Simultaneous Alignment and Structure Prediction of RNAs

Are Three Input Sequences Better than Two?
Beeta Masoumi and Marcel Turcotte
School of Information Technology and Engineering
University of Ottawa
Ottawa, Ontario, Canada

* 2005 International Workshop on Bioinformatics Research and Applications

A Web Appendix: Supplementary Material

Table 1: tRNA dataset.

IdLengthDescription
RD0260 77Asp Phage T5 (Virus)
RD0500 76Asp Haloferax volcanii (Archae)
RD4800 71Asp Aedes albopictus (Mitochondria, Animal)
RE2140 76Glu Synechocystis sp. (Eubacteria)
RE6781 76Glu Hordeum vulgare (Chloroplast)
RF6320 76Phe Schizosaccharomyces pombe (Cytoplasm, Fungi)
RL0503 88Leu Haloferax volcanii (Archae)
RL1141 89Leu Mycoplasma capricolum (Eubacteria)
RS0380 88Ser Halobacterium cutirubrum (Archae)
RS1141 92Ser Mycoplasma capricolum (Eubacteria)

Table 2: 5S rRNA dataset.

IdLengthDescription
AJ131594 117Delftia acidovorans
AJ251080 117Geobacillus stearothermophilus
K02682 120Micrococcus luteus
M10816 119Geobacillus stearothermophilus
M16532 121Thermus sp.
M25591 117Geobacillus stearothermophilus
V00336 120Escherichia coli
X02024 119Sporosarcina pasteurii
X02627 120Agrobacterium tumefaciens
X04585 119Rhodobacter capsulatus
X08000 122Arthrobacter oxydans
X08002 122Arthrobacter globiformis

PIC

PIC
PIC

PIC

Fig. 1: Effect of various gap penalty scores on PPV, sensitivity and MCC for the tRNA dataset.

PIC

PIC
PIC

PIC

Fig. 2: Effect of various gap penalty scores on MCC, PPV and Sensitivity for the 5S dataset.

Table 3: Sensitivity for the tRNA dataset.

Id NxdNdMinxdMindMaxxdMaxdAvexdAved
RD0260 4 5 95 57 100 100 98.8 90.5
RD0500 4 5 76 47 95 95 81.0 80.0
RD4800 5 5 95 57 100 100 99.0 91.4
RE2140 2 4 100 95 100 100 100.0 98.8
RE6781 2 4 100 81 100 100 100.0 95.2
RF6320 4 5 95 47 100 100 96.4 89.5
RL0503 1 2 95 95 95 95 95.8 95.8
RL1141 2 3 92 68 92 92 92.0 84.0
RS0380 1 2 92 80 92 80 92.0 80.0
RS1141 2 3 88 65 88 92 88.5 82.1

Table 4: MCC for the tRNA dataset.

Id NxdNdMinxdMindMaxxdMaxdAvexdAved
RD0260 4 5 97 67 100 100 99.4 93.0
RD0500 4 5 76 46 97 97 81.6 80.4
RD4800 5 5 97 67 100 100 99.5 93.5
RE2140 2 4 100 97 100 100 100.0 99.4
RE6781 2 4 100 79 100 100 100.0 94.8
RF6320 4 5 95 46 100 100 96.4 89.3
RL0503 1 2 97 97 97 97 97.9 97.9
RL1141 2 3 95 69 95 95 95.9 87.1
RS0380 1 2 95 81 95 83 95.9 82.5
RS1141 2 3 94 68 94 96 94.1 86.1

PIC

PIC
PIC

(a)

(b)
(c)

Fig. 3: Reference (a), Dynalign (b) and X-Dynalign (c) structures for the tRNA RS0380.

Table 5: Sensitivity for the 5S dataset.

Id NxdNdMinxdMindMaxxdMaxdAvexdAved
AJ131594 2 3 86 86 86 89 86.8 87.7
AJ251080 6 5 76 76 78 84 77.2 79.4
D11460 6 5 73 63 76 81 74.6 71.1
K02682 8 9 53 79 84 89 76.3 84.3
M10816 3 4 76 76 78 84 77.2 80.9
M16532 1 2 82 71 82 76 82.1 74.3
M25591 6 5 76 76 78 84 76.7 79.4
V00336 3 4 62 57 82 90 75.8 78.8
X02024 9 6 76 73 78 84 77.2 76.8
X02627 1 2 84 87 84 89 84.6 88.5
X04585 2 3 63 63 84 81 73.7 74.6
X08000 5 5 74 74 74 79 74.4 77.5
X08002 5 5 74 74 74 79 74.4 77.5

Table 6: MCC for the 5S dataset.

Id NxdNdMinxdMindMaxxdMaxdAvexdAved
AJ131594 2 3 93 89 93 93 93.2 91.0
AJ251080 6 5 83 79 84 85 83.5 82.1
D11460 6 5 80 64 81 85 80.8 75.1
K02682 8 9 58 85 92 93 82.4 88.1
M10816 3 4 83 80 84 86 83.7 84.3
M16532 1 2 87 74 87 81 87.9 78.0
M25591 6 5 81 79 83 85 83.0 82.1
V00336 3 4 68 61 90 94 83.5 84.8
X02024 9 6 83 79 84 86 83.4 81.1
X02627 1 2 92 90 92 93 92.0 92.2
X04585 2 3 67 65 89 87 78.4 78.5
X08000 5 5 82 82 82 83 82.1 83.2
X08002 5 5 82 82 82 83 82.1 83.2

PIC

(a)
PIC (b)

PIC

(c)

Fig. 4: Reference (a), Dynalign (b) and X-Dynalign (c) secondary structures for the 5S rRNA K02682.