DoOP: Databases of Orthologous Promoters,
collections of clusters of orthologous upstream
sequences from chordates and plants
Endre Barta*, Endre Sebestye´n, Tama´s B. Pa´lfy, Ga´bor To´th, Csaba P. Ortutay and
La´szlo´ Patthy1
Agricultural Biotechnology Center, Go¨do¨llo}, Szent-Gyo¨rgyi Albert u. 4, H-2100, Hungary and 1Institute of Enzymology,
Biological Research Center of the Hungarian Academy of Sciences, Budapest, Karolina
ut 29, H-1113, Hungary
Received August 15, 2004; Revised September 29, 2004; Accepted October 13, 2004
ABSTRACT
DoOP (http://doop.abc.hu/) is a database of eukaryotic
promoter sequences (upstream regions) aiming to
facilitate the recognition of regulatory sites con-
served between species. The annotated first
exons of human and Arabidopsis thaliana genes
were used as queries in BLAST searches to collect
the most closely related orthologous first exon
sequences from Chordata and Viridiplantae species.
Up to 3000 bp DNA segments upstream from these
first exons constitute the clusters in the chordate
and plant sections of the Database of Orthologous
Promoters. Release 1.0 of DoOP contains 21 061
chordate clusters from 284 different species and
7548 plant clusters from 269 different species. The
database can be used to find and retrieve promoter
sequences of a given gene from various species and
it is also suitable to see the most trivial conserved
sequence blocks in the orthologous upstream
regions. Users can search DoOP with either seq-
uence or text (annotation) to find promoter clusters
of various genes. In addition to the sequence data,
the positions of the conserved sequence blocks
derived from multiple alignments, the positions of
repetitive elements and the positions of transcription
start sites known from the Eukaryotic Promoter
Database (EPD) can be viewed graphically.
INTRODUCTION
Regulation of transcription initiation was among the first
successful targets of bioinformatic analyses. It is unequivocal
that this regulation always involves the binding of one or more
transcription factors to some parts of the promoter region (here
referred to as the region upstream from the transcription start
site of a gene), called transcription factor binding sites
(TFBSs) (1). The sequence of the promoter region of genes
can be determined experimentally, as well as the less exact
positions and scope of those regions where the different tran-
scription factors bind to the DNA. Although the experimental
approach is very difficult and slow, and in most cases gives
a rather ambiguous result, it soon had yielded enough data to
start collecting them into databases (2,3). From the bioinform-
atic point of view, it was easy to suppose that predicting
TFBSs would simply imply finding matches between experi-
mentally known TFBSs and the promoter sequence. It became
clear very soon, however, that it was more complicated, and
even using sophisticated methods for defining TFBSs such as
position-specific weight matrices gave unsatisfactory results in
most cases (4).
Besides the experimental approaches to identify TFBSs in
the promoter regions, several mostly string-based statistical
methods have been developed to allow their ab initio predic-
tion. As input, these methods can use promoter sequences of
a set of either co-regulated or orthologous genes. The first
approach has been applied with some success for example
in yeast, where the in silico data are in good agreement
with either the experimental results (5) or a set of well-
characterized promoters (6,7). The second approach where
a set of orthologous promoters are used to find conserved
or over-represented motifs is called phylogenetic footprinting
(8). Several examples prove that this is a good starting point to
discover TFBSs (9–12).
The lack of a publicly accessible orthologous promoter
database has hindered the widespread use of the phylogenetic
footprinting approach. The best known Eukaryotic Promoter
Database (EPD) (13) focuses rather on the specific features of
*To whom correspondence should be addressed. Tel: +36 28 526112; Fax: +36 28 526101; Email: barta@abc.hu
Present address:
Csaba P. Ortutay, Institute of Medical Technology, University of Tampere, F-33014 Tampere, Finland
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Nucleic Acids Research, Vol. 33, Database issue ª Oxford University Press 2005; all rights reserved
D86–D90 Nucleic Acids Research, 2005, Vol. 33, Database issue
doi:10.1093/nar/gki097

the promoter regions, such as transcription start sites (TSSs),
TATA-boxes, etc. than on defining orthologous sets of
promoters. The Hematopoiesis Promoter Database (HemoPDB)
(14) contains linked orthologous promoters, but only of those
genes that are involved in hematopoiesis. There are several
methods to cluster orthologous genes such as the COG data-
base at the National Center for Biotechnology Information
(NCBI) (15), the INPARANOID program (16) or the
OrthoMCL (17). They are all based on an all-against-all
BLAST search of protein sequences, thus it is difficult to
use their data to retrieve promoter sequences. The Ensembl
Genome Browser (18) provides links to orthologous genes,
but they are limited to the species involved in the Ensembl
system. The CORG database (19) also provides orthologous
promoter sequences, but initially only from the human and
mouse genomes and potentially from the species involved in
the Ensembl system.
We believe that all attempts to identify functionally import-
ant motifs in upstream cis-regulatory regions of genes can
largely benefit from a collection of orthologous promoter
sequences. Once we have a set of orthologous upstream
regions, we can locate known TFBSs, or try to predict
new ones applying ab initio methods, or simply search for
conserved regions. Several methods and databases are available
to carry out these tasks (20), but they always require a set of
orthologous genes.
We present here the first version of the Database of Ortho-
logous Promoters (DoOP), which provides clusters of ortho-
logous putative promoters within the phylum Chordata and
kingdom Viridiplantae. The chordate and plant sections
of DoOP are based on the NCBI gene annotation (21) of
the human and Arabidopsis thaliana genomes, respectively.
We use the first or the first two exons of genes as a query in a
BLAST (22) search to find orthologous first exons. Sub-
sequently, we extract the 500, 1000 and 3000 bp upstream
regions of these orthologous first exons to build the DoOP
clusters. A flow chart depicting the generation of the DoOP
databases can be seen in Figure 1. The aim of the DoOP is to
provide researchers with sets of orthologous promoters
to facilitate the analysis of regulatory regions, including the
discovery of conserved non-coding motifs.
CONSTRUCTION OF THE DoOP DATABASE
Searching for orthologs
We chose Homo sapiens (NCBI Human Build 34 available
from ftp://ftp.ncbi.nih.gov/genomes/H_sapiens) and A.thali-
ana (ftp://ftp.ncbi.nih.gov/genomes/Arabidopsis_thaliana/) as
reference species for the chordate and plant databases, respect-
ively. The first critical step of the database creation was to
choose the most suitable query sequences. We analyzed the
genome annotations provided by the NCBI, and decided to
divide the query types into six main categories. These types of
queries are distinguished according to the starting position of
the first mRNA exon with respect to the protein-coding (CDS)
sequence and the length of the first coding exon (Figure 2).
In the BLAST searches we used coding sequences whenever it
was possible (Figure 2, types 1–4), because the 50-untranslated
region (50-UTR) sequences are usually less conserved than
the coding regions. However, in types 5n and 6n, we had to
use the sequence of the annotated 50 fully UTR exons. To
improve sensitivity, we used the sequences of the first two
exons (type 6n) or the first two coding exons (types 2 and 4)
if the length of the first exon was <50 bp (Figure 2).
For building the local BLAST databases, we used the gss,
htgs, nt and wgs sections of the NCBI BLAST databases
(ftp://ftp.ncbi.nih.gov/blast/db/) and the whole genome
sequences of mouse, rat, fugu, zebrafish from Ensembl
(ftp://ftp.ensembl.org/pub) that were available at the end of
March 2004. We built two databases by removing all mRNA
(cDNA) sequences from both, and the non-chordate sequen-
ces from the chordate database and the non-Viridiplantae
sequences from the plant database utilizing the NCBI
taxid–gi number, phylum–kingdom assignment system (ftp://
ftp.ncbi.nih.gov/pub/taxonomy/). In the case of the reference
species, the 3000 bp upstream region with the first or the first
DIALIGN
RepeatMasker
sequences
Figure 1. The data flow of the generation of the chordate DoOP database.
The same method is used in the case of the plant DoOP database, except the
source BLAST database comes from all Viridiplantae sequences and the query
sequences are generated based on the NCBI A.thaliana annotation.
Figure 2.Different types of genes according to the positions of the first mRNA
and coding (cds) exons. The types 5 and 6 fall into different subcategories based
on the number of the first coding exon. If it is the second as in this figure, we call
it type 52 or 62, but otherwisewe are referring to themgenerally as 5n or 6n. The
positions of the query sequences relative to the first exons aremarkedwith green
boxes,while the 500, 1000 and 3000bp upstream regions that have been put into
the database are marked with red boxes.
Nucleic Acids Research, 2005, Vol. 33, Database issue D87

two exons corresponding to the selected query sequence was
used in the database. The details of the database creation,
searching and processing can be found at the DoOP web
pages (http://doop.abc.hu/details.html).
Processing the database
We analyzed the results of BLAST searches using Perl scripts
utilizing BioPerl modules (23). First, we had made a results
table (Figure 1) using a minimum threshold value of 50% for
identity and 75% for the combined hit length compared to the
query length. In the next step, we selected the best suitable
orthologous hits with the following simple algorithm. In each
species that had more than one hit, the hit with the best score
was chosen. If there were two or more hits with identical
scores from a species, the sequence that extended furthest
to the 50 direction relative to the hit was chosen (i.e. the
one that had a longer upstream region). After the most ortho-
logous hits were determined, 500, 1000 and 3000 bp long
upstream sequences were extracted from the original database
sequences if available.
In this paper we refer to the set of orthologous sequences as
a cluster. In order to find conserved regions within the clusters,
we generated multiple alignments. Since local alignments are
better for this purpose, we chose the DIALIGN2 program that
implements a segment-based alignment algorithm (24). The
conserved motifs were extracted from the consensus
sequences of the DIALIGN2 results using a Perl script. The
putative interspersed repeats were identified in the cluster
sequences using the RepeatMasker program (A. F. A. Smit
and P. Green, http://repeatmasker.genome.washington.edu/)
with the appropriate repeat libraries obtained from Repbase
Update (25). Positions of the TSSs as annotated in the EPD
database were determined in the two reference species using a
BLAST-based pairing of EPD sequences with the correspond-
ing DoOP entry. A similar method was used for the chordate
database to retrieve Ensembl links and definitions of genes.
The positions of the repeats and the conserved motifs along
with other information such as the positions of the annotated
50-UTRs for types 3 and 4 (Figure 2), and the TSSs were
compiled together in a graphics file using BioPerl. The data
obtained during database processing were fed into a MySQL
database.
CONTENTS OF THE CURRENT RELEASE
Release 1.0 of DoOP databank contains practically two
distinct databases, the plant and the chordate. They both reflect
the sequence and annotation data that were available at the end
of March 2004. The plant database contains 7548 clusters that
have sequences from at least two species. This number refers
to the clusters containing up to 500 bp promoter regions. The
number is lower in the case of the 1000 and 3000 bp clusters
(1088 and 973, respectively), because many hits, especially
those from the Whole Genome Shotgun sequences section,
contain <500 bp of promoter sequence depending on the posi-
tion of the hit within the sequence entry. Most of the plant
clusters contain sequences only from A.thaliana and Brassica
oleracea. There are only 1096 clusters that contain promoter
sequences of at least one species outside the Brassicaceae
family. The chordate database contains 21 061 500 bp,
20 009 1000 bp and 18 911 3000 bp promoter region clusters.
