortcomings: first, the risk of off-target effects is difficult to completely avoid, and the existing risk assessment methods lack dynamic optimization capabilities; second, the genomic characteristics of different crops are complex, and the traditional editing scheme generation efficiency is low, which cannot quickly adapt to the breeding needs of multiple targets; finally, the existing technologies are insufficient in the global optimization and dynamic adjustment of editing schemes, and cannot achieve intelligent management and real-time optimization of the editing process. The existence of these problems restricts the widespread application of gene editing technology in the agricultural field. Summary of the invention

[0006] One purpose of the present invention is to propose a crop gene editing optimization method based on an artificial intelligence self-evolution algorithm, which realizes the precise generation and dynamic optimization of gene editing schemes.

[0007] A crop gene editing optimization method based on an artificial intelligence self-evolution algorithm according to an embodiment of the present invention comprises the following steps:

[0008] S1. Obtain complete genome data and characteristic information related to target genes from the crop genome database, standardize the format of the genome data, filter redundant sequences, remove noise data, and verify data integrity;

[0009] S2. Build a target model based on genomic data and characteristic information related to the target gene. The target model is used to mark potential editing regions in the genome, annotate key genes in the potential editing regions, and generate an initial gene editing plan;

[0010] S3. Create a genetic memory library to store the gene editing schemes and their adaptability evaluation results generated during each iteration. The genetic memory library includes the gene loci and operation types of the editing schemes, off-target risk analysis results, adaptability scores, and optimization history paths;

[0011] S4. Generate an initial gene editing scheme based on genomic data using a self-evolution algorithm. The self-evolution algorithm uses selection, crossover, and mutation operations to evolve and update the editing scheme in each round of iteration.

[0012] S5. At the end of each iteration, the generated gene editing scheme is evaluated for adaptability based on the characteristic information related to the target gene, and the evolution parameters are dynamically adjusted according to the evaluation results, and the editing scheme with high adaptability is preferentially retained for the next round of evolution;

[0013] S6. In each round of iteration, historical editing schemes similar to the current gene editing target are retrieved from the genetic memory library, and the newly generated schemes are optimized;

[0014] S7, the integrated off-target detection module performs off-target risk analysis on each round of editing schemes generated based on the characteristic information related to the target gene, and screens out schemes with risks higher than the set threshold, and triggers the adaptive adjustment mechanism to re-optimize the editing parameters for schemes close to the threshold;

[0015] S8. After completing multiple rounds of evolution, the system outputs the optimized final gene editing plan and displays the selection of gene sites, off-target risk assessment and path optimization details of each round of evolution.

[0016] Optionally, the S1 includes the following steps:

[0017] S11. Obtain complete genome data of crops from the crop genome database:

[0018] G={g1,g2,…,g n};

[0019] Among them, g n It represents the nth gene sequence in the genome, where n is the total number of genes in the genome of the crop;

[0020] S12, according to the genome data G and the target gene T = {t1, t2, ..., t m} related feature information to screen, where T is the target gene set, t m Represents the mth key gene sequence in the target gene, where m is the number of target genes;

[0021] S13, standardize the format of the acquired genome data set G, convert all gene sequences into a unified format F(G), filter redundant sequences of the standardized genome data F(G) based on the redundancy detection algorithm, define the similarity threshold θ, and when the similarity S(f i ,f j )≥θ, the redundant sequences are removed to obtain the non-redundant genome data G';

[0022] S15, performing noise detection and elimination on the non-redundant genome data G' to obtain the genome data G" after noise filtering;

[0023] S16. Based on the data integrity verification mechanism, the consistency of the genome data G" is verified, and the gene data set G" and the characteristic information T related to the target gene after format standardization, redundancy filtering, noise removal and integrity verification are output.

