39 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.

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

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.

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.

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.

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.

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.

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.

Described herein are methods for the detection of molecular residual disease (MRD) and cancer by analysis of cell‑free nucleic acids. The invention provides a capture panel comprising target‑specific probes that are directed to one or more genomic regions encompassing a spectrum of variant types, namely structural variants (SV), large insertions / deletions, phased single‑nucleotide variants, small insertions / deletions, multi‑nucleotide variants and single‑nucleotide variants. By contacting a nucleic‑acid sample derived from a subject with the probe panel, the method enriches variant‑bearing molecules and subjects the captured set to sequencing. Disease presence or the likelihood thereof is determined on the basis of each target region, optionally integrating epigenetic information (e.g., differential methylation) obtained from a tumour biopsy. The probe design is adapted to technical noise, allocating higher probe concentrations to low‑noise, high‑complexity variants, thereby reducing reliance on molecular barcodes, lowering required read depth and improving analytical sensitivity for both fixed‑panel and patient‑specific MRD assays.

The invention relates to a whole genome structure variation recognition method and device based on assembly, and the method comprises the steps: obtaining a query genome, comparing the query genome with a reference genome, and obtaining a comparison file; the comparison file is preprocessed; according to the preprocessed comparison file, carrying out partitioning and clustering analysis on the comparison record to obtain a clustering result; variation recognition is carried out on the interior of each cluster and among different clusters; according to a variation recognition result, carrying out inversion recognition on the structure variable region in the comparison record through an iterative algorithm, and resetting a breakpoint so as to carry out re-clustering; obtaining a variation set of all haplotypes according to a variation recognition result and the re-clustered structure variable region; and performing variation combination and genetic typing on the variation set of all the haplotypes to obtain a final variation result. Compared with the prior art, the method has the advantages that large-scale structure variation can be accurately recognized, and cross-species variation recognition can be achieved.

Disclosed herein are methods and systems for phasing sequence reads. In some embodiments, the methods includes steps of generating sequence reads from fragments of the genomic DNA sample bound to a flow cell; determining linkage information between the sequence reads based on the geographic location of each fragment on the flow cell; mapping the sequence reads to a reference genome using the linkage information and nucleic acid sequence data from a set of candidate haplotypes; analyzing the sequence reads to determine likelihoods that the sequence reads correspond to a haplotype from the set of candidate haplotypes; and phasing the sequence reads based on the linkage information and on the likelihoods that the sequence reads correspond to a haplotype from the set of candidate haplotypes. Further disclosed herein are methods and systems for detecting a phased sequence variant, for determining a haplotype nucleotide sequence, and for detecting a structural variant.

The invention discloses a construction method and application of a multi-variety goat high-density genotype reference panel containing structural variation, and belongs to the technical field of genotype interpolation panel construction.The construction method comprises the following steps of data processing, quality control on original data, variation detection, structural variation genotyping and reference panel construction. According to the method, a global multi-variety goat high-density genotype interpolation reference panel is constructed, a reference group is expanded, and the accuracy and efficiency of interpolation are effectively improved; the data used in the interpolation process comprises whole genome sequencing (WGS) data of 1145 goats in total. The reference panel has wide adaptability to different data types, can keep consistent performance in different types of genetic data, verifies the reliability and practicability of the interpolation panel, reduces the interpolation error, and improves the interpolation effect.

The application provides a CRISPR gene editing structural variation detection method, taking the CRISPR edited cell genome DNA as an experimental group, taking the homologous wild type cell genome DNA as a control group, respectively performing Long-range PCR amplification, and using high-throughput double-end sequencing data of the amplification product for bioinformatics analysis, the bioinformatics analysis comprising: (1) using fastp software to perform quality control on the sequencing data; (2) screening target sequencing read pairs of large fragment deletion based on a local alignment algorithm combining k-mer hash table alignment and longest increasing subsequence identification; (3) screening large fragment deletion candidate sequencing read pairs crossing the cleavage site; (4) excluding false positive large fragment deletion based on a k-means clustering algorithm; (5) screening effective large fragment deletion structural variation events. The application can more efficiently and accurately detect large fragment deletion at a CRISPR gene editing target point.

The invention relates to the technical field of bioinformatics and genome sequencing data processing, in particular to a gastric cancer pathogenic gene sequence biological information comparative analysis method, which comprises the following steps: acquiring gene sequencing data of a target sample, loading a preset gastric cancer multi-dimensional topological reference map, and loading a preset mutation feature vector library; generating characteristic frequency distribution, and matching the characteristic frequency distribution with a preset etiological fingerprint spectrum to determine a pathogenic background type of the target sample; constructing a dynamic weight multi-modal diagram comparison model; calculating the fitting degree of the optimal comparison path and a theoretical variation path based on etiological deduction, and generating a pathogenic confidence score; generating a corrected second topological reference graph; according to the method, the limitation of linear comparison in processing complex structure variation is effectively overcome, and the variation recognition sensitivity is remarkably improved.

The present application belongs to the field of bioinformatics, and particularly relates to a method and device for detecting a thalassemia patient's copy number variation type, the method comprising the following steps: constructing a thalassemia copy number variation database, constructing a thalassemia-related gene copy number baseline database of a healthy population, using a region range of a copy number change of a to-be-tested sample and the copy number of the region, and combining the self-constructed thalassemia copy number variation database to determine a thalassemia copy number variation type to which the sample belongs; the present application can clearly detect a known structural variation carried by a sample and related to thalassemia, and can accurately find out an atypical thalassemia-related genomic structural variation; it has been verified that the method can be used for detecting known thalassemia copy number variation types and discovering new thalassemia copy number variation types according to a standard.

The embodiment of the application discloses a whole genome structural variation identification method based on third-generation sequencing, comprising: aligning the data to be sequenced with a preset reference genome, and sorting the preset alignment file to construct an index; for each sequencing read, the base alignment condition is analyzed, SV identification is carried out, and whether the signal of the variable DNA fragment is included is identified; the error and overlapping variable DNA fragment signal is corrected; the corrected sequence is output, compared with the preset reference genome, SV identification is carried out, and the identification result is output; and the data to be sequenced is typed according to the identification result. The application can improve the detection accuracy and sensitivity, and the alignment boundary for high-error third-generation sequencing sequences; improve the identification accuracy of complex or longer variable DNA fragments; accurately distinguish adjacent variable DNA fragments with close distances; and accurately type the variable DNA fragments.

Described herein are methods of preparing an enriched library of nucleic acids, comprising: (a) identifying a patient-specific panel of somatic variants present in a tumor sample from a patient, (b) generating a subset panel of somatic variants from the patient-specific panel, wherein the subset of somatic variants comprises one or more of: multi-nucleotide variants, insertions and deletions, and genomic rearrangements; (c) preparing a sample of cell-free DNA (cfDNA) fragments from the patient for sequencing; and (d) selectively enriching the cfDNA for the subset of somatic variants to generate an enriched library.

Disclosed herein are methods and systems for detecting a structural variant from a DNA sample placed on a flow cell. In some embodiments, the methods and systems obtain nucleic acid sequence reads and flow cell locations of sequence read clusters on the flow cell; align the sequence reads to one or more reference genomes to obtain a genomic location of the sequence reads; obtain linking information between pairs of sequence reads on the flow cell based on the flow cell location and genomic location of each sequence read in the pairs of sequence reads; generate colocation data based on the linking information; provide the colocation data to a machine learning model; and process the colocation data with the machine learning model to identify a predicted structural variant region within the genomic nucleic acids and determine a confidence score. Further disclosed are methods and systems for training a machine learning model.