7 results about "Representative sequences" patented technology

This application relates to the field of microbial detection technology, specifically to a method for detecting and analyzing the drug resistance of Mycobacterium tuberculosis based on edge computing. The method includes: classifying sequencing data of biological samples based on a metagenomic reference database to obtain a matching dataset aligned to Mycobacterium tuberculosis; extracting representative sequences of each amplified fragment from multiple amplified fragments in the first region based on the matching dataset; and performing a first alignment of the representative sequences of each amplified fragment with a mycobacterium reference database to identify Mycobacterium tuberculosis complexes based on sequence similarity. This method is suitable for operation on edge devices. The method proposed in this application, through process optimization, enables accurate, efficient, and low-cost detection of mycobacteria even with limited computing resources, thereby helping to prevent and control the spread of tuberculosis.

This application discloses a primer design method, system, and computer storage medium based on nucleic acid consistency. The method includes: obtaining a target nucleic acid sequence; classifying the target nucleic acid sequence to obtain multiple taxonomic units; using at least one target nucleic acid sequence from each taxonomic unit as a representative sequence; designing candidate primers for the representative sequence; sequentially selecting at most one pair of candidate primers from each taxonomic unit to establish a candidate primer pool; and excluding candidate primers in the candidate primer pool that exhibit non-specific amplification to obtain the final primer pool. This application is simple to operate and can handle large nucleic acid libraries, making it particularly suitable for universal primer design for pathogen libraries. Compared to similar software, the method in this application is more systematic and comprehensive, resulting in a primer pool with high coverage and low host contamination rate.

The application discloses a primer design method, system and computer storage medium based on minimum degeneracy, the method comprising: obtaining nucleotide sequences; dividing the nucleotide sequences into multiple classification units; selecting a representative sequence from each classification unit; designing a corresponding primer pair set for the representative sequence based on the base frequency in the representative sequence; determining the coverage of the primer pair set relative to all nucleotide sequences, and selecting multiple primer pairs in each primer pair set as a candidate primer pair set according to the coverage of each primer pair set; and selecting multiple candidate primers in the candidate primer pair set to establish a primer pool. The application considers the mismatch between primer pairs and the coverage in the primer design process, thereby allowing the primer pairs to have extremely high coverage and lower degeneracy.

The application discloses a high-throughput sequencing data sequence clustering method and system based on cyclic self-blast: the high-throughput sequencing data is de-duplicated; unique sequences with a number of repetitions lower than a threshold value a are filtered, and the filtered unique sequences are sorted according to the number of repetitions; the sorted unique sequences are divided into subsets and a parent set; the subsets are subjected to blast multiple alignment to obtain a de-redundant subset; the parent set and the de-redundant subset are subjected to cyclic blast alignment to obtain a de-redundant parent set; all representative sequences in a preliminary clustering set are combined, sorted according to the number of repetitions and distributed with identification tags to generate a final OTU clustering result set; in the prior art, when OTU clustering and ASV methods are used to process environmental DNA macro-barcode sequencing data, different OTU or ASV sequences are annotated to the same species, so that a large amount of redundancy still exists in the classified units after clustering, and the reliability of species analysis is improved.

The invention discloses a high-throughput sequencing data sequence clustering method and system based on circulating self-blast. The method comprises the following steps: carrying out duplicate removal processing on high-throughput sequencing data; filtering the unique sequences of which the repetition number is lower than a threshold value a, and sorting the filtered unique sequences according to the repetition number; dividing the sorted unique sequence into a subset and a mother set; performing blast multiple comparison on the subsets to obtain redundancy-removed subsets; performing cyclic blast comparison on the mother set and the redundancy-removed subset to obtain a redundancy-removed mother set; combining all representative sequences in the preliminary clustering set, sorting according to the number of repetitions and distributing identification labels, and generating a final OTU clustering result set; in the prior art, when an OTU clustering method and an ASV method are used for processing environment DNA macro bar code sequencing data, different OTU or ASV sequences are annotated to the same species, so that a large amount of redundancy still exists in a classified unit after clustering, and the reliability of species analysis is improved.

This classification method comprises: a measurement step for measuring the nucleotide sequence of a nucleic acid molecule in a measurement sample as a measurement sequence; and a classification step for classifying the measurement sequence into each of a plurality of groups on the basis of the degree of similarity between a representative sequence set as a base sequence representing each of the groups and the measurement sequence measured in the measurement step. The plurality of groups are obtained by grouping the base sequences of a plurality of nucleic acid molecules according to a specific rule.

Immunological entity sequences are clustered by a two-step process based on their similarity, i.e., their ability to react to the same antigen. In the first step, the sequences are converted into numerical vectors, and these numerical vectors are clustered into initial clusters. In the second step, the initial clusters are refined into final clusters, which can be done using the sequence data itself rather than numerical vectors. Each final cluster may be assigned a representative sequence to reduce the dimensionality of the cluster.