We describe a new method (T-Coffee) for multiple sequence alignment that pr
ovides a dramatic improvement in accuracy with a modest sacrifice in speed
as compared to the most commonly used alternatives. The method is broadly b
ased on the popular progressive approach to multiple alignment but avoids t
he most serious pitfalls caused by the greedy nature of this algorithm. Wit
h T-Coffee we pre-process a data set of all pair-wise alignments between th
e 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 al
l 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 appro
ach by using a combination of local and global pair-wise alignments to gene
rate 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 phyloge
netic spread of the sequences in the tests. (C) 2000 Academic Press.