State aggregation for fast likelihood computations in molecular evolution.

ABSTRACT:

Motivation

Codon models are widely used to identify the signature of selection at the molecular level and to test for changes in selective pressure during the evolution of genes encoding proteins. The large size of the state space of the Markov processes used to model codon evolution makes it difficult to use these models with large biological datasets. We propose here to use state aggregation to reduce the state space of codon models and, thus, improve the computational performance of likelihood estimation on these models.

Results

We show that this heuristic speeds up the computations of the M0 and branch-site models up to 6.8 times. We also show through simulations that state aggregation does not introduce a detectable bias. We analyzed a real dataset and show that aggrega

SUBMITTER: Davydov II 

PROVIDER: S-EPMC5408795 | biostudies-literature | 2017 Feb

REPOSITORIES: biostudies-literature