187 results about "RNA Sequence" patented technology

The application discloses an exosome small non-coding RNA molecule marker and application thereof, and belongs to the field of tumor liquid biopsy and molecular diagnosis. The exosome small non-coding RNA molecule marker is at least one of tRNA-GlyGCC-5 and sRESE; the nucleotide sequence of the tRNA-GlyGCC-5 is shown as SEQ ID NO:1; and the nucleotide sequence of the sRESE is shown as SEQ ID NO:2. The sRESE in the application is a brand new small non-coding RNA sequence, and is first discovered by the inventor, which is important for the marker of esophageal cancer and the research on disease progression mechanism. The tumor diagnosis kit provided by the application, which comprises the exosome small non-coding RNA molecule marker, is a liquid diagnosis monitoring kit with non-invasiveness, high sensitivity, high specificity and applicability to general survey or screening.

The present invention provides nucleic acids and methods for making circular RNAs (circRNAs), compositions comprising circular RNAs, and methods of use thereof. The present invention uses optimized self-splicing T4 bacteriophage td gene (T4td) intron sequences to produce circular RNAs. The optimized T4td intron sequences described herein mediate efficient circularization of a linear RNA sequence.

The invention provides an ischemic stroke auxiliary diagnosis system based on a peripheral blood T cell single cell transcriptome and application thereof, and the auxiliary diagnosis system comprises a peripheral blood mononuclear cell acquisition module, a single cell RNA sequencing module, a data processing module, a data analysis module and a result discrimination module. With a coding gene of a protein molecule significantly related to the occurrence of ischemic stroke as a target gene, a data processing module obtains the expression level of the target gene in each T cell; the data analysis module obtains a risk score of each T cell of the subject by using a built-in single cell risk scoring model, and weights the risk scores of all the T cells through distributed perception identification to obtain an individual risk score of the subject; and the result judgment module is used for classifying the subjects into ischemic stroke negative and ischemic stroke positive according to the individualized risk scores of the subjects. The auxiliary diagnosis system disclosed by the invention is high in ischemic stroke discrimination capability, and high in sensitivity and specificity.

Provided herein are compositions and methods for identifying the cell type origin of cells based on RNA sequencing (RNA-seq) data.The method provided herein may include: receiving a plurality of sequencing read counts; providing a set of detected cell types and a related gene set G for each cell type, such that each gene set G comprises a plurality of genes g; scoring the sequencing read counts to generate an assignment score; and assigning each cell among the plurality of cells to the cell type with the highest assignment score, thereby identifying the cell type origin of each cell.This method can be implemented, for example, using a computer system.

The present disclosure relates generally to (CRISPR) RNA (crRNA) for the precision silencing of transcripts. In some embodiments, the crRNA are enriched for guanosine (G) nucleotides at key spacer positions, which is useful in enhancing the silencing efficacy of otherwise inefficient crRNA, thereby expanding the targeting spectrum of Cas13 endonucleases, e.g., Cas13b and Cas13d. In other embodiments, the crRNA comprise a spacer sequence having at least one nucleotide mismatch relative to the target RNA sequence, wherein the target RNA sequence is a wild-type transcript and / or a variant transcript (e.g., a transcript comprising a single nucleotide variant (SNV)). The present disclosure also provides RNA editing systems comprising the crRNA described herein in complex a Cas13 effector protein and a target RNA sequence, methods for the selective targeting of transcripts encoding proteins that are difficult to target, or are not amenable to pharmacological targeting, e.g., oncogenic fusion transcripts or oncogenic transcripts comprising single nucleotide variant(s), and methods for the design and selection of potent crRNA.

The application discloses a dimension reduction processing method for single-cell RNA sequence data sets, and comprises the following steps: searching for k nearest neighbors (KNN) of each cell and counting RNN through the KNN; traversing the cells in descending order of RNN, and if the point is extracted, the KNN of the cell is removed until all cells are traversed, and then the anchor point cells obtained by sampling are output; constructing a KNN graph network of all anchor point cells, calculating the shortest path between all anchor point cells, and calculating their high-dimensional probability distribution; reducing all anchor point cells to a low-dimensional space and constantly updating the low-dimensional coordinates until the iteration condition is met and the iteration is terminated; for each non-anchor point cell that is not sampled, the mapping relationship between the low-dimensional coordinates before and after updating of d+1 anchor point cells is used to calculate the low-dimensional space coordinates of the non-anchor point cell; and integrating the low-dimensional coordinates of all anchor point cells and non-anchor point cells, and outputting the low-dimensional coordinates. The application greatly improves the dimension reduction efficiency and reduces the complexity of calculation.

This invention relates to an RNA sequence design method based on isovariant geometry and graph neural networks, comprising: first, RNA three-dimensional structure data and corresponding sequences; then, after dataset encapsulation and feature extraction, vector embeddings that can be processed by graph neural networks are generated; next, the data is fed into the Equiformer+GVP model for modeling, and the model outputs the probability distribution of four bases at each nucleotide position; finally, the bases are selected according to the probability distribution to form the predicted RNA sequence. Among these methods, E(3) isovariant constraints are introduced to achieve geometric consistency expression, and multi-scale geometric feature encoding is combined to enhance the model's understanding of local and global structures, significantly improving the model's physical rationality, prediction accuracy and universality; and the three-dimensional geometric skeleton of the RNA molecule is used as the only input, eliminating the dependence on secondary structures or external contact diagrams, and learning and generation are performed directly at the three-dimensional spatial coordinate level, thereby achieving significant improvements in physical consistency, geometric completeness and generalization ability.

The invention relates to the technical field of nucleic acid detection, and particularly provides a CRISPR-Cas12a cascade signal amplification system and application thereof. The system comprises a first-stage sensing unit, a second-stage sensing unit and a third-stage sensing unit, wherein the first-stage sensing unit is configured to recognize a target to be detected and activate the trans-cleavage activity of first-stage Cas12a protein; an engineered scaffold RNA composed of an RNA sequence and a DNA sequence is cut, the obtained RNA sequence serves as an activator of a second-stage Cas12a protein, and the second-stage Cas12a protein obtains trans-cleavage activity, so that a fluorescence report probe is cut, and a signal is output. According to the system, an activation event of a first Cas12a is converted into an exponential signal output unit of a second Cas12a, and a cascade amplification network is formed. According to the design, background signal leakage is eradicated from a physical mechanism, and the detection sensitivity is remarkably improved. The detection method provided by the invention is low in cost, high in sensitivity and high in reaction speed.

The present invention relates to a biomarker for diagnosing and prognostically predicting diabetes mellitus associated with pancreatic cancer and a use thereof, and more particularly, to a biomarker composition for diagnosing and prognostically predicting diabetes mellitus associated with pancreatic cancer, a kit and an information providing method, which comprise a preparation for determining the expression level of REG4 (REGEN 4) protein. Furthermore, the present invention relates to an information providing method for providing necessary information for the diagnosis and prognosis of diabetes-associated pancreatic cancer by measuring the level of REG4 in a biological sample isolated from a subject suffering from diabetes-associated pancreatic cancer, and for determining the prognosis of diabetes-associated pancreatic cancer by patient queue analysis, single-cell RNA sequencing (scRNAseq) analysis, and organoid methods. Provided is a complex prognostic prediction of REG4 against diabetes accompanied by pancreatic cancer. Therefore, an individualized treatment method can be provided for a patient, and prognosis prediction and treatment method determination can be more reasonably carried out on an object with diabetes mellitus accompanied by pancreatic cancer.

Provided herein is an antisense oligonucleotide (ASO) for use in the prevention or treatment of a disease or a condition associated with ApoB in a subject, wherein 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 ApoB gene.

Described herein are methods for determining a sequence of a region of interest from an mRNA molecule. Sequenced polynucleotides can include a barcode region, a homopolymer region (e.g., a poly-A region), and a target region associated with the mRNA molecule. According to some methods, the barcode region omits the same base present in the homopolymer region. According to some methods, extension of the primer used for sequencing is stalled within the homopolymer region. According to some methods, sequencing flow cycles and the different barcode regions of the polynucleotides configured are such that the primer is extended to the end of the barcode region across the plurality of polynucleotides before being extended into the homopolymer region. According to some methods, two primers or a cleavable primer is used to separately sequence the barcode region and the target region.

This invention provides an organ fibrosis-related micropeptide, OFMP, and its applications. This type of micropeptide, OFMP, is a translational product with endogenous biological activity and stable existence, encoded by an open reading frame (ORF) in a long non-coding RNA. It is the first time that the long non-coding RNA encoding the micropeptide has been found to be significantly related to organ fibrosis and can encode the micropeptide. Synthetic peptides prepared based on the aforementioned OFMP micropeptide show significant inhibitory effects on several key indicators of organ fibrosis, including type I collagen α1 chain gene, α-smooth muscle actin, transforming growth factor β1, and connective tissue growth factor. This indicates that the OFMP micropeptide, the nucleotide sequence (ORF) encoding the micropeptide, and the long non-coding RNA sequence containing the nucleotide sequence have important clinical diagnostic and therapeutic value for organ fibrosis.

The use of different primer sets in reverse transcription incorporates tags allowing for the selective amplification of cDNA transcribed using the different primers. Primers targeting non-desired RNA sequences such as ribosomal RNA can be used to prevent subsequent amplification of cDNA transcribed from those non-coding fragments. Accordingly effective depletion of non-desired sequences after cDNA amplification can be achieved. Systems and methods of the invention have applications in whole-transcriptome analysis. Non-coding sequence targeting primers can include nucleotide analogs that, when enzymatically processed, prevent subsequent amplification. Library preparation can include single primer isothermal amplification (SPIA) techniques wherein an RNA sequence required for SPIA is included in random primers but is absent from primers targeting non-coding RNA.

The invention provides a feature gene selection method and system based on deep learning attribution analysis and beam combination optimization, and relates to the technical field of signal analysis, and the method comprises the following steps: obtaining single cell RNA sequencing data and batch RNA sequencing data; the method comprises the following steps: based on single-cell RNA sequencing data, obtaining an initial gene pool aiming at a plurality of disease subtypes through a consensus screening strategy fusing XGBoost multi-dimensional importance measurement and deep learning SHAP attribution analysis; performing first-stage optimization on the gene list of each subtype by taking the initial gene pool as a starting point and adopting beam combination search and based on a global discrimination objective function to obtain a first-stage optimized gene set; integrating the first-stage optimized gene set with the differential expression gene set of the batch RNA sequencing data, and carrying out second-stage optimization by adopting bundle combination search again to obtain a second-stage optimized gene set; and performing cross validation integration on the second-stage optimized gene set, and outputting a final feature gene set.

Disclosed are methods, systems, and computer readable media for predicting interactions for RNA sequences, including long noncoding RNA (IncRNA), which can be used, for example, as biomarkers. In an example method of predicting one or more binding sites between a ribonucleotide (RNA) sequence and a RNA binding protein, the method includes at a computing platform including at least one processor and memory: preparing sequence data for a plurality of overlapping RNA segments derived from a target sequence; inputting the sequence data for the plurality of overlapping RNA segments into a machine learning model trained to predict a probability of an interaction between a RNA sequence and a RNA binding protein occurring in a RNA sequence; outputting from the machine learning model one or more segments in the sequence data for the plurality of overlapping RNA segments falling above a probability threshold indicative of a region of interaction between a RNA sequence and a RNA binding protein; and predicting one or more binding sites between a RNA sequence and a RNA binding protein in the sequence data for the plurality of overlapping RNA segments derived from the target sequence based on the output from the model.

The invention relates to the technical field of biology, and discloses a linker element and a method for constructing an oxford nanopore direct RNA sequencing library by the linker element, and the linker element comprises a partial double-strand dump structure formed by annealing a first nucleotide strand S and a second nucleotide strand AS; the first nucleotide chain S sequentially comprises a ligase recognition motif region, a sample tag sequence region, a first universal sequence region and a second universal sequence region in the 5 '-3' direction; the second nucleotide AS sequentially comprises a third universal sequence region, a first universal sequence reverse complementary region, a sample tag sequence reverse complementary region, a ligase recognition motif reverse complementary region and a polyadenylate tail recognition region according to the 5 '-3' direction. According to the method, RNA can be efficiently utilized, the high sequencing data utilization rate and high resolution uniformity are achieved, the technical problems that an existing Oxford nanopore direct RNA sequencing technology cannot effectively utilize initial RNA, and a plurality of library samples cannot be effectively split on the same chip are solved, and the economic and time cost of a practical application end is reduced.

In the present invention, the accuracy of prediction of the therapeutic effect of the immune checkpoint inhibitor is sufficiently high.　Test data and training data for model training are prepared using the effect of administering an immune checkpoint inhibitor to a patient group and the results of RNA sequence analysis pertaining to the patient group (S11). A combination of a plurality of genes including the NLRC5 gene is selected (S12), and a model is trained using training data relating to the combination of genes (S13). The trained model is tested using the test data (S14). After training all combinations of genes (S15, Yes), a test result is output (S16). A biomarker comprising a combination of genes having high accuracy of prediction of therapeutic effect is determined (S17).

Provided herein are RNA sequences encoding a chimeric antigen receptor (CAR) comprising a B-cell maturation antigen (BCMA) binding molecule, along with related compositions and methods. In some embodiments, circular RNAs encoding the BCMA binding molecule or BCMA CAR, along with related compositions, methods, and precursors, are described herein. In some embodiments, these compositions comprise a transfer vehicle, e.g., comprising an ionizable lipid described herein.

The application discloses an mRNA sequence scoring method and related equipment, and the method comprises the following steps: acquiring an mRNA sequence to be scored; extracting a key base sequence from the mRNA sequence, wherein the key base sequence comprises a start codon context and a stop codon context; inputting the key base sequence into a target scoring model to obtain a score of the mRNA sequence on a target index; wherein the target scoring model comprises a multi-modal RNA sequence encoding module and a large language model, and the target scoring model is obtained based on unsupervised pre-training of transcription sample data and supervised fine-tuning of a score label data set corresponding to the target index. The application integrates non-coding regions and key regions of coding regions (start / stop codon contexts) as inputs, and processes them by using a large language model trained based on massive data, so that the application can comprehensively consider various complex factors such as sequence context information and secondary structure, and can be widely applied to the technical field of gene sequence analysis.

Compositions and methods comprising artificial poly (A) sequences having at least one cytosine are provided. The artificial poly (A) sequences described herein can be used in methods for producing a polypeptide of interest in a cell. The artificial poly (A) sequences, when joined to the 3' end of an RNA sequence, can effectively enhance protein expression from the RNA sequence.

The application provides an RNA sequence classification method based on a Mamba model and semi-supervised learning, and belongs to the field of bioinformatics. First, multi-scale sparse features are extracted from the RNA sequence, high-dimensional features are compressed into low-dimensional potential space vectors through an encoder network, and L2 normalization is performed to enhance feature separability. Second, a Mamba model based on a selective state space model is introduced in a residual structure to efficiently model long-range dependencies with low complexity. Finally, a semi-supervised learning of unsupervised feature reconstruction and supervised classification is realized by constructing an encoder-decoder structure, and a weighted loss function is used to improve the generalization performance of the model under small sample conditions. Experimental results show that the method significantly improves the F1 score in the multi-class RNA sequence classification task. The application reduces the cost of long sequence modeling and labeling, and is suitable for the fields of RNA function prediction, biomedical research and disease diagnosis.

The invention discloses a modified linear arRNA (Ribonucleic Acid) or cyclized arRNA and an application of the modified linear arRNA or cyclized arRNA in RNA (Ribonucleic Acid) editing. The invention provides an RNA (referred to as a modified arRNA), comprising n copies of arRNA truncated bodies; and n is a natural number greater than 2. The modified arRNA can be a linear RNA (Ribonucleic Acid) or a circular RNA (Ribonucleic Acid). To minimize the effect of bystanders without reducing the editing efficiency. The RNA sequence structure is modified by minimizing the specific sequence, the recruitment structural domain is combined, and the cyclization mechanism is optimized. Compared with the existing ADAR technology, the method and the device have the advantage that the editing efficiency is remarkably improved.

The application provides application of an adenosine A2B receptor as a target in a medicine for preventing and / or treating itch, and belongs to the technical field of biological medicines. Based on previously reported single-cell RNA sequencing data of a mouse dorsal root ganglion (DRG), expression of genes encoding main ligand ion channels is detected, Adora2b highly expressed in Neuron_10 is detected, and it is indicated that Adora2b and a receptor (adenosine A2B receptor) of Adora2b can participate in regulating itch. Experiments prove that the activity of the adenosine A2B receptor can be effectively blocked to prevent a histamine-induced scratching behavior of a mouse, and it is indicated that the A2B receptor participates in histamine-induced itch sensation. The application also provides application of a reagent for inhibiting biological functions of the adenosine A2B receptor in preparation of a medicine for preventing and / or treating itch, and provides a new means for prevention and treatment of itch.

The invention relates to a single-cell RNA (Ribonucleic Acid) sequencing data clustering method, device and equipment based on graph structure information fusion and a medium, and the method comprises the following steps: respectively calculating soft distribution matrixes corresponding to cell attribute representation, cell structure representation and consensus embedding representation, and deriving target distribution based on the soft distribution matrixes of the cell consensus embedding representation, all parameters of a zero-expansion negative binomial autoencoder, a graph autoencoder and a cross-modal fusion module are jointly optimized by minimizing the KL divergence between target distribution and each soft distribution matrix, iterative training is performed until the model reaches a preset convergence condition, and optimized cell consensus embedding representation is determined; and carrying out clustering analysis on the optimized cell consensus embedded representation by adopting a preset K-means clustering algorithm to obtain a cell type tag corresponding to the gene expression data of single cell RNA sequencing. According to the method, the problems of zero expansion, sparsity and over-dispersion characteristics of single-cell RNA sequencing data can be solved.

The invention provides a peanut bacterial wilt resistant gene AhNHL24 as well as an identification method and application thereof, and belongs to the technical field of plant genetic engineering. The peanut disease resistance related gene AhNHL24 is separated from peanuts, the DNA sequence of the peanut disease resistance related gene AhNHL24 is shown as SEQ ID NO: 1, and the amino acid sequence of protein coded by the gene AhNHL24 is shown as SEQ ID NO: 2. According to the preparation method of the peanut disease-resistant gene, the AhNHL24 gene is separated and identified through Illumina RNA sequence and comparative transcriptome analysis, and transcription mode analysis and plant expression analysis find that the AhNHL24 gene can improve the resistance of plants to ralstonia solanacearum, so that the invention determines that the peanut AhNHL24 gene can be used as an important gene for peanut bacterial wilt resistance breeding.

The application relates to a method and device for identifying a modification site in an RNA sequence, computer equipment and a medium. The method comprises the following steps: obtaining sequence features, structure features of the RNA sequence to be identified, and a pre-trained modification site identification model; inputting the structure features into a structure convolution layer for convolution to obtain convolution structure features; in each attention encoding unit: inputting the sequence features into a query weight matrix, a key weight matrix and a value weight matrix respectively for linear transformation, thereby obtaining query features, key features and value features; fusing the query features, the key features and the convolution structure features to obtain first fusion features; fusing the first fusion features and the value features to obtain second fusion features; splicing the second fusion features output by all the attention encoding units to obtain spliced features; and performing feature adjustment on the spliced features based on a feature adjustment module to obtain an identification result. The method can improve the accuracy of the identification result.

This disclosure provides a method, apparatus, medium, and electronic device for predicting RNA-protein interactions; relating to the field of artificial intelligence technology. The method includes: acquiring an RNA-protein pair to be predicted; extracting features from the RNA-protein pair to obtain sequence features; vectorizing the RNA-protein pair to obtain RNA sequence representation vectors and protein sequence representation vectors; based on the sequence features, RNA sequence representation vectors, and protein sequence representation vectors of the RNA-protein pair, using an interaction prediction model to obtain predicted interaction values ​​for the RNA-protein pair; and determining the interaction between the RNA and protein based on the predicted interaction values.

Described herein are techniques for predicting whether a subject will experience an immune-related adverse event (irAE) in response to administration of an immune checkpoint inhibitor (ICI) therapy. In some embodiments, the techniques include: determining a likelihood that the subject will experience the irAE in response to administration of the ICI therapy, the determining comprising: performing: (a) processing clinical data using a first machine learning (ML) model to output a first likelihood that the subject will experience the irAE, (b) processing RNA sequencing data using a second ML model to output a second likelihood that the subject will experience the irAE, and / or (c) processing immune receptor data using a third ML model to output a third likelihood that the subject will experience the irAE; and processing the first, second, and / or third likelihoods using a fourth ML model trained to predict the likelihood that the subject will experience the irAE.

Compositions and methods for targeting pre-determined DNA or RNA sequences in plant pests are provided The methods result in the targeted elimination of plant pests that comprise the pre-determined DNA or RNA sequence(s). Compositions comprise a Cas12a2 protein, or a polynucleotide encoding the same, and at least one guide polynucleotide, or a polynucleotide encoding the same, wherein each guide polynucleotide comprises a portion complementary to a pre-determined target sequence of a plant pest. Methods to use these compositions to selectively target and eliminate plant pests that harbor the targeted DNA or RNA sequence(s) are described herein.

The invention discloses a nucleic acid molecule for inhibiting Zika virus, a pharmaceutical composition containing the nucleic acid molecule and application. The invention develops an anti-virus strategy for realizing the target host protein based on the RNA structure by changing the bottom-layer anti-virus logic. The MSI1 binding site in the nucleic acid molecule is highly conserved in all Zika virus strains, and the nucleic acid molecule has wide applicability. Meanwhile, the nucleic acid molecule can target an MSI1 high-expression region (namely virus-susceptible tissue), the problem of poor tissue specificity in existing delivery is solved, and the nucleic acid molecule is higher in targeting property, so that the nucleic acid molecule can cope with nervous system infection and pregnancy infection. In addition, the sequence design is relatively simple, and based on natural RNA sequence optimization, the modification demand is reduced, the natural affinity is high, and the chemical modification dependence is greatly reduced.