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Abstract

Phylogenies are increasingly used in all fields of medical and biological research. Moreover, because of the next generation sequencing revolution, datasets used for conducting phylogenetic analyses grow at an unprecedented pace. RAxML (Randomized Axelerated Maximum Likelihood) is a popular program for phylogenetic analyses of large datasets under maximum likelihood. Since the last RAxML paper in 2006, it has been continuously maintained and extended to accommodate the increasingly growing input datasets and to serve the needs of the user community. I present some of the most notable new features and extensions of RAxML, such as, a substantial extension of substitution models and supported data types, the introduction of SSE3, AVX, and AVX2 vector intrinsics, techniques for reducing the memory requirements of the code and a plethora of operations for conducting post-analyses on sets of trees. In addition, an up-to-date, 50 page user manual covering all new RAxML options is available. The code is available under GNU GPL at https://github.com/stamatak/standard-RAxML. Alexandros.Stamatakis@h-its.org.
Vol. 30 no. 9 2014, pages 1312–1313
BIOINFORMATICS APPLICATIONS NOTE doi:10.1093/bioinformatics/btu033
Phylogenetics Advance Access publication January 21, 2014
RAxML version 8: a tool for phylogenetic analysis and
post-analysis of large phylogenies
Alexandros Stamatakis
1,2
1
Scientific Computing Group, Heidelberg Institute for Theoretical Studies, 69118 Heidelberg and
2
Department of
Informatics, Institute of Theoretical Informatics, Karlsruhe Institute of Technology, 76128 Karlsruhe, Germany
Associate Editor: Jonathan Wren
ABSTRACT
Motivation: Phylogenies are increasingly used in all fields of medical
and biological research. Moreover, because of the next-generation
sequencing revolution, datasets used for conducting phylogenetic
analyses grow at an unprecedented pace. RAxML (Randomized
Axelerated Maximum Likelihood) is a popular program for phylogen-
etic analyses of large datasets under maximum likelihood. Since the
last RAxML paper in 2006, it has been continuously maintained and
extended to accommodate the increasingly growing input datasets
and to serve the needs of the user community.
Results: I present some of the most notable new features and
extensions of RAxML, such as a substantial extension of substitution
models and supported data types, the introduction of SSE3, AVX and
AVX2 vector intrinsics, techniques for reducing the memory require-
ments of the code and a plethora of operations for conducting post-
analyses on sets of trees. In addition, an up-to-date 50-page user
manual covering all new RAxML options is available.
Availability and implementation: The code is available under GNU
GPL at https://github.com/stamatak/standard-RAxML.
Contact: alexandros.stamatakis@h-its.org
Supplementary information: Supplementary data are available at
Bioinformatics online.
Received on December 22, 2013; revised and accepted on
January 14, 2014
1INTRODUCTION
RAxML (Randomized Axelerated Maximum Likelihood) is a
popular program for phylogenetic analysis of large datasets
under maximum likelihood. Its major strength is a fast maximum
likelihood tree search algorithm that returns trees with good
likelihood scores. Since the last RAxML paper (Stamatakis,
2006), it has been continuously maintained and extended to ac-
commodate the increasingly growing input datasets and to serve
the needs of the user community. In the following, I will present
some of the most notable new features and extensions of RAxML.
2 NEW FEATURES
2.1 Bootstrapping and support values
RAxML offers four different ways to obtain bootstrap support.
It implements the standard non-parametric bootstrap and also
the so-called rapid bootstrap (Stamatakis et al., 2008), which is a
standard bootstrap search that relies on algorithmic shortcuts
and approximations to speed up the search process.
It also offers an option to calculate the so-called SH-like
support values (Guindon et al., 2010). I recently implemented
a method that allows for computing RELL (Resampling
Estimated Log Likelihoods) bootstrap support as described by
Minh et al. (2013).
Apart from this, RAxML also offers a so-called bootstopping
option (Pattengale et al.,2010).Whenthisoptionisused,
RAxML will automatically determine how many bootstrap rep-
licates are required to obtain stable support values.
