|
Non-coding RNA detection using SVDD in comparing the stem kernels with the local alignment kernels. |
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| Stem kernels |
Local alignment kernels |
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|
|
|
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| ncRNA type |
Rfam Accession |
N |
ROC |
SP |
SN |
nSV |
ROC |
SP |
SN |
nSV |
|
|
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| 5S ribosomal RNA |
RF00001 |
449 |
1.000 |
1.000 |
0.940 |
27.8 (6.9) |
1.000 |
1.000 |
0.886 |
48.4 (12.0) |
| U2 spliceosomal RNA |
RF00004 |
566 |
0.997 |
0.999 |
0.912 |
51.8 (10.2) |
0.999 |
1.000 |
0.844 |
92.0 (18.1) |
| tRNA |
RF00005 |
495 |
0.983 |
0.948 |
0.939 |
26.8 (6.0) |
0.999 |
0.999 |
0.853 |
67.0 (15.0) |
| Hammerhead ribozyme III |
RF00008 |
588 |
1.000 |
0.998 |
0.971 |
14.2 (2.7) |
1.000 |
1.000 |
0.968 |
19.3 (3.6) |
| U3 snoRNA |
RF00012 |
471 |
1.000 |
1.000 |
0.915 |
36.3 (8.6) |
0.959 |
1.000 |
0.775 |
95.5 (22.5) |
| U5 spliceosomal RNA |
RF00020 |
510 |
0.999 |
0.998 |
0.939 |
30.3 (6.6) |
1.000 |
1.000 |
0.882 |
57.2 (12.5) |
| tmRNA |
RF00023 |
730 |
1.000 |
1.000 |
0.881 |
83.1 (12.6) |
0.757 |
1.000 |
0.037 |
636.5 (96.9) |
| Group II intron |
RF00029 |
604 |
0.996 |
0.989 |
0.942 |
30.9 (5.7) |
0.999 |
1.000 |
0.922 |
48.7 (9.0) |
| mir-10 |
RF00104 |
620 |
1.000 |
1.000 |
0.977 |
13.3 (2.4) |
1.000 |
1.000 |
0.984 |
10.7 (1.9) |
| U70 snoRNA |
RF00156 |
608 |
0.998 |
0.996 |
0.952 |
25.5 (4.7) |
1.000 |
1.000 |
0.951 |
29.0 (5.3) |
| RNAse P |
- |
656 |
0.998 |
1.000 |
0.887 |
66.2 (11.2) |
0.629 |
1.000 |
0.006 |
587.5 (99.5) |
| SRP RNA |
- |
872 |
1.000 |
1.000 |
0.939 |
54.4 (6.9) |
0.994 |
1.000 |
0.881 |
95.3 (12.1) |
|
|
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| Total |
7169 |
0.998 |
0.995 |
0.932 |
460.6 (7.1) |
0.938 |
1.000 |
0.729 |
1787.1 (27.7) |
|
|
ncRNA type: name of the target ncRNA family. Rfam Accession: accession number of the target ncRNA family in Rfam. N: number of alignments. ROC: ROC score, equal to the area under the ROC curve. SP: specificity of the discrimination of the target ncRNA family. SN: sensitivity of the discrimination of the target ncRNA family. nSV: number of support vectors collected in the training processes and their rates against the numbers of the training alignments within parentheses. | ||||||||||
Sato et al. BMC Bioinformatics 2008 9:318 doi:10.1186/1471-2105-9-318 |
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