572 results about "RNA Sequence" patented technology

The invention discloses a space transcriptomics cell type deconvolution method based on a generative adversarial network and a comparative learning framework. The method comprises the following steps: firstly, enhancing a gene expression matrix by using a histological image and spatial position information; then, in combination with GraphSAGE and self-supervised comparative learning, optimizing feature representation of space transcriptomics data, and reconstructing high-quality space gene expression data; single-cell RNA sequencing data gene expression features are extracted by using a variational automatic encoder, and meanwhile, the quality of a generated sample is optimized by using a discriminator, so that efficient feature extraction and accurate gene expression reconstruction are realized; and finally, mapping single-cell RNA sequencing data into spatial points through comparative learning and mapping matrix learning based on low-rank representation, thereby predicting the cell type of each point. According to the model, multi-modal information in single-cell RNA sequencing and space transcriptomics data is comprehensively considered, and a mapping matrix is constructed and optimized, so that the cell type of each space point is predicted.

The invention provides an UTR (Untranslated Region) element H2202 P1-G as well as a construction method and application thereof, and relates to the technical field of mRNA (messenger ribonucleic acid). According to the present invention, the ribosome load prediction and the secondary structure optimization are performed on the natural 5 'UTR of the HIV TAT 202 gene through the BaidleHelix platform, and the obtained HTAT 202 P1 sequence avoids the inhibitory hairpin structure so as to significantly improve the luciferase expression quantity compared to the natural UTR; an ncRNA sequence without a secondary structure is introduced on the basis of the HTAT 202 P1, translation inhibition of a 5 'cap region is further relieved, and the protein expression quantity of the constructed H2202 P1-G mutant (the DNA sequence of the H2202 P1-G is as shown in SEQ NO 1, and the RNA sequence is as shown in SEQ NO 2) is further improved.

The invention provides an UTR element NHP1 as well as a construction method and application thereof, and relates to the technical field of mRNA. A 5 'UTR with a good expression effect is designed by integrating dominant sequences of a human high-expression gene and a pathogen natural UTR, a chimeric structure NHP1 with high ribosome load is predicted through a calculation model, a DNA sequence of the NHP1 is as shown in SEQ NO 1, and an RNA sequence of the NHP1 is as shown in SEQ NO 2; an EGFP report system is adopted on the DNA level to rapidly screen UTR; the translation efficiency is quantitatively evaluated on the RNA level through luciferase mRNA (N1-methyl pseudouridine modification); and the particle size is controlled by a microfluidic technology, so that the optimized UTR-mRNA is efficiently expressed after being delivered.

The invention provides a spatial domain identification method based on data interpolation and cell type deconvolution, and belongs to the technical field of bioinformatics. In order to solve the problems that gap information between adjacent points cannot be utilized in low-resolution spatial transcriptome data and prior information of cell types in a tissue space structure level cannot be fully integrated in a traditional method, the method comprises the following steps: acquiring a spatial transcriptome data set and a single-cell RNA sequencing data set, and performing data preprocessing on the acquired data sets; and carrying out data interpolation on the preprocessed spatial transcriptome data, and carrying out cell type deconvolution in combination with single-cell RNA sequencing data. And constructing a deep learning model based on the graph convolutional network. And training a deep learning model according to gene expression information, spatial position information and cell type information of the spatial transcriptome data after cell type deconvolution by using a self-supervised contrast learning strategy. And performing spatial domain identification on the to-be-detected data based on the trained model.

The present disclosure provides a method for analyzing a polyribonucleotide, wherein the polyribonucleotide is an mRNA molecule with a poly(A) tail, said method comprising: (a) obtaining a labeled polyribonucleotide by GI tailing; followed by (b) providing a second molecule comprising a first primer recognition sequence followed by a sequence of C nucleotide residues linked to a sequence of T nucleotide residues; followed by (c) obtaining a complex of said labeled polyribonucleotide and said second molecule; followed by (d) obtaining an extended second molecule by (d1) extending the 3′ end of the second molecule by synthesizing a sequence that is complementary to the labeled polyribonucleotide; followed by (d2) extending the 3′ end of the second molecule by adding at least 1 to 5 C nucleotide residues, followed by a second primer recognition sequence; said method providing information on entire polyribonucleotides, in particular mRNA isoforms and their respective poly(A) tails.

