Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Identification of transcription factors dictating blood cell development using a bidirectional transcription network-based computational framework [CAGEseq_MLLAF9-AML]

ABSTRACT: Genome-wide transcription factor binding profiles provide valuable insights into hematopoietic development and malignancies. Today, advanced computational methods exploit cis-regulatory information to predict complex gene regulatory networks controlled by transcription factors. However, these prediction methods still require large amounts of reference data or experimental expertise. Here, we present a low-requirement network-based computational framework that exploits bidirectional transcription marked regulatory elements to identify important transcription factors driving hematopoietic decision making. Using CAGE-seq we predicted TF binding and confirmed experimentally validated TF important in cell conversion strategies by exploiting bidirectional regions. Next, we applied our framework to predict driving transcription factors in induced normal erythropoiesis and early myelopoiesis, as well as in acute myeloid leukemia associated MLL-AF9-driven immortalisation. Our approach allowed the identification of experimentally validated as well as thus far unexplored transcription factors in these processes.

ORGANISM(S): Homo sapiens

PROVIDER: GSE204707 | GEO | 2022/11/09

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Identification of transcription factors dictating blood cell development using a bidirectional transcription network-based computational framework [CAGEseq_THP1_GSK-LSD1]

Project description:Genome-wide transcription factor binding profiles provide valuable insights into hematopoietic development and malignancies. Today, advanced computational methods exploit cis-regulatory information to predict complex gene regulatory networks controlled by transcription factors. However, these prediction methods still require large amounts of reference data or experimental expertise. Here, we present a low-requirement network-based computational framework that exploits bidirectional transcription marked regulatory elements to identify important transcription factors driving hematopoietic decision making. Using CAGE-seq we predicted TF binding and confirmed experimentally validated TF important in cell conversion strategies by exploiting bidirectional regions. Next, we applied our framework to predict driving transcription factors in induced normal erythropoiesis and early myelopoiesis, as well as in acute myeloid leukemia associated MLL-AF9-driven immortalisation. Our approach allowed the identification of experimentally validated as well as thus far unexplored transcription factors in these processes.

2022-11-09 | GSE204708 | GEO

Identification of transcription factors dictating blood cell development using a bidirectional transcription network-based computational framework [RNAseq_THP1_GSK-LSD1]

2022-11-09 | GSE204710 | GEO

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

2022-02-24 | GSE183939 | GEO

PC3T: a signature-driven predictor of chemical compounds for cellular transition

Project description:Cellular transitions hold great promise in translational medicine research. However, therapeutic applications are limited by the low efficiency and safety concerns of using transcription factors. Small molecules provide a temporal and highly tuneable approach to overcome these issues. Here, we present PC3T, a computational framework to identify enrich molecules that induce desired cellular transitions, and PC3T was able to consistently identify enrich small molecules that had been experimentally validated in both bulk and single cell datasets. We then predicted and experimentally validated small molecules reprogramming of fibroblasts into hepatocyte-like cells and found that the fibroblasts exhibited epithelial cell morphology and hepatocyte-like specific gene expression pattern after treatment.

2023-05-09 | GSE231967 | GEO

Genome-wide detection of enhancer-hijacking events from chromatin interaction data in rearranged genomes

Project description:Recent efforts have shown that structural variations (SVs) can disrupt three-dimensional genome organization and induce enhancer hijacking, yet no computational tools exist to identify such events from chromatin interaction data. Here, we develop NeoLoopFinder, a computational framework to identify the chromatin interactions induced by SVs, including interchromosomal translocations, large deletions and inversions. Our framework can automatically resolve complex SVs, reconstruct local Hi-C maps surrounding the breakpoints, normalize copy number variation and allele effects and predict chromatin loops induced by SVs. We applied NeoLoopFinder in Hi-C data from 50 cancer cell lines and primary tumors and identified tens of recurrent genes associated with enhancer hijacking. To experimentally validate NeoLoopFinder, we deleted the hijacked enhancers in prostate adenocarcinoma cells using CRISPR–Cas9, which significantly reduced expression of the target oncogene. In summary, NeoLoopFinder enables identification of critical oncogenic regulatory elements that can potentially reveal therapeutic targets.

2021-03-23 | GSE161493 | GEO

Base-resolution methylation patterns accurately predict transcription factor bindings in vivo

Project description:Detecting in vivo transcription factor (TF) binding is important for understanding gene regulatory circuitries. ChIP-seq is a powerful technique to empirically define TF binding in vivo. However, the multitude of distinct TFs makes genome-wide profiling for them all labor-intensive and costly. Algorithms for in silico prediction of TF binding have been developed, based mostly on histone modification or DNase I hypersensitivity data in conjunction with DNA motif and other genomic features. However, technical limitations of these methods prevent them from being applied broadly, especially in clinical settings. We conducted a comprehensive survey involving multiple cell lines, TFs, and methylation types and found that there are intimate relationships between TF binding and methylation level changes around the binding sites. Exploiting the connection between DNA methylation and TF binding, we proposed a novel supervised learning approach to predict TF-DNA interaction using data from base-resolution whole-genome methylation sequencing experiments. We devised beta-binomial models to characterize methylation data around TF binding sites and the background. Along with other static genomic features, we adopted a random forest framework to predict TF-DNA interaction. After conducting comprehensive tests, we saw that the proposed method accurately predicts TF binding and performs favorably versus competing methods. Examine Oct4 genome-wide binding in mouse embryonic stem cells (E14)

2015-03-02 | E-GEOD-65093 | biostudies-arrayexpress

Expression data from REST knock-out versus REST wild type cells during in vitro neurogenesis

Project description:While changes in chromatin are integral to transcriptional reprogramming during cellular differentiation, it is currently unclear how chromatin modifications are targeted to specific loci. We developed a computational model on the premise that transcription factors (TFs) direct dynamic chromatin changes during cell fate decisions. When applied to a neurogenesis paradigm, this approach predicted the TF REST as a determinant of gain of Polycomb-mediated H3K27me3 in neuronal progenitor cells. We prove this prediction experimentally by showing that the absence of REST causes loss of H3K27me3 at target promoters in trans at the same cellular state. Moreover, promoter fragments containing a REST binding site are sufficient to recruit H3K27me3 in cis, while deletion of their REST site results in loss of H3K27me3. These findings illustrate that computational modeling can systematically identify TFs that regulate chromatin dynamics genome-wide. Local determination of Polycomb activity by REST exemplifies such TF based regulation of chromatin.

2012-09-04 | GSE27114 | GEO

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