71 results about "CpG site" patented technology

The invention discloses a biomarker combination for predicting calendar age, a model and application of the biomarker combination. 57 kinds of DNA methylated CpG sites screened from many elderly subjects are creatively introduced, through multiple detection and large-scale parallel DNA sequencing, operation is easy, the result is highly repeatable, and the age prediction model constructed based on the method is good in stability and accuracy and high in sensitivity and has good application prospects. Calendar age can be predicted from trace blood samples associated with forensic cases.

The invention provides a biomarker, a kit and a method for predicting the aging-related physiological age of a mouse, the biomarker comprises a plurality of CpG sites with different methylation levels, and the position information of the CpG sites is as follows: chr10: 122698929, chr11: 22600252, chr12: 86422875, chr13: 49379202, chr14: 55116926, chr15: 86222032, chr16: 87766091, chr17: 55971541, chr1: 109982829 and the like. The method can solve the problem of high cost of detecting the physiological age of the mouse through methylation level in the prior art, and is suitable for the field of animal age detection.

Disclosed are methods of preparing a composition of non-naturally occurring DNA, comprising: (a) extracting DNA from a sample of a subject; (b) contacting the extracted DNA or its derivative with a panel of oligonucleotide probes designed to hybridize to a plurality of preselected genomic regions, thereby generating selected DNA, wherein at least 50% of the preselected genomic regions are CpG regions that each comprises at least 3 CpG sites and a CpG density of at least 0.02 CpG / bp, wherein the preselected genomic regions cover 10-500 Mb of sequence space in human genome, wherein the extracted DNA or its derivative or the selected DNA or its derivative is further treated with an agent or a combination of agents that discriminates between methylated and unmethylated cytosines; and (c) performing high-throughput sequencing on the selected DNA or its derivative and generating sequencing reads.

The present application relates to a method and system for inferring the age of an individual from semen or a semen stain, and a methylation detection kit, which comprises the following steps: firstly, extracting the genomic DNA of the semen or semen stain of the individual; secondly, performing bisulfite treatment on the genomic DNA; thirdly, selecting a plurality of sites in the CpG sites in the genomic DNA and amplifying the selected CpG sites to obtain the methylation rate of the selected CpG sites; and finally, performing regression analysis on the methylation rate of the selected CpG sites and the age of the individual to construct a regression model for inferring the age of an unknown individual from semen or a semen stain. The average absolute error of the present application reaches 1.68 to 4.44 years.

The present application relates to the technical field of bioinformatics, and particularly relates to a method for constructing a biological age prediction model based on DNA methylation. The method comprises the following steps: obtaining whole genome methylation sequencing data of a human peripheral blood sample; classifying cell subpopulations of the human peripheral blood sample, and performing single-cell RNA sequencing processing on each cell subpopulation to obtain single-cell sequencing data including lymphocytes, neutrophils and monocytes; performing tissue-specific analysis on CpG sites within a range of 2000 base pairs upstream and downstream of each cell subpopulation-specific transcription factor binding site according to the single-cell sequencing data and the whole genome methylation sequencing data to obtain candidate marker site data. The present application can provide strong support for early detection of accelerated aging, prediction of related disease risks and guidance of precision medicine.

The present invention provides a device and a method capable of constructing a model that has high prediction accuracy and is suitable for a specific group from a small number of DNA methylation data items obtained from the specific group while reducing the number of samples required for model construction. A biological age measurement device 1 according to the present invention measures the biological age on the basis of the DNA methylation level. By using, as an explanatory variable matrix (n×p matrix (p≤q)), first explanatory variable information based on DNA methylation information in CpG sites at q points included in DNA of each sample of a first population composed of n samples, and using, as an objective variable vector (n-dimensional), first objective variable information based on biological age information of the n samples, the biological age measurement device 1 performs transfer learning on the basis of a biological age measurement model estimated by a second population different from the first population, and outputs the learning result as a trained biological age measurement model.

