Rapid likelihood analysis on large phylogenies using partial sampling of substitution histories.

ABSTRACT: Likelihood-based approaches can reconstruct evolutionary processes in greater detail and with better precision from larger data sets. The extremely large comparative genomic data sets that are now being generated thus create new opportunities for understanding molecular evolution, but analysis of such large quantities of data poses escalating computational challenges. Recently developed Markov chain Monte Carlo methods that augment substitution histories are a promising approach to alleviate these computational costs. We analyzed the computational costs of several such approaches, considering how they scale with model and data set complexity. This provided a theoretical framework to understand the most important computational bottlenecks, leading us to combine novel variations of our condi

SUBMITTER: de Koning AP 

PROVIDER: S-EPMC2877550 | biostudies-literature | 2010 Feb

REPOSITORIES: biostudies-literature