=Paper=
{{Paper
|id=Vol-1515/poster5
|storemode=property
|title=2015 Disease Ontology update: DO's expanded curation activities to connect disease-related data
|pdfUrl=https://ceur-ws.org/Vol-1515/poster5.pdf
|volume=Vol-1515
|dblpUrl=https://dblp.org/rec/conf/icbo/MitrakaS15
}}
==2015 Disease Ontology update: DO's expanded curation activities to connect disease-related data==
https://ceur-ws.org/Vol-1515/poster5.pdf
2015 Disease Ontology update: DO’s expanded curation activities
to connect disease-related data
Elvira Mitraka1 and Lynn M. Schriml1,*
1
Institute
for
Genome
Science,
University
of
Maryland
School
of
Medicine,
Baltimore,
MD,
USA
ABSTRACT and visualize the Disease Ontology and disease concept
The
Human
Disease
Ontology
is
a
widely
used
biomedical
resource,
data.
which
standardizes
and
classifies
common
and
rare
human
diseases.
Its
latest
iteration
makes
use
of
the
OWL
language
to
facilitate
easier
The latest version of DO has close to 9,000 disease terms,
curation
between
a
variety
of
working
groups
and
to
take
advantage
of
more than 16,000 synonyms and almost 39,000 cross-
the
analyses
available
using
OWL.
The
DO
integrates
disease
concepts
references to other biomedical resources. Those resources
from
ICD-‐9,
ICD-‐10,
the
National
Cancer
Institute
Thesaurus,
SNOMED-‐
include the ICD-9 and ICD-10, the National Cancer Institute
CT,
MeSH,
OMIM,
EFO
and
Orphanet.
The
DO
Team
is
focused
on
ena-‐
bling
mapping
and
curation
of
large
disease
datasets
for
major
Bio-‐ (NCI) Thesaurus (Sioutos et al., 2007), SNOMED-CT
medical
Resource
Centers
and
integration
of
their
disease
terms
into
(Donnelly, 2006) and MeSH (https://www.
DO.
Constant
updates
and
additions
to
the
ontology
allow
for
coverage
nlm.nih.gov/mesh/MBrowser.html) extracted from the Uni-
of
the
vast
field
of
human
diseases.
By
having
close
collaborations
with
a
variety
of
research
groups,
such
as
MGD,
EBI,
NCI,
the
Disease
Ontol-‐ fied Medical Language System (UMLS) (Bodenreider,
ogy
has
established
itself
as
the
go-‐to
tool
for
human
disease
curation.
2004) based on the UMLS Concept Unique Identifiers for
Implementing
a
combination
of
informatic
tools
and
manual
curation
each disease term. DO also includes disease terms extracted
DO
ensures
that
it
maintains
the
highest
standard
possible.
directly from Online Mendelian Inheritance in Man
(OMIM) (Ambereger et al., 2011), the Experimental Factor
1 INTRODUCTION Ontology (EFO, http://www.ebi.ac.uk/efo/) and Orphanet
The Disease Ontology (DO) (Schriml et al., 2012) is the (Maiella et al., 2013).
core disease data resource for the biomedical community. The DO files are available in both OBO and OWL format
Human disease data is a cornerstone of biomedical research from DO’s SourceForge site (http://sourceforge.net/p/ dis-
for identifying drug targets, connecting genetic variations to easeontology/code/HEAD/tree/trunk) and can be found at
phenotypes, understanding molecular pathways relevant to http://purl.obolibrary.org/obo/doid.obo and http:
novel treatments and coupling clinical care and biomedical //purl.obolibrary.org/obo/doid.owl. DO’s OBO and OWL
research. Consequently, across the multitude of biomedical files are also available from the OBO Foundry
resources there is a significant need for a standardized rep- (http://www.obofoundry.org/cgi-bin/detail. cgi?id=disease
resentation of human disease to map disease concepts across ontology) and GitHub
resources, to connect gene variation to phenotypes and drug (https://github.com/obophenotype/human-disease-
targets and to support development of computational tools ontology/tree/master/src/ontology).
that will enable robust data analysis and integration.
