REDUCE: an online tool for inferring cis-regulatory
elements and transcriptional module activities from
and Harmen J. Bussemaker1,2,*
Department of Biological Sciences and 2
Center for Computational Biology and Bioinformatics,
Columbia University, New York, NY, USA
Received February 18, 2003; Revised and Accepted April 28, 2003
REDUCE is a motif-based regression method for
microarray analysis. The only required inputs are (i)
a single genome-wide set of absolute or relative
mRNA abundances and (ii) the DNA sequence of the
regulatory region associated with each gene that is
probed. Currently supported organisms are yeast,
worm and ﬂy; it is an open question whether in its
current incarnation our approach can be used for
mouse or human. REDUCE uses unbiased statistics
to identify oligonucleotide motifs whose occurrence
in the regulatory region of a gene correlates with the
level of mRNA expression. Regression analysis is
used to infer the activity of the transcriptional
module associated with each motif. REDUCE is
available online at http://bussemaker.bio.columbia.
edu/reduce/. This web site provides functionality for
the upload and management of microarray data.
REDUCE analysis results can be viewed and down-
loaded, and optionally be shared with other users or
made publicly accessible.
In recent years, DNA microarrays have become a popular
experimental tool for monitoring the mRNA transcript
abundance for all genes in a cell simultaneously (1,2). The
DNA sequence of the non-coding part of the genome contains
a variety of regulatory signals that together are responsible for
the differential regulation of gene expression. Through the
combined analysis of transcriptome and genome, signiﬁcant
advances can therefore be made in understanding the
molecular mechanisms underlying transcription control (3,4).
A widely used strategy ﬁrst clusters genes based on their
expression proﬁle across multiple conditions (5) and then
searches for over-represented DNA motifs in the upstream
regions of each gene cluster (6–10). One problem with the
clustering approach to motif discovery, however, is that it
requires distinct microarray experiments to be combined in an
ad hoc fashion. Another problem is that it groups genes into
disjoint subsets, while a promoter region usually has multiple
transcription factor binding sites and receives input from a
number of different signaling cascades (11).
Our laboratory has pioneered the motif-based regression
analysis of a single transcriptome. Implemented as the
algorithm REDUCE—an acronym that stands for regulatory
element detection using correlation with expression—our
method naturally takes into account the combinatorial nature
of gene expression regulation and provides context-speciﬁc
information about transcription factor activities (12). REDUCE
works by ﬁtting a multivariate predictive model to a single
genome-wide expression pattern. The expression level of a
gene is modeled as a sum of independent contributions from
all transcription factors for which binding sites occur in the
promoter region. A forward parameter selection strategy is
used to select motifs from a large set of candidate motifs (e.g.
all oligonucleotides up to a given length).
We have used REDUCE to analyze microarray data for
mRNA expression in Saccharomyces cerevisiae (12,13) and
Drosophila melanogaster (14). We have also successfully
applied REDUCE to analyze genome-wide DNA–protein
interaction data in Drosophila (14,15). Our motif-centered
regression approach has been adopted and extended by other
USERS AND DATA MANAGEMENT
Users must ﬁrst register for an account via a fully automated
procedure. The login system and tree-based data management
structure allow for the safe and convenient storage of
expression data and REDUCE analysis results. The uploaded
data sets are private unless the user explicitly allows them to be
shared within a group or made publicly accessible through our
permissions management interface.
After registration and login, users are taken to the
‘Experiment View’ where they can upload and organize data
(Fig. 1). The upload mechanism subjects incoming data to a
series of ﬁlters and illegal expression values are discarded.
Expression values speciﬁed as ratios or fold-changes are
*To whom correspondence should be addressed. Tel: þ1 212 854 9932; Fax: þ1 212 865 8246; Email: email@example.com
# 2003 Oxford University Press Nucleic Acids Research, 2003, Vol. 31, No. 13 3487–3490
Nucleic Acids Research, Vol. 31, No. 13 # Oxford University Press 2003; all rights reserved
converted to log-ratios base two. The data can be uploaded one
experiment at a time, as a two-column data ﬁle with each line
containing an ORF and an expression value separated by a tab.
It is also possible to upload multiple experiments as a single
multi-column, tab-delimited ﬁle.
MULTIVARIATE REGRESSION ANALYSIS
REDUCE is based on the following multivariate linear model
for transcription initiation control:
Ag ¼ C þ
The power of multiple-motif regression analysis is that the
genome-wide transcriptional response measured in a micro-
array experiment can be decomposed in terms of distinct
regulatory modules. This greatly facilitates the biological
interpretation of transcriptome data. The regulatory modules
that are significantly affected in a particular experiment will
each be represented by one motif in the set M. The model tries
to explain the log-ratio Ag for each gene g in terms of the
number of occurrences Nmg of the motif m in its promoter.
The regression coefﬁcients or slopes Fm are uniquely
determined by the ﬁt to the log-ratios. They have a direct
interpretation as the change in the concentration of the (active
form of the) transcription factor in the nucleus. Note that it is
not possible to determine whether a transcription factor is an
activator or a repressor based on the sign of Fm. For instance,
both increased activation and decreased repression by a
transcription factor will manifest itself as an increase in the
expression level of its target genes. Only when an experiment
is analyzed in which a wild-type strain is compared to a strain
in which a transcription factor is over-expressed or has been
deleted (19) is it possible to infer the character of the
transcription factor from the sign of Fm.
Motifs can be ranked in terms of how much they improve the
ﬁt of the model to the data (as described in detail in 12). This
allows for an unbiased search of relevant motifs from a large
set of candidates, e.g. all oligonucleotides up to length 7. From
this set of 21 844 motifs, typically 100 motifs correlate
strongly enough with expression to be signiﬁcant. However,
these motifs fall into 10 classes of closely related, partially
overlapping motifs. The forward selection procedure as
described (12) selects one representative from each such class.
The set M will therefore typically contain 10 motifs.
Once a motif has been found to be of interest based on the
analysis of a single microarray experiment, scoring it against a
large set of other (published) experiments provides a useful
way of ‘annotating’ it, since the conditions under which the
corresponding (and often unknown) transcription factor
changes activity can be identiﬁed. It the case of mutant versus
wild-type data, factors that are upstream of the transcription
factor in a signal transduction pathway will be identiﬁed this
way. Our web site allows one to specify an IUPAC consensus
motif, select any number of experiments and compute the value
of Fm for each of them. We also provide functionality to plot
such inferred transcription factor activity proﬁles for a number
of different motifs. The usefulness of this approach has already
been demonstrated (12,13).
Figures 1–3 show the most important screens users will
encounter when uploading and analyzing their own expression
data. Using the ‘expression view’shown in Figure 1, users ﬁrst
select the directory to which they want to save their
experiment. We will use the 15 min time point of the heat
shock experiment performed by Gasch et al. (20) as an
example. Figure 2 shows how the two-column ﬁle containing
the log-ratio for each gene is uploaded to the server. Once this
step has been completed, the experiment can be submitted for
analysis by checking the box in front of it and pressing the
‘REDUCE’ button. The user will be prompted for a choice of
sequence data (in yeast, a typical choice is 600 bp upstream
from the translation start site) and the maximum length of the
candidate motifs. As soon as the analysis is complete—
typically after 1–2 min—the user is notiﬁed that the results can
be viewed. Figure 3 shows the resulting model. The motifs are
colored red or green according to the sign of the regression
coefﬁcient Fm. The number of genes with a match to the motif
is listed as well, and by clicking on it, the user will see a list of
all matched genes with a one-line description of their function,
and sorted by their log-ratio.
The computation server uses a modular architecture and
employs only open source components. To plan for growing
performance demands we have incorporated the CONDOR
batch processing system so that a higher throughput can be
achieved by just adding more servers (http://www.cs.wisc.edu/
condor). Caching in the database makes CONDOR even more
Figure 1. ‘Experiment View’ of the REDUCE analysis server. Uploaded
expression data can be organized in folders and shared with other users or
made publicly available.
3488 Nucleic Acids Research, 2003, Vol. 31, No. 13
efﬁcient, as we attempt to never perform the same operation
twice. Algorithms that take more than a few seconds to run are
disconnected from the application layer so that a user never
Our application server is built on Apache and mod_perl
(http://www.apache.org) using HTML::Mason (http://www.
masonhq.com) for presentation logic. We use Postgres
(http://www.postgresql.org/) as our database for session man-
agement caching and data storage. We use Apache::Session
from a centralized database so that we can add more appli-
cation servers without having to implement complicated load
The application itself is implemented as Perl (http://www.
perl.com) objects. We store all the data necessary to instantiate
these objects in the database. For example, a REDUCE batch
job is entered into the queue as a list of motif, sequence and
expression instance identiﬁers necessary to build the job. The
computation servers know how to rebuild the job and then run
the necessary functions.
One of our priorities is to bring our system into compliance
with the MIAME and MAGE-ML standards (21–24). This will
enable us to integrate with other databases and data curation
tools. We will also expand the list of organisms and sequence
types for which REDUCE analysis can be performed and make
it possible for users to upload their own sequence data in
FASTA format. Currently, users can select upstream and
downstream sequences for S.cerevisiae, Caenorhabditis
elegans and D.melanogaster. The analysis of transcription
regulation in human and mouse is inherently more difﬁcult
since enhancer regions are spread over distances of 10 kb or
more from the coding region. Ongoing research in our
laboratory is trying to answer the questions whether in its
current incarnation REDUCE can be used for there organisms;
if so, we will add the corresponding upstream sequences to our
server. Finally, the use of weight matrices was already shown
to have the potential of greatly increased performance of
REDUCE (12) and we are working on putting this approach on
a more solid basis. In general, the infrastructure provided by
our upload server and the expandable way in which it was
designed will allow us to quickly add new functionality as new
tools are developed.
We thank Andre Boorsma, Barrett Foat, Feng Gao and Marcel
van Batenburg for stimulating discussions and feedback, and
Laura Rogers for her help with the ﬁltering and conversion
modules. H.J.B. was partially supported by NIH grant
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Figure 2. The form for uploading a microarray experiment.
Figure 3. Typical result of a REDUCE analysis.
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