2. 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 func0on
• Pathway analysis
• Target ID
• Compound MoA
• Drug antagonist/
agonist
4. RNAi Analysis Workflow
Raw and GO
Processed annota0ons
Pathways
Data Interac0ons
• Summary
Normaliza0on
• Thresholding
Hit Triage
sta0s0cs • Median • Hypothesis • GO seman0c
• Correc0ons • Quar0le tes0ng similarity
• Background • Sum of ranks • Pathways
• Interac0ons
QC Hit Selec0on
Follow‐up Hit List
6. Back End Services
• Currently all computa0onal analysis performed
on the backend
• R & Bioconductor code
• Custom R package (ncgcrnai) to support NCGC
infrastructure
– Partly derived from cellHTS2
– Supports QC metrics, normaliza0on, adjustments,
selec0ons, triage, (sta0c) visualiza0on, reports
• Some Java tools for
– Data loading
– Library and plate registra0on
9. Challenge – siRNA Design &
Valida5on
• We mostly depend on quality controls
implemented by vendor
– siRNA design algorithms not a high priority
• Always interested in extra filters that help us
get a reliable hit list
• Would like to have measures of
– Off‐target effects
– Protein half lives
10. Challenge ‐ miRNA Target ID
• Screened a set of 885 human miRNA’s
for CPT sensi0za0on
• Iden0fied 23 sensi0zing miRNA’s
• But, we don’t have target informa0on
– Predic0ons aren’t par0cularly helpful
– Poor overlap with siRNA hits
miRAnda TargetScan
• Link pathogenic
miRNA’s to human
targets
11. Challenge ‐ RNAi & Small
Molecule Screens
What targets mediate activity
of siRNA and compound
Given a set of siRNA hits and
their targets, is there a
• Reuse pre-existing MLI data compound showing similar
• Develop new annotated libraries inhibition
CAGCATGAGTACTACAGGCCA
TACGGGAACTACCATAATTTA Target ID and validation
Link RNAi generated pathway
peturbations to small molecule
activities. Could provide insight
into polypharmacology
• Run parallel RNAi screen
Goal: Develop systems level view of small molecule activity
12. Challenge – RNAi Meta Analyses
• Building up a collec0on of screens
– Across cell lines, species, …
– Not necessarily “designed”
• What do we do with this?
– Iden0fy consistent markers
– Characterize differences between
cell lines
– Extrapolate from gene knockdown to pathway
and higher level differences
– Merge with gene expression data
13. The People
• Scoh Mar0n
RNAi
• Pinar Tuzmen
• Dac Trung Nguyen
Small Molecules
• Yuhong Wang
