Method and device for generating siRNA precursor double-stranded RNA sequence aiming at target organism and application of siRNA precursor double-stranded RNA sequence

Abstract

The invention discloses a method and a device for generating a siRNA precursor double-stranded RNA sequence aiming at a target organism in the field of bioinformatics and application of the siRNA precursor double-stranded RNA sequence. The invention aims to solve the technical problem of how to design a precursor double-stranded RNA (Ribonucleic Acid) aiming at specific siRNA (Small Interference Ribonucleic Acid) of a target species. The method comprises the following steps: analyzing a candidate conserved gene set of a target species based on transcriptome and protein data, and performing gene conservative analysis to obtain a target gene of the target species; then intercepting all precursor double-stranded RNA sequences based on transcriptome data of a target gene, and enumerating all available sequence fragments to obtain a candidate double-stranded RNA set 1; and screening from the candidate double-stranded RNA set 1 according to a preset off-target threshold to obtain a candidate double-stranded RNA set 2. The invention can be applied to RNAi prevention and control of pathogens or pests of agricultural related species.

Description

Technical Field

[0001] This invention relates to the methods, apparatus, and applications for generating siRNA precursor double-stranded RNA sequences for target organisms in the field of bioinformatics. Background Technology

[0002] RNA interference (RNAi) is a gene silencing mechanism based on small RNA molecules (such as double-stranded RNA (dsRNA) and small interfering RNA (siRNA)). It can regulate the physiological functions of organisms by specifically inhibiting the expression of target genes. Especially in non-model organisms where gene knockout and gene editing technologies are still immature, RNAi technology is highly advantageous as a highly efficient, specific, and relatively easy-to-operate molecular tool. In the field of agricultural pest control, RNAi technology is mainly achieved through transgenic expression or exogenous application of specific dsRNA. After pests ingest dsRNA, their Dicer-like proteins randomly cleave the dsRNA into 21-24 bp siRNAs. These siRNAs can target and silence key genes in pests, thereby affecting their growth, development, reproductive capacity, or survival rate. Compared to traditional chemical pesticides, RNAi technology is gradually becoming a new type of green biological pesticide technology due to its low environmental pollution and lower risk of pesticide resistance. However, despite the broad prospects of RNAi technology in agricultural pest control, existing technologies still have many bottlenecks that restrict its large-scale application.

[0003] During RNA interference, siRNA and mRNA specifically bind to target genes, silencing them. However, studies have shown that siRNA may also bind to non-target genes, leading to their silencing; this phenomenon is known as the siRNA off-target effect.

[0004] Currently, RNAi target gene screening and dsRNA design mainly rely on database comparison, manual screening, or traditional bioinformatics tools. These methods have the following technical limitations: (1) Low design efficiency: Existing methods typically employ a step-by-step approach of trying different gene fragments for design, which involves complex and time-consuming computation and analysis processes, impacting the feasibility of large-scale applications. Due to the massive amount of pest genome and transcriptome data, existing technologies face significant challenges in data cleaning, classification, and annotation, which limits the efficient screening of target genes. While related tools such as dsCheck, SnapDragon, and E-RNAi have optimized some aspects of the dsRNA design process, they still cannot achieve efficient RNAi design across genomes and transcriptomes.

[0005] (2) Insufficient control of off-target effects: dsRNA may induce gene silencing effects in non-target organisms (such as birds, beneficial insects, plants, livestock, and humans), leading to ecological security risks; Existing dsRNA design tools have limited consideration for off-target effects assessment, making it difficult to fully predict and reduce the potential impact of dsRNA on non-target organisms; Due to the complexity of genomic information in non-target organisms, accurately assessing the specificity of dsRNA between pest and non-pest species remains a challenge.

[0006] (3) Limited targeting accuracy: Existing tools often fail to simultaneously optimize the target matching and off-target risk of dsRNA fragments, resulting in poor interference effects or excessive side effects; Early dsRNA prediction methods mainly relied on sequence features (such as base composition and secondary structure), but ignored other important factors affecting RNAi efficiency, such as thermodynamic stability, the amount of effective siRNA generated, and the degradation mode of target gene mRNA.

[0007] (4) High computational cost, making large-scale screening difficult: Existing dsRNA design tools have high computational complexity, making it difficult to design co-targets across species; RNA structure prediction methods (such as RNAfold) are computationally expensive and difficult to apply to large-scale dsRNA design. Summary of the Invention

[0008] The technical problems to be solved by this invention are how to design precursor double-stranded RNAs of specific siRNAs for target species and / or how to reduce the off-target effects of precursor double-stranded RNAs (dsRNAs) of siRNAs on non-target organisms and / or how to optimize the design of precursor double-stranded RNA (dsRNA) fragments of siRNAs to improve RNA interference efficiency and / or how to design precursor double-stranded RNAs (dsRNAs) of siRNAs with broad spectrum and safety for plant pests or pathogens.

[0009] To address the aforementioned technical problems, the present invention first provides a method for generating a double-stranded RNA sequence of siRNA precursor targeting a specific species, the method comprising the following steps: S1) Target gene screening: Analyze the candidate conserved gene set of the target species based on transcriptome data and protein sequence data respectively; perform gene conservation analysis on the candidate conserved gene set, and obtain the target gene of the target species based on the results of the gene conservation analysis; S2) Obtaining a candidate siRNA set: Based on the transcriptome data of the target gene, all precursor double-stranded RNA sequences are extracted using the sliding window method; based on a predetermined length, all available sequence fragments of each precursor double-stranded RNA sequence are enumerated to obtain siRNAs; and candidate double-stranded RNA set 1 is obtained by filtering based on the siRNAs. S3) Off-target analysis: Receive sequence information of non-target species to obtain a non-target database, use a sequence alignment algorithm to compare the candidate double-stranded RNA set 1 with the non-target database, and select double-stranded RNA set 2 from the candidate double-stranded RNA set 1 according to a preset off-target threshold.

[0010] The candidate double-stranded RNA set 2 may be a set of candidate double-stranded RNA sequences for the target species.

[0011] The method described above further includes S4), which includes the following steps: using an RNA secondary structure prediction tool (based on a combination of thermodynamic and deep neural network models) to calculate the structural free energy of RNA in the double-stranded RNA set 2, and obtaining the siRNA precursor double-stranded RNA sequence from the double-stranded RNA set 2 based on the structural free energy.

[0012] In one specific embodiment of the present invention, the predetermined window length of the sliding window method is 400 bp; the predetermined sliding step size of the sliding window method is 50 bp. The predetermined length can be 21-23 bp. In one specific embodiment of the present invention, the predetermined length is 19 bp. In one specific embodiment of the present invention, the sequence alignment algorithm is Bowtie. In one specific embodiment of the present invention, the RNA secondary structure prediction tool is MXfold2.

[0013] In the above method, in step S1), The process of analyzing the candidate conserved gene set of the target species based on transcriptome data and protein sequence data includes receiving protein sequence data and transcriptome data of two or more individuals of the target species, performing multiple sequence alignment 1 on the transcriptome data to obtain transcriptome alignment results, performing multiple sequence alignment 2 on the protein sequence data to obtain protein alignment results, and obtaining the conserved gene set 1 of the target species based on the transcriptome alignment results and the protein alignment results. The gene conservation analysis includes performing gene conservation analysis on the conserved gene set 1 based on sequence, and removing non-conserved genes from the conserved gene set 1 based on the results of the gene conservation analysis to obtain the conserved gene set 2. Based on the results of the gene conservation analysis, the target genes of the target species are obtained by receiving the sequence information (genome or transcriptome) of non-target species to obtain a non-target database. Homologous genes in the non-target database are removed from the conserved gene set 2 to obtain a conserved gene set 3, which is the target gene of the target species.

