�ݺ�ߣ

Five years of DisGeNET: Lessons
learned and challenges ahead
Laura
I.
Furlong
IMIM-?�\UPF
Big
Data
in
Biomedicine
Barcelona
November
11,
2014

? Knowledge
plaDorm
on
human
diseases
and
their
genes
? Aims
to
cover
all
disease
therapeuHc
areas
? Developed
by
integraHon
of
data
from
expert-?�\curated
resources
and
from
the
literature
by
text
mining
? Centered
on
gene-?�\disease
associaHon
(GDA)
and
its
supporHng
evidence
and
provenance
11/11/14
Laura
I.
Furlong
2

Re#
Syndrome
UMLS:C0035372
MCEP2
NCBI:4204
11/11/14
Laura
I.
Furlong
3

Re#
Syndrome
UMLS:C0035372
MCEP2
NCBI:4204
��?
��?
��?
x
��?
11/11/14
Laura
I.
Furlong
4

UniProt
CTD
MGD
LHGDN
CTD
Curated
RGD
BEFREE
GAD
Predicted
Literature
11/11/14
Laura
I.
Furlong
5

UniProt
CTD
MGD
LHGDN
381,056
GDAs
16,666
CTD
genes
13,172
diseases
Curated
RGD
BEFREE
GAD
Predicted
Literature
11/11/14
Laura
I.
Furlong
6

Key
points
1. FragmentaPon
of
informaPon
as
a
barrier
to
knowledge
on
the
mechanisms
of
human
diseases
11/11/14
Laura
I.
Furlong
7

11/11/14
Laura
I.
Furlong
8

hVp://semanHcscience.org
hVp://lod-?�\cloud.net/
StandardizaPon
? Controlled
vocabularies
and
ontologies
? DisGeNET
associaHon
type
ontology
Open
access
? RDF
and
nanopublicaHons
? Data
distributed
under
the
Open
database
commons
license
hVp://opendatacommons.org/

Key
points
2. High
rate
of
data
generaPon
on
GDAs
poses
challenges
to
biocuraPon
pipelines
11/11/14
Laura
I.
Furlong
10

PublicaHons
on
diseases
and
genes
from
1980
(737,712
publicaHons)
Supervised
machine-?�\learning
approach
for
relaHon
extracHon
between
Text
Mining
530,347
associaPons
14,777
genes
12,650
diseases
355,976
publicaHons
genes
and
diseases
Aprox.
30,000
GDAs
11/11/14
Laura
I.
Furlong
11

? Nearly
70
%
of
GDAs
supported
by
only
one
publicaHon!
? 900
GDAs
supported
by
>
200
publicaHons
? Average
2.8
publicaHons/GDA
11/11/14
Laura
I.
Furlong
12

GDAs
supported
by
only
one
publicaHon
11/11/14
Laura
I.
Furlong
13

Li#le
overlap
between
text
mined
data
and
data
present
in
curated
repositories
? Other
publicaHons,
full
text,
etc
? FN
from
TM
? Non-?�\relevant
data
for
DBs
? Relevant
data
but
sHll
not
curated
? FP
from
TM
11/11/14
Laura
I.
Furlong
14

? This
is
not
raw
data
(e.g.
from
NGS
analysis),
is
data
already
collated
and
filtered
that
have
passed
peer
review
before
publicaHon
? Text
mined
from
abstracts:
? Not
mining
full
text,
nor
supplementary
material
? Not
mining
tables,
figures
? Focus
on
relaHons
stated
on
sentences,
not
handling
anaphoras
? We
are
just
looking
into
a
small
frac)on
of
all
the
available
data!!!
11/11/14
Laura
I.
Furlong
15

Key
points
2. High
rate
of
data
generaPon
on
GDAs
poses
challenges
to
biocuraPon
pipelines
? Need
to
find
alternaHve
strategies
to
expert
curaHon
? ��wisdom
of
the
crowds��
approaches
(e.g.
crowdsourcing)
? ?
11/11/14
Laura
I.
Furlong
16

Key
points
3. Data
prioriPzaPon
to
support
interpretaPon
of
data
on
the
genePc
determinants
of
human
diseases
11/11/14
Laura
I.
Furlong
17

Ranks
gene-?�\disease
associaHons
based
on
the
supporHng
evidence
DisGeNET
score
=
SCURATED
+
SPREDICTED
+
SLITERATURE
GDAs
prioriPzaPon
SCURATED
=
WUNIPROT
+
WCTD
SPREDICTED
=
WRat
+
WMouse
SLITERATURE
=
WGAD
+
WLHGDN
+
WBeFree
11/11/14
Laura
I.
Furlong
18

Disease
Gene
Score
UniProt
CTD
Rat
Mouse
Number
of
publicaPons
BeFree
GAD
LHGDN
Wilson��s
Disease
ATP7B
0.99
0.3
0.3
0.1
0.1
174
31
23
ReV
Syndrome
MECP2
0.9
0.3
0.3
0
0.1
438
27
43
CysHc
Fibrosis
CFTR
0.9
0.3
0.3
0
0.1
1429
150
78
Obesity
MC4R
0.94
0.3
0.3
0.1
0.1
220
46
0
Alzheimer
Disease
APP
0.88
0.3
0.3
0
0.1
1096
18
81
11/11/14
Laura
I.
Furlong
19

Key
points
3. Data
prioriPzaPon
to
support
interpretaPon
of
data
on
the
genePc
determinants
of
human
diseases
? SHll
we
have
a
large
dataset
with
low
score
(350,000
GDAs)
? Other
approaches
based
on:
? type
of
associaHon
of
the
gene
to
the
disease
? experimental
evidence
for
the
GDAs
? network-?�\based
gene-?�\prioriHzaHon
algorithms
? Take
into
account
contradictory
findings
? Different
prioriHzaHon
approaches
for
different
purposes?
11/11/14
Laura
I.
Furlong
20

Key
points
4. Large
number
of
genes
associated
to
some
diseases
might
reflect
phenotypic
diversity
11/11/14
Laura
I.
Furlong
21

11/11/14
Laura
I.
Furlong
22

Need
for
deep
phenotyping
of
diseases
to
idenHfy
disease
subtypes
associated
to
different
gene
networks
Human
Phenotype
Ontology
(HPO)
hVp://www.human-?�\phenotype-?�\ontology.org/
? Ontology
of
phenotypic
abnormaliHes
of
human
diseases
? Based
on
OMIM,
Orphanet
and
DECIPHER
11/11/14
Laura
I.
Furlong
23

DisGeNET-?�\HPO
annotaPons
33
%
of
DisGeNET
diseases
annotated
with
HPO
terms
Mainly
come
from
OMIM
diseases
(28
%
of
DisGeNET
diseases
come
from
OMIM)
Need
for
other
approaches
to
annotate
the
full
spectrum
of
diseases
at
phenotypic
level
11/11/14
Laura
I.
Furlong
24

Key
points
1. FragmentaHon
of
informaHon
as
a
barrier
to
knowledge
on
the
mechanisms
of
human
diseases
2. High
rate
of
data
generaHon
on
GDAs
poses
challenges
to
biocuraHon
pipelines
3. Data
prioriHzaHon
to
support
interpretaHon
of
data
on
the
geneHc
determinants
of
human
diseases
4. Large
number
of
genes
associated
to
some
diseases
might
reflect
phenotypic
diversity

IBI
Group
Alba
GuH��rrez
?lex
Bravo
Janet
Pi?ero
N��ria
Queralt-?�\Rosi?ach
Miguel
A.
Mayer
Pablo
Carbonell
Laura
I.
Furlong
Ferran
Sanz
IBI
Past
members
Montserrat
Cases
Michael
Rautschka
Anna
Bauer-?�\Mehren
Sol��ne
Grosdidier
11/11/14
Laura
I.
Furlong
26

�ݺ�ߣ

Laura Furlong. Big Data in Biomedicine debate. Barcelona, Nov 11 2014

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Laura Furlong. Big Data in Biomedicine debate. Barcelona, Nov 11 2014