55 results about "Genetic Sequence Databases" patented technology

The invention discloses a real-time data subscription method and device based on a time sequence database and a medium, belongs to the technical field of databases, and aims to solve the technical problems that the real-time performance is poor, the resource consumption is large and the time sequence data required by the current time sequence database in real time acquisition in mass data storage and processing is poor. According to the technical scheme, the method comprises the steps that a data subscription scheduling task is constructed through a predefined SQL statement, in combination with an efficient data change capture module based on a Raft log mechanism, data subscribed by a user is obtained in real time, and the data subscribed by the user is filtered according to a customization rule; then the screened data is automatically transmitted to a preset target sink component in a specified format, so that the whole data processing flow has the characteristics of real-time performance, accuracy and high availability; the method specifically comprises the following steps: constructing a data subscription scheduling task; executing the data subscription scheduling task; and capturing data change based on the raft log.

The invention provides an enzyme modification method based on artificial intelligence. The enzyme modification method comprises the following steps: training an AlphaFold2 model to obtain a glycosyltransferase tertiary structure prediction model; performing multi-sequence comparison on the protein and nucleic acid sequence database and the non-redundant protein feature sequence database based on the active point location recognition model to obtain a comparison result; initializing a matrix of which the length is equal to that of a comparison result, so as to construct a PSSM matrix and screen out potential mutation sites; according to the screened potential mutation sites, constructing an initial mutation library to form a virtual screening mutation library; performing structure reconstruction on the sequence in the virtual screening mutation library based on a glycosyl transferase tertiary structure prediction model so as to predict the glycosyl transferase tertiary structure and perform molecular docking and dynamic simulation on the glycosyl transferase tertiary structure; the method is used for analyzing binding pockets of proteins and ligands and determining dynamic interaction of amino acid residues and ligands in the binding pockets, and the contact number of each amino acid residue and the ligands is used for modifying the tertiary structure of the glycosyltransferase.

The invention discloses a method for constructing a lung cancer early screening model based on TCR sequencing, which comprises the following steps of: firstly, constructing a TCR-enriched sequence database based on TCR sequencing data from samples of lung cancer patients and healthy people, then performing TCR sequencing on each sample, and acquiring TCR characteristics of each sample; comprising proportion characteristics of V gene and J gene, statistical characteristics of a TCR immune repertoire, convergence sequence characteristics, TCR sequence amino acid proportion characteristics, proportion characteristics of TCR sequences with different lengths, proportion characteristics of TCR sequences with different frequencies and TCR enriched sequence characteristics; screening the TCR features of each sample by using a feature screening method, and constructing a machine learning model in combination with other information of the samples to obtain a lung cancer early screening model; a lung cancer early screening detection device is constructed based on the lung cancer early screening model, and lung cancer early screening can be achieved based on TCR sequencing information of a sample.

The invention provides a compatible system and a compatible control method of a cross-time-sequence database, and relates to the technical field of data processing, and the compatible system comprises a service kernel management module which is used for receiving preset source configuration information, creating corresponding kernel service processes according to the preset source configuration information, and controlling the start and stop of the kernel service processes, the kernel service process is used for connecting a corresponding time sequence database; the proxy module is used for acquiring request information of a client; and the data source driving module is used for accessing the time sequence database through the corresponding kernel service according to the request information and receiving a query result of the time sequence database. According to the method, efficient, flexible and compatible data access of the cross-time-sequence database is achieved, a developer only needs to achieve an interface once, the system can automatically process interaction of different types of databases, the requirement for writing specific interface codes for each type of database is reduced, and therefore the development cost of an application program is reduced.

The invention relates to a tumor neoantigen prediction method and a tumor neoantigen prediction system. The method comprises the following steps: acquiring a plurality of amino acid sequences in a gene of a person to be tested as a plurality of test peptides to be compared with a plurality of human protein sequences in a protein sequence database so as to find out a plurality of similar peptides matched with the human protein sequences; filtering similar peptides from the test peptides, and respectively inputting the filtered test peptides into a plurality of trained human leukocyte antigen models to obtain a plurality of sequencing results of the test peptides; calculating the weighted sum of the sequencing of each test peptide in the sequencing result as the score of the test peptide; and selecting at least one of the test peptides as a neoantigen suitable for the subject according to the fraction.

A method for predicting DNA binding residues based on a three-segment sliding window is disclosed. Firstly, according to the input protein sequence information P for DNA binding residue prediction, the number of residues is L, the protein sequence database UniRef90 is searched using the PSI-BLAST program to generate a position-specific scoring matrix of size L×20, a solvent accessibility feature matrix of size L×3 is obtained using the SANN program, and a feature matrix of size L×20 is obtained by one-hot encoding. Then, the three feature matrices are concatenated into a super feature matrix of size L×43, that is, each protein residue corresponds to a 1×43 feature vector, and the similarity of the feature vectors between two residues is obtained by cosine similarity, that is, the residue similarity. Secondly, the feature view processing is performed on each residue in the super matrix by using the three-segment sliding window technology, and these feature views are expanded into feature vectors. Thirdly, the corresponding feature vectors and labels are generated by using the protein sequence of known DNA binding residues, and input into the support vector machine for training. Finally, the feature vector generated by the protein sequence to be predicted is input into the trained model to predict whether the residues in the protein sequence are DNA binding residues. The present invention has low calculation cost and high prediction accuracy.

The application relates to a method and system for mining and analyzing the correlation of clinical comorbidity of discharged patients. The method comprises the following steps: constructing an individual diagnosis and treatment narrative graph for each patient according to the discharge medical record text, generating a time sequence transaction sequence corresponding to each patient, and constructing a sequence database; based on the database, the original time sequence frequent pattern set is constructed by analyzing through an improved generalized sequence pattern algorithm; each pattern in the set is classified to obtain multiple classification clusters, and the original time sequence frequent pattern in each classification cluster is processed through multi-sequence alignment to construct a generalized clinical path graph, and the information in the generalized clinical path graph is extracted to generate a natural language abstract. The method improves the time sequence logic, knowledge abstraction degree and clinical interpretability of the clinical comorbidity correlation mining by constructing a diagnosis and treatment narrative graph, mining a time sequence frequent pattern, and constructing a generalized clinical path graph through clustering and multi-sequence alignment.

The invention provides a construction method of a reference sequence for metagenome data compression, which comprises the following steps: constructing a basic reference sequence database according to a sample source of metagenome data; based on the basic reference sequence database, constructing an index of the basic reference sequence database; according to the index of the basic reference sequence database, a first read length sequence is compared with the basic reference sequence database, a comparison result is obtained, and the first read length sequence is a read length sequence of a part of samples randomly selected in metagenome data to be compressed; and according to the comparison result, determining the sequence abundance distribution of the first read length sequence, and constructing the reference sequence for metagenome data compression.

The invention provides a method for comparative evolution analysis based on soybean-related WD40 genes, and relates to the technical field of plant molecular markers. The method for comparative evolution analysis based on the soybean-related WD40 gene specifically comprises the following steps: S1, identifying WD40 gene family members; s2, chromosome distribution characteristics; s4, performing phylogenetic analysis; s5, carrying out Ka / Ks analysis and collinearity analysis; s6, gene structure, Motif and protein structure; and S7, analyzing a cis-regulatory element. The mining and analysis method of the WD40 gene related to the gray mold and the salt stress of the wild soybean and the cultivated soybean is based on a whole genome sequence database, and 497 and 500 WD40 family members are respectively identified by utilizing a bioinformatics method according to the whole genome database of the wild soybean and the cultivated soybean, so that the WD40 gene related to the gray mold and the salt stress of the wild soybean and the cultivated soybean is obtained. Gene characteristics and evolution comparative analysis is carried out on the genes, and the analysis result provides important reference value for improving gray mold resistance and salt tolerance of the cultivated soybeans through genetic engineering means in the future so as to improve the yield of the soybeans and improve the quality of the soybeans.

The application discloses a method for collecting active proteins of water leeches, a detection method, an analysis method and application thereof, and relates to the technical field of biological medicines. The application provides the water leech seedlings treated by hunger and the living host treated by emptying, so that the water leech seedlings adsorb and suck the host, and then the active proteins are injected into the body of the host; the sample of the host after being sucked is collected, and then is made into a freeze-dried powder after being frozen and dried, so that the active proteins of the water leeches are obtained; the proteins are extracted from the freeze-dried powder, and then are subjected to enzymolysis treatment to generate peptide segments; the liquid chromatography-mass spectrometry technology is used to detect the peptide segments; and the mass spectrometry data are subjected to database searching analysis based on the protein sequence database of the water leeches and the host, so that the active proteins from the water leeches are identified and the relative content is determined. The application utilizes the characteristic that the active proteins are injected into the body of the host in the sucking process of the water leeches, and realizes efficient enrichment of the target proteins. In combination with the proteomics technology, high-throughput and high-sensitivity protein identification is realized.

The invention discloses a method, a system and a device for identifying the age of a green-head diving duck based on multi-feature fusion. The method comprises the following steps: shooting a high-definition image of a target individual; obtaining one or more of feather state information of an ear feather region, a wing region, a hypochondrium region, a shoulder and back region, a tail region, a head and neck region, a chest and abdomen region, eye iris and beak color change information and chest and tail distance and beak length data information of the target individual in the high-definition image, and performing quantitative processing to obtain feature parameters corresponding to each feature; performing multi-dimensional fusion analysis on the characteristic parameters through an integrated machine learning model to realize accurate matching with corresponding week age / month age data in a feather moulting standard time sequence database of the green-head diving ducklings / young birds; and outputting a week age / month age identification result of the target individual. According to the method, a moulting standard time sequence database covering a young bird stage of the green-head diving ducks for the first time is constructed, high-precision and high-efficiency non-injury age identification is realized through combination of the database and a machine learning model, and the dependence on professionals is reduced.

This invention relates to the fields of molecular biology and forensic identification, specifically to a method for bird species identification based on multi-gene co-amplification and nanopore sequencing. The method involves extracting genomic DNA from avian biological samples; using the extracted DNA as a template, amplification is performed using four independent PCR primer pools that specifically target the avian mitochondrial cytochrome C oxidase subunit I gene, cytochrome B gene, 12S rRNA gene, and 16S rRNA gene; after purification of the amplification products, a nanopore sequencing library is constructed and sequenced; finally, bioinformatics analysis, including read length clustering, draft consensus sequence generation, and polishing, is performed on the raw sequencing data to obtain consensus sequences for each target gene. Species identification is then completed by comparing the sequence with a avian sequence database. This method effectively solves the problem that existing technologies cannot simultaneously meet the practical needs of large-scale, rapid, and high-success-rate identification of avian biological samples.

The application discloses a kind of based on TCR sequencing lung cancer early screening model construction method, first based on the TCR sequencing data of lung cancer patient and healthy person sample is constructed TCR sequence database enrichment, then the TCR sequencing of each sample is carried out and obtains the TCR characteristics of each sample, including V gene and J gene The proportion characteristics of gene, the statistical characteristics of TCR immune group, convergence sequence characteristics, TCR sequence amino acid proportion characteristics, different length TCR sequence proportion characteristics, different frequency TCR sequence proportion characteristics and TCR enrichment sequence characteristics;The TCR characteristics of each sample are screened using feature selection method, and a machine learning model is constructed by combining other information of the sample to obtain a lung cancer early screening model.Based on the lung cancer early screening model, a lung cancer early screening detection device is constructed, which can realize lung cancer early screening based on TCR sequencing information of the sample.

The invention discloses a multi-objective decision-oriented utility sequence pattern mining method and system and application, and the method comprises the following steps: SF1, scanning a DNA sequence database, obtaining a DNA sequence with only a single item as a T1 sequence, and constructing a quantity utility array as a QU array by the T1 sequence; sF2, performing an SQUD pruning strategy on each T1 sequence; calculating a sequence weighted utility value of each T1 sequence based on the QU array, marking the sequence weighted utility value as SWU, constructing a maximum utility number array as MUQA, and deleting the T1 sequence of which the SWU is smaller than the corresponding MUQA value; sF3, an array SPFA of the sequence Pareto front is initialized, and a T1 sequence of the MUQA after non-dominated sorting is recorded; generating a projection database QUPro for the T1 sequence in the SPFA based on a QU array; traversing each T1 sequence in the MUQA and generating an extension mode; and SF4, calculating a utility upper bound by using the PEU, and recursively expanding the current sequence by controlling any elements in the SPFA by the PEU value and quantity. According to the method, by introducing the utility upper bound and the pruning strategy, unnecessary calculation is reduced, and the efficiency of the algorithm is improved.

Provided are an artificial intelligence-based early cancer diagnosis method and device using a method of inputting information on a cell-free DNA distribution of a tissue-specific regulatory region to an artificial intelligence model learned to early diagnose cancer and analyzing the information, and an information providing device and a storage medium.SOLUTION: A method for providing information for early cancer diagnosis based on artificial intelligence includes extracting a nucleic acid from a biological sample to obtain sequence information, arranging the obtained sequence information in a reference chromosome sequence database, selecting a nucleic acid fragment of a regulatory region based on the arranged sequence information, generating the selected nucleic acid fragment as image data, and inputting the generated image data to an artificial intelligence model learned to distinguish a normal image and a cancer image, analyzing the image data, and comparing the image data with a reference value to determine the presence or absence of cancer.SELECTED DRAWING: Figure 1

Embodiments of the present disclosure relate to a massively parallel processing database for sequence and graph data structures. The systems and methods disclosed herein may include a parallel processing graph database solution for protein sequence analysis to determine viable therapeutic agents for a given condition, and may include determining a protein sequence for the given condition; using the sequence database to compare a query sequence of a sequence of a given disorder to sequences of other known proteins in the sequence database; based on the comparison, using the sequence database to determine a similarity of the query sequence to sequences of other known proteins in the sequence database; and based on the similarity of the sequences, querying the graph database to identify potential therapeutic agents that can have an inhibitory effect on a given condition.

The invention discloses a course recommendation method based on a large language model. The method comprises the following steps: 1, constructing a learner-course sequence database; 2, constructing a course sequence denoising module for filtering noise courses from the course sequence; 3, constructing a course dependency-enhanced learning interest module, which is used for constructing a course dependency graph and capturing dynamic learning interests; 4, constructing an interest-target comparison learning module for narrowing the potential course selection range of the learner; 5, constructing a prediction module used for obtaining the prediction probability of the course selected by the learner; and 6, training the course recommendation network to obtain an optimal course recommendation model which is used for predicting a course recommendation scheme for the learner to select the next course. According to the method, more accurate modeling of learning interests and targets of learners is facilitated, and the accuracy of course recommendation can be remarkably improved.

A tumor neoantigen prediction method and a tumor neoantigen prediction system are provided. In the method, multiple amino acid sequences in genes of a person to be tested are extracted as multiple test peptides to be compared with multiple human protein sequences in a protein sequence database to find multiple similar peptides that match the human protein sequences. The similar peptides are filtered out from the test peptides and the filtered test peptides are input to multiple trained human leukocyte antigen (HLA) models to obtain multiple ranking results of the test peptides. A weighted sum of rankings of each test peptide in the ranking results is calculated as a score of the test peptide. At least one of the test peptides is selected as a neoantigen adapted for the person to be tested according to the score.

The polypeptide design method and sequence of the present application combine mask language modeling and yeast surface display, comprising the following steps: cleaning a protein sequence database, selecting protein sequences meeting the requirements as a training set of a language model, and performing mask language modeling on the protein sequences contained in the training set; designing a set of downstream tasks on the basis of a pre-trained model, and fine-tuning the downstream tasks; randomly masking residues of a selected reference sequence, and predicting the masked residues; performing virtual screening on polypeptide candidates generated by the model; and determining the protein expression level and affinity of the screened polypeptides through yeast display technology. The present application can generate a large number of polypeptide candidates that may have specific properties or functions by transferring natural language processing technology to the polypeptide generation field, and combines artificial intelligence generation, virtual screening and wet experimental characterization to build a relatively complete "dry-wet combination" design process.

A virus amino acid sequence generation and screening method comprises the following steps: S1, a pre-training step: constructing a data set for a pre-training model based on a label-free amino acid sequence database, and carrying out preliminary training on the pre-training model to learn grammar and semantic information of an amino acid sequence; s2, conditional generation: based on a model obtained by pre-training, preliminarily generating an amino acid sequence by using a virus amino acid sequence data set; s3, a rational design step: based on the'conditionally generated 'amino acid sequence, performing optimization design on the amino acid sequence through a rational design module, and based on existing positive sample data with required functions, performing further optimization and transformation on the amino acid sequence output by the generation model by utilizing a heuristic algorithm, so as to obtain an optimized amino acid sequence; the sequence space is evolved towards the positive sample direction, and the sequence is optimized; and S4, a filtering step: performing function prediction on the virus amino acid sequences in the target virus amino acid sequence library, performing function prediction according to the functions of the required amino acid sequences, and filtering out the amino acid sequences with poor functions. The invention also provides a system for implementing the method, and a key amino acid sequence screened out based on the system and used as an AAV capsid sequence.

This application provides a privacy-preserving user behavior data processing method and system, relating to the field of information security technology. The method comprises: converting acquired user behavior data into an ordered utility sequence to form a utility sequence database; extracting high-utility sequence patterns from the utility sequence database and their position and utility information within the utility sequence; for each high-utility sequence pattern, obtaining the sum of the utilities of each item in the high-utility sequence pattern, selecting the item with the largest utility sum as the victim item, and modifying the utility of the victim item in the utility sequence to update the utility value of the high-utility sequence pattern until the utility value of the high-utility sequence pattern is less than a minimum utility threshold, at which point modification ceases; and sharing the modified utility sequence database. In this way, the utility of the original database can be maintained as much as possible while ensuring that sensitive information is hidden.

This invention discloses a general method for amino acid sequence generation and screening based on machine learning. This method can be used to generate various functional amino acid sequences, such as viral amino acid sequences. It uses a diffusion-based denoising probability model as a deep learning generation framework. Based on the prior distribution of the data, it progressively denoises the diffused data or random noise through a back-diffusion process. Based on existing amino acid sequence databases, it can achieve de novo design of various functional amino acid sequences. For example, when applied to viral amino acid sequence generation and screening, the method includes the following steps: S1, constructing a dataset for training the model from experimental data; S2, training the viral amino acid sequence generation and screening device using the dataset; S3, using the trained viral amino acid sequence generation and screening device to generate and screen viral amino acid sequences; and S4, validating the viral amino acid sequences in the target viral amino acid sequence library through wet experiments. This invention also provides systems and devices related to the general method for amino acid sequence generation and screening.

The invention provides a mode mining method, device and equipment for critical path data and a storage medium, and relates to a data mining technology, the method comprises the following steps: obtaining an analysis processing request; and reading a sequence database corresponding to the sequence database identifier according to the analysis processing request. Generating a candidate mode set; determining a candidate mode greater than or equal to a preset minimum support degree threshold in a plurality of candidate modes in the candidate mode set based on the plurality of sequences in the sequence database; taking the candidate modes greater than or equal to a preset minimum support degree threshold as high-support-degree modes, and generating a high-support-degree mode set; and if it is determined that the high-support-degree mode set is null or the second length is greater than the preset maximum mode length, stopping high-support-degree mode mining. According to the method, the technical problem that the accuracy of mode mining of the key path of the processor microstructure correlation diagram is low is solved.

The present disclosure provides compositions and methods, and combinations, test kits and systems comprising the compositions and methods, for samples, specifically, amplification, detection, characterization, evaluation, analysis and / or measurement of nucleic acids in biological samples. The compositions and methods provided herein comprise combinations of microbial species target-specific nucleic acid primers for selective amplification and / or combinations of primers for amplifying nucleic acids from a large group of taxonomically related microorganisms. On the one hand, the amplified nucleic acids obtained using the compositions and methods can be used for various processes, comprising nucleic acid sequencing, and for detecting the presence of microbial species and evaluating the microbial populations in various samples. According to teaching and principle, new methods, systems and non-transitory machine-readable storage media are provided to compress reference sequence databases, which are used to map sequence reads for analysis and analysis of microbial populations.

The invention discloses an IGH gene fusion identification method and device, a storage medium and a program product, and the method comprises the following steps: obtaining sequencing data, and preprocessing the obtained sequencing data; the preprocessed data is compared to an IGH library, one or more candidate sequences are obtained, the candidate sequences are sequences containing IGH sections and unknown sections, and the unknown sections are sequences with the length within a preset first length range and are not compared to IGH fragments; acquiring the unknown sections, and comparing the unknown sections to a DNA sequence database to obtain a plurality of comparison results; and analyzing the plurality of comparison results, and identifying the gene corresponding to the optimal comparison result meeting a preset similarity condition as the gene fused with the IGH segment.

