# Sequence embedding for fast construction of guide trees for multiple sequence alignment.

**ABSTRACT** The most widely used multiple sequence alignment methods require sequences to be clustered as an initial step. Most sequence clustering methods require a full distance matrix to be computed between all pairs of sequences. This requires memory and time proportional to N2 for N sequences. When N grows larger than 10,000 or so, this becomes increasingly prohibitive and can form a significant barrier to carrying out very large multiple alignments.

In this paper, we have tested variations on a class of embedding methods that have been designed for clustering large numbers of complex objects where the individual distance calculations are expensive. These methods involve embedding the sequences in a space where the similarities within a set of sequences can be closely approximated without having to compute all pair-wise distances.

We show how this approach greatly reduces computation time and memory requirements for clustering large numbers of sequences and demonstrate the quality of the clusterings by benchmarking them as guide trees for multiple alignment. Source code is available for download from http://www.clustal.org/mbed.tgz.

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**ABSTRACT:**Multiple sequence alignment is a crucial task in a number of biological analyses like secondary structure prediction, domain searching, phylogeny, etc. MSAProbs is currently the most accurate alignment algorithm, but its effectiveness is obtained at the expense of computational time. In the paper we present QuickProbs, the variant of MSAProbs customised for graphics processors. We selected the two most time consuming stages of MSAProbs to be redesigned for GPU execution: the posterior matrices calculation and the consistency transformation. Experiments on three popular benchmarks (BAliBASE, PREFAB, OXBench-X) on quad-core PC equipped with high-end graphics card show QuickProbs to be 5.7 to 9.7 times faster than original CPU-parallel MSAProbs. Additional tests performed on several protein families from Pfam database give overall speed-up of 6.7. Compared to other algorithms like MAFFT, MUSCLE, or ClustalW, QuickProbs proved to be much more accurate at similar speed. Additionally we introduce a tuned variant of QuickProbs which is significantly more accurate on sets of distantly related sequences than MSAProbs without exceeding its computation time. The GPU part of QuickProbs was implemented in OpenCL, thus the package is suitable for graphics processors produced by all major vendors.PLoS ONE 01/2014; 9(2):e88901. · 3.53 Impact Factor - [Show abstract] [Hide abstract]

**ABSTRACT:**MOTIVATION: Recent developments in sequence alignment software have made possible multiple sequence alignments of over 100,000 sequences in reasonable times. At present there are no systematic analyses concerning the scalability of the alignment quality as the number of aligned sequences is increased. RESULTS: We benchmarked a wide range of widely used MSA packages using a selection of protein families with some known structures and find that the accuracy of such alignments decreases markedly as the number of sequences grows. This is more or less true of all packages and protein families. The phenomenon is mostly due to the accumulation of alignment errors, rather than problems in guide-tree construction. This is partly alleviated by using iterative refinement or selectively adding sequences. The average accuracy of progressive methods by comparison with structure-based benchmarks can be improved by incorporating information derived from high-quality structural alignments of sequences with solved structures. This suggests that the availability of high quality curated alignments will have to complement algorithmic and/or software developments in the long term. AVAILABILITY: Benchmark data used in this study is available at http://www.clustal.org/omega/homfam-20110613-25.tar.gz and http://www.clustal.org/omega/bali3fam-26.tar.gz. CONTACT: fabian.sievers@ucd.ie.Bioinformatics 02/2013; · 5.47 Impact Factor - SourceAvailable from: Roney S Coimbra[Show abstract] [Hide abstract]

**ABSTRACT:**Multiple sequence alignment (MSA) is an extremely useful tool for molecular and evolutionary biology and there are several programs and algorithms available for this purpose. Although previous studies have compared the alignment accuracy of different MSA programs, their computational time and memory usage have not been systematically evaluated. Given the unprecedented amount of data produced by next generation deep sequencing platforms, and increasing demand for large-scale data analysis, it is imperative to optimize the application of software. Therefore, a balance between alignment accuracy and computational cost has become a critical indicator of the most suitable MSA program. We compared both accuracy and cost of nine popular MSA programs, namely CLUSTALW, CLUSTAL OMEGA, DIALIGN-TX, MAFFT, MUSCLE, POA, Probalign, Probcons and T-Coffee, against the benchmark alignment dataset BAliBASE and discuss the relevance of some implementations embedded in each program's algorithm. Accuracy of alignment was calculated with the two standard scoring functions provided by BAliBASE, the sum-of-pairs and total-column scores, and computational costs were determined by collecting peak memory usage and time of execution. Our results indicate that mostly the consistency-based programs Probcons, T-Coffee, Probalign and MAFFT outperformed the other programs in accuracy. Whenever sequences with large N/C terminal extensions were present in the BAliBASE suite, Probalign, MAFFT and also CLUSTAL OMEGA outperformed Probcons and T-Coffee. The drawback of these programs is that they are more memory-greedy and slower than POA, CLUSTALW, DIALIGN-TX, and MUSCLE. CLUSTALW and MUSCLE were the fastest programs, being CLUSTALW the least RAM memory demanding program. Based on the results presented herein, all four programs Probcons, T-Coffee, Probalign and MAFFT are well recommended for better accuracy of multiple sequence alignments. T-Coffee and recent versions of MAFFT can deliver faster and reliable alignments, which are specially suited for larger datasets than those encountered in the BAliBASE suite, if multi-core computers are available. In fact, parallelization of alignments for multi-core computers should probably be addressed by a higher number of programs in a near future, which will certainly improve performance significantly.Algorithms for Molecular Biology 03/2014; 9(1):4. · 1.61 Impact Factor

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Open Access

RESEARCH

Sequence embedding for fast construction of

guide trees for multiple sequence alignment

© 2010 Blackshields et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Com-

mons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduc-

tion in any medium, provided the original work is properly cited.

