To understand why the new model matters, it helps to revisit what RNA velocity actually measures. Standard single-cell RNA sequencing counts mature, spliced transcripts, but many protocols also capture unspliced nascent RNA that has not yet been processed. Because unspliced RNA reflects genes that a cell has recently started transcribing, the ratio between unspliced and spliced counts for each gene provides a hint about whether that gene is being induced or repressed. By combining these signals across thousands of genes, RNA velocity methods can infer a vector field over the cell population, pointing each cell toward its likely future state. This allows researchers to reconstruct developmental trajectories, identify lineage relationships, and predict the endpoints of differentiation without needing time-series measurements.

The original formulation of RNA velocity relied on a dynamical model of splicing kinetics, treating the interplay between unspliced and spliced RNA as a set of ordinary differential equations whose parameters could be estimated from the data. While elegant, this approach struggles with the noise, dropout, and sparsity that plague single-cell datasets, and it often produces inconsistent velocity estimates across complex, multi-lineage systems. Subsequent methods have taken different routes: some simplify the problem by assuming steady-state kinetics, while others turn to neural networks to learn the velocity field directly from latent representations of gene expression. These neural approaches have improved robustness, but they come with their own limitations, particularly in how they model relationships between cells.

A key weakness of many current neural velocity models, the authors of the new study argue, lies in their reliance on local neighborhood structure. Most pipelines begin by constructing a k-nearest neighbor graph in a reduced-dimensional space, typically using principal component analysis to compress the high-dimensional expression profiles. The graph is meant to approximate the local geometry of the transcriptional manifold, and velocity information is propagated only between neighboring cells. Yet in real tissues, biologically related cells may not be nearest neighbors in the embedding space. Cells that lie far apart in the graph can still share developmental ancestry or be destined for similar fates, and these long-range dependencies carry information that a purely local model discards. Capturing them requires an architecture capable of attending to arbitrary pairs of cells rather than only to immediate neighbors.

That is precisely where the transformer comes in. Transformers, the architecture family that underpins modern large language models, are built around self-attention, a mechanism that lets each element in a set compute weighted relationships with every other element. Multi-head attention extends this by running several attention computations in parallel, each with its own learned projection, so that different heads can specialize in different kinds of relationships. Applied to single-cell data, a graph transformer can in principle learn which cells are relevant to one another regardless of their distance in the neighborhood graph, while the graph structure itself provides an inductive bias that keeps the model grounded in the observed topology of the data. GTVelo combines these ingredients within a neural ordinary differential equation framework, meaning that the learned attention-based representation feeds into a continuous-time dynamical model of how cell states evolve.

The neural ordinary differential equation component is significant in its own right. Rather than discretizing time into steps, a neural ODE defines the derivative of the cell’s latent state as the output of a neural network, and the trajectory is obtained by integrating that derivative forward. This continuous formulation is a natural fit for RNA velocity, which is fundamentally about the instantaneous rate of change of transcriptional state. It also allows the model to produce smooth trajectories through latent space, which is important when the underlying biology involves gradual differentiation rather than abrupt jumps between discrete states. By embedding the transformer-based encoder inside this dynamical system, GTVelo seeks to couple rich relational modeling of the cell population with principled temporal dynamics.

One of the more distinctive features of the new model is what the authors call a multi-origin state mechanism. Many existing approaches effectively assume that all trajectories in a dataset can be described starting from a single initial state, which becomes problematic in datasets containing multiple lineages, branching differentiation paths, or heterogeneous cell populations. A single origin forces the model to squeeze fundamentally different developmental stories into one shared starting point, degrading the accuracy of velocity estimates for every lineage involved. GTVelo instead allows inference to proceed from multiple origins, which the authors say enables flexible handling of multi-branch trajectories. This flexibility also extends to data integration: the model is designed to accommodate multi-omics inputs, reflecting the growing trend in genomics toward assays that measure RNA alongside chromatin accessibility, protein abundance, or other molecular layers in the same cells.

According to the study, GTVelo was evaluated across multiple benchmark datasets and compared against established RNA velocity methods using standard evaluation metrics. The reported results show competitive performance overall, with GTVelo outperforming the compared methods on most of the standard metrics in most cases. Two of the evaluation concepts highlighted in the paper are cross-boundary directedness, which measures whether inferred velocities correctly point across the boundaries between clusters in a biologically consistent direction, and in-cluster coherence, which assesses whether velocity vectors within a cluster are mutually consistent rather than pointing in conflicting directions. Together, these metrics probe whether a velocity model captures both the global organization of a trajectory and the local smoothness of the inferred dynamics, two properties that are often in tension.

The implications for downstream biology could be substantial. RNA velocity is widely used to annotate developmental hierarchies, trace the origins of disease-associated cell states, and generate hypotheses about lineage commitment in contexts ranging from embryogenesis to tumor evolution. Models that handle branching trajectories and long-range cellular associations more reliably could improve the fidelity of these analyses, particularly in complex tissues where multiple lineages coexist and where rare transitional populations carry outsized biological importance. The stated ability to integrate multi-omics data points toward applications in which velocity is estimated from joint measurements, potentially revealing regulatory dynamics that transcript-only models cannot see. As with any computational method, real-world performance will depend on how the benchmarks translate to noisy, dataset-specific conditions, and independent validation by the community will be an important next step.

The research was carried out by Shensi Huang, Hongyu Zhang, and Jianping Zhao of Xinjiang University, together with Zile Wang of Dalian University of Technology, Haiyun Wang of Wuhan University of Science and Technology, and Junfeng Xia of Anhui University, and was supported by funding including grants from the National Natural Science Foundation of China. The article was published open access in BMC Bioinformatics on 15 September 2026, with the accepted manuscript shared early under a permanent DOI. For a field that has seen a rapid succession of velocity methods, each claiming incremental gains, GTVelo’s contribution lies in bringing the attention machinery of modern deep learning to bear on a problem that has long been constrained by local graph assumptions and single-origin trajectory models. Whether graph transformers become a standard component of the single-cell dynamics toolbox, the study adds a technically grounded option for researchers wrestling with the messy, branching reality of cellular differentiation.

Subject of Research: RNA velocity inference in single-cell transcriptomics using a graph transformer-based neural ordinary differential equation model

Article Title: RNA velocity inference based on graph transformer

Article References: Huang, S., Wang, Z., Zhang, H., Wang, H., Zhao, J., & Xia, J. (2026). RNA velocity inference based on graph transformer. BMC Bioinformatics. https://doi.org/10.1186/s12859-026-06550-9

Image Credits: AI Generated

DOI: 10.1186/s12859-026-06550-9

Keywords: RNA velocity, single-cell RNA sequencing, graph transformer, neural ordinary differential equations, trajectory inference, cell differentiation, deep learning, multi-head attention, multi-omics integration, transcriptomics, computational biology, BMC Bioinformatics