439 results about "RNA Sequence" patented technology

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 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 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.

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.

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 invention provides a library building method for direct sequencing of single-stranded nucleic acid, the library building method comprises providing a to-be-detected sequence fragment, and a preparation method of the to-be-detected sequence fragment comprises cutting a double-stranded molecular sequence formed by complementary pairing of internal bases of the single-stranded nucleic acid by using transposase so as to form the to-be-detected sequence fragment. Compared with an existing library building method of RNA direct sequencing, the library building method has the advantages that a pairing structure is formed by complementary bases of RNA or single-stranded DNA, and a transposase compound can be used for shearing and connecting the pairing structure. This is an attempt which has not been made before, and the library building efficiency is greatly improved through the library building mode.

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 discloses a sparse auto-encoder-based cell type deconvolution modeling method and system, and relates to the technical field of artificial intelligence technology and bioinformatics. Single cell RNA sequencing data is input into a trained sparse auto-encoder model to obtain a predicted cell type; the sparse auto-encoder model training process comprises the following steps: generating simulated bulk transcriptome data by using single-cell RNA sequencing data to construct a training set; constructing a sparse auto-encoder model, simulating bulk transcriptome data, generating a predicted cell type proportion through an encoder, and generating reconstructed bulk transcriptome data through a decoder according to the predicted cell type proportion; constructing a total loss function based on the KL divergence function, the reconstruction error and the prediction error so as to optimize trainable parameters in the sparse auto-encoder model; according to the deconvolution modeling method and system, the problems of low cell type prediction accuracy and low algorithm efficiency in the prior art are solved, and rapid and efficient bioinformatics analysis is realized.

The invention provides an RNA sequence classification method based on a Mangbar model and semi-supervised learning, and belongs to the field of bioinformatics. Firstly, multi-scale sparse features are extracted from an RNA sequence, high-dimensional features are compressed to low-dimensional potential space vectors through an encoder network, and L2 normalization is carried out to enhance feature separability. Secondly, a Mangbar model based on a selective state space model is introduced into a residual structure, and the long-range dependency relationship is efficiently modeled with low complexity; and finally, realizing unsupervised feature reconstruction and semi-supervised learning of supervised classification by constructing an encoder-decoder structure, and improving the generalization performance of the model under a small sample condition by adopting a weighted loss function. Experimental results show that the method significantly improves the F1 score in a classification task of multiple types of RNA sequences. According to the method, the long sequence modeling and labeling cost is reduced, and the method is suitable for the fields of RNA function prediction, biomedical research, disease diagnosis and the like.

The invention discloses a cross-graph modeling three-dimensional RNA inverse folding method based on multi-structure comparison, and the method comprises the steps: carrying out the multi-structure comparison of a target RNA structure and an RNA structure database at an offline stage, obtaining a closest similar structure, carrying out the alignment processing, extracting node and edge features from the target RNA structure, and generating a training set through cross-structure coding; the training module is used for training a sequence decoder; in the online stage, through a trained sequence decoder, real-time input and cross-structure coded features are decoded, and after sequence probability distribution is obtained, a design sequence is obtained through temperature coefficient sampling. According to the method, the multi-structure comparison technology and the graph neural network are combined, and the accuracy and diversity of RNA sequence design are remarkably improved by utilizing the evolutionary conservative property of the target RNA structure and the homologous structure of the target RNA structure.

Injectable compositions and methods of preparation, as well as therapeutic uses, of induced pluripotent stem cell (iPSC)-derived notochordal cell (iNC)-loaded microgels are provided. Microfluidic on-chip platform can be utilized to prepare microgels (or microgel particles / spheres) formed from block copolymers that exhibit reverse thermal gelation, so as to encapsulate iNCs. Also provided are preconditioned iNC-loaded microgels and iNCs in bulk hydrogel. Cell purity, identity, viability, sterility, and the stability of microencapsulated iNCs have been evaluated. Safety and efficacy of the compositions as therapeutic candidates has been tested via intradiscal injection in animal models of intervertebral disc (IVD) degeneration and discogenic low back pain. Biobehavioral testing, MRI, and immunohistochemical analyses were utilized to evaluate the regenerative potential and reproducibility of the compositions as therapeutic candidate. Single cell RNA sequencing of the treated IVDs may also reveal mechanism of action of the compositions.

The invention relates to the field of computational biology and bioinformatics, in particular to an RNA (Ribonucleic Acid) sequence screening and promoter function predicting and screening method based on machine learning, which comprises the following steps of: firstly, adopting a novel sequence algorithm based on a sliding window and a position weight matrix, and combining technologies such as k-mer frequency analysis and conservative motif recognition; candidate RNA sequences are efficiently identified from genomic sequences. Then, an integrated prediction model fusing deep learning and traditional machine learning is established, and multi-dimensional information such as sequence features, structural features, functional features and evolution features is extracted; and carrying out batch prediction on the candidate RNA sequences, outputting a function intensity score and providing credibility evaluation. And finally, screening an RNA sequence with an optimal prediction result based on a multi-objective optimization strategy, and designing an experimental verification scheme. According to the method, the prediction accuracy and the screening efficiency are improved, the experiment cost can be remarkably reduced, and an efficient promoter screening tool is provided for the fields of synthetic biology, gene therapy, biological pharmacy and the like.

The present invention provides nucleic acids and methods for making circular RNAs (circRNAs), and circular RNAs, compositions and methods of use thereof. The present invention uses selected self-splicing intron sequences (e.g., intronic sequences derived from self-splicing Group I or Group II introns) to produce circular RNAs. These self-splicing intron sequences described herein mediate efficient circularization of a linear RNA sequence.

The invention discloses an mRNA sequence scoring method and related equipment. 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 an initiation codon context and a termination codon context; inputting the key base sequence into a target scoring model, and processing to obtain a score of the mRNA sequence in the target index; wherein the target scoring model comprises a multi-modal RNA sequence coding 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. According to the method, a non-coding region and a coding region key region (start / stop codon context) are integrated as input, and a large language model trained based on mass data is used for processing, so that various complex factors such as sequence context information and secondary structures can be comprehensively considered, and the method can be widely applied to the technical field of gene sequence analysis.

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.

1. Name of the product of this design: the main body of the test kit. 2. Purpose of the product of this design: The kit and the main body of the kit can be used for sequencing operations, and are configured to contain one or more liquids, solutions, DNA, RNA, chemicals or other media and be used with an analytical unit for sequencing or biochemical analysis, wherein sequencing operations include genome sequencing, genome sequencing includes DNA or RNA sequencing, and other media include reagents. 3. The key point of the design of this product lies in its shape. 4. The picture or photo that best illustrates the design points: Stereoscopic drawing 1.

The present application relates to the technical field of bioinformatics, and particularly relates to a method for constructing a biological age prediction model based on DNA methylation. The method comprises the following steps: obtaining whole genome methylation sequencing data of a human peripheral blood sample; classifying cell subpopulations of the human peripheral blood sample, and performing single-cell RNA sequencing processing on each cell subpopulation to obtain single-cell sequencing data including lymphocytes, neutrophils and monocytes; performing tissue-specific analysis on CpG sites within a range of 2000 base pairs upstream and downstream of each cell subpopulation-specific transcription factor binding site according to the single-cell sequencing data and the whole genome methylation sequencing data to obtain candidate marker site data. The present application can provide strong support for early detection of accelerated aging, prediction of related disease risks and guidance of precision medicine.

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 present invention provides technology for inhibiting or suppressing proliferation of cells. Disclosed herein is a double-stranded RNA that has a first strand and a second strand which is complementary to the first strand, wherein the first strand has a main sequence which comprises 19-23 bases and in which a base at the 5'-end is guanine (G) or cytosine (C) and an additional sequence which comprises 2-4 bases and which is added to the 3'-end side of the main sequence. The main sequence is a portion of a base sequence that encodes semaphorin-3A, the portion including at least a portion of a base sequence that encodes a signal peptide region of the semaphorin-3A.

The present invention relates to the field of biotechnology, and discloses a dsRNA for efficiently controlling Phyllotreta striolata and its application. A dsRNA for efficiently controlling Phyllotreta striolata, the dsRNA is transcribed from an arylalkylamine N-acyltransferase gene, and the dsRNA has an RNA sequence as shown in SEQ ID NO.1. The arylalkylamine N-acyltransferase gene sequence is as shown in SEQ ID NO.2. By using the dsRNA for efficiently controlling Phyllotreta striolata and its application as described above, the provided target gene, the arylalkylamine N-acyltransferase gene, has specificity and is highly expressed in the digestive tissues of adult Phyllotreta striolata, which can reduce the difficulty of the dsRNA reaching the target tissue, improve the interference effect of the dsRNA on Phyllotreta striolata, and further improve the control effect of the dsRNA on Phyllotreta striolata.

The application discloses a kind of methods, systems, equipment and media for analyzing EML4-ALK fusion gene type, belong to the technical field of fusion gene detection.The method comprises obtaining RNA sequencing data of a sample to be tested;According to the primer combination coverage area, obtain the primer coverage area sequence file;The RNA sequencing data is compared with the primer coverage area sequence file for the second time;The number of sequencing data that can be aligned to the exon sequence of two genes at the same time is counted.Using the method and system of the application, unknown type of fusion gene can be accurately detected, which is more conducive to the development of downstream research on the related mechanism and function of fusion gene.

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.

The invention relates to a method for carrying out capping rate analysis on mRNA (messenger ribonucleic acid) by using an unmarked RNase H probe and LC-MS (liquid chromatography-mass spectrometry), and belongs to the technical field of biology. A DNA-RNA probe is designed according to a to-be-detected RNA sequence, RNA molecules are cut by utilizing the specificity of RNase H, short segments are separated by virtue of specific adsorption of a silica gel column, and the molecular weight and relative proportion of a cut RNA product are detected by utilizing an LC-MS method, so that the capping efficiency is determined, quantitative detection of the RNA capping efficiency is realized, radioactive contamination can be avoided, and the detection accuracy is high. The operation is simple and the cost is low.