214 results about "Genetic Sequence Databases" patented technology

A system and method for performing domain-specific knowledge based metasearches. A metasearch engine is provided for accessing a searching text-based documents using generic search engines while simultaneously being able to access publication based databases and sequence databases as well as in-house proprietary databases and any database capable of being interfaced with a web interface so as to produce search results in text format. A data mining module is also provided for organizing raw data obtained by unsupervised clustering, simple relevance ranking, and categorization, all of which are done independently of one another. The system is capable of storing previous search data for use in query refinement or subsequent searches based upon the stored data. A search results collection browser may be provided for analyzing current browsing patterns of the user for developing weighting factors to be used in ordering the results of future searches.

The present invention provides an index structure for managing weighted-sequences in large databases. A weighted-sequence is defined as a two-dimensional structure in which each element in the sequence is associated with a weight. A series of network events, for instance, is a weighted-sequence because each event is associated with a timestamp. Querying a large sequence database by events' occurrence patterns is a first step towards understanding the temporal causal relationships among the events. The index structure proposed herein enables the efficient retrieval from the database of all subsequences (contiguous and non-contiguous) that match a given query sequence both by events and by weights. The index structure also takes into consideration the nonuniform frequency distribution of events in the sequence data.

Sequence alignment and sequence database similarity searching are among the most important and challenging task in bio informatics, and are used for several purposes, including protein function prediction. An efficient parallelisation of the Smith-Waterman sequence alignment algorithm using parallel processing in the form of SIMD (Single-Instruction, Multiple-Data) technology is presented. The method has been implementation using the MMX (MultiMedia eXtensions) and SSE (Streaming SIMD Extensions) technology that is embedded in Intel's latest microprocessors, but the method can also be implemented using similar technology existing in other modern microprocessors. Near eight-fold speed-up relative to the fastest previously an optimised eight-way parallel processing approach achieved know non-parallel Smith-Waterman implementation on the same hardware. A speed of about 200 million cell updates per second has been obtained on a single Intel Pentium III 500 MHz microprocessor.

This invention includes methods for identifying nucleic acids in a sample of nucleic acids by observing sequence sets present in the nucleic acids of the sample and then identifying those sequences in a nucleic acid sequence database having the sequence sets observed. In a preferred embodiment, a sequence set consists of two primary subsequences and an additional subsequence having determined mutual relationships. The methods include those for observing the sequence sets and those for performing sequence database searches. This invention also includes devices for recognizing in parallel the additional subsequences in a sample of as well as methods for the use of these devices. In a preferred embodiment, the devices include probes bound to a planar surface that recognize additional subsequence by hybridization, and the methods of use include features to improve the specificity and reproducibility of this hybridization.

A method and system for conducting sequence searches in a sequence database wherein in one embodiment, the method includes: combining a plurality of query sequences into a combined query sequence; determining a plurality of subdivisions of the sequence database; performing a plurality of searches, wherein each search includes a comparison of the combined query sequence against one of the plurality of subdivisions of the database to produce a plurality of word matches; extending the length of the plurality of word matches to produce a plurality of High-scoring Segment Pairs, combining the plurality of High-scoring Segment Pairs; and producing a plurality of reports, each report representing the highest scoring matches for one of the plurality of query sequences.

The invention relates to a stratification synergic detection and judgment method for detecting the confused API call behavior and judging the maliciousness of the call behavior; the detection and judgment method includes three steps: firstly, the binary code for being analyzed is disassembled to establish the control flow graph (CFG) of the program; the known unconventional instruction or data sequence provided with the API function call capability and stored in the database DB1 is adopted to recognize the unconventional call behavior of the API function; secondly, the generation operation of the API sequence called by the target program is finished; the API function is recognized and recovered in the aspects of direct call and indirect call; finally, the extraction of the API sequence and the maliciousness judgment operation are finished; the extraction of the sequence is finished and based on the control flow graph (CFG) of the program; then the obtained sequence is normalized according to the format stored in the suspected API sequence database; the invention provides a stratification synergic detection and judgment method for detecting the confused API call behavior and judging the maliciousness of the call behavior, which has the advantages of wide recognition range, accurate recognition and high efficiency.