Research

Gordon Blackshields, Fabian Sievers*, Weifeng Shi, Andreas Wilm and Desmond G Higgins

Abstract

Background: The most widely used multiple sequence alignment methods require sequences to be clustered as an

initial step. Most sequence clustering methods require a full distance matrix to be computed between all pairs of

sequences. This requires memory and time proportional to N2 for N sequences. When N grows larger than 10,000 or so,

this becomes increasingly prohibitive and can form a significant barrier to carrying out very large multiple alignments.

Results: In this paper, we have tested variations on a class of embedding methods that have been designed for

clustering large numbers of complex objects where the individual distance calculations are expensive. These methods

involve embedding the sequences in a space where the similarities within a set of sequences can be closely

approximated without having to compute all pair-wise distances.

Conclusions: We show how this approach greatly reduces computation time and memory requirements for clustering

large numbers of sequences and demonstrate the quality of the clusterings by benchmarking them as guide trees for

multiple alignment. Source code is available for download from http://www.clustal.org/mbed.tgz.

Introduction

The majority of multiple sequence alignment (MSA)

methods use some form of progressive alignment [1-7].

In progressive alignment the usual first step is to compute

a pair-wise distance matrix which is then used to make a

so called guide tree, in order to determine the order of

alignment of the input sequences. The computation of

the distance matrix requires N (N - 1)/2 pair-wise com-

parisons, N being the number of sequences. Construction

of the guide tree, usually has an additional time complex-

ity of (N2) to (N3), depending on the algorithm used

and its implementation. The complexity of these steps

can become prohibitive when N becomes very large e.g.

when N is in the tens of thousands. There are very few

multiple alignment programs that can handle datasets of

this size, with MUSCLE and MAFFT being the most

familiar [6,7]. Some of the most accurate multiple

sequence alignment methods can only routinely handle

sequences numbering in the hundreds [4,8,9]. The explo-

sive growth in the number of sequences coming from

genomic studies means that the ability to cluster and

align greater numbers of sequences is becoming even

more important. For example, the Ribosomal Database

Project [10] Release 10 consists of more than a million

sequences.

In order to make very large guide trees, the first issue is

the sheer number of distance calculations. For example,

with 100,000 sequences, we need to compute approxi-

mately 5 billion distances to construct a complete dis-

tance matrix as needed by standard implementations of

Neighbor-Joining [11] or UPGMA [12]. Even if the

sequences are short, and pair-wise distance calculations

can be done relatively quickly, say at a rate of 5000s-1, this

still requires of the order of 1 million seconds (11.57 days)

of CPU time. Just to store the distance matrix is then dif-

ficult as it will take up of the order of 20 GB of disk space

and/or memory.

There are some shortcuts that can be taken to reduce

the number of distance calculations needed for cluster-

ing. For example, a recent paper by Katoh and Toh [13]

introduced the PartTree heuristic, which could rapidly

build a very rough guide tree from an initial small num-

ber of seed sequences, using a very fast 6-mer pair-wise

distance function and a divisive clustering algorithm with

an average time complexity of (N log N). This algo-

* Correspondence: fabian.sievers@ucd.ie

1 UCD Conway Institute of Biomolecular and Biomedical Sciences, University

College Dublin, Dublin 4, Ireland

Full list of author information is available at the end of the article

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rithm was incorporated into the MAFFT suite of multiple

sequence alignment programs [14]. They reported that

this heuristic allowed the rapid clustering and alignment

of approximately 60,000 sequences in only a few minutes.

When used for a progressive alignment this considerable

enhancement in speed came at a cost of several percent in

alignment accuracy, as benchmarked on the Pfam data-

base of aligned protein families [15].

In this study, we look at data embedding methods

[16,17] for rapidly calculating guide trees. Our goal is to

associate the sequences with a set of vectors in some t-

dimensional embedding space. Embedding is done in such

a way that the positioning of the vectors in the space

reflects the relationships between the original sequences

as best as possible. Having embedded a set of sequences,

the distances between the vectors will be much faster and

cheaper to calculate than distances computed using typi-

cal sequence alignment methods which require

(L3) time, L being the sequence length [18].

Several methods for embedding biological sequences

have already been applied to protein sequences. For

example, the Linial-London-Rabinovich (LLR) algorithm

[16] takes a number of subsets of sequences randomly

from the input dataset. Each individual sequence in the

dataset is then associated with a vector whose elements

are the distances between that sequence and the refer-

ence subsets (here, 'distance' is defined to be the mini-

mum distance between sequence and subset). The

number and size of the reference subsets only depends on

N, the number of sequences, such that each embedded

vector will be of dimensionality t = (log2 N)2. This algo-

rithm was reported to offer close distance preservation in

the embedded space, and was successfully applied to

38,000 sequences from the Swiss-Prot database [19],

revealing many natural biological groupings. However,

the original implementation meant that

distances had to be computed. SparseMap [17] was pro-

posed as a heuristic LLR variant which was applied in

much the same way as the original, but contains some

heuristics to speed up the embedding process, reducing

the number of pair-wise distances that had to be com-

puted from (N2) to (Nt).

The reference groups in both LLR and SparseMap are

generated randomly, meaning that a different embedding

is found after each run. For testing purposes, this means

the average result from several runs should therefore be

considered when comparing methods. When applying

UPGMA to the outputs from SparseMap embeddings

and using these clusterings as guide trees for multiple

alignments we found (results not shown) considerable

differences between runs, and these differences increase

as more divergent sequences are included. For these rea-

(L2) to

(N2) pair-wise

sons we introduced SeedMap [20] which is a simplifica-

tion of SparseMap which uses the same reference

sequences in every run and some heuristics to make fur-

ther increases in speed. SeedMap was found to be capable

of producing very fast embeddings of datasets numbering

in the 10s of thousands of sequences.

In this paper we look at the use of variations on Seed-

Map specifically for making guide trees for multiple

alignment. We name the resulting method mBed and

make it available with routines for sequence input and

options for the output of embedded vectors or guide

trees. This area of application requires high speed and

moderate memory requirements for routine use by biolo-

gists. Thus, we have tried to find a method that is as sim-

ple and fast as possible while losing as little accuracy as

possible compared to the use of a full distance matrix. We

test accuracy using standard multiple alignment bench-

marking methods [21,22]. We demonstrate the accuracy

of mBed guide trees by comparing these to randomised

guide trees and to guide trees directly calculated by

ClustalW [5]. We also compared the accuracy of the

guide trees to those from MAFFT and PartTree [7,13].

We demonstrate the scalability of the method by applying

it to a set of 380,000 tRNA sequences. Finally, we show a

useful by-product of the embedding process where we

can easily generate ordinations of large numbers of

sequences using Principal Coordinates Analysis (PCoA/

PCOORD) or Multi-Dimensional Scaling (MDS) [23].

Proposed method: mBed

Let X be our input dataset containing N sequences. We

need to consider two distance metrics associated with

these sequences. First we need a sequence distance [24]

to establish dis-similarities between any pair of sequences

x and y, denoted as d(x, y). In this paper we used the fast

k-tuple distance measure of Wilbur and Lipman [25], as

implemented in ClustalW [5], using the maximum possi-

ble k-tuple size of 2 (for protein), to make the distance

calculation as fast as possible. Each sequence x will even-

tually be associated with a vector F(x) in some t-dimen-

sional space, so we also need a metric to calculate the

distance between pairs of vectors. For this we simply use

the Euclidean distance metric which we denote as δ (F(x);

F(y)). The embedding is considered successful then if, for

all pairs of sequences, the embedded distances closely

approximate the sequence distances.

In SparseMap [17] and SeedMap [20], the t dimensions

above are distances from t subsets of the sequences. We

refer to these subsets as references. In Seed Map, we

aimed to improve the choice of reference groups by

attempting to identify natural clusters within the dataset

prior to embedding. This was found to be useful both for

increasing accuracy of the embedding but also for

increasing speed. In this paper, we try to gain further

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increases in speed by identifying single sequences from

our input data X to act as references. Ideally these

sequences, which shall be referred to here as "seeds",

should characterise the dataset as a whole, and should

therefore include representatives of natural groups/clus-

ters within the dataset, and also include outliers.

The number of references chosen by the LLR method

and SparseMap is a simple function of the number of

sequences. In our method, however, the number of seeds

chosen also depends to an extent on the nature of the

data. The aim is that when the input data contains very

homogeneous and similar sequences, very few seeds will

be required for the embedding, and the dimensionality t

will be small. Conversely, when more divergent sequences

are considered, the number of required seeds will natu-

rally increase. The proposed algorithm, which we name

mBed, is described next.

1. Initial seed selection

A number of t sequences are initially sampled from the

input dataset X. Following the LLR algorithm, this value

is set by default to t = (log2 N)2. This sampling is referred

to as R. Here, we chose to sample t sequences with con-

stant stride from a length-sorted X.

The seeds that have been chosen are then compared to

each other. If any two seeds are highly similar to each

other (below a certain distance threshold) the shorter one

is considered redundant, and is discarded. This threshold

is, by default, set to zero (so that only identical sequences

are excluded)

2. Analysis of potential seed sequences

The set of reference points R can now be used directly to

embed the input sequences (see step 3). Alternatively,

each seed sequence can be used to find extra seeds that

help better characterise the dataset. This can be done in

one of two ways.

'usePivotObjects' heuristic

Each seed sequence is used to find potential outliers.

First, the sequence that is furthest away from the seed is

identified. The sequence that is furthest away from that

sequence is then returned as a new seed.