[0014] In one specific embodiment of the present invention, the gene conservation analysis is a phylogenetic analysis.

[0015] The conserved gene set 1 obtained in S1) is selected by selecting transcriptome alignment results and protein alignment results with a sequence similarity greater than 1e. -25 The genes were obtained.

[0016] The conserved genes mentioned above can be those with sequence identity greater than 1e. -25 The genes.

[0017] In the above method, the multiple sequence alignment 1 uses MMseq2 for multiple sequence alignment; the multiple sequence alignment 2 uses DIAMOND for multiple sequence alignment.

[0018] In the above method, S2) the sliding window method is used to truncate the sequence based on a predetermined window length and a predetermined sliding step size; the filtering is to filter all available sequence fragments based on the following conditions to obtain candidate double-stranded RNA set 1: GC content, 5' end base bias, whether it contains a repeating region of more than 4 consecutive bases, and whether it completely matches the target gene. S3) The alignment is to enumerate all available sequence fragments based on a predetermined length to obtain the siRNA of the candidate double-stranded RNA in the candidate double-stranded RNA set 1, and then align the siRNA of the candidate double-stranded RNA with the non-target database.

[0019] In one specific embodiment of the present invention, the predetermined window length of the sliding window method is 400 bp; the predetermined sliding step size of the sliding window method is 50 bp.

[0020] The preset off-target threshold is set based on the average or total number of off-target events of the candidate double-stranded RNA in non-target species. If the average number of off-target events of a candidate double-stranded RNA in non-target species is greater than 3 or the total number of off-target events is greater than 100, the fragment is removed. In a specific embodiment of the present invention, the preset off-target threshold is an average number of off-target events = 3 or a total number of off-target events = 100. That is, if the average number of off-target events of a candidate double-stranded RNA in non-target species is greater than 3 or the total number of off-target events is greater than 100, the candidate double-stranded RNA is removed from the candidate double-stranded RNA set 1. If the average number of off-target events of a candidate double-stranded RNA in non-target species is less than or equal to 3 or the total number of off-target events is less than or equal to 100, the candidate double-stranded RNA in the candidate double-stranded RNA set 1 is retained.

[0021] To address the aforementioned technical problems, this invention also provides a method for generating (broad-spectrum) siRNA precursor double-stranded RNA sequences for harmful organisms. The method is characterized by comprising steps S1)-S3) as described above, and further comprising steps S4)' and S5). S4)' includes receiving genomic or transcriptomic data of more than one harmful organism other than the target species; using a clustering analysis tool to analyze conserved co-expressed genes in the genomic or transcriptomic data to obtain a conserved co-expressed gene set; performing sequence alignment of each double-stranded RNA sequence in the double-stranded RNA set 2 with the conserved co-expressed gene set, and retaining siRNA sequences in the candidate double-stranded RNA set 2 that match the sequences in the conserved co-expressed gene set to obtain candidate double-stranded RNA sequences for harmful organisms; S5) includes using an RNA secondary structure prediction tool to calculate the structural free energy of the candidate double-stranded RNA sequences for harmful organisms, and obtaining the siRNA precursor double-stranded RNA sequence for harmful organisms from the candidate double-stranded RNA sequences for harmful organisms based on the structural free energy. The target species can be organisms originating from Blattodea, Coleoptera, Diptera, Hemiptera, Hymenoptera, mites, nematodes, Orthoptera, plant pathogenic fungi, or Thysanoptera. The harmful organisms can be organisms that cause pests or diseases, such as pests or pathogens. Specifically, the harmful organisms can be organisms originating from Blattodea, Coleoptera, Diptera, Hemiptera, Hymenoptera, mites, nematodes, Orthoptera, plant pathogenic fungi, and / or Thysanoptera.

[0022] The non-target species can be organisms derived from aquatic animals, biocontrol bacteria, birds, crops, earthworms, livestock, mammals, symbiotic bacteria, non-target arthropods, and / or pollinating insects. In one specific embodiment of the invention, the transcriptome data of the harmful organisms are transcriptome or genomic data of 250 harmful organisms (including pests and pathogens).

[0023] The homologous genes can be genes inherited from a common ancestor to different species.

[0024] The aforementioned harmful organisms can be organisms that are harmful to plants, such as pests and / or pathogens that cause plant diseases (fungi, bacteria, viruses, or nematodes, etc.).

[0025] To address the aforementioned technical problems, the present invention also provides a data processing apparatus, which can be used to generate siRNA sequences targeting a target species. The apparatus may include the following modules: A1) Target gene screening module: used to analyze the candidate conserved gene set of the target species based on transcriptome data and protein sequence data respectively; to perform gene conservation analysis on the candidate conserved gene set, and to obtain the target gene of the target species based on the results of the gene conservation analysis; A2) Module for obtaining candidate dsRNA set: Based on the transcriptome data of the target gene, a sliding window method is used to extract all precursor double-stranded RNA sequences; based on a predetermined length, all available sequence fragments of each precursor double-stranded RNA sequence are enumerated to obtain siRNA; and candidate double-stranded RNA set 1 is obtained by filtering based on the siRNA. A3) Off-target analysis module: used to receive sequence information of non-target species to obtain a non-target database, use sequence alignment algorithm to compare the candidate double-stranded RNA set 1 with the non-target database, and select double-stranded RNA set 2 from the candidate double-stranded RNA set 1 according to a preset off-target threshold.

[0026] The candidate double-stranded RNA set 2 may be a set of candidate double-stranded RNA sequences for the target species.

[0027] In the above-mentioned data processing device, the data processing device may further include an A4) module, which is used to calculate the structural free energy of RNA in the double-stranded RNA set 2 using an RNA secondary structure prediction tool (based on a combination of thermodynamic model and deep neural network model), and obtain the siRNA precursor double-stranded RNA sequence from the double-stranded RNA set 2 based on the structural free energy.

[0028] In one specific embodiment of the present invention, the predetermined length may be 21–23 bp. In one specific embodiment of the present invention, the predetermined length is 19 bp. In one specific embodiment of the present invention, the sequence alignment algorithm is Bowtie. In one specific embodiment of the present invention, the RNA secondary structure prediction tool is MXfold2. In one specific embodiment of the present invention, the gene conservation analysis is phylogenetic analysis.

[0029] The conserved gene set 1 obtained in S1) is selected by selecting transcriptome alignment results and protein alignment results with a sequence similarity greater than 1e. -25 The genes were obtained.

[0030] The conserved genes mentioned above can be those with sequence identity greater than 1e. -25 The genes.

[0031] In the aforementioned data processing device, in A1): The process of analyzing the candidate conserved gene set of the target species based on transcriptome data and protein sequence data includes receiving protein sequence data and transcriptome data of two or more individuals of the target species, performing multiple sequence alignment 1 on the transcriptome data to obtain transcriptome alignment results, performing multiple sequence alignment 2 on the protein sequence data to obtain protein alignment results, and obtaining the conserved gene set 1 of the target species based on the transcriptome alignment results and the protein alignment results. The gene conservation analysis includes performing gene conservation analysis on the conserved gene set 1 based on sequence, and removing non-conserved genes from the conserved gene set 1 based on the results of the gene conservation analysis to obtain the conserved gene set 2. Based on the results of the gene conservation analysis, the target genes of the target species are obtained, including the sequence information of non-target species organisms to obtain a non-target database. Homologous genes in the non-target database are removed from the conserved gene set 2 to obtain a conserved gene set 3, which is the target gene of the target species.

[0032] In one specific embodiment of the present invention, the gene conservation analysis is a phylogenetic analysis.

[0033] In one specific embodiment of the present invention, the multiple sequence alignment 1 is performed using MMseq2; the multiple sequence alignment 2 is performed using DIAMOND.

[0034] In the aforementioned data processing device, S2) The sliding window method is used to truncate the sequence based on a predetermined window length and a predetermined sliding step size. The filtering can be performed by filtering all available sequence fragments based on the following conditions to obtain candidate double-stranded RNA set 1: GC content, 5' base bias, whether it contains a repeating region of more than 4 consecutive bases, and whether it completely matches the target gene. S3) The alignment can be performed by enumerating all available sequence fragments based on a predetermined length to obtain siRNAs of candidate double-stranded RNAs in candidate double-stranded RNA set 1, and then aligning the siRNAs of the candidate double-stranded RNAs with the non-target database.

[0035] In one specific embodiment of the present invention, the predetermined window length of the sliding window method is 400 bp; the predetermined sliding step size of the sliding window method is 50 bp.

[0036] The preset off-target threshold is set based on the average or total number of off-target events of the candidate double-stranded RNA in non-target species. If the average number of off-target events of a candidate double-stranded RNA in non-target species is greater than 3 or the total number of off-target events is greater than 100, the fragment is removed. In a specific embodiment of the present invention, the preset off-target threshold is an average number of off-target events = 3 or a total number of off-target events = 100. That is, if the average number of off-target events of a candidate double-stranded RNA in non-target species is greater than 3 or the total number of off-target events is greater than 100, the candidate double-stranded RNA is removed from the candidate double-stranded RNA set 1. If the average number of off-target events of a candidate double-stranded RNA in non-target species is less than or equal to 3 or the total number of off-target events is less than or equal to 100, the candidate double-stranded RNA in the candidate double-stranded RNA set 1 is retained.

[0037] To address the aforementioned technical problems, this invention also provides an apparatus for generating (broad-spectrum) siRNA precursor double-stranded RNA sequences for harmful organisms. The apparatus is characterized by comprising modules A1)-A3) of the data processing apparatus described above, and further comprising modules A4)' and A5). Module A4)' is used to receive genomic or transcriptomic data of more than one harmful organism other than the target species, and to analyze conserved co-expressed genes in the genomic or transcriptomic data using a clustering analysis tool to obtain a conserved co-expressed gene set. Each double-stranded RNA sequence in the double-stranded RNA set 2 is sequence-aligned with the conserved co-expressed gene set, and siRNA sequences in the candidate double-stranded RNA set 2 that match the sequences in the conserved co-expressed gene set are retained to obtain candidate double-stranded RNA sequences for harmful organisms. Module A5) is used to calculate the structural free energy of the RNA in the candidate double-stranded RNA sequences for harmful organisms using an RNA secondary structure prediction tool, and to obtain the siRNA precursor double-stranded RNA sequence for harmful organisms from the candidate double-stranded RNA sequences for harmful organisms based on the structural free energy.

[0038] The target species may be organisms originating from Blattodea, Coleoptera, Diptera, Hemiptera, Hymenoptera, mites, nematodes, Orthoptera, plant pathogenic fungi, or Thysanoptera. The harmful organisms may be organisms that cause pests or diseases, such as pests or pathogens. The harmful organisms may be organisms originating from Blattodea, Coleoptera, Diptera, Hemiptera, Hymenoptera, mites, nematodes, Orthoptera, plant pathogenic fungi, and / or Thysanoptera.

[0039] The non-target species may include aquatic animals, biocontrol bacteria, birds, crops, earthworms, livestock, mammals, symbiotic bacteria, non-target arthropods, and pollinating insects. In one specific embodiment of the invention, the number of pest species is 250.

[0040] The aforementioned harmful organisms can be organisms that are harmful to plants, such as pests and / or pathogens that cause plant diseases (fungi, bacteria, viruses, or nematodes, etc.).

[0041] Current RNAi technology in the design of dsRNAs for pest control still faces challenges such as low target gene screening efficiency, insufficient consideration of dsRNA design features, lack of off-target effect assessment, limited computational accuracy, and the inability to design broad-spectrum and efficient dsRNAs targeting multiple genes or pest species simultaneously. These technical bottlenecks severely restrict the large-scale application of RNAi technology in agricultural pest control.

[0042] This invention provides an efficient, safe, accurate, and scalable dsRNA design system, which optimizes the above-mentioned problems and mainly addresses the following core challenges: Improving the efficiency of RNAi target gene screening: By constructing a database of genomes and transcriptomes of non-target organisms and common target organisms, combined with phylogenetic analysis and multiple sequence alignment, efficient cross-species target gene screening can be achieved, improving the accuracy and applicability of screening.

[0043] Optimize dsRNA fragment design to improve RNAi interference efficiency: Combine sequence features, siRNA generation mechanism and secondary structure prediction to develop a comprehensive optimization algorithm to ensure that dsRNA fragments have efficient gene silencing capabilities.

[0044] Accurately predict off-target effects and improve biosafety: Establish a cross-species off-target effect analysis system to optimize the ecological safety of RNAi design.

[0045] Improve computational efficiency and support large-scale screening: Utilize GPU parallel computing and AI deep learning to optimize RNA structure prediction, accelerate the dsRNA design process, improve computational efficiency, and support large-scale screening of pest RNAi targets.

[0046] Achieving multi-target co-targeting and improving the applicability of dsRNA: By using a co-targeting model, conserved target genes among multiple pest species are screened, and broad-spectrum dsRNAs are designed to improve their application value in a variety of pests and enhance the potential for the promotion of RNAi pesticides.

[0047] This invention, SEED (Species-Specific Efficient Ecofriendly dsRNA Designer), belongs to the interdisciplinary field of biotechnology and bioinformatics, specifically involving the application of RNA interference (RNAi) technology in agricultural pest control and biopesticide development. RNAi, as a precise gene silencing technology, has shown great application potential in agriculture, medicine, and bioengineering. This invention aims to optimize the screening of target genes for pest RNAi, the design of dsRNA fragments, the assessment of off-target effects, and co-targeting analysis to improve the success rate of dsRNA design and ensure its high efficiency, safety, and specificity.

[0048] Biosafety Testing: Based on the research of Cheng et al., a dsRNA fragment designed targeting the essential chitin synthase gene (Chs3b) in Fusarium graminearum was used to assess the ecosafety of the dsRNA. The potential effects of this dsRNA on common natural enemy insects (ladybugs, parasitic wasps), important beneficial insects (honeybees), major crops (wheat, rice, corn), birds (zebra finches), poultry and livestock (chickens, cattle), and mammals (mice, human cells) were evaluated. The results showed that no significant off-target effects were detected in any of the tested non-target organisms, validating the biosafety of the published dsRNA sequence.

[0049] Compared to existing dsRNA design methods, this invention has significant advantages in terms of accuracy, comprehensiveness, safety, and ease of operation. Specifically, the innovative aspects and beneficial effects of this invention are as follows: 1. Improve the accuracy and comprehensiveness of dsRNA design.

[0050] Traditional dsRNA design methods mainly rely on single alignment of target gene sequences, lacking comprehensive consideration of the overall transcriptome of pests, resulting in low design efficiency and significant randomness and uncertainty in the selection of target genes.

[0051] By combining the alignment strategies of MMseq2 and DIAMOND, both nucleic acid and protein sequence information can be utilized simultaneously to ensure the high conservation of the selected target genes in the target pest species, while avoiding homology with non-target organisms.

[0052] By using Z-score normalization and phylogenetic analysis (NPP), the screening criteria for target genes were further optimized, thereby improving the reliability of RNAi design.

[0053] 2. Multifunctional optimization analysis enables precise targeted design.

[0054] By employing sliding window screening, Dicer enzyme digestion simulation, and RNA structure optimization, we ensured that the designed dsRNA fragments could effectively induce RNAi responses.

[0055] By combining transcriptome analysis of non-target organisms, the impact of dsRNA fragments on non-target organisms (such as ecologically critical species like bees, ladybugs, and birds) can be effectively reduced.

[0056] By employing a common targeting strategy, the broad adaptability of dsRNA design can be enhanced, ensuring its applicability across multiple pests and increasing the application value of dsRNA fragments.

[0057] 3. Improve the biosafety of dsRNA design.

[0058] The widespread adoption of RNAi technology in pest control is constrained by off-target effects and ecological safety concerns.

[0059] We employ Bowtie's efficient off-target analysis algorithm to construct a cross-species RNAi biosafety assessment system, achieving high-precision off-target prediction.

[0060] By comparing the genomic data of 241 non-target organisms, it was ensured that the designed dsRNA fragments had no significant match in the non-target organisms, thus reducing environmental risks.

[0061] 4. Improve the computational efficiency and operability of dsRNA design.

[0062] Traditional RNA structure prediction and off-target analysis methods are computationally complex and time-consuming, making them unsuitable for large-scale dsRNA design.

[0063] By employing GPU parallel computing and AI deep learning optimization, the speed of RNA structure prediction is increased by more than 5 times, making it suitable for large-scale dsRNA screening.

[0064] Through an automated process, users only need to input the target species or gene sequence to complete the entire dsRNA design process.

[0065] It provides visualized results (including off-target effect heatmaps and statistical bar charts of dsRNA in different categories of non-target organisms), enabling researchers to quickly understand dsRNA design results and improving user experience.

[0066] 5. Applicable to multiple pests for joint targeting, improving the broad-spectrum control of RNAi.

[0067] Traditional RNAi design methods target single pests and are difficult to apply to the control needs of multiple pests.

[0068] By using MMseq2 cluster analysis on data from 250 pest species, highly conserved target genes were identified, ensuring that the designed dsRNA fragments could act on multiple pest species.

[0069] By adopting a common targeting strategy, the research and development costs of RNAi control can be reduced, and the application value of dsRNA fragments in different pest species can be improved.

[0070] This invention covers several key technical fields, including but not limited to: Agricultural biotechnology: Using RNAi technology to control agricultural pests, increase crop yields, reduce pesticide use, and promote the development of green agriculture.

[0071] Molecular biology: Optimize dsRNA fragment design to improve interference efficiency and ensure its high efficiency in target organisms.

[0072] Bioinformatics: Computational analysis based on large-scale genomic and transcriptomic data to develop target gene screening algorithms and off-target effect prediction models.

[0073] Ecological safety assessment: By integrating cross-species data, predict the potential impact of dsRNA on non-target organisms and reduce the environmental risks of RNAi technology.

[0074] Artificial intelligence: Deep learning and high-performance computing are used to optimize the dsRNA design process, improve computational efficiency, and support large-scale RNAi target screening.

[0075] This invention overcomes the shortcomings of existing technologies in design efficiency, off-target control, and targeting accuracy by introducing multi-species genome and transcriptome databases, a non-targeted biosafety assessment system, and intelligent algorithms to optimize dsRNA design, providing reliable technical support for the application of RNAi technology in agricultural pest control.

[0076] This invention is a modular dsRNA design program developed in Python, designed to provide an efficient, safe, precise, and scalable solution for RNAi control of agricultural pests. This tool integrates genomic and transcriptomic data from multiple species, combines sequence alignment, sliding window dsRNA generation, siRNA design, off-target analysis, multi-target co-targeting design, dsRNA secondary structure prediction, and final screening. It can generate dsRNA design schemes applicable to various pests, ensuring good gene silencing effects on target pests while exhibiting low off-target effects on non-target species. Attached Figure Description

[0077] Figure 1 This is a flowchart of the species-specific double-stranded RNA design of the present invention.

[0078] Figure 2 This diagram illustrates the species-specific off-target effect analysis, showcasing the off-target effects of the dsRNA fragment designed based on the essential chitin synthase gene (Chs3b) from *Fusarium graminearum* in both non-target and target organisms. The top row shows the off-target analysis for representative species, and the bottom row shows the overall statistics for each representative species. A represents the off-target distribution of CHS3b in non-target organisms; B represents the co-target distribution of CHS3b in common target pests; C represents the total number of off-target hits for each NTO representative species; and D represents the total number of off-target hits for each CTO representative species.

[0079] Figure 3 This diagram illustrates the off-target effects of dsRNA fragments in harmful organisms, showing the statistical results of dsRNA fragments across all species grouped by NTO and CTO. The top row shows the average off-target or co-target distribution of different siRNA sites, and the bottom row shows the average statistics for each group. A represents the average off-target distribution of CHS3b in the NTO group (average off-target hit count); B represents the average co-target distribution of CHS3b in the CTO group; C represents the average off-target hit count for each NTO group; and D represents the average co-target hit count for each CTO group.

[0080] Figure 4 This diagram illustrates the species-specific off-target effects analysis of the Colorado potato beetle (Leptinotarsa ​​decemlineata), demonstrating the off-target effects of the dsRNA fragment designed for the PSMB5 gene in this invention in both non-target and target organisms. A represents the off-target distribution of the PSMB5 gene in non-target organisms; B represents the co-target distribution of the PSMB5 gene in common target pests; C represents the total number of off-target hits for each NTO (non-target organism) representative species; and D represents the total number of off-target hits for each CTO (common target organism) representative species.

[0081] Figure 5 This diagram illustrates the off-target effects of dsRNA fragments in the Colorado potato beetle, showing the statistical results of dsRNA fragments across all species grouped by NTO and CTO. A represents the average off-target distribution of the PSMB5 gene in the NTO group (average off-target hit count); B represents the average co-target distribution of the PSMB5 gene in the CTO group; C represents the average off-target hit count for each NTO group; and D represents the average co-target hit count for each CTO group.

[0082] Figure 6The optimal candidate dsRNA fragment designed for the PSMB5 gene mRNA sequence in this invention is compared with the PSMB5 gene dsRNA fragment reported in the literature. Ledprona A schematic diagram showing the sequence alignment results of the dsRNA fragment designed by the present invention. 1 represents a partial sequence of the optimal candidate dsRNA fragment designed by the method of the present invention (nucleotides 1-366 of sequence 4). 2 represents... Ledprona Partial sequence (nucleotides 113-459 of sequence 5). Detailed Implementation

[0083] The present invention will now be described in further detail with reference to specific embodiments. The given embodiments are merely illustrative of the invention and not intended to limit its scope. The embodiments provided below can serve as a guide for further improvements by those skilled in the art and do not constitute a limitation on the invention in any way.

[0084] Unless otherwise specified, the experimental methods used in the following examples are conventional methods, performed according to the techniques or conditions described in the literature in this field or according to the product instructions. Unless otherwise specified, the materials and reagents used in the following examples are commercially available.

[0085] The English-Chinese terminology glossary in this invention is as follows: Double-stranded RNA (dsRNA); RNA interference: RNA interference (RNAi); Small interfering RNA (siRNA); Target gene; Off-target effect; Non-target organisms (NTO); Co-target Organism (CTO); RNA Secondary Structure Prediction; Phylogenetic analysis: Normalized Phylogenetic Profile (NPP); Genome; Transcriptome.

[0086] Example 1. Species-specific double-stranded RNA design process.

[0087] This embodiment uses Fusarium graminearum PH-1 as the target species to illustrate the species-specific double-stranded RNA (siRNA precursor) design process of the present invention. Figure 1 ).

[0088] 1. Target gene screening.

[0089] This invention integrates genomic and transcriptomic data of the target species (Fusarium graminearum) and non-target organisms to construct a cross-species genomic and transcriptomic database; and uses phylogenetic analysis and multiple sequence alignment to screen for highly conserved target genes suitable for RNAi design.

[0090] 1.1 Data download.

[0091] Download the Fusarium graminearum genome (including gff annotation), protein sequence (faa), and transcriptome mRNA sequence (fna) from the FungiDB public database.

[0092] We downloaded and received genomic and transcriptomic data from 241 non-target organisms (NTOs, species composition shown in Table 1).

[0093]

[0094] In this embodiment, the non-target species are organisms derived from aquatic animals, biocontrol bacteria, birds, crops, earthworms, livestock, mammals, symbiotic bacteria, non-target arthropods, and / or pollinating insects.

[0095] 1.2 Target gene screening process.

[0096] 1) Gene matching analysis.

[0097] A dual-algorithm alignment strategy: MMseq2 combines speed and sensitivity, ensuring no potential homologous sequences are missed at the nucleic acid level. DIAMOND offers high reliability, accurately identifying function-related sequences at the protein level. This invention combines the alignment strategies of MMseq2 and DIAMOND to simultaneously utilize information from both nucleic acid and protein sequences, improving the accuracy of gene screening. Specifically: MMseq2 was used to align the protein and transcriptome data of 14,143 Fusarium graminearum samples, and DIAMOND was used for protein sequence alignment. Genes that were conserved in both transcriptome and protein sequence alignment results (to ensure functional conservation) were selected and retained to obtain conserved gene set 1.

[0098] 2) Phylogenetic Profile (NPP).

[0099] Gene conservation was calculated to ensure that the selected target genes were highly conserved in the target species (Fusarium graminearum). Z-score normalization was used to reduce the impact of phylogenetic distance comparisons on the results and improve the accuracy of cross-species target gene screening. Specifically: Gene conservation is calculated and Z-score normalization is performed to improve alignment accuracy. The core of identifying conserved genes in phylogenetic analysis lies in selecting gene segments that have remained highly stable throughout evolution. These segments typically possess the following characteristics (criteria for determining conserved genes): Sequence stability: The nucleotide or amino acid sequence of a conserved gene remains highly stable throughout the evolution of a species. Examples include the conserved sequence of nucleotides 9-13 on the E. coli chromosome, starting from the DNA replication origin (as the first nucleotide), and the flanking conserved sequences of microsatellite DNA. Functional criticality: Conserved regions are often involved in the basic life functions of organisms (such as DNA replication initiation, cell membrane fusion, etc.), and their variations may affect the survival ability of organisms; Cross-species applicability: Conserved sequences can be used for genetic analysis across different species, such as the application of human microsatellite primers in the detection of non-human primates.

[0100] Conserved genes obtained from phylogenetic tree analysis were retained in conserved gene set 1, and non-conserved genes (gene segments that do not have the ability to maintain high stability during evolution, i.e., segments that do not have sequence stability, functional criticality and cross-species adaptability) were removed to obtain conserved gene set 2.

[0101] 3) Target gene screening: Prioritize the selection of highly conserved key genes in the target species (Fusarium graminearum) that have no homology in non-target organisms to improve the biosafety of dsRNA design. Specifically: Transcriptome and protein sequence data from 241 non-target organisms were received to obtain a non-target database. Key genes highly conserved in *Fusarium graminearum* (as obtained through steps 1 and 2) and showing no significant homology in non-target organisms (non-homologous genes) were selected. Homologous genes from the non-target database in conserved gene set 2 were removed, resulting in conserved gene set 3. This conserved gene set 3 represents the target genes of *Fusarium graminearum*.

[0102] Output: A list of candidate target genes, including alignment results and conservation analysis (see Table 2 for partial list of target genes).

[0103]

[0104] 2. Generation and scoring of siRNA precursor dsRNA candidate fragments.

[0105] Input data: The transcriptome mRNA sequences corresponding to the candidate target genes screened in step 1.

[0106] Processing steps: 2.1 Simulation of sliding window cutting.

[0107] Using a sliding window, dsRNA fragments of 200–800 bp in length are dynamically generated, and the Dicer enzyme digestion process is simulated to generate all usable siRNA sequences, thus optimizing siRNA generation efficiency.

[0108] Specifically: All candidate dsRNA fragments were generated using the sliding window method (default window length 400 bp, step size 50 bp in this embodiment); and then all usable siRNA sequence fragments were generated by enumerating each candidate dsRNA based on the default length (simulating the Dicer enzyme cleavage process) (default length 19 bp in this embodiment).

[0109] 2.2 siRNA activity assessment system.

[0110] Candidate dsRNAs were filtered based on siRNA fragment validity: SiRNA generation was scored on candidate dsRNAs to assess the number of valid siRNAs generated, ensuring that each dsRNA fragment generated at least 5 valid siRNAs. Specifically: A sliding window method (19 bp, step size 1 nt) was used to simulate Dicer enzyme digestion. All possible siRNAs were enumerated and screened under the following conditions to obtain effective siRNAs: GC content (20%–50%), 5' end A / U preference, toxicity sequence screening, avoidance of ≥4 repetitive bases, and complete matching with the target gene (i.e., target-specific screening to ensure that the selected sequence completely matches the sequence of the candidate target gene).

[0111] The number of effective siRNAs for each dsRNA is counted. If the number of effective siRNAs is greater than or equal to 5, the candidate dsRNA fragment is considered to have sufficient siRNA production potential and is a siRNA with high targeting activity, which can be included in the subsequent off-target detection process; otherwise, it is discarded.

[0112] Output: The initial screening of candidate dsRNA fragments yielded a candidate double-stranded RNA set 1. This set included siRNA generation scores for each candidate double-stranded RNA (scoring criteria included the number of viable siRNAs that could be generated, GC content, etc.). The 10 fragments with the highest overall scores were selected as recommended fragments.

[0113] 3. Off-target effect assessment: Input data: Candidate double-stranded RNA set 1 (containing candidate dsRNA fragments).

[0114] The non-target database is obtained by receiving the genome or transcriptome database of non-target organisms (NTOs) (in this embodiment, a transcriptome database is used).

[0115] Processing steps: 3.1 Alignment with Non-Target Organisms (NTO): Using the Bowtie efficient alignment algorithm, the matching of siRNA fragments generated from candidate double-stranded RNAs in candidate double-stranded RNA set 1 with non-target organism genes is calculated by aligning them with the non-target database.

[0116] 3.2 Off-target threshold setting: If the average number of off-target events for a candidate double-stranded RNA in non-target species is >3 or the total number of off-target events is >100, then the candidate double-stranded RNA fragment in candidate double-stranded RNA set 1 is removed. If the average number of off-target events for a candidate double-stranded RNA in the genome sequence of a non-target species is less than or equal to 3 or the total number of off-target events is less than or equal to 100, then the candidate double-stranded RNA in candidate double-stranded RNA set 1 is retained, resulting in double-stranded RNA set 2, which serves as the initial target species-specific double-stranded RNA sequence.

[0117] Off-target hits: refers to the number of matching sites found in the genome of the non-target organism (NTO) for each individual siRNA fragment (usually 19-21 bp) among all siRNAs that can be cleaved by the candidate double-stranded RNA (which may be generated). A certain number of mismatches are allowed (the default is 1 mismatch site).

[0118] Calculation: For each siRNA fragment, Bowtie was used to align it to the NTO genome, and the number of sites meeting the matching criteria was counted. Average off-target hits are the average number of off-target hits across all (potentially generated) siRNA fragments from a given dsRNA fragment. The average off-target hits are the ratio of the total off-target hits of all siRNA fragments to the total number of siRNA fragments. Total off-target hits refer to the total number of off-target sites found across all non-target organisms for all siRNA fragments generated by a single dsRNA.

[0119] Output: Off-target analysis report of safety assessment (including sequences with high off-target risk) and double-stranded RNA set 2.

