It can also combine multiple sequences alignments obtained previously and in the latest versions can use structural information from Protein Data Bank (PDB) files (3D-Coffee).

It has advanced features to evaluate the quality of the alignments and some capacity for identifying occurrence of motifs (Mocca).

The most common input formats are supported (FASTA, Protein Information Resource (PIR)).

T-Coffee algorithm consist of two main features, the first by, using heterogeneous data sources, can provide simple and flexible means to generate multiple alignments.

[1] Efficient combination of local and global alignment information is an important factor of T-Coffee.

Pairwise projections can be produced using fast or slow methods, thus allowing a trade-off between speed and accuracy.

TCS has been shown to lead to significantly better estimates of structural accuracy and more accurate phylogenetic trees against Heads-or-Tails, GUIDANCE, Gblocks, and trimAl.