protocol
Using the T-Coffee package to build multiple
sequence alignments of protein, RNA, DNA
sequences and 3D structures
Jean-Francois Taly1,2, Cedrik Magis1,2, Giovanni Bussotti1, Jia-Ming Chang1, Paolo Di Tommaso1, Ionas Erb1,
Jose Espinosa-Carrasco1, Carsten Kemena1 & Cedric Notredame1
1
2
Comparative Bioinformatics Group, Bioinformatics and Genomics Program, Centre for Genomic Regulation (CRG), Universitat Pompeu Fabra (UPF), Barcelona, Spain.
These authors contributed equally to this work. Correspondence should be addressed to C.N. ([email protected]).
© 2011 Nature America, Inc. All rights reserved.
Published online 6 October 2011; doi:10.1038/nprot.2011.393
T-Coffee (Tree-based consistency objective function for alignment evaluation) is a versatile multiple sequence alignment (MSA)
method suitable for aligning most types of biological sequences. The main strength of T-Coffee is its ability to combine third party
aligners and to integrate structural (or homology) information when building MSAs. The series of protocols presented here show how
the package can be used to multiply align proteins, RNA and DNA sequences. The protein section shows how users can select the
most suitable T-Coffee mode for their data set. Detailed protocols include T-Coffee, the default mode, M-Coffee, a meta version able
to combine several third party aligners into one, PSI (position-specific iterated)-Coffee, the homology extended mode suitable for
remote homologs and Expresso, the structure-based multiple aligner. We then also show how the T-RMSD (tree based on root mean
square deviation) option can be used to produce a functionally informative structure-based clustering. RNA alignment procedures are
described for using R-Coffee, a mode able to use predicted RNA secondary structures when aligning RNA sequences. DNA alignments
are illustrated with Pro-Coffee, a multiple aligner specific of promoter regions. We also present some of the many reformatting
utilities bundled with T-Coffee. The package is an open-source freeware available from http://www.tcoffee.org/.
INTRODUCTION
MSA is one of the most widely used methods for the simultaneous
comparison of biological sequences1,2. It makes sense to build an
MSA when a collection of evolutionarily related sequences has been
assembled (for instance by homology search), and when one wants
to identify features shared by these sequences. The method presented
in this protocol is named T-Coffee3. It is a versatile aligner able to
handle nucleic acids and protein sequences alike. T-Coffee is one of
the three most widely used aligners (Table 1), and its key strengths
are the ability to combine the output of any third party aligner into
a single MSA model, as well as the capacity to combine sequence
and structural information. Since its original publication about
10 years ago, more than 900 updated versions of T-Coffee have been
released along with about 20 publications describing improvements
of the original algorithm. The most recent release of T-Coffee (on
which this protocol is based) is a state-of-the-art platform for data
integration in a distributed computational environment2,4,5.
Main applications of multiple sequence alignments in biology
The purpose of an MSA is to explicitly declare the relation of homology between all the residues (or nucleotides) within a set of related
sequences, thus making it possible to identify highly conserved positions, or positions whose variability may have a functional meaning
(e.g., substrate specificity). Phylogenetic reconstruction is arguably
the most common application of MSAs. Even though most tree
building procedures rely on a precomputed MSA, the relationship
between trees and MSAs is rather complex, and it has been shown
that the selection of a specific aligner is an important bias when
reconstructing trees6. Yet, with no simple criteria at hand to select
the best aligner, tree computation remains a trial and error process, during which users are encouraged to check the robustness of
their results with alternative aligners. The second most widely used
application for MSAs is profile estimation. Profiles can be described
as position-specific substitution matrices7. They summarize the
variability observed on various positions of a protein (or RNA)
family and can be used to identify distantly related homologs. In
the literature, profiles can be referred to as hidden Markov models
(HMMs), Pfam domains, weight matrices or even motifs. Profiles
are routinely used to infer gene functions, and most automated
annotation pipelines are now relying on established profile collections for proteins8 and RNA9. In most cases, profiles are estimated
from a seed MSA made of selected sequences. In practice, one can
easily derive one’s own profiles using packages such as HMMer10
to turn existing MSAs into Pfam-style HMMs11. The third most
common application of MSAs is homology modeling, a generic
term referring to the prediction of protein’s secondary and tertiary structures. Many other applications exist for MSA modeling,
and a non-exhaustive listing includes promoter analysis, active
site identification, RNA secondary structure prediction and PCR
primer design. Such a wide breadth of applications explains why
some of the publications describing MSA methods are among the
most cited in biology12.
Methods and algorithms
The MSA problem sits at the crossroads of biology and computer
science. Its biological side lies in the difficulty of defining a mathematical function (scoring scheme) able to estimate the biological
correctness of an MSA. To be accurate, this scoring scheme should
reflect the relationship between sequence, structure and function;
it should also integrate the effect of their mutual dependencies onto
sequence mutations. We still lack sufficient biological insights to
accurately define such a scoring scheme and in practice, sequence
alignments are evaluated using weighted sequence identity measurements carried out with log-odd matrices such as the BLOSUM13.
These scoring schemes are reasonably effective for closely related
nature protocols | VOL.6 NO.11 | 2011 | 1669
protocol
TABLE 1 | Comparisons of the computational time and the resulting accuracy of five popular aligners with T-Coffee.
© 2011 Nature America, Inc. All rights reserved.
Aligner/software
Algorithm
Version
Acc.
CPU-1
CPU-2
No. of citations
Expresso
Template based
8.99
55.1
4095 s
1880 s
93
PSI-Coffee
Template based
8.99
52.2
637 s
750 s
2
T-Coffee
Consistency
8.99
42.3
29 s
188 s
2,534
Fast M-Coffee
Consistency
8.99
34.9
42 s
23 s
121
Kalign
Progressive
2.04
36.5
0.5 s
1s
165
MAFFT
Iterative
6.603
26.2
2.7 s
1s
867
MUSCLE
Iterative
3.38
31.7
14 s
15 s
3,213
ProbCons
Consistency
1.12
41.4
61 s
556 s
467
ClustalW
Progressive
2.0.10
23.3
6s
33 s
33,000
Algorithm refers to the rough classification given in the introduction. Version, version of the software tested here. Acc., fraction of columns correctly aligned in the core region of the BAliBASE30 RV11 data set.
CPU-1, CPU time required to align RV11 on a Mac Workstation Xeon Quadricore (note that the RV11 data set contains a maximum of 14 sequences). CPU-2, CPU time required to align a data set made of 170
serine proteases. No. of citations refers to the most cited paper related to the considered method (Google Scholar, April 2011).
sequences, but they rapidly become non-informative when dealing
with distantly related sequences (less than 25% for proteins and
70% for nucleic acids). Identity-based scoring schemes are not only
limited in accuracy, but they are also difficult to optimize. This constitutes the computational challenge of MSA modeling: the impossibility to guarantee the calculation of an optimal alignment, except in
the simplest cases14. In computer science terms, MSA computation
is said to be a nondeterministic polynomial time complete problem
(NP-complete), which means that the required memory and computational time is likely to increase exponentially with the number
of sequences. The insoluble nature of the problem has resulted in the
development of a large number of heuristic algorithms, all relying
on different trade-off systems to deliver approximate solutions. In
April 2011, the Wikipedia entry dedicated to ‘sequence alignment
software’ listed no less than 38 MSA packages, and Table 1 displays
the key properties of the most widely used software. Overall, one
can distinguish four main categories as follows:
(1) S tandard progressive alignment methods, such as ClustalW15
or Kalign16, where sequences are first compared two by two
in order to estimate a guide tree and then aligned following the tree order. The progressive alignment algorithm is
the backbone of most aligners and one of the fastest ways to
multiply align sequences.
(2)Iterative methods: In MUSCLE17 and MAFFT18, the progressive
alignment step is followed with an iterative procedure, wherein
subgroups are realigned to improve the overall alignment.
(3) C
onsistency-based methods: These methods combine the
progressive alignment with a different scoring scheme. They
include T-Coffee3, ProbCons19 and PROMALS20. Their goal
is to optimize the consistency (i.e., the agreement) between
the MSA and a collection of weighted constraints. The constraints are usually matches between symbols of different
sequences.
(4) T
emplate-based method: This method uses external information such as experimental structures or evolutionary information to improve MSA accuracy.
The various ‘flavors’ of T-Coffee developed in this protocol
correspond to these two last categories.
1670 | VOL.6 NO.11 | 2011 | nature protocols
Consistency-based methods
These methods do not try to maximize a BLOSUM score, like
MUSCLE or ClustalW. They start by computing an all-against-all
comparison of the provided sequences, using any suitable pairwise
alignment method (i.e., alignment of two sequences). In T-Coffee,
this collection of pairwise alignments is named a library. In a library,
each entry corresponds to a pair of symbols (identified by their
index in their respective sequences) found aligned in the pairwise
alignments. When incorporating sequences within the MSA, the
score for matching two residues between two sequences is set equal
to the number of time these two residues appear aligned within the
library, either directly or indirectly through an intermediate residue. The intermediate residues are identified by considering all the
transitive alignments within the library. For instance, given three
sequences A, I, B and the two pairwise alignments AI, IB, there exists
a transitive alignment of AB through sequence I (i.e., AI + IB).
All symbols thus aligned via a third residue from an intermediate
sequence I can therefore contribute to the scoring scheme.
The guaranteed optimization of the consistency between a library
and an MSA is a NP-complete problem. The scoring scheme described
above is therefore a mere heuristic attempt to compute an MSA having
the highest possible consistency with a library. It has been shown to
perform well21, but it is not guaranteed to deliver an optimal alignment. The rest of the algorithm is a standard progressive alignment
method, similar to ClustalW. Sequences are first clustered using a fast
ktup indexing procedure in order to produce a guide tree using the
method described for MUSCLE17. Sequences are then incorporated
one by one in the MSA, following the order indicated by the guide
tree and using the Myers and Miller dynamic programming algorithm for pairwise sequence alignment22. The main difference between
T-Coffee and a regular progressive aligner is that, rather than using a
BLOSUM matrix to estimate the score of matches when incorporating the sequences into the MSA, these scores are evaluated using the
scoring scheme defined by the library.
Library computation
Most consistency-based packages use a principle very similar to the
one originally described for T-Coffee, which involves estimating
protocol
Box
1 | CORE INDEX
© 2011 Nature America, Inc. All rights reserved.
The color code in the HTML output indicates the agreement between the library and the considered alignment. It is important to
understand that this score does not only depend on the MSA, but it also depends on the library. In general, modes that combine the
output of multiple aligners, such as M-Coffee, tend to produce alignments with a higher CORE index than modes based on pairwise
comparisons, such as the default T-Coffee. This does not necessarily imply that one mode is more or less accurate than the other, it
simply reflects different bias in the library computation. For this reason, it does not make sense to compare the CORE index of alternative MSAs produced with different T-Coffee modes. Furthermore, the CORE index is most informative when used to identify low-scoring
portions within an MSA. It is also worth noting that the CORE index is not informative when aligning less than five sequences.
a pairwise library and resolving it into an MSA. The main dif- CPU time requirement can be reduced when using more efficient
ferences across programs lie in the library-building procedure. methods to estimate the library (as happens with M-Coffee; Table 1).
For instance, in the original T-Coffee, the library contained all In T-Coffee, the recent implementation of a parallel algorithm has
pairwise global and local alignments. The
RT
n-Src
Distal
consistency-based mode of MAFFT combines a different kind of local and global
1PHT
1BB9
alignments. ProbCons and PROMALS use
1UHC
a concept named probabilistic consistency,
Ref 1YCS
100T
1AB0
for which the library is estimated using
1FYN
1QCF
the posterior decoding of a pair-HMM.
*
*
* *
Over time T-Coffee has been improved to
1
2
3
4 5
accommodate any kind of third party proOK
NO
NO
tocol for building the library, including the
a
OK OK
OK
OK
NO
ProbCons pair-HMM. A recent independNO
1PHT
ent analysis has reported a higher accu1BB9
1UHC
racy for T-Coffee than for ProbCons when
1YCS
23
100T
building the libraries with a pair-HMM .
1AB0
1FYN
Although the ProbCons pair-HMM is now
1QCF
used for the default T-Coffee library, it is
Cons
*
*
* *
merely one of the 20 third-party methods
3
4 5
1
2
currently supported for T-Coffee and ready
NO
NO
to be combined in its consistency-based
OK
b
NO
OK
OK
OK OK
framework.
1PHT
1BB9
1UHC
1YCS
100T
1AB0
1FYN
1QCF
Computational requirements
Given a data set of n sequences, consistencybased aligners are about n times slower than
other progressive algorithms. This increased
Cons
Figure 1 | Multiple sequence alignments of eight
SH3 protein domains. ‘Ref’ indicates a manually
curated MSA from reference 36 used as benchmark
reference. Functional regions are colored in
grayscale. Gray residues: RT, n-Src and distal
loops; blue-gray: aromatic and polar motifs of
the RT loop, respectively; beige: conserved triad
in the binding pocket. Asterisk (*), conserved
amino acid in all sequences. Colon (:), a position
of the MSA composed of residues having the
physicochemical properties. Dot (.) indicates
the column of the MSA for which semi-conserved
substitutions are observed. (a–d) MSA of the
same SH3 sequences with T-Coffee default, fast
M-Coffee, PSI-Coffee and Expresso, respectively.
The alignments are colored according to their
consistency with their primary libraries (CORE
index). Regions in red have a high consistency,
whereas regions in green/blue have the lowest
consistency (see Box 1 for a detailed description
of the CORE index). Functional motifs correctly
aligned are tagged with the word ‘OK’. On the
contrary, a ‘NO’ points out misaligned regions.
*
*
1
c
2
OK
OK
OK
1
2
* *
3
OK
4
5
NO
OK
OK OK
1PHT
1BB9
1UHC
1YCS
100T
1AB0
1FYN
1QCF
Cons
*
*
d
OK
OK
OK
1
2
* *
3
OK
4
5
OK
OK
OK OK
1PHT
1BB9
1UHC
1YCS
100T
1AB0
1FYN
1QCF
Cons
*
*
* *
3
4
5
nature protocols | VOL.6 NO.11 | 2011 | 1671
protocol
Box
2 | POSTPROCESSING FUNCTIONS IN T-COFFEE
© 2011 Nature America, Inc. All rights reserved.
T-Coffee comes along with several utilities to reformat and post-process sequences, alignments or trees. All these utilities can be
invoked using
t_coffee -other_pg < utility > The list includes the following:
extract_from_pdb. A Perl program allowing the download of PDB structures from RCSB41 and the extraction of chains or
subfragments.
seq_reformat. A C-utility allowing the reformatting of the most common sequence alignment formats. It can extract sequences
and blocks according to user-specified criteria. The utility also supports the handling of phylogenetic trees in Newick format, enabling
their comparison, trimming or even clustering. This utility allows the programmatic editing of MSAs: by replacing one symbol into
another, changing the case (lower/upper) or coloring residues. A popular application of seq_reformat is the trimming option that
makes it possible to trim out of an MSA highly redundant sequences.
aln_compare. A C-utility designed for the comparison of alternative MSAs of the same sequences.
trmsd. A classification method using the UPGMA algorithm to cluster protein sequences on the basis of their structural similarity.
Note: the number of positions per line in the alignment can be controlled by exporting a value to the environment variable
ALN_LINE_LENGTH. In a terminal window type:
export ALN_LINE_LENGTH = 150
made it possible to significantly decrease the run-time and take full
advantage of the multi-core processor architecture now available
on most standard platforms4,5.
Aligning proteins with T-Coffee
Although, in theory, users can compose a quasi-infinite number
of protocols through various method combinations, in practice
each combination is a novel method that needs to be validated.
Over the years, we have developed and validated five protocols
and made them available as T-Coffee modes. M-Coffee24 is one
of the most popular. This flavor of T-Coffee is able to combine
the output of alternative multiple aligners into a unique MSA.
M-Coffee is especially well suited for very large data sets, where
one can use fast approximate methods such as Kalign, MAFFT
or MUSCLE and combine their output into a unique MSA. Our
benchmarks suggest that the resulting alignments tend to be more
accurate than the individual methods. Interestingly, the output
also indicates the level of agreement between the combined methods, and this measure of agreement has been shown to reflect the
structural accuracy of the considered alignments. Other modes
of T-Coffee are available for situations in which accuracy is more
important than speed. Expresso25,26 is potentially the most accurate,
provided the data set is suitable and contains enough sequences
with an experimentally established 3D structure. Expresso is a
template-based mode. It does not use the standard library building procedure and starts by BLASTing every considered sequence
against the protein database (Protein Data Bank or PDB), looking
for suitable templates. Once a template has been found for each
sequence, the library is produced by using a structure-based aligner
(SAP27) to align every pair of structural template. The library is
finalized by projecting the template alignments onto the original
sequences. In an optimal situation, and when each sequence in the
data set has a suitable structural template (i.e., a PDB sequence
with more than 50% identity), Expresso produces the equivalent
of a structure-based MSA, the most accurate modeling currently
available for protein sequences. Expresso can also be used even if
all the sequences do not have a suitable template, but its accuracy
decreases significantly. The best alternative to Expresso is another
1672 | VOL.6 NO.11 | 2011 | nature protocols
template-based mode named position-specific iterated coffee
(PSI-Coffee). PSI-Coffee is the newest T-Coffee mode. It is briefly
described and validated in reference 2. In PSI-Coffee, the template
is a profile. PSI-Coffee starts by BLASTing every sequence in the
data set against the nonredundant protein database. Each BLAST
output is then turned into a profile thus producing one profile
per sequence. The library is produced by computing all the pairwise profile alignments using either the pair-HMM or HH-align28.
When structural data is not available, the PSI-Coffee mode is the
one that delivers the most accurate alignments. Because it involves
using the information of related sequences, this alignment mode is
also known as homology extension, and similar approaches have
been described for MAFFT and PROMALS.
Alignment modes for nucleic acids
Template-based alignments are not only limited to proteins, and in
this protocol we show how the R-Coffee mode29 can be used in order
to align RNA sequences. This mode is suitable when considering
sequences with an evolutionarily conserved secondary structure.
The principle is to enrich the library by adding information derived
from secondary structure predictions. Benchmarks on BAliBASE30,
a database of reference alignments, indicate that this simple procedure results in a significant improvement of accuracy when considering RNA sequences with an evolutionarily conserved structure.
The R-Coffee mode is not, however, suitable for the computation of
genomic alignment. This task should be carried out using either the
default T-Coffee, M-Coffee or Pro-Coffee, a mode developed in the
context of this protocol for the alignment of homologous promoter
regions. The main specificity of Pro-Coffee is to use a dinucleotide
substitution matrix estimated on the TRANSFAC database to build
the library of pairwise alignments.
Estimating the accuracy of a multiple sequence alignment
Once an MSA has been computed, estimating its accuracy can
be a complicated task. The equivalent of an e-value does not
exist for MSAs and it is often difficult to establish the relative
merits of two alternative alignments of the same sequences.
T-Coffee’s output comes along with a local score called the
© 2011 Nature America, Inc. All rights reserved.
protocol
consistency of overall Residue evaluation index (CORE index).
The CORE index reflects the support given by the library to
the alignment of every individual residue. Its value is normalized between 1 and 10. A residue having a CORE index
of 10 has maximal support from the library. This means
that the library does not contain any alternative alignment that
would be incompatible with the way this residue is aligned within
the final MSA. Conversely, residues with a score of 1 have no support from the library and their alignment should not be considered trustworthy. The CORE index depends greatly on the
methods used to build the library31 and the size of the data set
(Box 1). With less than five sequences, it is usually not very trustable. It is also very prone to delivering more false positives when
combining MSAs (M-Coffee). In that case, the regions having a
low score are more informative (i.e., more likely not to be correctly aligned) while the values with high scores are not necessarily trustable. In summary, a low level of consistency is often
more informative than a high level. Figure 1 shows examples of
CORE representations, where residues with a high CORE index
are yellow, orange and red, whereas those with a low CORE are
pale yellow, green and blue (where blue reflects a CORE index of
1, the lowest possible value).
Postprocessing of multiple sequence alignments
The postprocessing of an alignment is a crucial step, as it may
strongly influence the conclusions one may draw from that alignment. Postprocessing ranges from trivial reformatting procedures
up to highly complex model estimations for 3D homology modeling or phylogenetic reconstruction. The T-Coffee package comes
along with a wealth of postprocessing options, allowing alignments
reformatting, block extraction, redundancy trimming and filtering
of low-confidence regions (Box 2). Among these procedures, the
most sophisticated is probably the T-RMSD for tree based on root
mean square deviation32. Whenever enough structural information is
available, the T-RMSD makes it possible to estimate the relationship
between the considered sequences based on their 3D similarity. This
method was recently validated as a fine grain structure–based clustering procedure and an example of its potential use is given here.
MATERIALS
EQUIPMENT SETUP
• Computer Any computer running Linux or Mac OSX with access to
the Internet. Note: Windows is not supported. We recommend installing a Linux guest on your workstation using any virtualization tool
(VMware, VirtualBox and so on). For structure-based alignments, the
amount of RAM might be a limiting factor. A minimum of 2 Gb of
memory is advisable.
• Software T-Coffee can be downloaded from http://tcoffee.org/Packages/Stable/
Latest/. It is distributed as a set of precompiled binaries for Linux and Mac OSX
platforms (32-bit or 64-bit) with a guided install procedure. This is the smoothest
and quickest way to install T-Coffee on a local machine, as it comes with all the
required components and does not require any special user privileges. It is also
possible to download the source code.
The version used for this protocol can be found at http://tcoffee.org/Packages/
NatureProtocols/.
• User data Any FASTA file containing either nucleic acid or protein sequences.
• Protocol data All files required to run the examples presented
in the different sections of this article are available from
http://tcoffee.org/Projects/Datasets. They can also be automatically
retrieved using the following command: > t_coffee -other_pg
nature_protocol.pl.
• For each corresponding protocol, all command lines have been collected
into the file README.sh.
• Databases It is not mandatory to install locally any of the databases used for
computation (BLAST, PDB). The way T-Coffee interacts with databases via
BLAST is detailed in Box 3.
PROCEDURE
T-Coffee: obtaining and installing T_Coffee ● TIMING ~5 min
1| Install T-Coffee by following one of the three subsequent options (options A, B or C) depending on your operating system.
(A) Linux
(i) Download the installer package from http://tcoffee.org/Packages/Stable/Latest/linux/.
(ii) Grant execution permission to the downloaded file with the following command:
chmod + x T-COFFEE_installer_ < version_xxx > .bin
(iii) Launch the installation wizard with
./T-COFFEE_installer_ < version_xxx > .bin
(iv) Follow the wizard instructions and complete the installation.
Box
3 | T-COFFEE, BLAST AND LOCAL DATABASES
Several modes of T-Coffee (psicoffee, expresso, accurate) rely on BLAST to identify the most suitable templates. In theory,
this requires the user to have BLAST installed locally with the suitable databases up-to-date. In practice, however, this requirement is
often too burdensome for occasional users. Accordingly, the latest T-Coffee releases come bundled with BLAST clients, connecting T-Coffee
with the web services provided by either the European Bioinformatics Institute (EBI) or the National Center for Biotechnology Information
(NCBI). The default server is the EBI, but it can easily be changed at the flip of a flag by adding to the command line -blast_server.
The reliance on web services can be a blessing if one is looking for a rapid result or a curse when the main concerns are control, reproducibility and speed. By definition, the databases used by the web-services are updated daily. It means that the same job may return
different results from one day to the next. Users concerned with this kind of instability should use their own local databases, along with
their local BLAST installation. T-Coffee is compliant with Blast+42, and a local version of this package can be used via the flag –blast_
server LOCAL. Databases must then be indicated via –pdb_db (for Expresso) and via –protein_db (for PSI-Coffee).
nature protocols | VOL.6 NO.11 | 2011 | 1673
© 2011 Nature America, Inc. All rights reserved.
protocol
(v) Open a new terminal session to be sure that your environment is updated.
(vi) Type the following command to verify that the installation was successful:
t_coffee –version
(B) Mac OS X
(i) Download the installer package from http://tcoffee.org/Packages/Stable/Latest/macosx/. With Mac OSX 10.5.x
(Leopard) and earlier versions, users have to use the 32-bit installer version; with Mac OSX 10.6.x (Snow Leopard) and
above, users have to use the 64-bit installer version.
(ii) Double-click on the DMG file to open it.
(iii) Double-click on the installer icon (within the mounted image) to start the installation.
(iv) Follow the wizard instructions and complete the installation.
(v) Open a new terminal session to be sure that your environment is updated.
(vi) Type the following command to verify that the installation is successful:
t_coffee –version
(C) Compilation from source
(i) Follow the instructions from the T-Coffee Google code in order to retrieve the source code: http://code.google.com/p/
tcoffee/source/checkout.
(ii) Compile the T-Coffee source distribution package on your target system. It will require the GNU development tools
(gcc, g77 or gfortran, make), the Perl interpreter, CPAN and the root user privileges. A detailed description of the
installation can be found here.
2| For proteins, RNAs or promoters, follow the protocol best suited according to the nature of the biological sequence:
Section 1
Protein sequences/structures
Steps 3–10
Section 2
RNA sequences/structures
Steps 11–16
Section 3
Promoter sequences
Steps 17–20
Section 1: Protein MSAs ● TIMING 1–10 min
3| Choose the appropriate T-Coffee mode for protein alignments. There is no ‘ideal’ mode and they all involve different
trade-offs between computational cost and alignment accuracy (see Box 4):
Option (see Step 4)
Mode
Description
A
T-Coffee default (tcoffee) This default mode runs on all types of sequences and provides a reasonable tradeoff
between speed and accuracy
B
Fast M-Coffee (fmcoffee) This generic mode runs on all types of sequences. It applies the three fastest aligners (Kalign, MAFFT and MUSCLE) and combines their output into a unique model. It
is much faster albeit less accurate than the default T-Coffee mode. It can align up to
1,000 sequences
C
PSI-Coffee (psicoffee)
This mode uses homology extension and is suitable for aligning remote homologs
when no structural information is available. It can align up to 200 sequences
D
Expresso/3D-Coffee
(expresso)
This mode automatically fetches and uses structural information to align protein
sequences. It is best suited when all the sequences in the database have a close
homolog with a known 3D structure. It is the most accurate but also the slowest
mode of T-Coffee. It can align up to a 100 sequences
! CAUTION The numbers of sequences is given as an indication. The capacity of T-Coffee to align a given data set will depend
on the size and the difficulty of each particular data set and the CPU time estimated by the user as acceptable for running it.
4| Align your sequences using the chosen mode by following the steps in options A, B, C or D.
(A) Default T-Coffee
(i) Type the following command line in a terminal window:
t_coffee –seq sh3.fasta
1674 | VOL.6 NO.11 | 2011 | nature protocols
protocol
Box
4 | TIME COMPLEXITY AND ACCURACY
The CPU requirement of T-Coffee depends on the methods it uses. By default, consistency-based methods are slower than regular
methods. In a worst-case scenario, given a data set of n sequences, this can make consistency-based methods n times slower than
a regular progressive aligner such as ClustalW. Fortunately, the final CPU requirement depends on the level of consistency within the
considered library. It means that closely related sequences can be aligned quite efficiently. Furthermore, T-Coffee was recently adapted
to take advantage of multicore processors.
Table 1 gives an idea of the effective running time on a standard Mac Workstation (16 GB of memory, single processor, Quadricore
Xenon). The considered data set is made of 170 Ser-proteases and the run time for fmcoffee includes the computation time of the
primary alignments by Kalign, MAFFT and MUSCLE. Interestingly, the running time differences between ProbCons and T-Coffee result
almost entirely from the multicore implementation.
(ii) (Optional) The user can specify the desired output format for the alignment. This option is available for any mode
described in this section.
t_coffee –seq sh3.fasta –output clustalw_aln fasta_aln phylip_aln score_html
© 2011 Nature America, Inc. All rights reserved.
(B) Fast M-Coffee
(i) Type the following command line in a terminal window:
t_coffee –seq sh3.fasta –mode fmcoffee
(ii) (Optional) Modify the list of combined methods using the following command line:
t_coffee –seq sh3.fasta method muscle_msa probcons_msa clustalw_msa
In the command line above, only MUSCLE, ProbCons and ClustalW are combined; however, M-Coffee allows the combination of virtually any aligners available. The list of all available methods implemented in the T-Coffee framework can
be displayed by simply running the command:
t_coffee
(C) Homology extended alignments with PSI-Coffee
(i) Type the following command line in a terminal window:
t_coffee –seq sh3.fasta –mode psicoffee
This command will generate a series of ‘.prf’ files (one per input sequence) containing the homologous sequences used
in the homology extension process. The file sh3_prf1.template_list contains the list of profile templates associated
with each sequence (see Box 5 for description of template files).
! CAUTION The number of sequences in the profiles and their variability is important: if very few or identical sequences
are gathered then PSI-Coffee becomes equivalent to T-Coffee. One can check the number of template sequences fetched
by looking at the output displayed on screen. In the example specified, PSI-Coffee found 49 homologous sequences in
the UniProt database for the input protein 1PHT:
Process: > 1PHT _R_ 1PHT.prf [49 Seq.] [EBI/blast/uniprot][COMPUTE CACHE]
? TROUBLESHOOTING
(D) Multiple structural alignments with Expresso
(i) Type the following command line in a terminal window:
t_coffee -seq sh3.fasta -mode expresso -pdb_type dn
Box
5 | TEMPLATE FILES
The idea of a template-based alignment is to assign a template to each sequence one wants to align. In T-Coffee, the template can
either be a structure (PDB file), a profile (MSA) or an RNA secondary structure. In the default modes, the templates are automatically
identified and fetched by T-Coffee. However, there are many situations where the user may want to force the usage of pre-defined templates. Whenever this happens, the binding between a sequence and a template must be specified. This is done using the template file
format, a FASTA-like format with one sequence name per line, followed with a template identifier and the template itself. For instance:
> 1BB9 _P_ 1BB9A
Indicates that the sequence 1BB9 will use as a PDB template (_P_) the PDB structure 1BB9A; where 1BB9A is a PDB identifier followed
with a chain identifier. Note that the template can either be a file or an identifier. Three types of templates supported in T-Coffee:
_R_: profile templates, where the template should be an MSA, having exactly the length of the considered sequence (i.e., positions
with gaps in the master sequence must be removed).
_S_: secondary structures, for RNA or proteins.
_P_: PDB structure. The template must either be a valid PDB file or a valid PDB identifier followed with a chain index (if the index is
missing, the first chain is assumed). When a file is provided, the first chain is used by default. Users are therefore advised to extract
their chain of interest with the extract_from_pdb utility (see Box 2).
Whenever an automated mode of T-Coffee is used that relies on templates (expresso, psicoffee, rcoffee), a template file
is produced, which makes it possible to rerun the analysis. This template file can be kept and further reused or adapted.
nature protocols | VOL.6 NO.11 | 2011 | 1675
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Expresso uses structural information to align sequences. Provided enough structural data is available, it is by far the most
accurate alignment mode. Structural templates are identified by blasting every query sequence against the PDB. In order to
build the T-Coffee library, Expresso subsequently applies a structural aligner (SAP by default) onto the templates associated
with every pair of sequences. By default Expresso will consider only crystallographic structures (field XRAY in the PDB header),
whose similarity to the original sequence is higher than 30%. Nevertheless, it is possible to modify these default parameters
using the flags –pdb_type dn (where ‘d’ stands for XRAY and ‘n’ for NMR structures) and –pdb_min_sim n (where ‘n’
represents the minimal percent identity accepted for the templates). Other parameters can be modified by the user; refer to
the documentation for a more detailed overview.
! CAUTION Whenever a template is not attributed, because no structure is available or not similar enough to the considered
sequence, proba_pair (the default T-Coffee method for building libraries) is used instead of the chosen structural aligner
(SAP by default) on every pairwise alignment involving the corresponding sequence. The structural templates associated with
each sequence of the input data set are listed in the file sh3_pdb1.template_list.
? TROUBLESHOOTING
(ii) (Alternative step to (i)) Define your own templates and align with 3D-Coffee:
t_coffee –seq sh3.fasta –method sap_pair –template_file sh3.template_file
3D-Coffee25 can be used as an alternative to Expresso, whereby users can specify the templates to be used for each
sequence and the method to be used to align the templates. It can even be used to replicate Expresso. For instance,
the above command line will produce an alignment similar to Expresso.
(iii) (Alternative step to (ii)) Choose/combine the structural aligner software:
t_coffee –seq sh3.fasta –method TMalign_pair sap_pair
–template_file EXPRESSO
T he above command line allows users to select their own combinations of structural aligners, whereas the selection of
templates is realized using the automatic EXPRESSO strategy.
5| Evaluate the consistency of the alignment resulting from the options in Step 4 (A, B, C or D).
Any of these modes will generate the output files described here:
sh3.html
This file contains the final alignment colored in function of the CORE index from red to blue (very to poorly consistent,
see Box 1 for more details). You can open this file using any internet browser
sh3.dnd
This file contains the guide tree used to assemble the progressive alignment. You can open this file with a text file viewer
sh3.aln
The final alignment given in ClustalW format by default. Reformatting of the output file can be achieved using T-Coffee
post-processing options (see Box 2). You can open this file with any text file viewer or MSA viewer (i.e., Jalview)
! CAUTION The guide tree in sh3.dnd is not a phylogenetic tree. It is computed on the basis of the distance matrix
that is generated from the pairwise scores. It should not be used to infer the evolutionary relationships between
aligned sequences.
6| Compare MSAs by running the aln_compare function:
t_coffee –other_pg aln_compare –al1 sh3_ref.aln –al2 sh3.aln
-compare_mode column
Comparing two alternative alignments of the same sequences can be very useful to estimate their robustness with respect
to changes in parameters or across alignment methods. The comparison metric can either be the column score (fraction
of columns of the first alignment aligned identically in the second one) or the sum-of-pair (fraction of residue pairs in al1
aligned identically in al2). The default mode (column) can be modified with the flag –compare_mode (column or
sp). All these parameters are documented and a list of flags can be obtained via the command:
t_coffee –other_pg aln_compare
? TROUBLESHOOTING
7| Interpret the output.
sh3-ref
seq(1)
Sim(2)
[ALL](3/4)
Tot(5)
8
30.2
69.8 [100.0]
[822]
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By default, T-Coffee will display on screen the results of the aln_compare command as illustrated above, where: seq
(1) is the number of sequences in the alignment provided via –al1; Sim(2) is the average identity of sequences found in
the first alignment; [ALL](3/4) is the fraction of columns (or sum-of-pair) in the first alignment that are found identically
aligned in the second alignment, followed by the fraction of columns used to estimate the similarity usually called column
score (by default it should be 100%); and Tot(5) is the number of paired residues contained in the first alignment al1.
! CAUTION The comparison is not symmetrical and the result will change if al1 and al2 are interchanged. Furthermore, it is
possible to change the fraction of positions considered for the comparison (the number in parentheses in the [ALL] column,
see online documentation).
© 2011 Nature America, Inc. All rights reserved.
Structure-based clustering of proteins using T-RMSD ● TIMING ~2 min (optional)
8| Select one structure for each of your sequences and align them using the following command line:
t_coffee -seq crd.fasta -template_file crd.template_file -method sap_pair
TMalign_pair
9| Run the T-RMSD on any alignment containing sequences with a known 3D structure and the corresponding template file:
t_coffee -other_pg trmsd -aln crd.aln -template_file crd.template_file
The T-RMSD can be used only if all sequences in the alignment have a structural template, then structure-based comparisons can be applied to estimate the distances between every sequence pair. The T-RMSD performs such analysis by comparing
intra-molecular distances between pairs of ungapped positions (in the MSA). The clustering is therefore a structure-based
classification of your sequences.
! CAUTION In order to insure a meaningful clustering, it may be useful to remove the sequences causing large number of
indels (as estimated by visual inspection).
10| Analyze/visualize the results.
The T-RMSD function will generate several output files:
crd.struc_tree.list (text)
List of trees in Newick format. Each tree is estimated for a single ungapped column of the input
MSA
crd.struc_tree.consensus (text)
Newick format file corresponding to the consensus tree made by Consense40 from the collection
of trees contained in the previous file
crd.struc_tree.consense_output (text)
Statistics produced by Consense when producing the consensus tree
crd.struc_tree.html (html)
Color-coded version of the MSA indicating the level of support given by every ungapped column
to the topology reported in crd.struc_tree.consensus
The Newick format is a standard tree format that can be visualized with online phylogenetic tools such as PhyloWidget33
(Fig. 2b). On this clustering, the numbers indicate the level of support for each corresponding node. This value is the
equivalent of a bootstrap and corresponds to the fraction of ungapped columns effectively supporting a split32.
! CAUTION The crd.struc_tree.consensus tree is a structural tree. It only reflects the structural relation between the proteins
aligned.
Section 2: RNA MSAs ● TIMING ~1 min
11| Start the process of aligning RNA sequences with R-Coffee by typing the following command line in a terminal window:
t_coffee –seq RNA_set.fa –mode rcoffee –outfile RNA_rcoffee.aln
The default R-Coffee relies on a three-step strategy: generation of a standard library with proba_pair, prediction of the
secondary structures of each sequence using RNAPlfold34, and RNA-specific library extension using the method described in
reference 29. The rest of the procedure is identical to the default mode of T-Coffee.
? TROUBLESHOOTING
12| (Optional) Reformat the output using predicted structures:
t_coffee –other_pg seq_reformat –in RNA_rcoffee.aln –action + add_alifold –output
Stockholm_aln –out RNA_rcoffee_predicted.stk
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Figure 2 | Structural classification of the
cysteine-rich domain (CRD) family. (a) Structurebased multiple sequence alignments of the
tumor necrosis factor receptor (TNFR)-associated
CRDs. The alignment is colored according to
its consistency, from red for highly consistent
to blue/green for poorly consistent. Highly
conserved positions are indicated by black boxes.
(b) Structural tree of the TNFR-associated CRDs
obtained using T-RMSD. The structural subgroups
indicated in red, blue and green also correspond
to different specific functions attributed to
CRDs: pre-ligand assembly domains, single
binding domains and multiple binding domains.
Unclassified domains are indicated in black. The
value attributed to each node corresponds to the
percentage of ungapped column of the alignment
supporting each split. (c) Classification of the
human TNFR-associated CRDs family.
a
CRME-3
CRME-2
CRME-1
TNFRSF4-4
TNFRSF4-2
TNFRSF4-1
TNFRSF12A-1
TNFRSF17-1
TNFRSF13B-2
TNFRSF1A-4
TNFRSF13C-1
TNFRSF1A-3
TNFRSF1A-2
TNFRSF1A-1
TNFRSF16-4
TNFRSF16-3
TNFRSF16-2
TNFRSF16-1
TNFRSF14-2
TNFRSF14-1
TNFRSF10B-3
TNFRSF10B-2
b
c
Outlier domains
Pre-ligand
assembly domain
(PLAD)
TNFRSF16-1
© 2011 Nature America, Inc. All rights reserved.
TNFRSF1A-3
69
TNFRSF14-1
TNFRSF1A-1
By default, the output of R-Coffee is in
ClustalW format. However, it is possible to reformat the MSA in Stockholm
format, a more versatile format that
supports the inclusion of RNA secondary structures. For instance, the
previous command generates a Stockholm-formatted MSA containing the
alifold-predicted35 secondary structure
based on the MSA.
CRME-3
TNFRSF10B-3
CRME-1
TNFRSF16-2
15
30
38
TNFRSF4-4
76
30
TNFRSF4-1
100 53
61
TNFRSF17-1
30
TNFRSF13B-2
7
53
15
23
30
46
TNFRSF1A-4
TNFRSF13C-1
Single binding
domains
TNFRSF16-4
30
46
30
TNFRSF1A-2
TNFRSF4-2
CRME-2
38
TNFRSF12A-1
TNFRSF10B-2
TNFRSF14-2
TNFRSF16-3
Multiple binding
domains
Gene
Receptor name
TNFRSF1A
TNF-R1
TNFRSF1B
TNF-R2
TNFRSF3
TNF-R3
TNFRSF4
Ox40
TNFRSF5
CD40
TNFRSF6
FAS
TNFRSF6B
DcR3
TNFRSF7
CD27
TNFRSF8
CD30
TNFRSF9
4-1BB
TNFRSF10A DR4
TNFRSF10B DR5
TNFRSF10C DcR1
TNFRSF10D DcR2
TNFRSF11A RANK
TNFRSF11B OPG
TNFRSF25
DR3
TNFRSF12A TWEAK-R
TNFRSF13B TACI
TNFRSF13C BAFF-R
TNFRSF14
HVEM
TNFRSF16
NGFR
TNFRSF17
BCMA
TNFRSF18
GITR
TNFRSF19
TROY
TNFRSF19L RELT
TNFRSF21
DR6
TNFRSF27
XEDAR
EDAR
EDAR
CRD Composition
13| (Conditional step) Reformat using known/available RNA structures:
t_coffee –other_pg = seq_reformat –in RNA_rcoffee.aln -in2 1F6X_A_real.struc -input2 alifold -action + add_alifold -output stockholm_aln -out RNA_rcoffee_real.stk
If the structure is known (1F6X_A_real.struc), one can provide it rather than predict it using the previous command
(structure provided in alifold output format).
14| Visualize your results (see Fig. 3 for an example):
RNA_rcoffee.aln.html
This file contains the final alignment colored in function of the CORE index from red to blue (very to poorly consistent, see Box 1 for more details). You can open this file using any internet browser
RNA_rcoffee.dnd
This file contains the guide tree guide tree used to assemble the progressive alignment. You can open the file
RNA_rcoffee.dnd with a text viewer
RNA_rcoffee.aln
The final alignment is given in ClustalW format by default. One can reformat the output file using T-Coffee postprocessing options (see Box 2). You can open this file with any text file viewer or MSA viewer (i.e., Jalview)
! CAUTION The RNA_rcoffee.dnd tree is not a phylogenetic tree. It is computed on the basis of the distance matrix that is
generated from the pairwise scores. It should not be used to infer evolution relationship between aligned sequences.
