368results about "Evolutionary biology" patented technology

The invention relates to the technical field of breeding planning, in particular to an intelligent breeding planning and decision-making method and system based on a large model. The method comprises the following steps: acquiring a multi-source breeding data set; constructing a structured breeding knowledge graph based on the multi-source breeding data set; performing breeding data association on the structured breeding knowledge graph according to a preset large model to generate a special breeding basic model; obtaining a breeding instruction input by a user; performing user semantic recognition on a breeding instruction input by a user to generate breeding semantic recognition data; inputting the breeding semantic recognition data into a breeding special basic model for breeding intention analysis, and generating user breeding intention data; and determining data information needing to be called based on the breeding intention data of the user, analyzing and screening to generate germplasm resource screening data and a breeding plan / breeding decision scheme. According to the method, the intelligence and operability of breeding planning are improved through integration of multi-source data, intelligent semantic recognition, combined genetic analysis and executable evaluation.

Systems and methods for outbreak analysis by performing taxonomic classification of the first LRS data to assign one or more taxonomic identifiers to each record in the first LRS data; identifying a plurality of species-specific candidate genomes from a reference genome database based on the assigned taxonomic identifiers; aligning the first LRS data with each of the plurality of species-specific candidate genomes to determine a first species identity of a microorganism species present in the sample; identifying a first nearest strain of the identified microorganism species present in the first sample by aligning the first LRS data with a plurality of strain-specific candidate genome dataset.

Systems and methods for predicting cell-type-specific gene expression from genetic sequence are described. A computational framework can comprise a computational model for predicting epigenetic regulatory markers using genetic sequence as input. A computational framework can additionally, or alternatively, comprise a computational model for predicting cell-type-specific gene expression utilizing epigenetic regulatory markers spaced along sequence of genes. The computational model for predicting cell-type-specific gene expression can be trained with single-cell genetic expression profiles, such as scRNA-seq or snRNA-seq. The computational framework can be utilized to assess polymorphic sequences, such as polymorphisms related to medical disorders. Various methods further develop research tools, perform diagnostics, and treat individuals based on cell-type-specific gene expression levels, which can be further based on differential gene expression patterns among polymorphic sequences.

The invention discloses a marine bacteriophage antibacterial lysin prediction method, device, medium and equipment, and relates to the field of biological medicine and computational biology. Comprising the following steps: constructing a training data set based on a globally known antibacterial protein sequence; and training a protein sequence classification model based on deep learning by using the antibacterial protein data set to obtain a marine antibacterial lysin prediction model. The method comprises the following steps: carrying out data standardization processing on a to-be-predicted bacteriophage protein sequence, inputting the to-be-predicted bacteriophage protein sequence into a protein language model, carrying out self-attention modeling on the input sequence through a ProtT5 module, and extracting global structure and function related features to obtain a first feature vector; extracting a short sequence local dependency feature through a ProtBERT module to obtain a second feature vector; performing weighted summation on the first feature vector and the second feature vector to generate a fusion feature vector; and carrying out probability prediction on the fusion feature vector, and outputting the antibacterial tag and the prediction confidence of the marine phage sequence to be predicted.

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 present invention relates to the field of genetic engineering. Specifically, the present invention relates to cytosine deaminase and use thereof in base editing. More specifically, the present invention relates to a base editing system based on a newly identified cytosine deaminase, a method for base editing a target sequence in the genome of an organism (e.g., a plant) using the base editing system, and a genetically modified organism (e.g., a plant) produced by the method and progenies thereof.

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.

Novel antibiotics are needed to combat the antibiotic-resistance crisis. Presented herein are machine learning-based approaches for predicting antimicrobial peptides (AMPs) within the global microbiome and leverage a vast dataset that can include metagenomes and prokaryotic genomes from environmental and host-associated habitats to create a comprehensive catalog comprising distinct, non-redundant peptides, the majority of which are novel. This platform provides insights into the evolutionary origins of peptides, including by duplication or gene truncation of longer sequences. To validate predictions, 100 AMPs were synthesized and tested against clinically relevant drug-resistant pathogens and human gut commensals, both in vitro and in vivo. Many of the synthesized peptides were active, with a large number of them targeting pathogens. The presently described computational approach can identify millions of prokaryotic AMP sequences, opening new avenues for antibiotic discovery.

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 discloses a functional protein mining and screening method and device, which combines multi-dimensional bioinformatics analysis such as protein structure prediction, protein function annotation, protein cluster mining and the like with an artificial intelligence method based on a protein big language to construct a process for mining and screening a functional protein. Protein structure analysis is introduced, and proteins with similar functions are explored by utilizing association of protein structures and functions and constructing a structure similarity map. The protein large language model can contain characteristic parameters related to protein functions, such as a protein structure, post-translational modification and biophysical characteristics, and reveals the inherent functional relevance of the protein in a vector matrix form. By introducing the two analysis means and combining with the traditional sequence-based protein function annotation method, the sequence similarity and affinity are lower, but proteins with corresponding functions are excavated and screened out, so that biomacromolecules can be utilized more efficiently and accurately.

