Cistrome: An integrative platform for transcriptional regulation studies

Abstract

The increasing volume of ChIP-chip and ChIP-seq data being generated creates a challenge for standard, integrative and reproducible bioinformatics data analysis platforms. We developed a web-based application called Cistrome, based on the Galaxy open source framework. In addition to the standard Galaxy functions, Cistrome has 29 ChIP-chip- and ChIP-seq-specific tools in three major categories, from preliminary peak calling and correlation analyses to downstream genome feature association, gene expression analyses, and motif discovery. Cistrome is available at http://cistrome.org/ap/.

Full text

SOFTWA R E Open Access
Cistrome: an integrative platform for
transcriptional regulation studies
Tao Liu
1,2†
, Jorge A Ortiz
3,4†
, Len Taing
1,2
, Clifford A Meyer
1
, Bernett Lee
3,5
, Yong Zhang
6
, Hyunjin Shin
1,2
,
Swee S Wong
3,7
, Jian Ma
6
, Ying Lei
8
, Utz J Pape
1
, Michael Poidinger
3,5
, Yiwen Chen
1
, Kevin Yeung
3,9
,
Myles Brown
2,10*
, Yaron Turpaz
3,11*
and X Shirley Liu
1,2*
Abstract
The increasing volume of ChIP-chip and ChIP-seq data being generated creates a challenge for standard,
integrative and reproducible bioinformatics data analysis platforms. We developed a web-based application called
Cistrome, based on the Galaxy open source framework. In addition to the standard Galaxy functions, Cistrome has
29 ChIP-chip- and ChIP-seq-specific tools in three major categories, from preliminary peak calling and correlation
analyses to downstream genome feature association, gene expression analyses, and motif discovery. Cistrome is
available at http://cistrome.org/ap/.
Rationale
The term ‘cistrome’refers to the set of cis-acting tar-
gets of a trans-acting factor on a genome-wide scale,
also known as the in vivo genome-wide location of
transcription factors or histone modifications. Cis-
tromes were initially identified using chromatin immu-
noprecipitation (ChIP) combined with microarrays
(ChIP-chip) [1]. However, with the recent advent of
next generation sequencing (NGS) technologies, ChIP
combined with NGS (ChIP-seq) [2] has become the
more popular technique due to its higher sensitivity
and resolution.
Computational analyses of cistrome data have become
increasingly complex and integrative. Investigators often
examine the data from many different angles by com-
bining cistrome, epigenome, genomic sequence, and
transcriptome analyses. Many algorithms and tools have
been published over the years to facilitate such analyses.
However, these tools require investigators to have both
the hardware resources and computational expertise to
install, configure, and run these different algorithms
effectively. Integrated platforms such as CisGenome [3]
and seqMINER [4] have been developed to streamline
data analyses; however, the maintenance of these plat-
forms demands suitable hardware resources and compu-
tational skills. In addition, these tools lack useful
features such as the integration of cistrome data with
gene expression analysis, data sharing between research-
ers, and reusable analysis workflows.
To address the above challenges, we developed the
Cistrome platform to provide a flexible bioinformatics
workbenchwithananalysisplatformforChIP-chip/
seq and gene expression microarray analysis. Cistrome
was built on top of Galaxy [5], an open-source web
based computational framework that allows the easy
integration of different tools. Cistrome integrates use-
ful functions specific for ChIP-chip/seq and gene
expression analyses. These functions were implemen-
ted in a modular fashion to allow easy incorporation
of new tools in the future. Cistrome was deployed on
a supercomputer server with a publicly available web
interface. The current Cistrome server allows 15 jobs
running at the same time. Restrictions of input files
for each Cistrome tool are described in Table S1 in
Additional file 1. We provide Cistrome source codes
freely available through bitbucket [6]. The various
functions within the analysis platform are explained in
the following sections, and a workflow summary is
illustrated in Figure 1.
* Correspondence: myles_brown@dfci.harvard.edu; yaron.turpaz@astrazeneca.
com; xsliu@jimmy.harvard.edu
†Contributed equally
1
Department of Biostatistics and Computational Biology, Dana-Farber Cancer
Institute and Harvard School of Public Health, 450 Brookline Ave, Boston, MA
02215, USA
2
Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute,
Boston, MA 02215, USA
Full list of author information is available at the end of the article
Liu et al.Genome Biology 2011, 12:R83
http://genomebiology.com/2011/12/8/R83
© 2011 Liu et al.; li censee BioMed Central Lt d. This is an open access arti cle distributed under the terms of the Crea tive Commons
Attribution License (http://creative commons.org/licenses/by/2.0), which permits unrestricted use, distribu tion, and reproduction in
any medium, pro vided the original work is properly cite d.

Data preprocessing
Before interpreting the biological results from ChIP-chip
or ChIP-seq data using the Cistrome platform, research-
ers can upload raw data from their microarray or
sequencing facilities and then preprocess those data
using Cistrome peak-calling tools. Alternatively,
researchers can also upload intermediate results from
their own analysis tools. As illustrated in Figure 1, the
peak calling step generates two types of intermediate
files: peak location files (in BED format), indicating the
Data Upload
DC Browser
Auto Retriever
from GEO
Import Data
Gene Expression
Index
Dierential
Expression
Highly Expressed TFs
Related Genes
Gene Ontology
Gene Expression
Global
Correlation
Local
Correlation
Venn
Diagram
Correlation
Conservation
SitePro
Gene Centered
Annotation
Peak Centered
Annotation
CEAS
Heatmap with
clustering
Association
Motif
enrichment
Motif Scan
DNA Motif
Integrative Analysis
Gene lists
MAT for Ay
MA2C for
NimbleGen
MACS for
ChIP-seq
MM-ChIP
NPS
Peak Calling
Data Preprocessing
Peak locations (BED)
Signal profiles (WIGGLE)
Galaxy Tools
Figure 1 Workflow within the Cistrome analysis platform. Cistrome functions can be divided into three categories: data preprocessing, gene
expression and integrative analysis. A general workflow using Cistrome is to upload datasets, preprocess them using peak calling tools to
generate peak locations in BED format and signal profiles in WIGGLE format, upload gene expression data to produce specific gene lists, and
then use various integrative analysis tools to generate figures and reports. The bottom figure shows the web interface of the Cistrome platform
based on the Galaxy framework. The left panel shows available tools, the middle panel shows messages, tool options, or result details, and the
right panel shows the datasets organized in the user’s history, including datasets that have been or are being processed (in green and yellow,
respectively), or waiting in the queue (in gray). CEAS,; DC, Data Collection module; GEO, Gene Expression Omnibus; NPS, Nucleosome Positioning
from Sequencing; TF, transcription factor.
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predicted transcription factor binding sites or histone
modification sites, and signal profile files (in WIGGLE
format) of binding or histone modification across the
genome.
Several methods can be used to import data into Cis-
trome. The ‘Upload File’function can import a file from
the user’s computer or from an HTTP or FTP file server
in the same manner as in Galaxy. In most cases, sequen-
cing facilities will manage the low level base calling and
read mapping processes. The least processed Cistrome
data formats that we allow are the SAM/BAM [7] or
BED formats for ChIP-seq sequencing mapping results,
CEL files for ChIP-chip using Affymetrix tiling arrays,
or PAIR files from NimbleGen custom arrays. Research-
ers may have already used other algorithms to generate
intermediate results, such as BED format files for
regions of interest on the genome or WIGGLE format
files for signal information. In such cases, users can also
upload intermediate result files onto Cistrome and apply
our downstream tools while being mindful of the accep-
table formats (Table S1 in Additional file 1). In addition,
we implemented two new data types for expression
microarray data sets from Affymetrix and NimbleGen
technologies. Raw expression microarray data and a text
file describing the phenotype information (for example,
before and after transcription factor activation) should
be packaged in a zip file before being uploaded through
the general upload tool.
Cistrome contains peak-calling tools for both ChIP-
chip and ChIP-seq data. We deployed the MAT tool [8]
for Affymetrix promoter or tiling arrays and have sup-
ported nine different array designs from Caenorhabditis
elegans to human. Affymetrix CEL files are required as
input. For NimbleGen two-color arrays, MA2C [9] was
deployed. Because researchers usually have their own
customized NimbleGen two-color array designs, array
design (.ndf) and position (.pos) files and raw probe raw
signal files (.pair) should all be uploaded to run MA2C
on the Cistrome website. Both MAT and MA2C are
able to handle control data or replicates as input data
and can generate a BED file for peak locations and
WIGGLE file for normalized probe signals as the out-
put. Cistrome provides the MACS (Model-based Analy-
sis of ChIP-Seq) [10] tool forChIP-seqdataobtained
from various short read sequencers (for example, Gen-
ome Analyzer and HiSeq 2000 from Illumina or SOLiD
from Applied Biosystems). MACS can improve the accu-
racy of the predicted binding sites by modeling the
length of the sequenced ChIP fragments and the local
bias due to chromatin openness. MACS can run with or
without controls and allows the widely used SAM/BAM
format and another six mapping result formats (Table
S1 in Additional file 1) as input. The outputs include
peak regions and peak summits (the precise binding
location estimated by the algorithm) in BED format and
ChIP fragment pileup along the whole genome at every
10 bp in WIGGLE format. When the diagnosis option is
turned on, MACS subsamples the data to determine the
number of peaks that can be recovered from a subset,
thus estimating the saturation status of the current
sequencing depth. We deployed MACS version 1.4rc2
on Cistrome, which supports single-end or paired-end
sequencing in BAM or SAM format.
With the rapid growth of ChIP-chip and ChIP-seq
datasets in public repositories, it has become increas-
ingly important to be able to integrate information from
cross-platform and between-laboratory ChIP-chip or
ChIP-seq datasets. We recently developed the powerful
meta-analysis tool MM-ChIP (Model-based Meta-analy-
sis of ChIP data) [11] and deployed it under the peak-
caller application category of Cistrome. The MM-ChIP
tool includes two separate functions: MMChIP-chip per-
forms ChIP-chip meta-analysis based on WIGGLE files
from the MA2C and MAT tools, and MMChIP-seq uses
NGS alignments in BED format as input to combine dif-
ferent ChIP-seq libraries of the same factor under the
same conditions. The resulting peak locations (in BED
files) and signal profiles (in WIGGLE files) can be visua-
lized as a custom track on the UCSC genome browser
and used as input for other downstream analysis tools
that will be discussed later. In addition to these specific
peak callers for different platforms or purposes, there is
a general peak caller in Cistrome that can take any
whole genome signal profile in WIGGLE format, nor-
malize the signals, and then attempt to find the signifi-
cant regions by comparing to a null distribution built
from background data.
Expression microarray analysis tools
The Cistrome Expression pipeline uses R and Biocon-
ductor [12] packages to perform basic gene expression
analyses. The data analysis starts with the processing of
asetofsignalintensityfiles for Affymetrix expression
arrays (.cel) or NimbleGen arrays (.xys). Datasets may
also include a phenotype (.txt) file that describes and
groups the set of expression files.Thenextstepinthe
pipeline calculates the expression index of this dataset
using one of four possible methods: robust multichip
average (RMA) [13], justRMA, gcRMA and MAS5. The
result is a normalized expression set (.eset) that can be
represented as refSeq, Entrez, or ProbeSet IDs in plain
text format. When mapping the ProbeSet IDs to refSeq
or Entrez IDs, the custom CDF files from BRAINAR-
RAY [14] are used. The genes that are differentially
expressed between conditions (for example, before and
after a transcription factor is knocked down) are often
used to explore the function of the transcription factor
together with cistrome data. When a normalized
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expression set is used as input, Cistrome can identify
differentially expressed genes using any of the following
methods: limma moderated t-test, ordinary least-
squares, and permutation by re-sampling. Correction for
falsepositive(typeI)errorsmaybeperformedusing
either the Bonferroni correction or Benjamini-Hochberg
false discovery rate (FDR) methods. The output from
this tool is a list of differentially expressed genes, log2-
transformed fold changes and FDR-corrected P-values of
differential expression. The differential expression result
can be processed into gene lists, such as up-regulated or
down-regulated genes, using one of the public work-
flows as described in Table S2 in Additional file 1. The
gene lists can be further incorporated with other Cis-
trome tools.
Several downstream analysis modules are also avail-
able. A transcription factor tool allows the user to find
the transcription factors with the highest level of expres-
sion. The selection is done based on an expression index
cutoff value, and further filtering can be performed to
restrict the resulting list to the Gene Ontology (GO)
terms for transcription regulation activities. A correla-
tion tool allows the user to detect all genes for which
their expressions correlate with another given gene. This
correlation result can also be filtered by applying the
GO terms. The GO enrichment tool helps researchers
explore the functions for a list of genes, such as the up-
regulated genes after a transcription factor knockdown
or the genes with transcription factor bound in promo-
ter regions. Enrichment can be compared to the back-
ground of all genes or a subset of genes on the array.
This tool uses Bioconductor GO and GOstats [15]
packages together with a query to the DAVID (Database
for Annotation, Visualization and Integrated Discovery)
web server [16]. The visualization tool in this category
allows users to visualize and compare the expression
index distributions of multiple lists of genes (for exam-
ple, genes with proximate transcription factor binding
compared with all genes) using box plots or histograms.
Integrative analysis
Downstream analyses for a cistrome study require speci-
fic or integrative tools. The value of Cistrome is that it
enables biologists to use a broad range of bioinformatics
tools to easily generate report-quality figures and tables,
and to simplify routine analysis using reproducible pipe-
lines. In Cistrome, we provide tools for correlation stu-
dies, genome feature association studies and motif
analysis together with public workflows to link these
tools together.
Usually, researchers require at least two biological
replicates to show the consistency of an experiment. An
intuitive way to show consistency is to ask if the repli-
cates can be correlated in some meaningful
measurement. Correlation can also answer the question
of whether or not two transcription factors are co-loca-
lized. For instance, two biological replicates with low
correlation might suggest poor data quality, or highly
overlapping cistromes between two factors might sug-
gest interactions between the factors. For these reasons,
we deployed two levels of tools in Cistrome to calculate
correlations: one to compare protein-DNA binding sig-
nals and the other to investigate the overlap of the pre-
dicted binding sites. First, Cistrome can calculate
Pearson correlation coefficients for multiple signal pro-
files on a whole-genome scale or by restricting the cal-
culation to a set of genomic regions defined by the user.
A Pearson correlation coefficient close to 1 implies that
the replicates are consistent or two factors are corre-
lated. To save computation time, these tools use win-
dow-smoothing methods to calculate the mean or
median values within non-overlapping fixed-size win-
dows. This approach decreases the number of data
points involved in the calculation. The results are repre-
sented as scatter plots or heatmap images in either PDF
or PNG format as illustrated in Figure 2a. The second
level of correlation can address how many of the pre-
dicted binding sites (peaks) from several replicates, dif-
ferent factors or different conditions overlap. We
provide a tool for drawing a Venn diagram using two to
three BED format peak files. The circles and overlapping
regions in the Venn diagram can be proportional to the
actual number of peaks and overlaps (Figure 2b).
Functional DNA regions in genomes are often evolu-
tionarily conserved between different species [17-19].
Therefore, evolutionary conservation of ChIP-chip/seq
peaks compared with flanking non-peak regions is often
a good indicator of good data quality and correct data
preprocessing. In Cistrome, the ‘Conservation Plot’tool
can take one or more cistromes in BED files as input,
and use UCSC PhastCons conservation scores [20] to
produce a figure showing the average conservation score
profiles around the peak centers (Figure 2d). This analy-
sis could be extended to compare the conservation dif-
ferences between multiple cistromes.
Another useful task is to find the genomic features or
genes associated with transcription factor binding or
histone modification sites. For instance, H3K4me3 is
enriched in the promoter regions of active genes [21],
and H3K36me3 is enriched in transcribed exons [22].
Finding the target genes is critical to understanding the
function of transcription factors, such as transcription
repression or activation. Therefore, a set of tools from
the CEAS (Cis-regulatory Element Annotation System)
[23] package, including SitePro, GCA (Gene Centered
Annotation), Peak2Gene and the CEAS main program,
has been deployed in the Cistrome web interface. Site-
Pro can draw the average signal profiles around given
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genomic locations. When multiple locations or sets of
signal files are used as input, SitePro can address ques-
tions such as how the signals of multiple factors change
at the same locations between different conditions or
how the same factor changes in different sets of geno-
mic locations. The GCA tool can find the peaks that are
closest to the transcription start site (TSS) of each gene
and calculate the coverage of the peaks of the gene body
in a spreadsheet. The Peak2Gene tool can find the near-
est genes for each peak. The CEAS main program gen-
erates multi-paged figures as either a PDF document or
PNG image. In general, when a BED file for peaks and a
WIGGLE file for signals are used as input, the resulting
report includes the peak enrichment on chromosomes
and various genomic features, such as gene promoters,
downstream regions, UTRs, coding exons or introns,
and the average signal profile around TSSs and tran-
scription termination sites (TTSs), the meta-gene body
(all genes are scaled to 3 kbps), concatenated exons
(coding regions), or concatenated introns. When gene
lists are provided (for example, a list of genes with the
highest and lowest levels of expression for the same
sample in a ChIP-chip or ChIP-seq experiment), CEAS
will plot the average signal profiles for different gene
groups in different colors for the TSS, TTS, gene bodies,
exons, or introns (Figure 2c). This function can be
coupled with gene expression tools described in the pre-
vious section to show whether the signals of the tran-
scription factor or histone marks are related to
transcription repression or activation.
In addition to the average signal profiles at a given set
of genomic locations, as shown in CEAS, the visualiza-
tion and clustering of signal profiles from different fac-
tors at specific locations provides another angle of
insight. Through the observation of patterns, we can
also find the co-factors (co-activators or co-repressors)
that tend to work together on their regulated genes. The
Cistrome ‘Heatmap’tool can extract the signals centered
at every given genomic location, perform either a k-
means clustering or a sorting by maximum, mean, or
TSS only (locations= 14527)
H3K4me3 peak only (locations= 1973)
TSS and H3K4me3 peak (shared locations= 3750
)
(a) (b)
(c) (d)
-1000 0 1000 2000 3000 4000
0.0 0.5 1.0 1.5 2.0
Aver age Gene Profiles
Upstream (bp), 3000 bp of Meta-gen e
, Do
wnstream (bp)
Average Profile
Top10
Bottom10
All
H3K27me3
H3K9me3
H3K36me3
MES4
H3K4me2
H3K4me3
H3K4me3
H3K4me2
MES4
H3K36me3
H3K9me3
H3K27me3
−0.51 −0.14 0.35 0.37 0.74 1
−0.41 −0.07 0.22 0.25 10.74
−0.79 −0.14 0.9 10.25 0.37
−0.83 −0.15 10.9 0.22 0.35
0.33 1−0.15 −0.14 −0.07 −0.14
10.33 −0.83−0.79 −0.41 −0.51
Averag e Phastcons a round the Center of Sites
Distance from the Center (bp)
Average Phastcons
−1500 −500 0 500 1500
0.06 0.10 0.14
AR binding sites
Figure 2 Correlation and association tools.(a) Correlation plots using different histone marks in C. elegans early embryos [43]. Cistrome
correlation tools can generate either a heatmap with hierarchical clustering according to pair-wise correlation coefficients or a grid of
scatterplots. (b) Venn diagram showing the overlap of H3K4me3 peaks (in blue) with transcription start sites (TSS) for all the genes (in red) in the
C. elegans genome. (c) Meta-gene plot generated by CEAS showing the H3K4me3 signals enriched at gene promoter regions; the top expressed
genes (red) have higher H3K4me3 signals than the bottom expressed genes (purple). (d) Conservation plot showing that the human androgen
receptor (AR) binding sites from ChIP-chip [24] are more conserved than their flanking regions in placental mammals.
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median values within each region, and then draw a heat-
map. For example, the group of TSSs for active genes
should have H3K4me3 enriched at the TSS and a gra-
dual H3K36me3 enrichment downstream of the TSS,
whereas the group of TSSs for inactive genes would
have low signals of both H3K4me3 and H3K36me3.
Additional detailed clustering will be revealed when sig-
nal profiles of multiple factors are used (Figure 3). Mul-
tiple WIGGLE files for different factors or different
conditions can be used as input together with a set of
genomic locations defined in a BED file. These regions
could be nucleosome-free regions or transcription factor
binding sites instead of TSSs of genes. Clustering or
sorting can be based on all or some of the WIGGLE
files. The color schema of the heatmap is configurable
to adjust the contrast for better visualization between
high and low signals.
Transcription factor motif analysis is a key to under-
stand the specific DNA patterns of in vivo transcrip-
tion factor binding. Motif analysis can also identify the
co-factors that work together to activate or repress
gene expression because the binding sites of co-factors
should have similar DNA motifs. We deployed a new
motif algorithm called ‘SeqPos’in Cistrome based on
the algorithm in [24]. By taking the peak locations as
the input, SeqPos can find motifs that are enriched
close to the peak centers. SeqPos can scan all of the
motifs that we collected from JASPAR [25], TRANS-
FAC [26], Protein Binding Microarray (PBM) [27],
Yeast-1-hybrid (y1h) [28], and the human protein-
DNA interaction (hPDI) databases [29]. SeqPos can
also find de novo motifs using the MDscan algorithm
[30]. The final significant motifs are listed in an
HTMLpage,asinFigure4,wheretheusercansort
themotifsbyz-scoreorP-value and click on each
motif to see detailed information, such as the probabil-
ity matrix, logos, and the motif consensus. A position-
specific scoring matrix can be copied or referred to
another tool within Cistrome called a ‘screen motif’to
search a given set of genomic locations for all occur-
rences of a particular motif.
Cistrome has many other useful tools to help users
better manipulate their data. A lift over tool can con-
vert WIGGLE files from one genome assembly to
another if users want to combine old analysis results
with a new genome annotation. However, ab initio re-
preprocessing is recommended to generate new WIG-
GLE files for the new genome assembly. A WIGGLE
file standardization tool can convert the resolution of a
WIGGLE file to 8, 32, 64 or 128 bps. Two other tools
can extract data for certain chromosome out of a BED
file or a WIGGLE file. Furthermore, many Galaxy
functionsthatweconsideredtobeveryusefulfor
ChIP-chip/seq data analyses are also enabled in Cis-
trome. For example, the intersect tool for two interval
files, and the filtering/sorting/cutting tool for tab-
delimited text files are widely used in many of our pre-
compiled public workflows to post-process intermedi-
ate results then feed them into downstream tools
(Table S2 in Additional file 1).
H3K27me3 MES4H3K36me3H3K4me3H3K4me2H3K9me3
Wormbase Gene
distance to TSS
-100 10000
0
20000
0
1
2
3
Figure 3 Heatmap analysis with k-means clustering. By combining H3K27me3, H3K9me3, H3K4me3, H3K4me2, H3K36me3 and MES-4 (the
histone H3K36 methyltransferase) ChIP-chip signals, as in Figure 2a, the Cistrome heatmap tool separates the ± 1-kbp regions for all of the C.
elegans TSSs into five clusters using k-means clustering. From top to bottom, the clusters are as follows: (1) about 3,000 TSSs related to active
genes have high H3K4me3 upstream of the TSSs and high H3K36me3 downstream of the TSSs; (2) about 2,000 TTSs have slightly lower
H3K4me3 levels downstream of the TSSs and no significant K36me3 enrichment; (3) about 2,000 TSSs have high H3K27me3 and H3K9me3
related to inactive genes; (4) about 2,500 TTSs with low H3K27me3, moderate H3K4me3 and high H3K36me3 enrichment around the TTS related
to genes in operons; and (5) about 10,000 TTSs have no strong marks.
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Comparison to existing software
Cistrome was built upon the Galaxy framework to pro-
vide a user-friendly, reproducible and transparent work-
bench for cistrome researchers. Researchers can easily
and intuitively reuse and share data, incorporate pub-
lished data, and publish their results on the website.
Compared with the more general Galaxy main site [31],
the Cistrome system was specifically designed for down-
stream data analysis accompanied by ChIP-chip or
ChIP-seq technologies and includes basic analyses from
peak calling to motif detection. In the future, the Cis-
trome analysis platform module will be linked to our
local Data Collection (DC) module where publicly avail-
able ChIP-chip and ChIP-seq data are downloaded and
preprocessed.
There are several integrative software packages
designed for ChIP-chip and ChIP-seq analysis, including
the widely used CisGenome platform [3] and the
recently published seqMINER platform [4]. CisGenome
works as a package of command line software for Linux,
Windows and Mac OSX and provides a GUI and gen-
ome browser only for the Windows operating system.
seqMINERworksasstandaloneGUIsoftwarebasedon
Java. The major difference between Cistrome and these
packages is that we focus on a web solution to eliminate
the trouble of maintaining various software and the
demand for powerful hardware from the user. Another
advantage of using a web server is that we can continue
to provide Cistrome improvements, such as bug fixes
and additional features, that are transparent to the user.
Galaxy infrastructure enables every Cistrome tool to
remember the run-time parameters in the server. When
a Cistrome function is updated, users can rerun an ana-
lysis or reproduce a result using several simple mouse
Figure 4 Cistrome SeqPos motif analysis. A screenshot of the SeqPos output. The enriched motifs at the androgen receptor binding sites
without FoxA1 binding are displayed in an interactive HTML page. When the user clicks on the row of a particular motif, the motif logo and
detail information are shown at the top of the page.
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clicks. Last but not least, Cistrome has been provided
with the workflow and data sharing features from the
Galaxy framework. Users can customize their own pipe-
line to increase productivity. Additionally, users can
share their raw data and analysis results with collabora-
tors and the public through the web interface. An over-
view of a comparison of the functionalities of Cistrome,
CisGenome and seqMINER is provided in Table 1
(detail in Table S3 in Additional file 1).
Conclusions and future directions
We have deployed a comprehensive ChIP-chip and
ChIP-seq analysis platform called Cistrome by integrat-
ing publicly available research tools and newly devel-
oped algorithms from our group under the Galaxy
framework. Cistrome covers most of ChIP-chip/seq ana-
lysis tasks, from data preprocessing, expression analysis,
integrative analysis, reproducible pipeline, to data pub-
lishing; this integrated approach allows biologists to ana-
lyze and visualize their own ChIP-chip/seq data for
publication. We plan to extend Cistrome in the follow-
ing areas: first will be to support the increasing number
of ChIP-seq datasets by building a Cistrome DC module;
second,weplantocontinueadding additional research
tools and improve the existing features to provide more
sophisticated integrative workflows, especially for
epigenomics data. We will address these plans in detail
in the following paragraphs.
Each ChIP-chip/seq platform has its own cistrome
data analysis challenges. ChIP-chip platforms include til-
ing arrays from Affymetrix, NimbleGen and Agilent, and
ChIP-seq platforms include NGS machines from Illu-
mina, Applied Biosciences and Helicos. A typical human
ChIP-seq experiment sequenced on one Illumina GAIIx
lane generates approximately 20 GB of fastq data. With
more researchers adopting ChIP-chip/seq methods and
NGS technologies that are improving at rates beyond
Moore’s law [32], the production of cistrome data is
increasing exponentially. Currently, databases such as
the National Center for Biotechnology Information
(NCBI) Gene Expression Omnibus (GEO) [33] and the
European Bioinformatics Institute (EBI) ArrayExpress
[34] host array data, and databases such as the NCBI
Sequence Reads Archive (SRA) [35] and the EBI SRA
host sequencing data [36]. However, experimental biolo-
gists often cannot understand or reuse these deposited
data in their raw form. Although some processed data-
sets have been submitted to these databases, they are
difficult to compare and integrate due to diverse data
generation platforms and analysis algorithms. Therefore,
parallel to the Cistrome data analysis module, we are
designing another major component of Cistrome: the
Table 1 Overview comparison of functionalities of Cistrome, CisGenome and SeqMINER
Cistrome CisGenome 2 SeqMINER 1.2.1
Data preprocessing
ChIP-chip
preprocessing
Yes. Affymetrix or NimbleGen platform Yes. Affymetrix or other
platform through conversions
Not available
ChIP-seq
preprocessing
Yes Yes. No support for SAM/BAM Not available
General peak calling Yes. Through wiggle file for signals No direct solution Not available
Cross-platform
analysis
Yes. Across different ChIP-chip platforms, or across
different ChIP-seq libraries
Not available Not available
Expression analysis
From normalization,
differential
expression, to gene
ontology
Yes. Affymetrix or NimbleGen platform Not available Not available
Integrative analysis
Genome association
study
Yes. Chromosome or gene feature enrichment;
aggregation plot; genes or peaks centered annotation;
conservation plot; k-means clustering heatmap
Yes. Closest genes around
peaks
Yes. K-means clustering at
peak sites; interactive
heatmap; aggregation plot
Correlation between
samples
Yes. Whole genome or peak centered Pearson
correlation; Venn diagram
Not available Yes. Pearson correlation at
enriched regions
Motif analysis Yes. Find enriched known or de novo motifs; map
motifs to genomic locations
Yes. Find de novo motifs; map
motifs to genomic locations
Not available
Other tools Liftover both BED/WIGGLE files; low level operations on
text manipulation and format conversion through
Galaxy
Many useful scripts for format
conversions, to calculate
overlaps and so on
Not available
Genome browser
visualization
Redirect to mirrored UCSC genome browser on
Cistrome, or external genome browsers supported by
Galaxy
Local installed genome
browser on Windows
operating system
Not available
Liu et al.Genome Biology 2011, 12:R83
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Page 8 of 10

DC module. The Cistrome DC will be a manually
curated data warehouse. The data stored in the DC
module include both raw and preprocessed data - peak
locations and signal profiles - that are ready to be
imported into the current Cistrome analysis platform.
We plan to develop a user-friendly interface to let users
easily search and browse the datasets. We also plan to
build a bridge from the current analysis module to the
Cistrome DC so that users can choose to package their
analyzed data and publish them in the Cistrome DC
upon paper publication.
Concurrent with an increasing interest in epigenomics
research, increasing amounts of histone modification
ChIP-seq, nucleosome-seq, and DNase-seq data are
becoming available to the public. We plan to add
another specific peak caller, Nucleosome Positioning
from Sequencing (NPS), to Cistrome to target histone
modification data [37]. When ChIP-seq data are used at
the nucleosome resolution (that is, where experimental-
ists use micrococcal nuclease to digest DNA) NPS can
provide better data interpretation than the general
ChIP-seq peak caller MACS. NPS can give the well-
positioned nucleosomes as output and further detect the
dynamic chromatin regions with moving nucleosome or
DNase sites between conditions. Our newly developed
algorithms, called Binding Inference from Nucleosome
Occupancy Changes (BINOCh) [38], can follow up with
motif analysis in the dynamic regions to better under-
stand the transcription factor binding changes.
Many new features and tools for cistrome analysis are
included in our future plans. Basic file manipulation
tools - for example, the BedTools [39] suite - will be
added to Cistrome in the future. The goal is to provide
more flexible workflows for different demands. Because
the WIGGLE format used to save whole genome signal
profiles is too big to maintain and manipulate, we plan
to switch to a more space-efficient self-indexed binary
format: the BigWig [40]. We also plan to support pre-
processed RNA-seq data (for example, in RPKM (reads
per kilobase of exon model per million mapped reads)
form) in our expression analysis module. Galaxy has
included Cufflinks tools in main codes, and we will pro-
vide functions that are similar to those of the current
expression tools such as DESeq [41] or edgeR [42] and
incorporate them into other integrative analysis tools.
For example, by combining expression profiles and tran-
scription factor motif enrichment, we could predict the
correct transcription factors that collaborate with the
ChIPed factor.
BecauseCistromewasbuiltonGalaxy,wewillcon-
tinue updating the Galaxy framework codes for new fea-
tures, such as Galaxy Pages for the reproducible and
interactive supplementary material or Galaxy Visualiza-
tion to show data tracks in a genome browser view. We
also plan to follow in the steps of Galaxy and provide a
cloud computing solution for future scalability. We wel-
come feedback from users regarding new features and
better representations to make Cistrome a better
resource for the community.
Additional material
Additional file 1: Supplementary Tables S1, S2 and S3. File formats
and restrictions on the Cistrome server; public workflows; and detailed
comparison between Cistrome and CisGenome or seqMINER. Online
demonstration of a general ChIP-seq analysis can be found at the public
Cistrome site [44].
Abbreviations
bp: base pair; ChIP: chromatin immunoprecipitation; DC: Data Collection; GO:
Gene Ontology; NGS: next-generation sequencing; TSS: transcription start
site; TTS: transcription termination site.
Acknowledgements
Cistrome was developed by the Cistrome team at both the Dana-Farber
Cancer Institute and Eli Lilly and Company. We thank Lingling Shen, Wenbo
Wang, Jacqueline Wentz, Josiah Altschuler and Kar Joon Chew for their
contributions to the system implementation. We also thank the many
collaborators who gave us suggestions and feedback. This work is supported
by the Dana-Farber Cancer Institute High Tech and Campaign Technology
Fund (XSL), the National Basic Research Program of China grant 973
Program No. 2010CB944904 (YZ), NIH grants HG004069-04S1 (LT), DK074967
(MB) and DK062434 (TL).
Author details
1
Department of Biostatistics and Computational Biology, Dana-Farber Cancer
Institute and Harvard School of Public Health, 450 Brookline Ave, Boston, MA
02215, USA.
2
Center for Functional Cancer Epigenetics, Dana-Farber Cancer
Institute, Boston, MA 02215, USA.
3
Lilly Singapore Centre for Drug Discovery,
8A Biomedical Grove, Immunos, Singapore 138648.
4
Beijing Genomics
Institute, Beishan Industrial Zone, Yantian District, Shenzhen 518083, China.
5
Singapore Immunology Network, 8A Biomedical Grove, Immunos Building
level 3, Singapore 138648.
6
School of Life Science and Technology, Tongji
University, 1239 Siping Road, Shanghai 200092, China.
7
Eli Lilly and
Company, Lilly Corporate Center, Indianapolis, IN 46285, USA.
8
Department
of Bioengineering, Stanford University, 318 Campus Drive, Stanford, CA
94305, USA.
9
Jardine Lloyd Thompson Asia, 1 Raffles Quay #27-01, One
Raffles Quay - North Tower, Singapore 048583.
10
Department of Medical
Oncology, Dana-Farber Cancer Institute and Harvard Medical School, 450
Brookline Ave, Boston, MA 02215, USA.
11
AstraZeneca Pharmaceuticals LP, 35
Gatehouse Drive, Waltham, MA 02451, USA.
Authors’contributions
TL, MB, and XSL designed the project. TL, JAO, and XSL wrote the
manuscript. TL, JAO, MP, MB, YT, and XSL revised the manuscript. TL, JAO,
LT, CAM, BL, YZ, HGS, SSW, JM, UJP, YC, and KY implemented the system. TL,
LT, and JM maintain the public server instance hosted in Dana-Farber
Cancer Institute. All authors read and approved the final manuscript.
Competing interests
The authors declare that they have no competing interests.
Received: 4 April 2011 Revised: 5 August 2011
Accepted: 22 August 2011 Published: 22 August 2011
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transcriptional regulation studies. Genome Biology 2011 12:R83.
Liu et al.Genome Biology 2011, 12:R83
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