179results about "Evolutionary biology" patented technology

The embodiment of the invention provides an end-to-end B cell clone pedigree forest construction method and related equipment, and can be applied to the technical field of data processing. According to the method, a plurality of obtained receptor sequencing sequences are subjected to germline comparison identification to obtain a first test Fv sequence corresponding to each receptor sequencing sequence, and a germline Fv sequence corresponding to each receptor sequencing sequence is generated; performing integrity filtering on the first test Fv sequence, performing clone type division to obtain a plurality of first clone type sets, constructing corresponding first evolutionary trees to form a first pedigree forest on the basis of a second clone type set contract type conversion probability, and performing node optimization on all the first evolutionary trees to obtain a second pedigree forest; and after it is determined that the homotype category conversion probability after updating based on all the second evolutionary trees meets the preset requirement, visualization processing is performed on all the second evolutionary trees, so that the systematic cognition efficiency of related personnel on the adaptive immune response mechanism can be improved.

Disclosed herein is a system for detection of a molecular community in a molecular network, the system comprising an information handling system that comprises at least one processor; and a non-transitory storage medium storing instructions readable and executable by the processor to perform a method comprising receiving molecular data from an analytical device and / or a data repository; generating an unpruned molecular network from the molecular data; treating the unpruned network with a community detection algorithm to form one or more molecular communities; parsing the molecular communities to prevent the formation of singletons; and identifying at least one molecule present in the molecular community by comparing the molecular community with reference spectra from a library of spectra or by using a molecular structure prediction tool.

The invention relates to the technical field of bioinformatics data processing, in particular to a genome sequence compression method and system. The method comprises the following steps: acquiring genome sequencing data; comparing the sequencing data with a reference genome to determine a difference site; differentiating the difference sites as sequencing errors or real variations through a time sequence difference neural network model; performing differential compression coding according to an identification result; a friendly variation detection format is constructed, and rapid variation query is supported through a multi-level index structure and a variation metadata table. According to the method provided by the invention, the sequencing error and the real variation can be accurately distinguished through the time sequence differential neural network model, and important biological variation information is protected while the compression efficiency is improved by adopting the differential compression coding strategy.

Disclosed are a genomic mating method for Huaxi cattle based on whole genome single nucleotide polymorphism (SNP) information and an application thereof. The method includes the following specific steps: step 1, extracting deoxyribonucleic acid (DNA) from to-be-hybridized Huaxi cattle individuals for genotyping; step 2, performing genotype data imputation to obtain high-density chip data; step 3, calculating an additive genetic relationship matrix, utilizing genomic best linear unbiased prediction (GBLUP) to obtain genomic estimated breeding values of five important economic traits of a to-be-hybridized Huaxi cattle population, and calculating a comprehensive selection index of the individuals; and step 4, using a genetic algorithm to construct a population optimal mating combination list. In the present invention, the breeding cost is greatly saved and an inbreeding level of offspring populations is reduced.

The invention discloses a method and a device for identifying conservative non-coding elements (CNEs) in a genome. The method comprises the following steps: respectively comparing other genomes in a plurality of high-quality genomes with a reference genome to obtain genome comparison results; identifying the comparison result of the species evolutionary relationship topological structure and the genome to obtain a conserved sequence position information gff file; the gff file is filtered, and a CNEs.gff file is obtained; a CNEs sequence is extracted from the CNEs.gff file; determining a CNEs differentiation sequence according to the CNEs sequence; and performing function enrichment analysis on the target gene of the CNEs differentiation sequence to obtain a plurality of identification results of the CNEs in the high-quality genome. According to the method and the device, the technical problem that identification of the CNEs is incomplete or misjudged due to the fact that identification of the CNEs is often influenced by genome structure variation in the prior art is solved.

The invention relates to the field of gene engineering. In particular, the present invention relates to cytosine deaminases and their use in base editing. More specifically, the invention relates to a method for screening and identifying a deaminase, a base editing system based on a newly identified cytosine deaminase, a method for editing a target sequence in a genome of an organism (such as a plant) by using the base editing system, and a method for screening and identifying the target sequence. As well as genetically modified organisms (e.g., plants) and progeny thereof produced by the method.

The present invention relates to a method, preferably a computer-implemented method, and to an apparatus for generating at least one intermediate cellular state of a cellular state evolution from an initial cellular state to a corresponding advanced cellular state. The present invention also relates to a computer program for generating at least one intermediate cellular state and to a non-transitory computer readable data medium storing the computer program. Moreover, the present invention relates to a method and to an apparatus for training a model for generating at least one intermediate cellular state of a cellular state evolution from an initial cellular state to a corresponding advanced cellular state. The present invention also relates to a computer program for training a model for generating at least one intermediate cellular state and to a non-transitory computer readable data medium storing the computer program. Furthermore, the present invention relates to a method for diagnosing a disease and forecasting patients' future clinical outcome making use of the method for generating at least one intermediate cellular state. Moreover, the present invention relates to an in-vitro method for transforming the identity of a cell.

The invention relates to the technical field of microorganisms, and discloses a pseudomonas putida strain and a preparation method and application thereof.The strain is pseudomonas putida which is separated from soil and has the capacity of dissimilatory reduction of nitrate into ammonium and is classified into a new pseudomonas putida strain based on GTDB-TK. A dissimilatory NOreductive gene cluster, namely an nirBD gene cluster, exists on a genome of the gene, nirK / nirS genes are deleted, and NOs can be reduced into NH under the anaerobic culture condition. According to the application of the pseudomonas putida strain in improvement of soil nitrogen management, the pseudomonas putida strain can be mixed with organic fertilizer for use so as to enhance the effective utilization and conversion capacity of nitrogen in soil. By means of the pseudomonas putida strain, NON can be reduced into NHH under the anaerobic condition, the culture method is simple, the growth speed is high, variation is not prone to occurring, and a brand new microbial resource is provided for soil nitrogen cycle research.

The invention provides a multi-stream fusion gene sequence prediction method based on Mama and a double-coding strategy, and aims to improve semantic understanding and modeling capability of DNA sequences, so as to improve accuracy and generalization performance of protein expression value prediction. The method comprises the following steps: synchronously processing a forward chain and a reverse complementary chain of a DNA sequence: carrying out word segmentation and vectorization on the reverse complementary chain by adopting BPE coding to capture a semantic fragment; and the forward chain is subjected to one-hot coding and is linearly mapped into dense representation. Deep features of the two features are extracted through Mama Block and then are spliced and fused, then LSTM is input to model context dependence, and finally an expression value is output through a full connection layer. According to the method, the problems that biological semantic fragments are difficult to capture by One-Hot coding and functional domains are likely to be split by BPE coding are effectively solved. On the basis of a Mama framework, a local structure and global semantics are effectively combined, and the method is good at capturing long-distance dependence while low calculation complexity is kept, and is suitable for tasks such as gene design and protein expression regulation and control.

The invention relates to the technical field of drug target prediction, and discloses a drug target intelligent prediction method and system, and the method comprises the following steps: 1, obtaining the structure data and sequence data of a target protein, and the homologous sequence data of the target protein; 2, performing molecular dynamics simulation according to the structural data, and calculating to obtain a dynamic conformation feature vector; according to the sequence data, an evolutionary information feature vector is obtained through calculation; analyzing the structural data to obtain an interaction feature vector; 3, obtaining an enhanced dynamic conformation feature vector, an enhanced evolutionary information feature vector and an enhanced interaction feature vector through an attention fusion mechanism, respectively obtaining importance weights through a gating fusion mechanism, and obtaining a fusion feature vector by adopting element-by-element multiplication and combining the importance weights; and 4, inputting the fusion feature vector into a graph neural network prediction model to obtain a target druggability probability.

