Zoological Research 33 (E5−6): E121−E128 doi: 10.3724/SP.J.1141.2012.E05-06E121
Volume 33 Issues E5−6
Peak identification for ChIP-seq data with no controls
Yanfeng ZHANG, Bing SU*
State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, the Chinese Academy of Sciences, Kunming 650223, China
Abstract: Chromatin immunoprecipitation followed by sequencing (ChIP-seq) is increasingly being used for genome-wide profiling
of transcriptional regulation, as this technique enables dissection of the gene regulatory networks. With input as control, a variety of
statistical methods have been proposed for identifying the enriched regions in the genome, i.e., the transcriptional factor binding sites
and chromatin modifications. However, when there are no controls, whether peak calling is still reliable awaits systematic
evaluations. To address this question, we used a Bayesian framework approach to show the effectiveness of peak calling without
controls (PCWC). Using several different types of ChIP-seq data, we demonstrated the relatively high accuracy of PCWC with less
than a 5% false discovery rate (FDR). Compared with previously published methods, e.g., the model-based analysis of ChIP-seq
(MACS), PCWC is reliable with lower FDR. Furthermore, to interpret the biological significance of the called peaks, in combination
with microarray gene expression data, gene ontology annotation and subsequent motif discovery, our results indicate PCWC
possesses a high efficiency. Additionally, using in silico data, only a small number of peaks were identified, suggesting the
significantly low FDR for PCWC.
Keywords: ChIP-seq; Bayesian; Peak calling; Gene regulation
With the advance of high-throughput sequencing
technologies, both global survey of the genome structure
and transcriptome and transcriptional regulation has
become more accurate and sensitive. As one of the more
powerful and widely used experimental techniques for
immunoprecipitation followed by sequencing (ChIP-seq)
is one of the early applications of next generation
sequencing (NGS). Since the first study in 2007
(Mikkelsen et al), ChIP-seq technology has been
increasingly used for mapping DNA-binding sites by
transcriptional factors and chromatin modifications.
Compared with the earlier method of chromatin
immunoprecipitation followed by tiling microarray
(ChIP-chip) (Kapranov et al, 2002), ChIP-seq has more
advantages, e.g., higher resolution, cost-effectiveness and
technical simplification. At the same time, ChIP-seq
itself also has several disadvantages, including efficiently
computational analysis techniques, sequencing depth
requirement, and the like (Park, 2009).
Currently, there are three commonly used types of
control samples in ChIP experiments: input DNA (no
immunoprecipitation (IP) DNA samples); mock IP DNA
(DNA obtained from IP without antibodies); and DNA
in vivo, chromatin
from non-specific IP (such as IP with immunoglobulin
G). Concomitantly, several tools for ChIP-seq analysis
have been developed, the majority of which require a
matched control sample to determine fold enrichment
and significance of peak signals. This idea of fold ratio
relative to controls used for ChIP-seq is similar with
ChIP-chip data analysis, which in the currently used
methods is necessary for peak calling (Rozowsky et al,
2009; Valouev et al, 2008). Meanwhile, several new
methods based on Validation Discriminant Analysis
(Micsinai et al, 2012), Hidden Markov Models1(Choi et
al, 2009) or Bayesian Models (Spyrou et al, 2009)
combine ChIP-seq and ChIP-chip data for peak
identification. Nonetheless, a systematic analysis of
ChIP-chip and ChIP-seq datasets revealed that the input
data has variable effects on peak finding (Ho et al, 2011),
suggesting of the need for a high quality input sample for
peak calling. Additionally, sequencing depth significantly
Received: 06 September 2012; Accepted: 31 October 2012
Foundation items: This study was supported by the National 973
project of China (2011CBA01101) and the National Natural Science
Foundation of China (30871343 and 31130051)
* Corresponding author, E-mail: email@example.com
E122 ZHANG, SU
Zoological Research www.zoores.ac.cn
impacts the peak calling, further causing bias of peak
identification (Chen et al, 2012). To improve peak calling
accuracy, Osmanbeyoglu et al (2012) utilized a strategy
of co-regulation binding by integrating multiple sources
of biological information. Recently, although several
novel tools for peak calling without controls have been
made available (Cairns et al, 2011; Fejes et al, 2008;
Hower et al, 2011), for example, the model-based
analysis of ChIP-seq (MACS) defines a dynamic
parameter to capture local biases when a control profile
is unavailable (Zhang et al, 2008). No systematic
evaluation of peak calling without controls has been
carried out, even for MACS or BayesPeak.
