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EM-Coffee: An Improvement of M-Coffee

机译:EM-Coffee:M-Coffee的改进

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摘要

Multiple sequence alignment is a basic of sequence analysis. In the development of multiple sequence alignment (MSA) approaches, M-Coffee [1] 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.
机译:多序列比对是序列分析的基础。在多序列比对(MSA)方法的发展中,M-Coffee [1]被提出作为一种元方法,用于将来自不同的多个比对器的输出组装成一个单一的MSA,以提高准确性。作者表明,M-Coffee优于单独的对齐方法。在本文中,我们通过引入用于合并输入对齐方式的新加权方案,提出了一种改进的M-咖啡(称为EM-Coffee)。使用基准数据集进行的实验表明,EM-Coffee产生的结果优于M-Coffee,T-Coffee,Muscle和其他一些广泛使用的方法。因此,我们为研究人员提供了比对序列的替代选择。

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