The invention relates to the technical field of biology, in particular to a nucleic acid aptamer combined with TIDG6 protein, application and a screening method of the nucleic acid aptamer, and the nucleic acid aptamer has a nucleotide sequence as shown in SEQ ID NO.1; or a nucleotide sequence which has at least 30% homology with SEQ ID NO.1 and is combined with TIDG6 protein; the nucleic acid aptamer is an RNA sequence transcribed by a nucleotide sequence as shown in SEQ ID NO.1, or an RNA sequence transcribed by a nucleotide sequence as shown in SEQ ID NO.1. The nucleic acid aptamer is small in molecular weight, stable in chemical property, easy to store and mark, capable of being combined with TIDG6 protein with high affinity, very good in specificity, capable of being used in the aspects of detection, diagnosis, imaging, treatment and the like and wide in application prospect.

The invention relates to the technical field of cell data analysis, and discloses a single-cell transcriptome data and text description conjoint analysis method based on a multi-modal language model, which comprises the following steps: acquiring a single-cell RNA sequencing expression matrix and a corresponding cell text description, preprocessing the single-cell RNA sequencing expression matrix and the corresponding cell text description, and analyzing the single-cell transcriptome data and the corresponding cell text description; according to the method, a multi-modal data set is constructed, deep fusion of gene expression data and text knowledge is realized by constructing a double-model and cross-modal projection module, limitation of a single mode is avoided, a gene expression value and an index sequence are reserved during preprocessing, a rough coding mode is changed, and the cell type identification accuracy is improved; based on a pre-training strategy of comparative learning, matching learning and a cross-modal projection module, fine-grained cross-modal information interaction and sharing are realized, and cross-modal task effects of text generation cells or cell generation texts and the like are optimized.

The invention belongs to the field of data processing, and discloses an RNA secondary structure prediction method and a related device, and the method comprises the steps: converting an RNA sequence into a one-hot coding form, and carrying out the Kronecker inner product operation to obtain a first tensor; obtaining implicit pairing information between basic groups in the RNA sequence to obtain a second tensor; combining to obtain an input tensor, and calling a pre-trained DUMFold model to obtain a predicted contact matrix of the RNA sequence; optimizing the prediction contact matrix of the RNA sequence based on the physical constraint of the RNA structure to obtain a binary contact matrix of the RNA sequence; wherein according to the DUMFold model, each layer of the DUMFold model is set to be of a double-branch structure, and double-branch fusion is carried out by adopting a dynamic gating fusion mechanism; the first branch is a fully connected convolutional neural network; and the second branch comprises a visual state space module and a two-dimensional selective scanning module. An RNA secondary structure prediction model with good performance and strong universality is realized, and the secondary structure prediction accuracy of a long RNA sequence is improved.

The present disclosure provides compositions and methods for the targeted modification of RNA molecules by RNA prime editing. The compositions and methods may be conducted invitro or in vivo within cells (e.g., human cells) for the therapeutic correction of disease-causing mutations and / or installation of motifs or mutations in RNA molecules of interest as a tool for scientific research. The disclosure provides compositions and methods for conducting RNA prime editing of a target RNA molecule (e.g., an RNA transcript) that enables the incorporation of one or more nucleotide changes and / or targeted mutagenesis of a target RNA molecule. The nucleotide change can include a single-nucleotide change, an insertion of one or more nucleotides, or a deletion of one or more nucleotides. More in particular, the disclosure provides a variety of configurations of the RNA prime editors each comprising a nucleic acid programmable RNA binding proteins (napRNAbp), such as Cas13, and an RNA-dependent RNA polymerase (RDRP), which are provided as fusion proteins or which can be separately provided in trans. The RNA prime editors are guided to a target RNA site by a guide RNA, which can be a rpegRNA that includes a template region for the synthesis of an RNA sequence to be installed on the RNA molecule attached to an available 3′ terminus. In others embodiments, the RNA template can be provided in trans.

The invention discloses a single-cell RNA sequencing data classification method and system based on deep learning. The invention provides a multi-scale neural network based on an improved Transform. The multi-scale neural network is used for classifying preprocessed single-cell RNA (Ribonucleic Acid) sequencing data. The multi-scale neural network integrates a multi-scale attention mechanism, relative position coding and a gating feedforward network, not only can the multi-level dependency relationship of data be captured, but also noise interference can be effectively reduced through feature screening. Compared with a traditional convolutional neural network or a classical Transform, the multi-scale neural network provided by the invention shows stronger robustness and generalization ability in sequence modeling and classification tasks. Through wide tests on a plurality of public data sets, the proposed multi-scale neural network shows better performance in a cell category classification task.

The invention relates to a cell differentiation trajectory inference method based on deep hyperbolic manifold embedding. The method comprises the following steps: acquiring single-cell RNA sequencing data; introducing hyperbolic space embedding with negative curvature, performing feature extraction on the single-cell RNA sequencing data through a deep feature extraction network, and mapping extracted high-dimensional features to a low-dimensional hyperbolic space; through track inference in a low-dimensional hyperbolic space, transformation paths of cells from progenitor cells to various differentiation end cell types are constructed, and key genes and pathways are evaluated by utilizing a sensitivity analysis method. The method has the beneficial effects that by introducing hyperbolic space embedding with negative curvature, the model can better capture the hierarchical structure of data, and the accuracy and consistency of differentiation tracks are enhanced. Moreover, high-dimensional single-cell sequencing data can be processed, the limitation of a traditional method under the high-dimensional condition is overcome, and the applicability of the model is improved.

The invention relates to the technical field of tumor neoantigen detection, and particularly discloses a neoantigen prediction method and system for a malignant solid tumor, and the method comprises the following steps: obtaining a tumor sample and a peripheral blood sample, and carrying out DNA and RNA sequencing; analyzing and processing to obtain DNA (Deoxyribonucleic Acid) sequencing data and RNA (Ribonucleic Acid) sequencing data; analyzing to obtain an HLA typing result and a sequence of the mutant protein; calculating to obtain related characteristic values of the new antigens; calculating a new antigen score; and intercepting the first N pieces with the highest scores to obtain a neoantigen prediction result of the malignant solid tumor. Starting from the action mechanism of the new antigens, according to key steps of the prediction process, a plurality of characteristic values are selected, a hard filtering index is not adopted, a product mode is adopted, and the score of the new antigens is calculated according to the score of the relevant characteristic values of the new antigens.

The invention relates to antisense oligonucleotides that are capable of bringing about specific editing of a target nucleotide (adenosine) in a target RNA sequence in a eukaryotic cell, wherein said oligonucleotide does not, in itself, form an intramolecular hairpin or stem-loop structure, and wherein said oligonucleotide comprises a non-complementary nucleotide in a position opposite to the nucleotide to be edited in the target RNA sequence.

The invention relates to the technical field of medical informatics, and particularly discloses a construction method of a breast cancer lung metastasis prognosis prediction model. Comprising the following steps: (1) acquiring breast cancer in-situ and lung metastasis single cell RNA sequencing data by a GEO database, and preliminarily screening differential genes; (2) acquiring clinical information and gene expression profile information data of the breast cancer patient by a TCGA database, performing single-factor COX regression analysis, and screening genes related to the total survival rate; and (3) screening genes related to the total survival rate through LASSO regression to obtain six model genes, and calculating a risk scoring formula according to the expression quantity of the six model genes to complete the construction of the breast cancer pulmonary metastasis prognosis prediction model. According to the invention, scRNA-seq technical data from GSE202695 and a TCGA database are combined to develop a breast cancer lung metastasis prognosis prediction model based on six model gene characteristics of breast cancer lung metastasis.

The invention discloses a knowledge-driven and data-driven magnaporthe oryzae AVR (Automatic Voltage Regulation) gene RNA (Ribonucleic Acid) secondary structure generator, which is characterized in that an on-chip memory of an ARM (Advanced RISC Machines) chip and an external DRAM (Dynamic Random Access Memory) chip are adopted for respectively storing RNA original data and intermediate results; an FPGA chip is adopted to realize mapping from an RNA sequence to a knowledge graph, and features such as base pairing are fused through LUT; a multi-core AI chip is adopted, an encoder-decoder network is executed in parallel, and knowledge and data are fused to generate a secondary structure; and the VAE-GAN network is executed in a streamlined manner through an AI chip, the quality of the generated structure is evaluated and iteratively optimized, and an accurate RNA secondary structure is generated. The invention further discloses a knowledge-driven and data-driven fused magnaporthe oryzae AVR gene RNA secondary structure prediction method, RNA physicochemical characteristics are utilized, deep learning, transfer learning and knowledge distillation technologies are combined, and the prediction accuracy and operation efficiency are improved.

The present disclosure, in some aspects, relates to antisense oligonucleotides (ASO) for use in the prevention or treatment of a disease or a condition associated with low- density lipoprotein (LDL) in a subject. In some embodiments, the ASO effects site-directed adenosine-to-inosine (A-to-l) editing of a target adenosine in a target RNA sequence derived from a sequence of an endogenous low-density lipoprotein receptor (LDLR) gene such that: a) the modified LDLR protein has: (i) reduced binding to the inducible degrader of the LDLR protein (IDOL); (ii) increased stability; (iii) improved resistance to IDOL-mediated degradation; (iv) increased LDLR protein expression; and / or (v) increased activity or function to take up LDL; and / or b) editing of the 3'-untranslated region (UTR) of the target RNA leads to an increase in LDLR protein expression and / or stability.

A reference model DRFORMER for downstream tasks of an RNA sequence comprises the following steps: S1, constructing 24-dimensional image visual features based on structural features, distance features and base pairing features of the RNA sequence; s2, pre-training the visual features of the image by using a SWIN model, and generating a visual reference model SWIN-RNA (Ribonucleic Acid); s3, the visual reference model SWIN-RNA is combined with an RNA sequence model, multi-modal feature fusion is achieved through a self-attention module and a cross attention module, and then a multi-modal reference model DRFORMER is constructed and used for RNA downstream task analysis. According to the scheme, the analysis and prediction performance of the reference model for downstream tasks can be improved.

The invention discloses a method for predicting an RNA (Ribonucleic Acid) methylation site, which is based on a multi-modal feature fusion and semantic vector embedding technology and is used for remarkably improving the recognition precision of an m7G modification site. The method comprises the following steps: firstly, constructing an RNA sequence data set containing positive and negative samples, and dividing the RNA sequence data set into a training set and an independent test set according to a predetermined proportion; then, multi-modal features are extracted through One-hot coding (One-hot), nucleotide chemical property coding (NCP), electron-ion interaction potential coding (EIIP) and local nucleotide composition coding (ENAC), and context semantic information of a nucleotide sequence is obtained in combination with a DNA2Vec model; according to the model, a multi-modal feature fusion path (MRF) and a DNA2Vec embedding path are adopted, after feature dimension compression is carried out through a full connection layer, a Transform encoder is used for capturing a long-range dependency relationship, and a prediction probability is calculated through a Sigmoid activation function. In the optimization process, batch normalization, Dropout and Adam optimizers are adopted, and the binary cross entropy loss function is minimized. Finally, the performance of the model is evaluated through five-fold cross validation, and the generalization ability of the model is verified on an independent test set. According to the method, through multi-source feature fusion and hierarchical modeling, the analysis capability of sequence information is remarkably improved, and an accurate calculation tool is provided for RNA modification prediction.

The invention provides a method for treating COVID-19 patients. Therapeutic antibodies of certain types of antibodies to COVID-19 are identified early by targeted diagnostic testing can be given to other COVID-19 patients to enhance their recovery. This invention also identifies bacterial pathogens, particularly those that are resistant to antibiotics, faster. Data from deep RNA sequencing of human blood is used to create a faster diagnostic test for infections and associated antimicrobial resistance. This invention thus also allows for appropriate antibiotic treatment at an earlier time point. The invention further provides improvements to RNA sequencing that can make RNA sequencing useful for diagnostic and therapeutic methods of treating sepsis. RNA sequencing can identify bacterial pathogens directly from the blood of patients with those infections.

The invention relates to a training method of an RNA sequence modification prediction model, a prediction method and a related device. The method comprises the following steps: acquiring modification site sequencing data of an RNA sequence; on the basis of sites with target chemical modification and sites without target chemical modification in the modification site sequencing data, a plurality of positive sample sequences with different lengths and a plurality of negative sample sequences with different basic groups added at the central position are obtained respectively, so that a multi-level classification database is constructed; inputting each sample sequence in the multi-level classification database into a to-be-trained modification prediction model to obtain a prediction result of a modification site of each sample sequence; and on the basis of the prediction result and the actual condition of each sample sequence, updating model parameters of the to-be-trained modification prediction model until the trained modification prediction model is obtained. Compared with the prior art, the method can improve the training efficiency and precision of the model.

The invention provides a single-cell drug sensitivity prediction algorithm based on multi-modal reinforcement learning, and aims to solve the problem that the accuracy of a traditional drug sensitivity prediction method is limited. According to the system, multi-modal bioinformatics data including cell line gene expression data, proteome data, metabolome data, drug reaction data and single-cell RNA sequencing data are integrated, and feature extraction, fusion and selection are performed by using a deep reinforcement learning technology, so that high-precision prediction of single-cell drug sensitivity is realized. The algorithm mainly comprises five core modules: a multi-modal data integration and preprocessing module, a deep reinforcement learning feature extraction and fusion module, a dynamic feature selection and importance evaluation module, a cross-domain migration and adaptive learning module and a deep reinforcement learning prediction model and performance optimization module. The modules work cooperatively, potential features can be learned automatically from multi-modal data, a feature fusion strategy can be optimized, a key feature subset can be selected dynamically, and a model structure and parameters can be adjusted adaptively according to features of target domain data, so that prediction accuracy and stability are improved remarkably. Experimental results show that the algorithm shows significant advantages in multiple evaluation indexes, and compared with a traditional prediction method, the algorithm has higher prediction accuracy and wider applicability. Therefore, the invention provides a novel and efficient drug sensitivity prediction tool for the fields of personalized medical treatment and drug research and development, and has important practical application value.

The invention discloses an RNA G-quadruplex prediction method and system based on a pre-training model and an RNA secondary structure, and the method comprises the steps: obtaining the position information of human rG4 on a human transcript; for each sequence, filling the same length to two sides according to the position coordinates of the sequence, so that the total length reaches a set length value; obtaining human cDNA sequence data as a reference sequence, and extracting rG4 data containing flanking sequence information from the cDNA sequence according to the filled sequence coordinates as a positive sample sequence; disorganizing each positive sample sequence to obtain a negative sample sequence; rNA secondary structure features of each sample sequence are generated; extracting sequence features of the sample sequence by using a pre-training model; and inputting the sequence features and the RNA secondary structure features into a prediction model for model training. According to the scheme, the secondary structural features of the RNA sequence are utilized, the secondary structural features are introduced as auxiliary information, and the prediction performance of the model can be remarkably improved.

Methods for facilitating drug discovery in various models and proposing single cell-type-specific drug candidates, and predicting cellular drug sensitivities. Predictions comprise obtaining bulk RNA sequence data, wherein bulk RNA seq data is pan-cancer cell line transcriptomic data from one or more sources; obtaining single cell RNA sequence data (scRNA-seq) from one or more sources; integrating the bulk RNA sequence data and scRNA-seq data; capturing similarities between the bulk data and the scRNA-seq data based on a shared drug response relative gene and using canonical correlation analysis; extracting one or more drug-response relevant features and selecting a pharmacogenomic subspace for accurately predicting a single-cell drug response.

The invention provides a plant viral vector comprising a heterologous nucleotide sequence that encodes, or corresponds to the RNA sequence of a functional minimal miRNA precursor, wherein said RNA sequence consists of S1 that is of a length of 56-400 ribonucleotides and that comprises regions R1, R2, R3, R4 and R5 directly linked to each other as indicated in formula (i): (i) 5'-R1-R2-R3-R4-R5-3', wherein: - R1 consists of the RNA sequence of a strand of a basal stem (BS) structure, - R2 consists of the RNA sequence of a miRNA, - R3 comprises an RNA sequence that has a length of at least 8 nucleotides, - R4 consists of an RNA sequence that base pairs with the RNA sequence of R2 to form a miRNA / miRNA* duplex, - R5 consists of the RNA sequence complementary to R1 in the BS comprising R1, and wherein the RNA sequence S1 forms a stem-loop structure where the stem comprises regions R1, R2, R4 and R5 wherein region R1 base pairs with region R5 and region R2 base pairs with region R4, and the loop comprises a sequence of at least 4 contiguous nucleotides of region R3. The invention also provides a virus encoded by or comprising the viral vector, a plasmid comprising or encoding the viral vector, a bacterium comprising the plasmid, a plant comprising the viral vector, the virus or the bacterium, a plant extract from the plant, and a method for obtaining the plant and a method for obtaining the extract.

The present disclosure is directed to a system 100 and a method 300 for predicting binding affinity of small molecules to Ribo-Nucleic Acid (RNA) targets The method comprises of receiving an input from a user, comprising a sequence of an RNA target, information related to a small molecule and a sub-type of the RNA target. The method further comprises of predicting a binding affinity of the small molecule to the RNA target of the sub-type, using a prediction model. Generating the prediction model comprises of, obtaining a plurality of RNA sequence based features for a plurality of sub-types of a plurality of RNA targets, and a plurality of small molecule features for a plurality of small molecules. Generating the prediction model further comprises of identifying a plurality of non-correlated features from the plurality of RNA sequence based features and the plurality of small molecule features and generating a final feature combination from the plurality of non- correlated features for each sub-type of the plurality of sub-types of the plurality of RNA targets based on a quasi-binding affinity of each of the plurality of small molecules with each sub-type of the plurality of sub-types of the plurality of RNA targets. The final feature combination is used to generate the prediction model.

The invention discloses an RNA (Ribonucleic Acid) comprehensive classification deep learning prediction method based on graph representation, which comprises the following steps of: adopting an RNAImage graph representation strategy, constructing a template graph through a graph representation RNA data set, performing feature mapping on each RNA sequence in a data set for an RNA key classification prediction task by utilizing the template graph to obtain a representation graph, all the obtained representation graphs form a data set after coding processing; a double-line multi-channel convolutional neural network model is adopted, and through data set training after coding processing, an RNA classification training model is obtained; evaluating the RNA classification training model by using the evaluation matrix, and performing hyper-parameter optimization to generate an RNA classification prediction model; the RNA is classified and predicted, and the classification result is used for RNA function exploration and biological medicine development. The method provided by the invention is helpful to comprehensively predict RNA classification and further excavate the biological interpretability of the RNA coding potential.

According to the screening method for the potential protein biomarkers and the drug targets of the gastric cancer, Mendel randomization analysis in a proteome range is adopted, the genetic causal relationship between circulating plasma protein and the risk of the gastric cancer is evaluated, and finally the remarkably related protein is identified. According to the screening method of the potential protein biomarker and the drug target of the gastric cancer, provided by the invention, the potential association between circulating plasma protein and the gastric cancer is systematically revealed by integrating Mendel randomization, single-cell RNA sequencing analysis, space transcriptome analysis, virtual drug screening, molecular docking, molecular dynamics simulation and other methods.

A method for deconvolution of bulk RNA sequencing data is provided. Input comprising single-cell RNA sequencing (RNA-seq) data is obtained, and diverse datasets are generated based on a principle of same generating mixture probability such that each of the diverse datasets has a same cell type mixture proportion. The generated diverse datasets are used as input datasets for training a prediction model using machine learning, including creating a causal prediction model in which virtual samples are generated from the generated diverse datasets, and performing contrastive learning on the causal prediction model, wherein a contrastive loss is used for the learning of invariant features with respect to a measurement mechanism by which the RNA-seq datasets have been generated. The trained prediction model is used to predict the mixture of cell type quantities in the bulk RNA sequencing data. It can also contribute to predictive optimization regarding patient-specific risks.

The invention provides a viral vector comprising a heterologous nucleotide sequence that encodes or corresponds to an RNA sequence S1 that has a length of 52-350 ribonucleotides and that comprises regions R1, R2 and R3 directly linked to each other as indicated in formula (i):         (i)     5'-R1-R2-R3-3', wherein: - region R1 consists of the target site of a sRNA, - region R2 consists of an RNA sequence of 11 ribonucleotides, and - Region R3 consists of the RNA sequence of one or more siRNAs, wherein when R3 is formed by more than one siRNA sequence, the siRNA sequences are directly linked to each other. The invention also provides a virus encoded by or comprising the viral vector, a plasmid comprising or encoding the viral vector, a bacterium comprising the plasmid, a plant comprising the viral vector, the virus or the bacterium, a plant extract from the plant, and a method for obtaining the plant.

The disclosure relates to compositions, systems, and methods related to repair RNA sequences, as well as related systems for trans-splicing a target nucleic acid using a repair RNA sequence. Various compositions and systems are described herein, including compositions comprising a repair RNA sequence comprising one or more exons and / or introns, a splice donor and / or splice acceptor, wherein the repRNA is suitable for trans-splicing. The compositions, systems, and methods described herein are suitable for targeting one or more Usher syndrome-associated genes, and treating Usher syndrome.