We did not attempt to merge the adjacent windows that are identified as

We did not attempt to merge the adjacent windows that

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then performed differential analysis using their default parameter values. We did not attempt to merge the adjacent windows that are identified as significant. For ChIPDiff and CCAT, the replicates in each group were pooled because they do not support the use of replicates. We then applied them with the window size of 1Kb. After CCAT found peaks in control and treatment groups, the union of the two peak lists was used to calculate read count for each replicate. These read count information were then given to DESeq to identify differential sites. ChIPDiff works by modeling the log fold change of each non-overlapping window and merges the adjacent windows that are classified as significant. The merged windows are used to define a differential site. However, ChIPDiff does not provide additional information about the differential sites besides their genomic coordinates. We were not able to rank those differential sites. Therefore, we decided to report the number of non-overlapping windows instead of the number of differential sites. As a result, the number for ChIPDiff reported in this paper is always larger than the program’s default number. The non-overlapping windows of ChIPDiff were ranked according to fold changes in descending order.
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Additional p-values for the sensitivity and specificity curves Additional p-values: 0.5, 0.4, 0.3, 0.2 and 0.1 are added in Figs. S3 & S5. Basically, the curves in this range follow similar trends as p-value from 1e-2 to 1e-8. However, the curves are not necessarily monotonic for diffReps at large p-values; at certain p- values, diffReps reports less differential sites than DESeq and edgeR. This is because
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