The invention relates to the field of cherry molecular biotechnology and molecular assisted breeding technology detection, in particular to a molecular marker for identifying the core SNP (single nucleotide polymorphism) of a Chinese cherry resource and a DNA (deoxyribonucleic acid) fingerprint spectrum database constructed by using marker sites. 185 resources representing the distribution and genetic diversity of Chinese cherries in China are utilized to perform whole genome re-sequencing and biological information analysis, 469776 SNP molecular markers are obtained, 1674 SNP molecular markers are screened out to serve as core SNP markers, and a DNA fingerprint database of cherry resources is constructed and can be used for identifying germplasm resources of Chinese cherries. DNA genetic information and genetic differences of the to-be-detected Chinese cherry resources are evaluated, and a scientific basis is provided for effective protection of the Chinese cherry resources, utilization of local germplasm and selection of cherry breeding.

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 disclosure relates to methods and systems for identifying genes associated with biosynthetic gene clusters (BGCs), including embedded target genes (ETaGs) that are homologs of potential therapeutic targets. The methods and systems described herein apply a comparative genomics and manual review or machine-learning model to analyze grid representations such as heat maps, which assess across a plurality of diverse genomes distribution of orthologs (e.g., bidirectional best hits) of a plurality of query genes that are co-localized with an anchor gene (e.g., a core synthase gene) of a BGC in a query genome.

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 belongs to the technical field of bioinformatics, and particularly relates to a method for screening cross-species HGT. The method comprises the steps of constructing a background database, inferring a homologous group, constructing a gene phylogenetic tree, inferring HGT based on the gene tree, inferring verification of candidate HGTs based on the gene tree, distributing the HGTs to a timeline and the like. According to the method, high-throughput and automatic screening of HGT events in a large number of gene datasets is realized by integrating a plurality of steps of phylogenetic analysis, sequence alignment, gene function annotation, statistical verification and the like. The method disclosed by the invention is particularly suitable for the HGT research of an ultra-long time length and an ultra-long evolution distance crossing a biological boundary, and an important scientific tool can be provided for understanding gene flow and function enhancement in a biological evolution process.

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.

Systems and methods are disclosed for predicting the rise of different strains of viruses. A method comprises reading a genetic sequence of a first strain of a virus; identifying a plurality of residue indices in the genetic sequence; for each of the plurality of indices, assigning a predictor, the predictor configured to predict a residue for its assigned index based upon a residue of at least one other index, the predictors thereby forming a network; and determining, based on the network of predictors, a probability of transition of the first strain to a second strain.

A method and system for constructing a genomic selection model based on a multi-trait phenotypic model are provided, the multi-trait model founded on phenotypic data, a predicted value or an estimated breeding value obtained by the genomic selection model is used as input data of a machine learning phenotypic prediction model to predict a final phenotypic value for line selection; a multi-trait machine learning phenotypic model is established to capture a linear or non-linear relationship between plant phenotypic traits, and on this basis, a predicted value and an estimated breeding value of each trait are obtained by combining it with the genomic selection model. The method enhances the prediction accuracy of target traits and accelerates the breeding process, improves the selection efficiency of the target traits and saves breeding costs, which is widely employed in the field of agricultural animal and plant breeding.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for predicting a property of an input biological entity. The system generates an evolutionary feature representation of the input biological entity based at least in part on an evolutionary tree, and processes a model input that comprises the evolutionary feature representation sing a machine learning model to generate the prediction for the property of the input biological entity.

The disclosure relates to comprehensive genomic profiling (CGP) and to consensus-based classification techniques for determining genetically inferred ancestry from CGP of tumor DNA. Aspects are directed towards accessing reference and subject sequencing files and identifying genomic variants using a hybrid variant tool. The reference variant file is consolidated into a datastore formatted file that is queried to perform joint variant calling to generate a final reference variant file. The final reference variant file and the subject variant file are merged. On the merged variant file, principal component (PC) analysis is performed, and the PCs are used by a first and second classification process to generate a first and second ancestry call. The merged variant file is input into a third classification process to generate a third ancestry call. A consensus genetically inferred ancestry (GIA) call is predicted based on the first, the second, and the third ancestry calls.

The present invention relates to the field of bioinformatics, and relates to a method for identifying variant sites that differentiate HPV risk levels, and a use thereof. The identifying method comprises: constructing a guide tree; constructing a phylogenetic tree; marking the phylogenetic tree; determining risk boundary points; performing risk distribution frequency analysis on variant sites; and performing screening to obtain variant sites that meet a preset distribution-frequency condition and variant genotype statuses, that is, characteristic variants for identifying high-risk HPV types. The method of the present invention is used to identify high-risk HPV types, so that large-scale rapid screening and detection based on single nucleotide sites can be achieved without sacrificing accuracy, thereby enabling rapid and precise prevention and control of high-risk HPV types.

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.

The present invention is directed to a filter assembly for capturing environmental DNA (eDNA), a kit comprising the filter assembly, a method of capturing eDNA using the filter assembly, a method of analysing eDNA captured in the filter assembly, and a method of providing biodiversity data by analysing eDNA collected in the filter assembly.

The systems and methods described herein are directed to amplifying microbial cell free DNA (mcfDNA). In an aspect, described herein is a method of amplifying microbial cell free DNA (mcfDNA), comprising using one or more degenerate primers with complementarity to one or more conserved regions and a second primer comprising complementarity to a repaired version of an adaptor ligated to ends of the mcfDNA, wherein the one or more degenerate primers are oriented to prime polymerase extension of the hypervariable region to generate amplified mcfDNA fragments.

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.