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The position-specific scoring matrix from the MEME analysis was input into Find Individual Motif Occurrence program (Bailey and Gribskov, 1998) with default parameters.
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The frequency of motif occurrence at each position, as defined by each motif discovery program, is shown as a graphic using the Chart: Clicker Perl module [ 36].
While changes in the significance threshold set for defining a motif occurrence impacted absolute percentage of peaks containing motifs, such changes did not alter the performance of the programs relative to one another (Figure S5).
A heatmap showing motif occurrence in each region was plotted using four subsets except a subset used for the discovery.
Top: Wikipedia motif occurrence in time.
The algorithm identifies motif occurrence, motif position and total length of each 3'UTR.
All categories showed significant enrichment of motif occurrence compared to background genomic regions (Table S3).
Bottom: probability of motif occurrence around Red1 peak summits.
In the present study, the terms – motif, motif occurrence, motif-set, consensus motif and motif-group – have been used and need to be explained.
Predicted motifs are reported if the probability of the motif occurrence is statistically non-random compared to the background.
For most Tompa data, the distribution of motif in each sequence makes it difficult to report the motif occurrence positions.
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