Identification of gene kinetics patterns improves RNA velocity and cell fate prediction using JuloVelo
- 1Institute of Biomedical Informatics, National Yang Ming Chiao Tung University, Taipei, Taiwan; Department of Computer Science and Information Engineering, Natio
Abstract
Single-cell RNA velocity has transformed our capacity to predict future cell states from sequencing snapshots, but existing methods rely on constant or binary kinetic assumptions that fail to capture the heterogeneous splicing dynamics of cellular differentiation. Here we present JuloVelo, a deep learning framework that demonstrates that identifying gene kinetics patterns to initialize neural network training dramatically improves RNA velocity model performance. By pre-determining whether each gene follows a Circle, Complete induction, or Complete repression pattern across cellular differentiation, JuloVelo initializes training with pattern-matched, pre-trained network weights, effectively guiding the cell-specific kinetics optimization away from biologically implausible parameter regimes. Additionally, JuloVelo demonstrates that multiome datasets can also be processed and modeled by utilizing promoter chromatin accessibility to guide kinetics pre-determination, and integrates an in silico perturbation framework to simulate transcription factor modulation on cell fate. Validated across eight datasets spanning erythrogenesis, neurogenesis, and endocrinogenesis, JuloVelo consistently outperformed constant-rate and cell-specific models, resolving the entire five-lineage differentiation program in hippocampal neurogenesis. Multiome processing improved both intrinsic and extrinsic velocity stability, and JuloVelo reduced computational execution time by approximately 70% compared to cellDancer. JuloVelo is available as an open-source package at https://github.com/jhlabtw/JuloVelo.jl.
Related
- SpectroVQ: Noise-Aware Compression of Proteomics Data via Vector-Quantized Deep Learning improves MS/MS data storage and Peptide Identification
- Fine-tuning of polygenic score to local data improves risk prediction and clinical implementation
- Identification and Expression Patterns of Opsin Genes in Phortica okadai, the Only Confirmed Vector of Thelazia callipaeda in China
- Genotype imputation improves GWAS resolution and facilitates candidate gene identification in pearl millet
- Neural prediction decorrelation reveals that adversarial robustness substantially improves DNN prediction accuracy across the entire human auditory cortex
BibTeX
@misc{tung2026identification,
title = {{Identification of gene kinetics patterns improves RNA velocity and cell fate prediction using JuloVelo}},
author = {K.-C. Tung and Y.-H. Tu and K.-Y. Hsieh and H.-F. Juan and H.-C. Huang},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.24.761531},
url = {https://www.biorxiv.org/content/10.64898/2026.10.24.761531v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - Identification of gene kinetics patterns improves RNA velocity and cell fate prediction using JuloVelo
AU - K.-C. Tung
AU - Y.-H. Tu
AU - K.-Y. Hsieh
AU - H.-F. Juan
AU - H.-C. Huang
PY - 2026
DA - 2026/10/24
PB - bioRxiv
DO - 10.64898/2026.10.24.761531
UR - https://www.biorxiv.org/content/10.64898/2026.10.24.761531v1
AB - Single-cell RNA velocity has transformed our capacity to predict future cell states from sequencing snapshots, but existing methods rely on constant or binary kinetic assumptions that fail to capture the heterogeneous splicing dynamics of cellular differentiation. Here we present JuloVelo, a deep learning framework that demonstrates that identifying gene kinetics patterns to initialize neural network training dramatically improves RNA velocity model performance. By pre-determining whether each gene follows a Circle, Complete induction, or Complete repression pattern across cellular differentiation, JuloVelo initializes training with pattern-matched, pre-trained network weights, effectively guiding the cell-specific kinetics optimization away from biologically implausible parameter regimes. Additionally, JuloVelo demonstrates that multiome datasets can also be processed and modeled by utilizing promoter chromatin accessibility to guide kinetics pre-determination, and integrates an in silico perturbation framework to simulate transcription factor modulation on cell fate. Validated across eight datasets spanning erythrogenesis, neurogenesis, and endocrinogenesis, JuloVelo consistently outperformed constant-rate and cell-specific models, resolving the entire five-lineage differentiation program in hippocampal neurogenesis. Multiome processing improved both intrinsic and extrinsic velocity stability, and JuloVelo reduced computational execution time by approximately 70% compared to cellDancer. JuloVelo is available as an open-source package at https://github.com/jhlabtw/JuloVelo.jl.
ER -