A Bayesian Framework for Inferring the Influence of Sequence Context on Point Mutations.

ABSTRACT: The probability of point mutations is expected to be highly influenced by the flanking nucleotides that surround them, known as the sequence context. This phenomenon may be mainly attributed to the enzyme that modifies or mutates the genetic material, because most enzymes tend to have specific sequence contexts that dictate their activity. Here, we develop a statistical model that allows for the detection and evaluation of the effects of different sequence contexts on mutation rates from deep population sequencing data. This task is computationally challenging, as the complexity of the model increases exponentially as the context size increases. We established our novel Bayesian method based on sparse model selection methods, with the leading assumption that the number of actual sequence c

SUBMITTER: Ling G 

PROVIDER: S-EPMC7038660 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature