|MACS|| The MACS Peak Finder is based on the peak detection algorithm (Zhang, et al., 2008). This is a model-based algorithm that expects there to be paired peaks of reads on either side of a true binding site. Paired flanking peaks reflect the fact that only the 5’ ends of immuno-precipitated fragments are usually sequenced. Because of this, the majority of the locations of sequences associated with peaks don’t correspond to the location of the binding site.
The MACS algorithm attempts to build a model of the distance between these peaks and takes this distance into account to shift reads forwards or backwards, resulting in a peak centered over the true binding site.
The MACS Peak Detection algorithm reports the number of reads within a peak as the signal value for that peak. It also calculates a P-value based on the distribution of reads near a peak region to try to compensate for uneven background noise across the genome. When present, control data are used to filter the peaks that are called and to assign each peak an FDR score which is the false discovery rate likelihood that the peak is not valid.
|ERANGE2||The ERANGE2 Peak Finder (Johnson et al., 2007) is a simple "sliding window" peak detection algorithm that looks for a specified number of reads within a window of a specified length. If a peak is found, it is extended as long as there are reads within the window width. If control data are present, it can be used to disqualify any peaks that do not have a minimum fold enrichment over the same region in the control data.|
|ERANGE3||The ERANGE3 Peak Finder is based on the ERANGE 3.1 Algorithm for ChIP-Seq and RNA-Seq Analysis (see Mortazavi et al., 2008). This peak detection algorithm calculates peaks in a normalized reads-per-million space. Features of this algorithm include simple read shifting and repeat read handling. This algorithm also considers the directionality of reads when calling peaks.|
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