26 results about "Exome sequencing" 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 invention relates to a double-gene rare variation and disease relevance prediction model and an establishment method and application thereof, and belongs to the technical field of biological medicines.The establishment method of the double-gene rare variation and disease relevance prediction model comprises the following steps that S1, a sample library is screened; s2, performing quality control on whole exome sequencing data (WES); s3, performing phenotype screening; s4, performing grouping design; s5, carrying out PheWAS logistic regression analysis; s6, performing Firth logistic regression analysis and verification; and S7, carrying out double-gene feature analysis and double-gene pathogenicity relevance prediction. The method for analyzing the correlation between the rare double-gene variation and all disease phenotypes is designed for the first time, and a new method is provided for screening hereditary pathogenic factors of various diseases.

The application relates to the technical field of gene sequencing data processing and bioinformation analysis, in particular to a Web-based whole-exome sequencing tumor gene mutation detection visual analysis method and system. The system collects user sequencing data and a reference genome version through a Web interactive interface; a program is called to perform quality control cleaning and evaluation on the data, and a visual report is generated; sequence alignment is completed based on the reference genome, and a variation site is identified through algorithm iteration; biological annotation of the variation is combined with a database, a candidate pathogenic mutation set is screened out in multiple levels according to a strategy, the candidate set is projected to a visual interface, a site state is confirmed or removed in response to a manual checking instruction, and a final gene mutation detection report is generated. The application greatly simplifies the whole-exome sequencing data processing procedure, makes it easy for clinical doctors or researchers without bioinformation background to start, and improves the popularization rate and work efficiency of tumor gene detection work.

The application relates to the field of intelligent medical treatment, in particular to a new antigen load calculation method, equipment and program product. The method comprises the following steps: S1, obtaining sequencing data of a to-be-tested person, wherein the sequencing data is exome sequencing data or panel sequencing data; S2, extracting sequence and structural features of an antigen peptide, sequence and structural features of an MHC binding groove and sequence of a TCR variable region based on the sequencing data; S3, calculating MHC-antigen peptide affinity based on the sequence and structural features of the MHC binding groove and the sequence and structural features of the antigen peptide; S4, calculating the probability of TCR recognizing MHC-antigen peptide based on the sequence of the TCR variable region; and S5, calculating the new antigen load of the to-be-tested person based on the probability of TCR recognizing MHC-antigen peptide and the MHC-antigen peptide affinity. The method can calculate the new antigen load and has good clinical value.

A computer-implemented, machine learning method for generating clone-specific tumor data includes obtaining a phased transcriptome file, a phased transcript file, and a clonal structure that represents a tumor clonal structure and comprises one or more nodes. For each node of the clonal structure: input mutational pools are determined for mutating the phase transcriptome file and the phased transcript file; DNA sequence reads are sampled from the phased transcript file and the sampled sequence DNA reads are mutated; RNA sequence reads are sampled from the phased transcriptome file and the sampled RNA sequence reads are mutated; a mutated genome is generated using the mutated DNA sequence reads; and a mutated transcriptome is generated using the mutated RNA sequence reads. The method has applications including, but not limited to, use cases in medical AI / healthcare for optimization of predictions or to support decision-making.

The application belongs to the technical field of bioinformatics, and relates to a method and system for detecting structural variation in transcript data of three-generation whole-exome sequencing, comprising: establishing a transcript set to be searched for structural variation; mapping and annotating transcript information according to TAGET software, cyclically reading the transcript set, and obtaining first structural variation of all transcripts; mapping transcript information according to Hisat2 software, cyclically reading the transcript set, and obtaining second structural variation of all transcripts; filtering and screening the first structural variation and the second structural variation respectively, and generating screened transcripts of the first structural variation and the second structural variation; and comprehensively screening the screened transcripts of the first structural variation and the second structural variation, and obtaining the final structural variation of the transcripts. The application improves the positive rate of structural variation and greatly reduces false positives by cross comparison of results of two kinds of software and strict control of the difference value of exon breakpoints between the transcript and the reference transcript.

The application discloses a quality control method for detecting copy number variation of second-generation whole exome sequencing and application. The quality control method comprises the following steps: obtaining whole exome sequencing data of a sample to be detected, and calculating average sequencing depth of a probe region; matching the average sequencing depth of the sample to be detected with average sequencing depths of reference samples in different batches; if a matching condition is met, selecting corresponding depth reference sample sequencing data, and performing subsequent quality control on the sample to be detected; if the matching condition is not met, the sample to be detected cannot be used for copy number variation analysis; calculating STD and NEDDI of the sample to be detected, and comparing the STD and NEDDI with a quality control model; if the STD and NEDDI are located below a curve of the quality control model, the sample to be detected passes the quality control; otherwise, the sample to be detected does not pass the quality control. The application innovatively performs quality control grading in a data preprocessing stage, judges whether a sample is suitable for copy number abnormal genetic analysis, significantly improves variation detection accuracy and reliability, and provides efficient technical support for clinical practical application.

The invention belongs to the technical field of gene engineering, biomedicine and molecular diagnosis, and discloses a hearing disorder related HSD17B7 gene mutant and application thereof, the HSD17B7 gene mutant is an autosomal dominant mutant, and the mutation site of the HSD17B7 gene mutant is that the 544th nucleotide G of an HSD17B7 gene coding region on a human 10th chromosome is mutated into T (c.544Ggt; and the 182th amino acid in the protein sequence is mutated from glutamic acid E to a termination codon (p.E182 *). Through exon group sequencing analysis and animal experiment verification on members of the deafness disease family, the invention determines that the mutation has obvious correlation with deafness for the first time, reveals the key effect of the mutation as deafness causing mutation, and constructs a diagnostic kit capable of early identifying the mutation according to the key effect. The method can be widely applied to screening and molecular diagnosis of deafness high-risk groups, and has important scientific research value and clinical application prospect.

The invention relates to a scoring model, method and system for screening tumor neoantigen peptides, and the scoring model is used for screening tumor neoantigen peptides. The construction method comprises the following steps: acquiring whole exome sequencing data of tumor tissues and normal tissues of a plurality of subjects, whole transcriptome sequencing data of the tumor tissues and immunogenicity results of variant polypeptide fragments; processing the sequencing data to obtain a score feature corresponding to each variant polypeptide fragment; and screening the scoring features by using a Lasso regression model to obtain scoring key features and regression coefficients thereof, and establishing a tumor neoantigen peptide scoring model. The scoring model constructed by the invention has better prediction efficiency, and is suitable for screening personalized tumor neoantigen peptides.

The invention relates to a somatic mutation detection method and device based on interpretable artificial intelligence and a computer storage medium, and belongs to the technical field of gene detection. And performing whole exome sequencing on the tumor-control sample by using a next-generation sequencing technology. Mutation data analyzed by various somatic variation detection tools are used as input features, and a deviation value var = (FP-FN) / (TP + FP) of each detection tool is calculated and extracted as a response variable. On the basis of a H2O AutoML framework, modeling and simulated annealing algorithms are integrated, an optimal variation detection tool combination strategy for a single sample is dynamically selected, and the challenges of diversification of sequencing depth, tumor purity and mutation detection algorithms in tumor mutation load evaluation are solved. Compared with a classic somatic variation detection tool, the method shows excellent prediction performance and robustness. The interpretability of the method provides necessary transparency for a complex machine learning model, fills the blank between calculation output and clinical interpretation, and promotes the application of the method in conversion research and clinical decision.

The application provides a processing method and processing system of whole exome sequencing data and a system for detecting short tandem repeat disease-related abnormal amplification. The application defines the STR-related genes that can be detected in the sample by the actual sample true coverage in the WES sequencing data, which is more accurate than the evaluation of whether the bed region of the WES probe and the bed+flanking region overlap. The processing method of whole exome sequencing data provided by the application is less affected by different algorithms, different sequencing platforms, different probes and different alignment software, and the data results obtained are more accurate.

The invention belongs to the field of medical diagnosis, and particularly relates to adolescent idiopathic scoliosis disease-causing gene mutation and a diagnostic reagent based on the adolescent idiopathic scoliosis disease-causing gene mutation, and it is found for the first time that ESR1 gene mutation (chr6: 151, 880, 656Agt, G, hg38) can cause adolescent idiopathic scoliosis disease through an exon group sequencing technology. Research results of the invention can be used for early screening of adolescent idiopathic scoliosis virulence gene mutation carriers to provide prenatal and postnatal rearing guidance on one hand, and can provide molecular diagnosis basis for adolescent idiopathic scoliosis patients on the other hand to provide a new direction for research and development of related scientific research and medical diagnosis products on the other hand, so that the research and development of the adolescent idiopathic scoliosis virulence gene mutation carriers can be promoted. Wide application prospects and market values are realized.

The invention relates to the technical field of somatic variation detection, and discloses a somatic variation detection method and device based on whole exon sequencing data of normal blood, and the method comprises the following steps: obtaining variation sites of blood whole exon sequencing data from normal blood; sequentially carrying out filtering, haploid typing and haplotype classification pretreatment on the variation sites; and inputting the preprocessed variation sites into the somatic variation prediction model to obtain somatic variation in normal blood. According to the method, variation sites in whole exon sequencing data from a normal blood sample are adopted, are sequentially subjected to filtering, haploid typing and haplotype classification preprocessing and then are input into the somatic cell variation prediction model, so that the somatic cell variation detection result is obtained, and the somatic cell variation detection accuracy can be improved.

The invention discloses a single gene level copy number variation recognition method, system and equipment based on a whole exome sequencing queue and a medium. The method comprises the following steps: recognizing CNV at a single gene level by using WES short read length data; the method not only can identify the copy number variation of a single exon, but also can identify the copy number variation of a cross-exon, so that CNV identification at a single gene level is really realized, the problem of false positive caused by the difference of sequencing data is avoided, and the diagnosis accuracy is effectively improved. And diagnosis of rare single-gene genetic diseases is facilitated.

The invention discloses a method for dynamically identifying low-abundance mutation of a tumor gene based on an isolated forest algorithm. The method comprises the following steps: constructing a background noise frequency database; extracting multi-dimensional characteristics of sequencing depth, mutation support number and base quality of a to-be-detected mutation site of the tumor gene; and calculating the path length and the abnormal score of the mutation site by using an isolated forest algorithm, dynamically adjusting the feature weight through the confidence coefficient, and distinguishing the real mutation from the background noise. According to the method, the calculation efficiency is improved, the method is suitable for large-scale tumor gene next-generation sequencing data analysis, the adopted isolated forest algorithm has linear time complexity, compared with a traditional machine learning method, the method has higher calculation efficiency, and the method is suitable for large-scale analysis of high-throughput sequencing data such as whole exome sequencing WES or whole genome sequencing WGS.

The application provides a miscarriage risk assessment method and system of whole-exome load and serum markers, comprising: independently scoring each variation in whole-exome sequencing data to obtain a comprehensive pathogenic score; constructing a gene pathogenic load matrix; mapping the gene pathogenic load to a biological pathway to obtain a pathway comprehensive score and construct a model; solving the model by jointly optimizing the regularization strength parameters of the two-layer ridge penalty terms, outputting significant pathway features; standardizing and preprocessing serum detection data; jointly modeling the output significant pathway features and the output standardized serum data under the same logistic regression probability framework, outputting a miscarriage-related risk probability value, and performing probability stratification. The application also outputs a double-track coordination coefficient to quantify the directional consistency of genetic signals and serum signals. It is significantly superior to a single omics solution and provides a high-precision individualized risk assessment tool for recurrent miscarriage with normal karyotype.

The application discloses a gene marker combination for evaluating HLH risk and use thereof, and belongs to the technical field of gene detection. The gene marker combination is obtained by whole genome association analysis of whole exome sequencing, covers more extensive genetic information, solves the narrowness of the prior art, and provides more comprehensive analysis of the polygenic heterogeneity of HLH. Moreover, the cumulative effect of alleles is comprehensively considered, the genetic susceptibility characteristics of an individual to HLH can be more accurately reflected, and the sensitivity and specificity of gene detection can be improved.

The application discloses a site detection limitation prediction method and device in whole exome sequencing, equipment and medium, relates to the field of biology and precision medicine high-throughput sequencing and variation detection technology, and comprises the following steps: extracting target sequence and original sequence from a reference genome based on coordinate information of a target variation site, obtaining multi-dimensional sequence characteristics of the target sequence; determining the distance value between each target variation site and the capture probe region, and counting the coverage of each target variation site according to the distance value; mutating the original sequence, simulating sampling of the mutated sequence, obtaining each simulated sequence, comparing the position of each simulated sequence with the reference genome, and obtaining the alignment quality value and alignment position consistency proportion of each simulated sequence; and predicting the detection limitation of the target variation site based on the multi-dimensional sequence characteristics, the coverage, the alignment quality value and the alignment position consistency proportion. The detection limitation of the site in the whole exome sequencing can be accurately predicted.

The specification of the present disclosure relates to a device, method, and computer program for searching for genes in a synthetic lethality relationship by using a Gaussian restricted Boltzmann machine. According to any one of the above-described means for solving the problem, a gene in a synthetic lethality relationship with a target gene may be output through an artificial intelligence model trained by receiving a training data set including an mRNA expression value in RNA Seq data, the presence or absence of a mutation in WES data, and a dependency score in CRISPR KO data. In addition, it is possible to increase the efficiency of anticancer treatment by using the genes in a synthetic lethality relationship calculated by using the trained artificial intelligence model. In addition, it is possible to present a treatment route with low drug resistance by using the genes in a synthetic lethality relationship calculated by using the trained artificial intelligence model.

The invention discloses a chromosome karyotype analysis method, device and equipment and a medium, and relates to the technical field of chromosome analysis, and the method comprises the following steps: carrying out genome comparison on whole exome sequencing data of a biological sample to be detected based on a preset mapping relation file to obtain a target counting matrix of a sequencing read segment, performing variation detection on the whole exome sequencing data to obtain a variation detection result file; determining sequencing read difference information and statistical significance information according to the target counting matrix and the standard counting matrix to obtain a first karyotype result; counting the number of variation sites of each chromosome of the biological sample to be detected based on the variation detection result file, and calculating allele frequency value density distribution of target chromosomes of which the number of variation sites reaches a preset variation site number threshold to analyze the target chromosomes to obtain a second karyotype result; and determining a target chromosome karyotype analysis result according to the first karyotype result and the second karyotype result. And full-exome sequencing data is directly and automatically processed.

The invention belongs to the technical field of bioinformatics and gene detection, and particularly relates to a method for realizing high-accuracy and high-resolution human leukocyte antigen HLA typing based on whole exon sequencing data. By incorporating complete exon and intron sequences, different alleles sharing a G-DOMAIN sequence are effectively distinguished, HLA-AP pseudogene sequence correction is combined, typing errors caused by pseudogene interference are effectively avoided, and the anti-interference capability is high. The HLA typing method provided by the invention is adaptive to high and low coverage WES data, can still obtain an accurate typing result for NGS data of fragmented or limited DNA samples, does not need strict coverage screening, and is wide in adaptability. The HLA allele dictionary can be conveniently expanded along with updating of the IMGT / HLA database. According to the HLA typing method, high-resolution typing can be achieved, the typing accuracy of high-coverage NGS data reaches 100%, and the typing precision is high.

