RAxML-NG: a fast, scalable and user-friendly tool for maximum likelihood phylogenetic inference.

ABSTRACT:

Motivation

Phylogenies are important for fundamental biological research, but also have numerous applications in biotechnology, agriculture and medicine. Finding the optimal tree under the popular maximum likelihood (ML) criterion is known to be NP-hard. Thus, highly optimized and scalable codes are needed to analyze constantly growing empirical datasets.

Results

We present RAxML-NG, a from-scratch re-implementation of the established greedy tree search algorithm of RAxML/ExaML. RAxML-NG offers improved accuracy, flexibility, speed, scalability, and usability compared with RAxML/ExaML. On taxon-rich datasets, RAxML-NG typically finds higher-scoring trees than IQTree, an increasingly popular recent tool for ML-based phylogenetic inference (although IQ-Tree shows better stabil

SUBMITTER: Kozlov AM 

PROVIDER: S-EPMC6821337 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature