628results about "Sequence analysis" patented technology

Methods, kits, and systems for assessing the risk of a human subject for developing a cancer, including genetic risk assessment, clinical risk assessment, and combinations of both to improve risk analysis. Technologies utilize, among other things, analyzing a sample of DNA obtained or derived from a subject to detect the genotype for a plurality of test genomic loci.

To provide a method for compression of genome sequence data, a hardware processor, a system, and a storage device.SOLUTION: The method includes the steps of: determining whether leads of sequences of nucleotides or bases that have been aligned to a reference sequence are perfectly or imperfectly mapped with the reference sequence or whether the leads are unmapped with the reference sequence; and encoding the perfectly mapped leads according to a first encoding process and encoding the unmapped leads according to a second encoding process. In the determining step, for each imperfectly mapped lead, the number of mismatches between the lead and the reference sequence is compared with a threshold value. In the encoding step, the imperfectly mapped leads, when the number of mismatches is larger than the threshold value, are encoded according to the second encoding process, and when the number of mismatches is smaller than the threshold value, are encoded according to a third encoding process.SELECTED DRAWING: Figure 1

A system may include an alignment algorithm and a sample deep learning model configured to output screening maps. The system may also include executable computing instructions that cause one or more processors to receive a plurality of experiment datasets comprising respective unaligned sample perturbation readouts from samples transfected with CRISPR-Cas9 reagents and respective control readouts from control samples transfected with intron targeting control reagents. The one or more processors may also identify intron feature characteristics for the control readouts using the alignment algorithm and generate aligned sample perturbation readouts from the unaligned sample perturbation readouts using the alignment algorithm and the intron feature characteristics identified. The one or more processors may also generate a screening map for the plurality of experiment datasets centered around the control readouts by processing the aligned sample perturbation readouts through the sample deep learning model.

This invention relates to the field of bioinformatics, and particularly to a method and system for denoising single-cell immune repertoire sequencing data. The method includes data preprocessing and feature extraction, bidirectional collaborative denoising, intelligent comprehensive judgment and classification, data archiving and background learning, and result output. Compared to existing technologies that primarily rely on static thresholds for cell filtering, which struggle to comprehensively assess and eliminate multi-dimensional noise, leading to incomplete purification and the potential deletion of high-value cell information, this invention employs a systematic denoising scheme integrating multi-parameter dynamic threshold filtering, specific gene contamination analysis, and targeted optimization of VDJ data. It sets dynamic thresholds by integrating multi-dimensional quality control indicators such as UMI number, gene number, and the proportion of mitochondrial and ribosomal genes, and specifically identifies and filters interfering genes and background sequences. This enables refined and hierarchical removal of complex noise, significantly improving the overall quality of cell datasets and the accuracy of VDJ receptor sequence analysis.

In some embodiments, a computer-implemented method of detecting a presence of a predetermined fusion gene in a biological sample is provided. A computing system generates an alignment of a read sequence to a reference genome. The alignment includes a first alignment result and a second alignment result. The computing system determines a breakpoint location indicated by the first alignment result and the second alignment result, distances between coordinates of the breakpoint location and coordinates of one or more expected breakpoint locations associated with the predetermined fusion gene, a gap size value and an overlap size value. In response to determining that the gap size value is less than a gap size value threshold, the overlap size value is less than an overlap size value threshold, and the distances are less than a breakpoint distance threshold, the computing system generates an indication of the presence of the predetermined fusion gene.

The application belongs to the field of biological information visualization, and discloses a three-dimensional visualization functional gene browser and system based on Z series closed loop coding, which solves the problems of track disorder, arc winding, dependence on scaling and incomplete coding of existing tools. The application constructs Z series closed loop coding, packs genes and non-gene regions into Z0~ZN units, and adapts repeat sequences and ring chromosomes. L-X-Y three-dimensional coordinates are established, 65 standardized character libraries are constructed, and amino acids are labeled in a six-color degenerate system. Modular labeling is adopted, continuous functional elements are integrated into M modules, three types of background colors are used to classify levels, the trunk area and the independent labeling area are displayed separately, and tracks and arcs are abandoned. The system supports Z unit addressing, three-level interaction, module linkage, variation marking, Hi-C double-end alignment and reverse complementary strand switching, realizes full-link visualization from whole genome to single base, and is suitable for genomics and clinical variation analysis.

The application provides an innovative screening method and application of a key gene of intraparenchymal cholangiocarcinoma causing vascular invasion. The application first identifies specific cells enriched in intraparenchymal cholangiocarcinoma tissues causing vascular invasion based on single-cell transcriptome data of tissues causing and not causing vascular invasion, and screens a plurality of highly expressed genes in the specific cells. At the same time, a plurality of up-regulated differential genes are screened based on ordinary transcriptome data of tissues causing and not causing vascular invasion. Subsequently, common genes from the two data sources are selected for subsequent cell phenotype screening and function verification experiments. According to the experimental results, the gene capable of reducing the budding ability of endothelial cells is identified as the key gene of intraparenchymal cholangiocarcinoma causing vascular invasion. The application aims to provide an innovative screening strategy for the discovery of the key gene of intraparenchymal cholangiocarcinoma causing vascular invasion, so as to provide a beneficial idea for the precise treatment of intraparenchymal cholangiocarcinoma.

The present invention provides a method for simultaneously amplifying multiple target nucleic acid regions in a single reaction volume, and a method for selecting a primer library to be used in such amplification. Furthermore, the present invention provides a primer library having desired properties, such as minimal formation of amplification primer dimers or other non-target amplification products. [Solution] For example, a method for amplifying a target gene locus in a nucleic acid sample is provided, comprising the steps of (a) contacting the nucleic acid sample with a test primer library that simultaneously hybridizes to at least 1,000 different target gene loci to generate a reaction mixture, and (b) subjecting the reaction mixture to primer extension reaction conditions to generate an amplification product containing a target amplification product.

Provided is a technique relating to codon optimization. A method for optimizing a nucleic acid sequence for expression of a protein in a host comprises: obtaining a protein subsequence-nucleic acid subsequence pair according to collected highly expressed protein sequences and encoding nucleic acid sequences thereof, so as to form a training set; using the training set to train a neural machine translation model, wherein the neural machine translation model is used to realize translation from an amino acid sequence to a codon sequence; cleaving the protein sequence requiring codon optimization into protein subsequences; using the trained neural machine translation model to translate the protein subsequences from amino acid sequences to codon sequences; and overlapping the translated subsequences, so as to combine same into a full-length codon sequence, wherein during overlapping to synthesize the codon sequence, the synonymous codon with the highest occurrence frequency or number is selected as the optimal codon for the position according to the frequency or the number of the synonymous codon corresponding to each amino acid position of the protein sequence.

Provided is a method for identifying an antibody that has a certain biological function in relation to an antigen using a machine learning model. The method comprises the steps of: a) obtaining antibodies from B cells of at least one animal immunized with the antigen; b) determining the sequences of the antibodies in a) or fragments thereof and at least one type of functional data thereof; c) building a machine learning model using one or more machine learning algorithms, and training the model using training data, wherein the training data comprises the sequences and functional data in b); d) using the trained model to predict the ability of sequences from B cells of the immunized animal in a) or from B cells of a different animal immunized with the antigen, to encode an antibody that has the biological function in relation to the antigen; e) generating one or more antibodies from the sequences in d) predicted to encode an antibody that has the biological function in relation to the antigen; and f) determining whether the antibodies in e) has the predicted biological function. Enrichment scores of the sequences, CDR groups, lineages and / or clusters of the selected sequences in step d) are calculated and those having a higher enrichment score are selected to generate the antibodies.

