.

1. A Torch to Use in Your Journey In the Dark Genome
Dac Trung Nguyen, Timothy Sheils, Geetha Mandava, Noel Southall, Rajarshi Guha
NCATS NIH

2. Why Bother with Unstudied Targets?
Data from Tudor Oprea & Christian Bologa
Leptin
SMO
S1PR1
Orexin
PCSK9
Ghrelin
1995 2000 2005 2010 2015

3. IDG Knowledge Management Center

4. Target Central Resource Database
Full data source list at http://targetcentral.ws/Pharos

5. From TCRD to Pharos
Identifiers, ontology terms, sequence, expression
data, publications, grants, phenotypes, PPI, …

6. Current Status
20,120 targets
15,094 diseases
2.3M publications
4,500 drugs
Nguyen & Mathias et al, NAR, 2017
191 facets
17.8 GB database
30 GB Lucene indexes
50K LoC
Image available
Source code available

7. The Principles of Pharos
Guidance Serendipity Summary

8. Entity browsing (filterable & linked)Search (full text, auto-suggest)
Detailed view of entities Built on top of a robust REST API
An Interface to the KMC

9. Biologists &
Clinical Researcher
• Characterize &
validate novel
targets
• Identify key small
molecules or
biologics
Program Staff
• Explore the
research
landscape
• New directions
for research &
funding
Informatics
Scientists
• Data mining
• Support target
validation
projects
Target Audience

10. Do You Know What You Want?
• Efficient full text search
• Primary entry point when exploring and for
hypothesis generation
• Fast autosuggestion facility
Ø Suggestions grouped
by type (disease,
ligand, …)
• Searches run across
all entity types
Ø But can be restricted
to specific ones

11. Multiple Search Options
Batch search Sequence search
Structure searchText search

12. (Possibly) Lots of Results

13. Filters

14. More Features …
• Visualizations used to
summarize and efficiently
use screen space
Multiple dossiers
Set operationsVisualization tools
Download
• Documented throughout
• Well known UI components

15. ......
f(...)
Going Beyond Presentation

16. Target Knowledge Vectors
...
{0.9, 0.0, ..., 0.1}
• Move from a structure-based representation to a
knowledge-based representation
Recommendations
Clustering
Prediction

17. Surprising Similarities
• For each Tdark target, identify the 5 most similar
targets from the remaining targets
Enzyme
Epigenetic
GPCR
IC
Kinase
NR
oGPCR
TF
TF; Epigenetic
Transporter
Enzym
e
EpigeneticG
PC
R
ICKinaseoG
PC
R
TF
TF;Epigenetic
Transporter
Tdark
MostSimilarTarget
0
50
100
150
Freq
0
1000
2000
3000
4000
Tbio Tchem Tclin Tdark
TDL of Most Similar Target
NumberofTdark

18. Surprising Similarities
ABHD8
ADAMTSL4−AS1
ALG3
ADGRG4
ADGRF3
ANKRD33B
ACSF2
AGAP11
ANKRD18B
AHDC1
ATXN7L1
ARL16
ADAMTS6
BSDC1
C1orf64
C1orf101
LINC00696
CCDC187
WASHC3
C2orf16
C4orf19
C7orf69
CECR5
CCDC177
CCDC179
C5orf52
C5orf51
LINC00313
COX7A2
FAM127A
LINC01549
CXorf49B
GAFA1
GRAPL
GPR148
CGB7
C9orf135
CNGA4
ARRDC1−AS1
C9orf170
LINC01551
CDRT4
COMMD8
CNPPD1
LINC01620
CYB561A3
CWC15
C19orf35
CSNK2A3
DEFB116
DHX37
DUSP21
FAM166A
FAM74A3
FAM201A
FAM65C
FAM159B
FAM87A
FAM53A
FAM171B
FAM57B
FRMD8
IFT22
IGDCC3
IPP
ISM2
IZUMO4
INTS8
METTL9
CD200R1L
OR51L1
NT5DC4
OR10G3
OR9I1
OR10A2
OR13C8
OR10D4P
OR4F4
OR5B3
OR7A5
P3H3
POM121L1P
PM20D1
OR2M2
OR6C4
P4HA3
NKIRAS2
KRTDAP
KLHL13
LCE2B
LCE2A
KLHL33
LUC7L2
KRTAP24−1
LIMS3
LRRC74B
LRRC14B
M1AP
METTL15P1
MTHFSD
MSANTD3
NLRP13
NOP14
NOP9
NKAIN1
NAXD
OR5AS1
NUTM2B
ERVK−7
PCDHA2
PCDHGB4
PHF21B
PERM1
PIGY
PPP1R21
PPP6R1
PODNL1
PRAMEF17
PRR34
PTAR1
SFXN3
GINS4
SHISA4
RNF113B
RCOR3
RBMY1E
POLR2J3
RPRD2
RNF175
SLC35C2
SLC35F4
RWDD2A
SLC35E2B
TSEN15
C11orf58
SLC37A3
SMYD4
SPATA45
SVBP
THSD4
TOPAZ1
TMEM143
TMEM78
TRIM10
TMEM51
TMEM95
USP17L4
USP17L8
TSTD2
TTC22
UBL3
UTP14A
ZNF622
YPEL2
WDR31
WDR89
XKR3
WIPF3
WBP1L
ZNF713
ZNF200
ZNF391
ZNF764
ZNF773
ZNF507
ZNF778
KRT39
ATXN7L3
C14orf1
HIST1H2AC
C16orf87
FAM221A
KRTAP9−9
KRTAP5−3
ZNF214
CACTIN−AS1
EMILIN2
TMED7
ZNF567
C1orf159
TMEM167B
TMEM169
CCDC17
MSANTD2
PRELID3B
TPRG1
UHRF1BP1L
ZC3H18
ZNF710
X1 X2 X3 X4 X5
ATXN7L3
C14orf1
HIST1H2AC
C16orf87
FAM221A
KRTAP9−9
KRTAP5−3
ZNF214
CACTIN−AS1
EMILIN2
TMED7
ZNF567
C1orf159
TMEM167B
TMEM169
CCDC17
MSANTD2
PRELID3B
TPRG1
UHRF1BP1L
ZC3H18
ZNF710
X1 X2 X3 X4 X5
Tbio
Tchem
Tclin
Tdark
100
1000
10000
0.01 1.00
Pubmed Score (Tdark)
PubmedScore(mostsimilarTclin)

19. ATXN7L3
• Associated with glioblastoma (via text mining)
• Most similar to CHRNB2, CYP3A7
CHRNB2ATXN7L3

20. Outreach & Dissemination Activities
User Feedback Deployment
Webinars Documentation
NER API for
targets & diseases
@idg_pharos
Recent papers to
Pharos links via
Tweets

21. Supporting Specific Domains
The Monarch Initiative
A target-centric
resource customized
for rare diseases

22. The Long Term Vision

23. Feedback
• Explore the UI, try it, break it, and let us know
what works and what doesn’t
• Are there data types and relations that would help
you but are not available?
• Contact us for webinars and learning sessions
https://pharos.nih.gov
pharos@nih.gov
@idg_pharos & Youtube

24. Acknowledgements
Dac-Trung Nguyen, Kyle Brimacombe, Timothy
Sheils, Geetha Mandava, Noel Southall, Ajit Jadhav
Steve Mathias, Oleg Ursu, Jeremy Yang,
Christian Bologa, Daniel Canon, Tudor Oprea
Nicholas Fernandez, Andrew Rouillard, Avi Mayan
Ajay Pillai, Aaron Pawlyk, Christine Colvis
Tomita Lab / Finkbeiner lab