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Therefore, to assess whether there was greater divergence between species within s-DMRs, we compared the sequence similarity between the s-DMRs and the total background set of peaks that these were identified from.
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For each peak, matching peaks in all accessions were identified, building a set of peaks for use in further analysis.
ChIPseek provides the option to compare two set of peaks.
The algorithmic task is to find a set of peaks.
Similarly as in MACS, a candidate set of peak regions is then identified in a ChIP-sample assuming a local Poisson background, taking into account also the variability in genomic mappability.
Let P be the set of peak pairs of E and E′ without zero-intensity peaks.
Our background set consists of 1000 sequences also from dbTSS.
The ability of other peaks to classify other groups fell between these two sets of peaks.
To get an estimate for the fraction of simultaneously detected peaks with non-overlapping sites expected by chance we performed a tenfold simulation on a random 1st order Markov chain background set that was generated by shuffling full-length peaks.
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After removing the background signals and detecting peak position using the second derivative test, an optimal peak model is selected from a set of predefined peak models, including Gaussian mixture (GMM), log-normal (LN), Poisson, gamma, hybrid of exponential and Gaussian (EGH), and exponentially modified Gaussian (EMG) models.
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