2.2 Models and data types
Apart from DNA and protein data, RAxML now also supports
binary, multi-state morphological and RNA secondary structure
data. It can correct for ascertainment bias (Lewis, 2001) for all of
the above data types. This might be useful not only for morpho-
logical data matrices that only contain variable sites but also for
alignments of SNPs.
The number of available protein substitution models has been
significantly extended and comprises a general time reversible
(GTR) model, as well as the computationally more complex
LG4M and LG4X models (Le et al., 2012). RAxML can also
automatically determine the best-scoring protein substitution
model.
Finally, a new option for conducting a maximum likelihood
estimate of the base frequencies has become available.
2.3 Parallel versions
RAxML offers a fine-grain parallelization of the likelihood func-
tion for multi-core systems via the PThreads-based version and a
coarse-grain parallelization of independent tree searches via MPI
(Message Passing Interface). It also supports coarse-grain/fine-
grain parallelism via the hybrid MPI/PThreads version (Pfeiffer
and Stamatakis, 2010).
Note that, for extremely large analyses on supercomputer s,
using the dedicated sister program ExaML [Exascale Maximum
Likelihood (Stamatakis and Aberer, 2013)] is recommended.
2.4 Post-analysis of trees
RAxML offers a plethora of post-analysis functions for sets of
trees. Apart from standard statistical significance tests, it offers
efficient (and partially parallelized) operations for computing
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Robinson–Foulds distances, as well as extended majority rule,
majority rule and strict consensus trees (Aberer et al., 2010).
Beyond this, it implements a method for identifying the so-
called rogue taxa (Pattengale et al., 2011), and I recently imple-
mented options for calculating the TC (Tree Certainty) and IC
(Internode Certainty) measures as introduced by Salichos and
Rokas (2013).
Finally, there is the new plausibility checker option (Dao et al.,
2013) that allows computing the RF distances between a huge phyl-
ogeny with tens of thousands of taxa and several smaller more
accurate reference phylogenies that contain a strict subset of the
taxa in the huge tree. This option can be used to automatically
assess the quality of huge trees that can not be inspected by eye.
2.5 Analyzing next-generation sequencing data
RAxML offers two algorithms for preparing and analyzing next-
generation sequencing data. A sliding-window approach (unpub-
lished) is available to assess which regions of a gene (e.g. 16S)
exhibit strong and stable phylogenetic signal to support decisions
about which regions to amplify. Apart from that, RAxML also
implements parsimony and maximum likelihood flavors of the
evolutionary placement algorithm [EPA (Berger et al., 2011)]
that places short reads into a given reference phylogeny obtained
from full-length sequences to determine the evolutionary origin
of the reads. It also offers placement support statistics for those
reads by calculating likelihood weights. This option can also be
used to place fossils into a given phylogeny (Berger and
Stamatakis, 2010) or to insert different outgroups into the tree
a posteriori, that is, after the inference of the ingroup phylogeny.
2.6 Vector intrinsics
RAxML uses manually inserted and optimized x86 vector intrin-
sics to accelerate the parsimony and likelihood calculations.
It supports SSE3, AVX and AVX2 (using fused multiply-add
instructions) intrinsics. For a small single-gene DNA alignment
using the model of rate heterogeneity, the unvectorized version
of RAxML requires 111.5 s, the SSE3 version 84.4 s and the
AVX version 66.22 s to complete a simple tree search on an
Intel i7-2620 M core running at 2.70 GHz under Ubuntu Linux.
The differences between AVX and AVX2 are less pronounced
and are typically below 5% run time improvement.
2.7 Saving memory
Because memory shortage is becoming an issue due to the grow-
ing dataset sizes, RAxML implements an option for reducing
memory footprints and potentially run times on large phyloge-
nomic datasets with missing data. The memory savings are pro-
portional to the amount of missing data in the alignment
(Izquierdo-Carrasco et al.,2011)
2.8 Miscellaneous new options
RAxML offers options to conduct fast and more superficial tree
searches on datasets with tens of thousands of taxa. It can also
compute marginal ancestral states and offers an algorithm for
rooting trees. Furthermore, it implements a sequential,
PThreads-parallelized and MPI-parallelized algorithm for com-
puting all quartets or a subset of quartets for a given alignment.
3 USER SUPPORT AND FUTURE WORK
User support is provided via the RAxML Google group
at: https://groups.google.com/forum/?hl¼en#!forum/raxml. The
RAxML source code contains a comprehensive manual and
there is a step-by-step tutorial with some basic commands avail-
able at http://www.exelixis-lab.org/web/software/raxml/hands_
on.html. Further resources are available via the RAxML soft-
ware page at http://www.exelixis-lab.org/web/software/raxml/
Future work includes the continued maintenance of RAxML,
the adaptation to novel computer architectures and the implemen-
tation of novel models and datatypes, in particular codon models.
ACKNOWLEDGEMENT
The author thank several colleagues for contributing code to
RAxML: Andre J. Aberer, Simon Berger, Alexey Kozlov, Nick
Pattengale, Wayne Pfeiffer, Akifumi S. Tanabe, David Dao and
Charlie Taylor.
Funding: This work was funded by institutional funding provided
by the Heidelberg Institute for Theoretical Studies.
Conflict of Interest: none declared.
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Abstract-A hybrid MPI/Pthreads parallelization was implemented in the RAxML phylogenetics code. New MPI code was added to the existing Pthreads production code to exploit parallelism at two algorithmic levels simultaneously: coarse-grained with MPI and fine-grained with Pthreads. This hybrid, multi-grained approach is well suited for current high-performance computers, which typically are clusters of multicore, shared-memory nodes. The hybrid version of RAxML is especially useful for a comprehensive phylogenetic analysis, i.e., execution of many rapid bootstraps followed by a full maximum likelihood search. Multiple multi-core nodes can be used in a single run to speed up the computation and, hence, reduce the turnaround time. The hybrid code also allows more efficient utilization of a given number of processor cores. Moreover, it often returns a better solution than the stand-alone Pthreads code, because additional maximum likelihood searches are conducted in parallel using MPI. The comprehensive analysis algorithm involves four stages, in which coarse-grained parallelism continually decreases from stage to stage. The first three stages speed up well with MPI, while the last stage speeds up only with Pthreads. This leads to a tradeoff in effectiveness between MPI and Pthreads parallelization. The useful number of MPI processes increases with the number of bootstraps performed, but typically is limited to 10 or 20 by the parameters of the algorithm. The optimal number of Pthreads increases with the number of distinct patterns in the columns of the multiple sequence alignment, but is limited to the number of cores per node of the computer being used. For a benchmark problem with 218 taxa, 1,846 patterns, and 100 bootstraps run on the Dash computer at SDSC, the speedup of the hybrid code on 10 nodes (80 cores) was 6.5 compared to the Pthreads-only code on one node (8 cores) and 35 compared to the serial code. This run used 10 MPI processes with 8 Pthreads each. For an- - other problem with 125 taxa, 19,436 patterns, and 100 bootstraps, the speedup on the Triton PDAF computer at SDSC was 38 on two nodes (64 cores) compared to the serial code. This run used 2 MPI processes with 32 Pthreads each.
Conference Paper
The molecular data avalanche generated by novel wet-lab sequencing technologies allows for reconstructing phylogenies (evolutionary trees) using hundreds of complete genomes as input data. Therefore, scalable codes are required to infer trees on these data under likelihood-based models of molecular evolution. We recently introduced a checkpointable and scalable MPI-based code for this purpose called RAxML-Light and are currently using it for several real-world data analysis projects. It turned out that the scalability of RAxML-Light is nonetheless still limited because of the fork-join parallelization approach that is deployed. To this end, we introduce a novel, generally applicable, approach to computing the phylogenetic likelihood in parallel on whole-genome datasets and implement it in ExaML (Exascale Maximum Likelihood). ExaML executes up to 3.2 times faster than RAxML-Light because of the more efficient parallelization and communication scheme, while implementing exactly the same tree search algorithm. Moreover, the new parallelization approach exhibits lower code complexity and a more appropriate structure for implementing fault tolerance with respect to hardware failures.
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