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Table 4 Comparison to existing methods

From: Improved accuracy of multiple ncRNA alignment by incorporating structural information into a MAFFT-based framework

    Accuracy of predicted structure (MCC)
Method Time (s.) SPS Pfold McCaskill-MEA RNAalifold (intrinsic)
ClustalW (iterative) 98 0.669 0.488 0.554 0.482  
ProbConsRNA 61 0.763 0.654 0.651 0.613  
G-INS-i 12 0.768 0.622 0.646 0.622  
LaRA 1.31 15,000 0.687 0.607 0.649 0.600  
Murlet 64,000 0.773 0.712 0.702 0.668  
MXSCARNA 2 700 0.769 0.718 0.712 0.666  
RNA Sampler (fast) 19,000 0.641 0.659 0.684 0.662 0.655
RNA Sampler 70,000 0.655 0.685 0.703 0.705 0.705
MASTR 24,000 0.662 0.570 0.616 0.592 0.601
X-INS-i-larapair 15,000 0.758 0.665 0.692 0.672  
X-INS-i-scarnapair 1,800 0.789 0.724 0.712 0.726  
  1. The KKA dataset was used as the benchmark. The accuracies of alignments measured by the SPS criterion are listed in the SPS column. The SPS value was computed for each alignment and then averaged across all the alignments. The accuracies of predicted common secondary structures are shown in the four columns on the right. The alignment by each method was subjected to three external prediction programs, Pfold, McCaskill-MEA and RNAalifold, and then the differences from the Rfam curated structure were assessed. The MCC values were computed for each sequence and then averaged across all the sequences. The accuracy values for secondary structure internally predicted by RNA Sampler and MASTR are shown in the (intrinsic) column. The highest score in each column is underlined. The scores close to the highest (p > 0.01 in the Wilcoxon test) are shown in bold. McCaskill-MEA was run with the default value α = 0.91.