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The predicted score is computed by PeakRegressor using the peak sequence information.
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We use these motifs to represent each peak sequence as a binary vector, indicating whether a motif is present or not in the peak sequence.
To evaluate syllable stereotypy, independent of sequence stereotypy, we used the peak of the spectral cross-correlation (Aronov et al., 2008), on a syllable-by-syllable basis.
As the FIE-HA+H3K27me3 FIE-HA+H3K27me3highest confidence FIE binding sites, we used these peakssequences in a MEME analysis.
When the true binding sites are not located near the center of some peaks and/or the peak sequences used in motif scan are not long enough to cover the actual motif, a motif-based approach would miss the site.
In the first step of PeakRegressor, we use MEME to find over-represented DNA motifs in the peak sequences.
The program uses one matrix representing the TF-binding motif and the peak sequences as input.
The peak sequences are analyzed for enrichment of transcription factor binding motifs using MEME-ChIP [ 15].
Locating and visualizing motifs Peak-motifs allows searching putative binding sites in the peak sequences.
Secondly, Schwarts et al. used the peak-calling software FindPeaks, whereas we used MACS, with only MACS also using input DNA sequence data for peak estimation [ 57– 57].
The residual water peak was suppressed using the WET sequence at the end of the relaxation delay (Smallcombe et al., 1995).
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