Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

DeepSTARR predicts enhancer activity from DNA sequence and enables the de novo design of synthetic enhancers [Drosophila genome-wide UMI-STARR-seq]

ABSTRACT: DeepSTARR predicts enhancer activity from DNA sequence and enables the de novo design of synthetic enhancers [Drosophila genome-wide UMI-STARR-seq]

PROVIDER: PRJNA762363 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	SRR15844840_1.fastq.gz	Fastqsanger.gz
	SRR15844840_2.fastq.gz	Fastqsanger.gz
	SRR15844841_1.fastq.gz	Fastqsanger.gz
	SRR15844841_2.fastq.gz	Fastqsanger.gz
	SRR15844842_1.fastq.gz	Fastqsanger.gz

Items per page:

1 - 5 of 12

Similar Datasets

DeepSTARR predicts enhancer activity from DNA sequence and enables the de novo design of synthetic enhancers [Drosophila oligo UMI-STARR-seq]

Project description:DeepSTARR predicts enhancer activity from DNA sequence and enables the de novo design of synthetic enhancers [Drosophila oligo UMI-STARR-seq]

| PRJNA762362 | ENA

DeepSTARR predicts enhancer activity from DNA sequence and enables the de novo design of synthetic enhancers [Human oligo UMI-STARR-seq]

Project description:DeepSTARR predicts enhancer activity from DNA sequence and enables the de novo design of synthetic enhancers [Human oligo UMI-STARR-seq]

| PRJNA762361 | ENA

DeepSTARR predicts enhancer activity from DNA sequence and enables the de novo design of synthetic enhancers [Human oligo UMI-STARR-seq]

Project description:Enhancer sequences control gene expression and comprise binding sites (motifs) for different transcription factors (TFs). Despite extensive genetic and computational studies, the relationship between DNA sequence and regulatory activity is poorly understood and enhancer de novo design is considered impossible. Here we built a deep learning model, DeepSTARR, to quantitatively predict the activities of thousands of developmental and housekeeping enhancers directly from DNA sequence in Drosophila melanogaster S2 cells. The model learned relevant TF motifs and higher-order syntax rules, including functionally non-equivalent instances of the same TF motif that are determined by motif-flanking sequence and inter-motif distances. We validated these rules experimentally and demonstrated their conservation in human by testing more than 40,000 wildtype and mutant Drosophila and human enhancers. Finally, we designed and functionally validated synthetic enhancers with desired activities de novo.

2022-02-24 | GSE183938 | GEO

DeepSTARR predicts enhancer activity from DNA sequence and enables the de novo design of synthetic enhancers [Drosophila oligo UMI-STARR-seq]

2022-02-24 | GSE183937 | GEO

DeepSTARR predicts enhancer activity from DNA sequence and enables the de novo design of synthetic enhancers [Drosophila genome-wide UMI-STARR-seq]

2022-02-24 | GSE183936 | GEO

DeepSTARR predicts enhancer activity from DNA sequence and enables the de novo design of synthetic enhancers

Project description:DeepSTARR predicts enhancer activity from DNA sequence and enables the de novo design of synthetic enhancers

| PRJNA762359 | ENA

DeepSTARR predicts enhancer activity from DNA sequence and enables the de novo design of synthetic enhancers

2022-02-24 | GSE183939 | GEO

UMI-4C for quantitative and targeted chromosomal contact profiling

Project description:We developed a targeted chromosome conformation capture (4C) approach that uses unique molecular identifiers (UMI) to derive high complexity quantitative chromosome contact profiles with controlled signal to noise ratios. We demonstrate that the method improves the sensitivity and specificity for detection of long-range chromosomal interactions, and that it allows the design of interaction screens with predictable statistical power. UMI-4C robustly quantifies contact intensity changes between cell types and conditions, opening the way toward incorporation of long-range interactions in quantitative models of gene regulation.

2016-06-15 | GSE76763 | GEO

UMI-4C for quantitative and targeted chromosomal contact profiling

2016-09-23 | GSE87209 | GEO

Studying enhancer activity by FAIRE-STARR-seq in the U2OS-GR and the U2OS-AR cells upon hormone treatment

Project description:To quantify functional enhancers, we performed STARR-seq (Self-Transcribing Active Regulatory Region sequencing) in the U2OS-GR and the U2OS-AR cell lines (derived from U2OS ATTC:HTB-96, stably transfected with an expression construct for rat GR or human AR, respectively). U2OS-GR cells were treated with dexamethasone (1 µM) or vehicle (ethanol) for 14 hours. U2OS-AR cells were treated with R1881 (5 nM) or vehicle (DMSO) for 14 hours. To limit the number of putative enhancers, the STARR library contained genomic regions isolated by FAIRE (Formaldehyde Assisted Isolation of Regulatory Elements) from dexamethasone-treated U2OS-GR cells to include regions that gain accessibility upon GR activation. We added unique molecular identifiers (UMIs) during the reverse transcription stage to facilitate quantitative measurements of enhancer activity for each fragment. The UMI for each read is present within the sequence identifier line (directly following the y coordinate and separated by a ':') of the fastq files.

2021-03-09 | E-MTAB-9614 | 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