Browse
Submit Data
Databases
API
Help

Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

47 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Bait-capture based single molecule footprinting of different mouse cell lines

ABSTRACT: Bait-capture based Single Molecule Footprinting (SMF) data from Kreibich et al., 2022. SMF data is obtained by treating extracted nuclei with a GpC methyltransferase, where binding of proteins on DNA, e.g. nucleosomes and transcription factors (TFs), leave behind unmethylated GpCs as footprints. Data in this experiment comprises SMF data obtained from WT embryonic stem cells (ES), DNMT TKO ES, TET TKO ES, F1 hybrid ES (129/CAST), neural progenitor (NP),�myoblast (C2C12) and�murine erythroleukemia (MEL)�cells. These data were generated by employing Agilent Sure-Select Mouse Methyl-Seq kit, enriching the sample for cis-regulatory regions of the mouse genome prior to library preparation. Thus, these data contain high coverage accessibility information at regulatory loci in different cell types. The SMF procedure maintains the endogenous DNA methtylation in CpG context, allowing the simultaneous detection of chromatin accessibility, TF binding and endogenous DNA methylation.

ORGANISM(S): Mus musculus

SUBMITTER:

PROVIDER: E-MTAB-11700 | biostudies-arrayexpress |

SECONDARY ACCESSION(S): ERP137220

REPOSITORIES: biostudies-arrayexpress

ACCESS DATA

Json Xml

Similar Datasets

Amplicon based single molecule footprinting on ES cells and derivatives

Project description:Single Molecule Footprinting (SMF) data from Sonmezer et al., 2020. SMF data is obtained by treating isolated nuclei with methyltransferases, where binding of proteins on DNA, e.g. nucleosomes and TFs, leave behind unmethylated cytosines as footprints. Data in this experiment comprises SMF data obtained from ES cells and various derivatives of ES cells, such DNMT-null, REST-knockout ES cells, neural progenitors and ES cells treated with NRF1 sirNA.

2020-12-26 | E-MTAB-9033 | biostudies-arrayexpress

Bait-capture based single molecule footprinting on ES cells and derivatives

Project description:Bait-capture based Single Molecule Footprinting (SMF) data from Sonmezer et al., 2020. SMF data is obtained by treating isolated nuclei with methyltransferases, where binding of proteins on DNA, e.g. nucleosomes and TFs, leave behind unmethylated cytosines as footprints. Data in this experiment comprises SMF data obtained from ES, DNMT-TKO, and neural progenitor (NP) cells. These data were generated by employing Agilent Sure-Select Mouse Methyl-Seq kit, enriching the sample for regulatory regions of mouse genome prior to library preparation. Thus, these data contain high coverage accessibility information at regulatory loci in different cell types.

2021-05-04 | E-MTAB-9123 | biostudies-arrayexpress

The effects of the global loss of DNA methylation on the functional cancer epigenome (Bisulfite-seq)

Project description:We compare the global DNA methylation and nucleosome occupancy patterns of HCT116 colon cancer cells with its genetic derivatives DKO1 cells which lack DNMT3B and DNMT1 activity Simultaneous genome-wide mapping of DNA methylation and nucleosome occupancy of HCT116 and DKO1 cells using NOMe-seq, rep2

2015-02-06 | E-GEOD-58695 | biostudies-arrayexpress

The effects of the global loss of DNA methylation on the functional cancer epigenome (K562 WGBS/whole-genome NOMe-seq)

Project description:We assess global chromatin accessibility following high salt wash Simultaneous genome-wide mapping of DNA methylation and nucleosome occupancy of K562 cells

2015-02-06 | E-GEOD-64929 | biostudies-arrayexpress

A novel bivalent chromatin state exists at enhancers

Project description:We assess global chromatin accessibility following 5-Aza-CdR treatment of HCT116 cells Simultaneous genome-wide mapping of DNA methylation and nucleosome occupancy of HCT116 cells

2016-06-02 | E-GEOD-68046 | biostudies-arrayexpress

Probability of stealth multiplets in sample-multiplexing for droplet-based single-cell analysis

Project description:These datasets are test datasets of sample-multiplexed scRNA-seq, consisting of cDNA (transcriptome) and sample barcode read files: Three-sample multiplexing experiment (JS009) is a MULTI-seq dataset containing mesenchyme embryonic hind limb bud cells, embryonic stem (ES) cells, and NIH3T3 cells. Each cell sample was labelled with a distinct MULTI-seq barcode. The barcode sequences were, CATAGAGC, TCCTCGAA, and GTGTACCT for the limb bud mesenchyme cells, the ES cells, and the NIH3T3 cells, respectively. Two-sample multiplexing experiment (JS010) is a CellPlex detaset, containing ES cells and NIH3T3 cells. Each cell sample was labelled with the 3'CellPlex Kit (10X Genomics). NIH3T3 cells and ES cells were labelled with CMO301 and CMO302, respectively.

2024-01-09 | E-MTAB-13181 | biostudies-arrayexpress

Comparison of the transcription (coding and lncRNAs) of in vitro cultured bovine SAM-treated blastocysts and non-treated blastocysts

Project description:Transcriptome (total RNA) profiling of bovine in vitro cultured expanded blastocysts (EB) comparing control non-treated expanded blastocysts with SAM-treated expanded blastocysts. S-Adenosyl methionine (SAM) is the global methyl donor providing methyl group for variety of biomolecules such as DNA , histone, RNA, lipids and etc. Two-condition experiment, bovine non-treated expanded blastocysts (pools of 10) vs bovine SAM-treated expanded blastocysts. Four biological replicates of each tissue were hybridized to four two-color arrays in a dye-balanced design.

2016-02-29 | E-GEOD-75430 | biostudies-arrayexpress

Comparison of nucleosome occupancy and chromatin states between normal and cancer cell lines

Project description:Background: Epigenetic modification is a hallmark of cancer cells achieved through altered patterns of DNA methylation, histone modifications and nucleosome positions. These events have only been described in detail at gene promoters. Results: Here, we integrate multiple epigenome-wide maps to evaluate the scope of global epigenetic reprogramming in cancer cells at enhancers and insulators. Using high-resolution simultaneous mapping of global DNA methylation and nucleosome positions within individual DNA strands (NOMe-Seq), we observe a reorganization of the DNA methylome coupled with simultaneous modulation of nucleosome depleted regions (NDRs) at distal regulatory elements in cancer cells. We show that while NDRs are preferentially found at enhancers in normal breast and prostate epithelial cells, there are fewer NDRs coupled with increased DNA methylation detected at enhancersin cancer cells. Our data suggests that the acquisition of a nucleosome is key to DNA hypermethylation susceptibility and contributes to epigenetic silencing of enhancers in cancer, which can notably occur without loss of H3K4me1. Further we observe that NDRs are not necessary for peripheral phasing of adjacent nucleosomes, contrary to the common dogma, and show that nucleosomes remain organized and phased, even in the absence of a NDR as an anchor. Finally we observe the potential for transcription factors (TF) to organize NDRs genome-wide; all TF-binding sites are strongly hypomethylated and some show extensive peripheral nucleosome phasing. Conclusion: Together our findings suggest that, in addition to epigenetic alterations to promoter regions in cancer, there is substantial disruption to the distal regulatory architecture that provides an additional layer of epigenetic plasticity in malignancy. NOMe-seq, ChIP-seq for 5 marks and input control for breast normal (HMEC), breast cancer (MCF7), prostate normal (PrEC) and prostate cancer (PC3) cell lines

2014-06-04 | E-GEOD-57498 | biostudies-arrayexpress

Gene expression profiling of granulocytes from primary and secondary myelofibrosis

Project description:Primary myelofibrosis (PMF) together with polycythemia vera (PV) and essential thrombocythemia (ET) belongs to the classic Philadelphia-negative myeloproliferative neoplasms (MPNs). PV and ET can evolve to secondary myelofibrosis (SMF) giving rise to post-PV (PPV) and post-ET (PET) myelofibrosis (MF). PMF and SMF patients are currently managed in the same way and prediction of survival is based on the same prognostic models, even if it has been demonstrated that they can’t accurately distinguish different risk categories in SMF. In the last few years interest grew concerning the ability of gene expression profiling (GEP) to provide valuable prognostic information for clinical decision making. To construct a molecular signature that can predict survival according to gene expression we studied GEP of granulocytes from 114 MF patients, including 35 prefibrotic/early PMF (Pre-PMF), 37 overt PMF (Overt-PMF), 26 PET and 16 PPV, using microarray platform.

2021-03-09 | GSE159514 | GEO

S-adenosylmethionine modifies cocaine-induced DNA methylation and increases locomotor sensitization in mice

Project description:The aim of the study was to investigate whether environmental factors like S-adenosylmethionine (SAM) via affecting epigenome could alter cocaine-induced gene expression and locomotor sensitization in mice. Using mouse nucleus accumbens (NAc) tissue, whole-genome gene expression profiling revealed that repeated SAM treatment affected a limited number of genes, but significantly modified cocaine-induced gene expression by blunting nonspecifically the cocaine response. At the gene level, we discovered that SAM modulated cocaine-induced DNA methylation by inhibiting both promoter-associated CpG-island hyper- and hypomethylation in the NAc but not in the reference tissue cerebellum. Total RNA was extracted from the mouse nucleus accumbens (NAc) tissue. Two tissues were combined to a sample, 4 samples per group used. RNA quality and quantity were assessed using the Nano-Drop -1000 spectrophotometer and the Agilent 2100 Bioanalyzer.

2013-06-27 | E-GEOD-48365 | biostudies-arrayexpress

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data