Published online 15 November 2007 Nucleic Acids Research, 2008, Vol. 36, Database issue D107–D113
doi:10.1093/nar/gkm967
ORegAnno: an open-access community-driven
resource for regulatory annotation
Obi L. Griffith
1,
*, Stephen B. Montgomery
2
, Bridget Bernier
1
, Bryan Chu
1
,
Katayoon Kasaian
1
, Stein Aerts
3
, Shaun Mahony
4
, Monica C. Sleumer
1
,
Mikhail Bilenky
1
, Maximilian Haeussler
5
, Malachi Griffith
1
, Steven M. Gallo
6
,
Belinda Giardine
7
, Bart Hooghe
8
, Peter Van Loo
3
, Enrique Blanco
9
, Amy Ticoll
10
,
Stuart Lithwick
10
, Elodie Portales-Casamar
10
, Ian J. Donaldson
11
, Gordon
Robertson
1
, Claes Wadelius
12
, Pieter De Bleser
8
, Dominique Vlieghe
8
,
Marc S. Halfon
6
, Wyeth Wasserman
10
, Ross Hardison
7
, Casey M. Bergman
11
,
Steven J.M. Jones
1
and The Open Regulatory Annotation Consortium
y
1
Canada’s Michael Smith Genome Sciences Centre, British Columbia Cancer Agency, Vancouver, BC V5Z 4S6,
Canada,
2
Wellcome Trust Sanger Institute, CB10 1SA Hinxton, UK,
3
VIB Department of Molecular and
Developmental Genetics, Katholieke Universiteit Leuven, 3000 Leuven, Belgium,
4
Department of Computational
Biology, School of Medicine, 3501 Fifth Avenue, University of Pittsburgh, Pittsburgh, PA 15213, USA,
5
DEPSN, Institut Alfred Fessard, CNRS, 91198 Gif-sur-Yvette, France,
6
New York State Center of Excellence in
Bioinformatics and the Life Sciences, Buffalo, NY 14203,
7
Center for Comparative Genomics and Bioinformatics,
Department of Biochemistry and Molecular Biology, The Pennsylvania State University, University Park, PA 16802,
USA,
8
VIB Department for Molecular Biomedical Research, Ghent University, 9052 Ghent, Belgium,
9
Bioinformatics and Genomics Program, Centre de Regulacio´ Geno`mica. Dr Aiguader 88, 08003 Barcelona,
Catalonia, Spain,
10
Centre for Molecular Medicine and Therapeutics, University of British Columbia, Vancouver,
BC V5Z 4H4, Canada,
11
Faculty of Life Sciences, University of Manchester, Michael Smith Building, Oxford Road,
Manchester, M13 9PT, UK and
12
Department of genetics and pathology, Uppsala University, SE-75185 Uppsala,
Sweden
Received September 15, 2007; Revised October 16, 2007; Accepted October 17, 2007
ABSTRACT
ORegAnno is an open-source, open-access data-
base and literature curation system for community-
based annotation of experimentally identified DNA
regulatory regions, transcription factor binding sites
and regulatory variants. The current release com-
prises 30 145 records curated from 922 publications
and describing regulatory sequences for over 3853
genes and 465 transcription factors from 19 species.
A new feature called the ‘publication queue’ allows
users to input relevant papers from scientific
literature as targets for annotation. The queue
contains 4438 gene regulation papers entered by
experts and another 54 351 identified by text-mining
methods. Users can enter or ‘check out’ papers
from the queue for manual curation using a series of
user-friendly annotation pages. A typical record
entry consists of species, sequence type, sequence,
target gene, binding factor, experimental outcome
and one or more lines of experimental evidence.
An evidence ontology was developed to describe
and categorize these experiments. Records are
cross-referenced to Ensembl or Entrez gene
identifiers, PubMed and dbSNP and can be
visualized in the Ensembl or UCSC genome
browsers. All data are freely available through
search pages, XML data dumps or web services
at: http://www.oreganno.org.
*To whom correspondence should be addressed. Tel: +1 604 707 5900 x. 5401; Fax: +1 604 876 3561; Email: obig@bcgsc.ca
Correspondence may also be addressed to Stephen Montgomery. Tel: +44 1223 834244 (ext 7297); Fax: +44 1223 494919;
Email: sm8@sanger.ac.uk; Steven J.M. Jones. Tel: +1 604 877 6083; Fax: +1 604 876 3561; Email: sjones@bcgsc.ca
y
The complete list of The Open Regulatory Annotation Consortium members has been listed at the end of the article.
The authors wish it to be known that, in their opinion, the first two authors should be regarded as joint First Authors.

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.

BACKGROUND
A consequence of the escalating pace of genomic
sequencing has been the requirement for novel methodol-
ogy and large-scale efforts to interpret and annotate
sequence function. Initial efforts to achieve this were
primarily focused on identifying protein-coding genes,
RNA genes and repetitive DNA, since the rules governing
their presence are generally tractable. However, less
annotated, due to their small size and variability, gene
regulatory sequences are widely regarded to be at least as
important to our understanding of biological systems. To
aid in their identification, computational techniques such
as phylogenetic footprinting, transcription factor (TF)-
binding matrices, and motif clustering have been devel-
oped (1–3). Unfortunately, the predictive ability of such
methods has been difficult to assess without large, well-
described and comprehensive collections of biologically
validated regulatory sequences (3). Sets of cis-regulatory
sequences have been annotated by curation from the
primary literature and several databases have been
developed to collect and disseminate these sets (4–11).
However, these databases are often species- or process-
specific, and do not provide sufficient details about the
experiments or conditions under which function was
demonstrated, and in some cases require payment for
access. Data access is generally limited to web-based
search pages without any option for the programmatic
interaction essential to most bioinformatics studies.
Finally, they are typically ‘closed systems’ in that they
do not allow continued addition or annotation by the
research community and as such are not maintainable
over the long term without vast resources. We have
developed the Open Regulatory Annotation database
(ORegAnno) to overcome these challenges and support
research in regulatory biology (12). ORegAnno provides
standardized technologies for the long-term, community-
driven, open-access curation of cis-regulatory data. Here
we provide an update of developments on the ORegAnno
database and progress in the field of open regulatory
annotation.
OVERVIEW
ORegAnno (http://www.oreganno.org) is a database and
literature curation system for community-based annota-
tion of experimentally proven DNA regulatory regions,
transcription factor binding sites (TFBS) and regulatory
variants. A ‘publication queue’ allows papers of interest
to be added to the system for future curation. Thus both
regulatory papers and their regulatory sequences are
managed in the system. ORegAnno is based on open-
source technology and is comprised of a MySQL database
with a Java-based web application that indexes new
annotations using the Lucene search engine (http://
lucene.apache.org/) and provides programmatic access
to the underlying data using Hibernate (http://www.
hibernate.org/) and SOAP Web Services. Figure 1 outlines
the annotation process and information flow. Users in the
gene regulation community can enter or ‘check out’ papers
from the publication queue for detailed manual curation,
using a series of annotation pages. A typical record entry
consists of species, sequence type, sequence (plus sufficient
flanking sequence for genome alignment), target gene,
binding factor, experimental outcome and one or more
detailed lines of experimental evidence demonstrating
function of the sequence. Records are cross-referenced to
Ensembl or Entrez Gene identifiers, PubMed and dbSNP
(for regulatory polymorphisms). Before committing a
record to the database, ORegAnno performs a number
of error checks (e.g. that the sequence has not been
entered previously) and asks the user to verify its contents.
A BLAST-based mapping agent then assigns genome
coordinates to each sequence, allowing it to be viewed as a
track in the Ensembl or UCSC genome browsers. Once
finished with a paper, a user will then ‘close’ it in the queue
and assign an annotation result (success, neutral or
failure). Existing records can be updated, commented
and scored (positive if verified as correct; negative if a
problem is identified) by any registered user or deprecated
and replaced by a ‘Validator’ user. The complete database
or any subset can be searched or downloaded in a number
of formats or accessed programmatically.
RECENT DEVELOPMENTS
New entries
Since ORegAnno was first released, the collection has
grown by
10-fold from 2691 to 30 145 records. This total
includes 15 738 regulatory regions, 14 229 TFBSs and 178
regulatory variants (polymorphisms and haplotypes) from
19 species (Table 1). A total of 29 433 records have been
mapped to one of 14 species representing a mapping
success rate of
98%. New additions were incorporated
from external datasets including a large set of human
promoters (13), the REDfly resource (9), HBB and
Erythroid modules (14,15), the Vista Enhancer dataset
(11), ChIP–chip sites for CTCF(16) and multiple yeast
TFs(17,18) and ChIP-Seq sites for STAT1 (19) and REST
(20). Apart from the 11 external datasets currently in
ORegAnno, extensive manual curation of the literature
has produced an additional 1293 original sequence
records. A large number of annotations were entered
during the RegCreative Jamboree (http://www.dmbr.
ugent.be/bioit/contents/regcreative/) at which 130 scienti-
fic articles were examined in depth with 96 papers meeting
the criteria for annotation and resulting in 501 new
regulatory sequence records. In total, 922 publications
have been curated by 45 contributing users (from >300
registered users). The complete set of records contain
regulatory sequences for over 3853 genes and 465 TFs,
describe 41 856 experimental sources of evidence referen-
cing 31 different cell types and are further annotated by
49 807 user-comments. The majority of records (98.9%)
had positive experimental outcomes (i.e. the experiments
demonstrated the sequence to be functional) but a small
set of negative or neutral results have also been
catalogued.
D108 Nucleic Acids Research, 2008, Vol. 36, Database issue

Recent applications
The ORegAnno resource has proven useful for the
development of both computational and experi-
mental methods for the identification of novel TFBSs
and regulatory polymorphisms. One such approach,
called cisRED (http://www.cisred.org), uses multiple
motif discovery methods applied to sequence sets that
include up to 42 orthologous sequence regions from
Figure 1. Information flow for ORegAnno annotation process. (A) Data input. A publication queue allows papers from scientific literature to be
added to the system for future curation. Users in the gene regulation community can enter or ‘check out’ papers from the queue for detailed manual
curation using a series of user-friendly annotation pages. It is also possible to ‘batch upload’ complete datasets (e.g. external databases) using the
ORegAnno XML data exchange format. (B) Data storage and processing. All functionality of the ORegAnno web application depends on storage
and retrieval of data from an underlying MySQL relational database. Records are cross-referenced to PubMed, Entrez, Ensembl, dbSNP and eVOC
where appropriate. A BLAST-based mapping agent assigns genome coordinates to each sequence. (C) Visualization. All mapped ORegAnno records
can be viewed as custom tracks in the Ensembl or UCSC genome browsers. Most records are also available as official tracks in UCSC. (D) Data
access. The web application provides an advanced search page for the entire record set. Each record page represents a complete summary of the data
for a verified regulatory sequence. Nightly data dumps are posted in XML format. Programmatic interaction with ORegAnno is available through
web services using the Perl SOAP modules.
