1. Data Translator: an Open Science Data Platform
for Mechanistic Disease Discovery
Melissa Haendel, PhD
@ontowonka

2. Prevailing clinical genomic pipelines
leverage only a tiny fraction of the available
data

3. Prevailing clinical genomic pipelines
leverage only a tiny fraction of the available
data
Under-utilized data 
Loss of discriminatory power
?

4. More species = more coverage
Number of human protein-coding genes in ExAC DB as per Lek et al. Nature 2016
19,008
9,739
51%

5. More species = more coverage
19,008
78%
14,779
Number of human protein-coding genes in ExAC DB as per Lek et al. Nature 2016
19,008
9,739
51%
Mungall et al Nucleic Acids Research bit.ly/monarch-nar-2016

6. More species = more coverage
Even inclusion of just four species boosts phenotypic coverage of genes by 38%
(5189%)
Combined = 89%
19,008
2,195 7,544 7,235 = 16,974
(union of coverage in any species)
Mungall et al Nucleic Acids Research bit.ly/monarch-nar-2016

7. Polydactyly
Triphalangeal
thumb
Extra thumb
bone
https://radiopaedia.org/cases/triphalangeal-thumb-in-fanconi-anemia
Pajni-Underwood, 2007, http://dev.biologists.org/content/134/12/2359
Different communities use different languages

8. Challenge: Each data source uses their own
vocabulary/ontology
MP
HP
MGI
HPOA

9. Challenge: Each data source uses their own
vocabulary/ontology
ZFA
MP
DPO
WPO
HP
OMIA
VT
FYPO
APO
SNOMED
…
…
…
WB
PB
FB
OMIA
MGI
RGD
ZFIN
SGD
IMPC
OMIM
…
QTLdb
HPOA
EHR

10. Can we help machines understand
phenotypes?
“Triphalangeal
thumb”
Human phenotype
I have absolutely
no idea what
that means

11. Decomposition of complex concepts allows
interoperability
“Triphalangeal
thumb”
Phalanx of manual
digit
=
Human phenotype PATO
Uberon
Species neutral ontologies, homologous concepts
Autopod
GO
=
duplicated
embryonic skeletal
system
morphogenesis

12. Decomposition of complex concepts allows
interoperability
“Triphalangeal
thumb”
Phalanx of manual
digit
=
Human phenotype PATO
Uberon
Species neutral ontologies, homologous concepts
Autopod
GO
“Polydactyly”
Mouse phenotype
=
duplicated
embryonic skeletal
system
morphogenesis

13. Example case solved by Exomiser
Phenotypic
profile
Genes
Heterozygous,
missense mutation
STIM-1
N/A
Heterozygous,
missense mutation
STIM-1
N/A
Stim1Sax/Sax
Ranked STIM-1 variant maximally pathogenic
based on cross-species G2P data,
in the absence of traditional data sources
https://exomiser.github.io/Exomiser/
bit.ly/stim1paper

14. Example case solved by Exomiser
Phenotypic
profile
Genes
Heterozygous,
missense mutation
STIM-1
N/A
Heterozygous,
missense mutation
STIM-1
N/A
Stim1Sax/Sax
Ranked STIM-1 variant maximally pathogenic
based on cross-species G2P data,
in the absence of traditional data sources
bit.ly/stim1paper
In Genomics England 100K Genomes, of first 1936 diagnosed
patients, 82% are in the top 5 Exomiser hits across a range
of rare diseases and family structures

16. Harmonizing diseases, phenotypes, anatomy, and genotypes
91% of our 2.2 Million G2P associations require integrating
2 or more data sources

17. Enabling phenotype comparisons across
diseases and species

18. Plain language synonyms for computable
phenotypes

19. Translational applicability for FA
 Tools can support more rapid diagnostics for FA
patients
 Integration of data enables mechanistic discovery
and new candidate gene targets
 Identification of models for FA hypothesis
validation
 Helping patients contribute data and participate
in their ongoing evaluation, care, and science

20. Acknowledgements
Lawrence Berkeley
Chris Mungall
Suzanna Lewis
Jeremy Nguyen
Seth Carbon
Nicole Washington
Charite
Sebastian Kohler
Garvan
Tudor Groza
Craig McNamara
RTI
Jim Balhoff
Boston Children’s
Ingrid Holm
Catherine Brownstein
John Brownstein
ClinGen
Heidi Rehm
Larry Babb
Harindra Arachchi
OHSU
Matt Brush
Kent Shefchek
Julie McMurry
Tom Conlin
Nicole Vasilevsky
Dan Keith
Maureen Hoatlin
Genomics England/Queen
Mary
Damian Smedley
Jules Jacobson
Tomasz Konopka
Pilar Cacheiro
Jackson Laboratory
Peter Robinson
Leigh Carmody
Hannah Blau
EBI
Helen Parkinson
David Osumi-Sutherland
With special thanks to Julie McMurry for excellent graphic design
Johns Hopkins
Chris Chute
Casey Overby
Ada Hamosh
Mayo
Hongfang Liu
Ravi Komandur
UCSC
David Haussler
Benedict Paten
Mark Deikhans
Scripps
Andrew Su
Ben Good
Chunlei Wu
Gregg Stupp
Sanford Health Imagenetics
Neal Boerkoel
Kayli Rageth
Murat Sincan

21. www.monarchinitiative.org
Chris Mungall, Peter Robinson, Damian Smedley
Funding:
NIH Office of Director: 1R24OD011883; NIH-UDP: HHSN268201300036C, HHSN268201400093P;
NCINCI/Leidos #15X143, BD2K U54HG007990-S2 (Haussler) & BD2K PA-15-144-U01 (Kesselman)

22. extra

23. Layperson-HPO driven phenotyping tool
https://www.pcori.org/research-results/2017/realization-standard-care-rare-
diseases-using-patient-engaged-phenotyping

24. Genes Environment Phenotypes
VCF PXFGFF
Standard exchange formats exist for genes … but
for phenotypes? Environment?
BED

25. What does a phenopacket look like?
 Alacrima
 Sleep Apnea
 Microcephaly
phenotype_profile:
- entity: ”patient16"
phenotype:
types:
- id: "HP:0000522"
label: ”Alacrima"
onset:
description: “at birth”
types:
- id: "HP:0003577"
label: "Congenital onset"
evidence:
- types:
- id: "ECO:0000033"
label: ”Traceable Author Statement"
source:
- id: ”PMID:"
 Clinical labs
 Public databases
 Journals

26. Layperson HPO + Phenopackets
 Dry eyes
 Stops breathing during sleep
 Small head
phenotype_profile:
- entity: “Grace”
phenotype:
types:
- id: "HP:0000522"
label: “Alacrima"
onset:
description: “at birth"
types:
- id: "HP:0003577"
label: "Congenital onset"
evidence:
- types:
- id: “ECO:0000033”
label: “Traceable Author Statement"
source:
- id: “
https://twitter.com/examplepatient/status/1
23456789”
• Patient registries
• Social media