There are 15 178 clusters where at least one entry exists from
a family other than Hominidae. A detailed database statistics
is available in the DoOP web pages.
DATABASE ACCESS AND WEB CONTENT
The information that is stored in the DoOP databases can be
accessed freely from the DoOP website at http://doop.abc.hu/.
After selecting the chordate or the plant database section, users
can search for a specific cluster or for a group of clusters. There
are four text search fields, where users can enter a specific
cluster ID, or a scientific taxon name, or a species taxonomy
ID (taxid) from the NCBI Taxonomy database, or a keyword to
search in the definition lines of the clusters. It is also possible to
select a cluster (i.e. a gene) by a fast BLAT (26) search using
any cDNA sequence from the reference species (H.sapiens or
A.thaliana) as a query. In the next step users can select any
cluster for further viewing from the results list. In the cluster
view (Figure 3), among other data there is a graphical repre-
sentation of the cluster sequences showing the species names,
possible 50-UTR and TSS in the reference sequence, any repe-
titive sequences and conserved motifs. By default the cluster
view starts with the cluster of 500 bp long promoter regions
(Figure 3), but users can select the 1000 or 3000 bp promoter
clusters, too. It is also possible to view and save the multiple
alignments and the FASTA format sequences of the clusters
and the sequences of the conserved motifs.
CONCLUSIONS AND PERSPECTIVES
The primary aim of the DoOP is to provide an easy way to
obtain clusters of orthologous promoter sequences. These clus-
ter sequences can be a source for a more detailed analysis of
possible promoter elements, including TFBSs. The data
provided by the DoOP web server is also suitable for a draft
view of the conserved motifs in the promoter regions of
different genes.
Our method to find orthologous upstream regions relies on
two important sources. The first is the genome annotation of
the reference species (H.sapiens and A.thaliana). Undoubt-
edly, we need to have the exact positions of the first exons,
including the 50-UTR regions. We know that the annotation
we had used is far from being complete. For example, there are
still a lot of annotated genes (4031 in the human genome
database) where the starting points of the first coding and
mRNA exons are the same (types 1 and 2 in Figure 2),
which is very unlikely in vivo. We expect that the genome
annotation of these two reference species will be steadily
bettered, thereby improving subsequent releases of DoOP as
well. The second important factor of our work is the available
genomic sequences, especially the number of complete gen-
omes. It is proven that the more the sequences are available for
phylogenetic footprinting, the better the result that can be
achieved (27). The situation for the chordate database is
quite promising, since already several mammalian whole gen-
ome sequences have been determined, and there are quite
a few ongoing projects, too. Unfortunately, there are only
two completed genomes from plants, A.thaliana and rice.
We hope however that the sequence data appearing from the
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ongoing plant genome sequencing projects such as Medicago
truncatula, Populus trichocarpa or tomato, will improve the
plant DoOP database significantly.
This is the first release of the DoOP database. The next
minor update will be available by the end of 2004. We plan
to generate two databases per year in the future, which will
reflect the sequence and annotation data available at the end
of March and September. Our plans to improve the DoOP
database include refining the BLAST searches to be more
sensitive, using other methods for multiple alignments and
to detect conserved motifs within the clusters, or relying
more on the data of other databases such as Ensembl.
Figure 3. Examples of the DoOP dataviews. In the picture of the cluster the boxes numbered from m1 to m13 show the conserved motifs, the black box shows
a predicted repetitive element, while the blue box shows the 50-UTR.
Nucleic Acids Research, 2005, Vol. 33, Database issue D89

The method we have developed here could also be applicable
for the generation of DoOP databases based on other reference
species, such as the yeast or the fruit fly.
ACKNOWLEDGEMENTS
This work was supported by Biotechnology 2001 grant
BIO-0117/01 from the Central Technical Development
Target Funds (Hungary). The recipients of ‘Bolyai
Fellowship’ awarded by the Hungarian Academy of
Sciences were E.B. and G.T.
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