The document discusses the integration of multiple orthology databases to enhance research capabilities, particularly in linking genes to diseases and functional activities. It highlights existing resources and tools, such as ogolod and orthoxml, along with their implications for data sharing and ontology mapping. The author emphasizes the need for standardization in orthology resources and addresses challenges faced in the field during the Biohackathon 2014.

1. Seman&c
integra&on
of
orthology
resources:
beyond
the
OGO
experience
Jesualdo
Tomás
Fernández
Breis
Universidad
de
Murcia,
IMIB-­‐Arrixaca
jfernand@um.es
hKp://webs.um.es/jfernand

2. Why?
• Applica&ons
able
to
combine
mu&ple
orthology
databases
with
other
sources
(e.g.,
diseases)
• Answer
queries
like
– “Rat
genes
whose
orthologs
are
associated
with
diseases
with
phenotype
Autosomal
dominant
inheritance”
– “Rat
genes
whose
orthologs
par8cipate
in
transcrip8on
repression
ac8vi8es
and
that
are
also
related
to
genes
involved
in
lung
cancer”

3. State of the art in orthology resources

4. State of the art in orthology resources
Pros
Cons
Many
different
formats
Local
meaning
of
fields
Trees
and
clusters
with
different
meanings
Difficult
to
share
and
reuse
data
Open
Many
community
resources
available
Increasing
interest
in
orthologs

5. Our original effort
• OGOLOD:
An
orthology
linked
dataset
• Resources
– KOG
– Inparanoid
– Homologene
– OrthoMCL
– OMIM
– NCBI
Taxonomy
– GO
– Human
Phenotype

6. OGOLOD: ontological issues
ClusterOrthologs hasResource
hasOrtholog
causedBy
connectedTo hasMethod
ECO_0000000
Method
PubmedArticle
x Name
x Identifier
hasPhenotype
GeneticDisease
x Name
x Identifier
x Location
relatedPubmedArticle
Protein
x Name
x Identifier
NCBITaxon_1
Gene
x Name
x Identifier
GO_0008150
GO_0003674 GO_0005575
HP_0000001
Resource
participates_in
located_in
isTranslatedTo
encodedBy
fromSpecies
evidenceCode
Gene Ontology
Human Phenotype
Ontology
Evidence
Code
Ontology
NCBI Taxonomy
Relations Ontology

7. OGOLOD: the process
Data
schemas
Data
Schema
mapping
Mapping
file
Ontology
Data
transforma&on
RDF
data
Linked
Data
sets
Data
enrichment
Linked
dataset

8. Linked Open Data Cloud
(http://lod-cloud.net/)

9. OGOLOD: linking data

10. Quest For Orthologs
(http://questfororthologs.com)

11. OrthoXML (http://orthoxml.org)
• Intended
community
standard
• Not
many
databases
use
it
so
far
• OrthoXML
2
OGO
ontology
mapping

12. OrthoXML2OGO Mapping Examples
Mapping
genes
Mapping
clusters
of
orthologs

13. SWIT
(http://sele.inf.um.es/swit)
• Ontology-­‐driven
transforma&on
and
integra&on
of
data
(Rela&onal
Databases
+
XML
Schemas)
• Output:
4-­‐stars
datasets
• Mapping
and
iden&ty
rules
– En&ty2Class
– En&ty2ObjectProperty
– En&ty2DatatypeProperty
– Complex
transforma&on
paKerns
coded
in
OPPL2.
• Only
logically
consistent
content
is
transformed

14. SWIT
(http://sele.inf.um.es/swit)

15. Next challenges….Biohackathon 2014?
• Need
for
a
standard
ontology
for
orthology
• OGO,
Ortho,
Homology
Ontology,
Compara&ve
Data
Analysis
Ontology
• The
evolu&onary
rela&on
between
two
genes
may
differ
in
two
different
contexts
• Need
for
gedng
more
orthology
resources
in
standardized
formats

16. Acknowledgements
• Mari
Carmen
Legaz
García,
José
Antonio
Miñarro
Giménez,
Mikel
Egaña
Aranguren,
Marisa
Madrid
• Biohackathon
2014
organizers
for
the
invita&on
TIN2010- 21388-C02-02 15295/PI/10

17. Thank
you
for
your
aKen&on
Ques&ons,
comments..
Jesualdo
Tomás
Fernández
Breis
Universidad
de
Murcia,
IMIB-­‐Arrixaca
jfernand@um.es
hKp://webs.um.es/jfernand