[0120] 4. dsRNA structure prediction and screening.

[0121] Input data: Double-stranded RNA set 2.

[0122] Processing steps: Secondary structure prediction: The RNA structure free energy (ΔG) of the dsRNA fragments in double-stranded RNA set 2 was calculated using MXfold2 to ensure the structural stability and rationality of the fragments, and to obtain the secondary structure score of each double-stranded RNA in double-stranded RNA set 2.

[0123] The calculated ΔG is the secondary structure free energy of the complete dsRNA, used to assess the stability of the fragment and avoid generating sequences that are too loose or form complex secondary structures.

[0124] Each double-stranded RNA was comprehensively sorted and screened by combining the secondary structure score and the number of effective siRNAs in step 2.2 to ensure that the selected dsRNAs have both efficient siRNA generation capabilities and reasonable stable structures. Finally, the fragment with the highest sorting (the first 10 fragments by default, and the information format of the first 3 fragments is shown in Table 3) was selected as the final design fragment, namely the target species (Fusarium graminearum) specific (siRNA precursor) double-stranded RNA.

[0125] Each mRNA target gene typically outputs 5 optimal dsRNA fragments, with their ΔG values ​​required to be between -20 kcal / mol and -40 kcal / mol, with -20 to -30 being more suitable.

[0126] Output: A list of Fusarium graminearum-specific (siRNA precursor) double-stranded RNA fragments, including structural scores and the number of siRNAs generated.

[0127]

[0128] Note: In the secondary structure of dsRNA, "(" and ")" represent hydrogen bond pairing, indicating the base pair structure in the RNA chain; "..." represents an unpaired base sequence.

[0129] 5. Results summary and visualization.

[0130] Output content: The final selected dsRNA fragments include the structure, score, and off-target assessment results for each fragment (Table 3). A visualization chart of off-target effects, showing the distribution of off-target species and the number of off-target events; All dsRNA fragment recording files.

[0131] This invention targets essential genes of Fusarium graminearum and completes automated dsRNA analysis and optimization of 84 candidate genes within 30 minutes, which is at least 10 times faster than manual analysis and significantly improves the efficiency and scalability of dsRNA design.

[0132] Based on the work of Cheng et al. (related literature: Cheng, W.; Song, XS; Li, HP; Cao, LH; Sun, K.; Qiu, XL; Liao, YC), host-induced gene silencing of an essential chitin synthase gene confers durable resistance to Fusarium head blight and seedling blight in wheat. Plant Biotechnol. J. (2015, 13, 1335-1345.) This study designed a dsRNA fragment targeting the chitin synthase gene (Chs3b) in Fusarium graminearum to conduct an ecosafety assessment.

[0133] Figure 2 Results shown in Figures A and C illustrate the composition of the Non-Target Organism (NTO) database and off-target effect analysis of representative species. The NTO database integrates genomic and transcriptomic data from 241 non-target organisms, and... Figure 2The study grouped and presented representative species, including: *Beauveria bassiana*, *Rhizocystis irregularis*, *Daphnia magna*, honeybee, planarian, zebrafish, chicken, cattle, humans, and rice. *Beauveria bassiana* and humans showed particularly high off-target hit statistics, indicating that these species have significant reference value in ecological security assessments. Through systematic analysis of the above-mentioned representative NTO species, this invention enables a comprehensive assessment of potential off-target effects during dsRNA design, ensuring the high conservation of selected target genes in pest populations while avoiding adverse effects on non-target species. Figure 2 The results in A and C provide a clear data source and scientific basis for the system of this invention in dsRNA target gene screening, cross-species off-target assessment and ecological safety control.

[0134] Figure 3 The results shown in A and C are derived from the construction and analysis of a non-target organism database. Specifically, the non-target organism database integrates genomic and transcriptomic data from 241 non-target organisms for cross-species off-target effect assessment. A dual-algorithm alignment strategy using MMseq2 and DIAMOND ensures both sensitivity and reliability at the nucleic acid and protein levels. Furthermore, phylogenetic analysis (NPP) and Z-score normalization are employed to calculate cross-species gene conservation, identifying key target genes that are highly conserved in the target species but show no significant homology in non-target organisms. Figure 3 The results in A and C provide data support for the method of this invention to achieve efficient target gene screening and ecological safety control in the dsRNA fragment design process.

[0135] In the implementation of this invention, the PSMB5 gene transcript (XM_023158308.2, updated May 27, 2025) from the Colorado potato beetle was subjected to sliding window cutting and secondary structure optimization to obtain multiple candidate dsRNA fragments. After comprehensive evaluation of the number of effective siRNAs, secondary structure stability, and folding fraction, the screening results showed that the best candidate dsRNA fragment (Sequence 4 in the sequence listing) started at nucleotide 551 of the PSMB5 gene transcript (counted based on the first nucleotide of the transcript) and ended at nucleotide 950, with a total length of 400 bp and containing 27 effective siRNAs (Table 4). It is noteworthy that this fragment completely overlaps with the transcript and is an effective dsRNA fragment experimentally validated in existing literature (related literature: Rodrigues TB, Mishra SK, Sridharan K, Barnes ER, Alyokhin A, Tuttle R, Kokulapalan W, Garby D, Skizim NJ, Tang YW, Manley B, Aulisa L, Flannagan RD, Cobb C and Narva KE (2021) First Sprayable Double-Stranded RNA-Based Biopesticide Product Targets Proteasome Subunit Beta Type-5 in Colorado Potato Beetle (Leptinotarsa ​​decemlineata). Front. Plant Sci. 12:728652. doi: 10.3389 / fpls.2021.728652). Ledprona Sequence 5 in the sequence list is highly consistent ( Figure 6 ). Figure 4 The results in A and C represent the species-specific off-target effects of the PSMB5 candidate dsRNA fragments obtained in this invention in non-target organisms (NTOs). Figure 5 The results in A and C represent the off-target effects analysis of the PSMB5 candidate dsRNA fragments obtained in this invention in non-target organisms (NTOs).

[0136] The above results further verify the accuracy and reliability of the SEED system of the present invention in dsRNA design.

[0137]

[0138] Note: In the secondary structure of dsRNA, "(" and ")" represent hydrogen bond pairing, indicating the base pair structure in the RNA chain; "..." represents an unpaired base sequence.

[0139] Example 2. Design of a common siRNA precursor double-stranded RNA sequence for harmful organisms.

[0140] 1. Based on the same process as steps 1-3 in Example 1, the target species double-stranded RNA set 2 is obtained as the initial target species-specific double-stranded RNA sequence.

[0141] The target species are organisms from Blattodea, Coleoptera, Diptera, Hemiptera, Hymenoptera, mites, nematodes, Orthoptera, plant pathogenic fungi, or Thysanoptera. The harmful organisms in this embodiment are pathogens or pests that cause plant diseases and pests, and are organisms from Blattodea, Coleoptera, Diptera, Hemiptera, Hymenoptera, mites, nematodes, Orthoptera, plant pathogenic fungi, and / or Thysanoptera.

[0142] The target species in this embodiment is the same as in Example 1, which is still Fusarium graminearum.

[0143] The non-target species in this embodiment are the same as in Example 1, and are organisms derived from aquatic animals, biocontrol bacteria, birds, crops, earthworms, livestock, mammals, symbiotic bacteria, non-target arthropods, and / or pollinating insects.

[0144] 2. Multi-gene co-targeting: Input data: Transcriptome databases of target species Fusarium graminearum and common target organisms (CTOs, Table 5).

[0145] Among them, the common target species (excluding the target species Fusarium graminearum) are, like the target species, organisms from the orders Blattodea, Coleoptera, Diptera, Hemiptera, Hymenoptera, mites, nematodes, Orthoptera, plant pathogenic fungi, or Thysanoptera.