The application provides a copy number variation detection method, system and storage medium, and belongs to the technical field of biological information, and comprises the following steps: constructing a control set, using a healthy person sample, obtaining a bin sequencing depth control set and a SNP control set; using the same method as the control set to sequence and process the to-be-detected sample, respectively correcting the sequencing depth of each bin of each to-be-detected sample at the chromosome level and the genome level based on the bin sequencing depth control set; obtaining a copy number variation detection result based on the z-score and the ratio after noise reduction; obtaining a copy number variation detection result according to the number of effective heterozygous SNP sites of the to-be-detected sample; and outputting quantitative and qualitative results of the copy number of the to-be-detected sample based on the copy number variation detection results of the sequencing depth method and the heterozygous SNP method. The detection method is more economical and flexible based on Panel sequencing, and the detection result is accurate.

The present application relates to a kind of based on splitting-CLS two-way signal-to-noise separation multi-instance learning's whole genome fragment level cancer detection method and its application in cancer screening and MRD monitoring.The method utilizes splitting-CLS and double-branch deep representation structure, realizes "signal-background" effective separation;Through the modeling of cfDNA whole genome fragment, and combined with multi-instance learning framework realizes cancer signal automatic extraction and discrimination under the condition of no fragment level labeling.The present application can effectively overcome the influence of different sequencing platforms and sequencing strategy difference (whole genome sequencing and targeted capture) on detection performance, so as to maintain the robustness and generalizability of the results.The technology breaks through the limitation of relying on specific known mutation or single sequencing scheme, significantly improves the sensitivity, specificity and cross-platform generalization ability of early cancer screening and minimal residual disease (MRD) monitoring, and provides a high robustness and high universality innovative solution for multi-cancer non-invasive screening and longitudinal disease monitoring.

A method for predicting the functional fitness of AAV capsid protein insertion at the 7-position short peptide sequence belongs to the interdisciplinary fields of bioinformatics, computational biology, and artificial intelligence. This method first preprocesses samples containing the 7-position short peptide sequence and its functional sequencing data to construct a basic feature vector that integrates amino acid one-hot encoding and physicochemical properties. Then, a chain-star hybrid graph structure containing global master nodes is constructed, and graph neural networks are used to extract topological association features between residues, while bidirectional long short-term memory networks are used to extract sequence context features. Finally, a linear regression model is used to fuse the prediction results of the two models to obtain the functional fitness score of the target sequence. This method can simultaneously characterize the structural associations and sequence information of short peptide sequences, and still exhibits good prediction accuracy and generalization ability under small sample conditions, providing technical support for the rational design and optimization of AAV vectors.

A device includes software instructions for a circular plot analysis agent and at least one circular plot definition. The circular plot analysis agent obtains a digital image of a circular plot, detects a perimeter of the circular plot within the digital image, detects a plurality of edges within the perimeter, identifies a set of endpoints on the perimeter as a function of the plurality of edges, generates a plot descriptor from the set of endpoints, and initiates a transaction with a second device as a function of the plot descriptor.

A method for biological sample analysis includes receiving reference nucleic acid sequence information and biological sample nucleic acid sequence data, comparing the biological sample nucleic acid sequence data to the reference nucleic acid sequence information, outputting overall consensus metrics of the biological sample nucleic acid sequence data and the reference nucleic acid sequence information based on the comparison and outputting one or more region level consensus metrics of one or more regions of each of the biological sample nucleic acid sequence data and the reference nucleic acid sequence information.

The application discloses a method and system for predicting protein drug binding sites based on a multi-modal dynamic graph, comprising: obtaining amino acid sequence data and three-dimensional structure data of a protein, and generating evolutionary conservation features, structure graph topology features and sequence features respectively; inputting the features into a multi-modal fusion encoder to generate first fusion features; inputting the first fusion features into a prediction decoder to generate initial prediction probabilities; iteratively updating the structure graph topology features according to a preset three-graph update rule according to the initial prediction probabilities and residue dynamic communication scores; re-inputting the updated structure graph topology features into the multi-modal fusion encoder and the prediction decoder, and repeatedly executing until a preset iteration number is reached, to generate final prediction probabilities; and generating a residue importance heat map and outputting a prediction report based on the final prediction probabilities through a gradient weighted class activation mapping algorithm. The application realizes the cooperative optimization of prediction and graph structure, and significantly improves the accuracy of binding site prediction.

This application discloses an MRL prediction and sequence generation method based on an encoding / decoding structure, comprising: step 1, one-hot encoding of a 5'UTR sequence; step 2, building a deep neural network model; step 3, training the deep neural network model; and step 4, generating a 5'UTR sequence; wherein the length of the 5'UTR sequence is 25nt~100nt; the neural network model training process of this application is divided into two training stages: supervised learning and self-supervised learning, which respectively complete the tasks of predicting MRL values ​​and reconstructing 5'UTR sequences; a gradient is introduced into the 5'UTR sequence generation method to achieve the generation of excellent 5'UTR sequences with high MRL values.

The present application relates to the technical field of animal health monitoring, and relates to a wild animal non-invasive genome monitoring and population health evaluation system. A time indication marker is generated based on the ratio of a predetermined indicator bacteria genus, and microenvironment data is input into a pre-trained microbial succession model to intelligently infer the actual exposure time of the sample in the wild. Then, the initial intestinal flora map of the sample at the instant of discharge is reconstructed. The high-fidelity flora map after reconstruction is further deeply coupled with health evaluation to realize scientific diagnosis and early warning of the physiological state of wild animal individuals. The accuracy of health evaluation, nutritional status analysis and pathogen detection is greatly improved, and major misjudgments such as misjudging environmental bacteria as pathogenic bacteria or misdiagnosing flora natural decay as ecological imbalance are avoided. A stable and reliable technical tool is provided for wild animal protection management.

Provided herein are methods of making negative predictions. In some aspects, methods of determining, including via epigenomic detection, that a first target nucleic acid variant is absent at a first genetic locus in a cell-free nucleic acid (cfNA) sample obtained from a subject having a given cancer type at least partially using a computer are provided. Certain of these methods include determining that the first target nucleic acid variant is not detected in the cfNA sample obtained from the subject, generating, by the computer, at least one tumor fraction based value including methylation based estimation; generating, by the computer, at least one mutual exclusivity value; and determining that the first target nucleic acid variant is absent at the first genetic locus in the cfNA sample using the tumor fraction based value and / or the mutual exclusivity value. Additional methods and related systems and computer readable media are also provided.

This invention relates to the field of bioinformatics processing and discloses a method and system for mining the structure-activity relationship (SMR) of porcine neurotrophic peptides based on sequence features. The method includes constructing an original sample index table and fusing multi-source production data, extracting peptide sequence features to generate a sequence feature matrix, constructing a sequence-process joint graph containing peptide nodes and process state nodes, training a structure-activity relationship graph neural network to mine SMR relationships, and deriving a process control decision table based on a process response sample set generated by the network, thereby achieving online optimization of the porcine neurotrophic peptide preparation process. This invention solves the problem of SMR mining caused by the separation of process parameters, sequence information, and activity data, achieving accurate characterization of the synergistic effect of sequence and process and reverse optimization of process parameters, thus improving the targeted enrichment efficiency and bioactivity retention level of target neurotrophic peptides.

The application provides a SNP molecular marker combination for constructing a SNP fingerprint of Trachurus and application thereof, and belongs to the technical field of molecular biology. Physical positions of the SNP molecular marker combination are determined based on sequence alignment of a Trachurus japonicus reference genome GCA_900607315.1, and the SNP molecular marker combination comprises 150 SNP sites located on chromosomes Chr01-Chr24. The SNP molecular marker combination provided by the application can be used for constructing a SNP fingerprint of Trachurus and rapid identification of Trachurus varieties. The application provides brand-new identity information specific to different Trachurus varieties, which can effectively identify the authenticity of individuals and trace the source of individuals, and has a good application prospect in classification and identification of Trachurus germplasm resources, molecular-assisted breeding and the like.

