A Rapid Bootstrap Algorithm for the RAxML Web Servers

Abstract

Despite recent advances achieved by application of high-performance computing methods and novel algorithmic techniques to maximum likelihood (ML)-based inference programs, the major computational bottleneck still consists in the computation of bootstrap support values. Conducting a probably insufficient number of 100 bootstrap (BS) analyses with current ML programs on large datasets-either with respect to the number of taxa or base pairs-can easily require a month of run time. Therefore, we have developed, implemented, and thoroughly tested rapid bootstrap heuristics in RAxML (Randomized Axelerated Maximum Likelihood) that are more than an order of magnitude faster than current algorithms. These new heuristics can contribute to resolving the computational bottleneck and improve current methodology in phylogenetic analyses. Computational experiments to assess the performance and relative accuracy of these heuristics were conducted on 22 diverse DNA and AA (amino acid), single gene as well as multigene, real-world alignments containing 125 up to 7764 sequences. The standard BS (SBS) and rapid BS (RBS) values drawn on the best-scoring ML tree are highly correlated and show almost identical average support values. The weighted RF (Robinson-Foulds) distance between SBS- and RBS-based consensus trees was smaller than 6% in all cases (average 4%). More importantly, RBS inferences are between 8 and 20 times faster (average 14.73) than SBS analyses with RAxML and between 18 and 495 times faster than BS analyses with competing programs, such as PHYML or GARLI. Moreover, this performance improvement increases with alignment size. Finally, we have set up two freely accessible Web servers for this significantly improved version of RAxML that provide access to the 200-CPU cluster of the Vital-IT unit at the Swiss Institute of Bioinformatics and the 128-CPU cluster of the CIPRES project at the San Diego Supercomputer Center. These Web servers offer the possibility to conduct large-scale phylogenetic inferences to a large part of the community that does not have access to, or the expertise to use, high-performance computing resources.

Evolution of pollen morphology in Loranthaceae

“…All data files, primary matrix NEXUS files optimised for Mesquite, can be found in the OSM (File S2). Phylogenetic tree inference and bootstrapping relied on RAxML v. 7.2.6 (Stamatakis 2006; Stamatakis et al 2008; set-up details are provided in File S2).…”

A nuclear DNA barcode for eastern North American oaks and application to a study of hybridization in an Arboretum setting

“…UPGMA trees were calculated in Geneious R9 (Kearse et al., 2012). The RAxML BlackBox Web server was used to run ML analysis with default settings (Stamatakis, Hoover, & Rougemont, 2008), and trees were uploaded to iTOL for annotation (Letunic & Bork, 2016). The Eurasian white oaks ( Q. petraea , Q. robur , Q. fabri , and Q. aliena ) were used to root the ML tree for purposes of visualization.…”

Genomic adaptation to polyphagy and insecticides in a major East Asian noctuid pest

“…The maximum likelihood tree was inferred by RaxML version 8 79 using the LG+ Gamma+ I model. To evaluate the confidence of the tree topology, the bootstrap method 80 was applied with 1000 replications using the rapid bootstrap algorithm 81 .…”