15| Evaluate your RNA MSAs (Steps 15 and 16). Estimate the compensated mutations of your RNA alignment:
t_coffee –other_pg seq_reformat –in RNA_rcoffee.aln –action + alifold2analyze
color_html > RNA_rcoffee.comp.html
One of the most informative analysis when aligning RNA sequences is the identification of compensated mutations. Given
an RNA alignment, with either a predicted or an estimated secondary structure, one can easily use the seq_reformat
option of T-Coffee to estimate the number of columns showing compensated mutations. With the given command line, compensated mutations are determined based on an alifold-predicted secondary structure (the structure is predicted taking into
account ungapped columns only). If available, an experimental (or alternative) structure may also be provided.
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16| Display the results in numerical form:
t_coffee –other_pg seq_reformat –in RNA_
rcoffee.aln –action + alifold2analyze
T-Coffee
R-Coffee
1F6X_A
1F78_A
1F7F_A
1F7H_A
1JOX_A
1JPO_A
1XSG_A
1XST_A
#=GC SS_cons
#=GC SS_analyze
© 2011 Nature America, Inc. All rights reserved.
Section 3: Promoter MSAs ● TIMING 1 min
17| Type the following command line in a terminal window
in order to align your promoter regions with Pro-Coffee
Figure 3 | Comparison of T-Coffee and R-Coffee alignments. The color code
(Steps 17 and 18).
indicates the type of complementarities between matching columns. Orange
t_coffee –seq c18orf19.fa –mode procoffee
columns marked with a ‘W’ correspond to perfect Watson-Crick pairing (WC),
without mutation. Green columns (marked with an ‘N’) are columns that
Pro-Coffee is designed for aligning orthologous promoter
either contain WC or GU pairs (neutral). Blue columns (marked with an ‘I’)
regions or other orthologous DNA regions containing functional
indicate position containing non-WC pairs. Red columns correspond to
elements, such as enhancers. It uses a substitution matrix estiperfect WC positions, including compensated mutations (marked with a ‘C’).
mated on the TRANSFAC transcription factor-­binding sites MSAs.
The resulting alignments can be used as input for motif finders
and motif scanning programs that make use of aligned sequences. It is important to realize that Pro-Coffee was designed for aligning
closely related promoter regions, where it is realistic to expect some colinearity between the various transcription factor-binding sites.
18| Parameter settings: modify the gap-opening and gap-extension penalties. One can do so using the following command,
where @EP stands for extra parameters:
t_coffee –seq c18orf19.fa -method promo_pair@EP@GOP@-60@GEP@-1
The current version of Pro-Coffee uses a default gap-opening penalty (GOP) of − 60 and a gap-extension penalty (GEP)
of − 1. Our current benchmarks on sequences with an average identity around 45% suggest that good alignments are
obtained keeping the GEP value of − 1 and using a GOP value between − 50 and − 70.
19| Extract a portion of the alignment by typing the following command line:
offee -other_pg seq_reformat -in c18orf19.aln -action + extract_block ′ ENSG00000177150 ′
c
1852 1983 > c18orf19_chipseq.aln
This command extracts the portion of the alignment corresponding to the human region (ENSG00000177150) from 1,852 to 1,983 bp
(17 to 148 bp upstream of the transcription start site; Fig. 4). To improve accuracy, it can be beneficial to align larger stretches of
DNA even if one is only interested in a smaller portion of the alignment, which can then be extracted with this command.
Pro-Coffee
HoSa
MuMu
CaFa
BoTa
GaGa
Cons
HoSa
MuMu
CaFa
BoTa
GaGa
Cons
T-Coffee
HoSa
MuMu
CaFa
BoTa
GaGa
Cons
HoSa
MuMu
CaFa
BoTa
GaGa
Cons
Figure 4 | Pro-Coffee: alignment of promoter regions using Pro-Coffee. Yellow boxes indicate the ChIP-seq regions for CEBPA (raw data taken from ref. 38). Denoted
in purple are predicted CEBPA-binding sites (predictions done with MatScan with a cut-off of 70% weight matrix identity39). Below the sequences the color code of
the CORE index indicates consistency between pairwise alignments (red: high, blue: low). T-Coffee: similar alignment produced by T-Coffee with default mode.
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20| Visualize your results. The procoffee mode produces by default three different types of output files:
c18orf19.aln and c18orf19_chipseq.aln
The final alignment is given in ClustalW format by default. Reformatting of the output file
can be achieved using T-Coffee post-processing options (see Box 2). You can open this file
with any text file viewer or MSA viewer (i.e., Jalview)
c18orf19.html
This file contains the final alignment colored in function of the CORE index from red to blue
(very to poorly consistent, see Box 1 for more details). You can open this file using any
internet browser
c18orf19.dnd
This file contains the guide tree used to assemble the progressive alignment. Open this file
with a text viewer
© 2011 Nature America, Inc. All rights reserved.
! CAUTION The c18orf19.dnd file is not a phylogenetic tree. It is computed based on the distance matrix that is generated
from the pairwise scores. It should not be used to infer the evolution relationship between aligned sequences.
? TROUBLESHOOTING
? TROUBLESHOOTING
See Table 2 for troubleshooting guidelines.
TABLE 2 | Troubleshooting table.
Step
Problem
Solution
4C(i),
4D(i)
EBI server is offline
-blast_server NCBI
This option will use the NCBI server instead of EBI
You need to use local database
-blast_server LOCAL -protein_db database -pdb_db
database
Where ‘database’ must be a BLAST preformatted database. PSI-Coffee will
use protein_db, whereas pdb_db will be used by Expresso
4C(i)
You need to control the level of similarity for
-prot_min_sim n -prot_max_sim n -prot_min_cov n
the sequences included in the PSI-Coffee profile Where n is a percentage, between 0 and 100. A BLAST hit will be selected if
matching the specified criteria: the minimum sequence similarity, the
maximum sequence similarity and the minimum coverage
4D(i)
You need to control the level of similarity for
the structures selected as PDB templates for
Expresso
-pdb_min_sim n -pdb_max_sim n -pdb_min_cov n
Where n is a percentage, between 0 and 100. A BLAST hit will be selected if
matching the specified criteria: the minimum sequence similarity, the
maximum sequence similarity and the minimum coverage
6
You need to make a sum of pair comparison of
an MSA
-compare_mode sp
11
You need to produce highly accurate RNA
alignments
-mode rcoffee_consan
This mode is very memory and CPU intensive, requires Consan to be installed
20 (can
You launched more than one T-Coffee instance
occur at and they are competing for your machine’s
any point) processor and memory resources)
-multi_core no
By default T-Coffee uses all the cores and processors detected on your
machine. It is also possible to limit the number of processors used by an
instance:
-max_n_proc=n
● TIMING
Steps 1 and 2, Obtaining T-Coffee and choosing the proper protocol: ~5 min
Steps 3–7, Protein MSAs: 1–10 min
Steps 8–10, (Optional) Structure-based clustering of proteins using T-RMSD: ~2 min
Steps 11–16, RNA MSAs: ~1 min
Steps 17–20, Promoter MSAs: 1 min
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ANTICIPATED RESULTS
© 2011 Nature America, Inc. All rights reserved.