The invention discloses a multi-layer heterogeneous network unicellular organism network inference method based on meta-path enhancement, which mainly comprises a gene regulation knowledge base enhanced multi-layer heterogeneous network construction module for integrating an external gene interaction network and multiple omics data such as scRNA-seq, scATAC-seq, ST and the like; constructing a single-cell multi-omics multilayer heterogeneous network containing cell-cell, cell-gene and gene-gene relationships, and fusing spatial constraints to consider cell positions and tissue structures; and the feature enhancement module based on the meta-path explores complex semantics of the network by designing a multi-hop meta-path mode, designs an adaptive multi-view learning framework and a multi-round enhancement mechanism, and optimizes feature representation by using cell-gene interaction and cross-modal attention fusion. The unicellular biological network can be effectively deduced, the deduction accuracy and biological interpretation are remarkably improved, the method plays an important role in understanding the cell biological process, developing and treating diseases and the like, has good expandability, and can further integrate multi-modal omics data such as proteomics and metabonomics.

The invention relates to the technical field of biostatistics, and discloses a noise language frequency hearing impairment prediction system based on a GEE model and genetic characteristics, and the system comprises a data collection module which collects follow-up visit data of a worker; the data processing module is used for generating a standardized modeling data set; the feature screening module is used for screening features through a statistical learning method to obtain a key feature subset; the time-varying interaction module is used for constructing a generalized estimation equation model and outputting regression coefficient estimation; the risk prediction module outputs the risk probability and the risk layering result of the individual; the decision support module is used for generating hearing protection suggestions of the individuals; according to the method, the generalized estimation equation model is constructed to process follow-up data of occupational noise exposure workers, so that the accuracy and the stability of language frequency hearing loss risk prediction are improved, a basis is provided for formulating a personalized hearing protection scheme and recommending a proper hearing protection device, and the method is suitable for popularization and application. And the transformation of occupational hearing loss from passive treatment to active prevention is facilitated.

Provided are a method and system for predicting an activation potency of agonist molecules on G Protein-Coupled Receptors (GPCRs). The method includes: blindly speculating complex structures formed by binding of a ligand to an activated receptor structure and an inactivated receptor structure, respectively, via global molecular docking; extracting an initial path enabling an inactivated complex structure to be activated to an activated complex structure based on an enhanced sampling algorithm; searching for a minimum free energy path closest to the initial path by applying an automatic path optimization algorithm; calculating a free energy distribution curve along the minimum free energy path by employing umbrella sampling and determining an energy barrier height and a free energy difference before and after activation, thereby determining the activation potency of the ligand structure on the GPCRs.

The application discloses an antiviral peptide prediction method and system based on federated learning and a neural network, utilizes multiple independent data sets to respectively perform training on a prediction model, constructs a double-channel deep neural network through the model, extracts features of different dimensions from original variable-length sequence data, analyzes peptide segment sequences from two aspects of sequence data and evolution levels, and finally transmits model gradients and parameters obtained to an intermediate server through encryption technology; the server aggregates the transmitted model parameters to form an intermediate model without contacting the data itself; the server transmits parameters of the intermediate model to participants, completes updating of participant models, and the above steps are circularly performed until model training is completed. The combination of federated learning and the neural network can accurately and quickly predict the antiviral activity of peptide segments under the premise of guaranteeing data security.

The invention discloses a poultry breeding pedigree coding method. The method comprises the following steps: firstly, determining strain codes of individuals; determining the appearance codes of the individuals; determining batch codes of the individuals; determining family numbers of the individuals; determining individual type codes of the individuals; determining individual sequence numbers of the individuals; generating a check code; combining the strain code, the provenance code, the batch code, the family number, the individual type code, the individual sequence number and the check code in sequence to form an individual code; according to the method, the structured coding rule containing information such as strains, generations, batches, families, types, individual serial numbers and the like is constructed, so that the information capacity borne by a single code is greatly improved, and key individual identity information which originally needs to depend on database query can be directly reflected in a coding character string; therefore, the problem that the traditional serial number information amount is too low is solved.

