64 results about "Structural variant" patented technology

The invention discloses a structural variation molecular marker located on a pig chromosome 7 and related to the lactation ability of a sow. The structural variation molecular marker is a DNA (deoxyribonucleic acid) fragment which is inserted between the 112024896 bp site and the 112024896 bp site on a chromosome 7 of an international pig reference genome 11.1 version, wherein the nucleotide sequence of the DNA fragment is shown as SEQ ID NO: 1; the genotype of the gene is T / T, T / INS and INS / INS. The structural variation molecular marker provided by the invention obviously influences the total weight gain character and the lactation ability character of the 7-day-old sow, and by breeding the pig with the structural variation molecular marker genotype of T / T, the lactation ability of the sow can be obviously improved, and the survival rate and the weaning weight of piglets are increased.

This invention discloses a system and method for verifying structural variations in genomic repetitive sequences, comprising: a sequence alignment module for aligning sequences in a sorted BAM file, a reference genome's FASTA file, and a VCF file to obtain re-alignment results, which are then sent to a deduplication module; a deduplication module for deduplicating the received re-alignment results and sending the deduplicated results to a structural variation evaluation module; and a structural variation evaluation module for evaluating the received deduplicated results using a distance-based structural variation evaluation method, thereby verifying structural variations in genomic repetitive sequences. This structural variation verification system helps people efficiently, accurately, and comprehensively examine sequencing evidence of structural variations, enhancing and simplifying the manual review process.

The invention discloses a primer group, a kit and a detection system for thalassemia gene detection. In order to more comprehensively and accurately detect point mutation, small insertion, small deletion, gene fusion mutation, large fragment deletion and gene structure variation on a thalassemia core gene and a modified gene in single detection, the invention provides a thalassemia gene detection primer group and a kit. Based on third-generation sequencing platforms such as a single-base nanopore sequencing platform and the like, all point mutation, small insertion, small deletion and gene fusion mutation in the amplification range of the thalassemia core gene and the modified gene can be detected by utilizing the primer group or the kit; and 60 alpha large fragment deletion mutations, 60 beta large fragment deletion mutations and 7 gene structure variations. The method is beneficial to rapid and comprehensive screening of thalassemia, and is high in detection sensitivity and good in accuracy.

Based on end-to-end assembly genome-based variation detection method, the present application relates to a variation detection method. The purpose of the present application is to solve the problems of incomplete detection, inaccurate detection, inability to identify complex variation types and low accuracy of genotyping in the current method based on splicing detection structural variation. The process of the end-to-end assembly genome-based variation detection method is as follows: step one, variation signal processing, obtaining the processed variation signal; step two, mixed sorting of the processed variation signal obtained in step one, spatial distribution clustering of the sorted variation signal, judging whether the spatial distribution clustered variation signal is a potential complex variation, if yes, judging the complex variation type and outputting, obtaining the complex variation signal, and executing step three; if not, it is a simple variation signal, and step three is executed; step three, genotyping of the variation signal obtained in step two. The present application is used in the field of variation detection.

Some embodiments relate to methods, systems, uses, or software for identification of structural variants (SVs) for regions of deoxynucleic acid (DNA) sequences. A device may receive, at a processor, a dataset comprising base pair data output from a sequencing by expansion process. A device may extract, at the processor, soft-clipped reads from the received dataset. A device may group, at the processor, the extracted soft-clipped reads based at least on their respective breakpoint. A device may determine, at the processor, a presence or an absence of a somatic structural variant by k-mer matching the grouped and extracted soft-clipped reads. A device may provide, at the processor, at least one determined somatic structural variant responsive to determining the presence of the somatic structural variant

Provided herein are, inter alia, are methods and systems for amplifying a chromosomal fragment DNA sequence. The methods and systems provided herein are, inter alia, useful for detecting copy number variation, structural variation, and / or extrachromosomal DNA within the chromosomal DNA of a plurality of cells. In addition, the methods provided herein are, inter alia, useful for jointly analyzing chromatin architecture and gene expression at a single cell level. The methods provided herein can be, inter alia, used to analyze chromosomal heterogeneity, clonal shifts, and / or evolutionary shifts between different cells.

The application discloses a method for identifying large fragment sequence insertion of a target genomic region and application thereof, and belongs to the technical field of bioinformatics. In view of the problem that large fragment insertion variation is difficult to be accurately recognized in clinical metagenomic sequencing due to short sequencing read length, insufficient coverage and other factors, the application proposes to construct a reference sequence which can represent the insertion variation by means of manual construction, and to realize efficient identification of the insertion event of the target genomic region by combining a short read-based fast alignment process. The method overcomes the dependence of existing structural variation detection tools on high sequencing depth and long read length, has the advantages of fast identification speed, high sensitivity and high accuracy, and is suitable for rapid screening of large fragment insertion related to drug resistance mechanism in clinical samples. Meanwhile, the method can be popularized for insertion variation analysis of other pathogen drug resistance related genes or genomic regions, and has a good clinical application prospect.

The invention discloses a structural variation recognition method and system based on a generic genome map. The generic genome map is expanded / enhanced by using third-generation sequencing data; receiving input sequencing data, comparing the sequencing data with the expanded / enhanced generic genome map to obtain a map reference comparison result, identifying structural variation in the sequencing data based on the map reference comparison result, and generating a candidate structural variation set; and performing credibility evaluation and classification on the candidate structure variation set by adopting a pre-trained deep learning model to obtain a filtered high-credibility structure variation set. According to the method, on the basis of utilizing a standard generic genome map, high-quality three-generation sequencing data owned by a user is introduced to dynamically construct or expand the map, so that a reference structure can cover more unknown or individual specific structure variations, the sensitivity and the accuracy of structure variation recognition are improved, and the application prospect is wide. And the capturing capability on complex and individual specific variation is enhanced.

A method for detecting variants within one or more genes of interest, the method comprising: designing a set of capture probes to cover at least one of the one or more genes of interest, including coding regions and non-coding regions; using the set of capture probes to prepare a sequencing library using a hybrid capture target-enrichment library preparation workflow; and identifying intra-genic structural variants.

The present invention relates to a method for detecting chromosomal structural variations using chromosome conformation capture technology and high-throughput sequencing technology, as well as detection products for use in the method, and diagnostic applications of the method and products, in particular in prenatal screening.

This invention discloses an algorithm, system, device, and medium for detecting structural variations based on third-generation sequencing data and a pan-genome. The detection algorithm includes: detecting SNARL structures in the pan-genome map; extracting reads corresponding to each SNARL path from the GAM alignment file; calculating the average coverage of all edges in each SNARL path and the number of edges with zero coverage; statistically analyzing the possible paths, path directions, reads aligned to the path, and path coverage information contained in each SNARL path; selecting the optimal path and the second path based on the read information and path coverage information; and comparing the optimized optimal path and the second path with the reference path to obtain variation information. This invention integrates the read information corresponding to the path, the base coverage information of the path, and the edge coverage information of the path as the basis for selecting potential variation paths, thus improving the accuracy of third-generation sequencing data detection.

Variant identification in the presence of alignment artifacts is described. Discordant reads of sequencing data may be identified with respect to a reference sequence. A variant-indicative signal may be generated based at least in part on a subread analysis of the discordant reads, the variant-indicative signal including one or both of a discordant junction signal or an alignment generated using a latent breakpoint graph. A variant call may be generated based at least in part on the variant-indicative signal, the variant call identifying a structural variant in the sequencing data as compared to the reference sequence.

The invention provides methods of sample identification using germline structural variants to assess sample contamination and sample swap.

The invention provides a genetic variation and chimera detection method, system and kit. The method comprises the following steps: (a) extracting high molecular weight DNA from a biological sample; (b) labeling the high molecular weight DNA by using a linker containing a UMI sequence to form a UMI labeled DNA library; (c) performing targeted enrichment on the DNA library marked by the UMI by using a probe aiming at a target gene locus to obtain an enriched long-fragment DNA library; (d) performing long read length sequencing on the enriched long fragment DNA library to obtain sequencing data; and (e) carrying out bioinformatics analysis on the sequencing data, wherein the bioinformatics analysis comprises error correction and variation detection based on a UMI sequence. The method is used for simultaneously detecting structural variation and low-frequency chimeric variation in a target genome region.

The present disclosure relates to methods and systems for detecting structural variants. The present disclosure further relates to methods and systems for phasing sequence reads. The present disclosure further relates to methods and systems for mapping sequence reads. The present disclosure further relates to methods and systems for generating a phased haplotype assembly from sequence reads.

Disclosed is a method of simultaneously capturing and identifying distinct targets within a DNA sample, wherein the distinct targets comprise a defined target region and an undefined target region, wherein the undefined target region comprises a structural variation or rearrangement or fusion. Also disclosed is a kit comprising the reagents for use in the methods as described herein.

The application discloses a primer pair, a method and a kit for detecting TUBB8 gene mutation. The primer pair is shown in SEQ ID NO:1 and SEQ ID NO:2, and a long fragment product covering a complete gene locus of the TUBB8 gene is specifically amplified. The method comprises the steps of extracting genomic DNA, long fragment PCR amplification, constructing a sequencing library, long read sequencing and gene variation analysis. The primer design of the application effectively avoids homologous sequence interference, solves the problem of high false positive rate in the prior art, has comprehensive detection range, can find point mutation, insertion and deletion, structural variation and regulatory region variation at one time, supports an efficient clinical sequential diagnosis path of "targeting TUBB8 first and then whole exon sequencing", and has significant accuracy, economy and clinical application value.

The invention discloses a genome structure variation automatic annotation method and system and a storage medium, and relates to the technical field of bioinformatics. The method comprises the following steps: receiving a structure variation file and analysis parameters through a graphical user interface; packaging the file path and the parameter into a structured data format task request, and sending the structured data format task request to a micro-service interface in a container through an HTTP protocol; the micro-service interface analyzes the request and calls an annotation engine in the container to read the file; an annotation engine automatically calls tools such as built-in AnnotSV based on an ACMG / ClinGen standard, integrates a multi-source reference database to perform pathogenicity grading calculation, and generates an annotation result; and finally, displaying a result in a table form through a graphical interface. According to the method, cross-platform operation is realized by utilizing a containerization technology, the operation threshold is reduced through a graphical interface, batch processing is supported, the efficiency is improved, and a complete automatic solution is provided for genome structure variation annotation.

The present disclosure provides techniques for analysis of genetic features. In particular, machine learning techniques can be used to analyze various statistical features in determining genetic features such as variants, markers, and traits, for example in a nucleotide sequence.

This disclosure relates to methods of predicting the probability that a circulating tumor DNA (ctDNA) structural variant (SV) is present in tumor tissue using a machine learning model. The methods can further include training and validating the machine learning model using paired ctDNA-derived SVs and tumor tissue-derived SVs. The training set data can include multiple passes of quantified sensitivity values and positive predictive values calculated from known true positive SVs, false positive SVs or false negative SVs until a favorable sensitivity values and / or favorable positive predictive value is achieved.

The invention discloses a structure variation analysis-oriented genome data visualization system and method, and the system comprises a data input module which is in data connection with a coordinate conversion module, an index module and a graph-comparison extraction module, the coordinate conversion module, the index module and the graph-comparison extraction module are in data connection with the function annotation visualization module, the coverage analysis module and the reading visualization module respectively, and the function annotation visualization module, the coverage analysis module and the reading visualization module jointly generate a group structure variation frequency visualization module. According to the method, the comparison result of the reading segment on the graphical generic genome is visualized in an interactive mode, and the limitation that a traditional linear genome browser cannot present graph structure comparison information is overcome; through the coordinate conversion module, the graphical generic genome is effectively connected with the traditional linear reference genome, and the system can automatically map a comparison result to the graphical genome and synchronously display corresponding function annotations, so that the operation intuition and compatibility are remarkably improved.

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.

The invention belongs to the technical field of biological information processing, and particularly relates to a genome structure variation detection method and system based on chromosome interaction. Comprising the following steps: acquiring a Hi-C interaction matrix and a sequencing comparison file; standardizing the Hi-C interaction matrix to obtain a standardized matrix; dividing the Hi-C interaction matrix into sub-matrix windows, and performing DETR filtering on the sub-matrix windows to obtain candidate sub-matrixes; extracting sequencing coverage rate information from the sequencing comparison file according to the genome coordinates of the candidate sub-matrixes; fusing the standardized matrix with the sequencing coverage rate information to obtain a three-channel RGB image; according to the method, the feature vectors of the three-channel RGB image are extracted, the structure variation type is determined based on the feature vectors, the structure variation type and genome position information thereof are output, the problems that in an existing chromosome structure variation detection method, single data source information is not fully utilized, and the false positive rate is high are solved, and the limitations of weak generalization ability and low calculation efficiency are overcome.

The application discloses a method for detecting genomic structural variations between distant species, comprising the following steps: step S1, preprocessing the genomes of different species to be detected which meet the requirements of distant species; step S2, taking the reference species genome as a benchmark, and performing pairwise alignment by using LASTZ; step S3, integrating the obtained alignment results by using MULTIZ, and generating a multi-genome alignment task processing alignment task file in batches; step S4, generating an alignment block optimization task in batches, and performing filtering and realignment of alignment blocks on a multi-sequence alignment file; step S5, in a high-confidence MAF file, identifying insertion, deletion, inversion, intrachromosomal translocation and interchromosomal translocation operations, and generating a BED format variation result file; and step S6, a program generates a variation detection task to calculate the average consistency of the flanking sequences of each structural variation breakpoint in the variation result file. The method is suitable for efficient, accurate and low resource consumption genomic structural variation detection of distant species.

In certain aspects, the technology relates to methods of selecting and / or treating subjects having cancer, where the subjects are identified as having at least one genetic structural variant associated with, or adjacent to, the PTPRD gene, the CD274 gene and / or the CD273, wherein the subject having cancer for potential responsiveness to treatment with a PTPRD-targeted therapeutic and / or an immune checkpoint blockade.

The invention relates to the technical field of genome data analysis, in particular to a whole genome map splicing method for structural variation detection, which comprises the steps of data preprocessing, feature map generation, multi-scale splicing frame construction, local optimization module design, global consistency correction, structural variation marking and result output and storage. According to the method, the data processing efficiency is improved through a distributed computing framework and a Hash index technology, the splicing precision and efficiency are improved by adopting deep learning to extract features and combining a multi-scale splicing framework, global consistency correction is realized by utilizing a graph theory method, and structural variation is detected through a sliding window scanning algorithm. According to the method, the splicing efficiency and accuracy of large-scale genome data can be remarkably improved, and reliable support is provided for structural variation detection.