Section 1: Protein MSAs
Src homology 3 (SH3) domains. In this section, we discuss the relative merits of the four T-Coffee modes dedicated to
protein sequences when aligning a set of eight SH3 domains with known 3D structures. We show how one can estimate the
accuracy of a T-Coffee mode by comparing its result to a manual specialist alignment36 (‘Ref’ in Fig. 1). In general, one
does not need such a reference alignment, but it is used here to objectively compare the various modes of T-Coffee that
we describe in this section. More precisely, we show how the capacity of T-Coffee to align specific structural features,
such as the RT, n-Src and distal loops (arrows), the conserved motifs in the RT loop (1 and 2) and a conserved triad in
the binding pocket (features 3, 4 and 5), varies across alignments and improves when one uses extended homology or
structural information.
Figure 1a shows the alignments obtained by the default mode of T-Coffee. The alignment is colored according to its
consistency with its reference library. As discussed in Box 1, residues with a low score (blue, green) are expected to be
inaccurately aligned. Indeed, when comparing this MSA to the reference, one can see that the overall alignment seems to be
correct with the exception of three functional regions: the W-φ motif (φ for hydrophobic, feature 3), n-Src and distal loops.
In particular, the motif W-φ involved in the binding pocket is not correctly aligned, as T-Coffee aligns the residues WN of
the sequence 1BB9 instead of residues WL. Such a misalignment is hard to identify without structural information, especially
when the inaccurate position contains a highly conserved tryptophan.
By contrast, the Fast M-Coffee MSAs (Fig. 1b) exhibit a much higher CORE index, owing to the fact that it results from
the combination of three multiple aligners (Kalign, MAFFT and MUSCLE). The agreement between fast methods is often high,
even in regions that are inaccurately aligned, thus making the CORE less informative when combining multiple aligners.
The third alignment (Fig. 1c) is produced by PSI-Coffee. It illustrates the advantage of using homology information to improve
accuracy of distantly related sequences exhibiting variation in specific functional regions: the n-Src loop and the W-φ motif. The
distal loop, however, remains incorrectly aligned, mostly because of the influence of 1BB9 that contains a long insertion.
Finally, the alignment displayed in Figure 1d is a structure-based sequence alignment, generated with the Expresso mode
of T-Coffee. When compared with the reference alignment, this alignment is the best produced so far, as it manages to
correctly align all the structural and functional features displayed in the reference alignment.
We used the aln_compare function of T-Coffee to produce a quantitative comparison of each of the alignments
produced and the alignment of reference. Results given in Table 3 show that there is an improvement in the fraction of
­columns correctly aligned from the default mode of T-Coffee (68.9%) to Expresso (90.0%).
CRD family. The data set used to illustrate the T-RMSD (section 1; Steps 8, 9 and 10) is rather challenging because it is
composed of very short ( <40 residues) domains with poorly conserved sequences ( <20% of sequence identity). For such a
data set, sequence-based aligners usually fail to deliver a biologically correct alignment. By contrast, structure-based methods, such as Expresso, can be used to deliver more informative alignments that can be further used to classify the considered
sequences on the basis of their structural similarity (Fig. 2). The alignment was performed using 3D-Coffee, the structural
templates were selected manually and a combination of two structural aligners (SAP and TM-align) was used to build the
pairwise library and estimate the structure-based MSA (Fig. 2a). The only remarkable feature of the resulting MSA is the
conserved Cys pattern (black boxes in Fig. 2a).
The T-RMSD clustering, when applied to the previous alignment, reveals at least three different subgroups (Fig. 2b). As
shown in the original T-RMSD publication32, this clustering is much more informative than any sequence-based phylogenetic
reconstruction, as it manages to group together domains having similar functions within their respective tumor necrosis ­factor
receptors: pre-ligand assembly domains (not ­binding), single binding domains and multiple binding domains. These functions
usually result from the position of the CRDs relative to the N terminus of their receptor, as shown in Figure 2c.
Section 2: RNA MSAs.
In this section, we show how the alignments resulting from
the T-Coffee mode for RNA sequences can be analyzed for
compensated mutations. For this purpose, we used an alignment derived from DARTS, a database of RNA sequences with
known 3D structure37.
The resulting output is shown in Figure 3, both for
R-Coffee and the default T-Coffee outputs. It shows that the
R-Coffee alignment contains four columns associated with
compensated mutations. Such a finding is interesting, as
it yields increased support to the existence of a secondary
structure shared by all the considered sequences, it also
Table 3 | Comparisons between a manually curated alignment of
eight SH3 domains and four T-Coffee MSAs
Method
Column scores
Time (s)
T-Coffee
69.8
0.5
Fast M-Coffee
74.7
0.8
PSI-Coffee
80.0
21.7
Expresso
90.0
32.6
The column scores indicate the percentage of identical columns. Time is computed with an 8 Intel
Xeon CPU [email protected] GHz.
nature protocols | VOL.6 NO.11 | 2011 | 1681
protocol
i­ncreases the level of confidence that one may have in the considered alignment. The result of the command line of
Step 15 is a list summarizing the number of base pairs with compensated mutations. In the example presented here, the
score is 0 for T-Coffee and 2 in the R-Coffee alignment.
Section 3: Promoter MSAs.
The example presented in this section shows how the Pro-Coffee mode can be used to align the 2,000 bp upstream region of
the human gene C18orf19 to its orthologous regions in mouse, dog, cow and chicken (as defined in Ensembl).
Figure 4 shows side by side the Pro-Coffee MSA and its T-Coffee counterpart. Notably, Pro-Coffee manages to match the
predicted CEBPA-binding sites that fall within the ChIP-seq regions corresponding to this transcription factor. By contrast,
the default version of T-Coffee fails to align these sites.
© 2011 Nature America, Inc. All rights reserved.
Acknowledgments We thank J. Ramón González-Vallinas and E. Eyras for
providing ChIP-seq analysis used in Figure 4. This project is supported by the Plan
Nacional BFU2008-00419, the LEISHDRUG (no. 223414) and the Quantomics (KBBE2A-222664) projects of the 7th Framework Programme of the European Commission
and by a ‘la Caixa’ International PhD Program fellowship. Computational resources
are provided by the Center for Genomic Regulation (CRG) of Barcelona.
AUTHOR CONTRIBUTIONS J.-F.T., C.M., J.-M.C. and C.N. conceived and executed
the experiments about protein sequences and structures. G.B., C.K. and C.N.
conceived and executed the experiments about RNA sequences. I.E. and C.N.
conceived and executed the experiments about DNA sequences. P.D.T. and C.N.
conceived and developed the installation procedure. J.-F.T., C.M., G.B., J.-M.C.,
P.D.T., I.E., J.E.-C., C.K. and C.N. wrote the manuscript.
COMPETING FINANCIAL INTERESTS The authors declare no competing financial
interests.
Published online at http://www.natureprotocols.com/.
Reprints and permissions information is available online at http://www.nature.
com/reprints/index.html.
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