19 results about "Regulatory region" patented technology

The invention discloses a gene function prediction method based on semantic correspondence of a regulatory region. The method comprises the following steps: firstly, constructing an inter-species regulation semantic correspondence relationship data set, constructing an artificial intelligence model structure, then, constructing a cross-species semantic correspondence network, and finally, carrying out function annotation on a target gene or identifying a candidate gene with a specific function in a target species. The accuracy of the PhytoBabel model constructed by the method is obviously higher than that of other model structures. By utilizing the method disclosed by the invention, the genes ZmERF104 and ZmGRF16 for promoting the regeneration of the corn somatic embryos and the gene ZmNAC17 for inhibiting the regeneration of the somatic embryos, which cannot be found by the traditional method, are successfully identified.

The invention discloses a gene expression profile prediction method and device, electronic equipment and a storage medium, and relates to the technical field of artificial intelligence. Specific genetic variation and regulation region sequences in a peripheral blood sample are detected, and a standardized SNP genotype matrix and a regulation annotation vector are obtained; constructing a gene-pathway-disease-drug four-layer regulation and control network based on multi-source heterogeneous data, and determining a core node gene based on the constructed network; obtaining LD structure information and chromatin accessibility characteristics corresponding to the core node gene; and taking the standardized SNP genotype matrix corresponding to the core node gene, the regulation and control annotation vector, the LD structure information and chromatin accessibility characteristics as input of a pre-trained gene expression profile prediction model to obtain a gene expression profile prediction value of an individual corresponding to the peripheral blood sample in a specified brain region. Therefore, an accurate mapping relation between the blood gene expression data and the gene expression data of the targeted CNS tissue is established.

The invention belongs to the technical field of animal gene breeding, and particularly discloses a functional mutation site combination based on a bovine immune disease-resistant tissue regulation region and application. According to the chip, the effect of bovine genome mutation sites on the influence of the activity of an immune tissue genome regulation element is fully considered, and 9394 mutation sites (SNP) which highly interfere with the binding strength of bovine transcription factors are screened from a large number of bovine genome mutation sites and immune tissue epigenetic regulation data; each marker provided by the invention is more closely associated with gene expression regulation and phenotype, and cattle disease resistance related character marker-assisted selection or cattle disease resistance breeding can be carried out by adopting a functional mutation site combination based on a cattle immune disease-resistant tissue regulation region.

This invention describes a transgenic non-human animal model for testing postnatal conditionally inducible plasmalogen deficiency. The genome of the animal model comprises genes capable of regulating the plasmalogen biosynthesis pathway, and these genes have regulatory regions. Within the regulatory regions are at least one conditionally inducible gene editing site that, when edited, blocks gene expression. The genome also has nucleic acid editing sequences that are integrated into a locus separate from the gene encoding the gene product. The gene product, when conditionally induced, edits the gene editing site, ultimately downregulating or disrupting the plasmalogen biosynthesis pathway. Methods and uses comprising the transgenic non-human animal model are also provided.

The application discloses a primer pair, a method and a kit for detecting TUBB8 gene mutation. The primer pair is shown in SEQ ID NO:1 and SEQ ID NO:2, and a long fragment product covering a complete gene locus of the TUBB8 gene is specifically amplified. The method comprises the steps of extracting genomic DNA, long fragment PCR amplification, constructing a sequencing library, long read sequencing and gene variation analysis. The primer design of the application effectively avoids homologous sequence interference, solves the problem of high false positive rate in the prior art, has comprehensive detection range, can find point mutation, insertion and deletion, structural variation and regulatory region variation at one time, supports an efficient clinical sequential diagnosis path of "targeting TUBB8 first and then whole exon sequencing", and has significant accuracy, economy and clinical application value.

The invention belongs to the field of bioinformatics, and particularly relates to a chromatin accessibility and transcription factor interaction deep learning method. The method comprises the following steps: firstly, providing a gene expression prediction framework based on deep learning, and simulating a cis-regulation effect by constructing a three-dimensional interaction tensor of a cell * transcription factor * chromatin region; secondly, designing a neural network containing a learnable interaction weight matrix, dynamically modeling specific combination of transcription factors and a regulation and control region by utilizing an attention mechanism, and synchronously optimizing prediction precision and correlation by adopting a joint loss function; and finally, introducing a gene specificity training and data enhancement strategy to realize personalized modeling and robust prediction of different gene regulation and control modes. According to the method, an interpretable deep learning system is established, potential interaction of transcription factors and chromatin can be deduced from multiple omics data, and a new calculation tool is provided for analyzing a gene regulation mechanism and screening key regulation elements.

Provided are an artificial intelligence-based early cancer diagnosis method and device using a method of inputting information on a cell-free DNA distribution of a tissue-specific regulatory region to an artificial intelligence model learned to early diagnose cancer and analyzing the information, and an information providing device and a storage medium.SOLUTION: A method for providing information for early cancer diagnosis based on artificial intelligence includes extracting a nucleic acid from a biological sample to obtain sequence information, arranging the obtained sequence information in a reference chromosome sequence database, selecting a nucleic acid fragment of a regulatory region based on the arranged sequence information, generating the selected nucleic acid fragment as image data, and inputting the generated image data to an artificial intelligence model learned to distinguish a normal image and a cancer image, analyzing the image data, and comparing the image data with a reference value to determine the presence or absence of cancer.SELECTED DRAWING: Figure 1

Provided herein are synthetic monovalent or polyvalent single-stranded non-coding nucleic acid molecules comprising a non-coding molecule complementary to the coding or regulatory region of a target gene for regulating gene expression or splicing, wherein the 5' and 3' ends of the single-stranded nucleic acid are joined to create a structure of a single-stranded non-coding nucleic acid molecule without free ends.

This invention relates to the field of gene editing technology, specifically to a gene circuit-based specific gene expression system and module, a pharmaceutical composition, and its applications. The system includes a first vector and a second vector. The first vector includes a first expression cassette containing a cell-specific promoter, a coding sequence encoding a transcriptionally activated fusion protein, and a regulatory region sequence downstream of the coding sequence. The regulatory region sequence is configured to form a response element in the 3' untranslated region of the fusion protein's mRNA after transcription. This response element binds to a specific long non-coding RNA within the silenced cell, leading to the degradation of the fusion protein's mRNA. The second vector includes a second expression cassette containing an associated promoter that can be activated by the transcriptionally activated fusion protein, and a target gene downstream of the associated promoter. Advantages: This ensures that the target protein is expressed only in target cells and not in cancer cells, avoiding adverse effects on non-target cells or tissues, and reducing treatment risks and side effects.

An engineered nucleic acid for regulating levels of gene expression, including in cells is provided. The nucleic acid has a regulatory region with a transcription factor binding site upstream of a transcription start site and separated by a spacer and includes at least one of multiple distinct transcription factor binding sites, a recombination regulation system and / or nested excisable spacers and orthogonal recombination sites. Methods of regulating gene expression are also provided.

The application discloses a water body drug-resistant gene risk reduction evaluation method and system based on regulatory region integrity and DNA fragment threshold, and belongs to the field of bioinformatics. The application extracts DNA in a water sample to be tested, detects DNA fragment length distribution and regulatory region integrity of a target antibiotic resistance gene by using high-throughput sequencing, determines a proportion of fragments with a length less than L in residual DNA fragments after treatment according to the DNA fragment length distribution, and determines a proportion P of regulatory regions still remaining in broken fragments according to the regulatory region integrity. When the proportion of fragments with a length less than L meets a first preset threshold condition and P meets a second preset threshold condition, it is determined that the risk of the target antibiotic resistance gene has been effectively reduced. The application proposes an evaluation method combining the DNA fragment threshold and the regulatory region integrity, overcomes the defect of simply relying on the abundance reduction to evaluate the risk reduction, and has higher accuracy and practical application value.

An isolated and / or artificial pG1-x promoter, which is a functional variant of the carbon source regulatable pG1 promoter of Pichia pastoris identified by SEQ ID 1, which pG1-x promoter consists of or comprises at least a part of SEQ ID 1 with a length of at least 293 bp, characterized by the following promoter regions:a) at least one core regulatory region comprising the nucleotide sequences SEQ ID 2 and SEQ ID 3; andb) a non-core regulatory region, which is any region within the pG1-x promoter sequence other than the core regulatory region;wherein the pG1-x promoter comprises at least one mutation in any of the promoter regions and a sequence identity of at least 80% in SEQ ID 2 and SEQ ID 3, and a sequence identity of at least 50% in any region other than SEQ ID 2 or SEQ ID 3; and furtherwherein the pG1-x promoter is characterized by the same or an increased promoter strength and induction ratio as compared to the pG1 promoter, whereinthe promoter strength is at least 1.1-fold increased in the induced state as compared to the pG1 promoter, and / orthe induction ratio is at least 1.1-fold increased as compared to the pG1 promoter.

The present application belongs to the field of bioinformatics, and particularly relates to a chromatin accessibility and transcription factor interaction deep learning method. First, a gene expression prediction framework based on deep learning is proposed, and a three-dimensional interaction tensor of cell x transcription factor x chromatin region is constructed to simulate cis-regulation; second, a neural network containing a learnable interaction weight matrix is designed, an attention mechanism is used to dynamically model the specific binding of transcription factors and regulatory regions, and a joint loss function is used to simultaneously optimize the prediction accuracy and correlation; finally, gene-specific training and data enhancement strategies are introduced to realize personalized modeling and robust prediction of different gene regulation modes. The present application establishes an interpretable deep learning system that can infer potential transcription factor and chromatin interactions from multi-omics data, providing a new computational tool for analyzing gene regulation mechanisms and screening key regulatory elements.

The present disclosure provides methods and compositions for modulating expression of a target gene in a cell by reducing binding of an endogenous transcription factor to a regulatory sequence of the target gene. The method includes introducing into the cell a DNA binding polypeptide (DBF) that binds a sequence in regulatory region of a target gene bound by a transcription factor (TF), thereby displacing the TF and modulating expression of the target gene. The DBF may be designed to bind a sequence comprising the binding site for the TF and additional nucleotides present on one or both sides of the sequence. Accordingly, the DBF specifically binds to binding site for the TF in the target gene but not in other genes that are also regulated by binding of the TF but do not include the nucleotides present on one or both sides of the sequence.

The invention belongs to the technical field of genetic engineering, particularly relates to creation and application of a ZmRap2.7 regulatory region edition-based corn early blossoming and yield conservation material, and more particularly relates to a method for regulating and controlling corn early blossoming and yield conservation, a biological material used by the method and application of the biological material. The technical problem to be solved by the invention is how to prepare the corn with early flowering and yield conservation. In order to solve the technical problem, the invention provides a method for preparing the corn with the early blossoming and yield keeping functions, the method comprises a step of performing gene editing on a transcription regulation element of a ZmRap2.7 gene in target corn to obtain the corn with the early blossoming and yield keeping functions, and the transcription regulation element comprises a Vgt1 enhancer or / and a ZmRap2.7 promoter. According to the application, the expression of the ZmRap2.7 gene is specifically regulated and controlled by precisely regulating and controlling the Vgt1 enhancer or the ZmRap2.7 promoter, and the goal of early flowering and yield conservation in agricultural production is achieved.

The application belongs to the field of modern agricultural technology, and particularly relates to a gene, haplotype, KASP marker and application for regulating and controlling cold tolerance of rice seedlings OsDMY03 The application can significantly improve the survival rate of rice seedlings under low-temperature stress by knocking out or overexpressing the gene through gene editing technology, which indicates that the gene is a positive regulation factor for cold tolerance of rice seedlings. OsDMY03 In the regulatory region and exon region of the gene, one linkage disequilibrium block (LD BLOCK) is identified, and two favorable haplotypes Hap3 and Hap5 significantly related to cold tolerance of seedlings are screened. OsDMY03 The KASP molecular markers corresponding to the favorable haplotypes are developed, which can be used for efficient and accurate screening and creation of cold-tolerant rice germplasm, and provide important gene resources and molecular tools for rice cold-tolerance molecular breeding, and have important breeding application value.

The present invention relates to an artificial intelligence-based method for early cancer diagnosis, more particularly, to an artificial intelligence-based method for early cancer diagnosis using a method of inputting information on cell-free DNA distribution of tissue-specific regulatory regions into an artificial intelligence model trained to diagnose cancer early, and analyzing the information. The method for early cancer diagnosis according to the present invention diagnoses cancer early based on artificial intelligence using cell-free nucleic acid distribution of tissue-specific regulatory regions obtained by next generation sequencing (NGS), and has high accuracy and sensitivity and is commercially applicable, so that the method of the present invention is useful for early cancer diagnosis.

The application belongs to the technical field of molecular genetics, and particularly relates to a detection method of an insertion / deletion marker of a Shanbei white cashmere goat ASIC2 gene and application thereof. The application uses the designed amplification primer pair, takes the whole genome DNA of the cashmere goat to be detected as a template, amplifies the 5' regulatory region fragment (NC_030826.1:g.16746415_16746441) of the cashmere goat ASIC2 gene through PCR, and then performs agarose gel electrophoresis and sequencing technology to identify the genotype of the insertion / deletion polymorphism site of the cashmere goat ASIC2 gene NC_030826.1:g.16746415_16746441del. The results of the examples show that the different genotypes of the InDel site of the cashmere goat ASIC2 gene are significantly related to the chest depth and cross section height of the Shanbei white cashmere goat, and can be used as an effective DNA marker of the growth traits of the Shanbei white cashmere goat. Further, the DNA marker can assist in breeding excellent cashmere goats.

The invention discloses an SLC31A1-based castration-resistant prostate cancer prognosis prediction model, and belongs to the technical field of bioinformatics and tumor precision medical treatment. The model is realized through the following data processing flow: acquiring cross-time-point molecular and clinical data of a patient, calculating a functional activity integration value of each time point and constructing a time sequence curve by fusing a transcription expression quantity of SLC31A1 and a methylation level of a specific regulation and control region; extracting dynamic morphological features of the curve, and performing matching analysis on the dynamic morphological features and a bad prognosis mode feature library pre-stored based on historical data; and inputting the obtained matching degree, the current activity integration value and the clinical features into a decision function, outputting a quantitative disease progress risk level, and further providing a tendency suggestion of a treatment strategy. According to the method, the dynamic, quantitative and explainable accurate prediction of the disease progress risk of the castration-resistant prostate cancer patient is realized, and a basis is provided for individualized treatment decision.