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Methylation

Methylation is the process of adding a methyl group (-CH3) to a molecule, typically a DNA or protein.
This chemical modification can regulate gene expression, alter protein function, and influence various cellular processes.
Methylation plays a crucial role in biological systems, including epigenetic regulation, cellular signaling, and disease pathogenesis.
Researchers studying methylation can utilize PubCompare.ai to optimize their research process by locating the most relevant protocols from literature, pre-prints, and patents using AI-driven comparisons.
This can enhance reproducibility and accuracy by identifying the best protocols and products, while simplifying the research process with PubCompare.ai's intuitive tools and data-driven insihgts.
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Most cited protocols related to «Methylation»

Specimens were obtained from patients with appropriate consent from institutional review boards. Using a co-isolation protocol, DNA and RNA were purified. In total, 800 patients were assayed on at least one platform. Different numbers of patients were used for each platform using the largest number of patients available at time of data freeze; 466 samples (463 patients) were in common across 5/6 platforms (excluding RPPA) and 348 patients were in common on 6/6 platforms. Technology platforms used include: 1) gene expression DNA microarrays51 (link), 2) DNA methylation arrays, 3) microRNA sequencing, 4) Affymetrix SNP arrays, 5) exome sequencing, and 6) Reverse Phase Protein Arrays. Each platform, except for the exome sequencing, was used in a de novo subtype discovery analysis (Supplemental Methods) which were included in a single analysis to define an overall subtype architecture. Additional integrated across platform computational analyses were preformed including PARADIGM32 (link) and MEMo40 (link).
All of the primary sequence files are deposited in CGHub (https://cghub.ucsc.edu/); all other data including mutation annotation file are deposited at the Data Coordinating Center (DCC) (http://cancergenome.nih.gov/). Sample lists, data matrices and supporting data can be found at (http://tcga-data.nci.nih.gov/docs/publications/brca_2012/). The data can be explored via the ISB Regulome Explorer (http://explorer.cancerregulome.org/) and the cBio Cancer Genomics Portal (http://cbioportal.org). Data descriptions can be found at (https://wiki.nci.nih.gov/display/TCGA/TCGA+Data+Primer) and in Supplementary Methods. Reprints and permissions information is available at www.nature.com/reprints.
Publication 2012
DNA Chips Ethics Committees, Research Freezing Gene Expression isolation Malignant Neoplasms Methylation MicroRNAs Mutation Oligonucleotide Primers Patients Protein Arrays
We used the Ensembl Variant Effect Predictor (VEP, Ensembl Gene annotation v68)16 (link) to obtain gene model annotation for single nucleotide and indel variants. For single nucleotide variants within coding sequence, we also obtained SIFT7 (link) and PolyPhen-26 (link) scores from VEP. We combined output lines describing MotifFeatures with the other annotation lines, reformatted it to a pure tabular format and reduced the different Consequence output values to 17 levels and implemented a four-level hierarchy in case of overlapping annotations (see Supplementary Note). To the 6 VEP input derived columns (chromosome, start, reference allele, alternative allele, variant type: SNV/INS/DEL, length) and 26 actual VEP output derived columns, we added 56 columns providing diverse annotations (e.g. mapability scores and segmental duplication annotation as distributed by UCSC51 (link),52 (link); PhastCons and phyloP conservation scores53 (link) for three multi-species alignments9 (link) excluding the human reference sequence in score calculation; GERP++ single-nucleotides scores, element scores and p-values54 (link), also defined from alignments with the human reference excluded; background selection score40 (link),55 (link); expression value, H3K27 acetylation, H3K4 methylation, H3K4 trimethylation, nucleosome occupancy and open chromatin tracks provided for ENCODE cell lines in the UCSC super tracks52 (link); genomic segment type assignment from Segway56 (link); predicted transcription factor binding sites and motifs11 (link); overlapping ENCODE ChIP-seq transcription factors11 (link), 1000 Genome variant14 (link) and Exome Sequencing Project57 (link) variant status and frequencies, Grantham scores20 (link) associated with a reported amino acid substitution). The Supplementary Note provides a full description and Supplementary Table 1 lists all columns of the obtained annotation matrix.
Publication 2014
Acetylation Alleles Amino Acid Substitution Binding Sites Cell Lines Chromatin Chromatin Immunoprecipitation Sequencing Chromosomes Gene Annotation Genome Homo sapiens INDEL Mutation Methylation Nucleosomes Nucleotides Open Reading Frames Segmental Duplications, Genomic Transcription, Genetic Transcription Factor
All patient specimens were obtained under appropriate IRB consent. DNA and RNA were collected from samples using the Allprep kit (Qiagen). We used commercial technology for capture and sequencing of exomes from whole genome amplified tumor and normal DNAs. DNA sequences were aligned to Human NCBI build 36; duplicate reads were excluded from mutation calling. Validation of mutations occurred on a separate whole genome amplification of DNA from the same tumor. Data is submitted to dbGaP under accession number PHS000178. Significantly mutated genes were identified by comparing to expectation models based on the exact measured rates of specific sequence lesions. CHASM 20 and MutationAssessor (Methods S4) were used to identify functional mutations. GISTIC analysis of the CBS segmented Agilent 1M feature copy number data was used to identify recurrent peaks comparing to the results from the other platforms to identify likely platform specific artifacts. Consensus clustering approaches were used to analyze mRNA, miRNA, and methylation subtypes as well as predictors of outcome using previous approaches46 (link). HotNet 33 was used to identify portions of the protein-protein interaction network that have more events than expected by chance. Networks that had a significant probability of being valid were evaluated for increased fraction of known annotations. PARADIGM40 (link) was used to estimate integrated pathway activity to identify portions of the network models differentially active in HGS-OvCa.
Publication 2011
Base Sequence DNA Sequence Genes Genome Homo sapiens Methylation MicroRNAs Mutation Neoplasms Patients RNA, Messenger
Biospecimens were screened from retrospective banks of Tissue Source Sites under appropriate IRB approvals for newly diagnosed GBM with minimal 80% tumor cell percentage. RNA and DNA extracted from qualified specimens were distributed to TCGA centers for analysis. Whole genome-amplified genomic DNA samples from tumors and normals were sequenced by the Sanger method. Mutations were called, verified using a second genotyping platform, and systematically analyzed to identify significantly mutated genes after correcting for the background mutation rate for nucleotide type and the sequence coverage of each gene. DNA copy number analyses were performed using the Agilent 244K, Affymetrix SNP6.0, and Illumina 550K DNA copy number platforms. Sample-specific and recurrent copy number changes were identified using various algorithms (GISTIC, GTS, RAE). mRNA and miRNA expression profiles were generated using Affymetrix U133A, Affymetrix Exon 1.0 ST, custom Agilent 244K, and Agilent miRNA array platforms. mRNA expression profiles were integrated into a single estimate of relative gene expression for each gene in each sample. Methylation at CpG dinucelotides was measured using the Illumina GoldenGate assay. All data for DNA sequence alterations, copy number, mRNA expression, miRNA expression, and CpG methylation were deposited in standard common formats in the TCGA DCC at http://cancergenome.nih.gov/dataportal/. All archives submitted to DCC were validated to ensure a common document structure and to ensure proper use of identifying information.
Publication 2008
Biological Assay Cells DNA, Neoplasm DNA Sequence Exons Gene Expression Genes Genome Methylation MicroRNAs Mutation Neoplasms Nucleotides RNA, Messenger
Biospecimens were screened from retrospective banks of Tissue Source Sites under appropriate IRB approvals for newly diagnosed GBM with minimal 80% tumor cell percentage. RNA and DNA extracted from qualified specimens were distributed to TCGA centers for analysis. Whole genome-amplified genomic DNA samples from tumors and normals were sequenced by the Sanger method. Mutations were called, verified using a second genotyping platform, and systematically analyzed to identify significantly mutated genes after correcting for the background mutation rate for nucleotide type and the sequence coverage of each gene. DNA copy number analyses were performed using the Agilent 244K, Affymetrix SNP6.0, and Illumina 550K DNA copy number platforms. Sample-specific and recurrent copy number changes were identified using various algorithms (GISTIC, GTS, RAE). mRNA and miRNA expression profiles were generated using Affymetrix U133A, Affymetrix Exon 1.0 ST, custom Agilent 244K, and Agilent miRNA array platforms. mRNA expression profiles were integrated into a single estimate of relative gene expression for each gene in each sample. Methylation at CpG dinucelotides was measured using the Illumina GoldenGate assay. All data for DNA sequence alterations, copy number, mRNA expression, miRNA expression, and CpG methylation were deposited in standard common formats in the TCGA DCC at http://cancergenome.nih.gov/dataportal/. All archives submitted to DCC were validated to ensure a common document structure and to ensure proper use of identifying information.
Publication 2008
Biological Assay Cells DNA, Neoplasm DNA Sequence Exons Gene Expression Genes Genome Methylation MicroRNAs Mutation Neoplasms Nucleotides RNA, Messenger

Most recents protocols related to «Methylation»

Example 8

Characterization of Absorption, Distribution, Metabolism, and Excretion of Oral [14C]Vorasidenib with Concomitant Intravenous Microdose Administration of [13C315N3]Vorasidenib in Humans

Metabolite profiling and identification of vorasidenib (AG-881) was performed in plasma, urine, and fecal samples collected from five healthy subjects after a single 50-mg (100 μCi) oral dose of [14C]AG-881 and concomitant intravenous microdose of [13C3 15N3]AG-881.

Plasma samples collected at selected time points from 0 through 336 hour postdose were pooled across subjects to generate 0—to 72 and 96-336-hour area under the concentration-time curve (AUC)-representative samples. Urine and feces samples were pooled by subject to generate individual urine and fecal pools. Plasma, urine, and feces samples were extracted, as appropriate, the extracts were profiled using high performance liquid chromatography (HPLC), and metabolites were identified by liquid chromatography-mass spectrometry (LC-MS and/or LC-MS/MS) analysis and by comparison of retention time with reference standards, when available.

Due to low radioactivity in samples, plasma metabolite profiling was performed by using accelerator mass spectrometry (AMS). In plasma, AG-881 was accounted for 66.24 and 29.47% of the total radioactivity in the pooled AUC0-72 h and AUC96-336 h plasma, respectively. The most abundant radioactive peak (P7; M458) represented 0.10 and 43.92% of total radioactivity for pooled AUC0-72 and AUC96-336 h plasma, respectively. All other radioactive peaks accounted for less than 6% of the total plasma radioactivity and were not identified.

The majority of the radioactivity recovered in feces was associated with unchanged AG-881 (55.5% of the dose), while no AG-881 was detected in urine. In comparison, metabolites in excreta accounted for approximately 18% of dose in feces and for approximately 4% of dose in urine. M515, M460-1, M499, M516/M460-2, and M472/M476 were the most abundant metabolites in feces, and each accounted for approximately 2 to 5% of the radioactive dose, while M266 was the most abundant metabolite identified in urine and accounted for a mean of 2.54% of the dose. The remaining radioactive components in urine and feces each accounted for <1% of the dose.

Overall, the data presented indicate [14C]AG-881 underwent moderate metabolism after a single oral dose of 50-mg (100 μCi) and was eliminated in humans via a combination of metabolism and excretion of unchanged parent. AG-881 metabolism involved the oxidation and conjugation with glutathione (GSH) by displacement of the chlorine at the chloropyridine moiety. Subsequent biotransformation of GSH intermediates resulted in elimination of both glutamic acid and glycine to form the cysteinyl conjugates (M515 and M499). The cysteinyl conjugates were further converted by a series of biotransformation reactions such as oxidation, S-dealkylation, S-methylation, S-oxidation, S-acetylation and N-dealkylation resulting in the formation multiple metabolites.

A summary of the metabolites observed is included in Table 2

TABLE 2
Retention
ComponentTimeMatrix
designation(Minutes)[M + H]+Type of BiotransformationPlasmaUrineFeces
Unidentified 17.00UnknownX
M2667.67a267N-dealkylationX
Unidentified 2UnknownX
Unidentified 3UnknownX
Unidentified 4UnknownX
Unidentified 5UnknownX
M51519.79b516OxidationX
M460-120.76b461OxidationX
M49921.22b500Dechloro-glutathioneXX
conjugation + hydrolysis
M51621.89b517Oxidative-deaminationX
M460-221.98b461OxidationX
M47222.76b473S-dealkylation + S-X
acetylation + reduction
M47622.76b477OxidationX
Unidentified 6UnknownX
M47423.63b475OxidationX
Unidentified 7UnknownX
M43025.88b431AG-881-oxidationX
M42630.62b427S-dealkylation + methylationX
M45831.03c459AG-69460X*
AG-88139.41b415AG-881XX
M42847.40b429S-dealkylation + oxidationX
Table 3 contains a summary of protonated molecular ions and characteristic product ions for AG-881 and identified metabolites

TABLE 3
RetentionCharacteristic
MetaboliteTimeProposed MetaboliteProduct Ions
designation(Minutes)[M + H]+Identification(m/z)Matrix
M266 7.88a267[Figure (not displayed)]
188, 187Urine
M51519.79b516[Figure (not displayed)]
429, 260, 164, 153Feces
M460-120.76b461[Figure (not displayed)]
379, 260, 164Feces
M49921.22b500[Figure (not displayed)]
437, 413, 260, 164, 137Urine Feces
M51621.89b517[Figure (not displayed)]
427, 260, 164, 153Feces
M460-221.98b461[Figure (not displayed)]
369, 260, 164, 139, 121, 93Feces
M47222.76b473[Figure (not displayed)]
429, 260, 179, 164, 153Feces
M47622.76b477[Figure (not displayed)]
395, 260, 164, 139, 119Feces
M47423.63b475[Figure (not displayed)]
260, 164, 68Feces
M43025.88b431[Figure (not displayed)]
260, 164, 155, 68Feces
M42630.62b427[Figure (not displayed)]
260, 164, 151Feces
M45831.03b459[Figure (not displayed)]
380, 311, 260, 183, 164, 130Plasma Fecesd
AG-88139.41b415[Figure (not displayed)]
319, 277, 260, 240, 164, 139, 119, 68Plasma Fecesd
M42847.40b429[Figure (not displayed)]
260, 164, 153Feces
Notes
aRetention time from analysis of a urine sample
bRetention time from analysis of a feces sample
cRetention time from analysis of a plasma sample
dM458 was only detected in feces by mass spectrometry, not by radioprofiling.
The proposed (theoretical) biotransformation pathways leading to the observed metabolites are shown in FIG. 1.

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Patent 2024
Acetylation AG 30 Biotransformation Chlorine Dealkylation Deamination Elements, Radioactive Feces Glutamic Acid Glutathione Glycine Healthy Volunteers High-Performance Liquid Chromatographies Homo sapiens Hydrolysis Intravenous Infusion Ions Liquid Chromatography Mass Spectrometry Metabolism Methylation Parent Plasma Radioactivity Retention (Psychology) Tandem Mass Spectrometry Urinalysis Urine vorasidenib
Not available on PMC !

Example 7

5′-capping of polynucleotides may be completed concomitantly during the in vitro-transcription reaction using the following chemical RNA cap analogs to generate the 5′-guanosine cap structure according to manufacturer protocols: 3′-O-Me-m7G(5′)ppp(5′) G [the ARCA cap]; G(5′)ppp(5′)A; G(5′)ppp(5′)G; m7G(5′)ppp(5′)A; m7G(5′)ppp(5′)G (New England BioLabs, Ipswich, MA). 5′-capping of modified RNA may be completed post-transcriptionally using a Vaccinia Virus Capping Enzyme to generate the “Cap 0” structure: m7G(5′)ppp(5′)G (New England BioLabs, Ipswich, MA). Cap 1 structure may be generated using both Vaccinia Virus Capping Enzyme and a 2′-O methyl-transferase to generate: m7G(5′)ppp(5′)G-2′-O-methyl. Cap 2 structure may be generated from the Cap 1 structure followed by the 2′-O-methylation of the 5′-antepenultimate nucleotide using a 2′-O methyl-transferase. Cap 3 structure may be generated from the Cap 2 structure followed by the 2′-O-methylation of the 5′-preantepenultimate nucleotide using a 2′-O methyl-transferase. Enzymes are preferably derived from a recombinant source.

When transfected into mammalian cells, the modified mRNAs have a stability of between 12-18 hours or more than 18 hours, e.g., 24, 36, 48, 60, 72 or greater than 72 hours.

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Patent 2024
capping enzyme, vaccinia virus Cells Enzymes Guanosine Mammals Methylation Nucleotides Polynucleotides RNA, Messenger RNA Cap Analogs TRAF3 protein, human Transcription, Genetic Transferase
Not available on PMC !

Example 6

5′-capping of polynucleotides can be completed concomitantly during the in vitro-transcription reaction using the following chemical RNA cap analogs to generate the 5′-guanosine cap structure according to manufacturer protocols: 3′-O-Me-m7G(5′)ppp(5′) G [the ARCA cap];G(5′)ppp(5′)A; G(5′)ppp(5′)G; m7G(5′)ppp(5′)A; m7G(5′)ppp(5′)G (New England BioLabs, Ipswich, MA). 5′-capping of modified RNA can be completed post-transcriptionally using a Vaccinia Virus Capping Enzyme to generate the “Cap 0” structure: m7G(5′)ppp(5′)G (New England BioLabs, Ipswich, MA). Cap 1 structure can be generated using both Vaccinia Virus Capping Enzyme and a 2′-O methyl-transferase to generate: m7G(5′)ppp(5′)G-2′-O-methyl. Cap 2 structure can be generated from the Cap 1 structure followed by the 2′-O-methylation of the 5′-antepenultimate nucleotide using a 2′-O methyl-transferase. Cap 3 structure can be generated from the Cap 2 structure followed by the 2′-O-methylation of the 5′-preantepenultimate nucleotide using a 2′-O methyl-transferase. Enzymes can be derived from a recombinant source.

When transfected into mammalian cells, the modified mRNAs can have a stability of between 12-18 hours or more than 18 hours, e.g., 24, 36, 48, 60, 72 or greater than 72 hours.

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Patent 2024
capping enzyme, vaccinia virus Cells Enzymes Guanosine Mammals Methylation Nucleotides Polynucleotides RNA, Messenger RNA Cap Analogs TRAF3 protein, human Transcription, Genetic Transferase
To find associations between TF targeting and promoter methylation status and copy number variation status, we selected 76 melanoma CCLE cell lines and we computed the significance of associations using ANOVA as implemented in the Python package statsmodels v0.13.2 [96 ]. Since we were mostly interested in finding strong associations and prominent regulatory hallmarks of melanoma, we discretized the input data by considering a gene to be amplified if it had more than three copies and to be deleted if both copies are lost. For promoter methylation data, promoters were defined in CCLE as the 1kb region downstream of the gene’s transcriptional start site (TSS). We defined hypermethylated promoter sites as those having methylation status with a z-score greater than three and we defined hypomethylated sites as those having methylation status with a z-score less than negative three; we considered a gene to be amplified if it had evidence of more than three copies in the genome and to be deleted if both copies are lost. We only computed the associations if they had at least three positive instances of the explanatory variable (for example, for a given gene at least three cell lines had a hypomethylation in that gene’s promoter) and corrected for multiple testing using a false discovery rate of less than 25% following the Benjamini-Hochberg procedure [97 ].
In all melanoma cell lines, for each modality (promoter hypomethylation, promoter hypermethylation, gene amplification, and gene deletion) and for each gene, we built an ANOVA model using TF targeting as the response variable across all melanoma cell lines while the status of that gene (either promoter methylation or copy number status) was the explanatory variable. For example, in modeling promoter hypermethylation, we chose positive instances to represent hypermethylated promoters and negative instances for nonmethylated promoters along with an additional factor correcting for the cell lineage. Similarly, for copy number variation analysis, we chose positive instance to represent amplified genes and negative instances for nonamplified genes while correcting for cell lineage. We only computed the associations if they had at least three positive instances of the explanatory variable (for example, promoter hypomethylation in at least three cell lines).
To predict drug response using TF targeting, we conducted a linear regression with elastic net [45 (link)] regularization as implemented in the Python package sklearn v1.1.3 using an equal weight of 0.5 for L1 and L2 penalties using regorafenib cell viability assays in melanoma cell lines as a response variable and the targeting scores of 1,132 TFs (Table S5) as the explanatory variable.
Finally, to model EMT in melanoma, we used MONSTER on two LIONESS networks of melanoma cancer cell lines, one representing a primary tumor (Depmap ID: ACH-000580) as the initial state and the other a metastasis cell line (Depmap ID: ACH-001569) as the end state. We modified the original implementation of MONSTER that implements its own network reconstruction procedure to take any input network, such as LIONESS networks. MONSTER identifies differentially involved TFs in the transition by shuffling the columns of the initial and final state adjacency matrices 1000 times to build a null distribution, which is then used to compute a standardized differential TF involvement score by scaling the obtained scores by those of the null distribution.
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Publication 2023
Biological Assay Cell Lines Cell Survival Copy Number Polymorphism Gene Amplification Gene Deletion Genes Genome Melanoma Methylation Neoplasm Metastasis Neoplasms neuro-oncological ventral antigen 2, human Pharmaceutical Preparations Promoter, Genetic Python Reconstructive Surgical Procedures regorafenib Transcription Initiation Site
To enable further exploration and discovery of biological associations, we built an online resource representing a multi-tiered regulatory network. First, to build a pan-cancer multi-tiered network that connects the genotype to cellular phenotypes, we extended DRAGON networks from modeling pairwise interactions between two biological variables to a multi-omic network that includes more than two node types by sequentially adding a new layer to an initial pairwise DRAGON network. In addition, since DRAGON networks are undirected, we added direction based on our understanding of how biological elements interact with each other. For example, gene expression nodes are upstream of protein level nodes and metabolite nodes. To facilitate browsing and limit exploration to potentially causal associations that best reflect our understanding of how different data types link to one another in cellular biology, our approach was to prune edges between the same node type to build bipartite DRAGON networks between each pair of genomic modalities. In particular, promoter methylation status, copy number variation, histone marks, and miRNA were linked to gene expression in a pairwise fashion. Then, gene expression was linked to protein levels, which in turn was associated with cellular phenotypes represented by metabolite levels, drug sensitivity, and cell fitness following CRISPR gene knockout. To reduce the size of the network to the most relevant positive and negative associations, only the 2000 most positive correlations and the 2000 most negative correlations in each pairwise association in each of the bipartite networks were retained in the final multi-omic network. The CCLE online pan-cancer map was built using Vis.js (v8.5.2) and can be queried for biological associations using user input queries at https://grand.networkmedicine.org/cclemap.
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Publication 2023
Biopharmaceuticals Clustered Regularly Interspaced Short Palindromic Repeats Copy Number Polymorphism Gene Expression Gene Knockout Techniques Genome Genotype Histone Code Hypersensitivity link protein Malignant Neoplasms Methylation MicroRNAs Pharmaceutical Preparations Phenotype Proteins Seizures

