Motivation: In order to increase the accuracy of multiple sequence alignments, we designed a new strategy for optimizing multiple sequence alignments by genetic algorithm. We named it COFFEE (Consistency based Objective Function For alignmEnt Evaluation). The COFFEE score reflects the level of consistency between a multiple sequence alignment and a library containing pairwise alignments of the same sequences.
Results: We show that multiple sequence alignments can be optimized for their COFFEE score with the genetic algorithm package SAGA. The COFFEE function is tested on 11 test cases made of structural alignments extracted from 3D_ali. These alignments are compared to those produced using five alternative methods. Results indicate that COFFEE outperforms the other methods when the level of identity between the sequences is low. Accuracy is evaluated by comparison with the structural alignments used as references. We also show that the COFFEE score can be used as a reliability index on multiple sequence alignments. Finally, we show that given a library of structure-based pairwise sequence alignments extracted from FSSP, SAGA can produce high-quality multiple sequence alignments. The main advantage of COFFEE is its flexibility. With COFFEE, any method suitable for making pairwise alignments can be extended to making multiple alignments.
Availability: The package is available along with the test cases through the WWW: http://www. ebi.ac.uk/cedric
Contact: cedric.notredame@ebi.ac.uk