AI Article Synopsis
- Phylogenies are crucial for biological research and have applications in fields like biotechnology and medicine, but finding optimal trees using maximum likelihood methods is computationally challenging.
- RAxML-NG is a new implementation that improves upon the previous RAxML/ExaML algorithm, offering better accuracy, speed, and new features while analyzing complex datasets.
- The RAxML-NG code is accessible under GNU GPL, with a web service available for users, and additional supplementary data is provided online.
Article 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 stability). Finally, RAxML-NG introduces several new features, such as the detection of terraces in tree space and the recently introduced transfer bootstrap support metric.
Availability And Implementation: The code is available under GNU GPL at https://github.com/amkozlov/raxml-ng. RAxML-NG web service (maintained by Vital-IT) is available at https://raxml-ng.vital-it.ch/.
Supplementary Information: Supplementary data are available at Bioinformatics online.
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Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6821337 | PMC |
| http://dx.doi.org/10.1093/bioinformatics/btz305 | DOI Listing |
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