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8,334 results for “Methylation”
Additional file 1 of Methylation data imputation performances under different representations and missingness patterns
<p>Additional file 1 Detailed imputation results per dataset.</p> <p><a href="https://springernature.figshare.com/articles/journal_contribution/Additional_file_1_of_Methylation_data_imputation_performances_under_different_representations_and_missingness_patterns/12585952/1">Additional file 1 of Methylation data imputation performances under different representations and missingness patterns (figshare.com)</a></p>
Additional file 3 of Methylation data imputation performances under different representations and missingness patterns
<p>Additional file 3 Performance comparison between complete (450k) and restricted (21k) datasets.</p> <p><a href="https://springernature.figshare.com/articles/journal_contribution/Additional_file_3_of_Methylation_data_imputation_performances_under_different_representations_and_missingness_patterns/12585958/1">Additional file 3 of Methylation data imputation performances under different representations and missingness patterns (figshare.com)</a></p> <p> </p>
Data for fitness analyses used in: Environmentally-induced DNA methylation is inherited across generations in water fleas (Daphnia magna)
<p><span>Data of</span> fitness effects of environmental stressors on <em>Daphnia magna</em> over multiple generations. Ages of first and second reproduction, and sizes of first and second brood were measured and used to calculate replacement rate. This data is part of a study on whole-genome bisulphate sequencing on individual <em>Daphnia magna</em> to assess whether environmentally-induced DNA methylation can persist for up to four generations.</p>
Modeling methyl-sensitive transcription factor motifs with an expanded epigenetic alphabet: transcription factor preferences
<p>The main output of our analysis, as a raw dataset. This data was used to create the plots depicting transcription factor preferences across our paper, including for our treemaps.</p>
Meta-omics-aided isolation of elusive anaerobic arsenic-methylating soil bacteria
<p>Data pertaining to the manuscript "<strong>Meta-omics-aided isolation of an elusive anaerobic arsenic-methylating soil bacterium"</strong> by Karen Viacava, Jiangtao, Qiao, Andrew Janowczyk, Suresh Poudel, Nicolas Jacquemin, Karin Lederballe Meibom, Him K. Shrestha, Matthew C. Reid, Robert L. Hettich and Rizlan Bernier-Latmani published in ISME journal.</p>
The impact of low input DNA on the reliability of DNA methylation as measured by the Illumina Infinium MethylationEPIC BeadChip, supplementary table 3
<p>Supplementary table 3: Summary statistics from an EWAS assessing the relationship between variance in DNA methylation value and DNA input level.</p>
Parent-of-origin detection and chromosome-scale haplotyping using long-read DNA methylation sequencing and Strand-seq
<p>Hundreds of loci in human genomes have alleles that are methylated differentially according to their parent of origin. These imprinted loci generally show little variation across tissues, individuals, and populations. We show that such loci can be used to distinguish the maternal and paternal homologs for all autosomes, without the need for the parental DNA. We integrate methylation-detecting nanopore sequencing with the long-range phase information in Strand-seq data to determine the parent of origin of chromosome-length haplotypes for both DNA sequence and DNA methylation in five trios with diverse genetic backgrounds.</p>
Data and code for "Differential methylation analysis of reduced representation bisulfite sequencing experiments using edgeR"
<p>This data set provides data files and R code to accompany the article <em>Differential methylation analysis of reduced representation bisulfite sequencing experiments using edgeR</em> published by F1000Research.</p> <p>The data consists of Reduced Representation BS-seq methylation profiles of epithelial populations from the mouse mammary gland, with n=2 biological replicates for each of three cell populations.</p> <p>RNA-seq expression profiles of luminal and basal mammary epithelial populations are also provided.</p> <p>The R code undertakes an differential methylation analysis of the BS-seq profiles and demonstrates a strong negative correlation between the differential methylation and differential expression results.</p>
Data for paper MethPhaser: methylation-based haplotype phasing of human genomes
<p>Data for paper MethPhaser: methylation-based haplotype phasing of human genomes. </p> <p>Files start with R9 or R10 are HG002 sample data.</p> <p>Zip files are block connection intermediate for MethPhaser. </p> <p>GTFs are block assignments. </p>
Dataset related to article: QUALITY ASSESSMENT OF THE MRI-RADIOMICS STUDIES FOR MGMT PROMOTER METHYLATION PREDICTION IN GLIOMA: A SYSTEMATIC REVIEW AND META-ANALYSIS
<p><strong><span>This table contains the raw data used to generate the heatmap illustrated in Fig 2. Each row of the table shows the distribution of the scores achieved by the studies for a domain. Colors from red to green denote progressive increase from minimum to maximum score obtainable for each domain.</span></strong></p>
Figure 2 in Mark-Release-Recapture Experiments on the Effectiveness of Methyl Eugenol-Spinosad Male Annihilation Technique Against an Invading Population of Bactrocera dorsalis
Figure 2. Visual representation of the MAT-ME saturation hypothesis. Darker areas represent higher concentrations of attractant odor in the air column, represented from above. (A) A single sentinel trap baited with 6 ml of methyl eugenol under control conditions. A release of males around the center of the area would lead some of the males to find the plume and successfully follow it to the trap for capture. (B) A single sentinel trap with some MAT-ME spots (low rate). More attractant is available, but gradients to point sources are still clear; note that the MAT-ME spots have a lower overall concentration at the source. (C) A single sentinel trap with a high density of MAT-ME spots. A haze of attractant exists, making gradients shorter. The sentinel trap is still about as effective as before due to higher final concentration, but the less concentrated MAT-ME spots are harder to find.
Figure 1 in Mark-Release-Recapture Experiments on the Effectiveness of Methyl Eugenol-Spinosad Male Annihilation Technique Against an Invading Population of Bactrocera dorsalis
Figure 1. Mean proportion recaptured by treatment. Whiskers indicate standard errors. Letters indicate statistically significant differences at α = 0.05 via two-sample z-test (see text for details)
Heterogeneous Biocatalytic Reduction of 5-(Hydroxy)methyl Furfural Using two Co-immobilised Alcohol Dehydrogenases
<p>Biocatalyst heterogenisation may enable robust processes that can be applied in biorefineries to selectively valorise highly functionalised platform chemicals. In this work, we co-immobilise two dehydrogenases and successfully apply them in the selective reduction of 5-hydroxymethylfurfural (HMF) to 2,5-bis(hydroxymethyl) furan (BHMF) with efficient <em>in situ</em> cofactor regeneration. First, we select the best enzyme candidates (an alcohol dehydrogenase from <em>Escherichia coli</em> together with a thermostable glucose dehydrogenase from <em>Bacillus subtilis</em>) and then screen a variety of carriers and chemistries to find the optimal individual immobilisation protocols for each dehydrogenase. As a result, methacrylate carriers (Purolite™) functionalised with either aldehydes or with epoxy and cobalt-chelate groups co-immobilise both enzymes in high yields with a sufficient activity recovery (>20%). These optimal heterogeneous biocatalysts enable the quantitative bio-reduction of HMF to BHMF with >99% selectivity in only fifteen minutes, exhibiting an outstanding reusability of >15 batch cycles with a total volumetric productivity of ∼5 g L<sup>−1</sup> h<sup>−1</sup> of BHMF. Preliminary experiments on a semipreparative scale with HMF loadings of 40 mM also reach high product conversions (86%). Overall, the judicious selection of enzymes, carriers and reaction conditions enables the design of robust biocatalysts that may contribute to paving the way to the valorisation of highly functionalised chemicals in biorefineries.</p>
DIAMOND_Methyl_Haplo_table
<p>methylationLevels.csv</p> <p>Metadata by Klaus Von GRAFENSTEIN, Engineer</p> <p>24/06/2024</p> <p>Data from Michel, et al. « Non-Invasive Multi-Cancer Diagnosis Using DNA Hypomethylation of LINE-1 Retrotransposons ». Preprint. Oncology, 23 janvier 2024. <a href="https://doi.org/10.1101/2024.01.20.23288905" target="_blank" rel="noopener">doi:10.1101/2024.01.20.23288905</a></p> <p>Generated with code : <a href="https://github.com/ProudhonLab/DIAMOND">https://github.com/ProudhonLab/DIAMOND</a></p> <p><br><strong>Columns:</strong></p> <ul> <li> Sample_ID = Sample identifier composed of : the sequencing dataset code, followed by "S" and the sample number inside the sequencing batch*</li> <li> Cohorte = Cohort of the sample (Discovery or Validation)</li> <li> Disease_status = Biological class of the sample</li> <li> Metastasis_status = Factor of if the sample is from a metastatic patient or not (M+ or M0). NA when the sample is healthy plasma or Metastasis_status is unknown</li> <li> Stage = Cancer stage of the sample ( 1 to 4 ). NA when the sample is healthy plasma or Stage is unknown</li> <li> Age.range = Age between a 5-year range of the patient or donor</li> <li> Sex = Sex of the patient or donor (F or M). NA when the sex is unknown</li> <li> Haplotype proportions = columns with the name of the amplicon (1,2,4,5,6 or 7) and haplotype code (0 unmethylated, 1 methylated, for each CpG site of the amplicon). Value between 0 and 1</li> <li> Methylation rate = columns with the name of the amplicon (1,2,4,5,6 or 7) and CpG site number. Value between 0 and 1</li> </ul>
A plant virus differentially alters DNA methylation in two cryptic species of a hemipteran vector
<p>This study investigated DNA methylation patterns in two cryptic species (B and Q) of the sweet potato whitefly, <em>Bemisia tabaci</em> (Gennadius), following the acquisition of the tomato yellow curl virus, a single-stranded DNA virus. The methylation levels in genomic features such as promoters, gene bodies, and transposable elements in both cryptic species were described in this study. While overall trends were found to be similar, specific differences in methylation levels were observed. Virus-induced differentially methylated regions (DMRs) were associated with different genes in each cryptic species and were negatively correlated with differential gene expression. These DMRs were analyzed for changes in gene expression and alternative splicing, revealing clusters of hyper- and hypomethylated genes related to virus-vector interactions, immune functions, and detoxification processes. These methylation differences may help explain the distinct biological and physiological traits observed between the B and Q cryptic species.</p>
Fig. 1 in The response of Phyllophaga brevidens and Phyllophaga lenis (Coleoptera: Scarabaeidae) to methyl 2-(methylthio) benzoate and light
Fig. 1. Numbers of Phyllophaga brevidens and Phyllophaga lenis adults trapped per species, sex, and treatment during 2 sampling periods in Tlaltizapan, Morelos, Mexico. A = attractant, L= light, C = control, M = males, F = females, nd = not determined.
Partially methylated domains are hypervariable in breast cancer and fuel widespread CpG island hypermethylation
<p>This dataset contains supplemental tables and tracks for the study entitled: "Partially methylated domains are hypervariable in breast cancer and fuel widespread CpG island hypermethylation".</p> <ul> <li>Files <ul> <li>PMDs_CGIs.zip <ul> <li>The included files contain</li> <li>Genome positions of detected PMDs with their mean methylation (weighted mean, see Methods)</li> <li>Genome positions of CpG islands with their mean methylation (weighted mean)</li> <li>The "Brinkman" directory contains files from breast cancer data produced in this study</li> <li>The "normals" directory contains files from normal tissues (external data) analyzed in this study</li> <li>The "tumors" directory contains files from tumors (external data) analyzed in this study</li> <li>All genome positions are based on GRCh37/hg19 </li> <li>All files are TAB-delimited text files (.tsv)</li> </ul> </li> <li>DNAme_bigwigs.zip <ul> <li>The included files are BIGWIG files (http://genome.ucsc.edu/goldenPath/help/bigWig.html) for viewing the DNA methylation profiles in a genome browser such as UCSC (http://genome.ucsc.edu). Each file represents a whole-Genome Bisulfite Sequencing (WGBS) DNA methylation profile from one tumor used in this study. The used genome build was GRCh37/hg19. For every CpG with a coverage of at least 4 reads, the DNA methylation value (range: 0-1) is included.</li> </ul> </li> </ul> </li> <li>Methods <ul> <li>Detection of partially methylated domains (PMDs) in all whole-genome bisulfite sequencing (WGBS) methylation profiles throughout this study was done using the MethylSeekR package for R (1). Before PMD calling, CpGs overlapping common SNPs (dbSNP build 137) were removed. The alpha distribution (1) was used to determine whether PMDs were present at all, along with visual inspection of WGBS profiles. After PMD calling, the resulting PMDs were further filtered by removing regions overlapping with centromers (undetermined sequence content).</li> <li>Mean methylation values from WGBS inside CGIs were calculated using the ‘weighted methylation level’ (2).</li> <li>Mean methylation values from WGBS inside PMDs were calculated using the ‘weighted methylation level’ (2). Calculation of mean methylation within PMDs involved removing all CpGs overlapping with CpG island(-shores) and promoters, as the high CpG densities within these elements yield unbalanced mean methylation values, not representative of PMD methylation. </li> </ul> </li> <li>References <ul> <li>(1) Burger, L., Gaidatzis, D., Schübeler, D. & Stadler, M. B. Identification of active regulatory regions from DNA methylation data. Nucleic Acids Research 41, (2013).</li> <li>(2) Schultz, M. D., Schmitz, R. J. & Ecker, J. R. ’Leveling’ the playing field for analyses of single-base resolution DNA methylomes. Trends in Genetics 28, 583–585 (2012).</li> </ul> </li> </ul>
New insights into the dissociation dynamics of methylated anilines
<p>Aniline, an important model system for biological chromophores, undergoes ultrafast H-atom loss upon absorption of an ultraviolet photon. By varying the number and position of methyl substituents on both the aromatic ring and amine functional group, we explore the ultrafast production of photofragments as a function of molecular structure. Both <em>N</em>-methyl aniline and 3,5-dimethyl aniline show altered H-atom loss pathways compared to aniline, while no evidence for CH<sub>3</sub> loss was found from either <em>N</em>-methyl aniline or <em>N,N</em>-dimethyl aniline. With the addition of time resolved photoelectron spectroscopy, the photofragment appearance times may be matched to excited state relaxation pathways. Evidence for a sequential excited state relaxation mechanism, potentially involving a valence-to-Rydberg decay mechanism, will be presented. Such a global, bottom-up approach to molecular photochemistry is crucial to understanding the dissociative pathways and excited state decay mechanisms of biomolecule photoprotection in nature.</p> <p> </p> <p>All data are saved as a .csv file. Each transient .csv file is labeled according to molecule, pump wavelength, data type, and a date (<em>e.g.</em> NMA_238_TRPES_11Oct2017.csv). The first column contains kinetic energies (either eKE or TKER) while the first row indexes each time delay. The normalised UV absorption spectra are saved in one file, with columns for each molecule.</p>
sirselim/immunecell_methylation_paper_data: First release of data for immune cell epigenetics (methylation) manuscript
<p>This is the first release of the data to be made public and accessible with the manuscript.</p>
Fig. 2 in Field capture of male oriental fruit flies (Diptera: Tephritidae) in traps baited with solid dispensers containing varying amounts of methyl eugenol
Fig. 2. Captures of Bactrocera dorsalis males on Oahu, Hawaii, USA, in Jackson traps baited with fresh liquid methyl eugenol or weathered, polymeric plugs containing 3, 6, or 10 g of methyl eugenol, where accompanying dichlorvos squares were either weathered (A, Experiment 2) or replaced prior to each trapping period (B, Experiment 3). Symbols represent means ± 1 SE; n = 12 in all cases. For a given trapping interval, means marked with different letters differed significantly (P <0.05, Tukey test).
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Allen Brain Atlas
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International Brain Laboratory public data
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OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.