This invention includes methods for identifying nucleic acids in a sample of nucleic acids by observing sequence sets present in the nucleic acids of the sample and then identifying those sequences in a nucleic acid sequence database having the sequence sets observed. In a preferred embodiment, a sequence set consists of two primary subsequences and an additional subsequence having determined mutual relationships. The methods include those for observing the sequence sets and those for performing sequence database searches. This invention also includes devices for recognizing in parallel the additional subsequences in a sample of as well as methods for the use of these devices. In a preferred embodiment, the devices include probes bound to a planar surface that recognize additional subsequence by hybridization, and the methods of use include features to improve the specificity and reproducibility of this hybridization.

The invention provides a digital image encryption method based on a chaotic system and a nucleotide sequence database. The method comprises the following steps: image pixel positions are scrambled by chaotic mapping index sequence; an image pixel value undergoes DNA coding, and the DNA-coded image pixel value and a DNA sequence in a nucleotide sequence database undergo base operation; and the DNA-coded pixel undergoes base replacement according to a quanternary hyperchaos sequence generated by a hyperchaos Chen system, and confusion and diffusion properties are further enhanced by iteration of a ciphertext feedback and chaotic system. By means of sensibility and pseudo-randomness of chaotic mapping to initial conditions, by combining inherent spatial configuration and unique information processing capability of DNA molecules and through transformation and operation between two chaotic sequences, a DNA sequence database and pixel gray value, the purpose of confusion and diffusion is achieved so as to realize encryption of digital images. It shows through experimental results that the method has a large keyspace, has strong sensitivity to a secret key and can effectively defense attack operations such as statistical analysis, exhaustion analysis and the like.

The invention provides a method for developing a mung bean simple sequence repeat (SSR) primer based on transcriptome sequencing. The method comprises the following steps: obtaining a set of mung bean genome-wide transcription, and forming a sequence database; splicing sequencing sequences into a transcriptome by Trinity; taking the longest transcript in each gene as Unigene; carrying out bioinformatics analysis of a Unigene sequence; carrying out SSR detection on the Unigene by adopting MISA1.0; carrying out SSR primer design by using a Primer 3, and carrying out SSR primer polymorphism identification. 13134 pairs of SSR primers are successfully designed by application of the method; 50 pairs of primers are randomly selected to verify 8 parts of mung bean deoxyribonucleic acids (DNAs) from different countries, wherein 32 pairs of polymorphic primers are formed in all; the mung bean materials with different geographical origins can be distinguished by using the 32 pairs of SSR primers. The method disclosed by the invention is convenient, fast and accurate, and low in cost, and a new thought is provided for development of the mung bean SSR primer.

The invention relates to data mining in the field of network security and especially relates to a method for mining an attack frequent sequence mode from a Web log. The method comprises the following steps: collecting website access log files, website information and attack feature codes; analyzing a website log structure, matching analyzed URLs with the collected attack feature codes, obtaining attack records, and clearing up the URLs; performing user identification on attack log data and distinguishing manual attacks and attacks of a loophole scanner; respectively performing session identification to obtain a sequence database of the manual attacks and a sequence database of the loophole scanner; converting a character string database into a digital database, and respectively mining a frequent sequence of the sequence database by use of a sequence mode mining method; and maximizing the frequent sequence obtained through mining and converting a sequence mode into visual figure language. The process is indicated in the first graph. The method provided by the invention can realize visualization of an attack mode and explores a scanning sequence in the loophole scanner.

The invention provides a mouse gesture identification method. A starting point coordinate and an end point coordinate before and after the movement of a mouse cursor point are recorded by a message processing module by establishing a multidirectional coordinate system and then calculated in a gesture computing module to judge the moving direction of the mouse cursor point; after the circulation of the steps, the direction value sequence obtained is sent to a sequence analysis module; and the group that is best matched is found in a feature sequence database by comparison and the corresponding action information is output. The method of the invention has fast computing speed and high identification efficiency, thus being convenient for the operation of a touch screen user on software greatly; meanwhile, a control manufactured by adopting the method is greatly convenient for the programmers for secondary development to develop the touch screen application program.

Sequence alignment and sequence database similarity searching are among the most important and challenging task in bio informatics, and are used for several purposes, including protein function prediction. An efficient parallelisation of the Smith-Waterman sequence alignment algorithm using parallel processing in the form of SIMD (Single-Instruction, Multiple-Data) technology is presented. The method has been implementation using the MMX (MultiMedia eXtensions) and SSE (Streaming SIMD Extensions) technology that is embedded in Intel's latest microprocessors, but the method can also be implemented using similar technology existing in other modern microprocessors. Near eight-fold speed-up relative to the fastest previously an optimised eight-way parallel processing approach achieved know non-parallel Smith-Waterman implementation on the same hardware. A speed of about 200 million cell updates per second has been obtained on a single Intel Pentium III 500 MHz microprocessor.

This invention describes the use of peptide profiling to identify, characterize, and classify biological samples. In complex samples, many thousands of different peptides will be present at varying concentrations. The invention uses liquid chromatography and similar methods to separate peptides, which are then identified and quantified using mass spectrometry. By identification it is meant that the correct sequence of the peptide is established through comparisons with genome sequence databases, since the majority of peptides and proteins are unannotated and have no ascribed name or function. Quantification means an estimate of the absolute or relative abundance of the peptide species using mass spectrometry and related techniques including, but not limited to, pre- or post-experimental stable or unstable isotope incorporation, molecular mass tagging, differential mass tagging, and amino acid analysis.

The invention provides a software security flaw detection method based on sequential pattern mining. Firstly, a great quantity of relevant operation sequence data of a detected software security flaw is used for building a relevant operation sequence database of the security flaw; then, a closed sequential pattern mining arithmetic in the data mining is used to mine frequently appearing relevant operation sequence of the flaw; the mined frequently appearing relevant operation sequence of the flaw is used for detecting the security flaw of the current software system so as to lower flaw detection missing report rate as well as improve security flaw detection efficiency. Along with the accumulation of flaw data, the advantage of high security flaw detection efficiency of the invention is more outstanding.

The present invention provides methods for efficiently generating and screening protein libraries for optimal proteins with desired biological functions, such as improved binding affinity for biologically and / or therapeutically important target molecules. The method is performed in silico in a high-throughput fashion by mining the ever-expanding database of protein sequences in all living things, especially humans. In one embodiment, a method of constructing a designer protein library comprises the steps of: providing an amino acid sequence derived from a lead protein, referred to as a lead sequence; comparing the lead sequence with a plurality of test protein sequences; Select at least two peptide fragments having at least 15% sequence identity with the leader sequence from each test protein sequence, and the selected peptide fragments form a selection library; a library of designed proteins is formed by replacing the leader sequence with the selection library. Libraries of designed proteins can be expressed in vitro or in vivo to generate libraries of recombinant proteins that can be screened for new or improved functions relative to lead proteins, such as antibodies against a therapeutically important target.

The invention discloses a multi-level acceleration method of flow-based biological sequence database search, which is to accelerate the search speed of a biological sequence database on the premise of ensuring search accuracy and relatively low cost. The technical proposal is that a cluster system composed of a plurality of personal computers shall be created firstly, and a master control node machine is assigned; the master control node machine distributes the database sequence and stores into each node machine in the cluster system, so as to fill and rearrange an inquiry sequence, and distribute the inquiry sequence to all the node machines in the cluster system; each node machine executes the search task in parallel, so as to be responsible for the completion of search tasks of the inquiry sequence in a local database sequence; the master control node machine collects, summarizes and outputs the results of parallel search tasks on all the node machines. The invention makes the search tasks be executed in parallel between the n node machines of the cluster, each node machine distributes the comparative calculation task of two sequences to p hardware calculation clusters to be conducted in parallel, thereby realizing the multi-level acceleration objective in parallel of three layers including a cluster node layer, a flow-level calculation layer, as well as a flow inner core command layer.

A method for rapid identification of biological materials is presented, which exploits the wealth of information contained in genome and protein sequence databases (5). In a preferred embodiment, the method utilizes the masses of a set of ions by MALDI TOF mass spectrometry of intact or treated cells (1). Subsequent correlation (4) of each ion in the set to a protein, along with the organismic source of the protein, is performed by searching a database comprising protein molecular weights (9).

An establishment method of a gene library of fecal flora based on high-throughput gene sequencing employs a ''nested PCR'' method for enrichment amplification on 16S rDNA, so as to reduce the host and food residue genome pollution by the maximum; and V3 and V6 are combined for specific amplification and massive parallel sequencing, so as to obtain the a target gene sequence database of the flora. The library can increase the identification of the bacterial flora from Genus level to Species level. The method can maximally avoid the interference of host cell nucleic acid, and completely and efficiently detect 16SrDNA tag sequence of all bacteria varieties, so as to accurately determine the ecological structure of the intestinal flora.

The invention discloses a detection method for abnormal behaviors based on a user access sequence. The detection method comprises the following steps: 1) capturing data from a local network, preprocessing the data, and performing serializing treatment on the acquired data; 2) storing a sequence formed in the step 1 into a sequence database, and generating a behavior sequence of each user on the basis of time; and 3) calculating the behavior similarity and the correlation coefficient between users according to the behavior sequence of each user, comparing the correlation coefficient for detecting the abnormal behaviors, and searching for the abnormal behaviors of the user. According to the method, on the basis of sequence pattern excavation, factors, such as, time and user behavior characteristics, are fully considered, an improved more accurate user behavior similarity algorithm is utilized to calculate, and the sequence rule of the user access is effectively extracted, so that an analysis result is more accurate and the defects of other analysis methods are overcome. Besides, on the basis of the user behavior similarity algorithm, the method has obvious advantages in noise interference, the used resources are few, and the running efficiency is high.

The invention relates to a network water army behavior detection method and a network water army behavior detection system based on a mixed Dirichlet process. The network water army behavior detection method includes: collecting original data comprising user behavior features and content features, performing quantization representation on the original data of each dimensionality so as to form history behavior vectors of a user, and forming a user data set to be clustered; clustering the history behavior vectors in the user data set to be clustered so as to obtain at least one classification user behavior set; converting data in the at least one classification user behavior set, and merging data with the same user identification in the data after being converted so as to obtain a sequence database; performing sequence pattern excavation on the sequence database through a pattern excavation module so as to obtain at least one affair sequence pattern corresponding to each classification user behavior set; judging out the classification user behavior set which is a water army username by comparing each affair sequence pattern through a water army judging module. The network water army behavior detection method and the network water army behavior detection system based on the mixed Dirichlet process can easily recognize which category belongs to the water army username.

The present invention discloses a primer, a method and a kit for detecting the animal clonorchiasis sinensis specificity, a nucleotide sequence of an upstream primer of the primer is represented by SEQ ID NO:1, a nucleotide sequence of a downstream primer is represented by SEQ ID NO:2. the present invention implements an PCR amplification to a detectingformwork DNA by the primer, an amplifying outcome yield is processed by an agarose gel electrophoresis and observed under an ultraviolet light, if it is a positive result, there will be a specificity amplifying band, otherwise there will not be a band. According to an ITS zone sequence database OF THE animal clonorchiasis sinensis, the invention designs the primer, builds a rapid, special and sensitive PCR method, and it is capable of authenticating the animal clonorchiasis sinensis accurately. The operation of the kit of the invention is simple and programmable, the method specificity is strong, the sensibility is high, the result judgement is objective, and the invention is capable of being used for diagnosing the animal clonorchiasis sinensis and inquiring epidemiology.

The invention relates to an approach of searching gene group sequence database based on characters. Approximate sequence is searched in database according to sequence statistic character. The search method is as follows: search approximate sequence according to distance of statistic character, namely, basic information of different species' sequence group serial data, including sequence's database login number, species name, chromosome number, original data, basic group composition character and base pair relativity character is memorized in database. For any a gene snippet submitted by client, its eigenvalue is computed according to client's request and distance between the eigenvalue and all corresponding eigenvalue in database is computed to compare approximate sequence; the most approximate sequence is arranged and displayed by distance.

The invention belongs to the time synchronization technical field and discloses a distributed network time synchronization method. The method includes following steps that: the difference of the clock of a center node and the clock of terminal nodes is determined; after receiving time synchronization request signals sent by different terminal nodes through different carrier frequencies, the center node captures, tracks and demodulates the request signals by using a local spread spectrum sequence database so as to obtain terminal node serial numbers, request sign information and terminal node clock information contained in the request signals, and center node clock information is compared with the local clock of the center node, time difference is measured; and offset correction is performed on the local clock of the terminal nodes according to the difference of the clock of the center node and the clock of the terminal nodes. Through synchronization, accuracy can reach a level of several hundreds of nanoseconds or even a level of dozens of nanoseconds.

The invention provides a method for recognizing posture and action of human limbs based on space division study. The method comprises the steps of (1) constructing a sequence database for some action and posture, acquiring lots of body joint action data, constructing action and posture characteristic vectors by a space dividing and encoding manner, and storing into the sequence database for some action and posture; (2) reading new body joint data when recognizing some action, organizing into the action and posture characteristics vector, finding out the action-rated action and posture sequence database to match so as to determine whether some posture and action are performed. The method has the characteristics of being high in real-time performance, high in accuracy, high in robustness and easily expanded; in addition, the method is suitably used for recognizing the postures and actions of many people.

The invention provides a vehicle driving path prediction method and system. The method includes the steps that a minimum internal storage is determined on the basis of a Hadoop platform, the largest length of a path is scanned, and an original path sequence database is evenly divided into n disjoint sub-path sequence databases; the original path sequence database and the n sub-path sequence databases are respectively uploaded to an HDFS; the n sub-path sequence databases are dispatched to different Map nodes by a master control node, each Map node executes an improved GSP algorithm, the sub-path sequence databases stored in a Map node internal storage are scanned according to a preset minimum supporting degree X, a local path sequence mode is worked out, and Reduce nodes are merged and processed so that an overall candidate sequence mode can be obtained; the original path sequence database is scanned again so that an overall path sequence mode can be obtained; the overall path sequence mode generates a path association rule and the confidence degree of the path association rule is calculated so that a vehicle driving path prediction result can be obtained.

The invention discloses a H3 subtype flu quick-detecting type classifying method based on an RT-LAMP technique. The method comprises the following steps: firstly, analyzing the HA gene sequence of the H3 subtype flu virus in a flu virus gene group sequence database, designing highly special and conserved RT-LAMP special primers and six primers corresponding to eight areas of target genes, wherein four primers are FIP, BIP, F3 and B3, and the other two primers are loop primers LP1 and LP2; secondly, treating a sample to be detected in advance, and extracting the total RNA of the sample to be used as a reaction template; thirdly, configuring an RT-LAMP reaction system; fourthly, mixing the RNA template into the RT-LAMP reaction system, arranging the RT-LAMP reaction system in a water bath, and carrying out RT-LAM enlargement at constant temperature; and fifthly, identifying the enlarged reaction product. The result shows that the reaction product contains H3 subtype flu virus when the reaction product is positive; and the reaction product is H3 subtype flu virus when the reaction product is negative.

The invention discloses an APT (Advanced Persistent Threat) attack detection method based on big data behavior sequence analysis. The method comprises the following steps of detecting an operation behavior of a user, and constructing a user operation behavior sequence database; optimizing the user operation behavior sequence database; extracting a user operation behavior sequence based on a big data method; performing matching analysis on the extracted user operation behavior sequence and a behavior sequence in the user operation behavior sequence database, and outputting a matching result; and analyzing the matching result and outputting a threatening behavior. The APT attack detection method based on the big data behavior sequence analysis provided by the invention can improve the accuracy of threat detection, so as to construct a safe and efficient network active defense system, and effectively overcome the threat of APT attacks on network security.

The invention discloses a method for analyzing the characteristic of a software vulnerability sequence based on a cluster, which belongs to the technical field of information security. The method comprises the following steps: firstly, establishing a software vulnerability sequence database by using the conventional software vulnerability sequence; secondly, analyzing the software vulnerability sequence in the database by using clustering technology so as to generate a plurality of software vulnerability sequence clustering clusters; thirdly, finding the software vulnerability sequence clustering cluster which is most similar to a doubtful vulnerability sequence of software to be tested through similarity computation; fourthly, comparing the doubtful vulnerability sequence of the software to be tested with all software vulnerability sequences in the most similar software vulnerability sequence clustering cluster through a sequence comparison method, and further finding the software vulnerability sequence which is most similar to the doubtful vulnerability sequence of the software to be tested from the software vulnerability clustering cluster; and finally, outputting related vulnerability information, in the software vulnerability sequence database, corresponding to the most similar software vulnerability sequence as a security report. The method improves the analysis efficiency of the software vulnerability sequence.

The invention relates to a metagenomics-based pathogen analysis method and device, equipment and a storage medium. According to the metagenomics-based automatic pathogen analysis method provided by the invention, on one hand, the flow and tools of a data analysis method are integrated and optimized, and the pathogen identification process is standardized, so that the data analysis time can be greatly shortened, the clinical detection period can be shortened, and rapid detection can be realized; and on the other hand, a platform effectively distinguishes pathogenic bacteria from background bacteria by establishing a microorganism reference sequence database and a pathogen information base and comprehensively evaluating parameters and evidence grades, so that missing detection caused by non-coverage can be effectively prevented, and the reference significance and interpretability of clinical detection results are further improved.

Techniques for identifying one or more communities in an information network are provided. The techniques include collecting one or more nodes and one or more edges from an information network, performing a random walk on the one or more nodes to produce a sequence of one or more nodes, creating a sequence database from one or more sequences produced via random walk, and mining the sequence database to determine one or more patterns in the network, wherein the one or more patterns identify one or more communities in the information network.