MultiPhyl: a high-throughput phylogenomics webserver using distributed computing.

Nucleic Acids Research, 2007, Vol. 35, Web Server issue W33–W37
doi:10.1093/nar/gkm359
MultiPhyl: a high-throughput phylogenomics
webserver using distributed computing
Thomas M. Keane1,2,*, Thomas J. Naughton3 and James O. McInerney2
1Pathogen Sequencing Unit, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, CB10
1SA Hinxton, UK, 2Department of Biology, National University of Ireland, Maynooth, Co. Kildare, Ireland and
3Department of Computer Science, National University of Ireland, Maynooth, Co. Kildare, Ireland
Received January 8, 2007; Revised April 18, 2007; Accepted April 25, 2007
ABSTRACT
With the number of fully sequenced genomes
increasing steadily, there is greater interest in
performing large-scale phylogenomic analyses
from large numbers of individual gene families.
Maximum likelihood (ML) has been shown repeat-
edly to be one of the most accurate methods
for phylogenetic construction. Recently, there have
been a number of algorithmic improvements in
maximum-likelihood-based tree search methods.
However, it can still take a long time to analyse
the evolutionary history of many gene families using
a single computer. Distributed computing refers to a
method of combining the computing power of
multiple computers in order to perform some
larger overall calculation. In this article, we present
the first high-throughput implementation of a
distributed phylogenetics platform, MultiPhyl,
capable of using the idle computational resources
of many heterogeneous non-dedicated machines
to form a phylogenetics supercomputer. MultiPhyl
allows a user to upload hundreds or thousands of
amino acid or nucleotide alignments simultaneously
and perform computationally intensive tasks
such as model selection, tree searching and boot-
strapping of each of the alignments using many
desktop machines. The program implements a set
of 88 amino acid models and 56 nucleotide max-
imum likelihood models and a variety of statistical
methods for choosing between alternative models.
A MultiPhyl webserver is available for public use at:
http://www.cs.nuim.ie/distributed/multiphyl.php.
INTRODUCTION
One of the greatest goals of biology is the reconstruction
of the tree of life. Recent advances in high-throughput
sequencing technologies have seen an exponential increase
in the amount of publicly available genomic data. The
huge increase in genomic data requires us to develop
efficient high-throughput methods for analysing this data.
Nowadays it is usual for whole-genome phylogenetic
studies to comprise of thousands of different genes and
search through large numbers of possible phylogenies.
For example, in supertree analysis, researchers first
construct a large number of fully optimized phylogenies
with partially overlapping taxa and combine these input
trees to produce more comprehensive phylogenetic
hypotheses (e.g. 1,2). One of the most accurate techniques
for constructing phylogenetic trees is using maximum
likelihood (ML) estimation (3).
Recently, there have been significant advances in model-
based tree search methods that can be mainly attributed to
algorithmic improvements rather than hardware improve-
ments. Programs such as Phyml (4), RaxML (5), MrBayes
(6) and IQPNNI (7) have each introduced better and
faster heuristics that have been shown to repeatedly
outperform traditional ML programs such as
fastDNAml (8) in terms of computing time and topo-
logical accuracy. The impressive results presented by
Guindon and Gascuel (4) serve as an example of the extent
to which these algorithmic improvements have made it
possible to use ML programs to construct relatively large
phylogenies quickly using standard hardware. Despite
these improvements, it can still take a considerable
amount of time to perform large-scale phylogenetic
analyses involving thousands of alignments using only a
single processor. One popular method of addressing this
limitation is to take a number of closely coupled
processors to form a dedicated processing cluster for
performing high-throughput phylogenetic analysis (9–11).
Although extremely effective, these clusters can be
prohibitively expensive as they often require dedicated
processors and specialized personnel to maintain these
systems. On the other hand, it has been shown in a recent
study by IBM that Windows and UNIX servers are idle
for approximately 95 and 85% of the day respectively (12)
while another study showed that on average, desktop
machines are idle for between 60 and 80% of the day (13).
*To whom correspondence should be addressed. Tel: þ44 (0) 1223 494954; Fax: þ44 (0)1223 494919; Email: tkeane@cs.nuim.ie
� 2007 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/
by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

The term distributed computing is often taken to mean
computing using a non-dedicated pre-existing pool of
heterogeneous processors that are not localized in a single
room or building and can fail arbitrarily. By harnessing the
spare clock cycles of multiple semi-idle desktop machines
(a semi-idle machine is amachine primarily used by another
individual that, because of the nature of the usage, is idle
some of the time, e.g. a computer in a university teaching
laboratory), it is possible to emulate the computing power
offered by specialized dedicated hardware at a fraction of
the cost (14). Recently, a number of authors have identified
the suitability of distributed computing for phylogenetic
analysis and shown how it is possible to harness the idle
clock cycles of hundreds of standard desktop machines to
perform phylogenetic analysis (15,16).
In this article, we introduce MultiPhyl, which is a fully
cross-platform high-throughput ML-based phylogeny
analysis program that allows researchers to create a
virtual phylogenetic supercomputer from a group of
semi-idle desktop machines. The MultiPhyl webserver is
aimed at researchers who wish to perform phylogenetic
analysis of large numbers of alignments in short amounts
of time but do not have access to dedicated high-
performance computational resources.
MULTIPHYL
MultiPhyl is one of a number of applications that runs on
our general-purpose distributed computing system (16,17).
The overall design of the system is based on the client-
server model. This model describes a system consisting of
a single server computer and a number of client
computers. Our system is divided into three separate
pieces of software: server, client and remote interface (18).
Apart from the server, the system is completely dynamic
meaning that new donor machines (a donor machine is
owned by someone that offers their machines spare
clock cycles to MultiPhyl) can be added at any time and
existing donor machines can fail arbitrarily with no
adverse effect to the computations. We have deployed
the distributed system across our university campus using
the idle computing resources of several hundred desk-
top computers (see http://www.cs.nuim.ie/distributed/
statistics.php for details).
The overall flow of control in MultiPhyl can be broken
up into three distinct stages: model selection, tree searching
and bootstrapping. The user uploads a group of n
nucleotide or amino acid alignments in either FASTA or
PHYLIP file format. MultiPhyl makes extensive use
of the Java-based Phylogenetic Analysis Library (PAL)
v1.5 (19) to perform all likelihood calculations. Each of the
analysis stages (model selection, tree searching and boot-
strapping) operates completely asynchronously across
different alignments, thus achieving maximum speedup
and avoiding performance bottlenecks. One of the essential
requirements for ML phylogenetic inference is a model of
amino acid or nucleotide substitution. It has been shown
that using incorrect or suboptimal substitution models can
produce less accurate or incorrect phylogenies when the
wrong model of evolution is assumed (20). Therefore,
it may be useful to first compute the most suitable model
before performing a treesearch. However, model selection
is one area that has been overlooked as an essential part of
the phylogeny construction process by many other parallel
phylogenetic construction programs. We have recognized
the importance of integrating model selection into all
phylogenetic studies and therefore MultiPhyl allows the
user to perform model selection on every alignment prior
to all tree searches (20). The task of testing multiple
substitution models against an alignment is very amenable
to parallelization as each model can be optimized com-
pletely independently and the corresponding likelihoods
can be collected and analysed at the server to determine the
best model. In order to gain the maximum speedup for
the model selection stage of the program, we implemented
an adaptive parallel strategy where the server first recruits a
single donor machine to construct the initial NJ tree to be
used to optimize each model. The parallel granularity of a
model selection stage is estimated dynamically by recording
howmanymodels are optimized by the first donor machine
inmmin (where the default value ofm is 15min). Although
it may seem that the optimal parallel granularity would be
to just issue a single model per donor machine, this could
have a number of adverse effects on the distributed system
such as overloading the non-dedicated network or over-
loading the server with many simultaneous connections.
After the server receives the first set of o optimized models,
it issues groups of o models to multiple donor machines
to be optimized. As MultiPhyl is based on a network of
heterogeneous processors with varying computational
abilities, each donor machine has the option to return a
partial set of p optimized models (where p is the number
of models optimized by the particular machine within
15min and p5o) with the unoptimized models being
redistributed to other machines, thereby avoiding a bottle-
neck that might be caused by a few slow donor machines in
the system.
In order to address the trade-off between running time
and accuracy when performing large-scale analysis, we
have implemented two different tree search algorithms in
MultiPhyl and given the user the option to select which
algorithm they wish to use to analyse their dataset.
