Single-cell sequencing has revolutionized the study of gene expression and its phenotypic consequences by enabling the simultaneous profiling of thousands of individual cells. Recent advancements in multimodal single-cell sequencing have further expanded the scope of these techniques, allowing for the integration of transcriptomics, epigenomics, proteomics, and other omic data to obtain a more comprehensive view of cellular states and dynamics. A specific application of multiomics single-cell sequencing is lineage tracing, which provides insights into the developmental process from pluripotent cell populations to fully differentiated states. This thesis proposes scVEMO, a multiomics-based approach to lineage tracing leveraging CellRank and the RNA-velocity estimation techniques, scVELO, and its extension to changes in chromatin states, Multivelo.