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Evaluating the Accuracy and Efficiency of Multiple Sequence Alignment Methods

机译:评估多序列对准方法的准确性和效率

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摘要

A comparison of 10 most popular Multiple Sequence Alignment (MSA) tools, namely, MUSCLE, MAFFT(L-INS-i), MAFFT (FFT-NS-2), T-Coffee, ProbCons, SATe, Clustal Omega, Kalign, Multalin, and Dialign-TX is presented. We also focused on the significance of some implementations embedded in algorithm of each tool. Based on 10 simulated trees of different number of taxa generated by R, 400 known alignments and sequence files were constructed using indel-Seq-Gen. A total of 4000 test alignments were generated to study the effect of sequence length, indel size, deletion rate, and insertion rate. Results showed that alignment quality was highly dependent on the number of deletions and insertions in the sequences and that the sequence length and indel size had a weaker effect. Overall, ProbCons was consistently on the top of list of the evaluated MSA tools. SATe, being little less accurate, was 529.10% faster than ProbCons and 236.72% faster than MAFFT(L-INS-i). Among other tools, Kalign and MUSCLE achieved the highest sum of pairs. We also considered BALiBASE benchmark datasets and the results relative to BAliBASE- and indel-Seq-Gen-generated alignments were consistent in the most cases.
机译:比较10个最受欢迎的多序列对准(MSA)工具,即肌肉,MAFFT(L-INS-I),MAFFT(FFT-NS-2),T-Coffee,Probonons,Sate,Clustal Omega,Kalign,Multalin而且展示了Sialign-Tx。我们还专注于在每个工具的算法中嵌入的一些实现的重要性。基于由R,400个已知的对齐和序列文件产生的不同数量的分布群的10种模拟树木使用Indel-SEQ-Gen构建。生成共4000个测试对准,以研究序列长度,诱导尺寸,缺失率和插入速率的效果。结果表明,对准质量高度依赖于序列中的缺失数量和插入次数,并且序列长度和吲哚尺寸具有较弱的效果。总体而言,验证牌一直在评估的MSA工具列表的顶部。 SATE,较少的准确性,比柱子快529.10%,比MAFFT(L-INS-I)快236.72%。在其他工具中,Kalign和肌肉实现了最高的成对总和。我们还考虑了Balibase基准数据集,并且相对于Balibase-和Indel-SEQ-Gen-生成的对准的结果在大多数情况下是一致的。

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