The invention discloses a locus detection limitation prediction method, device, equipment and medium in whole exome sequencing, and relates to the technical field of biology and precision medicine high-throughput sequencing and variation detection.The method comprises the steps that a target sequence and an original sequence are extracted from a reference genome based on coordinate information of a target variation locus; obtaining multi-dimensional sequence features of the target sequence; determining a distance value between each target variation point and the capture probe area, and counting the coverage condition of each target variation point according to the distance value; mutating the original sequence, performing simulation sampling on the mutated sequence to obtain each simulation sequence, and performing position comparison on each simulation sequence and a reference genome to obtain a comparison quality value and a comparison position consistency ratio of each simulation sequence; and predicting the detection limitation of the target variation site based on the multi-dimensional sequence features, the coverage condition, the comparison quality value and the comparison position consistency ratio. The prediction of the locus detection limitation in the whole exome sequencing can be accurately realized.

A computer-implemented machine learning method for generating clone-specific tumor data is provided, the method comprising obtaining a phasing transcriptome file, a phasing transcript file, and a clone structure representing a tumor clone structure and comprising one or more nodes. For each node of the clone structure: determining an input mutation pool for mutating the phasing transcriptome file and the phasing transcript file; sampling DNA sequence reads from the phasing transcript file, and mutating the sampled sequence DNA reads; sampling RNA sequence reads from the phasing transcriptome file, and mutating the sampled RNA sequence reads; generating a mutated genome using the mutated DNA sequence reads; and generating mutated transcriptomes using these mutated RNA sequence reads. The method has applications including, but not limited to, use cases for optimizing predictions or supporting decisions in medical AI / healthcare.

The application belongs to the field of biomedicine and particularly relates to a functional genomic region biomarker combination for diagnosing high myopia. Specifically, according to whole-exome sequencing data, correlation analysis is performed, the SNP sites with the most significant p values are screened, and the susceptibility risk prediction of two independent groups is performed, and the results show that these sites can significantly and effectively distinguish normal people and high myopia people.

The application discloses a reagent for detecting long fragment deletion mutation of an FHOD3 gene and application thereof, and belongs to the technical field of biological medicine. The reagent comprises nucleic acid molecules specifically recognizing long fragment deletion mutation of an intron starting region of the FHOD3 gene, in particular primers and probes for deletion mutation of the 12th-14th exon and / or deletion mutation of the 15th exon. The application first discovers and verifies the two pathogenic deletion mutations closely related to hypertrophic cardiomyopathy. In cooperation with a microdroplet digital PCR technology, the reagent has a sensitivity of 99%, a specificity of more than 95%, good repeatability, and an accuracy of 95%-99%. The new detection rate reaches 40% in a patient family with a negative result of previous whole-exome sequencing, effectively making up for the deficiency that the prior art cannot detect long fragment deletion in an intron starting region, and the reagent is suitable for gene diagnosis, family genetic screening and genetic consultation of hypertrophic cardiomyopathy.

The invention discloses a method for detecting different types of variations through family whole exome sequencing data, and belongs to the field of detection analysis, and the method comprises the following steps: S1, uploading a sequencing file and a family file to a PedMine2 website; s2, detecting chromosome number abnormality based on comparison of sequencing depths and heterozygous mutation proportions of different chromosomes; s3, comparing genotype distribution of parent specific carrying mutation to perform chromosome number abnormality typing; s4, based on deep learning, performing classification identification on a feature image formed by feature genotype combination mutation in Trios to realize CNV detection, positioning and annotation; and S5, obtaining pathogenicity evaluation and a visualization result. The PedMine2 integrates variation detection, variation annotation and variation pathogenicity evaluation, and compared with traditional experimental or analysis methods such as karyotype analysis, short tandem repeat amplification and exon sequencing depth detection copy number variation, the PedMine2 is higher in flux and more accurate in result. And for clinical genetic disease diagnosis and basic scientific research, the method has a relatively high application value.