Statistical Inference for the Evolutionary History of Cancer Genomes.

ABSTRACT: Recent years have seen considerable work on inference about cancer evolution from mutations identified in cancer samples. Much of the modeling work has been based on classical models of population genetics, generalized to accommodate time-varying cell population size. Reverse-time, genealogical views of such models, commonly known as coalescents, have been used to infer aspects of the past of growing populations. Another approach is to use branching processes, the simplest scenario being the classical linear birth-death process. Inference from evolutionary models of DNA often exploits summary statistics of the sequence data, a common one being the so-called Site Frequency Spectrum (SFS). In a bulk tumor sequencing experiment, we can estimate for each site at which a novel somatic point mut

SUBMITTER: Dinh KN 

PROVIDER: S-EPMC12519734 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature