Motivation: Trajectory inference for single cell RNA sequencing (scRNAseq) data is a powerful approach to understand time-dependent cellular processes such as cell cycle and cellular development. However, it is still not easy to infer the trajectory precisely by which cells differentiate to multiple lineages or exhibit cyclic transitions. Recent development of RNA velocity provides a way to visualize cell state transition without a prior knowledge. Trajectory inference that utilizes the

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... information will be highly useful to understand cellular dynamics. Results: We developed VeTra, a tool to infer the trajectories from scRNAseq data. Uniquely, VeTra can perform grouping of cells that are in the same stream of trajectory. For this, VeTra searches for weakly connected components of the directed graph obtained from RNA velocity. Therefore, VeTra makes it easy to define groups of cells from the origin and to the end of a certain trajectory. VeTra has been tested to infer the streams of cells for pancreatic development, neural development in hippocampus and cell cycle. VeTra is a useful tool to perform pseudo-time analysis from the start to the end of each group.