T-Coffee: A novel method for fast and accurate multiple sequence alignment.

By Cédric Notredame, Desmond G. Higgins, Jaap Heringa. 2000

7,235 citations recorded by the source corpus.

We describe a new method (T-Coffee) for multiple sequence alignment that provides a dramatic improvement in accuracy with a modest sacrifice in speed as compared to the most commonly used alternatives. The method is broadly based on the popular progressive approach to multiple alignment but avoids the most serious pitfalls caused by the greedy nature of this algorithm. With T-Coffee we pre-process a data set of all pair-wise alignments between the sequences. This provides us with a library of alignment information that can be used to guide the progressive alignment. Intermediate alignments are then based not only on the sequences to be aligned next but also on how all of the sequences align with each other. This alignment information can be derived from heterogeneous sources such as a mixture of alignment programs and/or structure superposition. Here, we illustrate the power of the approach by using a combination of local and global pair-wise alignments to generate the library. The resulting alignments are significantly more reliable, as determined by comparison with a set of 141 test cases, than any of the popular alternatives that we tried. The improvement, especially clear with the more difficult test cases, is always visible, regardless of the phylogenetic spread of the sequences in the tests.

Subfields: Molecular Biology

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Citations in the corpus

Improving homology modeling from low-sequence identity templates in Rosetta: A case study in GPCRs (2020, methodology): “As seen, despite using sequence-only (ClustalOmega [27] and Muscle [28]), structure-only (Mustang [30]), or automated blended alignments (T-Coffee, TM-PSI, and Espresso (PDB Mode) [29]), the knowledge-based alignment performs the best in all regions tested.”
Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega (2011, methodology): “…most (203 out of 218) BAliBASE test cases, the number of sequences is small and the script runs L-INS-i, which is the slow accurate program that uses the consistency heuristic (Notredame et al, 2000) that is also used by MSAprobs (Liu et al, 2010), Probalign, Probcons (Do et al, 2005) and T-Coffee.”
A position-specific distance-dependent statistical potential for protein structure and functional study (2012, background): “…(Maiorov and Crippen, 1992; Panchenko et al., 2000; Peng and Xu, 2009, 2010; Sippl and Weitckus, 1992; Skolnick et al., 2000), protein alignment (Notredame et al., 2000; Pei et al., 2008; Wu and Zhang, 2008b; Xu, 2005; Zhang and Skolnick , 2005), model quality assessment (Jones and Thornton,…”
Unicycler: Resolving bacterial genome assemblies from short and long sequencing reads (2016, methodology): “Such reads contain a sample of the gap sequence between those contigs, and if multiple long reads connect a pair of contigs, Unicycler uses SeqAn to produce a consensus gap sequence[18,19].”
MultiSeq: unifying sequence and structure data for evolutionary analysis (2006, methodology): “…[28], and Jalview [29]; there are tools for creating MSAs by aligning individual sequences and profiles, like ClustalW [30], HMMER [31] and T-Coffee [32]; there are tools for annotating sequence data such as Pfaat [33]; and BLAST [34] is used for searching through databases for related sequences.”
MAFFT version 5: improvement in accuracy of multiple sequence alignment (2005, methodology): “In an attempt to improve alignment accuracy, [GHF]-INS-i uses a TCoffee-like approach (20) in incorporating all pairwise alignment information into an objective function.”
GUIDANCE2: accurate detection of unreliable alignment regions accounting for the uncertainty of multiple parameters (2015, positive): “00 (40) were used as alignment algorithms.”
Exploiting evolutionary relationships for predicting protein structures (2003, methodology): “The correctness of the alignment can be improved by taking advantage of the knowledge of other members of the protein family and constructing multiple sequence alignments with programs such as CLUSTALW (Thompson et al., 1994) or T-COFFEE (Notredame et al., 2000).”