Motivation: Multiple molecular sequence alignment is among the most important and most challenging tasks in computational biology. The currently used alignment techniques are characterized by great computational complexity, which prevents their wider use. This research is aimed at developing a new technique for efficient multiple sequence alignment. Approach: The new method is based on genetic algorithms. Genetic algorithms are stochastic approaches for efficient and robust searching. By converting biomolecular sequence alignment into a problem of searching for optimal or near-optimal points in an 'alignment space', a genetic algorithm can be used to find good alignments very efficiently. Results: Experiments on real data sets have shown that the average computing time of this technique may be two or three orders lower than that of a technique based on pairwise dynamic programming, while the alignment qualities are very similar. Availability: A C program on UNIX has been written to implement the technique. It is available on request from the authors.
['Ching Zhang', 'Andrew K. C. Wong']
A genetic algorithm for multiple molecular sequence alignment