Relating mutational signature exposures to clinical data in cancers via signeR 2.0.

ABSTRACT:

Background

Cancer is a collection of diseases caused by the deregulation of cell processes, which is triggered by somatic mutations. The search for patterns in somatic mutations, known as mutational signatures, is a growing field of study that has already become a useful tool in oncology. Several algorithms have been proposed to perform one or both the following two tasks: (1) de novo estimation of signatures and their exposures, (2) estimation of the exposures of each one of a set of pre-defined signatures.

Results

Our group developed signeR, a Bayesian approach to both of these tasks. Here we present a new version of the software, signeR 2.0, which extends the possibilities of previous analyses to explore the relation of signature exposures to other data of clinical releva

SUBMITTER: Drummond RD 

PROVIDER: S-EPMC10664385 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature