Learning mutational graphs of individual tumour evolution from single-cell and multi-region sequencing data.

ABSTRACT:

Background

A large number of algorithms is being developed to reconstruct evolutionary models of individual tumours from genome sequencing data. Most methods can analyze multiple samples collected either through bulk multi-region sequencing experiments or the sequencing of individual cancer cells. However, rarely the same method can support both data types.

Results

We introduce TRaIT, a computational framework to infer mutational graphs that model the accumulation of multiple types of somatic alterations driving tumour evolution. Compared to other tools, TRaIT supports multi-region and single-cell sequencing data within the same statistical framework, and delivers expressive models that capture many complex evolutionary phenomena. TRaIT improves accuracy, robustness to data-

SUBMITTER: Ramazzotti D 

PROVIDER: S-EPMC6485126 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature