Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Classifying leukemia types with chromatin conformation data.

ABSTRACT: Although genetic or epigenetic alterations have been shown to affect the three-dimensional organization of genomes, the utility of chromatin conformation in the classification of human disease has never been addressed.Here, we explore whether chromatin conformation can be used to classify human leukemia. We map the conformation of the HOXA gene cluster in a panel of cell lines with 5C chromosome conformation capture technology, and use the data to train and test a support vector machine classifier named 3D-SP. We show that 3D-SP is able to accurately distinguish leukemias expressing MLL-fusion proteins from those expressing only wild-type MLL, and that it can also classify leukemia subtypes according to MLL fusion partner, based solely on 5C data.Our study provides the first proof-of-principle demonstration that chromatin conformation contains the information value necessary for classification of leukemia subtypes.

SUBMITTER: Rousseau M

PROVIDER: S-EPMC4038739 | biostudies-literature | 2014 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Classifying leukemia types with chromatin conformation data.

Rousseau Mathieu M Ferraiuolo Maria A MA Crutchley Jennifer L JL Wang Xue Qing XQ Miura Hisashi H Blanchette Mathieu M Dostie Josée J

Genome biology 20140430 4

<h4>Background</h4>Although genetic or epigenetic alterations have been shown to affect the three-dimensional organization of genomes, the utility of chromatin conformation in the classification of human disease has never been addressed.<h4>Results</h4>Here, we explore whether chromatin conformation can be used to classify human leukemia. We map the conformation of the HOXA gene cluster in a panel of cell lines with 5C chromosome conformation capture technology, and use the data to train and tes ...[more]

PMID: 24995990

Similar Datasets

Classifying leukemia types with chromatin conformation data

Project description:This SuperSeries is composed of the SubSeries listed below. Refer to individual Series

2014-02-27 | E-GEOD-55408 | biostudies-arrayexpress

Classifying leukemia types with chromatin conformation data

Project description:This SuperSeries is composed of the SubSeries listed below.

2014-02-27 | GSE55408 | GEO

Classifying leukemia types with chromatin conformation data (5C)

Project description:Background: Although genetic or epigenetic alterations have shown to affect the three-dimensional organization of genomes, the utility of chromatin conformation in the classification of human disease has never been addressed. Results: Here, we explore whether chromatin conformation can be used to classify human leukemia. We map the conformation of the HOXA gene cluster in a panel of cell lines with 5C chromosome conformation capture technology, and use the data to train and test a support vector machine classifier named 3D-SP. We show that 3D-SP is able to accurately distinguish leukemias expressing MLL-fusion proteins from those expressing only wild-type MLL, and that it can also classify leukemia subtypes according to MLL fusion partner, based solely on 5C data. Conclusions: Our study provides the first proof-of-principle demonstration that chromatin conformation contains the information value necessary for classification of leukemia subtypes. Analysis of 38 samples using 5C technology. All data normalized using a 'master' BAC consisting of 5C data from 6 samples.

2014-02-27 | E-GEOD-55406 | biostudies-arrayexpress

Classifying leukemia types with chromatin conformation data (ChIP-Seq)

Project description:Background: Although genetic or epigenetic alterations have shown to affect the three-dimensional organization of genomes, the utility of chromatin conformation in the classification of human disease has never been addressed. Results: Here, we explore whether chromatin conformation can be used to classify human leukemia. We map the conformation of the HOXA gene cluster in a panel of cell lines with 5C chromosome conformation capture technology, and use the data to train and test a support vector machine classifier named 3D-SP. We show that 3D-SP is able to accurately distinguish leukemias expressing MLL-fusion proteins from those expressing only wild-type MLL, and that it can also classify leukemia subtypes according to MLL fusion partner, based solely on 5C data. Conclusions: Our study provides the first proof-of-principle demonstration that chromatin conformation contains the information value necessary for classification of leukemia subtypes. Examination of CTCF and RAD21 binding sites in THP-1 cell.

2014-02-27 | E-GEOD-55407 | biostudies-arrayexpress

Classifying leukemia types with chromatin conformation data (5C)

2014-02-27 | GSE55406 | GEO

Classifying leukemia types with chromatin conformation data (ChIP-Seq)

2014-02-27 | GSE55407 | GEO

Alterations of specific chromatin conformation affect ATRA-induced leukemia cell differentiation.