The present invention is related to a method of determining suitability of at least one Chinese Hamster Ovary (CHO) test cell line for optimal heterologous protein production, the method comprising:(a) determining a test methylation profile from genomic material obtained from the CHO test cell line; and(b) comparing the test methylation profile obtained from (a) with a reference methylation profile, wherein the reference methylation profile comprises the methylation status of more than one CpG site from at least one CHO reference cell line that displays at least one phenotype of interest for optimal heterologous protein production,wherein a significant similarity in the test methylation profile of (a) compared to the reference methylation profile, is indicative of the CHO test cell line being suitable for optimal heterologous protein production and wherein the test methylation profile and reference methylation profile are from CpG sites from the CHO cell genome and are determined using DNA methylation-bead-based array.

The present invention relates to a method for assessing the prognosis or risk stratification of liver cancer by using a clinical specimen mixed with a normal tissue, wherein at least one CpG site that shows a low methylation level in normal and blood tissues but a high methylation level in only a cancer tissue is measured for methylation level.

A multi-modal pan-cancer early prediction method is disclosed, comprising: collecting cfDNA samples of i cancer populations and healthy populations respectively and extracting methylation data; merging CpG sites based on the methylation data to obtain multiple methylation intervals, screening multiple candidate marker intervals from the multiple methylation intervals, and further screening the multiple candidate marker intervals; training a first prediction model using the multiple candidate marker intervals, training a second prediction model based on the fragmentation characteristics of the chromosomes extracted from the reference genome, and training a third prediction model by extracting the methylation entropy characteristics of the chromosomes; training a multi-modal pan-cancer early prediction model through the cancer population prediction values of the first prediction model, the second prediction model and the third prediction model, which can provide precise joint early screening for lung cancer, intestinal cancer, gastric cancer, liver cancer, esophageal cancer, thyroid cancer and ovarian cancer.

Systems and methods of identifying methylation patterns discriminating or indicating a cancer condition are provided. First and second datasets are obtained. Each dataset comprises a plurality of fragment methylation patterns determined by methylation sequencing of nucleic acids obtained from a first or second set of subjects and comprising a methylation state of each CpG site in a corresponding plurality of CpG sites. Each plurality of subjects has a respective first or second state of the cancer condition. First and second interval maps are generated for each respective dataset, each comprising a plurality of nodes characterized by a start methylation site, an end methylation site, a representation of each different fragment methylation pattern and a count of fragments. The first and second interval maps are scanned for qualifying methylation patterns within a predetermined range of CpG sites, satisfying one or more selection criteria, thereby identifying methylation patterns discriminating a cancer condition.

The application provides a cancer primary focus tracing method, device, system and storage medium, and relates to the technical field of cancer tracing. The cancer primary focus tracing method comprises the following steps: acquiring sample tissue data, giving each cancer sample a primary focus label through a methylation chip; determining candidate CpG sites; and constructing a cancer tracing classifier. The application realizes the technology of tracing multiple cancers by using DNA methylation biological characteristics and machine learning methods, and has the following characteristics: due to the high stability of DNA molecules, cancer information is relatively completely retained, so that sample storage and transportation are more convenient; the cancers targeted by the application have a wide coverage range and include general common tumors; cancer-specific CpG sites have a wide coverage; through analysis of the cancer-specific sites, the interference of normal tissues is excluded, so that the accuracy and application range of the classifier are improved; and the high-precision classifier makes the cancer diagnosis process simpler, more accurate and more efficient.

Systems and methods for diagnosing a disease, a condition, or a characteristic in a subject are provided. In one such method, a future severity of an infection or inflammatory disease in a subject afflicted with the infection or inflammatory disease is predicted by obtaining a plurality of methylation levels. Each respective methylation level in the plurality of methylation levels represents a corresponding methylation level at a CpG site at a corresponding genetic locus in a plurality of genetic loci in a biological sample obtained from the subject. The plurality of methylation levels are inputted into a model comprising a plurality of parameters, where the model applies the plurality of parameters to the plurality of methylation levels to generate as output from the model an indication as to future severity of an infection or inflammatory disease in the subject.

The invention provides a gene methylation marker screening method, a kit, a storage medium and a system, and belongs to the technical field of gene detection.The gene methylation marker screening method comprises the steps that methylation chip sequencing data conforming to known typing of adult diffuse glioma is collected; aiming at a selected subtype, comparing methylation level differences of a CpG site in the subtype and other subtypes and contrasting brain tissues, and screening out a marker with a high median methylation level in a subtype sample; and according to the distinguishing contribution degree of each methylation marker in the random forest model to the classification result, carrying out importance evaluation on the marker, and screening out the key methylation marker having positive contribution to the classification result. On the basis of 11 methylation markers, an NGS detection method is adopted for typing the diffuse glioma of the adult, so that the problems that multiple molecular detection results in multiple omics need to be integrated in the conventional typing method, or the typing cost is high and the time period is long when a methylation chip is used for typing are solved.

The present invention relates to an in vitro method for predicting the chronological age of a test Chinese Hamster Ovary (CHO) cell or population of test CHO cells, the method comprising the steps of: (a) determining the test methylation level of a set of specific CpG sites from genomic material obtained from the test CHO cell or from the population of test CHO cells from a CHO cell line using a DNA-methylation bead-based array; (b) comparing the test methylation level determined from step (a) with a reference methylation level of the same set of specific CpG sites from an age-correlated reference sample, thereby establishing the epigenetic age and predicting the chronological age of the test CHO cell or the population of test CHO cells wherein the set of specific CpG sites comprises at least 10%, preferably 30%, more preferably 50% of the CpG sites indicated in Table 1.

The present invention relates generally to methods of screening for rheumatoid arthritis, as well as kits for screening for rheumatoid arthritis. More particularly, the invention relates to a method of screening for rheumatoid arthritis in a subject, the method comprising using methylation levels of CpG sites in DNA from a biological sample obtained from the subject in order to screen for rheumatoid arthritis in the subject, wherein said methylation levels are used to provide an indication of the presence or absence of rheumatoid arthritis in the subject.

The invention relates to the technical field of molecular biology, in particular to a methylation marker for Down's syndrome diagnosis. The invention particularly relates to application of a detection reagent for detecting the methylation level of a CpG site of a nucleic acid marker in preparation of a Down's syndrome diagnostic kit. The nucleic acid marker comprises a) an SIM2 gene segment as shown in SEQ ID NO: 1 or a polynucleotide segment complementary to the SIM2 gene segment, and / or b) a CRYBG1 gene segment as shown in SEQ ID NO: 2 or a polynucleotide segment complementary to the CRYBG1 gene segment.

The invention discloses a stem cell culture generation identification method based on an epigenetics clock. Belongs to the field of biotechnology and molecular biology, and designs a specific multiple PCR primer pool and establishes a high-precision generation prediction model by screening eight CpG sites highly related to stem cell replicative senescence. The method comprises the following steps: firstly, carrying out hydrosulfite conversion on sample DNA (Deoxyribonucleic Acid), carrying out targeted amplification by adopting a primer group with an optimized ratio, calculating a methylation level through high-throughput sequencing, and substituting into a formula to output a result. The model has small mean absolute error and is significantly related to aging function indexes. Compared with traditional chip detection, the cost is reduced, the detection period is shortened, and the method is suitable for release inspection of stem cell treatment products, anti-aging drug screening and cell bank quality management.

The present application relates to the field of DNA methylation modification detection, and particularly relates to nucleic acid sequence molecules and DNA methylation detection methods. The nucleic acid sequence molecules comprise at least a probe sequence, the probe sequence comprises at least one CpG site, at least one of which is a to-be-detected CpG site; the probe sequence comprises a methylation probe sequence and a non-methylation probe sequence, and the two probe sequences are modified with different fluorescent groups; and the probe sequence at least meets one of the following conditions: i) within the region of the to-be-detected CpG site and the 2 nt upstream and downstream of the to-be-detected CpG site, the overall GC content of the methylation probe is not more than 50%; ii) within the region of the to-be-detected CpG site and the 2 nt upstream and downstream of the to-be-detected CpG site, the overall GC content of the methylation probe is more than 50%, and the to-be-detected CpG site in the probe sequence is modified with LNA. The method of the present application can significantly improve the specificity and sensitivity of CpG site methylation detection.

The embodiments of the present application disclose a tumor marker, a kit and a use method for judging early hepatocellular carcinoma. The kit of the embodiments of the present application takes the first DNA shown as SEQ ID NO. 1 and / or the second DNA shown as SEQ ID NO. 2 as a judgment marker, judges whether the ex vivo sample is a positive sample of early hepatocellular carcinoma by judging the methylation degree of the CpG site in the first DNA and / or the second DNA in the ex vivo sample, thereby prompting whether the organism derived from the ex vivo sample has early hepatocellular carcinoma lesion. The kit of the embodiments of the present application has good specificity and sensitivity for the positive judgment of early hepatocellular carcinoma based on the specific tumor marker, and can be applied to the screening and judgment of early hepatocellular carcinoma.

The embodiment of the invention relates to a method, device and equipment for generating candidate probes, a medium and a product. The method includes obtaining a gene sequence corresponding to a selected gene region. The method further comprises generating a methylated gene sequence by performing different conversion strategies against cytosine C in the cytosine-phosphate-guanine (CPG) site and cytosine C in the non-CPG site in the gene sequence. The method further includes generating a plurality of candidate probes for the gene region based on the methylated gene sequence. Through the method, candidate probes which cover different methylation states and are adaptive to actual experiment scenes can be generated, and a foundation is laid for subsequent accurate screening of high-quality methylation capture probes.

Methods, devices and systems for analyzing cfDNA fragments in a biological sample. For example, techniques may use a total amount of cfDNA fragments terminally located at three or more different positions around different types of CpG sites to detect cancer. Another technique may use the amount of cfDNA fragments located in a set of open chromatin regions (OCRs) to detect cancer. Another technique may use abundance of long cfDNA fragments on a gene to estimate the expression level of the gene. Another technique may use abundance of long cfDNA fragments to identify open chromatin regions. Another technique may use abundance of long cfDNA fragments located in a set of OCRs to determine a proportional concentration of free DNA of a first tissue type. Another technique may use abundance of long cfDNA fragments to classify nuclease activity.

The invention discloses a TabNet-based interpretable multi-tissue DNA methylation age prediction method and system, and relates to the technical field of bioinformatics and computer crossing, and the method comprises the following steps: obtaining DNA methylation data of a to-be-detected sample, and preprocessing the data; a methylation beta value of a feature set composed of 2035 preset CpG sites is extracted from the preprocessed data to serve as an input feature; the input features are loaded to an age prediction model based on a TabNet framework obtained through a specific training process for calculation, the predicted DNA methylation age of the to-be-detected sample is output, and through screening of a simple preset CpG site with multi-tissue stability, tissue specific signals are accurately captured in cooperation with a TabNet dynamic attention mechanism, so that the DNA methylation age of the to-be-detected sample is predicted. The black box dilemma of other deep learning models is effectively solved, and meanwhile, the technical problems of insufficient prediction precision, weak generalization ability in extreme age groups, poor robustness in practical application and the like of a traditional machine learning model are solved.

The present invention relates to a method for diagnosing patients with rheumatoid arthritis by using novel epigenetic biomarkers linked to rheumatoid arthritis. These biomarkers are based on the DNA methylation levels in specific regions of the genome. Specifically, CpG sites associated with rheumatoid arthritis and CpG sites associated with the severity of the disease have been identified.

The application discloses a DNA methylation prediction method integrating multi-omics features, comprising determining a to-be-predicted CpG site, acquiring a multi-omics feature set of the CpG site in para-cancer tissue, acquiring a methylation feature set of the CpG site in cancer tissue, respectively calculating correlation coefficients between the methylation features of the cancer tissue CpG site and the miRNA, mRNA and methylation features of the para-cancer tissue based on Pearson correlation coefficients, respectively selecting K miRNAs, Q mRNAs and L methylation features according to the values of the correlation coefficients, and constructing a multi-omics correlation feature set, constructing a DNA methylation prediction model based on a deep neural network, training the DNA methylation prediction model according to the multi-omics correlation feature set, calculating evaluation indexes of the trained DNA methylation prediction model, acquiring an evaluated DNA methylation prediction model when the evaluation indexes meet threshold values, and predicting the methylation of the cancer tissue according to the evaluated DNA methylation prediction model. The DNA methylation prediction accuracy is improved.

The invention relates to a method of predicting quality of a heterologous protein to be produced from a test Chinese Hamster Ovary (CHO) cell, the method comprising the steps of: (a) determining a test methylation profile of one or more pre-selected methylation CpG sites within the DNA of the test CHO cell; (b) comparing the test methylation profile obtained from (a) with a reference methylation profile, wherein the reference methylation profile comprises the methylation status of the pre-selected methylation CpG sites from at least one CHO reference cell line that is capable of producing a known, preferably good, quality heterologous protein; wherein a significant similarity in the test methylation profile of (a) compared to the reference methylation profile, is indicative of the heterologous protein produced by the test cell being of known, preferably good, quality; and wherein a significant difference in the test methylation profile of (a) compared to the reference methylation profile, is indicative of the heterologous protein produced by the test cell being of unknown or poor quality; wherein the test and reference methylation profiles are determined using bead-based DNA methylation array; and wherein the quality of the heterologous protein is based on the presence of at least one protein quality metric; and the pre-selected methylation CpG sites are selected from Tables 4 and 5.

Provided is a method for detecting early ovarian cancer (EOC) for a patient, comprising: obtaining one or more embedding representations for each of a plurality of chromosomes of the patient; inputting one or more embedding representations into a machine-learning model, wherein the machine-learning model is trained by: obtaining DNA methylation data of a plurality of subjects; pretraining, based on the DNA methylation data of the plurality of subjects, the machine-learning model to predict a methylation level of a masked CpG site; and training the pretrained model to detect EOC; and detecting EOC for the patient based on an output of the machine-learning model. Provided is transformer-based AI for early cancer diagnosis using cfDNA methylation markers, such as a transformer-based AI technology that improves early ovarian cancer diagnosis using cfDNA methylation markers.

The invention discloses a design method of a specific methylation marker Panel, which comprises the following steps: obtaining a first candidate interval based on a methylation related gene of a selected disease, screening a first target interval in the first candidate interval, and extracting a first CpG site set in the first target interval; performing methylation sequencing data on lesion tissues of the selected disease crowd to obtain a second candidate interval, screening a second target interval in the second candidate interval, and extracting a second CpG site set in the second target interval; and combining the first CpG site set and the second CpG site set to obtain a Panel interval covering the CpG sites therein. According to the design method, researchers or medical personnel can be helped to obtain the methylation Panel of the required disease, the methylation level resolution based on high-depth sequencing data is improved, and the efficiency of screening methylation markers related to the specific disease is improved.

The invention discloses a primer probe composition for detecting the DNA methylation level of a TMEM213 gene promoter region and application of the primer probe composition, and belongs to the technical field of biology. The primer probe composition is designed aiming at a CpG site of a human TMEM213 gene promoter region, after sample DNA is modified through bisulfite, methylation sites of the gene promoter region in the modified DNA are detected through methylation specific real-time fluorescent quantitative PCR, and accurate quantitative analysis of the methylation level of the TMEM213 gene target CpG site in the sample DNA can be achieved on the basis of a Ct value obtained through amplification. The primer probe composition can be specifically combined with a target methylated DNA sequence, methylated and non-methylated templates can be efficiently distinguished, the detection sensitivity is high, and the repeatability is good. The primer probe composition is combined with a primer probe combination of a reference gene ACTB for use, and can be applied to preparation of a TMEM213 gene methylation level detection kit.

A method for discriminating a cancer state is provided. A first dataset is obtained for a plurality of subjects having a first cancer state. Each subject has a plurality of nucleic acid methylation fragments with methylation patterns comprising CpG site methylation states. An autoencoder including an encoder and decoder is trained by evaluating the error in the autoencoder reconstruction of the methylation pattern and nucleic acid sequence of each nucleic acid methylation fragment in the first dataset. A second dataset is obtained for a plurality of subjects having a second cancer state. A plurality of features is identified by inputting the methylation pattern and nucleic acid sequence of each nucleic acid methylation fragment in the second dataset into the trained autoencoder and computing a score determined by the autoencoder reconstruction of the methylation pattern. The plurality of features is used to train a supervised model that discriminates a cancer state.