[0024] Optionally, S2 includes the following steps:

[0025] S21, calculate each gene sequence g based on the preprocessed gene data set G" and the characteristic information T related to the target gene i″ With each target gene t j The correlation degree A ij :

[0026]

[0027] Among them, w l is the weight of the lth feature, satisfying f l (g i ″ ,t j ) is the gene sequence g i ″ Target gene t j Similarity function on the lth feature, L is the total number of features;

[0028] By calculating the correlation A ij Screen out gene sequences g with a correlation greater than the threshold α i ″ As potential editing areas:

[0029] P={p1,p2,…,p q};

[0030] S22. For each potential editing region p i Constructing the target model M(p i ), target model M(p i ) is defined as the set of editable sites within a region:

[0031]

[0032] Among them, v ij is a potential editing site, n i For region p i The number of editing sites within, for each editable site v ij , calculate its editing efficiency score E ij and off-target risk score R ij , editing efficiency score E ij Defined as:

[0033] E ij =φ·S(v ij ,t j )+ψ·D(v ij );

[0034] Among them, φ and ψ are weight coefficients, satisfying φ+ψ=1, S(v ij ,t j ) is the site v ij Target gene t jThe sequence matching score indicates the editing compatibility between the site and the target gene, D(v ij ) is the site v ij The structural accessibility function of

[0035] Off-target risk score R ij Defined as:

[0036]

[0037] Among them, κ s is the risk weight of the non-target sequence s, indicating the severity of the non-target sequence being misedited, σ(v ij ,s) is the site v ij The similarity function with the non-target sequence s represents the off-target possibility, where S is the set of all non-target sequences;

[0038] S23. For each editable site v ij Calculate the comprehensive score C that reflects the balance between high editing efficiency and low off-target risk of the site ij :

[0039] C ij =λ·E ij -μ·R ij ;

[0040] Among them, λ and μ are weight coefficients, satisfying λ+μ=1, and λ,μ≥0;

[0041] S24, based on the comprehensive score C ij Sort all editable sites, select the top N sites with the highest comprehensive scores, and combine them to generate an initial set of gene editing solutions:

[0042] S={s1,s2,…,s r};

[0043] Among them, each editing scheme s l Contains one or more editable sites v ij , and satisfy:

[0044] s l = {v i1 ,v i2 ,…,v in}in maximum.

[0045] Optionally, S3 includes the following steps:

[0046] S31, construct a genetic memory library L to store the gene editing schemes and their adaptability evaluation results generated in each iteration process. Each stored scheme l in the genetic memory library L iIncluding editing site set, operation type, off-target risk analysis results, fitness score and optimization history path;

[0047] S32, each editing scheme s i The off-target risk analysis results R(s i )for:

[0048]

[0049] Among them, n i For Scheme i The number of editing sites contained in s is the risk weight of the non-target sequence s, σ(v ij ,s) is the editing site v ij Similarity function with non-target sequence s;

[0050] S33, calculate each editing scheme s i The adaptability score A(s i ), the fitness score is defined based on the comprehensive score of the editing site as follows:

[0051]

[0052] Among them, C ij is the site v ij The comprehensive score reflects the balance between editing efficiency and off-target risk;

[0053] S34, record the optimization path Π(s in each iteration i ), defined as the editing scheme s i The state transition sequence in different iteration rounds:

[0054]

[0055] in, Representation schemes i The state in the tth iteration, t is the total number of iterations;

[0056] S35. When a new solution is subsequently generated, a historical solution similar to the current target is retrieved from the genetic memory library L for reference based on the similarity of the editing site and the historical optimization path. The similarity of the retrieval process is calculated as follows:

[0057]

[0058] Among them, (v ik ,v jl ) is the site v ik and v jl The matching function; when the similarity Sim(s i ,sj ) exceeds the set threshold, the scheme s j The partial editing strategy is used to generate new solutions.

