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5,596 results for “Epigenetics”
Backpain exercise therapy remodels human epigenetic profiles in buccal and human peripheral blood mononuclear cells: An exploratory study in young male participants
<pre><strong>###### Files description #####</strong><br> <strong>Notes</strong>. 1) "BT" refers to before therapy and "AT" to after therapy. 2) 0 refers to FALSE and 1 to TRUE for binary variables. The provided files have tab-separated columns except the .RDS which is and R output of the mixOmics DIABLO integration analysis. <strong># Questionnaire</strong> > participants_categories.tsv: per participant (rows), output of the clustering with the participant ("ID") category ("category") per class<br> ("class") > questionnaire_agility_metrics.tsv: questionnaire and agility metrics per participant (rows) for the participants ("ID") with at least one paired AT+BT data in one type of biological sample (indicated in the columns "swab", "PBMC", and "plasma") <strong># PTMs</strong> Samples´ names are encoded as PBMC_AT_8_batch1, i.e. cells origin_time upon therapy_ID_batch (we removed _batch column suffix for the <br>processed files). NA indicates an undetected intensity. > raw_PBMC_light_labelled_intensities.tsv: raw intensity of light/endogenous peptides (row) by precursor per sample (column) from PBMC > raw_swab_light_labelled_intensities.tsv: idem from buccal cells > raw_PBMC_heavy_labelled_intensities.tsv: raw intensity of light/endogenous peptides (row) by precursor per sample (column) from PBMC > raw_swab_heavy_labelled_intensities.tsv: idem from buccal cells > raw_PBMC_heavynormalized_intensities.tsv: raw intensity of light peptides normalized by heavy peptides intensity (row) by precursor per <br>sample (column) > raw_swab_heavynormalized_labelled_intensities.tsv: idem from buccal cells > processed_cleaned_PBMC_log2intensities.tsv: processed (heavy normalized, imputed, batch-corrected) intensity of peptides aggregated by modification (PTM, row) by precursor per sample (column) after log2-transformation. The relative abundances are computed from this file. Rows without me/ac suffix represents the amount of unmodified peptide for the considered site. > processed_cleaned_swab_log2intensities.tsv: idem from buccal cells > rel_abundance_PTM_PBMC.tsv: relative abundance computed per precursor, e.g. for a given sample, the H3_K4+H3_K4me1+H3_K4me2+H3_K4me3 <br>relative abundance values must sum to 100, with the relative abundance of H3_K4 representing the absence of modified K4. > rel_abundance_PTM_swab.tsv: idem from buccal cells > tests_from_rel_abundance_PTM_swab_PBMC.tsv: per type of samples ("Sample.origin", i.e.swab of PBMC) and per PTM (rows, "PTM"), report <br>the output of classic (p-values, adjusted with Benjamini-Hochberg (BH), or Benjamini-Yekutieli procedure (BY), from raw and arcsin square <br>root transformed percentage) and PLS-DA tests (VIP - Variable Importance score - and its 95% confidence interval). The percentage of change<br>of each PTM after therapy relative tobefore therapy is reported in "perc_change.AT.over.BT" column. The "is_candidate" indicates if the PTM has been considered as a hit in the swab or PBMC. <strong># Plasma</strong> Samples´ names are encoded as PLASMA_AT_8_batch1, i.e. cells origin_time upon therapy_ID_batch. NA indicates an undetected intensity. > raw_plasma_maxquant_log2ibaq_intensities.tsv: raw data from protein group MaxQuant file. The iBAQ columns are used in later steps. > processed_cleaned_plasma_log2intensities.tsv: processed (imputed, batch-corrected) intensity of protein groups after log2-transformation. > tests_from_intens_plasma.tsv: per protein group ("Proteins.ID"), report the output of classic (p-values, adjusted Benjamini-Hochberg (BH),<br>or Benjamini-Yekutieli procedure (BY), from log2-transformed intensities) and PLS-DA tests (VIP and its 95% confidence interval). The log2 <br>fold change after therapy relative to before therapy is reported in "log2FC.AT.over.BT" column. The "is_candidate" indicates if the protein group has been considered as a hit. <strong># Integration</strong> > circos_input: output of DIABLO analysis with correlation threshold set to 0.7. Use the readRDS R function to open.</pre> <p> </p>
Data to reproduce analysis in "Systematic analysis of transcriptional and epigenetic effects of genetic variation in Kupffer cells enables discrimination of cell intrinsic and environment-dependent mechanisms"
<p>Here you can find the datasets necessary to reproduce all analyses described in the Glass lab paper by <a href="https://www.biorxiv.org/content/10.1101/2022.09.22.509046v1">Bennett et al</a>. The python and R code for reproducing analysis and figures can be found on our linked <a href="https://github.com/HunterBennett/KupfferCell_NaturalGeneticVariation">github repository.</a></p> <p>Briefly, this paper explores the effect of natural genetic variation <em>in vivo</em>, using Kupffer cells as a model cell type. We collect and analyze transcriptional and epigenetic data (ATAC-seq, H3K27Ac ChIP-seq) to identify putative <em>trans</em> regulators driving differential gene expression across inbred strains of mice. Additionally, we provide evidence that <em>trans</em> effects control a majority of strain differential genes at homeostasis while <em>cis</em> effects dominate the transcriptional response to an external signal (lipopolysaccharide).</p> <p>References:</p> <p>Hunter Bennett, Ty D. Troutman, Enchen Zhou, Nathanael J. Spann, Verena M. Link, Jason S. Seidman, Christian K. Nickl, Yohei Abe, Mashito Sakai, Martina P. Pasillas, Justin M. Marlman, Carlos Guzman, Mojgan Hosseini, Bernd Schnabl, Christopher K. Glass bioRxiv 2022.09.22.509046; doi: <a href="https://doi.org/10.1101/2022.09.22.509046">https://doi.org/10.1101/2022.09.22.509046</a></p> <p> </p>
Supporting data for "A simple ATAC-seq protocol for population epigenetics"
<p>This is supporting data for an article in which we describe a protocol for the generation of sequence-ready libraries for population epigenomics studies. The protocol is a streamlined version of the Assay for transposase accessible chromatin with high-throughput sequencing (ATAC-seq) that provides a positive display of accessible, presumably euchromatic regions. The protocol is straightforward and can be used with small individuals such as daphnia and schistosome worms, and probably many other biological samples of comparable size, and it requires little molecular biology handling expertise.</p> <p>In "Agarose picture.Tif" the left lane shows the 100 bp size marker, first 10 bands from down to top: 100bp, 200bp, 300bp, 400bp, 500bp, 600bp, 700bp, 800bp, 900bp and 1kbp.</p> <p>Produced at IHPE (http://ihpe.univ-perp.fr/)</p>
Supporting data for "The methylome of Biomphalaria glabrata and other mollusks: enduring modification of epigenetic landscape and phenotypic traits by a new DNA methylation inhibitor"
<p>Methylome of the fresh water snail <em>Biomphalaria glabrata</em>. DNA was extracted from the feet of 10 individuals of <em>B. glabrata</em> originally isolated from Brazil. These snails have been cultivated in the laboratory since 1960. Tissue were grinded at 4°C and incubated in 1 ml volume of lysis buffer (20 mM TRIS pH 8; 1 mM EDTA; 100 mM NaCl; 0.5% SDS), with 0.3 mg of proteinase K at 55°C for 1 night. Afterwards, lysate was purified with phenol-chloroform and DNA was isopropanol precipitated. The extracted DNA (around 138ng/µL) was poled in equivalent amounts and Whole Genome Bisulfite Sequencing was done by GATC-biotech (www.gatc-biotech.com). The principle of this treatment is to convert non-methylated cytosines of gDNA into deoxy-uracil, whereas methylated cytosines remain intact. WGBS was done according to the Lister protocol (sequence 2 forward strands only). The reference genome (Biomphalaria-glabrata-BB02_SCAFFOLDS_BglaB1.fa) and annotation (Biomphalaria-glabrata-BB02_BASEFEATURES_BglaB1.3.gff3) used in this project are available on VectorBase (https://www.vectorbase.org/). To align our short reads, we chose to use two specific bisulfite mapping tools, BSMAP 1.0.0 (https://code.google.com/p/bsmap/) and Bismark 0.10.2 (www.bioinformatics.babraham.ac.uk /projects/bismark/), to compare their efficiency and convenience to finally work with the more suitable one on our datasets. IGV (Interactive Genomics Viewer, https://www.broadinstitute.org/igv/) was used to visualized final alignments.