W646 Nucleic Acids Research, 2007, Vol. 35, Web Server issue on the basis of its consistency with a collection of pairwise constraints. In practice, the constraints correspond to pairs of residues that could end up aligned in the ﬁnal alignment. These constraints, however, are not necessarily all compatible with one another and the goal of the algorithm is to ﬁt as many as possible within the ﬁnal alignment, while discarding those that were hopefully biologically less relevant. The term consistency refers to the notion that one tries to compute the alignment having the highest possible consistency with the constraint list. This notion was introduced by Gotoh (12) and later re-used in several algorithms (13,14). In 2000, Notredame et al. (1) described a variation of the progressive algorithm using consistency as a scoring scheme. This combination proved quite successful and is now at the core of several MSA packages (15–17). In its default mode, T-Coﬀee uses, as a list of constraints, all the pair-wise matches extracted from a compilation of all possible global pair- wise alignments and the 10 best local alignments from each pair of sequences. Yet, this is merely one of the possible recipes to assemble such a list of constraints, and alternatives are possible. For instance, ProbCons (16) uses suboptimal pairwise global alignments (as emitted by an HMM with posterior decoding); PCMA (15) uses pairwise proﬁle comparisons and Expresso (18) uses a mixture of sequence and structure-based alignments. Following the same principle, it is also possible to generate alternative MSAs and compile them into a single list of constraints. This latest approach forms the basis of M-Coﬀee (2), where eight MSA methods are used to generate alternative MSAs. Extensive benchmarking showed that this combination results in a modest but consistent improvement over each individual method, with M-Coﬀee producing the best scoring alignment on two of three of the datasets contained in BaliBase (5), Prefab (6) and Homstrad (2). Another interesting by-product of alignment combina- tion is the possibility of estimating the local consistency between the ﬁnal alignment and the individual alignments. This amounts to measuring, for every residue, the fraction of individual alignments that support its position in the ﬁnal alignment. This measure is named the CORE index (Consistency of Overall Residue Evaluation) and was shown to be very informative with respect to the overall alignment accuracy (19). These initial reports recently gained further support thanks to some extensive analysis carried out by Sonhammer et al. (20) whose results indicate that the consistency between an MSA and a pre-computed collection of alternative alignments gives very reliable information with respect to the structural correctness of that alignment. As such, the local consistency measure appears to be one of the most reliable predictors of alignment accuracy available today. The server we present here computes an alignment with eight of the most commonly used MSA packages. It then outputs a consensus alignment along with a CORE-based local evaluation that can either be color-coded or ASCII based. Two mirrors of these services currently run on separate clusters: one at the Swiss Institute of Bioinformatics on the Vital-IT framework, the other at the CNRS in Marseilles, France. Both mirrors can be accessed via the T-Coﬀee homepage: www.tcoﬀee.org and extra mirrors should be added in the close future. METHODS Primary library: computation of the initial MSAs The principle of M-Coﬀee is to compute several alter- native multiple alignments in order to combine them into one consensus alignment. By default, eight methods were chosen for this purpose: PCMA (Version 2.0) (15), POA (Version 2.0) (21), Dialign-t (Version 0.2.1) (22), MAFFT (Version 5.431, L-INS-i) (17), Muscle (Version 3.6), ProbCons (Version 1.2), ClustalW (23) and T-Coﬀee (1). Apart from MAFFT that is used in its most accurate mode (maﬀt- - localpair- -maxiterate 1000) all the methods are run on the initial dataset using the default parameters. This produces an MSA that is then turned into a T-Coﬀee primary library. All these libraries are then combined in order to generate an MSA. M-Coffee alignment computation In order to compute the ﬁnal alignment, the server runs the following command: t_coﬀee 5seq4 -method poa_msa, dialignt_msa, maﬀt_msa, clustalw_msa, muscle_msa, probcons_msa, t_coﬀee_msa, pcma_msa. Using the M-Coffee server The server can be accessed at www.tcoﬀee.org. Following the M-Coﬀee link will either take the user to the regular or advanced mode. The regular mode merely requires the user to cut and paste a set of sequences in FASTA format. The advanced mode (Figure 1) oﬀers more possibilities and guides the user with a series of bulleted points: (i) Cut and paste your sequences. Sequences should be in FASTA format. Duplicated names are now supported although not recommended. (ii) Alignment computation. This section deﬁnes the way the primary library is computed. For instance, selecting only lalign_id_pair and slow_pair will lead to the computation of a regular T-Coﬀee MSA. The lower section (xxx_msa) displays the list of available MSA methods. Selecting only one of these methods will generate the corresponding alignment. Selecting several methods (or all of them, as in the regular mode displayed on Figure 1) will lead to a consensus T-Coﬀee MSA. If the MSA method one wants to combine is missing on this form, another server named ‘Combine’ should be used (accessible from www.tcoﬀee.org). The ‘Combine’ server works on the same principle as M-Coﬀee but does not compute the MSAs itself and requires the user to cut and paste pre-computed MSAs. At this point it should be used if one wants to incorporate speciﬁc constraints or structure-based sequence alignments. (iii) Output. The Output section makes it possible to control the output format. The most notable