1. Hit Triage in RNAi Screens
Making Use of Interac1on and Pathway Data
to Enhance Hit Selec1on
Rajarshi Guha
NIH Chemical Genomics Center
March 18, 2010
GeneGO User Group Mee5ng, Boston, MA

2. Outline
• The NCGC RNAi infrastructure
• Selec:on & Triage
• Case Studies

3. Trans‐NIH RNAi Ini6a6ve ‐ Mission
To establish a state of the art RNAi screening facility to perform
genome-wide RNAi screens with investigators in the intramural
NIH community.
• Gene func:on
• Pathway analysis
• Target ID
• Compound MoA
• Drug antagonist/
agonist

4. Pilot Phase Screens

5. RNAi Informa6cs Toolset
• Local databases (screen data, pathways,
interac:ons, etc).
• Commercial pathway tools.
• Custom soKware for loading, analysis and
visualiza:on.

6. Back End Services
• Currently all computa:onal analysis performed
on the backend
• R & Bioconductor code
• Custom R package (ncgcrnai) to support NCGC
infrastructure
– Partly derived from cellHTS2
– Supports QC metrics, normaliza:on, adjustments,
selec:ons, triage, (sta:c) visualiza:on, reports
• Some Java tools for
– Data loading
– Library and plate registra:on

7. User Accessible Tools

8. RNAi Analysis Workflow
Raw and GO
Processed annota:ons
Pathways
Data Interac:ons
• Summary
Normaliza:on
• Thresholding
Hit Triage
sta:s:cs • Median • Hypothesis • GO seman:c
• Correc:ons • Quar:le tes:ng similarity
• Background • Sum of ranks • Pathways
• Interac:ons
QC Hit Selec:on
Follow‐up Hit List

9. Hit Selec6on Methods
• Thresholding Negative
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0
Plate Number
• In all cases, some degree of abitrariness
• In many cases, poor concordance between
selec:on methods
• Gives us a selec:on of siRNA’s
• Iden:fy genes based on number of siRNA’s

10. Hit Triage Methods
• Given that we end up with a rela:vely small
number of hit genes, we need to expand it
• Triage iden:fies other “relevant” or
“borderline” genes to be included for follow‐up
• Methods
– Protein‐protein interac:on data
– Pathway membership data
– GO terms
– Network sta:s:cs

11. CPT Sensi6za6on
TOP1 poisons prevent DNA religation resulting in replication-dependent double
strand breaks. Cell activates DNA damage response (e.g. ATR).
Pommier, Y., Nat. Rev. Cancer, 2006.

12. Screening Protocol
Screen conducted in the human breast cancer cell line MDA-MB-231.
Many variables to optimize including transfection conditions, cell seeding
density, assay conditions, and the selection of positive and negative
controls.

13. Hit Selection
Follow-Up Dose Response Analysis
ATR
Screen #1
siNeg
siATR-A
Viability (%)
siATR-B
siATR-C
Sensitization Ranked by Log2 Fold Change
CPT (Log M)
Screen #2
MAP3K7IP2
siNeg
siMAP3K7IP2-A
Viability (%)
siMAP3K7IP2-B
siMAP3K7IP2-C
siMAP3K7IP2-D
Sensitization Ranked by Log2 Fold Change
CPT (Log M)
Multiple active siRNAs for ATR, MAP3K7IP2, and BCL2L1.

14. Triage with PPIs
• For each gene in the hit list iden:fy interac:on
partners
– Currently using HPRD
– Will be switching to MIMI
• This added MAP3K7 to the hit
list, which
MAP3K7
reconfirmed nicely
siNeg
Viability (%)
siMAP3K7-A
siMAP3K7-B
siMAP3K7-C
•
siMAP3K7-D
CPT (Log M)
http://www.eecs.umich.edu/db/mimi/

15. Looking in Pathways
• Using the ini:al hit list based on differen:al
analysis we get a list of relevant pathways

16. Looking in Pathways
• We then iden:fy genes from these pathways
that were not ini:ally selected
• 3 genes from the IL‐10 signalling pathway
were each knocked down by a single siRNA
– CD14
– PIK3CD
– PIK3R3
• Similarly, we add in
genes from other
pathways

17. Looking in Pathways
• The reconfirma:on rate of pathway‐derived
hit list members was low (< 10%)
• Not too surprising
• PPI‐derived hit list members worked beger

18. Are These Genes Relevant?
• Some are well known to be CPT‐sensi:zers
• Consider a HPRD PPI sub‐network
corresponding to the Qiagen HDG gene set
• How “central” are these selected genes?
– Larger values of betweenness
3.0
indicate that the node lies on
2.5
many shortest paths
2.0
log Frequency
– Makes sense ‐ a number of
1.5
them are stress‐related
1.0
– But some of them have very low 0.5
betweenness values
0.0
0 2 4 6
log Betweenness