Accounting for Errors in Data Improves Divergence Time Estimates in Single-cell Cancer Evolution.

ABSTRACT: Single-cell sequencing provides a new way to explore the evolutionary history of cells. Compared to traditional bulk sequencing, where a population of heterogeneous cells is pooled to form a single observation, single-cell sequencing isolates and amplifies genetic material from individual cells, thereby preserving the information about the origin of the sequences. However, single-cell data are more error-prone than bulk sequencing data due to the limited genomic material available per cell. Here, we present error and mutation models for evolutionary inference of single-cell data within a mature and extensible Bayesian framework, BEAST2. Our framework enables integration with biologically informative models such as relaxed molecular clocks and population dynamic models. Our simulations show

SUBMITTER: Chen K 

PROVIDER: S-EPMC9356729 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature