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MACS Zhang et al. MACS then shifts and combines all forward and reverse strand tags toward the center by the estimated shift size, and calls peaks on the combined tags using a Poisson model through sliding windows. To account for the local variability of tag counts due to genomic features, MACS estimates a local Poisson parameter as the average tag counts from an up to 10kb neighboring region around each sliding window.

Due to the constraint of mean and variance equality in the Poisson distribution, however, MACS is not able to model peak data if the variability of tag counts far exceeds the mean.

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Also, MACS reports binding regions with highly variable sizes, ranging from bp to 7kb. Due to its wide range of sizes of the predicted binding intervals, MACS tends to call only a single peak at regions of clusters of peaks. SPP Kharchenko et al. SPP then chooses a window size based on the estimated peak shift size.

To detect binding, SPP utilizes a sliding window and calls a peak if a binding score is locally maximized, where the score is defined as twice of the difference between the geometric mean and the arithmetic mean of the forward tag counts in upstream window and the reverse tag counts in downstream window. Since SPP is not based on any statistical models, it estimates FDR by comparing binding scores in the signal data with the scores in a control data.


When there is no real control data, a randomized data set generated from the signal data is used. QuEST Valouev et al. QuEST then calls a peak if a specific score profile achieves a local maximum. CisGenome Ji et al. A peak is called if the observed tag counts within a sliding window default bp significantly exceeds the expected tag counts from a background distribution.

CisGenome estimates the negative binomial parameters from the non-binding regions.

It has been demonstrated that the negative binomial model can better fit ChIP-Seq datasets than the Poisson model Ji et al. The above programs all estimate a global and constant peak shift size for all potential binding regions.

They simply merge the forward and the reverse strand tags together before peak calling, and thus may loose power if the estimated peak shift size is inaccurate at some real binding sites. In addition, they do not incorporate any binding pattern information in their algorithm. At a real protein binding site, the tag counts often follow specific spatial patterns specific to the target protein, which provide valuable information for us to best distinguish between a real binding site from spurious peaks caused by means other than the target protein.

In this article, we propose a new negative binomial generalized linear model GLMNB that effectively uses information from both strands of the genome and model the background tag level using negative binomial distribution to account for the variation of tag counts along the genome.

Pepke et al. Different from most methods, we fit the tag count data in a sliding window bp by default for both forward and reverse strand simultaneously, rather than merging the tag counts.

Furthermore, we estimate a binding profile a pattern for the mean tag counts observed at peak regions to best separate real peaks from spurious ones.

The two programs were reported to perform the best among many existing peak callers in a comprehensive evaluation study Willbanks and Facciotti, Results 2. The simulated data contained peaks distributed in a Mb region.Best of all, you can connect with anyone with an email address and a web browser, without wasting time creating accounts or downloading files.

Your opinions are important to us. SPP then chooses a window size based on the estimated peak shift size.

Address correspondence to: Dr. One option was to substitute electrical components called capacitors for the transistors.


It has been demonstrated that the negative binomial model can better fit ChIP-Seq datasets than the Poisson model Ji et al. Apr 11,

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