The first tree- building algorithm initially constructs an NJ
tree and then iteratively improves the trees by repeatedly
performing all possible simultaneous NNI and local
branch length optimization swaps that improve the
likelihood until convergence (4). The model parameters
are also optimized after each round of NNI swaps.
Finally, all of the branch lengths are globally optimized to
improve the final likelihood of the tree. The second tree
search algorithm implemented in MultiPhyl allows the
user to define a maximal depth of subtree pruning and
regrafting (SPR) rearrangements to perform from each
node of the tree. The SPR tree search algorithm begins by
creating a starting tree by performing the NNI search
algorithm outlined above and then iteratively visits
each node of the tree and performs SPR rearrangements
spanning increasing levels of the tree (beginning at 2 levels
and continuing up to a user-defined maximum). In order
to accommodate both long tree searches and the varying
computational abilities of the available donor machines,
W34 Nucleic Acids Research, 2007, Vol. 35,Web Server issue

if an individual tree search has not converged after 60min
on a particular machine, then the current state of the tree
search is saved and returned to the server. This tree search
is then re-issued to another donor machine to continue
optimizing the tree. This also ensures that if a donor
machine fails (e.g. crashes or is switched off), it is only
possible to lose a maximum of approximately 60min
computing time for any tree search.
One significant challenge in a distributed computing
environment is the huge disparity of available memory on
the donor machines. This is a particular issue in
phylogenetics as the amount of memory consumed by
ML-based phylogeny programs increases linearly with
sequence length and number of taxa. MultiPhyl imple-
ments a dynamic memory scheme where the minimum
memory requirements of each task is first set to some
initial value (default is 60Mb). When a donor machine
requests a work unit, the donor machine can only be
issued a particular task (model optimization or tree
search) that has a memory requirement less than the
amount of memory available to the Java Virtual Machine
(JVM) on the donor machine. If a donor machine reports
that the JVM ran out of memory while attempting to
perform a particular task, the server dynamically increases
the minimum memory requirements of that individual
task to a level that is suitable for the task. If no machine
with sufficient memory can be detected after q attempts
(default value for q is 5), then the problem is removed
from the system with an appropriate entry in the log file
returned to the user. This dynamic scheme ensures that
each phylogenetic computation can gain access to the
maximum amount of computational resources available to
MultiPhyl as the user is not required to decide a priori
what types of machines are capable of processing their
dataset.
WEBSERVER
The MultiPhyl webserver (http://www.cs.nuim.ie/distributed/
multiphyl.php) allows any researcher to upload multiple
amino acid or nucleotide alignments (in FASTA or
PHYLIP format) to be analysed on our network of
computers. A user can upload either a single alignment file
or a zip file containing up to 1000 alignments. The user
must provide a valid email address to which the results are
sent when the analysis is finished. Each alignment is first
checked for formatting errors and an email is sent to
notify the user if any errors are found. The webserver is set
to perform model selection by default and the user chooses
which tree search algorithm to use and whether or not to
perform bootstrapping. For each alignment, a compre-
hensive report file is produced with information on the
alignment (number of sites, gap content, constant sites),
search parameters, model selection ranking information,
sequence-composition chi-squared test, final likelihood,
human-readable tree, phylip format tree and a
bipartition table constructed from the bootstrap
replicates.
A user is presented with a web submission form when
they log onto the MultiPhyl homepage. To avail of the
service, the user first selects their alignment file (or a zip
file containing their alignments). The user then uses the
check-boxes to specify which tree search algorithm (NNI
or SPR) to use for their analysis. Next, the user can select
whether to perform bootstrapping on their dataset and
enter how many bootstrap replicates to carry out in the
text box. All users must enter their name, institution and
email address in the text-boxes provided. Finally, the user
presses the execute button to start the analysis on the
distributed system. Any errors found in the submission
information are displayed on the screen immediately.
VALIDATION
In order to benchmark the tree search algorithms
implemented in MultiPhyl, we ran MultiPhyl on a single
processor and compared it to a number of other
prominent tree search programs, using a number of
previously published well-known real alignments. We
obtained the three datasets used to benchmark
parallel fastDNAml (11) corresponding to 50 (1858 bp),
101 (1858 bp) and 150 (1269 bp) taxa. We also used the
218RDPII (4128 bp) dataset used to benchmark Phyml (4)
and the 150ARB (3188 bp), 193V (465 bp), 200ARB
(3270 bp) and 250ARB (3638 bp) datasets used to bench-
mark RAxML (5). The performance of six different
phylogeny construction programs across three different
architectures was recorded. As MultiPhyl optimizes the
base frequencies of the model, Phyml was executed using
the ‘F’ option to also optimize the base frequencies. The
final likelihood values and running times of each program
using the GTR (21) nucleotide substitution model are
given in Table 1. In all cases, the final likelihood values of
the trees were calculated using Phyml using the branch
and model optimisation option to allow for a fair
comparison of likelihood values.