Nucleic Acids Research, 2008, Vol. 36, Database issue D109

vertebrates (21). The collection of known binding sites in
ORegAnno has proved an invaluable resource for the
parameter optimization and estimates of accuracy for this
resource. In another study, the set of known regulatory
SNPs (rSNPs) in ORegAnno was used to investigate and
prioritize various properties that may be important for
identifying novel regulatory polymorphisms (22). The
discriminatory potential of 23 properties related to gene
regulation and population genetics was assessed by
comparing these known rSNPs to a set of SNPs of
unknown function (ufSNPs). A support vector machine
classifier using these properties was able to discriminate
rSNPs from ufSNPs with a sensitivity and specificity of
82% and 71%, respectively (22). Finally, ORegAnno has
also served a critical role in the development of new
experimental approaches such as ChIP-Seq. ChIP-Seq is
similar to the well-described ChIP–chip method (23)
except that DNA fragments isolated from the protein–
DNA complex are identified by DNA sequencing instead
of hybridization to a tiling microarray. The approach was
first demonstrated for the STAT1 TF in interferon-
g-stimulated HeLa S3 cells (19). A set of 41 experimentally
verified sites representing 34 genomic loci for STAT1
binding were first collected from the literature and entered
into ORegAnno (Oreganno dataset: OREGDS00006).
Stimulated ChIP-Seq peaks were found to overlap 24 of
34 of these loci, suggesting a sensitivity of
71%. For the
ORegAnno STAT1 sites shown to be functional in HeLa
cells specifically, sensitivity was 100%. The collection of
known STAT1 sites and binding matrices derived from
them also allowed a set of high-confidence novel STAT1-
binding sites to be determined and entered into
ORegAnno as their own dataset (OREGDS00007). This
iterative process, whereby existing data drives the creation
of new data, demonstrates the utility and flexibility of the
ORegAnno system.
Publication queue
An important new feature of ORegAnno called the
‘publication queue’ was created as a literature manage-
ment system to allow registered users to input relevant
papers from the scientific literature as targets for annota-
tion. All that is required to enter a publication is a valid
PubMed identifier. Optionally, a TF can be specified,
allowing users to later search the queue for papers related
to TFs of interest. Normally, publications are added to the
queue with an entry type of ‘expert entry’, indicating that
a human expert reviewed the paper and found it to be
relevant. However, it is also possible to enter ‘text-mining
entry’ papers (see below). A publication enters the queue
with an initial state of ‘pending’. Any user can then ‘open’
the publication and begin the annotation process. Once
annotated, the paper is either ‘closed’ or reset to ‘pending’
if annotation work remains. Free-form comment fields are
optional for each change of state. However, when a
publication is closed, one of several standardized closure
comments must be chosen (success – addition of new
records, failure – did not describe regulatory element,
etc.). These allow the overall success rate and failure
causes to be tracked. The queue can be queried on a
number of fields including user, PubMed id, title, abstract,
author, publication date and journal. Search results can be
optionally filtered by state (pending, open or closed), TF,
entry type (expert or text mining) or text-mining score.
Each queue record contains a history of all state changes
and comments as well as links to the publication’s
PubMed abstract. The current set of ‘expert entry’
papers in the queue was obtained from existing sources
of curated publications including the Drosophila DNase I
Footprint Database (8), REDfly (9), a catalog of
regulatory elements for muscle-specific regulation of
transcription (24,25), ABS (4), TRED (7), ooTFD (26),
Table 1. Current content of ORegAnno database
Species Regulatory
haplotype
Regulatory
polymorphism
Regulatory
region
Transcription factor
binding site
Totals
Bos taurus 11
Caenorhabditis briggsae 21 21
Caenorhabditis elegans 13 194 207
Ciona intestinalis 7 7 4
Ciona savignyi
Cricetinae 33
Danio rerio 22 4
Drosophila melanogaster 680 1415 2095
Gallus gallus 9 37
Halocynthia roretzi 6
Homo sapiens 6 171 14 948 7834 22 959
HIV 1 2
Mus musculus 1 55 215 271
Oryctolagus cuniculus
Rattus norvegicus 15 99 114
Saccharomyces cerevisiae 1 4392 4393
Takifugu rubripes
Xenopus laevis 1 2
Xenopus tropicalis
Totals (19 species) 7 171 15 738 14 229 30 145
D110 Nucleic Acids Research, 2008, Vol. 36, Database issue

DBTGR (10) or added manually by individual
ORegAnno users from literature searches and review
articles. The expert entry queue currently contains 4438
gene regulation papers of which 3478 are open or pending
and 960 are closed.