For each seed sequence s in R:

1. Let l be the sequence in X that maximises d(l, s).

2. Let m be the sequence in X that maximises d(m, l).

3. Return m as a new seed.

'usePivotGroups' heuristic

This works in a similar way to the 'usePivotObjects' heu-

ristic, but finds groups rather than single sequences. It

first finds the sequence that is furthest away from the

seed, and then iteratively finds the sequence that is fur-

thest away from the group of those already chosen, i.e.:

For each seed sequence s in R:

1. Let l be the sequence in X that maximises d(l, s).

2. Let m be the sequence in X that maximises d(m, s)

+ d(m, l).

3. Let n be the sequence in X that maximises d(n, s) +

d(n, l) + d(n, m) ...etc.

The loop terminates if the same sequence is identified

more than once, or if the group reaches a set maximum

size. Each member of the group is then returned as a new

seed. As in step 1, before any sequences are accepted as

seeds, they are first compared to those already chosen,

and if they are found to be highly similar, they are

rejected as seeds.

3. Embedding of input sequences

After the seed sequences in R have been chosen, all

sequences in the input data are associated with a t-

dimensional vector. This is done simply by computing the

distances from all sequences to each of the t seeds. The

distances become the coordinate values of the embedded

vector, i.e. for each sequence s in X, let F(s) be the corre-

sponding embedded vector, such that F(s) = [d(s, R1), d(s,

R2)... d(s, Rt)].

Results

The embedding process entails the construction of vec-

tors representing biological sequences in such a way that

the distances between those vectors approximate the dis-

similarities between the sequences themselves. These

vector distances are orders of magnitude faster to calcu-

late than sequence distances, and this allows us to rapidly

generate a distance matrix δ (F(x), F(y)) from a set of

embedded sequences. For very large numbers of

sequences, perhaps numbering in the hundreds of thou-

sands, such distance matrices can become unmanageable,

due to sheer size. In these cases, the sequence vectors can

be clustered using an alternative clustering method such

as k-means. For this paper, our main aim is to be able to

rapidly generate guide trees which can be used to make

multiple alignments of the input sequences. Here, this is

done by applying the UPGMA clustering algorithm to the

embedded distance matrix. We then try to measure the

success of the overall procedure by (i) tree comparison

and (ii) comparing the multiple sequence alignments that

are generated using guide trees from embedded distance

matrices with those generated from full sequence dis-

tance matrices. This comparison is achieved using stan-

dard multiple alignment benchmarking procedures.

Attempts at directly comparing the distance matrices

using standard matrix comparison methods, such as

Stress [26], proved inconclusive, and results are not

shown here.

Quality assessment by direct tree comparison

As the mBed procedure progresses from distance matrix,

via guide tree to alignment, it should prove informative to

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assess the quality of the intermediate step, the guide tree.

For this we used the guide trees derived from (i) the full

distance matrix, (ii) the SparseMap method and (iii)

mBed. The full matrix guide trees were taken as the base-

line. We used the Robinson-Foulds (RF) metric [27], as

implemented by the treedist program of the PHYLIP

suite [28], to measure the distance of the SparseMap and

the mBed guide trees from the baseline. In Figure 1 we

plotted the RF distance of the SparseMap guide tree from

the full matrix guide tree versus the RF distance of the

mBed guide tree from the full matrix guide tree for the

BAliBase benchmark set of 386 test cases. As the RF mea-

sure has no immediate statistical interpretation, we sim-

ply make the qualitative observation that more points

(260 out of 386) lie above the bisectrix than on it (78) or

below it (48), suggesting that the SparseMap guide trees

are on average 'further away' from the full matrix guide

trees than the mBed guide trees.

Initial application to multiple sequence alignment

Typically, the quality of a multiple sequence alignment is

measured by comparison of the alignment to one from an

independently verified reference alignment. Initially, we

tested mBed on a small number of such test cases to

establish the approximate speed and accuracy of mBed

and its variations. The level of agreement between two

alignments can be assessed using the Column Score [29],

which measures the percentage of the columns of resi-

dues in the test alignment which agree with the columns

in the reference alignment. We use the qscore alignment

evaluation program to calculate the Column Score [6].

BAliBase [22,29] was the first large scale benchmark

dataset against which alignment programs were routinely

assessed. Test cases from this dataset are designed to

expose new methods to many different types of align-

ment problems. However, the test cases are relatively

small, and cannot show how alignment methods deal

with very large numbers of sequences. A collection of

larger test cases was therefore derived from Pfam [15,30]

so that accuracy when dealing with thousands of

sequences could be assessed. Each Pfam entry containing

up to 10,000 sequences and which had a corresponding

structural alignment for two or more of the sequences in

HOMSTRAD [21] was retrieved from the database. The

upper limit of 10,000 was set so that results derived from

using a full distance matrix could be included for com-

parison.

In each test case, assessment of the overall test align-

ment was made by using the sequences in common

between the Pfam and HOMSTRAD entry. This was usu-

ally just a relatively small number of sequences and

includes those with known 3D structures. The alignment

of these common sequences was then compared, using

qscore. This compares the alignment generated using the

guide tree, calculated using the embedded distances

against the corresponding HOMSTRAD structural align-

ment. We show the details of the timings and qscore

results for the ten largest of these test cases in Table 1.

Each entry contains 9,000-10,000 protein sequences. In

the same table, we also give the qscore results from using

a guide tree based on a full distance matrix from

sequence edit distances.

As can be seen in Table 1, the default mBed approach

(labelled (2)) requires an average of 53 seconds to embed

each entry, with a further 49 seconds to generate a dis-

tance matrix from the vectors. In total, this amounts to

less than 7% of the time required for computation of a full

pair-wise distance matrix (1533 seconds). This saving is

due to the considerable reduction in required distance

evaluations, and the increased speed at which distance

evaluations between the vectors can be made. The value

of t (the number of reference or seed sequences) ranged

from 143 to 169.

A UPGMA guide tree built from either distance matrix

then takes an average of 5 seconds to construct (data not

shown). This guide tree is passed to ClustalW to guide

the alignment of the input sequences. Assessment of the

alignment quality (and by association, of the embedding)

is made by comparison to the corresponding HOM-

STRAD entry using the Column Score (see last four col-

umns in Table 1). On average, there is 1.9% difference in

alignment quality between the mBed approach and the

Figure 1 Tree Distances. Tree distances of SparseMap and mBed

guide trees from full matrix guide trees for the BAliBase benchmark set

(386 families), using the Robinson-Foulds metric. Data points above

the bisectrix (red) indicate instances where the SparseMap tree is infe-

rior to the mBed tree, and vice versa. Multiple data points may lie on

top of each other.

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full distance matrix computation. There is of course a big

stochastic error because we only used 10 examples, but

the overall trend is clear: mBed reduces the time for guide

tree computation drastically, while the alignment quality

remains almost unchanged, on average.

Table 1 also shows the effect of different approaches for

the selection of seeds. The variation called 'usePivotOb-

jects' (labelled (3) in the table) brings no increase in align-

ment accuracy whereas 'usePivotGroups" (labelled (4))

increases the accuracy, but also almost triples the embed-

ding time. We therefore ignore these options in the rest of

this paper. The second option is of interest as it has an

obvious effect on accuracy, but is not used in mBed by

default. These two heuristics were just two among a long

series of heuristics that were examined during the devel-

opment of mBed and our earlier method, SeedMap. We

include these preliminary results as it shows that there is

more accuracy to be gained by careful consideration of

seed/reference selection. Nonetheless, the extra compu-

tational overhead and the complicated hand optimisation

that was needed to run these heuristics made us choose

to drop these as default options.

Embedding sequences scales well for large numbers of

sequences

The main advantage in using a data embedding approach

is the reduction in the number of pair-wise expensive dis-

tance evaluations that need to be calculated. The scatter

plot in Figure 2 shows the times required to calculate a

full pair-wise distance matrix directly from the sequence

data (red) for each entry in the HOMSTRAD/Pfam data-

set. As expected, these times scale quadratically, thus

appearing linear with a slope of two on a double-log plot.

However, due to the heterogeneity of the different test

cases used (for example, in terms of sequence lengths),

the data points do not fall neatly on to a well defined line,

but within a particular region. For comparison, the total

time required to (1) create a set of embedded vectors

from the sequence data and (2) create a distance matrix

from the vectors is plotted in blue. This plot shows a sav-

ing of an order of magnitude compared to the traditional

approach, as well as a more favourable scalability (that is

to say, a lesser slope).

To further illustrate this scalability we use RF00005, the

largest entry in the Rfam database [31]. RF00005 contains

381,601 tRNA sequences, ranging between 74-95 nucle-

otides in length. The similarity in length among all these

sequences means that the main deciding factor in compu-

tation time, for the alignment of any subset of this data-

set, is the number of sequences to be aligned. A series of

subsets of different sizes were extracted from this entry

and embedded. By default, the embedding process simply

selects t sequences to act as reference points, and calcu-

lates the distances from these references to all other

Table 1: mBed performance on the ten biggest Pfam/HOMSTRAD families.

NameSize Len%IDEmbedding Time (s)Distance Matrix Calculation

Time (s)

Alignment Column Score (%)

(1)(2)(3)(4)(1)(2)(3)(4)(1)(2) (3)(4)

PF0138199935323- 2555136 764 5755 175 13.3 26.725.334.7

PF000069796 209 43- 134248280 436448 49 88 42.8 36.636.638.0

PF009899681 9517- 4388 1971281 50 5115946.533.3 31.834.1

PF00486961575 30-34 69 107 9505552 10463.992.864.989.7

PF005719551 11919-731432681993 5450152 6.153.08 1.541.54

PF000979423 4133 -183894 517444311553.254.861.3 54.8

PF01479935247 32-174090 4964546124 58.391.789.679.2

PF00046930554 35-20 4385651 4142 77 59.444.9 46.460.9

PF005509249 6325- 2859136 794 47 4714151.332.955.359.2

PF001499072198 14-133 256552351547 46172 75.471.972.3 76.1

Average9503 95270 53 1041951533 4948 13147.048.948.552.8

The ten biggest Pfam entries containing 9,000-10,000 sequences, which have a corresponding HOMSTRAD alignment are used here. Four

different methods were applied to each entry to calculate a distance matrix. These methods are: (1) the traditional process of calculating a full

distance matrix from the sequence data using an alignment distance measure; (2) mBed default; (3) mBed followed by the 'usePivotObjects'

method; (4) mBed followed by the 'usePivotGroups' method. A UPGMA guide tree is constructed from each matrix and used as a guide tree for

progressive alignment of the sequences. The alignment is then scored against the corresponding HOMSTRAD structural alignment using

Column Score.

(1) Full d(x, y) distance matrix; (2) mBed; (3) mBed + usePivotObjects; (4) mBed + usePivotGroups

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sequences. Essentially, this is the same as calculating t

rows of a distance matrix. For 300,000 sequences the

method selected t = 303 seeds. Figure 3 shows that this

approach scales practically linearly with increasing values

of N. All 381,601 tRNA sequences can be embedded in

under 40 minutes, using 1 core of a 3.33 GHz Intel Xeon

with 6 MB cache.

Having embedded such large numbers of sequences, it

is not straightforward to use UPGMA to cluster these

without taking special steps [32]. The distance matrix

alone, becomes huge and difficult to generate or store in

memory. Nonetheless, there are alternative, efficient clus-

tering methods that can be used directly on the embed-

ded vectors. For example, k-means clustering, can cluster

300,000 of these sequences, in 6 minutes (using a k of

300) on a single processor, after embedding.

Choice of guide tree affects alignment quality

To demonstrate the precise effects of guide tree quality

on alignments of different degrees of difficulty, five test

cases of 1000 sequences each, were taken from Pfam.

Figure 2 Complexity of Embedding. Total time required to compute a full pair-wise distance matrix (red) is plotted against time taken to embed

sequences (blue) for each entry in the HOMSTRAD/Pfam dataset (containing up to 10,000 sequences per entry).

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These had between 17% and 61% pair-wise identity, on

average. In each case, a guide tree was constructed using

Clustal and the quality of the alignment was assessed by

comparing the alignment of the included HOMSTRAD

sequences against the HOMSTRAD reference alignment.

Five alignments were also generated using mBed guide

trees and scored. These scores are shown plotted in Fig-

ure 4.

For each test case, 1000 randomised guide trees were

generated by taking the Clustal default guide tree and

randomly shuffling the labels (the sequence names) on

each one. This generated a distribution of scores from

randomised trees of identical structure (topology and

branch lengths) to the test tree. These are shown as the

dark blue histograms in Figure 4. mBed is a simplification

over our earlier SeedMap method [20] which is in turn

related to the earlier SparseMap [17]. SparseMap, uses

random seed selection and thus gives a different guide

tree, each time it is run. This is an inconvenience for nor-

mal alignment purposes but in this case, it can be used to

generate a range of guide trees for each of these test cases.

Thus, we have also plotted the results from 1000

SparseMap runs on each part of Figure 4, using a pale

blue histogram.

The first thing that can be seen is that for the most diffi-

cult of the five test cases (in panel (a) of Figure 4), it

makes little difference which guide tree is used. Here, all

sequences are very dissimilar and the usual beneficial

effects of using a good guide tree, make little difference to

the final alignment quality. This is good news and bad

news. The good news is that, therefore, mBed will be no

worse that using the default guide trees. The bad news is

that all guide trees are ineffective anyway. For the remain-

ing four test cases, the randomised Clustal guide trees are

clearly inferior to both the default Clustal and mBed

guide trees. This says that the details of the guide tree do

matter a great deal, and is a very simple and direct mea-

sure of the effectiveness of progressive alignment itself.

This is true, even for the easiest test cases, where the use

of a good guide tree gives almost 100% correct align-

ments. The spread of scores from SparseMap is very

noticeable in the medium difficulty test cases in panels

(b) and (c). This is one reason for wanting to replace

SparseMap with a method that gives the same result on

every run. With very similar sequences (panels (d) and

(e)), the runs are fairly uniform but with the intermediate

difficulty alignments, the variation between runs is very

high.

Large-scale assessment of alignment quality

We carried out a broad assessment of alignment quality

using two complete sets of test sequences. We used BAli-

Base because it allows comparison with other work but

the numbers of test cases per reference alignment are rel-

atively small. We therefore, also used the HOMSTRAD/

Pfam test arrangement that we used earlier but now

report the average accuracies across all 646 test cases.

mBed, was applied to each dataset and the results are

listed in Table 2. The main mBed result is given in the last

line of the table which shows results for default mBed

guide trees and using ClustalW for making the align-

ments. Performance is also shown for alignments built

using guide trees generated using our earlier SeedMap

program. For comparison, at the top of the table, we give

results for alignments made using default ClustalW and

also with the -quicktree and -ktuple = 2 flags i.e. the

mBed equivalent. We also give results for MUSCLE and

MAFFT (with and without the -parttree heuristic), and

also from using the PartTree output as a guide tree for

ClustalW, and vice versa, using the mBed generated tree

as a guide tree for MAFFT and MUSCLE.

The left hand column of results in Table 2 gives the

results for the BAliBase test cases. The figures are aver-

ages across all test cases and all the numbers lie in a very

narrow range with default MUSCLE performing best

(35.80%), closely followed by MUSCLE using the mBed

tree (35.38%). This is encouraging in that it shows that

mBed does not incur any major loss in accuracy. For the

HOMSTRAD/Pfam data (right hand column), we were

unable to compute results for default MUSCLE due to

very long running time, which is mainly caused by Mus-

cle's iteration steps. The default version of MAFFT is the

most accurate (66.51%), followed by MUSCLE with itera-

tion switched off (60,45%). If the PartTree option is used

without refinement then MAFFT's accuracy drops mark-

Figure 3 Times for embedding up to 300,000 tRNA sequences.

Number of calls to the d(x, y) distance function made during computa-

tion of a full pair-wise distance matrix (red), plotted against number of

sequences for random subsets of Rfam entry RF00005 which contains

381,602 tRNA sequences. We only show the number of calls up to

40,000 sequences. In blue we show the times for embedding subsets

up to 300,000 sequences in size. The full data set takes 40 minutes to

embed.

1e+09

8e+08

6e+08

4e+08

2e+08

300,000 200,000100,000

Number of Sequences [N]

5.00

4.00

3.00

2.00

1.00

0.00

Distance Calls [N]

Time [h]

embedded

Full Matrix

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edly (59.27%). On the other hand, default ClustalW starts

off from a lower baseline (60.12%) but does not incur

such a large drop (59.24%) if mBed is used to make the

guide tree. This is the main focus of this paper. Our older

SeedMap method gives slightly lower performance

(58.85%).

A PartTree guide tree appears to be incompatible with

the ClustalW aligner (54.75%), while an mBed tree seems

to fare only slightly better as a guide tree for MAFFT

(57.57%). This appears to be due to differences in how the

two packages use guide trees. For example, ClustalW uses

branch length information for sequence weighting. It also

uses branch lengths to delay the alignment of very diver-

gent sequences until all other sequences have been

aligned. We used the -retree 0 option to generate the

PartTree guide tree so as to avoid the iterative refinement

step of MAFFT (Katoh, private communication). With

MUSCLE, initial guide trees are generated rapidly using

k-tuple counts and then refined by iteration. The initial

trees are fast and simple and the alignment quality is con-

siderable improved by the later iteration steps. We com-

pared MUSCLE without iteration, using mBed guide

trees and using the internal MUSCLE k-tuple based trees.

Use of the mBed tree improves on the MUSCLE result

(from 60.45% to 64.18%; iteration turned off).

Visualisation of embedded sequences

Data embedding methods give the user great flexibility

when visualising the relationships between sequences of

interest, without the specific need to cluster or align. To

give a simple example, mBed was used to generate 121

dimensional vectors for 3994 H3N2 influenza virus hae-

maglutinin sequences from GenBank http://www.ncbi.

nlm.nih.gov/genomes/FLU, selecting 'any region' and 'any

species'. These vectors were subjected to Principle Com-

ponents Analysis (PCA), and the first three axes of this

analysis were then used to directly visualise the virus

sequences in 3D space (Figure 5). The vectors were

coloured using a time-based colour scheme, representing

the year of isolation for each sequence. The oldest

sequences (from 1967) are coloured in blue, changing to

red as time progressed (up to 2008). Such a time series is

hard to visualise using simple hierarchical clustering but

the almost linear progression through time is very clear

using the PCA of the embedded sequences.

Figure 4 Variation in alignment score induced by choice of guide tree. Alignment quality scores for a collection of five test cases (a-e) taken from

the HOMSTRAD/Pfam dataset, and aligned with ClustalW using guide trees generated from a variety of sources. Quality scores using guide trees from

ClustalW -quicktree and from mBed are shown as arrows. Scores from 1000 randomised guide trees are shown in dark blue. Scores from 1000

SparseMap guide trees are shown in light blue.

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Conclusions

The method that we describe here (mBed) is fast and

simple but highly effective. It can be used to make guide

trees of the order of 10,000 sequences using modest

amounts of memory, in minutes. For very short

sequences, the times can be as little as 20 seconds or so to

embed the sequences. A further 5 to 10 seconds are

needed to cluster the sequences using UPGMA. This is

an enormous speed up over the traditional method which

requires every sequence to be aligned with every

sequence to generate a full distance matrix. The method

also scales well and can be used to embed datasets of the

size of 100s of thousands of sequences. In terms of being

useful for making guide trees, the method is equivalent to

the PartTree algorithm [13] which also generates guide

trees, very rapidly. The two algorithms are quite different

however, in detail, and mBed does have some features, for

example support for branch-lengths, which make the

method interesting as an alternative.

The most important criterion, ultimately, in judging an

embedding of a set of sequences, is quality of the results.

In earlier tests, we experimented with comparing the dis-

tance matrices from embedded sequences against full dis-

tance matrices from all-against-all comparisons using

standard matrix comparison measures such as Stress

[26]. The motivation was to use such comparisons to

compare different seed selection methods but the results

were very dataset dependant and therefore inconclusive

(results not shown). As an intermediate step we com-

pared guide trees produced by mBed and SparseMap to

guide trees derived from full distance matrices. For this

we used the Robinson-Foulds (RF) metric. We can see on

the comparison plot that mBed guide trees are on average

'closer' to full distance matrix tree guide trees than

SparseMap guide trees. In the end we chose to measure

quality of the final results, using alignment benchmarking

because this directly measures how well a guide tree

works. This is good because it measures quality of the

Table 2: Comparison of alignment accuracy between ClustalW, MAFFT, SparseMap and mBed.

