Table 1.

Publicly available ChIP-seq software packages discussed in this review

	Profile	Peak Criteria1	Tag Shift	Control Data2	Rank By	FDR3	User Input Parameters4	Artifact Filtering: Strand- based / Duplicate5	Refer- ence
CisGenome v1.1	Strand- specific window scan	1: Number of reads in window, 2: Number of reads in window – control reads	Average for highest ranking peak pairs	Conditional binomial used to estimate FDR	No. of reads under peak	1: Negative binomial, 2: conditional binomial	Target FDR, optional window width, window interval	Yes / Yes	10
ERANGE v3.1	Tag aggregation	1: Height cutoff, 2: Height and fold enrichment over control counts in region	Hiqh quality peak estimate, per-region estimate, or input	Used to calculate fold enrichment and optionally p-values	p- value	1: None 2: # control # ChIP	Optional peak height, ratio to background	Yes / No	4,18
FindPeaks v3.1.9.2	Aggregation of overlapped tags	Height threshold	Input or estimated	N/A	N	1: Monte Carlo simulation 2: N/A	Minimum peak height, subpeak valley depth	Yes / Yes	19
F-Seq v1.82	Kernel density estimation	s Standard deviations above kde for 1: random background, 2: control	Input or estimated	Kde for local background	Peak Height	1: None 2: None	Threshold standard deviation value, kde bandwidth	No / No	14
GLITR	Aggregation of overlapped tags	Classification by height and relative enrichment	User input tag extension	Multiply sampled to estimate background class values	Peak height and fold enrich -ment	2: # control # ChIP	Target FDR, number nearest neighbors for clustering	No / No	17
MACS v1.3.5	Tags shifted then window scan	Local region Poisson p value	Estimate from high quality peak pairs	Used for Poisson fit when available	p- value	1: None 2: # control # ChIP	p-value threshold, tag length, mfold for shift estimate	No / Yes	13
PeakSeq	Extended tag aggregation	Local region binomial p value	Input tag extension length	Used for significance of sample enrichment w/ binomial distribution	q- value	1: Poisson background assumption 2: From binomial for sample + control	Target FDR	No / No	5
QuEST v2.3	Kernel density estimation	2: Height threshold, background ratio	Mode of local shifts that maximize strand cross correlation	Kde for enrichment and empirical FDR estimation	q- value	1: N/A 2: # control # ChIP as a function of profile threshold	Kde bandwidth, peak height,subpeak valley depth,ratio to background	Yes / Yes	9
SICER v1.02	Window scan with gaps allowed	P value from random background model, enrichment relative to control	Input	Linearly rescaled for candidate peak rejection and p- values	q- value	1: None 2: From Poisson p- values	Window length, gap size, FDR (w/ control) or E-value (no control)	No / Yes	15
SiSSRs v1.4	Window scan	N+-N− sign change, N++N− threshold in region	Average nearest paired tag distance	Used to compute fold- enrichment distribution	p- value	1: Poisson 2: control distribution	1: FDR 1,2: N++N− threshold	Yes / Yes	11
spp v1.0	Strand specific window scan	Poisson p-value (paired peaks only)	Maximal strand cross- correlation	Subtracted before peak calling	p- value	1: Monte Carlo simulation 2: # control # ChIP	Ratio to background	Yes / No	12
USeq v4.2	Window scan	Binomial p- value	Estimated or user specified	Subtracted before peak calling	q- value	1, 2: Binomial 2: # control # ChIP	Target FDR	No / Yes	20

¹Throughout the table 1: and 2: refer to one sample and two-sample experiments, respectively.

²The ‘Control Data’ column is intended to give a rough idea of how control data is used by the software. ‘N/A’ means that control data is not handled.

³The “FDR’ column describes how the FDR is or optionally may be computed. Note that ‘None’ indicates an FDR is not computed, however the experimental data may still be analyzed; ‘N/A’ indicates the experimental setup (1 sample or 2) is not yet handled by the software.

⁴The lists of ‘User Input Parameters’ for each program are not exhaustive but rather comprise a subset of greatest interest to new users.

⁵’Strand-based’ artifiact filtering rejects peaks if the strand-specific distributions of reads do not conform to expectation, for example by exhibiting extreme bias of tag populations for one strand or the other in a region. ‘Duplicate’ filtering refers to either removal of reads that occur in excess of expectation at a location or filtering of called peaks to eliminate those due to low complexity read pileups that may be associated with, for example, microsatellite DNA.