This application relates to the field of aquatic pathogen detection technology, and provides a method and system for assessing the virulence level of *Nocardia amberjack* strains. The method includes: obtaining the whole genome sequence of the *Nocardia amberjack* strain to be tested; analyzing the whole genome sequence based on a classified and weighted specific virulence gene feature library of *Nocardia amberjack* to obtain a virulence feature vector; using a trained regression model to predict the LD50 of the virulence feature vector to obtain the predicted LD50 value of the *Nocardia amberjack* strain; and determining the virulence level of the *Nocardia amberjack* strain based on the predicted LD50 value and a preset virulence classification baseline. This application can achieve accurate assessment of the virulence level of *Nocardia amberjack* while significantly reducing experimental costs and shortening the cycle.

The application discloses a long-read data robust variant detection method and system based on data quality improvement, and belongs to the technical field of gene sequencing and biological information analysis. The method of the application is aimed at the characteristics of long-read data, and first, preprocessing is completed through sequence filtering and base correction, and a three-dimensional outlier feature system of sequencing support intensity, mapping quality reliability and strand-specific bias is constructed. Based on the median absolute deviation algorithm, the features are standardized and outlier scores are generated, a double-threshold dynamic optimization mechanism is constructed, and high-confidence data is screened. The variant candidate regions are identified through multi-tool joint comparison, the SNP and Indel are typed through a deep learning model, and the reliability of the results is ensured through triple cross-validation. The method does not need to manually set a fixed threshold, effectively suppresses interference factors, balances detection accuracy, robustness and scene universality, and provides high-quality variant data support for clinical diagnosis and genomics research.

The application relates to the technical field of gene sequencing data processing and bioinformation analysis, in particular to a Web-based whole-exome sequencing tumor gene mutation detection visual analysis method and system. The system collects user sequencing data and a reference genome version through a Web interactive interface; a program is called to perform quality control cleaning and evaluation on the data, and a visual report is generated; sequence alignment is completed based on the reference genome, and a variation site is identified through algorithm iteration; biological annotation of the variation is combined with a database, a candidate pathogenic mutation set is screened out in multiple levels according to a strategy, the candidate set is projected to a visual interface, a site state is confirmed or removed in response to a manual checking instruction, and a final gene mutation detection report is generated. The application greatly simplifies the whole-exome sequencing data processing procedure, makes it easy for clinical doctors or researchers without bioinformation background to start, and improves the popularization rate and work efficiency of tumor gene detection work.

A spatial in situ sequencing method, belonging to the field of biology, is proposed. It utilizes an electric field-assisted directed migration of mRNA and in-situ capture with primers on a microarray surface to enrich tissue and release mRNA. In-situ reverse transcription generates covalently fixed cDNA, ensuring high positional stability during multiple rounds of hybridization and imaging. After reverse transcription, tissue is digested to remove tissue, reducing spatial hindrance and background interference, while the cDNA remains at its original coordinates due to covalent anchoring. Combining coding probe hybridization and RCA, single-molecule-level signal amplification and recognition are achieved. Through decoding and single-cell segmentation, transcripts are mapped to their respective cells, constructing a single-cell resolution spatial gene expression map. This method does not rely on multi-round DAPI mapping or other endogenous morphological marker-based multi-cycle image registration methods, making it suitable for high-throughput spatial in situ sequencing and significantly improving robustness and versatility in complex imaging scenarios such as thick tissue sections and low signal-to-noise ratios. It is applicable to high spatial resolution, high-throughput spatial transcriptome research.

The application discloses a marker combination and a prognosis evaluation model for evaluating the therapeutic effect of an anti-PD-1 antibody on melanoma, and belongs to the technical field of biological markers. The technical problem to be solved is that the prior art lacks a prognosis evaluation model for evaluating the therapeutic effect of an anti-PD-1 (programmed death receptor 1) antibody on melanoma. The technical solution is characterized in that a prognosis evaluation model for evaluating the therapeutic effect of an anti-PD-1 antibody on melanoma is provided, wherein the prognosis evaluation model adopts a marker combination of KIR (killer immunoglobulin-like receptor) and HLA (human leukocyte antigen) as parameters for prediction and evaluation, and the evaluation model has a remarkable effect and high accuracy in evaluating the therapeutic effect of an anti-PD-1 antibody on melanoma.