Table 5.
Evaluation of rigid gap motifs on Dilimot datasets.
| Motif | NumSeqs | Abs. Supp | Supp Rank | IG | Pratt | LogOdd | Zscore |
| LPSN | 15 | 4 | 1294 | 520 | 2429 | 4 | 6 |
| WS.WS | 34 | 7 | 15 | 22 | 31 | 28 | 28 |
| Q.RLQ..Q | 15 | 4 | 5259 | 660 | 5213 | 1 | 1 |
| P.LP.K | 24 | 8 | 1334 | 336 | 592 | 22 | 23 |
| L.DL.K | 7 | 7 | 1 | 1 | 12 | 1 | 1 |
| M.C..S.E.K.A | 5 | 4 | 101 | 14 | 424 | 17 | 17 |
| GS...G.P | 25 | 5 | 22554 | 10428 | 11292 | 1155 | 1243 |
| G...E.GE | 40 | 9 | 4735 | 1257 | 3617 | 30 | 32 |
| R.RS.S | 32 | 6 | 3497 | 1319 | 1395 | 42 | 52 |
| G...RGRG | 15 | 8 | 97 | 1 | 136 | 1 | 1 |
| Rm | 0.0003 | 0.0007 | 0.0004 | 0.0077 | 0.0071 | ||
Evaluation of motif ranking results for ten datasets from the Dilimot database. For each dataset the number of sequences and the absolute support value (Abs. Supp.) of the target motif are given. Motifs are ranked with Information-theoretic measures and support (Supp rank). Last row gives the Rm values of each measure, where LoggOdd obtained the best results.