3 CURRENT WORK
2 CURRENT STATUS Due to the huge amount of data generated at an increasingly
DO has proven to be an invaluable genomics and genetic rapid pace, the genomics community is trying to streamline
disease data resource used for evaluating and connecting its data processing efforts. Ontologies are an avenue that
diverse sets of data, used by diverse curation groups to con- lead to this, but even they can become too big and unwieldy
nect human disease to animal models and genomic re- in their effort to capture all available data. There are in-
sources and used to informatically identify representative stances where the multitude of information captured is not
phenotype sets (Köhler et al., 2014; Schofield et al., 2010), needed.
functionally similar genes (Fang and Gough, 2013; Single- The Gene Ontology is one the most widely used ontology
ton et al., 2014), human gene and genome annotations (Peng and one of the most comprehensive. It covers the molecular
et al., 2013; Osborne et al., 2009), pathways, cancer variants functions, biological processes and location in cellular com-
(Wu et al., 2014) and immune epitopes (Vita et al., 2014). ponents of gene products, containing more than 40,000
The DO website (http://www.disease-ontology.org), is a terms. In order to make it more accessible and less resource
web-based application that allows users to query, browse, intensive the Gene Ontology Consortium has created slim
version of the ontology. These “GO slims” are smaller ver-
* To whom correspondence should be addressed: sions of GO that contain only a subset of the terms, repre-
lschriml@som.umaryland.edu
Copyright c 2015 for this paper by its authors. Copying permitted for private and academic purposes 1
Mitraka et al.
senting a general knowledge of a specific field, without go- Maiella,S., Rath,A., Angin,C., Mousson,F. and Kremp,O. (2013) Orphanet
ing too deep into the hierarchy. and its consortium: where to find expert-validated information on rare
Due to the breadth of its user base the DO team decided diseases. Rev. Neurol. (Paris), 169, S3–S8.
Osborne,J.D., Flatow,J., Holko,M,. Lin,S.M., Kibbe,W.A., Zhu,L.J., Da-
to create its own slim files, the DO Cancer Slim (Wu et al.,
nila,M.I., Feng,G. and Chisholm,R.L. (2009) Annotating the human ge-
2015) being the most prominent, containing terms needed
nome with Disease Ontology. BMC Genomics. 10 Suppl 1:S6.
by the pan-cancer community. In the same vein a DO MGI
Peng,K., Xu,W., Zheng,J., Huang,K., Wang,H., Tong,J., Lin,Z., Liu,J.,
slim is being created. It contains all the terms that were Cheng,W., Fu,D., Du,P., Kibbe,W.A., Lin,S.M. and Xia,T. (2013) The
modified or created during an intensive curatorial effort to Disease and Gene Annotations (DGA): an annotation resource for hu-
map concepts between DO and OMIM. It will give insight man disease. Nucleic Acids Res. 41:D553-D560.
into the overlap between DO and OMIM, as well as which Schofield,P.N., Gkoutos,G.V., Gruenberger,M., Sundberg,J.P. and Han-
disease types are more heavily featured in the MGD. Mean- cock, J.M. (2010) Phenotype ontologies for mouse and man: bridging
ing it can give even more information about which diseases the semantic gap. Dis. Model Mech., 3:281–289.
do not have a mouse model to represent them. Schriml,L.M., Arze,C., Nadendla,S., Chang,Y.W., Mazaitis,M., Felix,V.,
Feng,G. and Kibbe,W.A. (2012) Disease Ontology: a backbone for dis-
ease semantic integration. Nucleic Acids Res., 40, D940–D946.
4 UPCOMING WORK
Singleton MV, Guthery SL, Voelkerding KV, Chen K, Kennedy B, Mar-
Future plans include the definition of all disease terms in graf RL, Durtschi J, Eilbeck K, Reese MG, Jorde LB, Huff CD, Yandell
DO and the creation of DO slims for every major curation M (2014) Phevor combines multiple biomedical ontologies for accurate
project of all the MODs. These slims will enable DO users identification of disease-causing alleles in single individuals and small
to review the representation and classification of MOD as- nuclear families. Am. J. Hum. Genet., 94:599-610.
sociated diseases, to compare the diseases represented be- Sioutos,N., de Coronado,S., Ha.ber,M.W., Hartel,F.W., Shaiu,W.L. and
tween MODs and to compare the different animal models Wright,L.W. (2007) NCI Thesaurus: a semantic model integrating can-
associated with a particular disease or types of diseases cer-related clinical and molecular information. J. Biomed. Inform., 40,
30–43.
across species.
Vita,R., Overton,J.A., Greenbaum,J.A., Ponomarenko,J., Clark,J.D.,
Cantrell,J.R., Wheeler,D.K., Gabbard,J.L., Hix,D., Sette,A. and Pe-
ACKNOWLEDGEMENTS ters,B (2014) The immune epitope database (IEDB) 3.0. Nucleic Acids
This work was supported in part by the National Institute of Res. 43:D405-D412
Health – National Center for Research Resources Wu,T.J., Shamsaddini,A., Pan,Y., Smith,K., Crichton,D.J., Simonyan,V.
(R01RR025341) and NIH/NIGMS (R01 GM 089820-06). and Mazumder,R. (2014) A framework for organizing cancer-related
variations from existing databases, publications and NGS data using a
REFERENCES High-performance Integrated Virtual Environment (HIVE). Database
(Oxford), bau:022.
Alexandrescu,A. (2001) Modern C++ Design: Generic Programming and
Wu,T.J., Schriml,L.M., Chen, Q.R., Colbert,M., Crichton,D.J., Finney,R.,
Design Patterens Applied. Addision Wesley Professional, Boston.
Hu,Y., Kibbe,W.A., Kincaid,H., Meerzaman,D., Mitraka,E., Pan,Y.,
Amberger,J., Bocchini,C. and Hamosh,A. (2011) A new face and new
Smith,K.M., Srivastava,S., Ward,S., Yan,C. and Mazumder,R. (2015)
challenges for Online Mendelian Inheritance in Man (OMIM). Hum.
Generating a focused view of disease ontology cancer terms for pan-
Mutat., 32, 564–567
cancer data integration and analysis. Database (Oxford), bav:032.
Bodenreider,O. (2004) The Unified Medical Language System (UMLS):
integrating biomedical terminology. Nucleic Acids Res., 32, D267–
D270.
Donnelly,K. (2006) SNOMED-CT: the advanced terminology and coding
system for eHealth. Stud. Health Technol. Inform., 121, 279–290.
Fang, H. and Gough,J. (2013) DcGO: database of domain-centric ontolo-
gies on functions, phenotypes, diseases and more. Nucleic Acids Res.
41:D536-D544.
Köhler,S., Doelken,S.C., Mungall,C.J., Bauer,S., Firth,H.V., Bailleul-
Forestier,I., Black,G.C., Brown,D.L., Brudno,M., Campbell,J., FitzPat-
rick,D.R., Eppig,J.T., Jackson,A.P., Freson,K., Girdea,M., Helbig,I.,
Hurst,J.A., Jähn,J., Jackson,L.G., Kelly,A.M., Ledbetter,D.H.,
Mansour,S., Martin,C.L., Moss,C., Mumford,A., Ouwehand,W.H.,
Park,S.M., Riggs,E.R., Scott,R.H., Sisodiya,S., Van Vooren,S., Wap-
ner,R.J., Wilkie,A.O., Wright,C.F., Vulto-van Silfhout,A.T., de
Leeuw,N., de Vries,B.B., Washingthon,N.L., Smith,C.L., Wester-
field,M., Schofield,P., Ruef,B.J., Gkoutos,G.V., Haendel,M., Smedle,
D., Lewis,S.E. and Robinson,P.N. (2014) The Human Phenotype On-
tology project: linking molecular biology and disease through pheno-
type data. Nucleic Acids Res. 4:D966-D974.
2 Copyright c 2015 for this paper by its authors. Copying permitted for private and academic purposes