387 results about "Mutation detection" patented technology

The invention discloses an intelligent substation communication link fault self-healing regulation and control method and system, and relates to the technical field of fault self-healing regulation and control, and the method comprises the following steps: constructing a link disturbance intensity sequence reflecting link quality fluctuation; performing trend fitting and mutation identification on the link disturbance intensity sequence based on a sliding window mechanism, and generating a link state transaction index; when the link state transaction index exceeds a first threshold value, identifying an affected key easy-to-disturb service set according to the service path topological data of the abnormal link; constructing a context-aware path migration cost model based on the key easy-to-disturb service set, and generating an optimal switching path sequence; and implementing hierarchical link regulation and control according to the path switching optimal sequence. According to the method, trend fitting and mutation detection are carried out by adopting an overlapped sliding window mechanism and a mutation scoring function, so that intermittent or hidden link degradation abnormity which is difficult to capture can be effectively identified, and a link state transaction index has higher judgment precision.

The invention provides an SNP (Single Nucleotide Polymorphism) site of an ABO blood group variant, and an A subtype variant blood group identification kit and method, and relates to the technical field of biology. The SNP sites of the ABO blood group variant comprise the 155th c.155Ggt from an initiation codon of an ABO gene coding region, and the 155th c.155Ggt from the initiation codon of the ABO gene coding region; mutation of T. A substance for detecting the SNP site of the ABO blood group variant can be applied to preparation of an A-subtype variant blood group identification method and product so as to be effectively applied to rapid detection of patients and blood donors, the A-subtype variant blood group of an individual can be defined, and possible blood transfusion reaction can be prevented in time.

The invention provides an SNP (Single Nucleotide Polymorphism) site of an ABO blood group variant, and a B subtype variant blood group identification kit and method, and relates to the technical field of biology. The SNP (Single Nucleotide Polymorphism) sites of the ABO blood group variant comprise the 842th c.842Cgt from an initiation codon of an ABO * B.01 allele coding region of an ABO gene; mutation of T. A substance for detecting the SNP site of the ABO blood group variant can be applied to preparation of a B subtype variant blood group identification method and product, so that the substance can be effectively applied to rapid detection of patients and blood donors, the B subtype variant blood group of an individual can be defined, and potential possible blood transfusion reaction can be prevented in time.

The invention relates to the field of medical image analysis, in particular to a lung adenocarcinoma EGFR gene mutation detection system and method based on PET / CT deep learning, and the system comprises a data collection module which is used for obtaining PET / CT image data and clinical information of a lung adenocarcinoma patient; the image preprocessing module is connected with the data acquisition module and is used for carrying out standardization processing and ROI extraction on the PET / CT image data; the feature extraction module is connected with the image preprocessing module and used for extracting depth features, metabolic parameter features and CT sign features from the PET / CT image data; the multi-modal data fusion module is connected with the feature extraction module and used for fusing the extracted multi-modal features; and the prediction model module is connected with the multi-modal data fusion module and is used for predicting the lung adenocarcinoma EGFR gene mutation state and prognosis based on the fusion features, and the prediction accuracy is improved through multi-modal data fusion and deep learning technologies.

The invention relates to the technical field of power system monitoring, in particular to a bus duct temperature and humidity abnormity monitoring method, which comprises the following steps of S1, periodically acquiring temperature and humidity data through a temperature and humidity sensor, and performing data preprocessing to obtain processed data; s2, mutation detection and trend analysis are carried out according to the processed data, and mutation data and trend data are obtained respectively; s3, according to the abrupt change data and the trend data, a weighting strategy is adjusted in a self-adaptive mode; when the method is used, the credibility of data can be improved, the noise and errors of the sensor can be effectively reduced, the adaptability of the system can be improved, the data subjected to denoising and abnormal value elimination can be used as high-quality input of mutation detection, trend analysis and time sequence prediction, the accuracy of the whole temperature and humidity abnormity monitoring is improved, and the reliability of the system is improved. And short-time window detection and long-time window detection are integrated, so that the accuracy of anomaly monitoring is improved, false alarm caused by purely based on mutation detection is avoided, and the response speed of the method is improved.

The invention relates to the technical field of debugging systems, in particular to an adaptive debugging system and method for touch screen FW parameters, and the system comprises a signal acquisition module, a trend triggering module, a temperature compensation calculation module, a window generation module and a parameter output module. According to the invention, the edge of the driving voltage is carefully collected, the sliding difference algorithm is introduced to filter out high-frequency noise, meanwhile, real-time sampling of environment temperature and baseline capacitance adjustment are combined, the influence of external variables on signals can be dynamically corrected, and accurate judgment of capacitance fluctuation is improved by utilizing periodic delay deviation and regression trend analysis. By means of main frequency feature extraction and zero crossing point sudden change detection, threshold setting can be refined, self-adaptive window expansion can be achieved, touch signal response is kept sensitive and consistent all the time, adjustment and optimization can still be automatically completed even under the condition that the temperature changes frequently or signal interference is complex, parameter drift and debugging failure are effectively prevented, and user experience is improved. And the parameter automatic matching efficiency and the finished product consistency in batch production are improved.

The invention relates to the technical field of plant phenotype data processing, in particular to a plant phenotype automatic acquisition and feature processing method oriented to a multi-element environment. According to the method, the plant multi-modal phenotypic data are synchronously collected through the Internet of Things nodes, the coupling strength is calculated in combination with the environmental factors, the phenotypic parameters are divided into an environment sensitive type and a steady type, and the data classification accuracy is improved. A growth trend field is constructed by means of phenological phase decoupling analysis and geographic space superposition, abnormal segments are dynamically eliminated by combining physiological synergy degree and a mutation detection algorithm, and trend analysis stability is enhanced. A phenotype-environment response map is generated through standardized processing and an environment equivalent value model, a key environment sensitive window is recognized, a regional and staged cross-environment regulation scheme is output, intelligent and accurate management of the crop growth environment is achieved, and the method is suitable for agricultural yield increase and breeding optimization.

A method of biological sample analysis includes subjecting a biological sample comprising nucleic acid to an amplification reaction conducted in the presence of a detector probe and a calibrator probe, wherein the detector probe is configured to specifically interact with a first region of a wild type target nucleic acid sequence and emit a first emission signal in response to the interaction, and wherein the calibrator probe is configured to specifically interact with a second region of the wild type target nucleic acid sequence and emit a second emission signal in response to the interaction, the first region and the second region differing from each other. The method further includes calculating a ratio of the change in the first emission signal to the change of the second emission signal at a point in time of the amplification reaction.

The invention provides a cattle behavior recognition method based on a three-axis attitude angle, and belongs to the technical field of intelligent animal husbandry management. According to the method, three-axis attitude angle signals such as the yaw angle (Yaw), the pitch angle (Pitch) and the roll angle (Roll) of the head of a cattle are collected in real time through a gyroscope sensor arranged in wearable equipment such as a cattle ear tag. The method does not depend on images, accelerometers, microphones or other modalities, only based on attitude angle signals, multiple algorithms such as a time sequence sliding window, frequency domain analysis, weighted scoring, sudden change detection and state transition modeling are utilized, and multiple typical behaviors such as ingestion, rumination, water drinking, bed lying, limping, licking, fright jumping and low activity are recognized. The method is suitable for multiple cattle breeds such as dairy cattle, beef cattle and buffalo, has the advantages of low power consumption, lightweight calculation, low deployment cost, accurate identification and the like, and is convenient for realizing behavior monitoring and health management of large-scale cattle individuals. The algorithm path can be flexibly selected, but a core input signal is only limited to attitude angle data, so that the technical boundary of the patent protection of the method is formed.

The invention discloses a student mental health prediction method based on artificial intelligence, and particularly relates to the technical field of mental health prediction. A time sequence sample fusing multi-source behavior data is constructed, a deep learning model of an abrupt change detection mechanism is combined, the ability of recognizing short-term psychological abnormal changes of students is effectively improved, psychological risk signals are dynamically output when abrupt change occurs, and an individual intervention decision module is called to generate intervention suggestions, so that the accuracy of the short-term psychological abnormal changes is improved. Closed-loop management from risk identification to intelligent intervention is realized, the early warning capability and intervention timeliness of the system for sudden psychological crisis are remarkably enhanced, and the system has relatively high practical value and popularization prospect.

The invention belongs to the technical field of power system operation control, and relates to a real-time estimation method and system for comprehensive inertia containing a network construction VSG, a storage medium and electronic equipment, and the method comprises the following steps: S1, constructing a system model; s2, inertia constant state conversion is carried out; s3, mutation detection and correction are carried out; aiming at the problem of real-time evaluation when inertia sudden change occurs in a power system, on the basis of an improved unscented Kalman filtering algorithm, conversion from parameter estimation to real-time state estimation and verification and correction of inertia sudden change conditions are realized, the real-time performance and accuracy of an inertia evaluation result are ensured, and the real-time performance of the power system is improved. And the evaluation method of inertia real-time estimation is enriched.

The invention discloses an underground pipe network water quality sudden change detection system and method. Comprising a multi-parameter distributed sensing network module, a data transmission module, a data processing and analyzing module, a space-time traceability positioning module and a user interaction and alarm module, wherein the multi-parameter distributed sensing network module comprises various types of water quality sensors and hydraulic sensors; the data acquisition module is used for acquiring water quality parameter data and hydraulic parameter data of each node in real time; the invention relates to the technical field of water quality monitoring. According to the underground pipe network water quality abrupt change detection system and method, multi-dimensional data are collected in real time through a distributed anti-interference sensing network, and an intelligent closed loop from data collection to abrupt change recognition is constructed in combination with'mixed transmission architecture + improved Bayesian network fusion analysis + LSTM-AE deep learning detection '; a complex sudden change mode of multi-parameter cooperative change can be rapidly identified, and risk expansion caused by monitoring lag or shallow layer analysis is avoided.

A machine learning-assisted intelligent device for optimizing personalized cancer treatment based on genomic data, the device comprising the following: a genome data input unit configured to receive and preprocess genome sequences from a variety of external data sources, the preprocessing including noise reduction, sequence alignment, and filtering of irrelevant genome segments; a mutation detection unit comprising a deep learning-based processing module, wherein the mutation detection unit is configured to compare the preprocessed genome sequences with a database of known cancer-related mutations and biomarkers and to create a mutation profile associated with a patient's cancer; an optimization unit for machine learning models comprising at least one neural network architecture, wherein the optimization unit is configured to receive the mutation profile, analyze the identified mutations, correlate the mutations with historical patient outcomes, and generate an initial treatment recommendation; a treatment recommendation engine configured to use reinforcement learning algorithms, wherein the engine continuously learns from real-time patient responses to previous treatments and dynamically adapts treatment strategies based on updated genomic data and evolving patient conditions, thus providing an optimized treatment plan; an outcome prediction module configured to analyze potential outcomes of the recommended treatments based on predictive analysis models, whereby the module generates a probability value for treatment success and presents a detailed explanation of the correlation between genomic markers and the expected efficacy of the treatment; a secure data transmission interface configured for bidirectional communication with hospital networks and genome research centers, with the interface complying with data protection standards to protect sensitive patient information; a display interface configured to visually represent the results of genomic analyses, treatment recommendations, and outcome predictions, wherein the display interface allows user interaction to adjust treatment parameters based on clinical inputs; and a power management unit configured to regulate energy consumption, wherein the power management unit includes a cooling system to maintain the operating temperature of the device during high-performance data processing tasks.

The invention provides a method for evaluating the miss risk of gene editing based on targeted enrichment sequencing, which comprises the following steps: respectively screening out miss sequences by adopting a plurality of prediction software, summarizing the miss sequences to obtain a plurality of miss sequence tables, and selecting the miss sequences which appear in the miss sequence tables and have high potential miss risk as miss sites; performing high-depth sequencing on the off-target sites in the genomes of the to-be-detected sample and the control sample by adopting a TES method, comparing the obtained sequencing result with the reference genome, and performing mutation detection and structural variation detection on the comparison result to obtain mutation sites and structural variation sites only existing in the editing sample, the authenticity of the off-target site is identified; evaluating an off-target risk and an editing effect; and visualizing the off-target site information, and automatically generating an off-target risk assessment report. The evaluation method has high sensitivity and high specificity, the accuracy and sensitivity of gene editing off-target detection can be improved, and the overall sequencing cost is remarkably reduced.

In order to address problems in the prior art, the present invention provides a novel agent for raising the melting temperature (Tm) of a double-stranded nucleic acid, methods for detecting mismatching of a double-stranded nucleic acid or mutation with respect to a standard nucleic acid in a sample nucleic acid, and kits for carrying out these methods.Embodiments of the present invention include: an agent for raising the melting temperature of a double-stranded nucleic acid, said agent containing an amphoteric copolymer that includes a specific cationic constituent unit (1) and a specific anionic constituent unit (2); a method for detecting a nucleic acid mismatch and a method for detecting mutation with respect to a standard nucleic acid in a sample nucleic acid both using the agent for raising the melting temperature of a double-stranded nucleic acid; and a nucleic acid mismatch detection kit and a mutation detection kit including the agent for raising the melting temperature of a double-stranded nucleic acid and the method for detecting a nucleic acid mismatch or the method for detecting mutation.

The present disclosure relates to somatic mutation determinations. An example method includes performing whole genome sequencing using a high-throughput sequencing system on nucleic acids from a sample obtained from a subject to generate raw data, transmitting the raw data from the high-throughput sequencing system to a computing system, processing the raw data by the computing system to generate candidate variants and a set of feature values for each candidate variant of the candidate variants, processing the feature values by the computing system using a trained machine learning model to identify somatic mutations from the candidate variants, transmitting results based on the somatic mutations from the computing system to an end device, and displaying a report on the end device based on the results.

The invention discloses a psychological state dynamic evaluation and early warning system based on multi-modal behavior data, which belongs to the field of medical care informatics and comprises a multi-modal behavior data hierarchical coding module, a time sequence causal atlas construction and reasoning module, a double-stage self-adaptive early warning decision module and a context awareness intervention strategy generation module. A cross-modal association mode is extracted through a double-layer coding mechanism, a time sequence graph containing a causal relationship is constructed, causal reasoning is performed, a double-stage mechanism of short-term mutation detection and long-term trend prediction is adopted to generate graded early warning, and an optimal intervention strategy is selected based on a deep Q network according to a user situation. According to the method, the accuracy, timeliness and intervention effectiveness of psychological health assessment are improved, and dynamic monitoring and early warning of the psychological state are realized.

A molecular marker related to drought resistance of tomatoes and a use of the molecular marker, relating to the technical field of molecular breeding. The nucleotide sequence of the molecular marker is shown as SEQ ID NO. 4, and T / G mutation is present at position 22 bp of the sequence shown in SEQ ID NO. 4. A PARMS primer group for detecting the molecular marker comprises two allele specific primers and one marker site specific primer, the drought resistance of tomatoes can be accurately identified by using the PARMS primer group, and the drought resistance of materials can be determined at the time of young tomato seedling, so that the seed production cost is reduced, and the breeding efficiency is improved. Additionally, the transformation process of new tomatoes or other fruiting vegetables can also be accelerated.

The invention relates to the technical field of pathogenic bacterium detection, in particular to a method and a kit for detecting helicobacter pylori gene mutation in excrement. According to the technical scheme, the kit for detecting helicobacter pylori gene mutation in excrement is developed by combining multiple advanced technical means such as ARMS-PCR design, blocking primers, locked nucleic acid modification (peptide nucleic acid modification), MGB probes, nested PCR design and degenerate base design. The kit can detect nine gene mutation sites related to the drug resistance of helicobacter pylori at one time, and has a wide coverage range. The kit in the scheme can be used for detecting the drug resistance of quinolone antibiotics and / or clarithromycin antibiotics of helicobacter pylori, and the technical problem that a high-specificity and high-sensitivity excrement helicobacter pylori gene mutation detection method is lacked in the prior art is solved. The method is simple and convenient to operate and can be completed within 1-2 hours, the detection cost is reduced, and the method has ideal application and popularization prospects.

The invention belongs to the technical field of visual identification, and particularly discloses a thick plate saddle-shaped weld groove feature extraction and size measurement method and system. Comprising the steps that based on multi-scale geometric feature mutation detection, a current frame weld groove three-dimensional point cloud is segmented into a branch pipe face, a groove side wall, a backing weld surface and a main pipe face, and four area three-dimensional point clouds are obtained; according to the three-dimensional point cloud of each area, determining an intersection point of a branch pipe surface and a groove side wall, an intersection point of the groove side wall and a backing welding surface, an intersection point of the backing welding surface and a main pipe surface, an intersection point of the branch pipe surface and the main pipe surface, and a virtual intersection point of the groove side wall and an extension line of the main pipe surface as weld groove feature points; and constructing vectors between the feature points, and calculating the geometric dimension of the weld groove through vector operation. The method is based on multi-scale geometric feature mutation detection and adaptive point cloud segmentation, a fitting curve slope threshold value does not need to be preset, and the method can be adaptive to different included angles between the branch pipes and the main pipe in the pipe tubular joint.

The invention relates to the field of intelligent medical treatment, in particular to a prediction method for predicting MET 14 jump mutation. Comprising the following steps: S1, acquiring MET gene data of a patient, including a chromosome number, a physical coordinate, a reference genome basic group and a mutant basic group; s2, calculating the basic group change number of the reference genome basic groups and the mutation basic groups to obtain the basic group change number of the reference genome and the change number of the mutation basic groups; s3, the chromosome number, the physical coordinates, the reference genome base change number and the mutation base change number are input into a prediction model, and a prediction result whether MET 14 jump mutation exists or not is obtained. The method can be used for detecting classical shearing and hidden shearing of MET 14 jump mutation, the mutation detection rate is increased, and the method has good clinical value.

