Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Single-molecule simultaneous profiling of DNA methylation and DNA-protein interactions with Nanopore-DamID

ABSTRACT: Single-molecule simultaneous profiling of DNA methylation and DNA-protein interactions with Nanopore-DamID

PROVIDER: PRJNA838582 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	SRR19224431.fastq.gz	Fastqsanger.gz
	SRR19224432.fastq.gz	Fastqsanger.gz
	SRR19224433.fastq.gz	Fastqsanger.gz
	SRR19224434_1.fastq.gz	Fastqsanger.gz
	SRR19224435_1.fastq.gz	Fastqsanger.gz

Items per page:

1 - 5 of 10

Similar Datasets

Single-molecule simultaneous profiling of DNA methylation and DNA-protein interactions with Nanopore-DamID

Project description:We present Nanopore-DamID, a method to simultaneously detect cytosine methylation and DNA-protein interactions from single molecules, via selective sequencing of adenine-labelled DNA. Assaying LaminB1 and CTCF binding with Nanopore-DamID, we identify escape from LAD-associated repression of hypomethylated promoters amidst generalised hypermethylation of LaminB1-associated regulatory elements. We detect novel CTCF binding sites in highly repetitive regions, and allele-specific CTCF binding to imprinted genes and the active X chromosome. Nanopore-DamID highlights the importance of DNA methylation to transcription factor activity.

2025-05-15 | GSE203109 | GEO

Project description:Single-molecule simultaneous profiling of DNA methylation and DNA-protein interactions with Nanopore-DamID

| PRJNA850798 | ENA

Project description:Single-molecule simultaneous profiling of DNA methylation and DNA-protein-DNA interactions with Nanopore-DamID

| PRJNA751437 | ENA

Genome-wide DNA-binding analysis for Stat92E in Drosophila testis by DamID

Project description:Purpose: Genome-wide DNA-binding analysis for Stat92E in Drosophila testis cyst cells by DNA adenine methyltransferase identification(DamID).Methods: DNA adenine methyltransferase identification (DamID) on Stat92E driven by c587Gal4ts;hopTum-l

2023-09-10 | GSE226582 | GEO

Genome-wide DNA-binding analysis for Stat92E and escargot(esg) in Drosophila testis by DamID

Project description:Purpose: Genome-wide DNA-binding analysis for Stat92E and escargot(esg) in Drosophila testis cyst cells by DNA adenine methyltransferase identification(DamID).Methods: DNA adenine methyltransferase identification (DamID) on Stat92E and escargot(esg) driven by c587Gal4ts

2024-10-23 | GSE243698 | GEO

Genome-wide DNA-binding analysis for E(spl)m8 in intestinal stem cells by DamID

Project description:Purpose: Genome-wide DNA-binding analysis for E(spl)m8 in intestinal stem cells in Drosophila midgut by DNA adenine methyltransferase identification(DamID) Methods: DNA adenine methyltransferase identification (DamID) on E(spl)m8 driven by Dl-Gal4 Results:

2018-02-02 | GSE102568 | GEO

Simultaneous profiling of ac4C and m5C modifications from nanopore direct RNA sequencing

Project description:Long-read nanopore sequencing has emerged as a potent tool for studying RNA modifications. However, the detection of N4-acetylcytidine (ac4C) based on nanopore sequencing remains largely unexplored. Here, we introduce ac4Cnet, a deep learning frame utilizing Oxford Nanopore direct RNA sequencing to accurately identify ac4C sites. Our methodology involves training ac4Cnet capable of distinguishing ac4C from unmodified cytidine and 5-methylcytosine (m5C), as well as estimating the modification rate at each ac4C site. We demonstrate the robustness of our approach through validations on independent in vitro datasets and a human cell line, highlighting its versatility and potential for advancing the study of ac4C modifications.

2025-02-08 | GSE267558 | GEO

Genome-wide DNA-binding analysis for Sox100B in intestinal stem cells by DamID

Project description:Purpose: Genome-wide DNA-binding analysis for Sox100B in intestinal stem cells in Drosophila midgut by DNA adenine methyltransferase identification(DamID) Methods: DNA adenine methyltransferase identification (DamID) on Sox100B driven by ESG-Gal4 Results: 5046 significant genomic binding sites, corresponding to 3182 genes were identified via iDamID following Sox100B overexpression in progenitor cells compared to WT. About a third of these genes were in the list of significantly altered genes following Sox100B overexpression and/or depletion.

2020-05-18 | GSE130304 | GEO

damidseq_pipeline: an automated pipeline for processing DamID sequencing datasets

Project description:DamID is a powerful technique for identifying regions of the genome bound by a DNA-binding (or DNA-associated) protein. Currently no method exists for automatically processing next-generation sequencing DamID (DamID-seq) data, and the use of DamID-seq datasets with normalisation based on read-counts alone can lead to high background and the loss of bound signal. DamID-seq thus presents novel challenges in terms of normalisation and background minimisation. We describe here damidseq_pipeline, a software pipeline that performs automatic normalisation and background reduction on multiple DamID-seq FASTQ or BAM datasets.

2015-06-26 | GSE69184 | GEO

Tissue-specific DamID protocol using nanopore sequencing.

Project description:DNA adenine methylation identification (DamID) is a powerful method to determine DNA binding profiles of proteins at a genomic scale. The method leverages the fusion between a protein of interest and the Dam methyltransferase of E. coli, which methylates proximal DNA in vivo. Here, we present an optimized procedure, which was developed for tissue-specific analyses in Caenorhabditis elegans and successfully used to footprint genes actively transcribed by RNA polymerases and to map transcription factor binding in gene regulatory regions. The present protocol details C. elegans-specific steps involved in the preparation of transgenic lines and genomic DNA samples, as well as broadly applicable steps for the DamID procedure, including the isolation of methylated DNA fragments, the preparation of multiplexed libraries, Nanopore sequencing, and data analysis. Two distinctive features of the approach are (i) the use of an efficient recombination-based strategy to selectively analyze rare cell types and (ii) the use of Nanopore sequencing, which streamlines the process. The method allows researchers to go from genomic DNA samples to sequencing results in less than a week, while being sensitive enough to report reliable DNA footprints in cell types as rare as 2 cells per animal.

| S-EPMC8411031 | biostudies-literature

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