92 results about "Allele frequency" patented technology

The invention relates to the technical field of molecular markers and animal genetic breeding, in particular to an SNP marker influencing the content of porcine inosine and application of the SNP marker. The 10 SNP molecular markers closely related to the porcine inosine content are researched and determined on the basis of American-series purebred landrace, American-series purebred large white pigs and American-series purebred duroc, the dominant allele frequency can be increased generation by generation by optimizing the dominant allele of the SNP, the inosine content can be reduced, and the molecular markers can be used for identifying the porcine inosine content. The pig genetic improvement progress is accelerated, so that the economic benefit of breeding pig breeding is effectively improved.

The invention relates to the technical field of biomedicine, in particular to a method for constructing a plasma ctDNA organ distribution characteristic spectrum of advanced colorectal cancer. The method comprises the following steps: collecting a peripheral blood sample at multiple time points, separating plasma by adopting a double-centrifugal method, and extracting circulating tumor DNA (Deoxyribose Nucleic Acid); carrying out whole exome sequencing based on ctDNA to obtain genome variation information and calculating variation allele frequency, and synchronously detecting the expression quantity of immune-related proteins by adopting an Olink proteomics technology; integrating the genome data, the protein expression data and the clinical pathological features, and constructing a multi-dimensional feature data matrix; and taking the organ metastasis condition confirmed by iconography as a supervision label, training a model by applying a machine learning algorithm, screening key prediction factors, constructing a quantitative prediction model, and finally generating a visual organ metastasis tendency prediction map. According to the method, early and accurate prediction of the advanced colorectal cancer organ metastasis tendency is realized through multi-omics data collaborative analysis and machine learning modeling.

To improve allele discrimination, the inventors adapted the system combining Cas13a detection sensitivity with allele-specific PCR amplification to propose CASPER (Cas13a Allele-Specific PCR Enzyme Recognition) as a new versatile, easy-to-implement, and highly sensitive method to detect low-frequency of sequence variant. CASPER enabled specific and sensitive detection of KRASG12D with low DNA input such as DNA extracted from patient's pancreatic ultrasound-guided fine-needle aspiration fluids. CASPER is easy to implement and a versatile reliable method virtually adaptable to any point mutation.

The invention belongs to the technical field of pig molecular marker-assisted selection, and particularly relates to application of an SNP molecular marker related to the total litter size character of pigs. The SNP molecular marker is located at the basic group of the 33289690th site of the ninth chromosome in a pig genome Sscrofa11.1. 109, the SNP site of the molecular marker has C / T polymorphism, and T is the favorable allelic variation of the pig total litter size character. The dominant alleles of the SNP are preferably selected, the frequency of the dominant alleles can be increased generation by generation, the total litter size of pigs is increased, and the genetic improvement progress of the pigs is accelerated, so that the breeding and production benefits are effectively improved.

Disclosed herein are methods of determining a tumor fraction of a sample from a subject. The methods can include, for example, acquiring a value for a target variable associated with a subgenomic interval m the sample; determining, from the target variable, a certainty metric: accessing a determined relationship between a stored certainty metric and a stored tumor fraction; and determining, with reference to the certainty metric and the determined relationship, the tumor fraction of the sample. In some instances, the method can further include determining a tumor fraction of the sample using a determination of somatic allele frequency when the estimate of tumor fraction calculated from the certainty metric falls below a cut-off threshold.

The invention discloses a corn selective breeding effect analysis method integrating population inheritance and deep learning, and the method comprises the steps: collecting corn germplasm resource data, including phenotype data, genotype data and environmental data; performing standardization processing and missing value filling on the phenotypic data, and calculating population genetic parameters including allele frequency, linkage imbalance and population structure stability; performing correlation analysis on the population genetic parameters and phenotypic data by using a deep learning model, predicting phenotypic values of candidate parents by using the trained model, calculating breeding indexes in combination with the population genetic parameters, and providing guidance for corn breeding decisions based on prediction results; through organic combination of a deep learning technology and a population genetics principle, accurate prediction and evaluation of the corn selective breeding effect are realized, the breeding efficiency is improved, and a new technical path is provided for corn variety improvement.

The invention belongs to the field of molecular genetics, and particularly relates to an SNP (Single Nucleotide Polymorphism) molecular marker combination for genetic relationship identification of wuzhu cattle and application of the SNP molecular marker combination. The SNP marker combination is composed of 140 high-polymorphism SNP sites distributed on a plurality of chromosomes of a bovine whole genome, the average distance between the sites is reasonable, and the SNP marker combination has good genome coverage. The selected SNP site has high allele frequency (MAF), expected heterozygosity (He) and polymorphic information content (PIC), and the cumulative exclusion probability is 0.9999. King and Plink software are combined to deduce a genetic relationship coefficient (Kinship) and an IBD shared value (PIHAT) between samples, and a result shows that the marker combination can realize a genetic relationship recognition effect equivalent to that of whole genome SNP data in Wuzhu white cattle (grassland white cattle), and the scientificity and practicability of the marker combination are verified. The SNP molecular marker combination can be widely applied to the processes of germplasm resource management, pedigree correction and cattle breeding, and has good popularization prospects and economic benefits.

This invention discloses a combination of SNP loci, a gene chip, and their applications for cotton variety identification, relating to the fields of bioinformatics and molecular breeding. From 100 cotton whole-genome resequencing data, this invention screened out 128 SNPs, which can be used as SNP fingerprints for cotton varieties. Using the SNP molecular markers and liquid-phase probes developed in this invention, cotton identification achieved a 100% detection rate, with a minimum allele frequency greater than 0.05, capable of distinguishing any two cotton varieties, enabling efficient and rapid cotton variety identification. Furthermore, this invention utilizes next-generation sequencing technology, resulting in a simple and standardized procedure that reduces human error, leading to extremely low unit costs for batch identification analysis. It is economical and efficient, particularly suitable for large-sample analysis and identification, and has promising application prospects.

The present invention discloses a molecular marker associated with the age at which pigs reach 115 kg of weight. The molecular marker is a G>A mutation located at the 160284793 bp site of chromosome 1 of the international porcine genome Sscrofa11.1 version or a G>A mutation represented by M at the 131st site as shown in SEQ ID NO: 1. The molecular marker is significantly associated with the age at which pigs reach 115 kg of weight. By using the molecular marker, an efficient and accurate molecular marker-assisted breeding technology can be established, which can be applied to genetic improvement of reducing the age at which breeding pigs reach 115 kg of weight, thereby increasing the growth rate of the pig population, improving the economic profit of the enterprise, and increasing core competitiveness. By optimizing the dominant allele of the SNP molecular marker, the dominant allele frequency can be increased from generation to generation, the age at which core group breeding pigs reach 115 kg of weight can be reduced, and the improvement progress of breeding pig-related growth traits can be accelerated, thereby effectively improving the economic benefits of breeding pigs.

The application provides a STR typing method applied to second-generation sequencing data, and relates to the technical field of biology.The method specifically comprises the following steps: customizing an STR locus configuration file; constructing a k-mer index library of a flanking sequence of the STR locus; extracting STR allele sequences from each READ of a sequence alignment file in combination with a CIGAR value and the k-mer index; constructing a PCR ghost peak model of the STR locus, and estimating parameters of the PCR ghost peak model by using a maximum likelihood estimation method; estimating candidate allele frequencies by using a second-generation sequencing sample data set; calculating posterior probabilities of all candidate alleles by using an allele set extracted from a given sequence alignment file, and inferring the most possible allele.The STR typing method has higher detection rate and typing accuracy, and has faster typing speed, and can be used for whole genome sequencing data, high-coverage targeted sequencing data or amplicon sequencing data.

The invention belongs to the technical field of molecular biotechnology and molecular markers, and particularly relates to an SNP (Single Nucleotide Polymorphism) molecular marker located on a pig chromosome 15 and related to an average daily feed intake character and an application of the SNP molecular marker. The SNP site of the SNP molecular marker located on the No.15 chromosome of the pig and related to the average daily feed intake character corresponds to the 122335983th site Cgt on the No.15 chromosome of a reference sequence of an international pig reference genome version 11.1; t mutation. By optimizing the dominant alleles of the SNP, the frequency of the dominant alleles can be increased generation by generation, the average daily feed intake of the boars is reduced to a certain extent, the feed efficiency of the boars is improved, the excellent boars with the characters are bred, and the progress of genetic improvement of the boars is accelerated, so that the economic benefit of breeding of the boars is effectively improved.

The invention relates to the technical field of molecular genetic breeding, and particularly discloses an eggplant core SNP molecular marker set and application, and the molecular marker set comprises 23 SNP markers; screening representative eggplant germplasm for re-sequencing; the method comprises the following steps: by taking an eggplant HQ-1315V1.0 genome as a reference genome, carrying out comparison to obtain 4869502 SNP (Single Nucleotide Polymorphism) markers; the method comprises the following steps: screening to obtain a first SNP molecular marker set based on a material discrimination degree, a distribution uniformity degree on a genome, specificity intensity, a heterozygosity degree, a gene diversity index, a polymorphic information amount PIC and a minimum allele frequency MAF principle of SNP markers; 96 SNP molecular markers are randomly selected, and genetic typing verification is carried out on the SNP molecular markers by using eggplant resource materials to obtain 32 candidate SNP molecular markers; and further performing genetic diversity evaluation, clustering analysis and population structure analysis on the 32 candidate SNP molecular markers, and screening to obtain 23 core SNP molecular marker sets. The method has the characteristics that germplasm resources can be effectively distinguished, and variety identification and authenticity detection are convenient.

The application discloses a kind of molecular markers of wheat stripe rust SNP homozygous site based on whole genome sequence and application thereof.The application is analyzed to 28 strains of global wheat stripe rust genome sequence, compared with reference genome, and more than a million SNP sites are mined out.Sequence depth screening, linkage disequilibrium analysis and heterozygosity detection, identify 1076 SNP homozygous sites.Further based on the frequency of secondary allele, screening of deletion rate and adjacent simple repeat sequence, finally determine 37 core SNP markers.Through primer design to these SNP sites, and add fluorescent linker, develop KASP-SNP molecular marker, which can be used for accurate genotyping identification of wheat stripe rust population.The molecular marker developed based on global wheat stripe rust whole genome is suitable for wheat stripe rust population research in all regions of the world.Based on the development of molecular marker of homozygous site, polymorphism difference caused by heterozygous site can be effectively excluded, to ensure the accuracy of detection site.The set of KASP-SNP molecular marker has high polymorphism, good repeatability and high detection efficiency, and can be widely applied to genetic research of wheat stripe rust population.

The application discloses a kind of based on molecular marker clustering analysis's cotton hybrid breeding combination screening method, the screening method includes: step S1: using multi-traits related molecular marker to the gene typing experiment of test cotton variety, data collection and arrangement are carried out;Step S2: excellent allele frequency calculation and feature matrix construction;Step S3: data standardization and principal component analysis dimension reduction;Step S4: optimal cluster number selection and genetic clustering;Step S5: each population character analysis;Step S6: heterosis potential score and combination screening.The technical problem that present cotton breeding parent selection lacks multi-traits comprehensive quantitative evaluation is solved, and the data of breeding decision-making and intelligentization are realized.

The invention relates to the field of bioinformatics and artificial intelligence analysis, in particular to a chromosome anomaly detection system and method based on a random forest model. According to the method, variation detection is carried out on the purified file, base replacement, insertion and deletion events are recognized, the allele frequency of each variation site is calculated, a standardized VCF format file is generated, the allele frequency spectrum of the variation sites is divided into a plurality of continuous intervals according to the preset step length, and the variation sites are extracted. Counting the number of sites of the sample data falling into each interval in a whole genome range and on each chromosome, and carrying out normalization calculation to generate a standardized feature vector; a random forest classification model trained by historical sample sequencing data with diagnosis tags is adopted to automatically judge all chromosome anomalies of the samples based on the standardized feature vectors of the samples; the method is superior to the existing SNP Array technology in the aspects of detection precision, detection range, chimeric recognition capability, intelligent degree, platform compatibility and the like.

The present application belongs to the technical field of sample pollution evaluation, and discloses a sample cross-contamination evaluation method, device, equipment and medium, comprising: performing quality control on batch sequencing data, and performing mutation site detection and genetic marker typing according to a human reference genome sequence and a site interval file; according to the genetic marker typing result and the site interval file, the number of heterozygous genotype sites is counted and the sample heterozygosity is calculated, according to the sample heterozygosity and a preset threshold, the sample contamination state is determined; according to the copy number estimation of the sample, the allele frequency is diploid standardized correction; based on the allele frequency in the genetic marker typing result and the mutation site detection result, the cross-contamination relationship between each two samples is analyzed by using identification rules to determine the pollution source; the site for calculating the pollution proportion is selected, and the weighted average algorithm is used to calculate the pollution proportion. The sample pollution degree can be accurately estimated and the pollution source sample can be identified.

The application discloses a method for detecting glioma chromosome abnormalities based on targeted sequencing, and belongs to the technical field of biological medicine. The method first acquires the allele frequency of a to-be-detected sample at preset SNP sites (covering 1p, 1q, 19p, 19q, chromosome 7 and chromosome 10), and then calculates and determines whether specific chromosome arms or chromosomes have loss of heterozygosity. Meanwhile, the copy number of the region where each SNP site is located is calculated based on the sequencing depth, and the total copy number of the above-mentioned chromosomes is obtained by integration. Finally, the loss of heterozygosity determination result and the chromosome copy number information are comprehensively combined, so that the simultaneous identification of 1p / 19q co-deletion, gain of chromosome 7 (+7) and deletion of chromosome 10 (-10) is realized. The method does not require paired samples, can accurately quantify the copy number, avoid false positives, and only needs to detect part of the SNP sites, that is, can be combined with hot spot mutation detection, thereby saving cost and improving detection efficiency.