<br> BSMAP performed better than Bismark and was used for downstream analyses. Without default parameters alignement efficiency for BSMAP is 47.1%, allowing for 2 mismatches increases it to 55.6%. Methylation occurs predominantly in CpGs. (C methylated in CpG context: 12.4%, C methylated in CHG context: 0.5%, C methylated in CHH context: 0.5%) The major part of CpG sites, 95.7% were unmethylated, of the remaining 4.3% of CpG sites around 3.8% had low methylation, and 0.5% were completely methylated. Methylation is of the mosaic type. Methylation is relatively low with 1.2% of total cytosines. Our analyses suggested that conserved genes and genes with stable expression are localized in high methylated regions of the genome. Finally, we see that repetitive sequences were predominantly situated in low methylated regions of <em>B. glabrata</em>. </p> <p>Wiggle files were generated for CpG pairs only.</p> <p>Produced at IHPE (http://ihpe.univ-perp.fr/)</p>
What is epigenetics? and how our choices affect our genes
<p>The first video of the EPIBOOST Project, which integrates researchers Cecília Guerra and Maria José Loureiro from CIDTFF, and David Oliveira from DigiMedia, in their multidisciplinary team, is now available. As experts in Didactics and Educational Technology, researchers from CIDTFF and DigiMedia are responsible for producing scientific dissemination videos related to epigenetics, targeting society and to be disseminated on online platforms such as Educast (PT) and YouTube (for global accessibility). The first video explains "What is Epigenetics?" and how our choices affect our genes, with scientific review by Joana Luísa Pereira and Guilherme Jeremias, is already available on the project's page and on YouTube (<a href="https://youtu.be/1Tu1JA1_ICY">https://youtu.be/1Tu1JA1_ICY</a>).</p> <p>Date of the description: May, 2, 2024</p>
Genetic and epigenetic regulation of zebrafish intestinal development
<p>This dataset contains zebrafish (<em>Danio rerio</em>) raw RNA and ChIP (paired-end) sequencing data:</p> <ul> <li>RNA-seq <ul> <li>lane1_BSwt5dpf*: 3 biological replicates of RNA-seq data from 5dpf wild-type (AB background) pooled intestines</li> <li>lane1_BSwt7dpf*: 3 biological replicates of RNA-seq data from 7dpf wild-type (AB background) pooled intestines</li> <li>lane1_BSwt9dpf*: 3 biological replicates of RNA-seq data from 9dpf wild-type (AB background) pooled intestines</li> </ul> </li> <li>ChIP-seq <ul> <li>Cldn-wt-int-5dpf-H3K27me3*: 2 biological replicates of H3K27me3 ChIP-seq data from 5dpf wild-type (AB background) pooled intestines</li> <li>Cldn-wt-int-5dpf-H3K4me3*: 2 biological replicates of H3K4me3 ChIP-seq data from 5dpf wild-type (AB background) pooled intestines</li> <li>Cldn-wt-int-5dpf-input-12727_R[12].fastq.gz: 1 sample of input ChIP-seq data from 5dpf wild-type (AB background) pooled intestines</li> <li>Cldn-wt-int-7dpf-H3K27me3*: 2 biological replicates of H3K27me3 ChIP-seq data from 7dpf wild-type (AB background) pooled intestines</li> <li>Cldn-wt-int-7dpf-H3K4me3*: 2 biological replicates of H3K4me3 ChIP-seq data from 7dpf wild-type (AB background) pooled intestines</li> <li>Cldn-wt-int-7dpf-input-12727_R[12].fastq.gz: 1 sample of input ChIP-seq data from 7dpf wild-type (AB background) pooled intestines</li> <li>Cldn-wt-int-9dpf-H3K27me3*: 2 biological replicates of H3K27me3 ChIP-seq data from 9dpf wild-type (AB background) pooled intestines</li> <li>Cldn-wt-int-9dpf-H3K4me3*: 2 biological replicates of H3K4me3 ChIP-seq data from 9dpf wild-type (AB background) pooled intestines</li> <li>Cldn-wt-int-9dpf-input-12727_R[12].fastq.gz: 1 sample of input ChIP-seq data from 9dpf wild-type (AB background) pooled intestines</li> </ul> </li> </ul>
Increased egg shell temperature during incubation leads to changes in transcriptional and epigenetic profiles in chicken lungs
<p>These RDS files contain <strong>DESeqDataSet </strong>objects subsets per broiler age and treatment. These objects are the result of DESeq2::DESeq( … ,betaPrior=FALSE).The .txt-objects contain the normalized sequencing counts per broiler age and treatment group. These objects are the result of DESeq2::counts( … , normalized=TRUE). Data was generated using STAR v2.7.10a and DESeq2 v1.36. Metadata is included as Excel file.</p> <p>Sequencing data is deposited at NCBI-SRA under BioProject: PRJNA949139. </p> <p> </p> <p><strong>Study abstract</strong></p> <p>D. Schokker, J. de Vos, P.B. Stege, O. Madsen, H.J. Wijnen, S.K. Kar, and J.M.J. Rebel</p> <p>Health and resilience against respiratory diseases are important features for broiler chicken. In this study, epigenetic and transcriptomic changes in the lungs of broiler chickens of different ages during rearing that were either exposed to elevated egg shell temperature (HIGH) of 38.9°C during mid-incubation or normal egg shell temperature (control; CON). The objective was to better understand how environmental challenges, such as heat stress during egg incubation, affect the development of the immune system and health of broiler chicken at later age. To this end we generated both epigenetic and transcriptomic data of lung tissue of elevated HIGH and CON chicken, furthermore these chicken were challenged by introducing either an infectious E. coli or an IBV vaccination to monitor the respiratory response. Thousands of differential methylated sites were observed at days 15 and 33, when comparing HIGH vs. CON. Pathway enrichment analysis of HIGH vs. CON showed that differentially expressed genes were mainly involved in cilium, cytoskeleton, and immune processes. These findings provide insight into the underlying biological mechanisms of early life conditions, like elevated EST, and their potential role in health of broilers.</p>
Epigenetic and transcriptional landscape of stress memory in woodland strawberry
<p>Bedfiles of differentially methylated regions (DMRs) that were detected in stressed mother plants (M) and their (themselves unstressed) clonal daughter plants that were formed <em>via</em> stolon formation (St1, St2, St3). The M plants were grown and sampled <em>in vitro</em> and the St1, St2, St3 plants in the green house. The DMRs were called using the the EpiDiverse/dmr bioinformatic analysis pipeline (Nunn et al., 2021).</p> <p><strong>Stress assays <em>in vitro</em></strong></p> <p>One-month-old seedlings were transferred to a fresh MS media and growth chambers at 24<sup>o</sup>C/21<sup>o</sup>C (day/night),16 h light/8 h dark, as control conditions<em>. </em>For heat-stress, plants were exposed to 30<sup>o</sup>C (day/night) for one week followed by 2 days of recovery (24<sup>o</sup>C/21<sup>o</sup>C) on fresh medium as well as the control plants. Then, the plates were transferred to 37<sup>o</sup>C (day/night) for 1 week with 2 recovery days (Figure 1A). We sampled aerial parts of plants for the molecular analyses. To reduce variability resulting from individual plants, three biological replicates of 5 pooled plants were collected per condition. Samples were harvested in 1.5 mL tubes between 9:00-11:00 a.m. and immediately frozen in liquid nitrogen and stored at -80<sup>o</sup>C until required.</p> <p><strong>Greenhouse propagation assays</strong></p> <p><em>In vitro</em> plants after heat and control treatment were transferred to soil (one plant per pot) in square plastic pots (size: 12x12x10 cm) and to a greenhouse with long day conditions (24<sup>o</sup>C/21<sup>o</sup>C day/night and 60%-70% humidity).</p> <p>Twelve mother plants (M) from control (CM; n=12) and heat-stress (HM; n=12) conditions were used for asexual propagation. From each mother plant, the two first stolons (St) were kept for producing the daughter plants of the first asexual propagation (St1) in individual pots. After two weeks, following root formation, the stolons were cut to get independent daughter plants from their mother plant (M). This process was continued until St3.</p> <p>The DMRs can also be visualized here: <a href="https://jbrowse.agroscope.info/jbrowse/?data=fragaria_sub">https://jbrowse.agroscope.info/jbrowse/?data=fragaria_sub</a></p> <p>And the raw bisulfite sequencing data can be found here: <a href="https://www.ebi.ac.uk/ena/browser/text-search?query=ERP135585">https://www.ebi.ac.uk/ena/browser/text-search?query=ERP135585</a></p> <p> </p> <p> How many daughter plants per stolon? One stolon produce a chain of daughter plants.</p>
CRISPR/dCas9-mediated DNA demethylation screen identifies driver epigenetic determinants of colorectal cancer (Processed data)
<p><strong>Background:</strong> Promoter hypermethylation of tumour suppressor genes is frequently observed during the malignant transformation of colorectal cancer (CRC). However, whether this epigenetic mechanism is an actual driver of cancer or is a mere consequence of the carcinogenic process remains to be elucidated.</p> <p><strong>Results: </strong>In this work we performed an integrative multi -omic approach to identify gene candidates with strong correlations between DNA methylation and gene expression in human CRC samples and a set of 8 colon cancer cell lines. As a proof of concept, we combined recent CRISPR-Cas9 epigenome editing tools (dCas9-TET1, dCas9-TET-IM) with a custom arrayed gRNA library to modulate the DNA methylation status of 56 promoters previously linked with strong epigenetic repression in CRC, and we monitored the potential functional consequences of such DNA methylation loss by means of a high-content cell proliferation screen. Overall, the epigenetic modulation of most of these DNA methylated regions had a mild impact in the reactivation of gene expression and in the viability of cancer cells. Interestingly, we found that epigenetic reactivation of RSPO2 in the tumour context was associated with a significant impairment in cell proliferation in p53-/- cancer cell lines and further validation with human samples demonstrated that the epigenetic silencing of RSPO2 is a mid-late event in the adenoma to carcinoma sequence.</p> <p><strong>Conclusions: </strong>These results highlight the potential role of DNA methylation as a driver mechanism of CRC and open up the venue for the identification of novel therapeutic windows based on the epigenetic reactivation of certain tumour suppressor genes.</p>
FIGURE 2 in Paul Kammerer and epigenetics - a reappraisal of his experiments
FIGURE 2 Example of the spot distribution development in a salamander individual (the second individual from the right in the bottom row shown in fig. 1). The salamander was continuously kept on black background, but still became progressively yellow, increasingly resembling its mother. Documentation dates of A, B, and C: 29.06.1917, 28.09.1918, and 10.10.1922. D, E, F: Example of the development of a specimen continuously kept on yellow background. Nevertheless, it turned increasingly dark. Documentation dates of D, E, and F: 13.06.1918, 12.09.1919, and Downloaded 21.09.1921 from. Brill.com 12/12/2023 04:18:33PM SOURCES: A, B, AND C via REPRESENTOpen FIG Access. 21.IN This is HERBSTan (open 1924); D access, E, AND articleF REPRESENT distributed FIG under. 10the IN terms HERBST (1924); BOTH ARE DIGITALLY REVAMPED BY M. NAHM of the CC-BY 4.0 license. https://creativecommons.org/licenses/by/4.0/
Epigenetic aging of Māui and Hector's dolphins
<p>The age of an individual is an essential demographic parameter but is difficult to estimate without long-term monitoring or invasive sampling. Epigenetic approaches are increasingly used to age organisms, including non-model organisms such as cetaceans. Māui dolphins (<em>Cephalorhynchus hectori maui</em>) are a critically endangered subspecies endemic to Aotearoa New Zealand, and the age structure of this population is important for informing conservation. Here we present an epigenetic clock for aging Māui and Hector's dolphins (<em>C. h. hectori</em>) developed from methylation data using DNA from tooth aged individuals (<em>n </em>= 48). Based on this training dataset, the optimal model required only eight methylation sites, provided an age correlation of 0.95, and had a median absolute age error of 1.54 years. A leave-one-out cross-validation analysis with the same parameters resulted in an age correlation of 0.87 and median absolute age error of 2.09 years. To improve age estimate, we included previously published beluga whale (<em>Delphinapterus leucas</em>) data to develop a joint beluga/dolphin clock, resulting in a clock with comparable performance and improved estimation of older individuals. Application of the models to DNA from skin biopsy samples of living Māui dolphins revealed a shift in the median age of 8–9 years to a younger population aged 7–8 years 10 years later. These models could be applied to other dolphin species and demonstrate the ability to construct a clock even when the number of known age samples is limited, removing this impediment to estimating demographic parameters vital to the conservation of critically endangered species.</p>
Pollution induces epigenetic effects that are stably transmitted across multiple generations
<p>It has been hypothesised that the effects of pollutants on phenotypes can be passed to subsequent generations through epigenetic inheritance, affecting populations long after the removal of a pollutant. But there is still little evidence that pollutants can induce persistent epigenetic effects in animals. Here we show that low doses of commonly used pollutants induce genome-wide differences in cytosine methylation in the freshwater crustacean Daphnia pulex. Uniclonal populations were either continually exposed to pollutants or switched to clean water, and methylation was compared to control populations that did not experience pollutant exposure. While some direct changes to methylation were only present in the continually exposed populations, others were present in both the continually exposed and switched to clean water treatments, suggesting that these modifications had persisted for seven months (> 15 generations). We also identified modifications which were only present in the populations that had switched to clean water, indicating a long-term legacy of pollutant exposure distinct from the persistent effects. Pollutant-induced differential methylation tended to occur at sites that were highly methylated in controls. Modifications that were observed in both continually and switched treatments were highly methylated in controls and showed reduced methylation in the treatments. On the other hand, modifications found just in the switched treatment tended to have lower levels of methylation in the controls and showed increase methylation in the switched treatment. In a second experiment we confirmed that sub-lethal doses of the same pollutants generate effects on life-histories for at least three generations following the removal of the pollutant. Our results demonstrate that even low doses of pollutants can induce transgenerational epigenetic effects that are stably transmitted over many generations. Persistent effects are likely to influence phenotypic development, which could contribute to the rapid adaptation, or extinction, of populations confronted by anthropogenic stressors.</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>
Supplementary Figures, Files and Datasets: Reduction of Metastasis via Epigenetic Modulation in a Murine Model of Metastatic Triple Negative Breast Cancer (TNBC)
<p>*denotes authors contributed equally to this work</p> <p>FileS1_Figures_Proofread.pdf: (Updated) Supplementary Figures (Figure S1: RNA-sequencing experimental design; Figure S2: Experiments measuring proliferation between drug-treated and control conditions indicate no significant difference 6 hrs. after scratch; Figure S3: Effect of 4SC-202 treatment on 4T1 tumor volume in mice; Figure S4: Differential expression between 4SC-202- and Vorinostat-treated 4T1 tumors; Figure S5: Top underexpressed differentially expressed genes 4SC-202 vs Control; Figure S6: HDACi target genes are not differentially expressed in RNA-sequencing data from 4SC-202-treated mice relative to control mice; Figure S7: Differential expression and expression