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To determine motif intervals, we built a histogram of the number of occurrences in a sliding window.
To determine motif enrichment or depletion, the algorithm initially extracts promoter sequences from the provided FASTA formatted input data file to create the experimental and randomized reference datasets.
To calculate the false-positive error rate; a script was written to reiteratively call the Cluster Analysis Real Randomization algorithm to determine motif enrichment for various sample sizes and motifs and is available upon request.
Attempts at a heuristic to automatically determine motif width in a deterministic (Expectation-Maximization, or EM-based) algorithm have included functions based on the maximum likelihood ratio test (LRT) [ 11], methods based on V -fold cross-validation [ 7] and the Bayesian Information Criterion (BIC) [ 12].
This is possible as the algorithm simply defines the number of motifs and/or modules in the genomic, randomized and experimental datasets to determine motif enrichment or depletion without seeking for optimal concurrent cis-elements to explain a given dataset clustering in its entity.
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The HOMER program (Heinz et al. 2010) was used to determine motifs that had diverged between paralogous regions discordant in their chromatin states.
We find that models assuming purely unspecific or lysine site-specific acetylation rates were insufficient to explain the experimentally determined motif abundances.
FIRE-pro also determines motif pairs that co-occur within the same proteins and co-localize in the primary protein sequences (Text S1).
Hence, accurate p-values for determining motif enrichment could be calculated using Gaussian Z-statistics.
However, these criteria have generally not performed well at determining motif width in known datasets [ 5].
We demonstrate the performance of MCOIN as part of a deterministic motif discovery algorithm and conclude that MCOIN outperforms current methods for determining motif width.
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