The application discloses a method for detecting sample cross contamination based on SNP sites and application thereof. The method comprises the following steps: screening SNP sites, simulating data, constructing a model, and evaluating contamination degree according to the model. The screening of SNP sites comprises the following steps: (1) downloading sequencing-related FASTQ files or bam files from a database; (2) performing variation detection on high-throughput sequencing data of each sample in the files to obtain gvcf format files of variation information of the samples; (3) merging all gvcf files of the samples to obtain vcf format variation information of all the samples; and (4) screening SNP sites according to heterozygous state population frequency and variation allele frequency. The application simulates data of stable SNP sites carrying high population frequency, constructs a model, and evaluates contamination degree according to the model. The AF values of these SNP sites in different samples are relatively stable, and are concentratedly distributed near 0, 0.5 and 1; and the dose change of the AF values after the SNP sites contaminate other samples can accurately reflect the degree of sample cross contamination, and the operation is simple, fast and convenient.

The present disclosure is directed to a method for producing a Vpb4Da2 resistant insect pest species colony and / or a Mpp75Aa1.1 resistant insect pest species colony from an insect pest species isolated from an agricultural field. The present disclosure is also directed to methods for determining the inheritance of resistance to Vpb4Da2 and / or Mpp75Aa1.1, determining the allele frequency of resistance in an agricultural field, and / or mapping the locus of disease resistant genes or loci in the resistant insect pest species.

The invention belongs to the field of biological detection, and relates to an SNP (Single Nucleotide Polymorphism) combination for identifying hybrid lineage of foreign varieties and northeast local beef cattle and an application method of the SNP combination. A whole genome re-sequencing technology is utilized, northeast local beef Yanbian cattle is taken as a target group, European common cattle is taken as a reference group, Asian common cattle is taken as a transition group, and variation sites and a group genetic structure of northeast local beef cattle are analyzed; it is found that hybrid individuals of lizan or simmental and Yanbian cattle of foreign varieties exist in local beef cattle groups in northeast China. By calculating inter-population differentiation index (Fst), linkage imbalance (LD) and allele frequency (AF) of variation sites in the population, 1640 SNP sites are screened, and the lineage of Yanbian cattle can be identified.

This invention belongs to the field of animal molecular genetics and breeding technology, specifically relating to SNP molecular marker combinations for identifying kinship in Sunite Mongolian cattle and their applications. The marker combination consists of 141 highly polymorphic SNP loci distributed on bovine autosomes 1-29, with an average physical distance of 19.93 Mb between loci, exhibiting good genome coverage and locus independence. The selected SNP loci show high minimum allele frequency (MAF), expected heterozygosity (He), and polymorphism information content (PIC) in the Sunite Mongolian cattle population, with average values ​​of 0.4843, 0.4990, and 0.3745, respectively, and a cumulative exclusion probability of no less than 0.9999 under unknown maternal genotype conditions. The SNP molecular marker combination and its application method of this invention can provide a reliable molecular tool for the protection of Sunite Mongolian cattle germplasm resources, the construction of conservation populations, the selection of breeding bulls and cows, and the development of the Sunite Mongolian beef cattle industry, offering advantages such as ease of operation, moderate cost, and high accuracy.

The disclosure provides methods for estimating tumor purity from tumor samples without use of matched-normal controls. A set of genomic regions are identified based on a nucleic acid sequence data that is aligned to a reference genome. Each genomic region of the set of genomic regions includes one or more nucleotide-sequence variants relative to a corresponding genomic region of the reference genome. A B-allele frequency distribution for the biological sample is determined based on a B-allele frequency determined for each genomic region of the set of genomic regions. The B-allele frequency distribution is processed using a trained machine-learning model to estimate a metric identifying tumor purity in the biological sample.

The disclosure provides methods for estimating tumor purity from tumor samples without use of matched-normal controls. A set of genomic regions are identified based on a nucleic acid sequence data that is aligned to a reference genome. Each genomic region of the set of genomic regions includes one or more nucleotide-sequence variants relative to a corresponding genomic region of the reference genome. A B-allele frequency distribution for the biological sample is determined based on a B-allele frequency determined for each genomic region of the set of genomic regions. The B-allele frequency distribution is processed using a trained machine-learning model to estimate a metric identifying tumor purity in the biological sample.