Among other things, the present disclosure provides technologies for efficient and effective identification of ETaGs, for example, from fungi genomes. In some embodiments, provided technologies are particularly useful for identifying mammalian targets of biosynthetic products of fungi. In some embodiments, provided technologies are particularly useful for identifying and / or prioritizing human targets for drug development. In some embodiments, provided technologies are particularly useful for developing modulators for human targets based on biosynthetic products of fungi.

The invention discloses a method, a system and equipment for splitting an allopolyploid based on a perl language and a storage medium, and particularly relates to the technical field of bioinformatics analys.The method comprises the steps that a diploid species close to a to-be-split allopolyploid species is selected as a diploid reference species; calculating a distance SD value between chromosomes to form a matrix; a homologous chromosome group HG1. HGN is formed; according to the homologous chromosome group HG1... HGN, an allopolyploid to be split is split into AN subgenomes. The subgenome is split based on the mutation accumulation difference of the sexual maturity cycle, and the method has high flexibility; the method disclosed by the invention has wide applicability; the splitting method disclosed by the invention has the advantages of high accuracy, good visualization effect, simplicity and convenience in operation, reliable data support, capability of promoting genome research and the like; according to the invention, how different mutations are accumulated by different species under the difference of sexual maturity cycles can be well revealed, so that deep understanding of the evolutionary mechanism of the species is promoted.

The application provides a global ancestor estimation method and system based on principal component analysis, which comprises the following steps: based on the principal component analysis method, according to single nucleotide mutation data, projecting the principal components of the to-be-tested offspring samples and the principal components of the ancestral samples into the same two-dimensional plane, and dividing the projected two-dimensional plane into multiple corresponding population regions; fitting the population region of each ancestral sample to obtain an ancestral population fitting region, and obtaining the region density of each ancestral population fitting region; extending each ancestral population fitting region outward at the same proportion, stopping the outward extension when any ancestral population fitting region contains a to-be-tested offspring sample, and obtaining the ancestral population proportion of the current to-be-tested offspring sample according to the region area after the extension and the region density. The application directly calculates the proportion of the corresponding ancestor in the offspring individual or population through the principal component analysis result, and has high accuracy.

According to an example aspect of the present invention, there is provided a method for generating a transmission timeline, the method comprising: determining, based on SNP information, SNP evolutionary distance from a reference genome for each sample; determining, based on the SNP information, SNP evolutionary distance between each sample; and generating, based on: the SNP information, the SNP evolutionary distances from the reference genome, the SNP evolutionary distance between the samples, the mutation rate, generation rules and the corresponding timestamps; a dated phylogenetic tree, said tree comprising sample nodes) and non-sample nodes, wherein each sample may correspond to a node, for example.

An example computer-implemented method includes receiving agricultural data records comprising a first set of yield properties for a first set of seeds grown in a first set of environments, and receiving genetic feature data related to a second set of seeds. The method further includes generating a second set of yield properties for the second set of seeds associated with a second set of environments by applying a model using the genetic feature data and the agricultural data records. In addition, the method includes determining predicted yield performance for a third set of seeds associated with one or more target environments by applying the second set of yield properties, and generating seed recommendations for the one or more target environments based on the predicted yield performance for the third set of seeds. In the present example, the method also includes causing display, on a display device communicatively coupled to the server computer system, the seed recommendations.

The invention relates to the technical field of agricultural breeding, and discloses a variety screening and promoting method based on partial repeated test design, which comprises the following steps: step 1, variety grouping and sowing quantity preparation: determining the number of repeated varieties and non-repeated varieties according to the total number of varieties to be detected and a preset proportion, wherein the repeated variety is planted in the field for at least two times, the non-repeated variety is planted in the field for one time, and the seeding rate of the corresponding variety is prepared according to the planting times; step 2, experimental plot planning: dividing an experimental plot into latticed cells with clear row numbers and column numbers; according to the method, partial repetition design is combined with a space analysis model based on row and column information. Through a mixed linear model, whether the variety is repeated or not is used as a fixed effect to correct group level difference, the variety is used as a random effect to estimate a real breeding value, and a first-order autoregression structure is used for effectively correcting errors caused by field microenvironment heterogeneity.