of genes implicated gene ontology biological processes of interest; Figure S8: IPA visualization of the Regulation of Epithelial Mesenchymal Transition By Growth Factors Pathway emphasizing influence of 4SC-202-induced consensus DEGs; Figure S9: 4SC-202 modulates gene networks related to Cancer, Endocrine System Disorders, and Organismal Injury and Abnormalities; Figure S10: 4SC-202 modulates gene networks related to Cancer, Cellular Movement, and Organismal Injury and Abnormalities; Figure S11: 4SC-202 modulates gene networks related to Cell-mediated Immune Response, Cellular Movement, and Hematological System Development and Function; Figure S12: 4SC-202 differentially modulates gene networks related to Cellular Movement, Hematological System Development and Function, and Immune Cell Trafficking relative to Vorinostat); File S2: DAVID 4SC vs. Control 70DEG results: DAVID Annotation 4SC-202 vs Control 70 DEGs: Full functional annotation clustering results from DAVID Bioinformatics Resource for the 4SC-202-induced, consensus differentially expressed genes.; File S3: DAVID 4SC vs. Vori 33 DEGs results: DAVID Annotation 4SC-202 vs Control 33 DEGs: Full functional annotation clustering results from DAVID Bioinformatics Resource for the 4SC-202 versus Vorinostat consensus differentially expressed genes.; File S4: IPA 70 All Results: IPA Canonical Pathways Enrichment 70 DEGs: Full Ingenuity Pathway Analysis (IPA) canonical pathways enrichment results for the 4SC-202-induced, consensus differentially expressed genes.; File S5: IPA 33 Summary: Ingenuity Pathway Analysis (IPA) summary of the enrichment results for the 4SC-202-induced, consensus differentially expressed genes against Vorinostat.; File S6: Experiment RIN Numbers: RNA extraction quality control step, one of the various steps of quality control within the RNA-sequencing workflow. These RNA Integrity numbers are from the Agilent 2100 Bioanalyzer that looks for RNA contamination and degradation.; File S7: 4SC vs. Control all DEGs: Workflow results including all DEGs for 4SC-202 vs Control: Full excel file that contains all of the DEGs from the results of all workflows for 4SC-202.</p>
Raw Microscopy and Western Blot Data for "Distinct silencer states generate epigenetic states of heterochromatin"
<p>Raw microscopy and western blot images for "Distinct silencer states generate epigenetic states of heterochromatin"</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>
Supplemental Data to PhD Dissertation entitled "Non-Coding and Epigenetic Regulators of Ambient Temperature Sensitive Flowering"
<p>Supplemental Data to the PhD dissertation entitled "Non-Coding and Epigenetic Regulators of Ambient Temperature Sensitive Flowering" from Suze Blom, Wageningen University & Research (doi:10.18174/670822). Supplementary data to the chapter entitled "Ambient temperature dependent changes in DNA methylation in Arabidopsis thaliana Col-0 and the decrease in DNA methylation 1 mutant" [manuscript in preparation]</p> <p>Includes Supplemental Data:</p> <p>Supplementary Data 1: Significantly differently expressed (DE) genes in Col-0 after the 24 hour ambient temperature switch from 16°C to 25°C. padj value of 0.05 was set as the cutoff value for statistical significance.</p> <p>Supplementary Data 2: Significantly differently expressed (DE) genes in ddm1-10 after the 24 hour ambient temperature switch from 16°C to 25°C. padj value of 0.05 was set as the cutoff value for statistical significance.</p> <p>Supplementary Data 3: Alternative splicing (AS) of transcripts after an ambient temperature change from 16°C to 25°C in Col-0. Gene ID, splicing event, genomic location, AS frequency and padj values are indicated.</p> <p>Supplementary Data 4: Alternative splicing (AS) of transcripts after an ambient temperature change from 16°C to 25°C in ddm1-10. Gene ID, splicing event, genomic location, AS frequency and padj values are indicated.</p> <p>Supplementary Data 5: List of genes in Col-0 (sheet 1) and ddm1-10 (sheet 2) with both significant AS and DMC associated with them. Gene ID and number of DMCs associated with the gene are indicated.</p>