Given the bias of control data in the ChIP-seq peak
calling, we sought to address whether ChIP-seq peak
calling without controls (PCWC) is plausible. Employing
the Bayesian theorem and simulation-based empirical
estimates of background distribution, we demonstrated
that our method ChIP-seq peak identification with no
control is reliable, with an FDR lower than 5%.
Compared with the Poisson-based MACS method, our
Bayesian framework strategies showed lower FDRs,
suggesting high selectivity and effectiveness for peak
calling. The systematic analysis of PCWC we present in
the current study could serve as an alternative strategy
for ChIP-seq analysis and subsequent biological
interpretation of gene regulation.
MATERIALS AND METHODS
In order to present the comprehensive performance
of PCWC, we used the FASTQ-formatted ChIP-seq data
sets downloaded from Gene Expression Omnibus (GEO)
(Edgar et al, 2002), including the ChIP-seq data of
transcription factor E2F1 in mESCs (Chen et al, 2008),
VDR binding in human
(Ramagopalan et al, 2010) and H3K4me3 in mESCs
(Creyghton et al, 2010), as well as the ABI SOLID ChIP-
seq data sets of EGR1 ChIP-seq data (Tang et al, 2010)
and MNase-seq data in two replicates (Valouev et al,
2011). Furthermore, we integrated the microarray gene
expression dataset to evaluate the effectiveness of PCWC.
Additionally, we in silico generated 1×107 36-bp reads
using the simreads program of the Rmap package (Smith
et al, 2009) to simulate control samples for peak calling.
The detailed description for ChIP-seq is shown in Table
Peak calling based on the Bayesian framework
On the genome-wide scale, peaks in the ChIP-seq
experiment are those regions significantly enriched by
reads. Thus, regarding all uniquely mapped reads of the
ChIP-seq as background, the density of reads in peak
regions should be dominantly enriched. In the Bayesian
framework (Madigan & Ridgeway, 2003), the posterior
density for θ is obtained, up to a proportionality constant
by multiplying the prior density g(θ) by the likelihood
L(θ) where θ denotes the probability that a region is a
Based on global deep sequencing that reaches to
single nucleotide resolution, a uniform prior distribution
is usually supposed, which has been adopted in the
RNA-seq analysis (Sultan et al, 2008; Wang et al, 2008).
Here, for simplification and feasibility, we assume a
uniform distribution for the prior distribution g(θ)
( ) ~gU
( ) 1, for g θ=
For measuring the significant enrichment of reads in
a peak, we considered that the candidate peaks with the
number of reads (#reads, a) below a cutoff threshold (c,
usually 0.01) would fit the following equation,
≥ = −
) ( ) ( )g
, leading to the
where the cumulative density of #reads reaching to a is
based on likelihood function L(θ) and L(θ) is empirically
estimated from the genome-wide scanning of tag-count
(10,000 w bp random bins per chromosome).
Following the optimal parameter a below c, we can
obtain the read-count (uniquely mapped total reads) with
a sliding window of size w/2 bp (w=50 bp in default).
Afterward, the #reads in each w/2 bp sliding window size
above a is regarded as candidate peaks. Peaks spanning
lower than 100 bp are discarded and the neighboring
peaks within 1 kb are merged (Guenther et al, 2008) to
counteract the shifting effects of aligned tags from
forward and reverse reads.
False discovery rate (FDR) estimate for the number
Similar to methods used by Valouev et al (2008), the
overlapped number of called peaks (using the same
threshold) between the input dataset and ChIP dataset are
the false discovery number, and the FDR is the false
discovery number divided by the total number of peaks
called in the ChIP experiment.