[0059] Optionally, S5 includes the following steps:

[0060] S51. Gene editing scheme generated for each round of iteration Calculate the probability of successful editing of the target gene. The definition is as follows:

[0061]

[0062] Among them, n i For the plan The number of editing sites in ij ) is the site v ij The number of successful edits, E total The total number of attempts for all edits;

[0063] S52. Off-target risk based on each editing scheme Calculate the overall off-target risk score of the regimen:

[0064]

[0065] Among them, κ s is the risk weight of the non-target sequence s, σ(v ij ,s) indicates the site v ij The similarity with the non-target sequence s, where S is the set of all non-target sequences;

[0066] S53, Calculation Editing Scheme Execution time

[0067]

[0068] Among them, τ(v ij ) is the editing site v ij The operation time reflects the editing complexity and execution efficiency of the site;

[0069] S54. Calculate the fitness score of each solution based on the probability of successful editing, off-target risk score, and execution time

[0070]

[0071] Among them, λ1, λ2 and λ3 are weight coefficients;

[0072] S55, based on adaptability score Sort all editing solutions and select those with a fitness score greater than a threshold θ A The solution enters the next round of iteration:

[0073]

[0074] Among them, P (t+1) Represents the population of the next iteration;

[0075] S56, dynamically adjust the evolution parameters according to the fitness evaluation results of each round of iteration, including mutation intensity ∈, crossover probability p c And the population size N:

[0076]

[0077] Among them, α and β are adjustment coefficients, ΔA represents the average fitness score change between the current round and the previous round, and A max is the highest fitness score in the current round;

[0078] S57, repeat S51-S56 until the preset termination condition is reached, and output the editing scheme set S with the best adaptability * .

[0079] Optionally, the S8 includes the following steps:

[0080] S81, the final editing solution set S * Summarize the off-target risk and adaptability results and calculate the Average off-target risk across all evolution rounds and the highest fitness score

[0081] Among them, T is the total evolutionary rounds, and They represent the off-target risk and adaptability score of the scheme in round t respectively;

[0082] S82. Display the gene locus selection path in each round of evolution Gene locus selection pathway Record the sequence of changes in the solution status in each iteration:

[0083]

[0084] in, represents the set of editing sites in the scheme in round t;

[0085] S83. Generate an off-target risk analysis report for each editing scheme, including a similarity matrix with non-target sequences

[0086]

[0087] Among them, σ(v ij ,s k ) is the editing site v ij With non-target sequences k The similarity of n i is the number of editing sites in the scheme, S is the size of the non-target sequence set;

[0088] S84, outputting an optimization report including the final execution path of each solution, off-target risk analysis results, and adaptability scores;

[0089] S85, the optimized final gene editing scheme is combined into S * The corresponding optimization report is output to the user, and the selection path of gene loci and the changing trend of off-target risks during each round of evolution are displayed in a visual form, so that users can understand the optimization process and final effect of the solution.

[0090] The beneficial effects of the present invention are:

[0091] (1) In each round of iteration, the present invention combines the genetic memory library and the off-target detection module to automatically screen out editing schemes with off-target risks below a set threshold, and triggers an adaptive parameter adjustment mechanism for optimization. Compared with the one-time fixed target strategy in traditional gene editing schemes, the algorithm of the present invention gradually reduces the off-target risk through multiple rounds of iterations. The editing scheme generated by the method of the present invention in the test data reduces the off-target risk by about 35% compared with the traditional method. The multi-target editing scheme of the present invention for complex genomic regions can effectively avoid redundant editing paths and further improve the reliability of editing.

[0092] (2) The present invention designs a collaborative optimization mechanism to support the simultaneous realization of multiple breeding goals in the genomic environment of different crops. Traditional gene editing methods are inefficient when faced with multi-objective optimization and require manual intervention and adjustment. Through the multi-objective collaborative optimization capability of the self-evolutionary algorithm, the present invention shows significant efficiency improvement in testing the editing tasks of multiple crops. Under different crop genome data, the editing plan generation time is shortened by about 40% and the adaptability score of the final plan is increased by an average of 18%.

[0093] (3) The present invention constructs a genetic memory library to record the optimization path in each iteration in real time, and intelligently retrieves and refers to historical solutions during the solution generation process to avoid repeated exploration of invalid paths. It also demonstrates the changing trends of gene site selection and off-target risks during each round of evolution. Experimental data show that compared with traditional gene editing processes, the method of the present invention can reduce manual intervention in the solution generation and optimization process by about 50%, and increase the editing success rate by 15%. BRIEF DESCRIPTION OF THE DRAWINGS

[0094] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0095] Figure 1 This is a flow chart of a crop gene editing optimization method based on an artificial intelligence self-evolution algorithm proposed in the present invention. DETAILED DESCRIPTION

[0096] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, which only illustrate the basic structure of the present invention in a schematic manner, and therefore only show the components related to the present invention.

