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

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

PROVIDER: PRJNA705145 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Similar Datasets

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 (IMR90)

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

| PRJNA705150 | ENA

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 (mESCs)

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

2022-04-25 | GSE167608 | GEO

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

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

Universal prediction of cell cycle position using transfer learning

Project description:Universal prediction of cell cycle position using transfer learning

| PRJNA720385 | ENA

RespectM revealed metabolic heterogeneity powers deep learning for reshaping the DBTL cycle

Project description:<p>Synthetic biology, relying on Design-Build-Test-Learn (DBTL) cycle, aims to solve medicine, manufacturing and agriculture problems. However, the DBTL cycle’s Learn (L) step lacks predictive power for the behavior of biological systems, resulting from the incompatibility between sparse testing data and chaotic metabolic networks. Herein, we develop a method, 'RespectM', based on mass spectrometry imaging, which is able to detect metabolites at a rate of 500 cells per hour with high efficiency. In this study, 4321 single cell level metabolomics data were acquired, representing metabolic heterogeneity. An optimizable deep neural network was applied to learn from metabolic heterogeneity and a 'heterogeneity-powered learning (HPL)' based model was trained as well. By testing the HPL based model, we suggest minimal operations to achieve high triglyceride production for engineering. The HPL strategy could revolutionize rational design and reshape the DBTL cycle.</p>

2023-06-29 | MTBLS3830 | MetaboLights

learning

Project description:learning

| PRJEB26278 | ENA

Universal prediction of cell cycle position using transfer learning

Project description:The cell cycle is a highly conserved, continuous process which controls faithful replication and division of cells. Single-cell technologies have enabled increasingly precise measurements of the cell cycle as both as a biological process of interest and as a possible confounding factor. Despite its importance and conservation, there is no universally applicable approach to infer position in the cell cycle with high-resolution from single-cell RNA-seq data. Here, we present tricycle, an R/Bioconductor package,to address this challenge by leveraging key features of the biology of the cell cycle, the mathematical properties of principal component analysis of periodic functions, and the ubiquitous applicability of transfer learning. We show that tricycle can predict any cell’s position in the cell cycle regardless of the cell type, species of origin, and even sequencing assay. The accuracy of tricycle compares favorably to gold-standard experimental assays which generally require specialized measurements in specifically constructed in vitro systems. Unlike gold-standard assays, tricycle is applicable to any single-cell RNA-seq dataset. Tricycle is highly scalable, universally accurate, and eminently pertinent for atlas-level data.

2021-04-07 | GSE171636 | GEO

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

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