Biology Reference
In-Depth Information
Chapter 7
T-Coffee: Tree-Based Consistency Objective Function
for Alignment Evaluation
Cedrik Magis, Jean-Fran ¸ ois Taly, Giovanni Bussotti,
Jia-Ming Chang, Paolo Di Tommaso, Ionas Erb,
Jos
´
Espinosa-Carrasco, and Cedric Notredame
Abstract
T-Coffee, for Tree-based consistency objective function for alignment evaluation, is a versatile multiple
sequence alignment (MSA) method suitable for aligning virtually any type of biological sequences.
T-Coffee provides more than a simple sequence aligner; rather it is a framework in which alternative
alignment methods and/or extra information (i.e., structural, evolutionary, or experimental information)
can be combined to reach more accurate and more meaningful MSAs. T-Coffee can be used either by
running input data via the Web server (
http://tcoffee.crg.cat/apps/tcoffee/index.html
) or by download-
ing the T-Coffee package. Here, we present how the package can be used in its command line mode to carry
out the most common tasks and multiply align proteins, DNA, and RNA sequences. This chapter particu-
larly emphasizes on the description of T-Coffee special flavors also called “modes,” designed to address
particular biological problems.
Key words
MSA, 3D structure, Protein sequences, Transmembrane protein, Homolog sequences,
DNA/RNA sequences, Promoter alignment, RNA secondary structure
1
Introduction
Multiple sequence alignment (MSA) is one of the most widely used
bioinformatic methods in biology for the simultaneous comparison
of evolutionarily related sequences [
1
,
2
]. In an MSA, the relation-
ship between all residues of the considered sequences is explicitly
described, thus making it possible to identify highly conserved
positions, or positions whose variability has a functional signifi-
cance. These MSA models are rarely used for their own sake and
their computation is usually an intermediate step towards more
sophisticated applications: phylogenetic reconstruction, profile
estimation (often referred to as hidden Markov models, HMM),
structural predictions, promoter analysis, active site identification,
and RNA secondary structure prediction. Building the most
