Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Epigenomic Tensor Predicts Disease Subtypes and Reveals Constrained Tumor Evolution [RNA-Seq II]

ABSTRACT: Epigenomic Tensor Predicts Disease Subtypes and Reveals Constrained Tumor Evolution [RNA-Seq II]

PROVIDER: PRJNA670967 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	SRR12884790_1.fastq.gz	Fastqsanger.gz
	SRR12884790_2.fastq.gz	Fastqsanger.gz
	SRR12884791_1.fastq.gz	Fastqsanger.gz
	SRR12884791_2.fastq.gz	Fastqsanger.gz
	SRR12884792_1.fastq.gz	Fastqsanger.gz

Items per page:

1 - 5 of 12

Similar Datasets

Epigenomic Tensor Predicts Disease Subtypes and Reveals Constrained Tumor Evolution

Project description:Epigenomic Tensor Predicts Disease Subtypes and Reveals Constrained Tumor Evolution

| PRJNA596655 | ENA

Epigenomic Tensor Predicts Disease Subtypes and Reveals Constrained Tumor Evolution

Project description:Epigenomic Tensor Predicts Disease Subtypes and Reveals Constrained Tumor Evolution

| PRJNA596649 | ENA

Epigenomic Tensor Predicts Disease Subtypes and Reveals Constrained Tumor Evolution [ATAC-seq]

Project description:Epigenomic Tensor Predicts Disease Subtypes and Reveals Constrained Tumor Evolution [ATAC-seq]

| PRJNA596653 | ENA

Epigenomic Tensor Predicts Disease Subtypes and Reveals Constrained Tumor Evolution [RNA-seq]

Project description:Epigenomic Tensor Predicts Disease Subtypes and Reveals Constrained Tumor Evolution [RNA-seq]

| PRJNA596654 | ENA

Epigenomic Tensor Predicts Disease Subtypes and Reveals Constrained Tumor Evolution [RNA-seq_HOXA13 KD]

Project description:Epigenomic Tensor Predicts Disease Subtypes and Reveals Constrained Tumor Evolution [RNA-seq_HOXA13 KD]

| PRJNA596652 | ENA

Epigenomic Tensor Predicts Disease Subtypes and Reveals Constrained Tumor Evolution

Project description:Understanding the epigenomic evolution and specificity of disease subtypes from complex patient data remains a major biomedical problem. We here present DeCET (Decomposition and Classification of Epigenomic Tensors), an integrative computational approach for simultaneously analyzing hierarchical heterogeneous data, to identify robust epigenomic differences between tissue types, differentiation states, and disease subtypes. Applying DeCET to our own data from 21 uterine benign tumor (leiomyoma) patients identifies distinct epigenomic features discriminating normal myometrium and leiomyoma subtypes. Leiomyomas possess preponderant alterations in distal enhancers and long-range histone modifications confined to chromatin contact domains that constrain the evolution of pathological epigenomes. Moreover, we demonstrate the power and advantage of DeCET on multiple publicly available epigenomic datasets representing different cancers and cellular states. Epigenomic features extracted by DeCET can thus help improve our understanding of disease states, cellular development, and differentiation, thereby facilitating future therapeutic, diagnostic and prognostic strategies.

2021-03-30 | GSE142328 | GEO

Epigenomic Tensor Predicts Disease Subtypes and Reveals Constrained Tumor Evolution

2021-03-30 | GSE142332 | GEO