8 results about "Cellular transcription" patented technology

The application discloses application of an IRS1 gene promoter region SNP site as a pig sexual maturity molecular genetic marker. The present application takes the IRS1 gene promoter region as a research object, and studies the correlation between the IRS1 gene promoter region and sow sexual maturity by using molecular and cell biology methods: it is found that g.128307611C>A and g.128307308C>T are significantly correlated with the age of sexual maturity of Duhei pig populations; then, the influence of different genotypes of the above sites on the activity of the IRS1 gene promoter is studied by transfecting different vectors. Further, the sow ovarian granulosa cells are taken as experimental materials, and experimental techniques such as transcriptome sequencing, total iron colorimetry and malondialdehyde colorimetry are used to study the influence of the IRS1 gene on the transcription level and ferroptosis of the sow ovarian granulosa cells, and it is found that the IRS1 gene influences the different genes of the cell transcriptome, and is mainly enriched in signal pathways such as iron ion homeostasis, and inhibits the ferroptosis of the cells.

The application discloses a single-cell transcriptome analysis method, system and storage medium, relates to the technical field of biological statistical data analysis, and comprises the following steps: receiving single-cell transcriptome sequencing data and analysis parameters corresponding to a current analysis task, and determining a hash value corresponding to the analysis parameters; comparing the hash value with a preset hash value, and determining whether a target preset hash value matching the hash value exists; if yes, taking an analysis result associated with the target hash value as a target analysis result of the current analysis task; if no, analyzing the single-cell transcriptome sequencing data based on the analysis parameters, generating the target analysis result, associating the target analysis result with the hash value, and outputting the target analysis result. The application realizes cache reuse and version isolation of single-cell transcriptome analysis results by calculating and comparing the hash value of the analysis parameters, and solves the technical problem that researchers need to repeatedly calculate due to result coverage in traditional analysis.

The application belongs to the technical field of biological medicine, and particularly relates to application of VSIG4 as a biomarker in evaluation of risk of lung fibrosis progression. The application first discovers and verifies that VSIG4 is significantly highly expressed in ILD patients with active inflammatory response, and the high expression is closely related to a signal of continuous progression of the disease by integrating peripheral blood exosome proteomics data and lung tissue single-cell transcriptome data. Based on the discovery, the application provides a kit and a detection method for detecting a VSIG4 protein level in peripheral blood. Data show that the PPF patients can be effectively identified by detecting the content or level of the exosome protein VSIG4 in peripheral blood, and the method has high sensitivity and specificity. The application provides a new non-invasive solution for early risk prediction and disease progression monitoring of lung fibrosis, and has important clinical value.

The application relates to the technical field of biological information, in particular to a single-cell transcription gene gene regulation network inference method, model, system and storage medium. A correlation vector of each gene pair in to-be-measured single-cell transcription gene data is determined, and each gene pair is represented in the form of a histogram; time characteristics in the correlation vector are captured through a time sequence convolution network, and space characteristics in the histogram are captured through a convolution network; the time characteristics and the space characteristics are weighted and fused to obtain fused characteristics; and a gene regulation network inference result of the to-be-measured single-cell transcription gene data is predicted based on the fused characteristics. The application aims to solve the problem of how to predict single-cell transcriptome data in a gene regulation network.

The application relates to a single cell perturbation prediction method based on a flow matching and mixed expert architecture. Single cell transcriptome data and perturbation information are acquired, input into a pre-trained single cell variational autoencoder, compressed to obtain low-dimensional latent representations of control group and perturbation group cells, and then optimal transport pairing is used to construct a mapping between the two groups of cells to obtain a target velocity vector of the flow matching model. Subsequently, a mixed expert enhanced flow matching generation model is built, the mapping relationship and the velocity vector are used as training data, prior knowledge is combined to form a physical-biological dual constraint loss function to complete training, and a perturbation prediction model is obtained. Finally, actual single cell transcriptome data is encoded into latent representations, and the model can output the complete evolution trajectory of the cell to the target perturbation. Compared with the prior art, the application has the advantages of breaking the limitation of unpaired data and establishing a mapping relationship conforming to physical and biological laws.

The application discloses a single-cell transcriptional program screening method based on donor stability, and the method comprises the following steps: obtaining single-cell transcriptional expression data set, donor identification information and pathological staging information, establishing the corresponding relationship between cells and donors and screening a donor subset; extracting a target cell set in the donor subset, calculating the continuous state coordinates of each cell based on a quasi-time sequence algorithm, and constructing a cell continuous state representation; modeling the gene expression dynamics based on the continuous state representation to obtain a trend vector, clustering the genes according to the trend similarity, and forming a candidate transcriptional program set; performing donor resampling in different stages and layers for the candidate transcriptional program set, aggregating the program values in the donor layer, establishing a fitting relationship with the pathological staging information to obtain an evaluation result, calculating the effect stability index, the direction consistency index and the donor coverage index of cross-resampling, constructing a donor layer scoring index, and evaluating the correlation and the distinguishing ability in different source verification data sets according to the donor layer.

This invention discloses the application of ginseng stem cell transcription factors and related biological materials in upregulating ginsenoside synthesis. This invention discovers that overexpression of ginsenosides in ginseng... WOX11 The gene effectively promotes the upregulation of expression of genes related to ginsenoside synthesis, significantly increasing the content of total ginsenosides, ginsenoside Rh1, and Re in transgenic hairy roots. This invention provides a new approach for creating transgenic hairy roots with high ginsenoside yield and also provides a superior biomaterial for the industrial production of ginsenosides based on ginseng cells.