Nucleic Acids Research, 2007, Vol. 35, Web Server issue W253–W258
doi:10.1093/nar/gkm272
STAMP: a web tool for exploring DNA-binding
motif similarities
Shaun Mahony1 and Panayiotis V. Benos1,2,*
1Department of Computational Biology, School of Medicine, University of Pittsburgh and 2Department of Human
Genetics, Graduate School of Public Health, and University of Pittsburgh Cancer Institute, School of Medicine,
University of Pittsburgh, Pittsburgh, PA, USA
Received January 31, 2007; Accepted April 10, 2007
ABSTRACT
STAMP is a newly developed web server that is
designed to support the study of DNA-binding
motifs. STAMP may be used to query motifs against
databases of known motifs; the software
aligns input motifs against the chosen database
(or alternatively against a user-provided dataset),
and lists of the highest-scoring matches
are returned. Such similarity-search functionality
is expected to facilitate the identification of tran-
scription factors that potentially interact with newly
discovered motifs. STAMP also automatically builds
multiple alignments, familial binding profiles and
similarity trees when more than one motif is
inputted. These functions are expected to enable
evolutionary studies on sets of related motifs
and fixed-order regulatory modules, as well as
illustrating similarities and redundancies within
the input motif collection. STAMP is a highly
flexible alignment platform, allowing users to
‘mix-and-match’ between various implemented
comparison metrics, alignment methods (local or
global, gapped or ungapped), multiple alignment
strategies and tree-building methods. Motifs may
be inputted as frequency matrices (in many of the
commonly used formats), consensus sequences, or
alignments of known binding sites. STAMP also
directly accepts the output files from 12 supported
motif-finders, enabling quick interpretation of
motif-discovery analyses. STAMP is available at
http://www.benoslab.pitt.edu/stamp
INTRODUCTION
‘Position-specific scoring matrices’ (PSSMs) and their
derivatives have become the standard representation of
a transcription factor’s (TF) DNA-binding preference.
For example, experimentally derived DNA-binding
preferences for a growing number of TFs are stored as
frequency matrices in databases such as JASPAR (1) and
TRANSFAC (2). In addition, most de novo motif-finding
software tools report statistically over-represented
degenerate sequence features in the form of frequency
matrices or consensus sequences.
Motif-discovery is often one of the first steps performed
during computational analysis of gene-regulation.
For instance, researchers often wish to discover over-
represented motifs that are common to sets of genes with
similar expression patterns. However, interpretation of
the output from motif-finders is often daunting; many
distinct motifs may be reported with little or no indication
as to whether each may potentially possesses regulatory
function. Furthermore, no information is provided about
the TF protein that may bind to them. It is therefore
surprising that few tools currently exist that can assess
similarity between novel, computationally identified
motifs and the known motifs stored in the databases.
Available tools [such as T-Reg Comparator (3) and
MACO (4)] currently allow for only a single type of
alignment method, which may not be suitable for all
database searches, and none support the direct analysis of
motif-finder output files.
Recently, a number of studies have focused on
the evolution of binding preference amongst related
TFs. For example, generalized models of the binding
preferences from a group of structurally related TFs have
been described (5). Such ‘familial binding profiles’ (FBPs)
have been shown to have wide applicability in
improving the performance of motif-finders (5,6) and in
predicting the structural class of the TF associated with
novel motifs (5,7). Other studies have shown evolutionary
conservation and change in fixed-order cis-regulatory
modules (e.g. in the SXY modules controlling vertebrate
MHC gene expression (8)). Currently, however, there is
no publicly available software to support evolutionary
analyses of DNA-binding motifs and facilitate the
study of FBPs.
In response to the gap in the current bio-
informatics software repertoire outlined above, the
*To whom correspondence should be addressed. Tel: þ412 648 3315; Fax: þ412 648 3163; Email: benos@pitt.edu or shaun.mahony@ccbb.pitt.edu

2007 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/
by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

STAMP web server aims to provide a platform for
‘BLAST-like’ database searching and ‘ClustalW-like’
multiple alignment and tree building for DNA-binding
frequency matrices and motifs. Instead of limiting
analyses to a single ungapped alignment strategy,
STAMP allows various combinations between the
implemented scoring metrics, pairwise alignment methods,
gap penalties, multiple alignment strategies and tree-
building algorithms. The web server accepts many
commonly used motif and frequency matrix formats,
and in addition allows the uploading of entire output files
from 12 supported motif-finders. STAMP therefore offers
a highly flexible and comprehensive toolbox for the study
of relationships between TF-binding motifs.
MATERIALS AND METHODS
Pairwise comparison and alignment of motifs
At the core of STAMP’s functionality is the efficient
comparison and alignment of two motifs. Two motifs
can be aligned using Needleman–Wunsch (global) or
Smith–Waterman (local) alignment methods, based on
column comparison scores calculated by one of the five
supported distance metrics: (i) Pearson’s correlation
coefficient (9), (ii) Kullback–Leibler information con-
tent (3), b sum of squared distances (5), (iv) average
log-likelihood ratio (ALLR) (10) and (v) ALLR with a
lower limit of –2 imposed on the score. The latter option is
provided as an attempt to ease the negative effect the
ALLR scoring function has on the motif alignment due to
its highly skewed scores (7).
A special ungapped type of Smith–Waterman local
alignment is also provided, where the motif ‘cores’
(defined as the four most informative adjacent matrix
columns) are aligned before extending the alignment.
Various gap-opening penalty options are also offered,
and differ according to the column-comparison
metric employed. The gap extension penalty is currently
set to half of the value of the gap-opening penalty.
For Smith–Waterman alignments, users may choose to
only recover alignments that overlap by at least three
matrix positions, and can require that the local alignment
be extrapolated to the matrix edges.
In order to avoid length biases when comparing motifs
of different lengths, we used the method of Sandelin and
Wasserman for the calculation of empirical P-values based
on simulated PSSM models (5). Construction of a dataset
of 10 000 simulated PSSMs that reflect the properties
of PSSMs in the JASPAR database was performed
as described by Sandelin and Wasserman (http://fork
head2.cgb.ki.se/jaspar/additional).
Multiple motif alignment and phylogenetic tree construction
Users may choose between two motif multiple alignment
strategies: ‘progressive profile alignment’ and ‘iterative
refinement’. In progressive profile alignment, the multiple
alignment is built up by progressively aligning the nodes
on an approximate guide tree in order of decreasing
similarity. Iterative refinement initializes the alignment
using the most similar pair of input motifs, and
progressively adds the remaining motifs according to
similarity to a profile based on the current alignment.
Once the initial alignment is built, each motif is removed
from the alignment in turn and is realigned to a profile
based on the other aligned motifs. Gaps are encouraged to
open in the same positions in the multiple alignments by
negatively weighting the gap penalties in positions of the
multiple alignment that already contain gaps.
Finally, users may choose between two tree-building
algorithms; an agglomerative method [UPGMA (11)] and
a divisive method that is based on a self-organizing tree
algorithm [SOTA (12)]. UPGMA begins by assigning
each input motif its own leaf node. At each time-step,
the two nodes with the maximum average pairwise
similarity are joined. The tree is built up through
successive combinations of nodes until only one node
(the root) remains. SOTA follows the opposite strategy.
The tree is initialized with only one node (the root), which
contains a rough alignment of all input PSSMs, and the
node model is generated from this alignment. The root
node then produces two identical offspring leaf nodes.
During each time-step, the algorithm assigns the PSSMs
to their most similar leaf nodes and then allows the node
model to be updated in accordance with their current
contents. SOTA also allows for small contributions
from neighboring nodes during the update step. These
contributions are designed to keep neighboring nodes
similar. After a number of time-steps, the node with the
highest degree of dissimilarity amongst its members is
allowed to produce two identical offspring nodes. This
competitive learning scheme continues until each leaf node
contains a single PSSM.
Database matching
Besides motif alignments and tree-building construction,
STAMP automatically queries each of the input
motifs against a user-specified motif database to identify
their ‘best matching’ known motifs. The following
motif databases are currently supported: (i) JASPAR,
(ii) TRANSFAC, (iii) Saccharomyces cerevisiae
‘regulatory code’ motifs [predicted by Harbison et al.
(13) and MacIsaac et al. (14)], (iv) Drosophila motifs
[DNase I footprinting data from (15), motifs generated
by Dan Pollard], (v) DPInteract Escherichia coli motifs
(16) and (vi) RegTransBase prokaryotic motifs (17).
Alternatively, users may upload their own dataset of
motifs to query the input motifs against. Users may
choose to get listings of 1, 5 or 10 of the best-matching
motifs in the queried database.
Input data types and formats
STAMP accepts queries of one or more motifs
(no maximum query size is currently enforced). In order
to enhance accessibility, the web server supports a wide
variety of motif input formats. For example, users may
input DNA-binding motifs as collections of position
frequency matrices (also known as count matrices or
PSSMs) in TRANSFAC, JASPAR, MEME, or ‘Raw
count’ formats. Motifs may also be entered as consensus
sequences in the IUPAC degenerate sequence alphabet,
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or as multiple alignments of sample-binding sites.
Users do not have to tell the system which data format
is being used in a query; the system automatically senses if
a supported format has been entered. Users may also
mix-and-match input formats in a single run of the
platform.
Alternatively, users may upload the entire output
files from a number of supported de novo motif-finders.
This option is expected to be useful to those users who
wish to interpret the results of a DNA motif-finding
analysis. STAMP can be used to match the motif-finder
output against databases of known motifs, and can also
illustrate similarities between the discovered motifs [some
motif-finders, such as BioProspector (18), may report
multiple copies of similar motifs in any given run].
Currently supported STAMP input formats include the
output files from motif-finder programs like SOMBRERO
(19), BioProspector (18), MDScan (20), AlignACE (21),
MEME (22), Weeder (23), MotifSampler (24), YMF (25),
ANN-Spec (26), Consensus (27), Improbizer and
Co-Bind (28). Examples of all supported formats are
illustrated on STAMP’s help web page.
Parameters
The users may specify any combination of the alignment
parameters (comparison metric, gap penalty, alignment
and tree-building strategy) and search database described
above. Users may also specify the ‘information content’
for edge trimming. Motifs predicted by many motif-
finders or stored in the databases often contain a ‘core’
region of high information content flanked by low
information-content columns at the edges. Many research-
ers assume that most of these flanking columns are
irrelevant to the protein–DNA interaction. Whether or
not this assumption is true, STAMP allows the option of
trimming these low information-content edges from
the input motifs. Since STAMP’s motif alignment
P-value calculation is dependent on the length of the
compared motifs, removing the low information-content
edges can assist accurate alignment. STAMP allows
the user to choose an information content threshold
(between 0 and 1) for the purposes of excluding edge
columns. The motif will not be shortened below the
minimum motif length of four columns.
Implementation
The STAMP platform is written in Cþþ, and is modularly
designed to allow any combination of the implemented
column comparison metrics, alignment methods, multiple
alignment strategies and tree-building algorithms. Some of
the implemented algorithms make use of functions
provided by the open-source GNU scientific library
(http://www.gnu.org/software/gsl). The web server is
written in PHP with supporting scripts written in Perl.
Sequence logos are generated using ‘WebLogo’ (29), and
the similarity tree is drawn using the Phylip software
package (30). Conversion of the results pages to Portable
Document Format (PDF) is achieved using the open-
source htmldoc program (http://www.htmldoc.org/).
STAMP does not have excessive computing requirements.
In its current deployment (on a Dell PowerEdge
2650 server with 2.8GHz dual Xeon processors), and
under typical server loading conditions, STAMP processes
a typical input dataset (e.g. 10 motifs) in
5 s.
RESULTS AND DISCUSSION
STAMP functionality
STAMP’s typical functionality is demonstrated in
Figure 1 using a selection of four bHLH structural
class motifs taken from the JASPAR database. The
tested motifs are NHLH1 (MA0048), TAL1-TCF3
(MA0091), MAX (MA0058) and USF1 (MA0093). Note
that the matrices are of different lengths. The four
JASPAR matrices are pasted into the motif input box,
and we choose to perform an ungapped Smith–Waterman
alignment using Pearson’s correlation coefficient, an
iterative refinement multiple alignment, and a UPGMA
tree. In this example, we choose to match the input motifs
against the MacIsaac et al. (14) dataset of S. cerevisiae
motifs. STAMP’s results page is displayed as a HTML
document, but may also be downloaded as a PDF.
As illustrated in Figure 1, STAMP results include the
multiple alignment of the input motifs (when two or more
motifs are provided in the input), a similarity tree (when
three or more motifs are provided in the input), and a
ranked list of matches in the chosen dataset for each input
motif.
Although the multiple alignment algorithm is carried
out on the original inputted frequency matrices,
the resulting motif multiple alignment is displayed
in IUPAC consensus sequence format (Figure 1A).
The multiple alignment is accompanied by the generalized
profile, which represents the average profile of the multiple
alignment. Generalized profiles (including FBPs) are
useful when studying the binding properties common
to a set of related motifs. For example, the familial
binding motif in Figure 1A illustrates the ‘CA’- and
‘TG’- binding positions that are shared between all four
input motifs. Clicking on the ‘Alignment Profile’ hyperlink
allows the generalized profile’s frequency matrix to be
downloaded.
A simple figure representing the motif tree is also
displayed on the results page (Figure 1B). In the example
in Figure 1B, the UPGMA tree successfully separates
the bHLH motifs (NHLH1 and TAL-TCF3) from the
bHLH-ZIP motifs (MAX and USF1), since the binding
preference of these two subclasses are distinct
(‘CAGCTG’ and ‘CACGTG’, respectively). The logos of
the input motifs are plotted beside the tree figure.
Finally, the results of the database queries are shown
(Figure 1C for an example for the USF1 motif). For each
input motif, STAMP lists a user-defined number of
best-matching motifs from the user-specified database.
