Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Gene Regulation Dynamics during the Cell Cycle uncovered by RNA velocity and deep-learning (IMR90)

ABSTRACT: Gene Regulation Dynamics during the Cell Cycle uncovered by RNA velocity and deep-learning (IMR90)

PROVIDER: PRJNA705150 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	SRR13790992_1.fastq.gz	Fastqsanger.gz
	SRR13790992_2.fastq.gz	Fastqsanger.gz

Items per page:

1 - 2 of 2

Similar Datasets

Gene Regulation Dynamics during the Cell Cycle uncovered by RNA velocity and deep-learning (IMR90)

Project description:Investigation of the cell cycle regulations in human fibroblasts (IMR90)

2022-04-25 | GSE167607 | GEO

Gene Regulation Dynamics during the Cell Cycle uncovered by RNA velocity and deep-learning

Project description:Gene Regulation Dynamics during the Cell Cycle uncovered by RNA velocity and deep-learning

| PRJNA705145 | ENA

Gene Regulation Dynamics during the Cell Cycle uncovered by RNA velocity and deep-learning (mESCs)

Project description:Gene Regulation Dynamics during the Cell Cycle uncovered by RNA velocity and deep-learning (mESCs)

| PRJNA705149 | ENA

Gene Regulation Dynamics during the Cell Cycle uncovered by RNA velocity and deep-learning

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

2022-04-25 | GSE167609 | GEO

Gene Regulation Dynamics during the Cell Cycle uncovered by RNA velocity and deep-learning (mESCs)

Project description:Investigation of the cell cycle regulations in mouse embryonic stem cells (mESCs)

2022-04-25 | GSE167608 | GEO

Statistical inference with a manifold-constrained RNA velocity model uncovers cell cycle speed modulations

Project description:Across a range of biological processes, cells undergo coordinated changes in gene expression, resulting in transcriptome dynamics that unfold within a low-dimensional manifold. Single-cell RNA-sequencing (scRNA-seq) only measures temporal snapshots of gene expression, yet information on the underlying low-dimensional dynamics can be extracted using RNA velocity, which models unspliced and spliced RNA abundances to estimate the rate of change of gene expression. Available RNA velocity algorithms can be fragile and rely on heuristics that lack statistical control. Moreover, the estimated vector field is not dynamically consistent with the traversed gene expression manifold. Here, we develop a generative model of RNA velocity and a Bayesian inference approach that solves these problems. Our model couples velocity field and manifold estimation in a reformulated, unified framework, so as to coherently identify the parameters of an autonomous dynamical system. Focusing on the cell cycle, we implemented VeloCycle to study gene regulation dynamics on one-dimensional periodic manifolds and validated using live-imaging its ability to infer actual cell cycle periods. We benchmarked RNA velocity inference with sensitivity analyses and demonstrated one- and multiple-sample testing. We also conducted Markov chain Monte Carlo inference on the model, uncovering key relationships between gene-specific kinetics and our gene-independent velocity estimate. Finally, we applied VeloCycle to in vivo samples and in vitro genome-wide Perturb-seq, revealing regionally defined proliferation modes in neural progenitors and the effect of gene knockdowns on cell cycle speed. Ultimately, VeloCycle expands the scRNA-seq analysis toolkit with a modular and statistically rigorous RNA velocity inference framework.

2023-12-18 | GSE250148 | GEO

Gene expression data from IMR90 Control, IMR90 HRAS-G12V, IMR90 HRAS-G12V+shDOT1L, IMR90 DOT1L Overexpression

Project description:Gene expression data from IMR90 Control, IMR90 HRAS-G12V, IMR90 HRAS-G12V+shDOT1L, IMR90 DOT1L Overexpression

| PRJNA658579 | ENA

Cell cycle gene regulation dynamics revealed by RNA velocity and deep-learning.

Project description:Despite the fact that the cell cycle is a fundamental process of life, a detailed quantitative understanding of gene regulation dynamics throughout the cell cycle is far from complete. Single-cell RNA-sequencing (scRNA-seq) technology gives access to these dynamics without externally perturbing the cell. Here, by generating scRNA-seq libraries in different cell systems, we observe cycling patterns in the unspliced-spliced RNA space of cell cycle-related genes. Since existing methods to analyze scRNA-seq are not efficient to measure cycling gene dynamics, we propose a deep learning approach (DeepCycle) to fit these patterns and build a high-resolution map of the entire cell cycle transcriptome. Characterizing the cell cycle in embryonic and somatic cells, we identify major waves of transcription during the G1 phase and systematically study the stages of the cell cycle. Our work will facilitate the study of the cell cycle in multiple cellular models and different biological contexts.

| S-EPMC9126911 | biostudies-literature

Subslab ultra low velocity anomaly uncovered by and facilitating the largest deep earthquake.

Project description:It is enigmatic that M8+ earthquakes can take place at depth greater than 600 km inside the slab, where the P-T conditions generally do not favor seismic slip rate (~m/s) on faults. Here we provide fresh insights to the initial rupture and mechanism of the Mw 8.3 Sea of Okhotsk earthquake by analyzing high-frequency (up to 0.8 Hz) teleseismic array data. We determine the relative location and timing of two early subevents, and the geometry and velocity perturbation of a nearby structure anomaly. We found a small-scale (~30 × 60 × 60 km) ultralow (-18 ± 2%) P-wave velocity anomaly located beneath the Pacific slab around the 660 km discontinuity. The volatile-bearing highly melted nature of the anomaly provides significant buoyancy, stressing the slab dramatically closer to the critical condition for thermal runaway weakening that allows the rupture to propagate beyond the metastable olivine wedge, forming M8+ events. Enormous velocity reduction urges for further mineral physics and geodynamic investigations.

| S-EPMC10980710 | biostudies-literature

Gene expression data from IMR90 control, IMR90 shRRM2 and shRRM2/shp16

Project description:Gene expression data from IMR90 control, IMR90 shRRM2 and shRRM2/shp16

| PRJNA552100 | ENA