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Multiple sequence alignment is a basic of sequence analysis. In the development of multiple sequence alignment (MSA) approaches, M-Coffee  was proposed as a meta-method for assembling outputs from different individual multiple aligners into one single MSA to boost the accuracy. Authors showed that M-Coffee outperformed individual alignment methods. In this paper, we propose an improvement of M-coffee, called EM-Coffee, by introducing a new weighting scheme for combining input alignments. Experiments with benchmark datasets showed that EM-Coffee produced better results than M-Coffee, T-Coffee, Muscle and some other widely used methods. Thus, we provide an alternative option for researchers to align sequences.