16 results about "Variant allele" patented technology

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.

The present disclosure provides methods and compositions for producing, detecting, and selecting soybean plants and seeds comprising at least one high protein CCT (CONSTANS, CO-like and TOC1) domain containing variant allele and introgressing the high protein CCT variant allele into soybean plants. The present disclosure also provides methods and compositions for producing, detecting, and selecting soybean plants producing seeds having a high protein content including breeding methods for introgressing high protein alleles into soybean plants using marker assisted selection using markers linked to or associated with high protein CCT in soybean.

Methods, systems, and software are provided for determining a homologous recombination pathway status of a cancer in a test subject, e.g., to improve cancer treatment predictions and outcomes. In some embodiments, classifiers using one or more of (i) a heterozygosity status for DNA damage repair genes in a cancerous tissue, (ii) a measure of the loss of heterozygosity across the genome of the cancerous tissue, (iii) a measure of variant alleles detected in a second plurality of DNA damage repair genes in the genome of the cancerous tissue, (iv) a measure of variant alleles detected in the second plurality of DNA damage repair genes in the genome of a non-cancerous tissue, and (v) tumor sample purity are provided.

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.

Some embodiments of the methods and systems provided herein relate to variant calling from sequence data obtained from a single sample. In some embodiments, a somatic variant can be distinguished from a germline variant based on variant allele frequency in a sample and location in a genome.

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.

In one aspect, the disclosure relates to methods for monitoring clonal hematopoiesis of indeterminate potential (CHIP) in a subject, the method including at least performing DNA methylation sequencing on DNA from the subject, performing cell-type deconvolution from the methylation data to estimate a first set of cell-type proportions in the subject, repeating the method to determine a second set of cell-type proportions in the subject, and monitoring a change in variant allele fraction (VAF) using methylation data collected during performance of the method in conjunction with the first set of cell-type proportions and the second set of cell-type proportions. Also disclosed are methods for assessing the performance of a drug for treating a blood cancer.

To provide methods useful in determining the cellular origin of a cell-free nucleic acid (cfNA) fragment from a cfNA sample, such as a liquid biopsy sample.SOLUTION: The methods disclosed herein generally improve the specificity and / or sensitivity of an assay for detecting diseased cell nucleic acid (e.g., cancer cell DNA) in a cfNA sample by identifying variant alleles that are produced by non-target cells, such as hematopoietic stem cells in certain embodiments. Still other aspects include, inter alia, related systems and computer-readable media.SELECTED DRAWING: None

A method for detecting de novo structural variations includes: a genome analysis apparatus extracting k-mer data of a target individual from genome sequencing data of a target individual. The method may include: extracting k-mer data of a target individual from genome sequencing data, comparing a reference-genome k-mer database—including parents' and pan-genome k-mers—with the target individual's k-mers to select target-individual-specific k-mers, determining target-individual-specific reads, identifying candidate de novo structural-variation regions, predicting discordant read pairs using a machine-learning model, selecting final de novo regions based on an estimated variant allele frequency, and generating a clinical report.

An example method includes receiving sequence read data of nucleic acid molecules in a sample treated with a corrective enzyme after being obtained from a subject and generating a variant allele frequency (VAF) distribution of at least one type of substitution variant of the sequence read data by comparing the sequence read data to a reference genome. Candidate variants are identified in the VAF distribution. At least a portion of the candidate variants are determined to be false positive variants by determining that greater than a threshold number of the candidate variants are present within a threshold range of the VAF distribution. A report is generated based on one or more additional variants, which omit the false positive variants.

The invention belongs to the field of biological medicines, and provides a device for predicting disease recurrence based on an ultrahigh-sensitivity mutation load, an MRD gene detection panel covering 28 hotspot mutation regions of acute myelogenous leukemia is designed by integrating famous tumor public databases such as COSMIC, a quantitative blocker displacement amplification QBDA technology is combined, and the disease recurrence is predicted. According to the method, ultra-low abundance mutation with the variation allele frequency lower than 0.01% can be detected, a set of device for predicting relapse in advance based on ultra-high sensitivity mutation load change in the complete remission period of an acute myelogenous leukemia patient is developed, and the important clinical application value is achieved.

The invention relates to the technical field of chimera variation detection, in particular to an NGS data chimera variation efficient detection method based on deep learning. The method comprises the following steps: for any biological sample, comparing a sequencing read segment in NGS data with a preset reference genome to obtain a variation allele frequency of each comparison variation site, and screening out effective variation sites; for any preset scale, obtaining a variation degree coefficient of each biological sample at each preset scale according to the number of effective variation sites of each biological sample in each sliding window and variation allele frequency distribution; taking the NGS data of each biological sample as input of a neural network, and obtaining an activation value of each neural node; and further obtaining a pruning threshold to perform pruning optimization of the neural network, and performing chimera variation detection on NGS data of a new biological sample. According to the method, pruning optimization is carried out on the neural network in a self-adaptive mode, and the efficiency and effectiveness of chimera variation detection are improved.

Systems and methods for cancer subject tumor fraction estimation comprise obtaining a first plurality of nucleic acid fragment sequences from the subject's liquid biological sample. The first plurality of sequences represent cell-free nucleic acids in the liquid sample. A second plurality of nucleic acid fragment sequences is obtained from the subject's tumor sample. The second plurality of sequences represent nucleic acid molecules in the tumor. Smoothed noise rates, each determined using nucleic acid fragment sequences from non-cancer samples mapping to a corresponding allele position in a plurality of allele positions, are obtained. Variant allele counts and coverages are determined for the allele positions using the first plurality of sequences. Solid variant allele fractions are determined for the plurality of allele positions using the second plurality of sequences. The subject tumor fraction is calculated using the smoothed noise rates, variant allele counts, coverages, and solid variant allele fractions.

Methods for more accurately determining tumor mutational burden (TMB) based on sequence read data for a sample from a subject are described. The methods may comprise, for example, receiving sample data comprising tumor purity data, variant data, variant allele fraction (VAF) data, or any combination thereof, for a sample from a subject; providing the sample data as input to a machine learning model configured to classify the sample according to TMB status based on the input sample data; and outputting a classification of the TMB status of the sample.

In alternative embodiments, the invention provides nucleic acid sequences that are genetic polymorphic variations of the human TMEM216 gene, and TMEM216 polypeptide encoded by these variant alleles. In alternative embodiments, the invention provides methods of determining or predicting a predisposition to, or the presence of, a ciliopathy (or any genetic disorder of a cellular cilia or cilia anchoring structure, basal body or ciliary function) in an individual, such as a Joubert Syndrome (JS), a Joubert Syndrome Related Disorder (JSRD) or a Meckel Syndrome (MKS). In alternative embodiments, the invention provides compositions and methods for the identification of genetic polymorphic variations in the human TMEM216 gene, and methods of using the identified genetic polymorphisms and the proteins they encode, e.g., to screen for compounds that can modulate the human TMEM216 gene product, and possibly treat JS, JSRD or MKS. In alternative embodiments, the invention provides cells, cell lines and / or non-human transgenic animals that can be used as screening or model systems for studying ciliopathies and testing various therapeutic approaches in treating ciliopathies, e.g., JS, JSRD or MKS.

The present disclosure provides a method for enrichment of at least one target nucleic acid in a library of nucleic acids. This present disclosure is also directed to a faster and easier method of target capture using primer extension reactions that can improve ease of use, turnaround time, and variant allele specificity by designing target enrichment primers to specifically enrich library fragments based on the relative location of the variant base(s) in the primer, the utilization of polymerases with better priming specificity, designing the variant bases in the capture primer, designing the variant bases in the release primer, and / or designing variant specific primers to the both the plus and minus strands of the target library fragment.