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>
Epigenetic modifications modify the rate of spontaneous mutations in a pathogenic fungus
<p>Mutations are the source of genetic variation and the substrate for evolution. Genome-wide mutation rates appear to be affected by selection and are probably adaptive. Mutation rates are also known to vary along genomes, possibly in response to epigenetic modifications, but causality is only assumed. In this study we determine the direct impact of epigenetic modifications and temperature stress on mitotic mutation rates in a fungal pathogen using a mutation accumulation approach. Deletion mutants lacking epigenetic modifications confirm that histone mark H3K27me3 increases whereas H3K9me3 decreases the mutation rate. Furthermore, cytosine methylation in transposable elements (TE) increases the mutation rate 15‑fold resulting in significantly less TE mobilization. Also accessory chromosomes have significantly higher mutation rates. Finally, we find that temperature stress substantially elevates the mutation rate. Taken together, we find that epigenetic modifications and environmental conditions modify the rate and the location of spontaneous mutations in the genome and alter its evolutionary trajectory.</p>
Experimental evidence for short term directional selection of epigenetic trait variation
<p>This Arabidosis data folder includes 5 folders providing the data and code associated with the publication entitled "Experimental evidence for short term directional selection of epigenetic trait variation" by Pujol et al. in Peer Community Journal:</p> <p>"in Silico 1000 resampling" folder includes two folders; one for Population 1, and one for Population 2, each including 24 files. Each file presents the 1000 lists of plant IDs that were randomly sampled to build control groups (listrand), and the corresponding lists of plant IDs selected on the basis of trait values to build the corresponding high (Top) and low (Bot) selection lines, for the strong (20) and weak (60) selection intensities.</p> <p>“phenotypic and epiril data” includes two csv files; data_pop1 and data_pop2, respectively for population 1 and 2, and a readme txt file that presents the data included in the files.</p> <p>“scripts R epigenomic selection” includes two R script files: “PCA_arabidopsis”, which includes the R code used to conduct the PCA analysis on epiRIL data and “Arabette_analysis_loop_Epigenotypes_data”, which is used to insert that epigenotype data in the analysis.</p> <p>“scripts R epigenomic validation” includes nine R scripts used to conduct tests of molecular epigenomic data validation and comparison with existing data from the literature: “Script 1 Extract 126 markers from BSMAP ouput files and apply methylkit”, “Script 2 Boxplots of the BS signal distribution for the 126 markers and their correspondence to published HMM classification”, “Script 3 Correlation between EM-seq and BS seq_published_data for the 126 markers”, “Script 4 Correlation between the BS signals of EM-seq and WGBS data for the 126markers”, “Script 5 Hierarchical clustering of the epiRILs EM-seq and published epigenomic data”, “cluster and PCA analysis of Col wt vs 24 sequenced epiRILs”, which filenames are self-explanatory. “methylkit_epiRILs_vs_Col_wt_DMCs” was used to use methylkit in order to identify Differentially Methylated Cytosines between epiRILs and in comparison to Col-0. “methylkit_tiles_script” was used to build the tiles in Methylkit. “verif DMR CHG context” was used to verify differentially methylated regions in the CHG context.</p> <p>“scripts R phenotypic selection” includes four R scripts “Arabette_analysis”, “Arabette_analysis_loop_Phenotypes_data”, “Arabette_analysis_loop_pop1_final”, “Arabette_analysis_loop_pop2_final” that were used to estimate the parameters used for comparing selection treatments, in other words, to estimate means and confidence intervals (“loop” scripts) in population 1 (“pop1” script) and population 2 (“pop2” script)</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.