The invention relates to the technical field of bioinformatics, and discloses a method, a system, equipment and a medium for searching for base mutation aiming at third-generation full-length transcript sequencing data, the base mutation is accurately positioned to a specific transcript by directly processing the third-generation full-length transcript sequencing data, and the method and the system for searching for the base mutation aiming at the third-generation full-length transcript sequencing data are provided. The co-occurrence relation of a plurality of mutations on the same transcript is accurately analyzed, and the defects of calculation redundancy, function misjudgment and the like caused by the fact that a mutation transcript source cannot be determined due to fragmentation splicing and the co-occurrence is simulated by depending on permutation and combination in a second-generation short-read-long sequencing technology are effectively overcome; the recall rate of transcripts which are not mapped due to a high-variation region is improved through mapping correction assisted by structural variation, and false positive is effectively inhibited through a multi-dimensional filtering condition, so that the sensitivity and reliability of mutation detection are remarkably improved; particularly, mutation events with function remodeling due to reading frame change can be accurately recognized in scenes such as neoantigen prediction where mutation function consequences need to be accurately evaluated, and the method has important value in application in the fields of precision medical treatment and the like.

The invention discloses a time sequence change extraction and mutation detection method for a water area of a reservoir in a plain farmland area. Acquiring a spatial position and a historical maximum water area range of the target reservoir; preprocessing the Sentinel-1 radar image covering the maximum water area of the reservoir and the surrounding land area, and then carrying out water body crude extraction; removing farmland elements by using optical land utilization data matched with the radar image, and removing mountain shadows; abnormal values are removed through sliding window detection, intra-month mean value synthesis is carried out on non-abnormal values, and a monthly water body area time sequence of the reservoir is obtained; and performing mutation detection by using a PELT algorithm, and identifying a mutation time point of water body change. The method does not depend on field observation data, efficiently monitors the water body area change of the reservoir in the plain farmland area, analyzes the response characteristics of extreme events, adapts to a complex environment, timely identifies the water body area sudden change time point, contributes to water resource management and protection, and ensures the safety of regional drinking water resources.

The invention discloses a current abrupt change identification method based on comparative learning. The method comprises the following steps: S1, collecting three-phase current time sequence data and generating a sample; s2, performing normalization and noise suppression on the sample to obtain a standardized time sequence vector; s3, constructing positive and negative sample pairs based on the current change trend for comparative learning; s4, inputting the sample pair into the multiple perceptual hash network to generate a fusion vector; s5, through channel difference calculation and residual fusion, extracting mutation features; s6, introducing a time position coding gating mechanism, and dynamically adjusting the channel weight; s7, optimizing a model structure and parameters by adopting an improved black hole optimization algorithm; and S8, performing mutation detection based on similarity calculation and outputting a time position. According to the invention, high precision and high robustness of current abrupt change detection are realized, the power system monitoring efficiency is improved, and the method is suitable for intelligent fault diagnosis in a complex power environment.

The invention provides a typhoon path prediction method and system fusing physical constraint and path mutation recognition. The method comprises the steps that a multi-dimensional input feature tensor is obtained through a feature tensor generation module; performing time modeling through a multi-scale modeling module to obtain a deep feature tensor, and performing significance guidance through a significance guidance module to obtain a prediction path sequence; the bending event identification module identifies a bending angle to obtain a plurality of path abnormal scores, the adversarial disturbance analysis module introduces a plurality of preset disturbances to determine the path confidence, and the path prediction output module outputs a path prediction result. According to the technical scheme of the embodiment of the invention, refined modeling can be carried out on the typhoon path, the PINNs physical constraint module is utilized to ensure that the prediction result meets the physical conservation principle, the path mutation detection capability is enhanced through bending event recognition, uncertainty evaluation is realized through disturbance analysis, and the accuracy of the typhoon path detection is improved. And a typhoon path prediction result which is more accurate and credible and has physical consistency is output.

The invention discloses a mitochondrial genome variation screening and automatic guide annotation platform, and a platform processor is configured to realize the following steps: data quality control: performing quality control on gene sequencing offline data to obtain a high-quality sequence; comparing data, namely comparing a high-quality sequence obtained after quality control to a human mitochondrial genome; re-comparing the data, and locally re-comparing the sequence of the region near the indel on the human mitochondrial genome; and mutation detection: carrying out gene mutation detection on the re-compared sequence. Mutation filtering: filtering gene mutation according to mutation frequency, sequencing depth and mutation reads number; mutation annotation: annotating the filtered gene mutation, and filtering the gene mutation again according to an annotation result after annotation of the gene mutation is completed. And quality control analysis: carrying out statistical analysis on data quality control according to the data volume, the comparison rate, the insert size and / or the coverage uniformity index.

The invention discloses a detection kit and method for Mycobacterium tuberculosis complex and rifampicin drug-resistant mutation, and belongs to the technical field of biologication.A Taqman probe and a molecular beacon are combined, a fluorescent signal is generated based on a Taq enzyme hydrolysis fluorescent probe, the fluorescent signal is accumulated, and the molecular beacon is used for detecting the drug-resistant mutation of the Mycobacterium tuberculosis complex and the rifampicin drug-resistant mutation of the Mycobacterium tuberculosis complex and the rifampicin drug-resistant mutation. According to two technical principles that a molecular beacon and a fluorescent product are hybridized to generate a fluorescent signal, fluorescent PCR amplification is adopted for detection of one target spot in a monochromatic fluorescent channel, and a melting curve mode of a probe and a target gene is adopted for detection of the other target spot. Therefore, two target spots can be detected and analyzed in a monochromatic fluorescence channel at the same time. Single-tube multiple detection is achieved, and mycobacterium tuberculosis complex group detection and mycobacterium tuberculosis rifampicin drug-resistant mutation site detection can be achieved at the same time.

The invention relates to the technical field of biological medicine, in particular to an RNF / CLR / Cas / crRNA compound as well as a preparation method and application thereof. The RNF / CLR / Cas / crRNA compound disclosed by the invention is composed of an RNA nanoflower (RNF), a circular fluorescent reporter molecule (CLR) and a Cas / crRNA compound, and the circular fluorescent reporter molecule and the Cas / crRNA compound are combined on the RNA nanoflower through base complementary pairing; the Cas / crRNA compound is composed of a Cas protein and crRNA, and the crRNA is used for specifically recognizing the crRNA of a nucleic acid molecule. The RNF / CLR / Cas / crRNA compound can be used for nucleic acid molecule specificity detection and nucleic acid molecule base mutation detection, can also be used for fluorescence in situ hybridization tissue imaging, and has a good application prospect.

The invention belongs to the technical field of gene detection, and particularly relates to application of a CFTR gene mutation combination, an amplification reagent and a product in preparation of a CF risk assessment product. The CFTR gene mutation combination consists of an intron region mutation combination and an exon region mutation combination, based on the CFTR gene mutation combination, the invention further develops an amplification reagent and a CFTR gene mutation detection product, the amplification reagent and the CFTR gene mutation detection product comprise a primer combination for multiplex PCR amplification of the CFTR gene mutation combination, and the primer combination has the characteristics of high accuracy, high specificity and high sensitivity, can accurately detect related gene mutation, and provides a reliable basis for risk assessment and diagnosis of CF. When the amplification reagent and the CFTR gene mutation detection product are combined with sweat chlorine detection for use, the diagnosis rate of CF can be remarkably improved, and the amplification reagent has important application value in clinical diagnosis of CF and is expected to provide more powerful support for early diagnosis and treatment of CF patients.

The invention belongs to the technical field of genes, and particularly relates to an amplification primer group of a renal cyst disease gene PKD1, a mutation detection method and application, the amplification primer group comprises four pairs of primers for specifically amplifying first to 34 exons of the PKD1 gene; the mutation detection method comprises the following steps: acquiring genome DNA (Deoxyribose Nucleic Acid); carrying out ultra-long fragment PCR amplification on the genome DNA through the primers, carrying out electrophoresis detection, and purifying a target fragment; mixing the purified amplification product with genome DNA in proportion, constructing a sequencing library, and performing high-throughput sequencing through target area capture; and analyzing sequencing data, and screening pathogenic PKD1 gene mutation. Compared with the prior art, the PKD1 ultra-long fragment PCR amplification primer and the renal cyst disease gene PKD1 mutation detection method have the advantages that true and false genes of PKD1 can be distinguished, the diagnosis accuracy is greatly improved, the operation process is simplified, the timeliness is improved, and the detection flux is improved.