Development of text-mining strategies and the
‘text-mining queue’
The publication queue represents an unprecedented
resource for researchers interested in developing text-
mining approaches to identify papers involved in gene
regulation and/or extract regulatory data from these
papers. We used both the ‘success’ and the ‘failure’
papers from the ‘expert-entry’ queue to validate and
compare different vector space models (27) for
cis-regulatory document retrieval (Aerts and coworkers,
manuscript in preparation). The model with the best
performance in terms of sensitivity and specificity was
chosen to rank the entire corpus of PubMed abstracts. By
manually curating uniformly distributed samples from the
top 100 000 scoring abstracts, a cut-off was set at
58 000
so that the positive predictive value (PPV) of top-scoring
abstracts reached 50%, a success rate similar to that
achieved during the RegCreative Jamboree (54%), and
judged satisfactory by the Jamboree participants. These
58 000 papers, containing an estimated 29 000 papers that
will result in regulatory annotations, have been added
to the ORegAnno queue (54 351 new additions after
removing duplications). We estimate that this large
cis-regulatory text corpus will require around 15–30
person-years to be fully curated. Therefore, the Open
Regulatory Annotation Consortium is actively pursuing
research in text-mining techniques to identify the actual
cis-regulatory sequences, the species and the target gene
automatically from the full text papers. In a pilot study,
sequences were extracted from full-text articles for papers
in the ORegAnno expert-based queue and the top 4501
papers from the text-mining-based queue. When
comparing the automatically extracted data with the
collection of manual ORegAnno annotations, this study
achieved a reasonably high PPV (62%) at the sequence
level, showing that automatic draft annotation of
cis-regulatory elements is indeed feasible by text-mining
(Aerts and coworkers, manuscript in preparation). Such
draft annotations should help accelerate the manual
curation and can also serve directly as benchmark data
to validate cis-element prediction algorithms.
Ontologies in ORegAnno
The ORegAnno evidence ontology is a simple ontology
of evidence classes, types and subtypes for describing
experiments that demonstrate the identity and/or function
of regulatory sequences and their factors. These lines
of evidence capture critical details from primary experi-
ments and allow end users to filter the ORegAnno
sequence set, based on their own criteria for experi-
mental credibility. The ontology has been considerably
extended since last published, and currently consists of
six classes (e.g. Transcription regulator site), 14 evidence
types (e.g. Reporter gene assay) and 72 evidence subtypes
(e.g. Transient transfection luciferase assay). This ontol-
ogy has been adopted by the PAZAR resource (28) and
is being developed in collaboration with that group
using Prote´ ge´ (http://protege.stanford.edu/). The complete
evidence ontology can be obtained in XML format
(http://www.oreganno.org/oregano/evidence.xml) or as a
Prote´ ge´ project file (http://www.pazar.info/ontologies/
newevidence.pprj). Within each line of evidence, a user
can also specify the cell type in which experiments were
conducted using the eVOC cell type ontology (29). We are
working to incorporate additional Ontologies such as
the BRENDA Tissue Ontology, and improvements to the
Sequence Ontology are currently being developed for the
cis-regulatory domain.
Other improvements
The ORegAnno website has been updated to use Ajax
technology, improving the ease of annotation. Ajax
improves a web page’s usability by exchanging small
amounts of data with the server behind the scenes, so that
the entire web page does not have to be reloaded each time
the user requests a change (http://www.xul.fr/en-xml-
ajax.html). A detailed case study has been added to the
help pages to guide users through the entire process of
annotating a paper. Annotation pages have been
improved so that individual ‘help bubbles’ are available
next to each field. Additional web services methods have
been created to allow programmatic access to the
publication queue and genome mappings.
DATA ACCESS
The website (http://www.oreganno.org) provides access to
an advanced search page for the entire record set, the
publication queue, simple tools for scanning or extracting
sequences, database dumps and extensive help documen-
tation. Each record page represents a complete summary
of the data for a verified regulatory sequence along with
links to external sources such as UCSC, Ensembl and
PubMed. All data are freely available in a number of
formats without any user registration. Users are required
to register and login only if they wish to add records,
comments or scores. Nightly data dumps of the database
are posted in XML format on the website. Human (hg18)
and fly (dm3) records are available through the UCSC
genome browser (http://genome.ucsc.edu/) as a standard
track under the ‘Expression and Regulation’ tab. Mouse
(mm8), worm (ce4) and rat (rn4) are available through
the UCSC ‘genome-test’ browser (http://genome-test.
cse.ucsc.edu/). The ORegAnno dataset is also in the
process of being incorporated into the PAZAR database
(760 records to date). Programmatic interaction with
ORegAnno is available through web services using the
Perl SOAP modules (see ‘Dump’ page for examples).
Requests for the entire database (e.g. a MySQL dump) or
other formats can be addressed to the authors. ORegAnno
records are typically mapped to only the most current
genome build for each species as provided by UCSC
(e.g. hg18 for human). However, mapping can easily be
performed for any other genome build upon request.
Nucleic Acids Research, 2008, Vol. 36, Database issue D111

A mailing list exists for updates and user assistance
(oreganno@bcgsc.ca). The ORegAnno web application is
available open-source under the Lesser GNU Public
License at https://oreganno.dev.java.net/.
ACKNOWLEDGEMENTS
We thank the Open Regulatory Annotation Consortium
for their continuing efforts to improve this resource
through manual curation and record validation. We also
thank the owners of regulatory sequence databases that
made their data available for inclusion in ORegAnno.
This work was funded by British Columbia Cancer Foun-
dation; Genome Canada; Genome British Columbia;
European Network of Excellence (ENFIN); BioSapiens
Network of Excellence; Research Foundation – Flanders
(FWO); The Pleiades Promoter Project; Michael Smith
Foundation for Health Research to O.L.G., M.C.S., M.G.
and S.J.M.J.; Canadian Institutes of Health Research to
O.L.G.; European Molecular Biology Laboratory to
S.B.M.; Marie Curie Early Stage Research Training
Fellowship (MEST-CT-2004-504854) to M.H.; Natural
Sciences and Engineering Research Council to S.B.M.,
and M.G.; Research Foundation – Flanders (FWO) to
P.V.L.; Swedish Research Council to C.W. Funding to
pay the Open Access publication charges for this article
was provided by Genome Canada and Genome British
Columbia.
Conflict of interest statement. None declared.
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D112 Nucleic Acids Research, 2008, Vol. 36, Database issue

THE OPEN REGULATORY ANNOTATION
CONSORTIUM MEMBERS
Amy Ticoll, Andy Schroeder, Arun Ramani, Bart
Hooghe, Belinda Giardine, Boris Adryan, Bridget
Bernier, Casey Bergman, Claes Wadelius, Daniel Sobral,
Debra Fulton, Denis Thieffry, Dominique Vlieghe, Elodie
Portales-Casamar, Enrique Blanco, Erin D. Pleasance,
Florian Leitner, Gordon Robertson, Hedi Peterson, Helge
Roider, Ian J. Donaldson, Ildefonso Cases, Jean Imbert,
Jean-Valery Turatsinze, Jonathan Mudge, Katayoon
Kasaian, Maggie Zhang, Malachi Griffith, Marc Halfon,
Maximilian Haeussler, Misha Bilenky, Monica Sleumer,
Nathalie Theret, Nikiforos Karamanis, Obi Griffith, Paco
Hulpiau, Peter Van Loo, Pieter De Bleser, Priit Adler,
Ross Hardison, Shaun Mahony, Stein Aerts, Stephen
Montgomery, Steven J.M. Jones, Steven M. Gallo, Wyeth
Wasserman, Yves Moreau.
Nucleic Acids Research, 2008, Vol. 36, Database issue D113