31 results about "Nucleotide variation" patented technology

The present disclosure relates to a method for in situ analysis of single nucleotide variations in an RNA sample, the method comprising the steps of: (a) contacting the RNA sample with a plurality of chimeric padlock probes comprising a modified base at the 3'end portion; (b) adding an RNA ligase to generate a cyclized padlock oligonucleotide for a padlock probe comprising a modified base complementary to the corresponding position of the RNA sample under conditions and reagents allowing the ligation of the padlock probe with single nucleotide specificity; (c) amplifying the cyclized padlock oligonucleotide under conditions and reagents that allow rolling circle amplification, thereby producing an amplified cyclized padlock oligonucleotide; and (d) detecting the amplified cyclized padlock oligonucleotide so as to obtain a map of the mononucleotide in the RNA sample. The disclosure further relates to a kit for in situ analysis of single nucleotide variations in an RNA sample.

The present disclosure relates generally to (CRISPR) RNA (crRNA) for the precision silencing of transcripts. In some embodiments, the crRNA are enriched for guanosine (G) nucleotides at key spacer positions, which is useful in enhancing the silencing efficacy of otherwise inefficient crRNA, thereby expanding the targeting spectrum of Cas13 endonucleases, e.g., Cas13b and Cas13d. In other embodiments, the crRNA comprise a spacer sequence having at least one nucleotide mismatch relative to the target RNA sequence, wherein the target RNA sequence is a wild-type transcript and / or a variant transcript (e.g., a transcript comprising a single nucleotide variant (SNV)). The present disclosure also provides RNA editing systems comprising the crRNA described herein in complex a Cas13 effector protein and a target RNA sequence, methods for the selective targeting of transcripts encoding proteins that are difficult to target, or are not amenable to pharmacological targeting, e.g., oncogenic fusion transcripts or oncogenic transcripts comprising single nucleotide variant(s), and methods for the design and selection of potent crRNA.

Methods are provided for detecting gene deletions and driver mutations using tumor-specific methylation patterns from cell-free DNA and tissue biopsies. The methods exploit the mutual exclusivity between tumor-specific methylation and homozygous gene deletion, enabling detection through absence of expected methylation signals. Applications include detection of MTAP, PTEN, and RB1 deletions, as well as prediction of EGFR single nucleotide (SNV) and small (up to 50 base pair) insertions and deletions (indel) driver mutations, enabling identification of patients eligible for targeted therapies.

The invention discloses a method for rapidly detecting sodium ion channel L1014F mutation of liriomyza trifoliata and a special kit, and belongs to the technical field of agricultural biology. The method comprises the following steps: extracting the DNA of the liriomyza trifoliate; carrying out PCR (Polymerase Chain Reaction) amplification by adopting two groups of primer pairs; an upstream primer of the first group of primer pair is as shown in SEQ ID NO.2, an upstream primer of the second group of primer pair is as shown in SEQ ID NO.1, and a downstream primer of the second group of primer pair is as shown in SEQ ID NO.3. Result judgment: when a 502 bp band appears in amplification of the first group of primer pairs, the L1014F site of the sample is not mutated; and when a 502 bp band appears in the amplification of the second group of primer pairs, the L1014F site of the sample is mutated. According to the method, mononucleotide variation typing can be achieved only through a conventional PCR instrument and electrophoresis, operation is easy and convenient, and the method is suitable for rapid monitoring of pyrethroid drug resistance of liriomyza trifoliate and investigation of field population resistance gene frequency.

The present application relates to the technical field of bioinformatics, in particular to a method for detecting microbial structural variation, mutation and insertion deletion based on second-generation sequencing. The present application is based on the short read Illumina second-generation sequencing strategy, through testing, using the current mainstream biological tools and combining the self-developed program, the automatic analysis of microbial metagenome structural variation (SV), single nucleotide variation (SNV / mutation) and small fragment insertion deletion (Indel) is realized, and the problems of low sensitivity, high false positive rate and low efficiency in the prior art for detecting variation under the metagenome background are significantly solved.

The invention discloses an evaluation method, system and device for cell specific genetic variation disturbance and a storage medium. An integrated single cell data set is used for training a variational auto-encoder, and a high-dimensional gene expression profile is mapped into a cell state vector in a low-dimensional hidden space through an encoder of the variational auto-encoder; freezing parameters of the encoder, training a conditional disturbance module by using training data with a mononucleotide variation tag, and generating conditional variation embedding; performing anti-fact prediction for target cell and target mononucleotide variation, and generating a reference prediction expression profile and a disturbance prediction expression profile through a conditional disturbance module and a decoder of a variational auto-encoder based on the original genotype and the overturned genotype of the target cell; and calculating the difference between the two to obtain the disturbance effect score of the single nucleotide variation in the target cell, and quantitatively describing and predicting the specific disturbance mode of the SNV in different cell states.

This invention discloses the application of single nucleotide genetic markers in the breeding of Tibetan cattle adapted to high altitudes. Using whole-genome infiltration analysis, the whole-genome SNPs of Tibetan cattle were analyzed, revealing that three single nucleotide variations on the promoter of the Tibetan cattle EGLN1 gene may be associated with high-altitude adaptation. Furthermore, molecular markers for breeding high-altitude adapted Tibetan cattle were identified, which can be used to rapidly establish high-altitude adapted cattle populations, providing a basis for breeding cattle breeds adapted to high-altitude environments.

The invention relates to the technical field of bioinformatics, in particular to a method for detecting microbial structure variation, mutation and insertion and deletion based on next-generation sequencing. On the basis of a short-read-length Illumina next-generation sequencing strategy, a current mainstream biological tool is tested, and automatic analysis of microbial metagenome structure variation (SV), single nucleotide variation (SNV / mutation) and small fragment insertion and deletion (Indel) is realized by applying and combining a self-developed program; the problems of low sensitivity, high false positive and low efficiency during variation detection under the metagenome background in the prior art are obviously solved.

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.

The present disclosure relates to a method for analyzing mutations, insertions, deletions and / or single or nucleotide variations in an RNA sample via nick filling, the method comprising the steps of: (a) providing an RNA sample comprising at least one nucleotide extension to be identified having an unknown identity, the nucleotide extension segment with unknown identity is laterally connected with a nucleotide extension segment with known identity; (b) contacting the RNA sample with at least one DNA probe, such as at least two linear probes or at least one DNA padlock probe having a first end and a second end, under conditions and reagents that allow hybridization, the first end and the second end are designed to hybridize with a sequence having a known identity that flanks the at least one nucleotide extension having an unknown identity, thereby creating a gap between the hybridized first end and second end of the at least one DNA probe; (c) adding a polymerase having reverse transcriptase activity and optionally having nick translation activity and optionally having limited strand displacement under conditions and reagents allowing reverse transcription, thereby allowing reverse transcription of the at least one DNA probe from the first end to the second end using the RNA sample as a template, such that the reverse transcribed portion of the at least one DNA probe comprises a complementary sequence of the nucleotide extension having the unknown identity in the RNA sample; (d) adding a ligase which can use RNA as a splint molecule under conditions and reagents which allow ligation, thereby allowing the blocking of the at least one reverse transcribed DNA probe; (e) the probe containing the nucleotide subjected to reverse transcription can be amplified; and (f) sequencing the at least one amplified cyclized oligonucleotide, thereby revealing the identity of the at least one nucleotide extension having an unknown identity of the RNA sample. The present disclosure further relates to a kit for use in a method of analyzing mutations for both in situ and in vitro applications.

The present invention proposes a method for detecting single nucleotide variants in functional noncoding regions based on ensemble learning, which primarily addresses the technical problem of low prediction accuracy in the prior art. The method comprises the following steps: (1) obtaining single nucleotide variant data in functional noncoding regions and constructing a training sample set and a test sample set; (2) constructing a single nucleotide variant prediction model for functional noncoding regions; (3) training the single nucleotide variant prediction model for functional noncoding regions; and (4) obtaining detection results for single nucleotide variants in functional noncoding regions. The present invention has a simple experimental process and high detection accuracy, and can be applied to detecting single nucleotide variants in functional noncoding regions.

The present disclosure relates to methods for analyzing target nucleic acids in a biological sample using Argonaute proteins and single cell RNA-templated ligation probe chemistry. Variant sequences (e.g., single nucleotide variations such as SNPs or point mutations) in a plurality of target nucleic acids in a cell or tissue sample are analyzed in the sample. The application is drawn to assay methods, and further drawn to compositions and kits for use in accordance with the methods. One or more processes of the methods described herein may be performed within a partition, such as a droplet or well.

A method, device, medium and program for detecting mutations on the basis of methylation data. The method comprises obtaining methylation data of a sample to be tested; on the basis of the methylation data, obtaining base composition data of a 3-base genome after chemical or enzymatic conversion; on the basis of the base composition data of the 3-base genome after chemical or enzymatic conversion, determining base composition data of a 4-base genome before chemical or enzymatic conversion according to the complementary base-pairing relationship between a positive strand and a negative strand, and / or the corresponding relationship between genotypes before and after chemical or enzymatic conversion; on the basis of the base composition data of the 4-base genome, identifying candidate mutation sites; and filtering the candidate mutation sites to generate detection results relating to single nucleotide variation mutation sites. According to the method, the single nucleotide variations can be detected from whole genome methylation data, and the accuracy of detection for low abundance mutations can be significantly improved.

The techniques described herein disclose a method or a system for analyzing genomic data, calculating a predictor and making a quantitative assessment of a biological effect based on the predictor. A biological effect such as the pathogenicity of a cancer a risk that a subject may develop a particular cancer may be determined based on the predictor. The predictor may comprise the observed number of occurrences of a gene variant divided by the expected number of occurrences of the gene variant. The prediction of a drug treatment may comprise prioritization of gene variants according to a selective variant effect and determining which drug treatment to prioritize. The predictions may further comprise using genomic coordinates for each gene variant and nucleotide alterations from various databases, but filtering out duplicate samples from the same subject.

The present application relates to a sample homology determination model and its establishment method and application, belong to gene detection technical field. The method comprises the following steps: sample collection: two samples of the same source are used as positive sample pair to form a positive sample set, and two samples of different sources are used as negative sample pair to form a negative sample set; SNV data collection: the sequencing data is compared to the human reference genome to obtain the single nucleotide variation site SNV condition of each sample, any sample pair is selected, the ratio of the number of typing inconsistent sites and the number of common detection sites is the mismatch rate; Model construction: using sample sequencing data volume, target sequence region, SNV filtering condition and genotype difference as model parameter condition, using mismatch rate as determination index, according to the gradient range of sample common detection site quantity corresponding to matched mismatch rate, the classification model is constructed. The model can be used in the scene with low sample sequencing depth, complete the homology determination of sample, has the advantages of low cost and high efficiency.

The invention relates to the technical field of nucleic acid molecule detection, and discloses a thyroid cancer NGS conjoint analysis method based on fusion gene dynamic monitoring. According to the method, high-throughput sequencing and a DNABERT deep learning algorithm are combined, so that highly specific single nucleotide variation (SNV) and fusion gene markers can be identified from massive sequencing data and are used for judging whether residual tumors or recurrence signs exist in the body of a patient or not, and whether the patient needs to pass a further detection method or not is judged; and diagnosis is carried out, or effect evaluation on the drug reaction is realized. According to the method, a deep learning algorithm is introduced into dynamic monitoring of the thyroid cancer ctDNA, so that the detection rate and the accuracy of the micro marker are improved.

The application provides a SNP marker in a pig ACSL3 gene promoter region and application thereof in pig PRRS resistance breeding. SNPs in the ACSL3 gene promoter region are analyzed by using whole genome resequencing data, it is found that three single nucleotide variations in the ACSL3 gene promoter are related to pig PRRS resistance, and it is found that a CCA haplotype can improve the PRRS resistance of a population, and thus early selection of individuals can be implemented by using the molecular marker, and pig PRRS resistant varieties can be selected. The application provides a reliable molecular marker for genetic improvement of pig PRRS resistance, and has important significance for pig disease resistance breeding.

Described herein are methods, devices, and systems for measuring disease (e.g., cancer) levels, such as fractions of nucleic acid molecules (e.g., cell-free DNA) in samples associated with diseased tissue (e.g., cancer tissue) from individuals. Methods, devices, and systems for measuring the presence, recurrence, progression, or regression of a disease in an individual are also described. Some methods include using nucleic acid sequencing data associated with an individual, where an indication is selected from a combination of individualized disease-related small nucleotide variations (SNV) loci, and a signal indicating a ratio of the sequenced loci derived from the diseased tissue is compared to a background factor indicating a false positive error rate for sequencing across the selected loci, or a noise factor indicating a sampling variance across the selected loci.

The invention discloses an oil body calcium protein gene GhCLO1 molecular marker related to cotton fiber quality characters and application of the oil body calcium protein gene GhCLO1 molecular marker. The SNAP molecular marker can be used for detecting two haplotypes of the cotton oil body calcium protein gene GhCLO1, the promoter sequence of the GhCLO1 of the haplotype 1 is shown as SEQ ID NO.1, the promoter sequence of the GhCLO1 of the haplotype 2 is shown as SEQ ID NO.2, and compared with the promoter sequence of the haplotype 2, the haplotype 1 has a C-A nucleotide variation at the position away from an initiation codon by-623bp. The fiber quality character of the material with the haplotype 1 is superior to that of the haplotype 2. And selecting a to-be-detected cotton germplasm material carrying the haplotype 1 as a cotton germplasm with a high fiber quality character for cotton breeding. The oil body calcium protein gene GhCLO1 and the molecular marker thereof have important effects and application prospects in improvement of cotton fiber quality characters and cultivation of new high-quality cotton varieties.

The present invention discloses a single-base mutation imaging method based on in situ ARMS-PCR and Cas labeling. This method belongs to the field of single-cell analysis. By adding base mismatch sites to PCR primers, effective amplification and specific identification of single nucleotide variation information within bacteria are achieved. At the same time, the amplified products are labeled by modified gRNA / Cas12a. This method can also accurately quantify Salmonella resistant bacteria in complex environments and obtain cellular heterogeneity and spatial location information.

The present application relates to SNP markers related to tobacco leaf width and application thereof. The present application mines single nucleotide polymorphism sites located in different chromosomes and gene regions, and the nucleotide variation forms are conventional SNP types such as A / T, A / G, C / T, C / G or G / T. These SNPs can be typed by conventional molecular detection means, and these sites all show significant and stable characteristic contribution values in the XGBoost model of the present application, which can effectively reflect the genetic influence of leaf width, and can be used for rapid prediction of leaf width of tobacco materials, material screening and molecular marker assisted selection, so as to realize early identification of target leaf width materials, reduce breeding cost and accelerate new strain breeding.

The present application relates to SNP markers related to tobacco leaf length and application thereof. The present application mines single nucleotide polymorphism sites located in different chromosomes and gene regions, and the nucleotide variation forms include common SNP types such as A / T, A / G, C / T, C / G or G / T, and these SNPs can be typed by conventional molecular detection means. These sites all show significant and stable characteristic contribution values in the XGBoost model of the present application, can effectively reflect the genetic influence of leaf length, and can be used for rapid prediction of tobacco material leaf length, material screening and molecular marker assisted selection, so as to achieve early identification of target leaf length material, reduce breeding cost and accelerate new strain cultivation.

The present application relates to SNP markers related to tobacco reducing sugar content and application thereof. The present application mines single nucleotide polymorphism sites located in different chromosomes and gene regions, and the nucleotide variation forms include common SNP types such as A / T, A / G, C / T, C / G or G / T, and these SNPs can be typed by conventional molecular detection means. These sites all show significant and stable characteristic contribution values in the XGBoost model of the present application, and can effectively reflect the genetic influence of reducing sugar content, and can be used for rapid prediction of tobacco material reducing sugar content, material screening and molecular marker assisted selection, so as to achieve early identification of target reducing sugar content material, reduce breeding cost and accelerate new strain breeding.

Variation in a single nucleotide of a target or template nucleic acid may be significant in many ways. For infectious disease, many drug resistance mutations are known and a simple means to confine their absence would more quickly match a patient with an infection with the most effective treatment. This disclosure provides for a simple, low resource strategy allowing the detection of single nucleotide variation using only low-cost diagnostic methods already available in many locations. A key to this approach is the use of left-handed DNA (L-DNA) as a comparator molecule for D-DNA targets. L-DNA provides numerous additional advantages such as low cost and stability.

The application discloses a method, a probe, a kit and application thereof for DNA single nucleotide variation detection. The method greatly expands the detection window for distinguishing single nucleotide variation in double-stranded DNA through the conversion of the quantitative relationship between the user-definable detection signal and the target concentration. Through computer simulation and experimental verification, the effectiveness of the method for expanding the detection window and improving sequence selectivity is proved. Since the method directly acts on dsDNA, it is easy to be applied to nucleic acid amplification technology, such as polymerase chain reaction (PCR). The practicability of the method is proved by detecting infection and screening drug resistance of clinical parasite samples collected from rural areas in Honduras. In addition, the method does not need to use high-cost reagents such as enzymes and has relatively high requirements on reaction conditions, so that the method is simple in operation, low in cost and convenient for enterprise and laboratory application.

We disclose computational models that alleviate the effects of human ascertainment biases in curated pathogenic non-coding variant databases by generating pathogenicity scores for variants occurring in the promoter regions (referred to herein as promoter single nucleotide variants (pSNVs)). We train deep learning networks (referred to herein as pathogenicity classifiers) using a semi-supervised approach to discriminate between a set of labeled benign variants and an unlabeled set of variants that were matched to remove biases.

The invention discloses a primer group, a kit and a method for detecting STRC gene variation and application. The primer group comprises one or more of primer groups 1-8. Targeted enrichment based on the primer group is combined with three-generation sequencing, so that single nucleotide variation, insertion / deletion, exon copy number variation and structural variation caused by non-allelic homologous recombination of the STRC gene can be comprehensively and accurately detected, the cis-trans relationship between the variations can be defined, and an efficient tool is provided for diagnosis of hereditary hearing loss.

The invention relates to the field of clinical pathogens, and provides a machine learning-based clinical pathogen infection interpretation method, which comprises the following steps of: acquiring original sequencing data, removing low-quality read segments, removing noise comprising a carrier sequence and a linker sequence by using a bioinformatics tool, and standardizing all data into an FASTQ format to obtain cleaned data; comparing the cleaned data to a reference genome, identifying single nucleotide variation, insertion deletion, single nucleotide polymorphism and copy number variation in a sample, annotating a sequencing read section, and identifying a known pathogen gene and a drug-resistant gene; judging the type of an infected pathogen according to a preset rule in combination with a generation signal pre-judgment result, amplicon coverage, pathogen pathogenicity, a strong positive ortho-position, a sample type and clinical evidence; and training the model, inputting the processed features of the to-be-tested sample into the optimized model, and outputting a pathogen infection interpretation result. When the kit is used, the accuracy and efficiency of clinical pathogen infection interpretation are improved.

The application discloses a kind of based on machine learning and immune escape mechanism optimization molecular marker algorithm method, the method includes: obtaining the NGS sequencing data of tumor tissue and normal tissue, the NGS sequencing data of tumor tissue and matched normal tissue are quality controlled, alignment, obtain original alignment file, the original alignment file is pretreated to obtain final alignment file;Tumor somatic single nucleotide variation and small fragment insertion and deletion detection are carried out to alignment file by software, obtain somatic variation information;Construct multivariate linear regression model, calculate the prediction ability of neoantigen of SNV site and INDEL site;The TMB calculation method is optimized in combination with the two aspects of factors of different neoantigen prediction ability and tumor patient immune escape mechanism.The application more truly reflects the real immune state of patient, and also has better prompting effect on patient immunotherapy response and prognosis.

Provided herein are reagents and methods for simultaneously enriching many potential rare genetic variants at different genetic loci. The rare variants enriched can include single nucleotide polymorphisms (SNPs), single nucleotide variants, or small insertions and deletions. Embodiments of the invention include procedures for integration with downstream next generation sequencing (NGS) analysis. Embodiments of the invention include analysis of nonpathogenic SNPs for the determination of cell identity and detection of cell contamination using qPCR or NGS.