Biology Reference
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motifs, the original algorithm offered the possibility of restricting motif
evaluation to a random subset of k- letter words.
The algorithm for iterative refinement of a locally overrepresented
sequence motif was published under the name PATOP. 34 The three
different components of the motif description — the borders of the pre-
ferred region, the weight matrix, and the cut-off value — are updated
Input:
Output:
A: 3 0 23 0 48 0 0 0 0 1 5
C: 5 1 22 41 32 52 51 31 29 11 0
G: 4 3 26 47 44 40 54 22 13 0 0
T A T A A A
Cutoff: 2 mismatches
T: 0 4 0 11 0 23 13 42 6 7 8
Cutoff: 74
Fig. 6. Optimization of the TATA box motif by the PATOP algorithm. A new weight
matrix description for the TATA box motif was optimized using local overrepresen-
tation (LOR), as explained in Fig. 5, as an objective function. A consensus sequence-
based motif description was used as the seed, and the 1867 promoter sequences from
EPD (see legend for Fig. 4) served as the training set for optimization. The positional
distributions of the input and output motifs are shown at the bottom. Note that the
optimized weight matrix has both a higher peak frequency near position −30 and
a lower background frequency elsewhere.
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