In terms of final likelihood values, the performance of
MultiPhyl (NNI) and Phyml is quite similar. Phyml
achieves higher final likelihood values in four out of the
eight datasets (150SC, 193V, 218RDPII and 250ARB)
with MultiPhyl (NNI) achieving higher likelihoods in the
other four datasets (50SC, 101SC, 150ARB and 200ARB).
As both programs implement very similar tree search
algorithms, the differences in performance can be attrib-
uted to slight implementation differences of the search
algorithm in the two programs. For the smaller datasets
(50SC, 101SC, 150SC, 150ARB and 193V), the runtimes
of the two programs are quite comparable with MultiPhyl
(NNI) producing runtimes up to 1.5 times slower than
Phyml. However, the runtimes from the three largest
datasets (200ARB, 218RDPII, 250ARB) show more
significant differences between the two programs. The
performance of the more extensive tree search programs
[MultiPhyl (SPR), RAxML and IQPNNI] shows a
number of patterns. RAxML clearly outperforms the
two other programs in terms of final likelihood values.
In five out of the eight datasets (101SC, 150SC, 193V,
200ARB and 218RDPII), RAxML produced the highest
likelihoods of any of the programs tested. MultiPhyl
(SPR) produced the highest likelihood in one dataset
Nucleic Acids Research, 2007, Vol. 35,Web Server issue W35

(150ARB) with IQPNNI also producing the highest
likelihood for the 250ARB dataset. The scale of the
algorithmic improvements in recent years is apparent by
examining the performance of DPRml compared to all of
the other programs. DPRml is based on a traditional tree
search algorithm that has been in use for many years
(8,16). However, in three of the four datasets that DPRml
was executed on, it produced the lowest (worst) likelihood
values of all of the programs.
CONCLUSION
As the number of fully completed genomes increases, we
are becoming greatly interested in performing whole-
genome phylogenetic analyses by examining the evolu-
tionary history of large numbers of gene families. Despite
the recent improvements in ML-based tree search
programs, it can still take a long time to perform a full
ML phylogenetic analysis of multiple genes on a single
computer. In order to address these computational
challenges, we have recently developed the first high-
throughput implementation of a distributed phylogenetics
platform, MultiPhyl, capable of using the idle computa-
tional resources of many heterogeneous non-dedicated
machines to form a phylogenetics supercomputer.
MultiPhyl allows users to upload multiple amino acid or
nucleotide alignments and perform the tasks of ML model
selection, tree searching, and bootstrapping. We have
shown that the tree search algorithms implemented in
MultiPhyl are quite comparable to the fastest serial
phylogenetic programs. In future versions of MultiPhyl,
we intend to integrate some of the other computationally
intensive processes that are performed when carrying out a
typical phylogenomic analysis such as multiple sequence
alignment and simultaneous estimation of alignment and
tree parameters. We also intend to incorporate more
complex models of sequence evolution such as hetero-
geneous mixture models of nucleotide and amino acid
evolution. MultiPhyl is currently installed on several
hundred desktop computers in our university campus
and we encourage other institutions to also install
MultiPhyl. Perhaps the true significance of the
MultiPhyl webserver is that it allows researchers who
otherwise do not have access to high performance
computational hardware (e.g. in developing countries)
the opportunity to perform large phylogenomic studies.
ACKNOWLEDGEMENTS
Financial support for this project was provided by the
Irish research council for Science, Engineering and
Technology. We would like to thank all of the computer
technicians at the Department of Computer Science and
the Computer Center at NUIM for their continued
support and assistance in deploying the software across
the university campus. Funding to pay the Open Access
publication charges for this article was provided by the
Department of Biology, National University of Ireland,
Maynooth, Co. Kildare, Ireland.
Conflict of interest statement. None declared.
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50SC �43664 �43691 �43735 �43651 �43630 �43630
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