Methods for identifying biosynthetic gene clusters that include genes for producing compounds that interact with specific target proteins are disclosed. Some methods relate to bioinformatics methods for identifying and / or prioritizing biosynthetic gene clusters. Related systems, components, and tools for the identification and expression of such gene clusters are also disclosed.

Methods and systems are disclosed for covariate prediction from methylation features. A system trains methylation state models that are configured to regress one or more methylation features at a genomic region based on covariates for a given sample. The system utilizes the methylation state models to determine information gain of genomic regions in predicting covariates of interest. The system may, based on the information gain, identify covariate-informative genomic regions. The system trains a covariate prediction model using non-cancer training samples with reported covariate label(s) and methylation features at a plurality of covariate-informative genomic regions. The system may deploy the covariate prediction model for sample swap detection. Additionally, the system may utilize prediction(s) from covariate prediction model(s) to serve as a feature to cancer classification.

Disclosed is a configuration for determining a genotyping label composition of a target individual using direct acyclic paths. The configuration includes receiving a phased genotype of the target individual, including a first haplotype and a second haplotype. The configuration initiates a full-ethnicity hidden Markov model (HMM) including nodes with a set of ethnicity labels. The first haplotype is input to determine a first subset of ethnicity labels that match the first haplotype. The second haplotype is input to determine a second subset of ethnicity labels that match the second haplotype. The first and second subsets of ethnicity labels are combined to create a candidate subset of ethnicity labels for the target individual. The configuration initiates a simplified HMM with nodes from the candidate subset of ethnicity labels. The phased genotype of the target individual is input to the simplified HMM to determine genotyping label composition of the target individual.

Provided herein are methods of determining tumoral sensitivity to hormonal (endocrine) therapy based upon an index of estrogen receptor (ER)- and progesterone receptor (PR)-related genes, referred to as the sensitivity to endocrine therapy index (SETER / PR index), and may have additional consideration for the proportion of ER gene (ESR1) RNA transcripts that contain a mutation relative to the value of the SETER / PR index. Further provided are methods of treating breast cancer patients determined to be sensitive to an endocrine therapy by the SETER / PR index.

The application provides a causal network discovery system based on reinforcement learning, takes biomolecules as variables, obtains observation data of the biomolecules, and filters possible parent variables of the variables according to the data to obtain a sparse graph after initial neighborhood selection; uses a graph attention network to extract embedded features of each biomolecule from the observation data of the biomolecules and the sparse graph after the initial neighborhood selection; calculates a weight vector of all biomolecules according to the embedded features of each biomolecule, samples on the weight vector in a loop, and sequentially sorts the extracted variables from back to front; calculates a reward for the sorting according to a scoring function, updates parameters of each network under a reinforcement learning framework according to the reward, and guides generation of variable sorting with a higher reward according to the updated parameters; selects variables for the sorting with the highest reward to obtain a final biomolecular causal regulation network; and the application improves the explainability of a causal graph search process and thus improves the accuracy of causal structure discovery.

The technology disclosed relates to generating species-differentiable evolutionary profiles using a weighting logic. In particular, the technology disclosed relates to determining a weighted summary statistic for a given residue category at a given position in a multiple sequence alignment based on one or more weights of one or more sequences in the multiple sequence alignment that have a residue of the given residue category at the given position.

Systems and methods are provided for automatically imaging and analyzing cell samples in an incubator. An actuated microscope operates to generate images of samples within wells of a sample container across days, weeks, or months. A plurality of images is generated for each scan of a particular well, and the images within such a scan are used to image and analysis metabolically active cells in the well. Tins analysis includes generating a "range image" by subtracting the minimum intensity value, across the scan, for each pixel from the maximum intensity value. This range image thus emphasizes cell s or portions of cells that exhibit changes in activity over a scan period (e.g., neurons, myocytes, cardiomyocytes) while de-emphasizing regions that exhibit consistently high intensities when images (e.g., regions exhibiting a great deal of autofluorescence unrelated to cell activity).