[0146]

[0147] Processing steps: 2.1 Gene clustering analysis: MMseq2 clustering analysis was used to analyze the data of 250 common target pests (Table 5, for plant pests or pathogens) to identify conserved co-expressed genes (i.e., genes co-expressed in each of the 250 common target pests).

[0148] The reason for choosing MMseqs2 for cluster analysis in this invention is as follows: MMseqs2 exhibits high sensitivity: it can detect sequences with as low as 30% sequence identity, and can also identify conserved genes with distant homology. It can cluster 250 pests and diseases with relatively large evolutionary distances.

[0149] MMseqs2 boasts high speed: compared to BLASTclust, CD-HIT, and USEARCH, MMseqs2 can be hundreds of times faster at a scale of millions of sequences.

[0150] MMseqs2 is a multi-level clustering system: it supports stepwise clustering from low sensitivity to high sensitivity, which can better separate conservative core clusters.

[0151] MMseqs2 is suitable for massive amounts of data: it can be indexed in memory in blocks, and can be processed in parallel on multiple cores without requiring a huge amount of memory.

[0152] MMseqs2 is easy to update: clusters can be quickly updated as new pest species data are added.

[0153] 2.2 Co-target Adaptability Optimization: Cross-pest alignment was performed on each dsRNA fragment in double-stranded RNA set 2 to optimize its broad-spectrum adaptability. Specifically: The MMseq2 clustering analysis method was used to cluster the genome and transcriptome sequences of 250 target harmful organisms, and a highly conserved set of co-expressed genes was screened to ensure that the dsRNAs designed subsequently can act across species.

[0154] Each dsRNA generated by the sliding window was simulated with Dicer cutting to obtain a large number of siRNA sequences. Bowtie was then used to accurately compare these siRNAs with all pest databases and cluster core sequences to statistically analyze their matching in different pest species.

[0155] A multi-target fitness matrix is ​​constructed based on the matching results. A weighted coverage algorithm is used to prioritize the selection of dsRNA fragments that can generate efficient siRNA matching in more harmful biological species, thereby maximizing their broad-spectrum fitness.

[0156] Output the co-target adaptation analysis file, as well as the top-ranked dsRNAs (default 10) based on the overall score, to ensure that the designed fragments can exert RNAi effects on multiple pests that cause plant diseases and pests at the same time, reducing the risk of failure caused by adaptation to a single pest.

[0157] Output: dsRNA fragments that are co-targeted by multiple invasive species (CTOs).

[0158] In the co-targeting screening module for multiple pests, the system outputs a file of co-targeted dsRNA fragments, detailing the compatibility of each dsRNA fragment selected through co-targeting optimization in the target pest species (Fusarium graminearum), including: The number of harmful species covered, target_species_count. Matching co-expressed conserved gene names co_target_genes The list of matched pest species is matched_species_list. The average number of effective siRNAs that can be produced per pest species is avg_siRNA_per_species. The number of harmful species covered, target_species_count. Matching co-expressed conserved gene names co_target_genes The list of matched pest species is matched_species_list. The average number of effective siRNAs that can be generated per pest species: avg_siRNA_per_species.

[0159] 3. dsRNA structure prediction and screening.

[0160] Input data: dsRNA fragments that are co-targeted with CTO.

[0161] Processing steps: Secondary structure prediction: MXfold2 is used to calculate the RNA structure free energy (ΔG) of the dsRNA fragments (candidate double-stranded RNA sequences targeting harmful organisms) that are co-targeted with CTO, to ensure the structural stability and rationality of the fragments and to obtain the secondary structure score of the dsRNA fragments that are co-targeted with CTO.

[0162] The calculated ΔG is the secondary structure free energy of the complete dsRNA, used to assess the stability of the fragment and avoid generating sequences that are too loose or form complex secondary structures.

[0163] Each double-stranded RNA was comprehensively sorted and screened using secondary structure scoring to ensure that the selected dsRNAs possess both efficient siRNA generation capabilities and a reasonable stable structure. Finally, the fragment ranked first (the first 10 fragments by default) was selected as the final design fragment, which is a universal (broadly applicable) siRNA precursor double-stranded RNA sequence targeting harmful organisms causing plant diseases and pests.

[0164] The ΔG value distribution should be between -20 kcal / mol and -40 kcal / mol, with -20 to -30 being more suitable.

[0165] Output: A list of general-purpose (broadly applicable) siRNA precursor double-stranded RNA sequence fragments for harmful organisms causing plant diseases and pests, including structural scores and siRNA generation quantities.

[0166] Figure 2 The results shown in B and D illustrate the composition of the Common Target Pest (CTO) database and the off-target effect analysis of representative species.

[0167] The CTO database integrates genomic and transcriptomic data of 250 agricultural pests (agricultural insects or pathogens, etc.), and in Figure 3 The samples were grouped and displayed according to representative species, including: Fusarium graminearum, Caenorhabditis elegans, Tetranychus spp., Thrips spp., Whitefly, Red Flood Flood Beetle, Drosophila melanogaster, American cockroach, Red imported fire ant, Fall Armyworm, and Desert Locust. Among them, Fusarium graminearum had a significantly higher total off-target hit count than other species, indicating that it has strong representativeness and conservation among pathogenic fungi.

[0168] Through systematic analysis of the above-mentioned representative CTO species, this invention enables a comprehensive assessment of potential off-target effects during the dsRNA design process, ensuring the high conservation of the selected target gene in harmful organism populations while avoiding adverse effects on non-target species. Figure 2 The results from B and D provide a clear data source and scientific basis for the system in dsRNA target gene screening, cross-species off-target assessment, and ecological safety control.

[0169] Figure 3 The results shown in B and D are derived from the construction and analysis of a common target (pest) database. This database aggregates genomic and transcriptomic data from 250 agricultural pests, and employs a dual-algorithm alignment strategy using MMseq2 and DIAMOND to ensure both sensitivity and reliability at the nucleic acid and protein levels. Furthermore, phylogenetic analysis (NPP) and Z-score normalization were used to calculate cross-species gene conservation, identifying key target genes that are highly conserved in pest species but show no significant homology in non-target organisms (based on the results of Example 1).

[0170] Figure 4Results B and D represent the species-specific co-targeting effects of the PSMB5 candidate dsRNA fragments obtained in this invention in common target organisms (CTOs). Figure 5 Results B and D represent the co-targeting effect analysis results of the PSMB5 candidate dsRNA fragments obtained in this invention in common target organisms (CTOs).

[0171] The above results provide data support for the method of the present invention to achieve efficient target gene screening and ecological safety control in the dsRNA fragment design process.

[0172] The present invention has been described in detail above. Those skilled in the art will recognize that the invention can be practiced in a wide range of ways with equivalent parameters, concentrations, and conditions without departing from its spirit and scope, and without requiring unnecessary experiments. While specific embodiments have been provided, it should be understood that further modifications can be made to the invention. In summary, according to the principles of the invention, this application is intended to include any changes, uses, or improvements to the invention, including changes made using conventional techniques known in the art that depart from the scope disclosed herein.

Claims

1. A method for generating a siRNA precursor double-stranded RNA sequence targeting a target species, characterized in that, The method includes the following steps: S1) Target gene screening: Analyze the candidate conserved gene set of the target species based on transcriptome data and protein sequence data respectively; perform gene conservation analysis on the candidate conserved gene set, and obtain the target gene of the target species based on the results of the gene conservation analysis; S2) Obtaining a candidate siRNA set: Based on the transcriptome data of the target gene, all precursor double-stranded RNA sequences are extracted using the sliding window method; based on a predetermined length, all available sequence fragments of each precursor double-stranded RNA sequence are enumerated to obtain siRNAs; and candidate double-stranded RNA set 1 is obtained by filtering based on the siRNAs. S3) Off-target analysis: Receive sequence information of non-target species to obtain a non-target database, use a sequence alignment algorithm to compare the candidate double-stranded RNA set 1 with the non-target database, and select double-stranded RNA set 2 from the candidate double-stranded RNA set 1 according to a preset off-target threshold.

2. The method according to claim 1, characterized in that: The method further includes S4), which includes the following steps: using an RNA secondary structure prediction tool to calculate the structural free energy of the RNA in the double-stranded RNA set 2, and obtaining the siRNA precursor double-stranded RNA sequence from the double-stranded RNA set 2 based on the structural free energy.

3. The method according to claim 1 or 2, characterized in that: In S1), The process of analyzing the candidate conserved gene set of the target species based on transcriptome data and protein sequence data includes receiving protein sequence data and transcriptome data of two or more individuals of the target species, performing multiple sequence alignment 1 on the transcriptome data to obtain transcriptome alignment results, performing multiple sequence alignment 2 on the protein sequence data to obtain protein alignment results, and obtaining the conserved gene set 1 of the target species based on the transcriptome alignment results and the protein alignment results. The gene conservation analysis includes performing gene conservation analysis on the conserved gene set 1 based on sequence, and removing non-conserved genes from the conserved gene set 1 based on the results of the gene conservation analysis to obtain the conserved gene set 2. Based on the results of the gene conservation analysis, the target genes of the target species are obtained, including the sequence information of non-target species organisms to obtain a non-target database. Homologous genes in the non-target database are removed from the conserved gene set 2 to obtain a conserved gene set 3, which is the target gene of the target species.

4. The method according to claim 3, characterized in that: The multiple sequence alignment 1 was performed using MMseq2; the multiple sequence alignment 2 was performed using DIAMOND.

5. The method according to claim 1 or 2, characterized in that: S2) The sliding window method is used to truncate the sequence based on a predetermined window length and a predetermined sliding step size; the filtering is to filter all available sequence fragments based on the following conditions to obtain candidate double-stranded RNA set 1: GC content, 5' base bias, whether it contains a repeating region of more than 4 consecutive bases, and whether it completely matches the target gene; S3) The alignment is to enumerate all available sequence fragments based on a predetermined length to obtain the siRNA of the candidate double-stranded RNA in the candidate double-stranded RNA set 1, and then align the siRNA of the candidate double-stranded RNA with the non-target database.

6. A method for generating a double-stranded RNA sequence of siRNA precursor for harmful organisms, characterized in that, The method includes steps S1)-S3) of the method according to any one of claims 1-5, and further includes steps S4)' and S5). S4)' includes receiving genomic or transcriptomic data of more than one type of harmful organism (excluding the target species); using a clustering analysis tool to analyze conserved co-expressed genes in the genomic or transcriptomic data to obtain a conserved co-expressed gene set; performing sequence alignment of each double-stranded RNA sequence in the double-stranded RNA set 2 with the conserved co-expressed gene set, and retaining siRNA sequences in the candidate double-stranded RNA set 2 that match the sequences in the conserved co-expressed gene set to obtain candidate double-stranded RNA sequences for the harmful organism; S5) includes using an RNA secondary structure prediction tool to calculate the structural free energy of the RNA in the candidate double-stranded RNA sequences for the harmful organism, and obtaining the siRNA precursor double-stranded RNA sequence for the harmful organism from the candidate double-stranded RNA sequences for the harmful organism based on the structural free energy. The target species are organisms derived from Blattodea, Coleoptera, Diptera, Hemiptera, Hymenoptera, mites, nematodes, Orthoptera, plant pathogenic fungi, or Thysanoptera; the harmful organisms are organisms derived from Blattodea, Coleoptera, Diptera, Hemiptera, Hymenoptera, mites, nematodes, Orthoptera, plant pathogenic fungi, and / or Thysanoptera.

7. A data processing device, characterized in that: The data processing device is used to generate siRNA sequences for target species, and the device includes the following modules: A1) Target gene screening module: used to analyze the candidate conserved gene set of the target species based on transcriptome data and protein sequence data respectively; to perform gene conservation analysis on the candidate conserved gene set, and to obtain the target gene of the target species based on the results of the gene conservation analysis; A2) Obtaining candidate dsRNA set module: Based on the transcriptome data of the target gene, all precursor double-stranded RNA sequences are extracted using the sliding window method; based on a predetermined length, all available sequence fragments of each precursor double-stranded RNA sequence are enumerated to obtain siRNA; and double-stranded RNA set 1 is obtained by filtering based on the siRNA. A3) Off-target analysis module: used to receive sequence information of non-target species to obtain a non-target database, use sequence alignment algorithm to compare the candidate double-stranded RNA set 1 with the non-target database, and select candidate double-stranded RNA set 2 from the candidate double-stranded RNA set 1 according to a preset off-target threshold.

8. The data processing apparatus according to claim 7, characterized in that: The data processing device further includes an A4 module, which is used to calculate the structural free energy of RNA in the double-stranded RNA set 2 using an RNA secondary structure prediction tool, and obtain the siRNA precursor double-stranded RNA sequence from the double-stranded RNA set 2 based on the structural free energy.

9. The data processing apparatus according to claim 7 or 8, characterized in that: In A1): The process of analyzing the candidate conserved gene set of the target species based on transcriptome data and protein sequence data includes receiving protein sequence data and transcriptome data of two or more individuals of the target species, performing multiple sequence alignment 1 on the transcriptome data to obtain transcriptome alignment results, performing multiple sequence alignment 2 on the protein sequence data to obtain protein alignment results, and obtaining the conserved gene set 1 of the target species based on the transcriptome alignment results and the protein alignment results. The gene conservation analysis includes performing gene conservation analysis on the conserved gene set 1 based on sequence, and removing non-conserved genes from the conserved gene set 1 based on the results of the gene conservation analysis to obtain the conserved gene set 2. Based on the results of the gene conservation analysis, the target genes of the target species are obtained, including the sequence information of non-target species organisms to obtain a non-target database. Homologous genes in the non-target database are removed from the conserved gene set 2 to obtain a conserved gene set 3, which is the target gene of the target species.

10. An apparatus for generating a double-stranded RNA sequence of siRNA precursor for harmful organisms, characterized in that, The device includes modules A1)-A3) of the data processing device according to any one of claims 7-9, and further includes modules A4)' and A5)'. Module A4)' is used to receive genomic or transcriptomic data of more than one type of harmful organism other than the target species, and to analyze conserved co-expressed genes in the genomic or transcriptomic data using a clustering analysis tool to obtain a set of conserved co-expressed genes; to perform sequence alignment of each double-stranded RNA sequence in the double-stranded RNA set 2 with the set of conserved co-expressed genes, and to retain siRNA sequences in the candidate double-stranded RNA set 2 that match the sequences in the set of conserved co-expressed genes to obtain candidate double-stranded RNA sequences for the harmful organism; module A5) is used to calculate the structural free energy of the RNA of the candidate double-stranded RNA sequences for the harmful organism using an RNA secondary structure prediction tool, and to obtain the siRNA precursor double-stranded RNA sequence for the harmful organism from the candidate double-stranded RNA sequences for the harmful organism based on the structural free energy; The target species are organisms derived from Blattodea, Coleoptera, Diptera, Hemiptera, Hymenoptera, mites, nematodes, Orthoptera, plant pathogenic fungi, or Thysanoptera; the harmful organisms are organisms derived from Blattodea, Coleoptera, Diptera, Hemiptera, Hymenoptera, mites, nematodes, Orthoptera, plant pathogenic fungi, and / or Thysanoptera.

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