43 results about "Snp data" patented technology

The invention provides a method for single cell classification and screening and a device therefor. The method comprises the following steps of carrying out comparison of reads obtained by sample sequencing and a reference genome, carrying out data filtration of a comparison result, determining a consistent genotype of all single cell samples according to filtered data, saving the consistent genotype of the all single cell samples into a SNP data set, extracting genotype files of loci corresponding to reference genome SNP data set positions from the saved SNP data set, selecting a cell mutation SNP locus, and carrying out cell classification and functional gene screening according to a genotype file of the cell mutation SNP locus. The method and the device avoid cell marking, solve the problem that the traditional single cell classification method can not realize classification of a certain cell subset having no corresponding specific markers, realize complete analysis of genetic variation information of a single cell genome, and greatly improve the accuracy of cell subset classification.

The invention discloses a feature selection method facing to SNP (Single Nucleotide Polymorphism) data, which specifically comprises the following steps: firstly, carrying out data pre-processing; secondly, removing unrelated SNP features by using a newly-designed Relief algorithm; thirdly, carrying out critical degree sorting on the SNP features by using an improved SVM-RFE algorithm; and finally, screening the critical SNP sorting by using cross validation. The feature selection method has the beneficial effects that the advantages of Filter feature selection and Wrapper feature selection are combined, and a secondary division method is used in the machine learning process, so that the problems of a high-dimensional small sample in the SNP feature selection and a SNP pathogenic combination mode are solved, and the analysis efficiency and the accuracy are improved.

The present invention relates generally to the detection and identification of various forms of genetic markers, and various forms of proteins, which have the potential utility as diagnostic markers. By determining the level of a plurality of biomarkers and genetic markers in a patient sample, and combining the obtained values according to a predefined formula, it is possible to forecast if it is likely that the prostate cancer patient will require active therapy like radiation therapy or surgery. A method based on a redundantly designed combination of data is disclosed for estimating if prostate cancer is aggressive or indolent. Said method combines SNP data to form a composite value, wherein at least 5% of the SNPs can be disregarded.

The invention relates to a genome information assisted breeding method for parent selection by means of SNP clustering and PAV variation. The essence of the genome information assisted breeding method is to obtain genome sequencing information of candidate parents with the help of genomic and bioinformatics methods; on one hand, a high-quality SNP data set is obtained through sequence alignment, a genetic distance matrix of the candidate parents is calculated, and the affinity between the candidate parents is judged with the help of a clustering tree; on the other hand, a Denovo assembled candidate parent contig is positioned to a reference genome, and the PAV variation of candidate parent target trait related genes is obtained according to the physical location. By combining the PAV variation and affinity information based on SNP, a parent subset is screened out from a large number of candidate parents for phenotype identification; finally, a selected breeding parent is determined by combining the phenotype identification result of the parent subset. The genome information assisted breeding method belongs to the field of rice molecular breeding, the range of the materials for phenotype identification can be effectively narrowed from the large number of the candidate parents, the workload of phenotype identification is reduced, and the breeding work efficiency is improved.

The invention belongs to the technical field of data processing, and discloses a method for establishing a correlation model between SNP pathogenic factors and diseases, collecting sample data sets corresponding to current SNP pathogenic factors; absolutely dividing the sample data sets according to initial values; based on machine learning Methods SNP pathogenic factors and disease association modeling; modeling results accuracy evaluation; determine the SNP pathogenic factors and disease association model. The present invention reduces the degree of interaction among various SNP pathogenic factors through the method of absolute division, so that the established correlation model between each SNP pathogenic factor and the disease is more accurate. The present invention is simple to operate, only needs to input the original SNP data and all SNP pathogenic factors, and can obtain a more accurate correlation model between each SNP pathogenic factor and the disease.