ArticleNature communications2022
Cell cycle gene regulation dynamics revealed by RNA velocity and deep-learning.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 63 papers.
What it found
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Who cites it
63 citing papers in PubMed, 120 citations in OpenAlex.
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- Temperature regulation of cell cycle and growth dynamics in Arabidopsis.Biochemical Society transactions · 2026Review
- Multiscale learning of gene network-driven phenotypic dynamics of single cells.Molecular systems biology · 2026Article
- Cell Cycle Sensing Shapes Human T Cell Fate and Exhaustion Programs.bioRxiv : the preprint server for biology · 2026Article
- ArchVelo: archetypal velocity modeling for single-cell multi-omic trajectories.Nature communications · 2026Article
- Cell Cycle Control of Nuclear Metabolism Couples Phosphatidylinositol Signaling to Histone Methylation.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- A velocity-informed framework for resolving functional stratification in rare human stem cells.Leukemia · 2026Article
- CycleVI: isolating cell cycle variation with an interpretable deep generative model.Bioinformatics (Oxford, England) · 2026Article
- Scoring gene importance by interpreting single-cell foundation models.Nature biotechnology · 2026Article
- Dystrophins DP71 and DP427 determine cell viability during proliferation and myofibre differentiation.Cell death & disease · 2026Article
- Locat: Joint enrichment and depletion testing identifies localized marker genes in single-cell transcriptomics.bioRxiv : the preprint server for biology · 2026Article
- Stress-dependent growth in breast cancer arises from a mechano-osmotic coupling and cell-sizing checkpoint.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- From noise to models to numbers: Evaluating negative binomial models and parameter estimations in single-cell RNA-seq.PLoS computational biology · 2026Article
- Mathematical analysis of G1/S sub-networks in hematopoietic proliferation: Sequential and lineage-specific activation.iScience · 2026Article
- CellUntangler: Separating distinct biological signals in single-cell data with deep generative models.Cell genomics · 2026Article
- Metabolic reprogramming in diabetes and other endocrine and metabolic disorders: exploring the Warburg effect, ketones, and SGLT2 inhibitors.Reviews in endocrine & metabolic disorders · 2026Review
- Sequential RNA polymerase II activation drives human hematopoiesis.Cell reports · 2026Article
- Interpretable trajectory inference with single-cell linear adaptive negative-binomial expression (scLANE) testing.Nucleic acids research · 2026Article
- Artificial intelligence in mitotic checkpoint modeling: transforming our understanding of cellular division through machine learning and predictive biology.Briefings in bioinformatics · 2026Review
3 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 1 institution in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.