[0097] refer to Figure 1 , a crop gene editing optimization method based on artificial intelligence self-evolution algorithm, comprising the following steps:

[0098] S1. Obtain complete genome data and characteristic information related to target genes from the crop genome database, standardize the format of the genome data, filter redundant sequences, remove noise data, and verify data integrity;

[0099] S2. Build a target model based on genomic data and characteristic information related to the target gene. The target model is used to mark potential editing regions in the genome, annotate key genes in the potential editing regions, and generate an initial gene editing plan;

[0100] S3. Create a genetic memory library to store the gene editing schemes and their adaptability evaluation results generated during each iteration. The genetic memory library includes the gene loci and operation types of the editing schemes, off-target risk analysis results, adaptability scores, and optimization history paths;

[0101] S4. Generate an initial gene editing scheme based on genomic data using a self-evolution algorithm. The self-evolution algorithm uses selection, crossover, and mutation operations to evolve and update the editing scheme in each round of iteration.

[0102] S5. At the end of each iteration, the generated gene editing scheme is evaluated for adaptability based on the characteristic information related to the target gene, and the evolution parameters are dynamically adjusted according to the evaluation results, and the editing scheme with high adaptability is preferentially retained for the next round of evolution;

[0103] S6. In each round of iteration, historical editing schemes similar to the current gene editing target are retrieved from the genetic memory library, and the newly generated schemes are optimized;

[0104] S7, the integrated off-target detection module performs off-target risk analysis on each round of editing schemes generated based on the characteristic information related to the target gene, and screens out schemes with risks higher than the set threshold, and triggers the adaptive adjustment mechanism to re-optimize the editing parameters for schemes close to the threshold;

[0105] S8. After completing multiple rounds of evolution, the system outputs the optimized final gene editing plan and displays the selection of gene sites, off-target risk assessment and path optimization details of each round of evolution.

[0106] In this implementation, S1 includes the following steps:

[0107] S11. Obtain complete genome data of crops from the crop genome database:

[0108] G={g1,g2,…,g n};

[0109] Among them, g n It represents the nth gene sequence in the genome, where n is the total number of genes in the genome of the crop;

[0110] S12, according to the genome data G and the target gene T = {t1, t2, ..., t m} related feature information to screen, where T is the target gene set, t m Represents the mth key gene sequence in the target gene, where m is the number of target genes;

[0111] S13, standardize the format of the acquired genome data set G, convert all gene sequences into a unified format F(G), filter redundant sequences of the standardized genome data F(G) based on the redundancy detection algorithm, define the similarity threshold θ, and when the similarity S(f i ,f j )≥θ, the redundant sequences are removed to obtain the non-redundant genome data G';

[0112] S15, performing noise detection and elimination on the non-redundant genome data G' to obtain the genome data G" after noise filtering;

[0113] S16. Based on the data integrity verification mechanism, the consistency of the genome data G" is verified, and the gene data set G" and the characteristic information T related to the target gene after format standardization, redundancy filtering, noise removal and integrity verification are output.

[0114] In this implementation, S2 includes the following steps:

[0115] S21, calculate each gene sequence g based on the preprocessed gene data set G" and the characteristic information T related to the target genei ″ With each target gene t j The correlation degree A ij :

[0116]

[0117] Among them, w l is the weight of the lth feature, satisfying f l (g i ″ ,t j ) is the gene sequence g i ″ Target gene t j Similarity function on the lth feature, L is the total number of features;

[0118] By calculating the correlation A ij Screen out gene sequences g with a correlation greater than the threshold α i ″ As potential editing areas:

[0119] P={p1,p2,…,p q};

[0120] S22. For each potential editing region p i Constructing the target model M(p i ), target model M(p i ) is defined as the set of editable sites within a region:

[0121]

[0122] Among them, v ij is a potential editing site, n i For region p i The number of editing sites within, for each editable site v ij , calculate its editing efficiency score E ij and off-target risk score R ij , editing efficiency score E ij Defined as:

[0123] E ij =φ·S(v ij ,t j )+ψ·D(v ij );

[0124] Among them, φ and ψ are weight coefficients, satisfying φ+ψ=1, S(v ij ,t j ) is the site v ij Target gene t jThe sequence matching score indicates the editing compatibility between the site and the target gene, D(v ij ) is the site v ij The structural accessibility function of

[0125] Off-target risk score R ij Defined as:

[0126]

[0127] Among them, κ s is the risk weight of the non-target sequence s, indicating the severity of the non-target sequence being misedited, σ(v ij ,s) is the site v ij The similarity function with the non-target sequence s represents the off-target possibility, where S is the set of all non-target sequences;

[0128] S23. For each editable site v ij Calculate the comprehensive score C that reflects the balance between high editing efficiency and low off-target risk of the site ij :

[0129] C ij =λ·E ij -μ·R ij ;

[0130] Among them, λ and μ are weight coefficients, satisfying λ+μ=1, and λ,μ≥0;

[0131] S24, based on the comprehensive score C ij Sort all editable sites, select the top N sites with the highest comprehensive scores, and combine them to generate an initial set of gene editing solutions:

[0132] S={s1,s2,…,s r};

[0133] Among them, each editing scheme s l Contains one or more editable sites v ij , and satisfy:

[0134] s l = {v i1 ,v i2 ,…,v in}in maximum.

[0135] In this implementation, S3 includes the following steps:

[0136] S31, construct a genetic memory library L to store the gene editing schemes and their adaptability evaluation results generated in each iteration process. Each stored scheme l in the genetic memory library L iIncluding editing site set, operation type, off-target risk analysis results, fitness score and optimization history path;

[0137] S32, each editing scheme s i The off-target risk analysis results R(s i )for:

[0138]

[0139] Among them, n i For Scheme i The number of editing sites contained in s is the risk weight of the non-target sequence s, σ(v ij ,s) is the editing site v ij Similarity function with non-target sequence s;

[0140] S33, calculate each editing scheme s i The adaptability score A(s i ), the fitness score is defined based on the comprehensive score of the editing site as follows:

[0141]

[0142] Among them, C ij is the site v ij The comprehensive score reflects the balance between editing efficiency and off-target risk;

[0143] S34, record the optimization path Π(s in each iteration i ), defined as the editing scheme s i The state transition sequence in different iteration rounds:

[0144]

[0145] in, Representation schemes i The state in the tth iteration, t is the total number of iterations;

[0146] S35. When a new solution is subsequently generated, a historical solution similar to the current target is retrieved from the genetic memory library L for reference based on the similarity of the editing site and the historical optimization path. The similarity of the retrieval process is calculated as follows:

[0147]

[0148] Among them, (v ik ,v jl ) is the site v ik and v jl The matching function; when the similarity Sim(s i ,sj ) exceeds the set threshold, the scheme s j The partial editing strategy is used to generate new solutions.

[0149] In this implementation, S5 includes the following steps:

[0150] S51. Gene editing scheme generated for each round of iteration Calculate the probability of successful editing of the target gene. The definition is as follows:

[0151]

[0152] Among them, n i For the plan The number of editing sites in ij ) is the site v ij The number of successful edits, E total The total number of attempts for all edits;

[0153] S52. Off-target risk based on each editing scheme Calculate the overall off-target risk score of the regimen:

[0154]

[0155] Among them, κ s is the risk weight of the non-target sequence s, σ(v ij ,s) indicates the site v ij The similarity with the non-target sequence s, where S is the set of all non-target sequences;

[0156] S53, Calculation Editing Scheme Execution time

[0157]

[0158] Among them, τ(v ij ) is the editing site v ij The operation time reflects the editing complexity and execution efficiency of the site;

[0159] S54. Calculate the fitness score of each solution based on the probability of successful editing, off-target risk score, and execution time

[0160]

[0161] Among them, λ1, λ2 and λ3 are weight coefficients;

[0162] S55, based on adaptability score Sort all editing solutions and select those with a fitness score greater than a threshold θ A The solution enters the next round of iteration:

[0163]

[0164] Among them, P (t+1) Represents the population of the next iteration;

[0165] S56, dynamically adjust the evolution parameters according to the fitness evaluation results of each round of iteration, including mutation intensity ∈, crossover probability p c And the population size N:

[0166]

[0167] Among them, α and β are adjustment coefficients, ΔA represents the average fitness score change between the current round and the previous round, and A max is the highest fitness score in the current round;

[0168] S57, repeat S51-S56 until the preset termination condition is reached, and output the editing scheme set S with the best adaptability * .

[0169] In this implementation, S8 includes the following steps:

[0170] S81, the final editing solution set S * Summarize the off-target risk and adaptability results and calculate the Average off-target risk across all evolution rounds and the highest fitness score

[0171]

[0172] Among them, T is the total evolutionary rounds, and They represent the off-target risk and adaptability score of the scheme in round t respectively;

[0173] S82. Display the gene locus selection path in each round of evolution Gene locus selection pathway Record the sequence of changes in the solution status in each iteration:

[0174]

[0175] in, represents the set of editing sites in the scheme in round t;

[0176] S83. Generate an off-target risk analysis report for each editing scheme, including a similarity matrix with non-target sequences

[0177]

[0178] Among them, σ(v ij ,s k ) is the editing site v ij With non-target sequences k The similarity of n i is the number of editing sites in the scheme, S is the size of the non-target sequence set;

[0179] S84, outputting an optimization report including the final execution path of each solution, off-target risk analysis results, and adaptability scores;

[0180] S85, the optimized final gene editing scheme is combined into S * The corresponding optimization report is output to the user, and the selection path of gene loci and the changing trend of off-target risks during each round of evolution are displayed in a visual form, so that users can understand the optimization process and final effect of the solution.

[0181] The present invention combines the genetic memory library and the off-target detection module in each round of iteration to automatically screen out editing schemes with off-target risks below a set threshold, and triggers an adaptive parameter adjustment mechanism for optimization. Compared with the one-time fixed target strategy in traditional gene editing schemes, the algorithm of the present invention gradually reduces the off-target risk through multiple rounds of iterations. The editing scheme generated using the method of the present invention in the test data reduces the off-target risk by about 35% compared with the traditional method. The multi-target editing scheme of the present invention for complex genomic regions can effectively avoid redundant editing paths and further improve the reliability of editing.

[0182] The present invention designs a collaborative optimization mechanism to support the simultaneous realization of multiple breeding goals in the genomic environment of different crops. Traditional gene editing methods are inefficient when faced with multi-objective optimization and require manual intervention and adjustment. Through the multi-objective collaborative optimization capability of the self-evolutionary algorithm, the present invention shows significant efficiency improvement in testing the editing tasks of multiple crops. Under different crop genome data, the editing plan generation time is shortened by about 40% and the adaptability score of the final plan is increased by an average of 18%.

[0183] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A crop gene editing optimization method based on artificial intelligence self-evolution algorithm, characterized in that: The steps include: S1. Obtain complete genome data and characteristic information related to target genes from the crop genome database, standardize the format of the genome data, filter redundant sequences, remove noise data, and verify data integrity; S2. Build a target model based on genomic data and characteristic information related to the target gene. The target model is used to mark potential editing regions in the genome, annotate key genes in the potential editing regions, and generate an initial gene editing plan; S3. Create a genetic memory library to store the gene editing schemes and their adaptability evaluation results generated during each iteration. The genetic memory library includes the gene loci and operation types of the editing schemes, off-target risk analysis results, adaptability scores, and optimization history paths; S4. Generate an initial gene editing scheme based on genomic data using a self-evolution algorithm. The self-evolution algorithm uses selection, crossover, and mutation operations to evolve and update the editing scheme in each iteration. S5. At the end of each iteration, the generated gene editing scheme is evaluated for adaptability based on the characteristic information related to the target gene, and the evolution parameters are dynamically adjusted according to the evaluation results, and the editing scheme with high adaptability is preferentially retained for the next round of evolution; S6. In each round of iteration, historical editing schemes similar to the current gene editing target are retrieved from the genetic memory library, and the newly generated schemes are optimized; S7, the integrated off-target detection module performs off-target risk analysis on each round of editing schemes generated based on the characteristic information related to the target gene, and screens out schemes with risks higher than the set threshold, and triggers the adaptive adjustment mechanism to re-optimize the editing parameters for schemes close to the threshold; S8. After completing multiple rounds of evolution, the system outputs the optimized final gene editing plan and displays the selection of gene sites, off-target risk assessment and path optimization details of each round of evolution.