Project description:Chromatin conformation plays a key role in regulating gene expression and controlling cell differentiation. However, the whole-genome chromatin conformation changes that occur during leukemia cell differentiation are poorly understood. Here, we characterized the changes in chromatin conformation, histone states, chromatin accessibility, and gene expression using an all-trans retinoic acid (ATRA)-induced HL-60 cell differentiation model. The results showed that the boundaries of topological associated domains (TADs) were stable during differentiation; however, the chromatin conformations within several specific TADs were obviously changed. By combining H3K4me3, H3K27ac, and Hi-C signals, we annotated the differential gene-regulatory chromatin interactions upon ATRA induction. The gains and losses of the gene-regulatory chromatin interactions are significantly correlated with gene expression and chromatin accessibility. Finally, we found that the loss of GATA2 expression and DNA binding are crucial for the differentiation process, and changes in the chromatin structure around the GATA2 regulate its expression upon ATRA induction. This study provided both statistical insights and experimental details regarding the relationship between chromatin conformation changes and transcription regulation during leukemia cell differentiation, and the results suggested that the chromatin conformation is a new type of potential drug target for cancer therapy.

| S-EPMC5833835 | biostudies-literature

Integrative prediction of gene expression with chromatin accessibility and conformation data.

Project description:BackgroundEnhancers play a fundamental role in orchestrating cell state and development. Although several methods have been developed to identify enhancers, linking them to their target genes is still an open problem. Several theories have been proposed on the functional mechanisms of enhancers, which triggered the development of various methods to infer promoter-enhancer interactions (PEIs). The advancement of high-throughput techniques describing the three-dimensional organization of the chromatin, paved the way to pinpoint long-range PEIs. Here we investigated whether including PEIs in computational models for the prediction of gene expression improves performance and interpretability.ResultsWe have extended our [Formula: see text] framework to include DNA contacts deduced from chromatin conformation capture experiments and compared various methods to determine PEIs using predictive modelling of gene expression from chromatin accessibility data and predicted transcription factor (TF) motif data. We designed a novel machine learning approach that allows the prioritization of TFs binding to distal loop and promoter regions with respect to their importance for gene expression regulation. Our analysis revealed a set of core TFs that are part of enhancer-promoter loops involving YY1 in different cell lines.ConclusionWe present a novel approach that can be used to prioritize TFs involved in distal and promoter-proximal regulatory events by integrating chromatin accessibility, conformation, and gene expression data. We show that the integration of chromatin conformation data can improve gene expression prediction and aids model interpretability.

| S-EPMC7003490 | biostudies-literature

Joint annotation of chromatin state and chromatin conformation reveals relationships among domain types and identifies domains of cell-type-specific expression.

Project description:The genomic neighborhood of a gene influences its activity, a behavior that is attributable in part to domain-scale regulation. Previous genomic studies have identified many types of regulatory domains. However, due to the difficulty of integrating genomics data sets, the relationships among these domain types are poorly understood. Semi-automated genome annotation (SAGA) algorithms facilitate human interpretation of heterogeneous collections of genomics data by simultaneously partitioning the human genome and assigning labels to the resulting genomic segments. However, existing SAGA methods cannot integrate inherently pairwise chromatin conformation data. We developed a new computational method, called graph-based regularization (GBR), for expressing a pairwise prior that encourages certain pairs of genomic loci to receive the same label in a genome annotation. We used GBR to exploit chromatin conformation information during genome annotation by encouraging positions that are close in 3D to occupy the same type of domain. Using this approach, we produced a model of chromatin domains in eight human cell types, thereby revealing the relationships among known domain types. Through this model, we identified clusters of tightly regulated genes expressed in only a small number of cell types, which we term "specific expression domains." We found that domain boundaries marked by promoters and CTCF motifs are consistent between cell types even when domain activity changes. Finally, we showed that GBR can be used to transfer information from well-studied cell types to less well-characterized cell types during genome annotation, making it possible to produce high-quality annotations of the hundreds of cell types with limited available data.

| S-EPMC4381526 | biostudies-literature

Perspectives for the reconstruction of 3D chromatin conformation using single cell Hi-C data.

Project description:Construction of chromosomes 3D models based on single cell Hi-C data constitute an important challenge. We present a reconstruction approach, DPDchrom, that incorporates basic knowledge whether the reconstructed conformation should be coil-like or globular and spring relaxation at contact sites. In contrast to previously published protocols, DPDchrom can naturally form globular conformation due to the presence of explicit solvent. Benchmarking of this and several other methods on artificial polymer models reveals similar reconstruction accuracy at high contact density and DPDchrom advantage at low contact density. To compare 3D structures insensitively to spatial orientation and scale, we propose the Modified Jaccard Index. We analyzed two sources of the contact dropout: contact radius change and random contact sampling. We found that the reconstruction accuracy exponentially depends on the number of contacts per genomic bin allowing to estimate the reconstruction accuracy in advance. We applied DPDchrom to model chromosome configurations based on single-cell Hi-C data of mouse oocytes and found that these configurations differ significantly from a random one, that is consistent with other studies.

| S-EPMC8601426 | 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