Method Alignment Column Score (%)

BAliBaseHOMSTRAD/Pfam

Guide Trees constructed internal to method

ClustalW32.6660.12

ClustalW -quicktree -ktuple=232.8459.92

MAFFT31.9766.51

MAFFT -retree 131.24 60.09

MAFFT -retree 1 -parttree30.0459.27

MUSCLE35.80NA

MUSCLE -maxiters 132.0460.45

Guide Trees constructed external to method

MUSCLE+mBed35.38 NA

MUSCLE -maxiters 1+ mBed32.8664.18

MAFFT + mBed29.7957.57

ClustalW + "MAFFT -retree 0 -parttree"31.6454.75

ClustalW + SeedMap 29.82 58.85

ClustalW + mBed30.20 59.24

# of alignments386 646

Average Column scores (%) are given for each method. Accuracies are measured on two datasets. The HOMSTRAD-Pfam dataset comprises

646 test cases. Each test consists of a Pfam alignment containing between 3-10,000 sequences, which has been paired with a corresponding

structural alignment from HOMSTRAD.

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end product. It does not, however, say how well an

embedding of a set of sequences will work for other pur-

poses such as determining the main groups of homolo-

gous sequences in an entire database.

For our purposes, we were mainly interested in a fast

way of generating guide trees for multiple alignment,

especially for future versions of the ClustalW package.

For this purpose, mBed works extremely well. There is a

modest loss in accuracy compared to using a full distance

matrix. Further, we found the guide trees worked better

with ClustalW than those from PartTree although that

may be due to differences between the packages and how

they use guide trees. PartTree works fine when used

directly with the MAFFT package.

The trees from mBed are generated strictly by grouping

similar sequences rather than by attempting to accurately

reconstruct phylogenetic branching orders. This would

make us advise against using mBed directly for large scale

phylogeny. The sequence alignments, however, may actu-

ally be improved by using guide trees that are based on

similarity rather than phylogeny [6,8]. Progressive align-

ment works by using the guide tree to align the next most

closely related sequences to each other. The most similar

sequences will be the easiest to align most accurately and

this delays the more difficult alignments until last. The

method we have described uses a very crude method for

selecting seed sequences. Ideally, we would like a much

more rigorous approach that would chose seed sequences

as being as representative as possible of the full diversity

of sequences in a dataset. In this paper we tried a couple

of modifications of the basic method and found some

useful increases in accuracy but at the expense of speed.

Nonetheless, the results are good, as measured by the

benchmarking.

Finally, by embedding a set of sequences, we get an

alternative representation of the sequences that is very

flexible with regards to how the sequences can be viewed.

By using the embedded sequence vectors as input to

PCA, we get very elegant and clear visualisations of large

numbers of sequences. For a fixed number of seed

sequences one can, in principle, visualise any number of

sequences, once they have been embedded. This could be

used to carry out PCA on entire databases of sequences

or entire outputs from high throughput sequencing runs.

Methods

Program Versions and Command-line Arguments

We used MAFFT version 6.705b [14], Clustal version

2.0.11 [33] and MUSCLE version 3.7 [6]. Non-default

command-line arguments are given in Table 2. For evalu-

ation of alignment quality we used qscore version 1.1

http://www.drive5.com/qscore with default arguments

[6]. The Robinson-Foulds metric was computed with

PHYLIP's treedist, version 3.68 http://evolution.genetics.

washington.edu/phylip/general.html. The mBed source

code is available on http://www.clustal.org/mbed.tgz.

Benchmark

For benchmarking of alignment quality we used Pfam

version 22.0 [15], BAliBase Version 3 [22] and HOM-

STRAD, downloaded on 2009-06-09 [21]. The HOM-

STRAD/Pfam benchmark comprises of Pfam entries

containing up to 10,000 sequences, which had a corre-

sponding structural alignment for two or more of the

sequences in HOMSTRAD. Alignment quality was then

measured on the corresponding HOMSTRAD sequences

only.

Competing interests

The authors declare that they have no competing interests.

Authors' contributions

This project was conceived by DH and initiated and developed by GB with

advice from AW and FS. The benchmarking was done by GB and FS. The soft-

ware was developed by GB and FS. The influenza virus example came from WS.

The final manuscript was written and approved by all authors.

Acknowledgements

The authors wish to thank Kazutaka Katoh for useful discussions and help with

the use of MAFFT/PartTree. This work was supported by funding from the Sci-

ence Foundation Ireland (PI grant 07/IN.1/B1783).

Author Details

UCD Conway Institute of Biomolecular and Biomedical Sciences, University

College Dublin, Dublin 4, Ireland

Figure 5 PCA visualisation of embedded H3 Influenza virus se-

quences. An embedding of 3994 GenBank haemaglutinin sequences

from H3N2 influenza viruses, generated using mBed, and visualised us-

ing the first three axes of a PCA of the embedded vectors. Each se-

quence has been coloured by year of isolation to show the progression

of sequence change between the years 1967 (blue) and 2008 (red).

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doi: 10.1186/1748-7188-5-21

Cite this article as: Blackshields et al., Sequence embedding for fast con-

struction of guide trees for multiple sequence alignment Algorithms for

Molecular Biology 2010, 5:21

Received: 12 February 2010 Accepted: 14 May 2010

Published: 14 May 2010

This article is available from: http://www.almob.org/content/5/1/21© 2010 Blackshields et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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