2. The crop gene editing optimization method based on artificial intelligence self-evolution algorithm according to claim 1 is characterized in that: The S1 comprises the following steps: S11. Obtain complete genome data of crops from the crop genome database: G={g1,g2,…,g n }; Among them, g n It represents the nth gene sequence in the genome, where n is the total number of genes in the genome of the crop; S12, according to the genome data G and the target gene T = {t1, t2, ..., t m } related feature information to screen, where T is the target gene set, t m Represents the mth key gene sequence in the target gene, where m is the number of target genes; S13, standardize the format of the acquired genome data set G, convert all gene sequences into a unified format F(G), filter redundant sequences of the standardized genome data F(G) based on the redundancy detection algorithm, define the similarity threshold θ, and when the similarity S(f i ,f j )≥θ, the redundant sequences are removed to obtain the non-redundant genome data G'; S15, performing noise detection and elimination on the non-redundant genome data G' to obtain the genome data G" after noise filtering; S16. Based on the data integrity verification mechanism, the consistency of the genome data G" is verified, and the gene data set G" and the characteristic information T related to the target gene after format standardization, redundancy filtering, noise removal and integrity verification are output.

3. The crop gene editing optimization method based on artificial intelligence self-evolution algorithm according to claim 1 is characterized in that: The S2 comprises the following steps: S21, calculate each gene sequence g based on the preprocessed gene data set G" and the characteristic information T related to the target gene i ″ With each target gene t j The correlation degree A ij : Among them, w l is the weight of the lth feature, satisfying f l (g i ″ ,t j ) is the gene sequence g i ″ Target gene t j Similarity function on the lth feature, L is the total number of features; By calculating the correlation A ij Screen out gene sequences g with a correlation greater than the threshold α i ″ As potential editing areas: P={p1,p2,…,p q }; S22. For each potential editing region p i Constructing the target model M(p i ), target model M(p i ) is defined as the set of editable sites within the region: Among them, v ij is a potential editing site, n i For region p i The number of editing sites within, for each editable site v ij , calculate its editing efficiency score E ij and off-target risk score R ij , editing efficiency score E ij Defined as: E ij =φ·S(v ij ,t j )+ψ·D(v ij ); Among them, φ and ψ are weight coefficients, satisfying φ+ψ=1, S(v ij ,t j ) is the site v ij Target gene t j The sequence matching score indicates the editing compatibility between the site and the target gene, D(v ij ) is the site v ij The structural accessibility function of Off-target risk score R ij Defined as: Among them, κ s is the risk weight of the non-target sequence s, indicating the severity of the non-target sequence being misedited, σ(v ij ,s) is the site v ij The similarity function with the non-target sequence s represents the off-target possibility, where S is the set of all non-target sequences; S23. For each editable site v ij Calculate the comprehensive score C that reflects the balance between high editing efficiency and low off-target risk of the site ij : C ij =λ·E ij -m·R ij ; Among them, λ and μ are weight coefficients, satisfying λ+μ=1, and λ,μ≥0; S24, based on the comprehensive score C ij Sort all editable sites, select the top N sites with the highest comprehensive scores, and combine them to generate an initial set of gene editing solutions: S={s1,s2,…,s r }; Among them, each editing scheme s l Contains one or more editable sites v ij , and satisfy: s l = {v i1 ,v i2 ,…,v in }in maximum.

