11 results about "Sequence assembly" patented technology

The invention relates to the technical field of metagenomics, discloses an automatic analysis method and system for pit mud metagenomic data, and aims at solving the problem that an existing method is poor in efficiency and accuracy, and the scheme mainly comprises the steps that a sequencing data type, a file path and analysis parameters are received; performing quality control on the original offline data; sequence assembly is carried out, and a contigs file is generated; carrying out assembly quality evaluation on the contigs file; carrying out genome binning by using at least two binning tools; integrating output results of the binning tool, and performing optimization based on a preset integrity threshold value and a preset pollution degree threshold value to obtain an optimized binning genome data set; evaluating and optimizing the integrity, the pollution degree and the strain heterogeneity of the binning genome; calculating coverage and relative abundance; performing species classification annotation and function annotation; and integrating the result data of the previous steps to generate an analysis report. According to the method, automatic analysis of metagenome data is realized, and the analysis efficiency and accuracy are improved.

The application discloses a macrovirus group data analysis method, and belongs to the technical field of macrovirology. The method comprises the following steps: sequence quality control, sequence assembly, sequence clustering, virus sequence identification, virus sequence checking, virus abundance calculation, species annotation, virus lifestyle judgment, virus host prediction and virus auxiliary metabolism gene analysis. The application uses Trimmomatic software, BWA-MEN, Megahit software, CD-HIT and other tools to execute the analysis process of macrovirus group data. Practice proves that the application can accurately identify and annotate virus species, comprehensively and systematically deeply analyze and mine macrovirus group data, the steps are simple and clear, the analysis time is short, and the effect of macrovirus identification research is greatly improved.

A method of compressing genomic data. The method has the steps of: aligning the genomic data with reference data, obtaining difference between the genomic data and the reference data, and compressing the difference using a statistical compression method to obtain compressed genomic data. In some embodiments, the statistical compression method may be an arithmetic coding method. In some embodiments, the method may further has a step of processing the difference using one or more statistical modeling methods, and compressing the processed difference using the statistical compression method. In some embodiments, the method further has a step of assembling a plurality of reads to form the reference data. In some embodiments, the method further has a step of storing compressed genomic data in a blockchain.

Described herein are methods and systems for assembling sequence reads from a genomic nucleic acid sample. In some embodiments, the sequence reads are read from clusters of nucleic acids on a flow cell. In some embodiments, the methods and systems use information relating to the proximity of clusters determined from their flow cell locations to assemble the sequence reads. For example, flow cell proximity information may be used during steps of generating a first assembly, recruiting sequence reads, generating a second assembly, and / or scaffolding an assembly.

The application provides a de Bruijn graph-based peptide sequence assembly method and device, the method comprises the following steps: obtaining light and heavy chain sequence data, and creating a sequence alignment database using the light and heavy chain sequence data; performing k-mer processing on peptide sequence test data to obtain a k-mer sequence set; aligning the k-mer sequence set with the sequence alignment database to obtain an alignment score; if the k-mer sequence set is mixed light and heavy chain data, the alignment score is divided into two categories of light chain and heavy chain, and a de Bruijn graph is constructed respectively, if the k-mer sequence set is light chain or heavy chain data, a de Bruijn graph is directly constructed; performing sequence assembly in the de Bruijn graph to obtain assembled peptide sequences. The application can perform peptide sequence assembly under the condition of distinguishing light and heavy chains, the assembly efficiency is greatly improved, and the assembly result is effective and reliable.

The application discloses an aspergillus telomere-to-telomere genome assembly method, device, equipment and storage medium. The method comprises the following steps: using a plurality of sequencing sequence assembly tools to assemble target aspergillus long read genome sequencing data from scratch to obtain a first assembled genome; selecting a first assembled genome meeting a preset condition as an initial assembled genome; integrating other first assembled genomes to fill gaps between repeat regions of the initial assembled genome to obtain a second assembled genome; aligning the long read genome sequencing data to the second assembled genome, identifying abnormal coverage regions and correcting sequences to obtain a third assembled genome; aligning a reference genome to the third assembled genome, connecting and orienting different contigs, and mounting the contigs to chromosomes to obtain a fourth assembled genome; and aligning the long read genome sequencing data and short read sequencing data to the fourth assembled genome for correction to obtain an aspergillus telomere-to-telomere genome assembly result.

The invention belongs to the technical field of biological analysis, and relates to an antibody sequencing method and device, in particular to an antibody peptide sequence assembling method and device based on beam search. Specifically, the invention relates to an antibody sequencing method which comprises the following steps: S1, obtaining a homologous template of an antibody to be detected; s2, cleaning the de novo sequencing data of the peptide fragment to obtain a cleaned peptide fragment sequence; s3, segmenting the cleaned peptide fragment sequence into a short peptide sequence with a fixed length; and S4, based on the amino acid information on the homologous template and the signal intensity of the oligopeptide sequence, constructing a Debrueine map through beam search, and carrying out sequence assembly to obtain an antibody sequence. The flux of the monoclonal antibody from the beginning sequencing can be improved, so that the mass spectrometric detection time and the cost of experimental consumables are reduced, and the method has a good application prospect.

The present disclosure provides a method and device for mixed sample sequencing, an electronic device and a storage medium, wherein after a first to-be-sequenced library is sequenced to obtain a first test sequence set, a second to-be-sequenced library is sequenced to obtain a second test sequence set; the test sequences in the test sequence set are assembled; each assembled sequence set is evaluated to obtain an assembled sequence corresponding to each to-be-sequenced sample; and the assembled sequences corresponding to each to-be-sequenced sample are determined based on the similarity between the assembled sequences. According to the present disclosure, after the first to-be-sequenced library and the second to-be-sequenced library are continuously sequenced in the same sequencing chip, the sequences obtained by sequencing are assembled and evaluated to obtain the assembled sequence corresponding to the first to-be-sequenced library and the assembled sequence corresponding to the second to-be-sequenced library; and the present disclosure can reduce the time spent on cleaning the sequencing chip when sequencing two adjacent batches, thereby improving the sequencing efficiency.

Disclosed herein are compositions, systems and methods related to sequence assembly, such as nucleic acid sequence assembly of single reads and contigs into larger contigs and scaffolds through the use of read pair sequence information, such as read pair information indicative of nucleic acid sequence phase or physical linkage.

The invention discloses a CRISPR-Cas12a-based plasmid pool DNA database management method and a CRISPR-Cas12a-based plasmid pool DNA database management system. The method comprises the following steps: S1, data writing: encoding data required to be stored by a user, assembling an encoded DNA sequence and a designed multi-layer address sequence into a plasmid map, and synthesizing and cloning to form a plasmid pool database; s2, data retrieval: S21, analyzing a target address; s22, assembling the one-way guide RNA and a Cas12a protein into a ribonucleoprotein complex; s23, carrying out specific cleavage on the plasmid pool database by utilizing a ribonucleoprotein complex; s24, enriching a cutting product; s25, performing amplification and sequencing on the enriched product, and restoring target data after decoding; s3, data deletion: S31, designing a one-way guide RNA (Ribonucleic Acid) aiming at a to-be-deleted data address sequence; s32, cutting a target plasmid through a Cas12a protein; and S33, separating and removing the cut plasmids through a gel electrophoresis method so as to realize data deletion, and promoting DNA storage from a static archiving mode to a dynamic database management mode.

The application discloses a virus-host RNA sequence classification method and device based on AI, and relates to the field of biological detection.The method comprises the following steps: mapping preprocessed short read RNA sequences to a host genome twice, assembling short read RNA sequences which are not mapped to the host genome into continuous RNA sequences, and screening RNA sequences with a length greater than 1000bp from the continuous RNA sequences; and performing AI classification on the RNA sequences with a length greater than 1000bp to obtain virus RNA sequences.The application combines host filtering, rapid assembly and AI classification, can significantly reduce the calculation amount and hardware pressure, realizes efficient and accurate virus sequence classification, and can quickly distinguish unknown viruses.