We used MEME 4.6.1 to discover motifs (Bailey &
Elkan, 1994). Since the running time can be prohibitively
long for large sets, the peaks are ranked by the number of
uniquely aligned reads and only the top 10% of the peaks
were selected for motif
methodological strategy was used by Ramagopalan et al
(2010) and Jothi et al (2008). The location of the peak is
centered and extended by 100 bp on either side. Motifs
discovery. A similar
Peak Identification for ChIP-seq data with no controls E123
Kunming Institute of Zoology (CAS), China Zoological Society Volume 33 Issues E5−6
between 5 and 30 bp in length were determined on both
Microarray data preprocessing and analysis
A total of 45 microarray data sets for calcitriol
stimulation on human lymphoblastoid cell lines were
used for expression analysis. The multi-array average
(RMA) expression values were calculated using the
Bioconductor “affy” package (Gentleman et al, 2004).
The log2 expression signals were used to calculate fold
Genes regulated by trans-acting factors (or by
chromatin modification, such as H3K4me3) were defined
by no more than 5 kb distance of peaks to Refseq
transcription start site (TSS). The enriched analysis of
genes was achieved using the DAVID annotation system
(Huang da et al, 2009). After Benjamini-Hochberg
correction, P<0.01 were considered significantly
Peak calling based on the Bayesian model and other
bioinformatics analyses were implemented in Perl and R
(data available upon request).
RESULTS AND DISCUSSION
To demonstrate the effectiveness of ChIP-seq
PCWC, we selected five representative ChIP-seq data
sets from two platforms (three from Illumina and two
from ABI SOLID platforms, respectively) and one in
silico data (See Materials and Methods for details).
We first applied the Bayesian-based approach on
recently published ChIP-seq data for transcription factor
E2F1 in mouse embryonic stem cells (mESCs) (Chen et
al, 2008), vitamin D receptor (VDR) binding in human
lymphoblastoid cell (Ramagopalan et al, 2010) and H3
trimethylated at lysine 4 (H3K4me3) in mESCs
(Creyghton et al, 2010), all of which were generated
from the Illumina platform. Employing SOAP2 program
(Li et al, 2009) with maximal 1 mismatch, the uniquely
mapped reads are considered for further analysis. Using a
cutoff threshold (c =0.01 or 0.001), the number of reads
a based on 10 000 random bins per chromosome with
window size (w=50) was estimated (Figure 1). With
these two parameters, we can empirically determine the
candidate peaks for each data set.
Figure 1 Cumulative density of reads based on genome-wide
Grey dashed line represents the read cutoff for peak calling.
Vitamin D receptor data
For the VDR ChIP-seq samples, it is highly suitable
for evaluating the ChIP-seq peak calling without controls
due to the inclusion of conditional data (calcitriol treated
or not) with deep sequencing (ranging from 7.9×106 to
1.46×107 uniquely mapped reads, see Table 1) and the
available microarray gene expression data set.
Table 1 Summary of the peak calling analysis using the VDR ChIP-seq data
Samplesa Description #Reads #Uniquely aligned Pr (#reads≥a)≤0.001 # Peaks Mean lengthb
SRX022390 unstimulated_rep1 18 252 156 13 487 568 7 1 989 633.9
SRX022391 unstimulated_rep2 15 379 663 10 761 914 7 548 708.6
SRX022392 vitaminD_rep1 18 391 888 13 937 615 7 2 920 455.4
SRX022393 vitaminD_rep2 18 965 010 14 613 990 7 2 835 467.3
SRX022394 unstimulated_rep1 12 264 149 10 149 547 7 2 223 519.3
SRX022395 unstimulated_rep2 10 145 026 7 930 475 7 5 298 460.7
SRX022396 vitaminD_rep1 13 302 506 10 657 775 9 6 755 428.9
SRX022397 vitaminD_rep2 14 526 186 11 755 087 9 6 981 434.9
SRX022398 Input1 15 001 356 11 403 930 6 348 −
SRX022399 Input2 13 682 506 11 402 869 6 545 −
a: Sample ID is the accession ID for meta-data documented in GEO database; b: Mean peak length reflects the resolution of binding sites.
E124 ZHANG, SU
Zoological Research www.zoores.ac.cn
Firstly, we sought to call peaks in both conditions.
In the samples not treated with calcitriol, the number of
peaks without control is between 548 to 5 298, while in
the calcitriol-treated samples the number of peaks is
between 2 835 to 6 981 (Table 1), consistent with the
Ramagopalan’s reports (Ramagopalan et al, 2010), where
they used MACS with two independent controls for peak
calling. Interestingly, our Bayesian-based PCWC get a
similar number of peaks compared with the data using
MACS with controls.
Next, we conducted analyses on multiple scales.
The resolution of peaks (VDR binding sites) is also
comparable with the reported study (Table 1).
Furthermore, the gene ontology (GO) annotation
indicates that immune system development (GO:0002520),
lymphocyte activation (GO:0046649) and T cell
activation (GO:0042110) are significantly enriched
(P≤0.01, after Benjamini correction), also highly
concordant with the report (Ramagopalan et al, 2010).
Additionally, previous studies have experimentally
validated that VDR (auto-regulation) (Zella et al, 2010),
CCNC (Sinkkonen et al, 2005), ALOX5 (Seuter et al,
2007), IRF8 and PTPN2 (Ramagopalan et al, 2010)
modulated by VDR have significant enrichment of peaks
(VDR binding) under calcitriol-treated conditions, which
were all confirmed using our method (Figure S1),
suggesting the effectiveness of our method for ChIP-seq
We then integrated the microarray gene expression
data (a total of 45 microarrays) to evaluate the accuracy
of PCWC. Compared with the untreated condition, a
total of 205 genes (fold change≥1.5) were up-regulated
with calcitriol treatment, of which 134 (65.4%) genes
were significantly enriched with the called peaks. Of the
134 genes, 81 (60.4%) genes enriched with peaks are
located in the intronic regions, 33 genes in the TSS
regions (TSS±500) bp, consistent with the report that
under the condition of the calcitriol stimulation, there is
an increased VDR binding in the intronic regions
(Ramagopalan et al, 2010), further suggesting the high
accuracy of our Bayesian model for PCWC.
For ChIP-seq data analysis, in addition to the
integration of gene expression data, the subsequent motif
discovery analysis for the enriched regions is also
informative to interpret the biological implications (Park,
2009). Thus, we carried out a motif analysis of VDR
binding (see Methods). Due to the prohibitively long
running time for large data sets, only the top 10% of the
peaks were used for motif discovery. Figure 2 shows the
motifs discovered by our method, which are nearly
identical with the reported data (Ramagopalan et al,
2010). Collectively, the data presented using the VDR
dataset indicates that the PCWC is reliable.
Figure 2 Inferred consensus binding motif for VDR
For PCWC, evaluate whether the called peaks are
real signals relative to the flanking regions is crucial. We
therefore conducted such analysis based on the ratio of
peaks to the flanking regions. For each peak, we selected
300 bp region at both the 5' and 3' flanking sequences.
As shown in Figure 3, compared with the 5' and 3'
flanking regions, the peak regions have much higher (at
least 2 times higher) read coverage, indicating the called
peaks without control are relatively reliable. Furthermore,
the distribution of the uniquely mapped bases (in 1 kb
bins) in the genome for the ChIP and input data also
indicates that the peak regions in the ChIP data are
Figure 3 Density of read coverage ratio between peak and flank (5' and 3') regions
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Kunming Institute of Zoology (CAS), China Zoological Society Volume 33 Issues E5−6
enriched in reads, while in the input data, they are
relatively uniformly distributed (Figure S2).
We also evaluated the FDR of the Bayesian model
for PCWC. Similar with the VDR ChIP-seq data analysis,
peak calling (
(# Pr reads ≥
independent controls (from the VDR data sets, Table 1)
was conducted to evaluate the FDR. We totally identified
348 and 545 peaks in two independent controls (Table 1),
respectively. Only less than 3% of peaks in the two
controls overlap with the ChIP data, indicating that the
FDR for our method is small. It should be noted that the
PCWC is also available in the MACS (version 1.4)
program. Thus we compared the relative effectiveness of
our Bayesian model with the Poisson-based MACS
model. Under the default parameter condition, the
MACS program failed for peak calling for the two
independent controls due to the large -mfold (-mfold=32)
parameter, resulting in unavailable construction of the
background model. When we decreased the -mfold
parameter to 8 (suitable for building the background
model), a total of 3 278 and 3 486 candidate peaks are
called with the FDRs of 5% and 8%, respectively, which
is higher than the FDR of our Bayesian model. The
advantages of the Bayesian model relies on the genome-
wide scanning to empirically measure the background
distribution, while the Poisson-based model is subject to
overestimation of the number of candidate peaks,
therefore resulting in higher FDRs (Kharchenko et al,
We also used the H3K4me3 ChIP-seq data
(8.77×106 uniquely mapped reads, accounting for 69.7%)
to compare the peak calling between the Bayesian model
and the MACS model. Using the MACS with no controls,
a total of 14 346 peaks (Table 2) were identified when the
cutoff was set to P=1e-05 (default). With the use of the
) for two
Bayesian model (
7 539 peaks, of which 7 518 (99.7%) were overlapped
with the MACS model with no controls, indicating a
high accuracy for the Bayesian model. To avoid the
potentially high false negative rate resulting from the
stringent cutoff applied, we also tested a relaxed cutoff
for the Bayesian model (
we identified a total of 11,536 peaks. Compared with the
MACS model using different cutoffs, the relaxed
Bayesian model remained high overlapping rates with
the MACS data P=1e-08, again indicating the
effectiveness of PCWC using our Bayesian model.
(#9) 0.01 Pr reads ≥≤
), we identified
(# 4) 0.05 reads ≥≤
Table 2 Comparison of peak callings between Bayesian
and MACS models
Options MACS Bayesian Overlap
1e-05 14 346 11 312 98.06
1e-08 10 841 10 432 90.43
1e-09 10 235 10 020 86.86
For the Bayesian model, the cutoff was set to
As the likelihood function used in the Bayesian
model is subject to simulation for optimizing the number
of reads a, we next addressed whether the simulation
method based on 10 000 bootstrapping per chromosome
with w=50 bp random bins would be biased in our
method. Firstly, we evaluated the effects of number of
bootstrappings (ranging from 1 000 to 50 000), and no
bias was observed when analyzing the H3K4me3 ChIP-
seq data (Table 3). Similar results were obtained when
using the VDR ChIP-seq data (data not shown). We then
assessed the effects of random bin size (w) and found no
bias there either, suggesting that the simulation method
itself would not generate bias for the parameters a and c
(# 4) 0.05 Prreads ≥≤
Table 3 Effects of bootstrapping on the cutoff threshold
#Reads 1 000 bootstrap 5 000 bootstrap 10 000 bootstrap 20 000 bootstrap 50 000 bootstrap
1 0.148 0.148 0.144 0.141 0.144
2 0.077 0.083 0.084 0.076 0.081
3 0.052 0.060 0.062 0.052 0.056
4 0.037 0.040 0.048 0.038 0.042
5 0.027 0.028 0.036 0.029 0.031
6 0.023 0.021 0.026 0.021 0.023
7 0.017 0.016 0.018 0.015 0.016
8 0.012 0.011 0.013 0.010 0.011
9a 0.008 0.008 0.008 0.007 0.007
10 0.004 0.005 0.007 0.004 0.005
11 0.002 0.004 0.004 0.003 0.004
13 0.000 0.002 0.003 0.001 0.002
a: bolded row indicates that bootstraps are not biased.
E126 ZHANG, SU
Zoological Research www.zoores.ac.cn
Figure 4 Effects of random bin size on the number of reads a
For the E2F1 ChIP-seq data, a total of 1.188×107
reads (~39.0% of total reads) were uniquely mapped onto
the genome. Using the PCWC (
we identified a total of 11 470 peaks, of which the
majority (75.3%, n=8 639) span across the annotated
Refseq TSSs, consistent with Chen et al’s (2008) report
that about ~50% of all genes are regulated by E2F1. Our
analysis also indicated E2F1 binding regions were very
close to TSS (Figure S3), consistent with the previous
ChIP-seq and ChIP-chip results (Chen et al, 2008; Xu et
al, 2007). The 5 genes (Figure S4) experimentally
verified to be positively regulated by E2F1，Cdc6 (Yan
et al, 1998), Ccne1, Ccna2 (DeGregori et al, 1995),
Mcm4 and Mcm7 (Arata et al, 2000)，were all identified
in the E2F1- regulated regions (peaks).
Using the Illumina platform of ChIP-seq data, we
demonstrated the effectiveness and accuracy of PCWC.
To further test the effectiveness of the Bayesian model,
we used two ABI SOLID platform data sets. The raw
colorspace-formatted data were aligned using Bowtie
software (Langmead et al, 2009) with maximal 2
mismatches and uniquely mapped reads were used for
For the early growth response gene 1 (EGR1) ChIP-
seq data (Tang et al, 2010), we identified a total of 7 302
peaks. The KEGG pathway annotation result indicates
that genes regulated by EGR1 are significantly enriched
in the MAPK, the Wnt and the TGF-beta signaling
pathways (hsa04010, hsa04310, hsa04350, respectively),
congruent with earlier reported data (Tang et al, 2010).
For the MNase-seq data in two replicates (Valouev
et al, 2011), a total of 533 401 and 514 135 peaks were
identified, respectively, where 83.12% overlapped
between the two replicates, further supporting the
effectiveness of peak calling with no control.
In silico data
To demonstrate the effectiveness of ChIP-seq
(#9) 0.01Pr reads ≥≤
PCWC, we in silico generated 1×107 36-bp reads using
simreads of the Rmap package (Smith et al, 2009) to
produce a simulated control sample. As expected, only
428 peaks were generated based on the Bayesian model.
Notably, the number of peaks called from the in silico
data is equivalent with the two independent controls
(Table 1), suggesting that the PCWC is sensitive.
In this study, we demonstrated an informative
analysis of ChIP-seq PCWC based on the Bayesian
framework approach. By the application to multiple
ChIP-seq data sets, we have demonstrated the
effectiveness of our method for both transcriptional
factor and chromatin modification ChIP-seq experiments.
Notably, the demonstration PCWC’s effectiveness
showed no superiority over the method with controls.
The purpose of this study is to demonstrate a potential
alternative strategy for designing ChIP-seq experiments
and identifying transcriptional factor binding sequences
without using controls.
For ChIP-chip analysis, the tiling array may suffer
from cross-hybridizations, therefore, the fold ratio for
peak calling relative to background is required. By
contrast, our analyses indicated the effectiveness of
ChIP-seq PCWC, likely due to the finer resolution and
greater signal-to-noise ratio of the ChIP-seq data
(Rozowsky et al, 2009). Meanwhile, as opposed to the
Poisson-based model for ChIP-seq peak calling, our
Bayesian model is dependent on genome-wide scanning
to empirically measure the background distribution,
resulting in the higher selectivity and lower FDRs for the
Contrary to RNA-seq, ChIP-seq is more confined
by relatively complicated pre-ChIP experiments, e.g.,
antibody specificity, large amount of cells (usually no
less than 1×107 cells), formaldehyde cross-link and
supersonic shearing, etc. If the ChIP experiments ensure
high antibody specificity, the following high-throughput
sequencing with deep coverage to identify peaks without
control is both plausible and robust. While depth-of-
sequencing issues (Kharchenko et al, 2008) exist in the
ChIP-seq experiments, our analyses suggest that no less
than 10 million effective reads (uniquely mapped) are
necessary for PCWC.
Moreover, improvements of ChIP-seq experimental
strategies without (or with) control data in an appropriate
way may be preferable. Based on our survey, two
biologically independent replicates is highly replicable (s
shown in Table 1) as previously suggested (Park, 2009).
Therefore, two replicates of ChIP-seq would be sufficient
for peak calling. Meanwhile, if PCWC is determined, the
integration with other data types will be essential. For
example, the integration of ChIP-seq data with gene
expression data (including microarray gene expression
Peak Identification for ChIP-seq data with no controls E127
Kunming Institute of Zoology (CAS), China Zoological Society Volume 33 Issues E5−6
data or RNA-seq data) may maximize the interpretation
of gene regulatory network.
immunoprecipitation followed by sequencing; NGS: next
immunoprecipitation followed by tiling microarray;
MACS: model-based analysis of ChIP-seq; PCWC: peak
calling without controls; FDR: false discovery rates; GO:
gene ontology; VDR: vitamin D receptor; H3K4me3: H3
trimethylated at lysine 4; ESCs: embryonic stem cells
Acknowledgments: We are thankful to Shao-Bin
XU (Kunming Institute of Zoology, CAS) for his support
on super-computing service, and to Yu-qi ZHAO
(Kunming Institute of Zoology, CAS) for his helpful
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