Fast and robust multiple sequence alignment with phylogeny-aware gap placement

BMC Bioinformatics

Table 2 Performance comparison on BAliBASE 3.0

Ranking of MSA tools on BAliBASE
	RV11	RV12	RV20	RV30	all
POA	0.26	0.279	0.217	0.183	0.239
Prank+F	0.252	0.6^***	0.256	0.272^*	0.357^***
Prank	0.261	0.607	0.261	0.277	0.363^**
Mafft	0.245	0.607	0.293^**	0.321^**	0.377^**
ProGraphMSA D (noCS)	0.313^**	0.63^*	0.328	0.321	0.41^***
ProGraphMSA D	0.343	0.647^**	0.368^**	0.357^**	0.44^***
ClustalW	0.309	0.679^**	0.338	0.326	0.427
Muscle	0.307	0.663^*	0.34	0.358^*	0.428
ProGraphMSA	0.361^*	0.656	0.383	0.376	0.455
Muscle-i	0.396^**	0.716^***	0.358	0.372	0.473^***
Mafft-i	0.435^**	0.731	0.446^***	0.471^***	0.53^***
Mummals	0.404	0.766^***	0.41	0.425^*	0.514

Displayed are the average true column scores (CS) for the truncated (BBS*) alignments of the RV11, RV12, RV20, and RV30 sets as well as the average over all these sets. Apart from a few exceptions the listing order of the tools implies significantly improving performance. Between each pair of subsequent scores for two different tools we perform a Wilcoxon signed-rank test. Stars indicate a significant difference at a p<0.05,p<0.01,p<0.001 level, respectively. In particular, the use of context-sensitive profiles significantly improves ProGraphMSA D's alignments, whereas our optimized version of ProGraphMSA significantly outperforms ClustalW (p=0.0024) but does scarcely not outperform Muscle without refinement (p=0.067) at the defined significance level.

ISSN: 1471-2105