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8,334 results for “Methylation”
Fig. 1 in Field capture of male oriental fruit flies (Diptera: Tephritidae) in traps baited with solid dispensers containing varying amounts of methyl eugenol
Fig. 1. Captures of Bactrocera dorsalis males on the Big Island, Hawaii, USA (Experiment 1) in Jackson traps baited with fresh liquid methyl eugenol or weathered, polymeric plugs containing 3, 6, or 10 g of methyl eugenol. Symbols represent means ± 1 SE; n = 12 in all cases.
Maternal smoking DNA methylation risk score associated with health outcomes in offspring of European and South Asian ancestry
<p>These are a collection of EWAS summary statistics for the following publication:</p> <p>Deng Wei Q, Cawte Nathan, Campbell Natalie, Azab Sandi M, de Souza Russell J, Lamri Amel, Morrison Katherine M, Atkinson Stephanie A, Subbarao Padmaja, Turvey Stuart E, Moraes Theo J, Teo Koon K, Mandhane Piush, Azad Meghan B, Simons Elinor, Pare Guillaume, Anand Sonia S (2024) Maternal smoking DNA methylation risk score associated with health outcomes in offspring of European and South Asian ancestry eLife 13:RP93260, https://doi.org/10.7554/eLife.93260.3</p> <p>1. CHILD_450K_R1_March2024_mateversmk_Regression_CpGWide.csv</p> <p>Maternal smoking using ever definition in CHILD (HM450K array).</p> <p>2. CHILD_450K_R1_March2024_matsmoke_Regression_CpGWide.csv</p> <p>Maternal smoking using current smoking definition in CHILD (HM450K array).</p> <p>3. CHILD_450K_R1_March2024_mblsmkexp_Regression_CpG_Wide.csv</p> <p>Maternal smoking exposure (hours per week) in CHILD (HM450K array).</p> <p>4. FAMILY_EPIC_R1_March2024_mateversmk_Regression_CpGWide.csv</p> <p>Maternal smoking using ever definition in FAMILY (customized EPIC array).</p> <p>5. FAMILY_EPIC_R1_March2024_matsmoke_Regression_CpGWide.csv</p> <p>Maternal smoking using current smoking definition in FAMILY (customized EPIC array).</p> <p>6. FAMILY_EPIC_R1_March2024_mblsmkexp_Regression_CpG_Wide.csv</p> <p>Maternal smoking exposure (hours per week) in FAMILY (customized EPIC array).</p> <p>7. START_450K_R1_March2024_mblsmkexp_Regression_CpG_Wide.csv</p> <p>Maternal smoking exposure (hours per week) in START (HM450K array).</p> <p>8. mateversmk_meta_annot_R1.csv</p> <p>Meta-analyzed european EWAS of maternal smoking using ever definition.</p> <p>9. matsmoke_meta_annot_R1.csv</p> <p>Meta-analyzed european EWAS of maternal smoking using current smoking definition.</p> <p>10. mblsmkexp_meta_annot_R1.csv</p> <p>Meta-analyzed european EWAS of maternal smoking exposure (hours per week).</p>
Research Data for the Journal Article: Metal-free catalytic systems based on imidazolium chloride and strong bases for selective oxidative esterification of furfural to methyl furoate
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Research Data for the Journal Article: Insertion of CO2 to 2-methyl furoate promoted by a cobalt hypercrosslinked polymer catalyst to obtain a monomer of CO2-based biopolyesters
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A Data-Driven Epigenetic Characterization of Morning Fatigue Severity in Oncology Patients Receiving Chemotherapy: Associations with Epigenetic Age Acceleration, Blood Cell Types, and Expression-Associated Methylation
<p>This dataset contains supplementary materials including the eCpG mapping analysis results and annotation. The manuscript has been accepted for publication at Cancer Medicine. Please cite both the paper as well as the DOI of this dataset if you make use of the data.</p>
Dataset for publication Electron-induced ligand loss from iron tetracarbonyl methyl acrylate
<p>Experimental dataset for the publication including readme files with description and all metadata.</p> <p><a href="https://doi.org/10.3762/bjnano.15.66">https://doi.org/10.3762/bjnano.15.66</a></p> <p> </p>
Data to: Sulfur Amino Acid Status Controls Selenium Methylation in Pseudomonas tolaasii...
<p>data to: Sulfur Amino Acid Status Controls Selenium Methylation in Pseudomonas tolaasii: Identification of a Novel Metabolite from Promiscuous Enzyme Reactions</p> <p>Appl Environ Microbiol 2021 May 26;87(12):e0010421.</p> <p>doi: 10.1128/AEM.00104-21. Epub 2021 May 26.</p> <p> </p>
Epigenome-wide DNA Methylation and Pesticide Use in the Agricultural Lung Health Study
<p>An epigenome-wide association study of blood DNA methylation and pesticide use was conducted in adults in the Agricultural Lung Health Study. Sixteen specific pesticides were analyzed: dicamba, picloram, mesotrione, acetochlor, metolachlor, glyphosate, 2,4-Dichlorophenoxyacetic acid (2,4-D), atrazine, malathion, aldrin, chlordane, DDT, dieldrin, heptachlor, lindane, and toxaphene. 162 differentially methylated CpGs across 9 specific pesticides (acetochlor, atrazine, dicamba, glyphosate, malathion, metolachlor, mesotrione, picloram, and heptachlor.</p>
Data from: Age estimation using methylation-sensitive high-resolution melting (MS-HRM) in both healthy felines and those with chronic kidney disease
<p>Age is an important ecological tool in wildlife conservation. However, it is difficult to estimate in most animals, including felines — most of whom are endangered. Here, we developed the first DNA methylation-based age-estimation technique — as an alternative to current age-estimation methods — for two feline species that share a relatively long genetic distance with each other: domestic cat (<i>Felis catus</i>; 79 blood samples) and an endangered <i>Panthera</i>, the snow leopard (<i>Panthera uncia</i>; 11 blood samples). We measured the methylation rates of two gene regions <span>using </span>methylation-sensitive high-resolution melting (MS-HRM). Domestic cat age was estimated with a mean absolute deviation (MAD) of 3.83 years. Health conditions influenced accuracy of the model. Specifically, the models built on cats with chronic kidney disease (CKD) had lower accuracy than those built on healthy cats. The snow leopard-specific model (i.e. the model that resets the model settings for snow leopards) had a better accuracy (MAD = 2.10 years) than that obtained on using the domestic cat model directly. This implies that our markers could be utilised across species, although changing the model settings when targeting different species could lead to better estimation accuracy. The snow leopard-specific model also successfully distinguished between sexually immature and mature individuals.</p>
Data for: Analysis of Conformational Exchange Processes using Methyl-TROSY-Based Hahn Echo Measurements of Quadruple-Quantum Relaxation
<p>Raw experimental data used in associated publication. A full list of experiments is provided in the README.md file.</p>
Simultaneous profiling of histone modifications and DNA methylation via nanopore sequencing
<p>Datasets that contain a minimum of nanopore reads sufficient for hidden Markov model training and for evaluating the performance of our computational tool - nanoHiMe at simultaneously calling CpG and/or adenine methylation on individual nanopore reads.<em> Ecoli</em>_PCR_amplicons_100k.tgz, <em>Ecoli</em>_PCR_MSssI_100k.tar.gz and <em>Ecoli</em>_PCR_pA-Hia5_100k.tar.gz are used for training new parameters of the emission distributions of individual <em>k</em>-mers from DNA template without modification, with fully methylated CpGs, and with partially methylated adenines, respectively. nanoHiMe_H3K27me3.fast5.tgz are the nanopore sequencing reads from H3K27me3 nanoHiMe-seq experiments in GM12878 cells and used for evaluating the performance of nanoHiMe at jointly calling CpG and adenine methylation.</p>
Deciphering methylation effects on S2(ππ∗) internal conversion in the simplest linear α,β-unsaturated carbonyl
<p>Here you will find the dataset related to the article entitled: Deciphering methylation effects on S2(ππ∗) internal conversion in the simplest linear α,β-unsaturated carbonyl.</p> <p>------------------------------------------------------------------------------</p> <p><strong>critical_points_geometries.zip</strong></p> <p>This repository contains the XYZ files of the critical points computed at the hh-TDA-ωPBEh/6-31G(d,p) (hh-TDA) and SA5-XMS(Im=0.3)-CASPT2(10,9)/cc-pVDZ (XMSPT2) levels of theory. Also, exmaple input files for MECI and geometry optimizations for TeraChem (hh-TDA level) and BAGEL (XMS-CASPT2).</p> <p>Notation:</p> <ul> <li>AC - Acrolein </li> <li>CR - Crotanaldehyde</li> <li>MVK - Methylvinylketone </li> <li>MA - Methacrolein </li> <li>S0min - Minimum of S0 electronic state</li> <li>S1min - Minimum of S1 electronic state</li> <li>S2min - Minimum of S2 electronic state</li> <li>S1S0_MECI - Minimum energy conical intersection at S1 and S0 electronic states intersection</li> <li>S2S1_MECI - Minimum energy conical intersection at S2 and S1 electronic states intersection</li> <li>NTpyr, CCpyr, N.... relates to the label of different MECI structures. Information regarding this nomenclature can be found in the Supporting Information of the paper.</li> </ul> <p>Computational details and extra information regarding these structures can be found in the main text and supporting information of the article. </p> <p>------------------------------------------------------------------------------------------------------------------------------------------</p> <p><strong>AIMS_ICs.zip</strong></p> <p>This repository contains all the initial conditions (ICs) sampled to perform the ab-initio multiple spawning dynamics simulations for acrolein (AC), crotanaldehyde (CR), methylvinylketone (MVK), and methacrolein (MA).</p> <p>For AC, CR, MVK and MA, 50 ICs were randomly sampled from a narrow window of 0.05 eV around 6.20 eV (from the calculated absorption spectra)</p> <p>For CR, 10 additional ICs were also sampled in addition to the 50, to ensure that the observed stalling in population decay around 600-900fs in not due to undersampling.</p> <p>All IC files are named as ICXXXX.dat where XXXX = randomly selected IC number</p> <p>Each IC file contains the cartesian coordinates in Bohr and the corresponding nuclear velocities in Bohr/atomic unit.</p>
Data from: Developmental stress does not induce genome-wide DNA methylation changes in wild great tit (Parus major) nestlings
<p class="MsoNormal"><span>The environment experienced during early life is a crucial factor in the life of many organisms. This early life environment has been shown to have profound effects on morphology, physiology and fitness. However, the molecular mechanisms that mediate these effects are largely unknown, even though this is essential for our understanding of the processes that induce phenotypic variation in natural populations. DNA methylation is an epigenetic mechanism that has been suggested to explain such environmentally induced phenotypic changes early in life. To investigate whether DNA methylation changes are associated with experimentally induced early developmental effects, we cross-fostered great tit (<em>Parus major</em>) nestlings and manipulated their brood sizes in a natural study population. We assessed experimental brood size effects on pre-fledging biometry and behaviour. We linked this to genome-wide DNA methylation levels of CpG sites in erythrocyte DNA, using 122 individuals and an improved epiGBS2 laboratory protocol. Brood enlargement caused developmental stress and negatively affected nestling condition, predominantly during the second half of the breeding season, when conditions are harsher. Brood enlargement, however, affected nestling DNA methylation in only one CpG site and only if hatch date was taken into account. In conclusion, this study shows that nutritional stress in enlarged broods does not associate with direct effects on genome-wide DNA methylation. Future studies should assess whether genome-wide DNA methylation variation may arise later in life as a consequence of phenotypic changes during early development.</span></p>
Methylation-free E.coli nanopore sequencing (ONT R9.4.1) data set
<p>The data set consists of fast5 files divided into 5 zip files (fast5_[1-5].zip), a genome record (Ecoli_K12_MG1655.fasta), an Illumina assembly genome (illumina_contigs.fasta) and a fastq file from Guppy 5 (guppy_basecalled.fastq.gz). We sequenced the Ecoli non-methylated genomic DNA (D5016, Zymo Research) with an ONT MinION device. The sequencing libraries were prepared by fragmenting the genomic DNA using Covaris g-TUBE and a Ligation sequencing kit (SQK-LSK109, Oxford Nanopore) with Flow Cell chemistry R9.4.1. We also performed short-read Illumina sequencing on the same sample using the TruSeq PCR-free library preparation on the MiSeq sequencing platform (Illumina, USA), and constructed a draft assembly from the Illumina sequencing results using SPAdes v3.6.0. We also upload a reference genome directly obtained from the E.coli sample producer website. </p> <p>In addition, the data set contains two fastq files that produced by the Lokatt basecaller (lokatt_basecalled.fasta.gz) and local-trained Bonito basecaller (bonito_local_basecalled.fastq.gz ), respectively, which are used for benchmarking in the Lokatt basecaller paper.</p>
Associated Dataset for Genome-wide DNA methylation patterns in bumble bee (Bombus vosnesenskii) populations from spatial-environmental range extremes
<p>The dataset contains the final methylation call set (n=14,627,533), variant calling file for population genomics analyses, analysis codes/scripts, and other associated files related to the research (Constitutive and variable patterns of genome-wide DNA methylation in populations from spatial-environmental range extremes of the bumble bee <em>Bombus vosnesenskii)</em>. Raw WGBS reads generated in this study have been deposited and are currently available at the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) under NCBI BioProject PRJNA956115.</p>
Codeletion of 1p and 19q determines distinct gene methylation and expression profiles in IDH-mutated oligodendroglial tumors _ Dataset
<p>Overall design: Genome-wide DNA methylation profiling of oligodendroglial tumors (OTs) and five non tumoral brain tissue (NTBT) samples. The Illumina Infinium Human DNA methylation 450k Beadchip was used to obtain DNA methylation profiles across approximately 450,000 CpGs in tumoral samples. Samples included 46 OTs and 5 NTBT.</p> <p>Bisulphite converted DNA from the 51 samples were hybridised to the Illumina Infinium 450k Human Methylation Beadchip</p> <p>Extracted molecule: genomic DNA</p> <p>Platform: Illumina HumanMethylation450 BeadChip (HumanMethylation450_15017482)</p> <p>Label protocol: Standard Illumina Protocol</p> <p>Hybridization protocol: bisulphite converted DNA was amplified, fragmented and hybridised to Illumina Infinium Human Methylation 450K Beadchip using standard Illumina protocol</p> <p>Scan protocol: Arrays were imaged using BeadArray Reader using standard recommended Illumina scanner setting </p> <p>Data processing: BeadStudio software v3.2</p> <p>Data format: IDAT files</p>
DNA methylation differences between stick insect ecotypes
<p><span>Epigenetic mechanisms, such as DNA methylation, can influence gene regulation and affect phenotypic variation, raising the possibility that they contribute to ecological adaptation. To begin to address this issue requires high-resolution sequencing studies of natural populations to pinpoint epigenetic regions of potential ecological and evolutionary significance. However, such studies are still relatively uncommon, especially in insects, and are mainly restricted to a few model organisms. Here, we characterize patterns of DNA methylation for natural populations of </span><span><em>Timema</em> <em>cristinae</em></span> <span>adapted to two host plant species (</span><span>i.e., </span><span>ecotypes).</span> <span>By integrating results from sequencing of whole transcriptomes, genomes, and methylomes, we investigate whether environmental, host, and genetic differences of these stick insects are associated with methylation levels of cytosine nucleotides in CpG context. We report an overall genome-wide methylation level for </span><em><span>T. cristinae</span></em> <span>of ~14%, being enriched in gene bodies and impoverished in repetitive elements. Genome-wide DNA methylation variation was strongly positively correlated with genetic distance (relatedness) but also exhibited significant host-plant effects. Using methylome-environment association analysis, we pinpointed specific genomic regions that are differentially methylated between ecotypes, with these regions being enriched for genes with functions in membrane processes. The observed association between methylation variation with genetic relatedness and the ecologically-important variable of host plant suggest a potential role for epigenetic modification in </span><em><span>T. cristinae</span></em> <span>adaptation. To substantiate such adaptive significance, future studies could test if methylation has a heritable component and the extent to which it responds to experimental manipulation in field and laboratory studies</span><span>.</span></p>
DNA methylation-based age prediction and sex-specific epigenetic aging in a lizard with female-biased longevity
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Data from: A cost-effective blood DNA methylation-based age estimation method in domestic cats, Tsushima leopard cats (Prionailurus bengalensis euptilurus), and Panthera species, using targeted bisulfite sequencing and machine learning models
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DNA methylation differences between stick insect ecotypes
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
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.