The results reproduce the name and sequence logo of the
database hit, along with the pairwise alignment
(in consensus sequence format) and the E-value
corresponding to the alignment. From the partial results
reproduced in Figure 1C, it may be seen that the human
Nucleic Acids Research, 2007, Vol. 35,Web Server issue W255

USF1 motif has a number of close matches in the dataset
of known S. cerevisiae motifs.
Other uses for STAMP
Aside from STAMP’s more obvious database searching
and multiple alignment functionality (Figure 1), the web
server may also be used to support various other types of
analyses. Some examples follow.
Obtain information about the identity of a TF that binds to
a DNA motif. When de novo pattern discovery methods
are used to analyze sets of unaligned DNA sequences
(typically, the promoters of co-expressed genes identified
Figure 1. An illustration of STAMP’s analysis for four bHLH DNA-binding motifs. Motifs are inputted as PSSM models or consensus sequences.
For illustration purposes, this figure represents them as LOGOs. The user-specified parameters include the distance metric to be used, and the
alignment and tree-building strategy of choice. In the output STAMP reports the multiple alignment of the input motifs (A), the corresponding
distance tree (B) and the best similarity matches against the database of choice (C).
W256 Nucleic Acids Research, 2007, Vol. 35,Web Server issue

through some high-throughput gene expression tech-
nique), they usually identify a number of statistically
significant DNA motifs. In general, the TF that binds to
these motifs is unknown. With the database search
functionality of STAMP, the user can now find for each
new motif its ‘closest known relative’ motif or its most
likely FBP membership.
Furthermore, it has been shown that TFs from the same
structural class often share similar binding preferences (5).
This fact has been used to allow prediction of the
structural class of TFs associated with novel DNA
motifs, either by comparing the novel motif to generalized
FBPs (5) or by using the best hit in a well-represented
database to provide the prediction (7). Thus, even if the
TF that recognizes a newly discovered motif is currently
unknown, using STAMP to compare novel motifs against
the JASPAR, TRANSFAC or the supported sets of FBPs
can provide information about the structure of the
associated TF.
FBP construction. FBPs are generalized models of
DNA binding for TFs that share structural similarities (5).
Their uses include the identification of the structural
group of the TF that recognizes a newly discovered DNA
motif and utilization as priors for DNA pattern discovery
algorithms (5). FBPs are constructed through multiple
alignments of structurally related DNA-binding motifs.
STAMP automatically generates a generalized profile
from the final multiple alignment of the input motifs.
An FBP for particular TF structural class or sub-class
may therefore be constructed simply by providing
STAMP with a collection of the corresponding motifs.
The web server allows the exploration of different distance
metrics and alignment strategies in order to construct
optimal FBPs. In addition, STAMP’s construction of
hierarchical motif trees can be used to guide the definition
of structural class subfamilies if more specific FBPs are
required.
Future improvements
We are interested in expanding the number of supported
databases of known motifs that STAMP can query, and
we will try to respond to any specific suggestions in
respect to this. Similarly, the number of supported
motif input formats may be expanded in the future.
We would encourage the developers of any currently
over-looked motif-finders to provide us with sample
output files, making us aware of any unique properties
of the output format that distinguishes it from other
motif-finder output. We also hope to incorporate other
column comparison metrics, alignment methods and
tree-building algorithms into the STAMP platform in
the future.
CONCLUSIONS
STAMP is the first web server that facilitates multiple
alignment and tree-building for collections of DNA-
binding motifs. STAMP therefore aims to provide a
platform for the evolutionary study of TF-binding motifs,
just as ClustalW (31) and similar tools provide a platform
for the evolutionary analysis of sequence information.
A small number of other programs currently provide
some of STAMP’s database search functionality,
including T-Reg Comparator (3), MACO (4) and the
matrix query component in JASPAR (1). However,
limited numbers of input formats are supported by these
services, and each supports only a single-alignment
strategy. In contrast, STAMP allows the user to mix-
and-match between the five supported column comparison
metrics, the three supported pairwise alignment methods
and various gap penalties. Many different input formats
are supported, and the user may directly upload the entire
output files from 12 supported de novo motif-finders.
In addition, a growing collection of general and species-
specific known motif databases may be queried using
STAMP.
We are confident that STAMP will be a useful resource
for future studies of motif evolution, as well as allowing
greater power in interpreting the often voluminous result
files generated by de novo motif-finders.
ACKNOWLEDGEMENTS
This work was supported by NIH grants RR014214
and NO1 AI-50018 and NSF grant MCB0316255. P.V.B.
was also supported by NIH grant 1R01LM007994-01
and TATRC/DoD USAMRAA Prime Award
W81XWH-05-2-0066. Funding to pay the Open Access
publication charges for this article was provided by NSF
(grant no.: MCB0316255).
Conflict of interest statement. None declared.
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