The application discloses a kind of based on double cross coupling correlation's unsupervised KPI anomaly detection method, first construct by historical KPI sequence database, preprocessing module, input module, based on double cross coupling correlation's KPI fragment feature extraction model, feature conversion module, feature space distance calculation module, training optimization module and online anomaly detection module constitute based on double cross coupling correlation's unsupervised KPI anomaly detection system.First to the unsupervised KPI anomaly detection system is trained, KPI fragment feature extraction model simultaneously models the coupling correlation of KPI data internal time sequence dimension and the coupling correlation between KPI sequence, extracts the feature vector of KPI data to be detected, feature space distance calculation module calculates the distance of feature vector and feature space center point, and online anomaly detection module determines whether KPI data to be detected is abnormal according to distance.The application can effectively improve the accuracy of unsupervised KPI anomaly detection.

The invention discloses a distributed sequence alignment method and system based on Spark, and relates to the technical field of bioinformatics, and the method comprises the steps: receiving a Minimap2 reference sequence, and generating a multi-part index of the reference sequence based on a multi-part index generation function of the Minimap2; partitioning the multi-part index of the reference sequence based on a character string in the multi-part index of the reference sequence as an identifier to obtain a partitioned copy, and distributing the partitioned copy to each node of a Spark cluster; receiving a query sequence, partitioning the query sequence through a file format of FASTA or FASTQ, and generating a multi-node shared task queue based on the number of partitions of the reference sequence, the number of partitions of the query sequence and partition copies on each node; and based on the multi-node shared task queue, each node selects the task in the task queue which needs to process all the remaining tasks and takes the longest time in the task queues corresponding to all the index file partition copies of the node to process, and a task processing result is obtained. According to the method, strategies of query sequence partitioning and reference sequence database partitioning are combined, dynamic load balancing is achieved by establishing a multi-node shared task queue, and the efficiency of comparison between Minimap2 and a large-scale data set is optimized. The method is suitable for long sequence data generated by a third-generation sequencing technology, and is especially suitable for application scenes of on-site rapid sequencing and analysis.

In a first aspect, the present invention relates to a computer-implemented method for obtaining information about a biological entity based on at least one biological sequence, comprising: (a) providing a repository of fingerprint data strings for a biological sequence database, each fingerprint data string representing a characteristic biological subsequence composed of sequence units, each characteristic biological subsequence having a combination number less than the total number of different sequence units available in the biological sequence database, the combination number of the biological subsequence being defined as the number of different sequence units that appear as consecutive sequence units of the biological subsequence in the biological sequence database; (b) determining one or more fingerprint data strings representative of the biological entity; (c) searching a repository including information associated with the fingerprint data strings for information associated with the one or more representative fingerprint data strings; and (d) processing the information.

The invention discloses a data quality evaluation method and system based on a real-sequence database, and the method comprises the steps: collecting factory bit number time sequence data from at least one data source, and storing the time sequence data in a time sequence data storage layer; based on preset multi-dimensional data quality evaluation indexes, the time series data are evaluated, a corresponding quality evaluation result is generated, and the multi-dimensional data quality evaluation indexes at least comprise data integrity, data consistency, data accuracy, data credibility and data stability; according to the quality evaluation result, a factory data quality space is constructed and maintained, and the data quality space is used for centralized management and visual display of the data quality state of the factory position number; and providing an access interface for the data quality space for a user through the client. A statistical analysis technology is utilized to research factory data quality, and a user is helped to better understand and solve a data quality problem.

The invention provides a proteome tandem mass spectrum quality evaluation method based on a machine learning self-attention model encoder, which is used for evaluating the quality of proteome mass spectrum experimental data. The method can be used for identifying high-quality mass spectra (such as search parameters which are not optimized, unspecified post-translational modifications and incomplete sequence databases) which are not identified due to data analysis reasons, can also be used for evaluating the overall data quality of one experiment, provides an overall statistical overview of the proteome mass spectrum experiment data quality, and can be used for analyzing the mass spectrum of the proteome. The method is used for constructing a proteome standard data set with consistent data quality and the like, and the reliability and reproducibility of proteomics analysis conclusions are promoted.