Top products related to «Methylation»

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The EZ DNA Methylation Kit is a product offered by Zymo Research for the bisulfite conversion of DNA. The kit is designed to convert unmethylated cytosine residues to uracil, while leaving methylated cytosines unchanged, enabling the detection of DNA methylation patterns.
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The EZ DNA Methylation-Gold Kit is a product offered by Zymo Research for bisulfite conversion of DNA samples. It is designed to convert unmethylated cytosine residues to uracil, while leaving methylated cytosines unchanged, enabling the detection and analysis of DNA methylation patterns.
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The EpiTect Bisulfite Kit is a laboratory equipment product designed for bisulfite conversion of DNA samples. It facilitates the chemical modification of DNA to determine the methylation status of specific genomic regions.
Sourced in United States
The Infinium HumanMethylation450 BeadChip is a DNA methylation microarray platform developed by Illumina. It is designed to examine DNA methylation patterns across the human genome at over 450,000 CpG sites.
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The QIAamp DNA Mini Kit is a laboratory equipment product designed for the purification of genomic DNA from a variety of sample types. It utilizes a silica-membrane-based technology to efficiently capture and purify DNA, which can then be used for various downstream applications.
Sourced in United States, Netherlands
The HumanMethylation450 BeadChip is a microarray-based technology used for the analysis of DNA methylation patterns. It provides a comprehensive coverage of CpG sites across the genome, allowing for the assessment of DNA methylation levels at over 450,000 specific locations.
Sourced in Germany, United States, Sweden, Netherlands, Canada, Japan, United Kingdom, Australia, France
The PyroMark Q24 is a automated pyrosequencing system designed for DNA sequencing and analysis. It provides rapid and accurate DNA sequence information, making it a useful tool for applications such as SNP genotyping, methylation analysis, and mutation detection.
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The PyroMark PCR Kit is a laboratory equipment product designed for performing Pyrosequencing PCR reactions. It contains the necessary reagents and components required to amplify DNA sequences prior to Pyrosequencing analysis.
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The DNeasy Blood & Tissue Kit is a DNA extraction and purification kit designed for the efficient isolation of high-quality genomic DNA from a variety of sample types, including whole blood, tissue, and cultured cells. The kit utilizes a silica-based membrane technology to capture and purify DNA, providing a reliable and consistent method for DNA extraction.
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The Infinium MethylationEPIC BeadChip is a lab equipment product designed for genome-wide DNA methylation analysis. It provides comprehensive coverage of over 850,000 methylation sites across the human genome.

More about "Methylation"

Methylation is a crucial biological process that involves the addition of a methyl group (-CH3) to DNA, RNA, or proteins.
This chemical modification can regulate gene expression, alter protein function, and influence various cellular processes.
Methylation is integral to epigenetic regulation, cellular signaling, and disease pathogenesis.
Researchers studying methylation can leverage powerful tools like the EZ DNA Methylation Kit, EZ DNA Methylation-Gold Kit, and EpiTect Bisulfite Kit to analyze DNA methylation patterns.
The Infinium HumanMethylation450 BeadChip and Infinium MethylationEPIC BeadChip provide comprehensive genome-wide methylation analysis, while the QIAamp DNA Mini Kit and DNeasy Blood & Tissue Kit enable efficient DNA extraction and purification.
Downstream techniques such as PyroMark Q24 and the PyroMark PCR Kit can be used to quantify and analyze methylation levels.
These advanced tools and kits, when combined with the AI-powered insights of PubCompare.ai, can help researchers optimize their methylation research process, enhance reproducibility and accuracy, and simplify their workflow.
PubCompare.ai empowers researchers to locate the most relevant protocols from literature, preprints, and patents using AI-driven comparisons.
This can lead to the identification of the best protocols and products, ultimately improving the quality and efficiency of methylation research.
Experince the power of AI-assisted methylation research optimization today and take your research to new heights.