129 results about "Genomics" patented technology

The invention relates to the field of medical information technology and medical data analysis, in particular to a VTE risk prediction method and system based on multi-modal data fusion and dynamic risk modeling, and the system comprises a first feature extraction module, a second feature extraction module, a two-stage risk assessment module and a final risk judgment module. According to the application, through integration of clinical diagnosis and treatment, iconography and genomics data, a deep learning algorithm, a dynamic weight adjustment mechanism and a graph neural network technology are utilized to realize accurate prediction of the VTE risk of the active-stage lung cancer patient. And meanwhile, the problem of cross-mechanism data distribution difference is solved by adopting transfer learning, and the generalization ability of the model is improved. The method can significantly improve the prediction accuracy, reduces the manual intervention demands, and optimizes the medical resource distribution efficiency.

The invention relates to the technical field of genomics, molecular biology, bioinformatics and whole-genome selective breeding, in particular to a whole-genome liquid chip for apostichopus japonicus and application of the whole-genome liquid chip. The liquid phase chip contains sequences of background SNP loci and functional SNP loci which are used for gene analysis and are positioned on an apostichopus japonicus reference genome; wherein the functional SNP sites are associated with important economic characters of the apostichopus japonicus. The invention also discloses application of the liquid chip in apostichopus japonicus genome selective breeding, important economic character gene positioning, genetic diversity analysis, germplasm resource improvement and protection.

Described herein are methods such as multi-omic methods for assessing a disease such as cancer. The multi-omic methods may integrate proteomic, transcriptomic, genomic, lipidomic, or metabolomic data. The method screening diseases or disease states. Also described herein are methods for screening for diseases or disease states from biological samples. The methods may include assessing whether a nodule, mass, or cyst is cancerous.

The invention discloses a marker screening and model building method for precise evaluation of parent genome breeding values, and belongs to the technical field of molecular breeding and genomics. The method comprises the following steps: acquiring genetic typing data and target character phenotype data of a reference group, and performing digital coding; calculating a secondary allele frequency (MAF) and a linkage imbalance score (LD score) of each marker site; based on the joint screening rule of the MAF and the LD score, screening to obtain a core marker subset with relatively high genetic stability and linkage structure representativeness; and constructing a differentially weighted genome prediction model based on the core marker subset to estimate the genome breeding value of the parent individual. According to the method, allele frequency information and linkage imbalance structure information are jointly utilized, interference of low-frequency or weak linkage sites on the prediction model is reduced, the prediction accuracy of parent breeding values in cross-generation predictor representative types is improved, and the method is suitable for genome prediction and molecular breeding of complex characters.

This invention relates to the fields of genomics, molecular biology, bioinformatics, and genome-wide selection breeding, specifically a 20k liquid-phase breeding chip for the whole genome of *S. esculenta* and its applications. The liquid-phase chip contains background SNPs and functional SNPs located on the *S. esculenta* reference genome; wherein the background SNPs are uniformly distributed within the genome; and the functional SNPs are associated with important economic traits of *S. esculenta*; these important economic traits include one or more of the following: saponin content, polysaccharide content, and heat tolerance. The chip can be applied to the assessment of genetic diversity in *S. esculenta*, identification of germplasm resources and phylogenetic relationships, genome-wide association analysis of important economic traits, and genome-wide selection breeding. This chip has advantages such as high throughput, high region coverage, high locus detection rate, and high flexibility, providing powerful tool support for molecular breeding of *S. esculenta*.

Compositions and methods for detecting structural variations (SVs) in target genes or for genetic mapping of movable transposable elements are disclosed, the target genes relating to disease pathologies commonly found in large Mendelian genomics projects, and the movable transposable elements relating to genetic diseases, cancer, and aging. The method comprises: (i) contacting a sample containing genomic DNA with a DNA endonuclease for an effective amount of time to cleave the genomic DNA into fragments, the genomic DNA being uncrosslinked; (ii) subjecting the fragments obtained from step (b) to a DNA ligase to obtain circularized DNA; (iii) subjecting the circularized DNA to reverse PCR amplification containing a reverse primer, wherein the reverse primer is designed to match a expected wild-type sequence near a suspected mutant locus in the gene; and (iv) sequencing the amplified products. Exemplary conditions include Bardet-Biedel syndrome; severe upper and lower limb defects; retinitis pigmentosa; syndromic microcephaly; spastic paraplegia; and atypical hemolytic uremic syndrome.

In various aspects and embodiments, the Disclosure provides methods for evaluating subjects for the presence or absence of colorectal neoplasms, such as colorectal cancer (CRC), colorectal adenoma (CRA), and progressive colorectal adenoma (CRAA), by metagenomic and multi-omics analysis of feces or other biological samples. In other aspects, the Disclosure provides methods for generating machine learning models or “signatures” based on metagenomic and multi-omics analysis of feces or other biological samples to evaluate subjects for the presence or absence of colorectal diseases, including but not limited to CRC, CRA, and CRAA.

Fluorescence imaging system designs are described that provide larger fields-of-view, increased spatial resolution, improved modulation transfer and image quality, higher spatial sampling frequency, faster transitions between image capture when repositioning the fields-of-view, improved imaging system duty cycle and a more compact system, and thus enable higher throughput image acquisition and analysis for genomics and other imaging applications at a lower cost.

The invention relates to the technical field of forensic genomics, in particular to a primer group and a kit for detecting genetic markers of 365 Y chromosomes and application of the primer group and the kit. According to the invention, an amplification primer group for specific 57 Y-STRs and 308 Y-SNPs of Chinese population is optimally designed, and a joint detection method with high sensitivity and high resolution is constructed. Y-STR haplotype analysis and Y-SNP haplogroup accurate typing can be synchronously realized, and the problems of high family checking false positive rate, insufficient geographic ancestor inference resolution and the like caused by single site type detection in the traditional technology are solved. The method is suitable for diversified biological samples such as blood, seminal stains and saliva, and provides a novel technical scheme of high-throughput, low-cost and multi-dimensional paternal genetic information analysis for forensic practice. Comprising construction of a Y-STR and Y-SNP composite amplification system based on next-generation sequencing and application of the Y-STR and Y-SNP composite amplification system in forensic medicine individual recognition, family investigation and biogeography ancestor inference.

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.

Described herein are particle-driven radiogenomics systems and methods that can be used to identify imaging features for prediction of intratumoral and interstitial nanoparticle distributions in cancers (e.g., in low grade and / or high-grade brain cancers (e.g., gliomas, e.g., primary gliomas)). In certain embodiments, the systems and methods described herein extract and combine quantitative multi-dimensional data generated from structural, functional, and / or metabolic imaging. In certain embodiments, the combined multidimensional data is linked to intratumoral and interstitial nanoparticle distributions. For example, this linked data can be used to determine quantitative functional-metabolic multimodality particle-based imaging features and to predict treatment efficacy. These techniques provide an improved quantitative ability to measure treatment response and determine tumor progressions compared to traditional size-based imaging methods.

The present application belongs to the field of biotechnology and genomics, and particularly relates to a method and system for determining the chromosome base number of Macrobrachium rosenbergii based on multi-omics joint analysis. The method obtains de novo assembly sequencing data and Hi-C sequencing data, generates a chromosome-level candidate assembly without presetting the number of chromosomes by using Hi-C interaction signals after primary assembly, and performs whole-genome collinearity alignment with no less than two published reference genomes; in combination with quality constraints such as collinearity continuity, Hi-C boundary characteristics and BUSCO / LAI, the candidate chromosome boundary is comprehensively judged and iteratively converged, and finally the chromosome base number and reviewable evidence chain are output. The embodiments show that the present application can identify and correct the number redundancy caused by over-splitting of the reference genome, determine the base number of Macrobrachium rosenbergii as n=57 (2n=114), and improve the objectivity and reliability of base number determination.

The invention belongs to the field of microorganisms and medicine health care, and relates to an ackermania muciniphila strain and application thereof, the preservation number of the ackermania muciniphila strain is CGMCC (China General Microbiological Culture Collection Center) No.34370, and a 16SrDNA (Deoxyribose Nucleic Acid) sequence of the ackermania muciniphila strain has 93% similarity with a sequence shown as SEQ ID NO: 1. The new ackermania muciniphila strain (AKKBC300) is separated from a healthy human body and has good safety; good tolerance to low pH value and bile salt is shown in vitro, and it is indicated that the compound can reach the intestinal tract through the upper digestive tract and play a role; according to the present invention, genomics analysis results prove that the bacterial strain contains the gene Akkermansia BC00573 with high homology with the known probiotic functional protein Amuc1100, such that the molecular basis is provided;

Systems and methods are provided for trait prediction coordination. One embodiment is a method that includes launching a genomics application at a user device, receiving a command to present a partner application within the genomics application, selecting characteristics to predict for an individual, based on an identifier that distinguishes the partner application from other partner applications, operating polygenic models that generate predictions for the characteristics based on genetic records for the individual, acquiring media for the partner application, based on the predictions, and operating the partner application to update a display at the user device with the media, thereby providing the predictions in a format specific to the partner application.

The present invention provides a computer implemented method for the prediction of clinical outcomes in patients with cancer or pre-neoplastic conditions through the integration of genomic evolutionary, genomic and clinical. More specifically, the invention provides systems and algorithms that generate prognostic and predictive models based on the combined analysis of molecular features, inferred evolutionary routes, and clinical parameters, enabling patient risk stratification and individualized outcome estimation.

The invention discloses an adsorption material screening method and system for transfer learning auxiliary material genome design, and belongs to the technical field of intelligent material design and high-throughput screening. The method comprises the following steps: S1, collecting an adsorption material data set of a source domain and a target domain, wherein the adsorption material data set comprises structural parameters, adsorption performance and environmental condition information; s2, establishing a prediction model for a source domain, constructing a transferable feature subspace through maximum mean difference (MMD) and KL divergence analysis, comparing and verifying cross-domain advantages of transfer learning compared with direct training, and adopting random sampling and grouping demonstration to ensure model stability; s3, screening a common framework structure of the source domain and the target domain; s4, designing and optimizing a functional group combination based on material genomics, and generating a target adsorbent design scheme; and S5, synthesizing a preferable material and testing the adsorption performance of the preferable material. According to the method, the limitation of a traditional trial and error method is broken through, cross-system knowledge reuse is achieved through transfer learning, directional development of a high-performance adsorbent is guided in combination with material genome design, and an intelligent solution is provided for adsorption material research and development in the fields of environmental governance, resource recovery and the like.

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.

Provided are methods, systems and computer program product embodiments for analyzing multi-omic data using a tensor regression model for genome-wide association studies in the life sciences. The unique structure of tensor covariates is leveraged to find associations between the omics data and complex diseases. Within this framework, the excessive dimensionality is reduced to a manageable level, leading to efficient estimations and predictions. The method is superior to using classical regression techniques in genome-wide association studies, which are challenged by analyzing multi-dimensional and uniquely structured data from the health and life sciences, in which covariates can take on more intricate forms such as multi-dimensional arrays. Embodiments have multiple uses in genomics, proteomics, metabolomics, multi-omics data integration, drug discovery, personalized medicine and predictive modeling, demonstrating the versatility and importance of tensor regression models to understand the associations between omics data and complex diseases.

The invention belongs to the field of biotechnology and genomics, and particularly relates to a method and system for determining the chromosome cardinal number of macrobrachium rosenbergii based on multi-omics conjoint analysis. According to the method, de novo assembly sequencing data and Hi-C sequencing data are obtained, after primary assembly is completed, chromosome-level candidate assemblies are generated under the condition that the number of chromosomes is not preset through Hi-C interaction signals, and whole-genome collinearity comparison is carried out on the chromosome-level candidate assemblies and no less than two disclosed reference genomes; and in combination with quality constraints such as collinearity continuity, Hi-C boundary features and BUSCO / LAI, carrying out comprehensive decision and iterative convergence on candidate chromosome boundaries, and finally outputting chromosome cardinal numbers and reviewable evidence chains. The embodiment of the invention shows that the method can identify and correct the number redundancy caused by the over-resolution of the reference genome, determines the cardinal number of macrobrachium rosenbergii as n = 57 (2n = 114), and improves the objectivity and reliability of cardinal number determination.

The invention discloses a dairy cow and live pig high-quality breeding method based on AI genomics, and relates to the field of breeding. Comprising the following steps: multi-dimensional data acquisition: aiming at a target breeding group, acquiring whole genome variation data, various phenotype data and environment management data of each individual, establishing unique identification association for all the data, and storing the data in a central database; data preprocessing and feature enhancement: performing quality control, filling and standardization processing on the genome data, and constructing an effective feature set for model training from the original data based on statistics and machine learning methods; and training an AI prediction model. By introducing the artificial intelligence deep learning model, the complex non-additive effect and gene-environment interaction between the genotype and the phenotype can be efficiently captured, the prediction precision of important economic characters is greatly improved, and earlier and more accurate selection and optimized hybridization are realized, so that the genetic progress is greatly accelerated, the breeding cost is reduced, and the method is suitable for large-scale popularization and application. The breeding efficiency and benefits are comprehensively improved.

The invention relates to the technical field of biological detection, and provides an integrated gradient spinning fiber membrane for multi-omics analysis and a preparation method of the integrated gradient spinning fiber membrane. According to the fiber membrane, a coaxial electrostatic spinning technology is adopted, and functional zones are continuously formed on a single membrane by controlling the flow velocity of a shell layer (PLGA / chitosan solution) and a core layer (PEO / GPC3 aptamer solution): a nucleic acid enrichment end adsorbs and captures nucleic acid through positive electricity of chitosan, and a protein capture end recognizes a liver cancer marker GPC3 protein through a fixed GPC3 specific nucleic acid aptamer G625. According to the design, the capture environment of nucleic acid and protein is physically isolated, the problems of mutual interference and cross contamination of functional sites in a traditional method are fundamentally solved, and parallel and in-situ capture and enrichment of genomics, transcriptomics and proteomics information in the same trace biological sample are realized. The method is easy and convenient to operate, GPC3 protein is specifically captured and recognized, the nucleic acid enrichment capacity and the cross contamination resistance capacity are high, and a new strategy is provided for early diagnosis and precise typing of liver cancer.

This invention belongs to the interdisciplinary field of bioscience and information technology, and relates to a method for assessing the intensity of gene transcriptional regulation based on multi-omics data. Addressing the challenges of systematically integrating multi-omics data and quantitatively characterizing the correspondence between regulatory elements and genes, as well as the intensity of regulation, in existing transcriptional regulation analyses, this invention integrates epigenomics, transcriptomics, and three-dimensional genomics data to construct a site-gene regulatory intensity model. This method extracts candidate cis-regulatory sites and calculates their regulatory activities. It then determines the site-gene regulatory weights by combining distance weighting and three-dimensional genomic contact information, thereby obtaining the regulatory intensity at the site-gene pair and gene levels. An iterative algorithm distinguishes between positive and negative regulatory effects, achieving correction and summarization of regulatory intensity. This invention can quantitatively assess the regulatory intensity of cis-regulatory elements on target genes and the overall regulatory effect on genes, and is suitable for studying gene transcriptional regulation mechanisms and analyzing gene expression changes under different treatment conditions.

The present disclosure relates to a computer-implemented method for extracting a subsample of cells from a plurality of cells in a single-cell genomics dataset, the method comprising: obtaining a single-cell genomics dataset represented in at least two dimensions, where information about each cell is represented in a first dimension and information about genetic features is represented in a second dimension; generating a cell-cell neighborhood graph from the single-cell genomics dataset, where the cell-cell neighborhood graph provides information about similarities in the genetic features of cells and where each cell is represented as a vertex in the cell-cell neighborhood graph; partitioning cells in the cell-cell neighborhood graph into seed cells and non-seed cells; assigning at least one first bounty to seed cells and at least one second bounty to non-seed cells in the cell-cell neighborhood graph; and traversing the cell-cell neighborhood graph using a prize-collecting Steiner tree algorithm to obtain a subsample of cells. The present disclosure further relates to a computer system for extracting a subsample of cells from a plurality of cells in a single-cell genomics dataset.

The invention relates to a SrCas13a-3 protein as well as a gene editing system and application thereof. The amino acid sequence of the SrCas13a-3 protein is shown as SEQ ID NO. 1. The invention provides endonuclease SrCas13a-3 identified by utilizing metagenomics in combination with experiments, and the endonuclease SrCas13a-3 is a new member with low CRISPR / Cas13a family homology. The invention also establishes a nucleic acid visual detection technology based on CRISPR / SrCas13a-3 system mediation, and the nucleic acid visual detection technology has a wide application prospect in the field of nucleic acid detection.

The application discloses a whole-genome DNA cytosine hydroxymethylation modification single-base precision library construction method, relates to the technical field of genomics, epigenetics and molecular biology, and contains the following steps: 5hmC labeling reaction, click chemistry reaction, sample DNA fragmentation, purification of the fragmentation product through magnetic beads, 5hmC fragmented DNA capture, washing of the capture product, reduction reaction, APOBEC enzyme deamination reaction, purification of the deamination product, double-strand conversion reaction, end repair and 3' end A addition reaction, linker ligation, purification of the ligation product and double sorting, library amplification and library purification. The library construction method integrates low data volume, single-base precision and conventional double-strand library construction kit in one under the premise of ensuring data authenticity, and has the technical advantages of high universality and highly repeatable data.

The invention discloses a ribosome group or selective ribosome group detection and analysis method based on an immune principle, and relates to the field of molecular biology and genomics. Comprising the following steps: sample preparation and cross-linking: treating cells by using a translation inhibitor to suspend ribosome translation, and then cross-linking RNA-protein in the cells by using a membrane permeable cross-linking agent; then cracking the cells, digesting RNA of a cracking product by using ribonuclease, and retaining ribosome and mRNA fragments protected by the ribosome; immune enrichment: carrying out specific immune enrichment on the digested sample by using an antibody combined with a carrier, and eluting to obtain a ribosome-mRNA compound; then decrosslinking and removing protein, extracting mRNA fragments and carrying out phosphorylation treatment so as to add joints, build a library and carry out sequencing; and finally, library construction, sequencing and bioinformatics analysis are carried out. The method has the advantages of high-specificity enrichment, accurate positioning of translation sites, wide application range and accurate sequence information.

The invention relates to the technical field of genomics and microbial science, in particular to a myxobacteria T2T genome assembling method. The assembling method mainly comprises the four steps of global assembling, telomere sequence screening, local sequence clustering screening and second-generation data multi-round correction. According to the method provided by the invention, pollution removal strategies of telomere sequence screening and local sequence clustering screening are newly added, so that the problem that tandem repeat sequences in a myxobacteria genome are complex and short is effectively solved, the analysis of a complex region becomes possible, the integrity and continuity of assembly are improved, and the assembly efficiency is improved. The method does not need fingerprint spectrum, Hi-C technology or optical spectrum sequencing technology, reduces the experiment cost, ensures that all the sequences belonging to myxobacteria are extracted, effectively removes the interference of repetitive sequences and deep pronucleus pollution, enables highly similar repetitive sequence fragments to be reduced and positioned to an accurate genome position, and improves the accuracy of the detection result. The integrity and continuity of the genome are obviously improved.

The present disclosure is directed a configurable, end-to-end clinical assay that ingests clinician-collected samples and harmonizes short-amplicon 16S, optional shotgun metagenomics, and alternative full-length 16S workflows into a unified analytics stack for decision-grade diagnostics. In some embodiments, the assay performs high-resolution ASV or full-length species calling, taxonomic assignment, consensus clustering, healthy-reference cross-correlation, bias-corrective differential abundance testing, and MetaCyc pathway prediction to surface conserved SCFA-linked signatures. The composite diagnostic framework fuses expanded biomarkers, CST stratification, network-module context, and pathway-level signals to adjudicate borderline or indeterminate cases with cross-method concordance to panel PCR outputs. Reporting includes normal-range contextualization and LIMS-integrated, auditable summaries of taxonomic, network, and pathway results. A throughput-oriented, auditable compute architecture employs streaming ingestion, hardware-accelerated basecalling, caching, asynchronous orchestration, and persistent provenance to enable reproducible clinical deployment across modalities.

The invention discloses a multi-modal biomedical data security fusion query treatment method and system. The method comprises the following steps: receiving a fusion query request; legality verification is carried out through the block chain smart contract; decomposing the ontology model based on the multi-modal metadata into sub-query tasks and distributing the sub-query tasks; each data holder node generates an intermediate result identified by a unified pseudonym identifier in a local privacy protection computing environment; executing data alignment and aggregation operation of privacy protection; and returning a fusion result and recording the key event in the block chain smart contract. According to the method, integrated treatment with data availability and invisibility, flexible query, process auditing and contribution incentive is achieved, and the method is suitable for safety collaborative analysis of multi-mode biomedical data such as genomes, images and electronic medical records.

The application discloses a non-small cell lung cancer auxiliary decision-making method based on multi-modal causal representation and belongs to the field of clinical auxiliary decision-making. The method comprises the following steps: S1, obtaining clinical texts of non-small cell lung cancer patients, inputting the texts into a text feature extraction network, and obtaining text features; S2, obtaining imaging examination data of the non-small cell lung cancer patients, inputting the data into an image feature extraction network, and obtaining image features; S3, obtaining genomic data of the non-small cell lung cancer patients, inputting the data into an omics feature extraction network, and obtaining gene features; S4, mapping and splicing the text features, the image features and the gene features to obtain mixed variable representation, inputting the mixed variable representation into a random causal relationship network, and combining a pessimistic estimation mechanism to generate an auxiliary decision. Through the introduction of long text analysis of rotary position coding, lesion image extraction of multi-order gating aggregation and gene pathway analysis of graph attention mechanism, efficient representation of multi-modal data is realized.