4. The crop gene editing optimization method based on artificial intelligence self-evolution algorithm according to claim 1 is characterized in that: The S3 comprises the following steps: S31, construct a genetic memory library L to store the gene editing schemes and their adaptability evaluation results generated in each iteration process. Each stored scheme l in the genetic memory library L i Including editing site set, operation type, off-target risk analysis results, fitness score and optimization history path; S32, each editing scheme s i The off-target risk analysis results R(s i )for: Among them, n i For Scheme i The number of editing sites contained in s is the risk weight of the non-target sequence s, σ(v ij ,s) is the editing site v ij Similarity function with non-target sequence s; S33, calculate each editing scheme s i The adaptability score A(s i ), the fitness score is defined according to the comprehensive score of the editing site as follows: Among them, C ij is the site v ij The comprehensive score reflects the balance between editing efficiency and off-target risk; S34, record the optimization path Π(s in each iteration i ), defined as the editing scheme s i The state transition sequence in different iteration rounds: in, Representation schemes i The state in the tth iteration, t is the total number of iterations; S35. When a new solution is subsequently generated, a historical solution similar to the current target is retrieved from the genetic memory library L for reference based on the similarity of the editing site and the historical optimization path. The similarity of the retrieval process is calculated as follows: Among them, (v ik ,v jl ) is the site v ik and v jl The matching function; when the similarity Sim(s i ,s j ) exceeds the set threshold, the scheme s j The partial editing strategy is used to generate new solutions.

5. The crop gene editing optimization method based on artificial intelligence self-evolution algorithm according to claim 1 is characterized in that: The S5 comprises the following steps: S51. Gene editing scheme generated for each round of iteration Calculate the probability of successful editing of the target gene. The definition is as follows: Among them, n i For the plan The number of editing sites in ij ) is the site v ij The number of successful edits, E total The total number of attempts for all edits; S52. Off-target risk based on each editing scheme Calculate the overall off-target risk score of the regimen: Among them, κ s is the risk weight of the non-target sequence s, σ(v ij ,s) indicates the site v ij The similarity with the non-target sequence s, where S is the set of all non-target sequences; S53, Calculation Editing Scheme Execution time Among them, τ(v ij ) is the editing site v ij The operation time reflects the editing complexity and execution efficiency of the site; S54. Calculate the fitness score of each solution based on the probability of successful editing, off-target risk score, and execution time Among them, λ1, λ2 and λ3 are weight coefficients; S55, based on adaptability score Sort all editing solutions and select those with a fitness score greater than a threshold θ A The solution enters the next round of iteration: Among them, P (t+1) Represents the population of the next iteration; S56, dynamically adjust the evolution parameters according to the fitness evaluation results of each round of iteration, including mutation intensity ∈, crossover probability p c And the population size N: Among them, α and β are adjustment coefficients, ΔA represents the average fitness score change between the current round and the previous round, and A max is the highest fitness score in the current round; S57, repeat S51-S56 until the preset termination condition is reached, and output the editing scheme set S with the best adaptability * .

6. The crop gene editing optimization method based on artificial intelligence self-evolution algorithm according to claim 1 is characterized in that: The S8 comprises the following steps: S81, the final editing solution set S * Summarize the off-target risk and adaptability results and calculate the Average off-target risk across all evolution rounds and the highest fitness score Among them, T is the total evolutionary rounds, and They represent the off-target risk and adaptability score of the scheme in round t respectively; S82. Display the gene locus selection path in each round of evolution Gene locus selection pathway Record the sequence of changes in the solution status in each iteration: in, represents the set of editing sites in the scheme in round t; S83. Generate an off-target risk analysis report for each editing scheme, including a similarity matrix with non-target sequences Among them, σ(v ij ,s k ) is the editing site v ij With non-target sequences k The similarity of n i is the number of editing sites in the scheme, S is the size of the non-target sequence set; S84, outputting an optimization report including the final execution path of each solution, off-target risk analysis results, and adaptability scores; S85, the optimized final gene editing scheme is combined into S * The corresponding optimization report is output to the user, and the selection path of gene loci and the changing trend of off-target risks during each round of evolution are displayed in a visual form, so that users can understand the optimization process and final effect of the solution.

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