The invention relates to the technical field of crop molecular breeding and biology, and discloses a sorghum whole genome 24K-SNP liquid chip and application thereof.The chip obtains 24062 SNP loci through process screening on the basis of whole genome re-sequencing data of global sorghum germplasm, and screening standards include that variation with the minimum allele frequency larger than 0.01 is reserved; it is ensured that the site flanking 100bp sequence is uniquely compared in a reference genome, and the consistency reaches 100%; removing redundant variation within 30bp at the downstream of the site; and implementing a distribution strategy that 6-7 sites are reserved in each 100Kb interval, a coding region is anchored preferentially, and a centromere region is filled. According to the method, the specific probe is used for liquid-phase hybrid capture, so that the method has the advantages of wide genetic background coverage, high capture specificity, uniform genome coverage and the like, can effectively solve the genetic typing problem of wild species and different-place germplasm, and is widely applicable to sorghum germplasm resource identification, whole-genome association analysis and molecular breeding research.

The present invention relates to a method for analyzing genetic material in a subject. More specifically, it relates to a method for analyzing genetic material using unphased genotypic information of polymorphic variants from one parent and one parent, in combination with the allele frequency of these variants in the subject's genetic material. Furthermore, the method of the present invention is particularly useful for analyzing isolated genetic material from a sample with a low amount of genetic material and / or for detecting low-level chromosomal mosaicism.

The present disclosure provides a probabilistic model for accurate and sensitive somatic single nucleotide variant (SNV) detection in cell-free nucleic acid samples comprising a set of sequence data. A joint genotype may be determined for each locus in the set of sequence data, and germline mutations may be intrinsically removed. A set of filtrations can be applied to eliminate low quality somatic variant calls. Further, a global tumor cell-free deoxyribonucleic acid (cfDNA) fraction and overlapping read mates can be considered, thereby enabling accurate SNV detection and variant allele frequency estimation from samples with low tumor cfDNA fraction. A sensitive early detection of minimal residual disease (MRD) is designed by using the probabilistic model and the machine learning model for distinguishing true variants from sequencing errors.

A method of detecting loss of HLA allele heterozygosity is provided. The method may include accessing a trained machine learning model, the model being trained using a training dataset, the training dataset including at least the following training datasets: an adjusted B allele frequency, it represents a ratio between a first B allele frequency of a heterozygous allele corresponding to the genomic region in the tumor sample and a second B allele frequency of a heterozygous allele associated with one or more control samples in the genomic region. The method may also include using the machine learning model to generate a result corresponding to a probability of whether a loss of heterozygosity exists in HLA alleles identified in a biological sample of a particular subject by processing sequence data using the machine learning model.

The present invention discloses a method for analyzing the selective breeding effects of corn by integrating population genetics and deep learning. The method collects corn germplasm resource data, including phenotypic data, genotypic data and environmental data; standardizes the phenotypic data and fills in missing values, and calculates population genetic parameters, including allele frequency, linkage disequilibrium and population structure stability; uses a deep learning model to perform association analysis between the population genetic parameters and the phenotypic data, uses the trained model to predict the phenotypic values ​​of candidate parents, calculates the breeding index in combination with the population genetic parameters, and provides guidance for corn breeding decisions based on the prediction results. The present invention achieves accurate prediction and evaluation of the selective breeding effects of corn by organically combining deep learning technology with the principles of population genetics, thereby improving breeding efficiency and providing a new technical path for corn variety improvement.

The disclosed embodiments concern methods, apparatus, systems and computer program products for determining sequences of interest using unique molecular index (UMI) sequences that are uniquely associable with individual polynucleotide fragments, including sequences with low allele frequencies and long sequence length. In some implementations, the UMIs include both physical UMIs and virtual UMIs. In some implementations, the unique molecular index sequences include non-random sequences. System, apparatus, and computer program products are also provided for determining a sequence of interest implementing the methods disclosed.

The application discloses a method for detecting thyroid cancer driving gene mutations based on multiplex fluorescence PCR, and belongs to the technical field of molecular biology detection. The method simultaneously detects nine mutation sites, namely BRAF V600E, NRAS Q61R, NRAS Q61K, HRAS Q61R, HRAS Q61K, KRAS G12V, KRAS G12D, TERT C228T and TERT C250T in a single tube reaction. The method enhances the distinguishing ability of wild type and mutant types by using a locked nucleic acid modified allele-specific primer, suppresses wild type amplification by using a peptide nucleic acid clamp, introduces a competitive internal reference probe to correct amplification efficiency difference, and reduces the sample usage to one fifth of that of a traditional multi-tube scheme. The mutation detection lower limit reaches 0.5% allele frequency, and the total time consumption is not more than 2.5 h.