Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

ABSTRACT: Multi-omics data and stochastic simulations support a cancer heterogeneity framework unifying genetic, epigenetic, and stochastic variabilities [Bulk-WXS-seq] (human)

PROVIDER: PRJNA632351 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	SRR11746966_1.fastq.gz	Fastqsanger.gz
	SRR11746966_2.fastq.gz	Fastqsanger.gz
	SRR11746967_1.fastq.gz	Fastqsanger.gz
	SRR11746967_2.fastq.gz	Fastqsanger.gz
	SRR11746968_1.fastq.gz	Fastqsanger.gz

Items per page:

1 - 5 of 16

Similar Datasets

Multi-omics data and stochastic simulations support a cancer heterogeneity framework unifying genetic, epigenetic, and stochastic variabilities

Project description:Multi-omics data and stochastic simulations support a cancer heterogeneity framework unifying genetic, epigenetic, and stochastic variabilities

| PRJNA631050 | ENA

Multi-omics data and stochastic simulations support a cancer heterogeneity framework unifying genetic, epigenetic, and stochastic variabilities [Bulk-RNA-seq]

Project description:Multi-omics data and stochastic simulations support a cancer heterogeneity framework unifying genetic, epigenetic, and stochastic variabilities [Bulk-RNA-seq]

| PRJNA630953 | ENA

Multi-omics data and stochastic simulations support a cancer heterogeneity framework unifying genetic, epigenetic, and stochastic variabilities [scRNA-seq]

Project description:Multi-omics data and stochastic simulations support a cancer heterogeneity framework unifying genetic, epigenetic, and stochastic variabilities [scRNA-seq]

| PRJNA631051 | ENA

Multi-omics data and stochastic simulations support a cancer heterogeneity framework unifying genetic, epigenetic, and stochastic variabilities [Bulk-RNA-seq]

Project description:We subject multiple versions and single cell-derived sublines of an archetypal non-small cell lung cancer cell line to experimental scrutiny at the genomic, transcriptomic, and cell population levels. We find evidence of genetic variability among cell line versions and one subline. Additional sublines show no genetic variation but epigenetic differences are detected at the transcriptomic level. Stochastic simulations of subline growth dynamics confirm that one subline likely contains at least two genetic states while all others are isogenic. Overall, our results substantiate a view of intratumoral heterogeneity in which different genetic states give rise to multiple epigenetic “basins of attraction,” across which cells can transition driven by stochastic factors such as gene expression noise and asymmetric cell division. Deconvolving intratumoral heterogeneity into genetic, epigenetic, and stochastic components provides a lens through which to view tumor drug response and acquired treatment resistance that may lead to novel therapeutic strategies in the future.

2020-05-08 | GSE150025 | GEO

Multi-omics data and stochastic simulations support a cancer heterogeneity framework unifying genetic, epigenetic, and stochastic variabilities [scRNA-seq]

2020-05-08 | GSE150083 | GEO

Combining mathematical and statistical modeling to simulate time course bulk and single cell gene expression data in cancer with CancerInSilico

Project description:We develop an R/Bioconductor package CancerInSilico to simulate bulk and single cell transcriptional data from a known ground truth obtained from mathematical models of cellular systems. This package contains a general R infrastructure for cell-based mathematical model, implemented for an off-lattice, cell-center Monte Carlo mathematical model. We also adapt this model to simulate the impact of growth suppression by targeted therapeutics in cancer and benchmark simulations against bulk in vitro experimental data. Sensitivity to parameters is evaluated and used to predict the relative impact of variation in cellular growth parameters and cell types on tumor heterogeneity in therapeutic response.

2020-07-09 | GSE114375 | GEO

Testing effects of cell density on NGN2 iN transcriptom

Project description:We previously established a standard protocol for NGN2 iN produciton using iP11N iPSC line. Here we aim to explore whether plating density contributes to batch-to-batch variabilities in the transcriptome. Cells were plated at high (400K/cm2) or normal (150K/cm2 densities) and cultured according to the established protocol. Day 28 neurons were harvested and total RNA extracted for bulk RNA sequencing

2024-09-13 | GSE273553 | GEO

Small RNA profiles of the rice non-pollen male sterile line Wuxiang S reveal miRNAs involved in the fertility transition

Project description:Illumina sequencing was employed to examine the expression profiles of rice anther miRNAs from the a non-pollen male sterile line Wuxiang S (WXS), one of photo-thermo sensitive genical male sterile (PTGMS) line rice, during in the fertility transition stage. A total of 493 known miRNAs and 273 novel miRNAs were identified during rice anther development. Based on the number of sequencing reads, a total of 26 miRNAs were discovered to be significant difference expression between WXS(S, Sterility) and WXS(F, Fertility), and the results were partially validated by qRT-PCR. Among these, 11 miRNAs were decreased and 15 miRNAs were increased in WXS(S) compared with WXS(F). The expression patterns for targets of osa-miR156a-j, osa-miR3879, osa-miR159c/d/e, osa-miR171a/c/e/i, osa-miR398b, osa-miR164d, osa-miR528 and osa-miR408 were selectively examined, and the results showed that there was a negative correlation on the expression patterns between miRNAs and their targets. These targets have previously been reported to be related with pollen development and male sterility, suggesting that miRNAs might act as regulators of rice anthers. Furthermore, miRNA editing events were observed. The U-to-C and U-to-A editing phenomenon was validated by molecular cloning and sequencing. Examine small RNA profiles change of four tissues of the rice non-pollen male sterile line Wuxiang S under two different environments.

2016-02-14 | E-GEOD-74003 | biostudies-arrayexpress

Small RNA profiles of the rice non-pollen male sterile line Wuxiang S reveal miRNAs involved in the fertility transition

2016-02-14 | GSE74003 | GEO

Transcriptome analysis of cells of different cycling speed during Yamanaka reprogramming

Project description:Reprogramming somatic cells to induced pluripotency by Yamanaka factors is usually slow and inefficient, and is thought to be a stochastic process. We identified a privileged somatic cell state, from which acquisition of pluripotency could occur in a non-stochastic manner. Subsets of murine hematopoietic progenitors are privileged, whose progeny cells predominantly adopt the pluripotent fate with activation of endogenous Oct4 locus after 4-5 divisions in reprogramming conditions. Privileged cells display an ultrafast cell cycle of ~8 hours. In fibroblasts, a subpopulation cycling at a similar ultrafast speed is observed after 6 days of factor expression, and is increased by p53-knockdown. This ultrafast-cycling population accounts for >99% of the bulk reprogramming activity in wildtype or p53-knockdown fibroblasts. We compared the transcriptomes of the fast cycling cells with those of slower hematopoietic progenitors, bulk fibroblasts and established iPS cells. 3-5 replicates for each of the six cell types were included: 4 replicates for established iPS cells, 4 replicates for bulk mouse embryonic fibroblasts (MEF), 4 replicates for fast cycling MEF, 4 replicates for slow cycling MEF, 5 replicates for fast cycling granulocyte monocyte progenitors (GMP) and 3 replicates for slow cycling GMP.

2014-02-12 | E-GEOD-53074 | 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