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Table 1 Results on the CMfinder-SARSE dataset

From: RNAalifold: improved consensus structure prediction for RNA alignments

RNA #seq MPI RIBOSUM RNAalifold Pfold KNetFold McC_mea
Antizyme_FSE 13 87 1.000 1.000 1.000 1.000 1.000
ctRNA_pGA1 15 72 1.000 1.000 1.000 0.976 1.000
Entero_5_CRE 160 84 1.000 0.848 0.478 1.000 0.942
Entero_CRE 56 81 1.000 0.736 1.000 0.953 0.953
GcvB 17 64 0.939 0.799 0.889 0.939 0.921
glmS 11 60 0.986 0.972 0.972 0.809 0.837
HACA_sno_Snake 22 90 0.871 0.407 0.414 0.915 0.884
HCV_SLIV 110 89 1.000 0.922 1.000 1.000 0.961
HDV_ribozyme 15 95 0.953 -0.015 0.590 0.460 0.460
HepC_CRE 52 87 1.000 0.962 1.000 1.000 1.000
Histone3 64 78 1.000 1.000 1.000 1.000 1.000
Hsp90_CRE 4 98 0.855 0.855 0.413 0.867 0.874
IBV_D-RNA 10 96 1.000 0.928 0.928 1.000 1.000
Intron_gpII 114 54 1.000 0.779 1.000 1.000 1.000
IRE 39 63 1.000 0.938 1.000 1.000 0.938
let-7 14 73 1.000 0.979 1.000 1.000 0.957
lin-4 9 73 1.000 0.973 1.000 1.000 1.000
Lysine 43 49 0.990 0.918 0.960 0.990 0.990
mir-10 11 67 0.973 0.888 0.916 0.973 0.973
mir-194 4 79 0.870 0.849 1.000 0.866 0.698
mir-BART1 3 93 0.977 0.977 0.861 1.000 0.977
nos_TCE 3 90 0.975 0.975 0.951 1.000 0.975
Purine 22 56 0.945 0.917 1.000 0.945 0.945
Rhino_CRE 12 72 0.734 0.734 0.680 0.974 0.756
RNA-OUT 4 96 0.775 0.775 0.834 0.740 0.775
rncO 6 80 0.903 0.923 0.668 0.896 0.825
Rota_CRE 14 86 1.000 0.764 0.682 0.099 -0.011
s2m 38 79 0.739 1.000 0.774 0.652 0.861
SCARNA14 4 67 0.969 0.748 -0.005 0.532 0.777
SCARNA15 3 96 1.000 1.000 0.601 0.971 0.925
SECIS 63 43 0.941 0.813 0.943 0.971 0.813
SNORA14 3 92 0.944 0.944 0.853 0.959 0.869
SNORA18 6 79 0.913 0.503 0.702 0.971 0.893
SNORA38 5 84 0.759 0.743 0.858 0.410 0.734
SNORA40 7 80 0.962 0.962 0.704 0.948 0.920
SNORA56 4 97 0.816 0.922 0.446 0.779 0.741
SNORD105 2 89 1.000 1.000 -0.007 0.648 0.971
SNORD64 3 94 1.000 0.539 0.539 0.661 -0.014
SNORD86 6 82 0.641 -0.012 -0.007 0.511 0.000
snoU83B 4 87 0.927 0.927 0.846 0.895 0.927
TCV_H5 3 97 1.000 1.000 0.685 1.000 1.000
TCV_Pr 4 95 1.000 1.000 0.688 1.000 1.000
Tymo_tRNA-like 28 64 1.000 0.916 1.000 0.973 1.000
ykoK 36 61 0.856 0.756 0.906 0.841 0.794
mean    0.937 0.831 0.765 0.866 0.837
  1. Performance comparisons on the CMfinder-SARSE dataset. We list the MCC for different alignments. Best performance bold.