1 cis 4930/6930 – recent advances in bioinformatics spring 2014 network construction from rnai...
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CIS 4930/6930 – Recent Advances in Bioinformatics
Spring 2014
Network construction from RNAi data
Tamer Kahveci
Signaling Networks
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MAPK network
Signal reachability
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Receptor Reporter
Luciferase
Signaling and RNA Interference
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Receptor Reporter
Luciferase
X Not critical
X Critical
Signaling Network Reconstruction from RNAi data
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Receptor Reporter
Not criticalCritical
RNAi data and Reference Network
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Receptor Reporter
Not criticalCritical
Reference network
Inse
rt
Delete
Not consistent !Consistent
!
Overview
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GR = (VR, ER)
Reference network
Constraints
1 10 0 0
GT = (VT, ET)
Target network
1 0
SiNeC (Signal Network Constructor)
S-SiNeC (Scalable Signal Network Constructor)
Giv
en Find
Goal: Minimize the number of edit
operations to make the reference
consistent.
NP-Complete !
SiNeC algorithm
Three steps
1. Order the critical genes left to right based on the topology of GR. [Sloan, 1986]
– v1, v2, …, vc
2. Edge deletion phase
3. Edge insertion phase
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Step 1: Order critical genes
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Receptor Reporter3
1
2
Prioritize based on distance to the reporter + degree
Step 2: Edge deletion
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Purpose: Eliminate detours around critical genes
Receptor Reportervi vkvj
• Find all (undesirable) paths between non-consecutive critical genes.
• i.e., Paths which go through only noncritical genes• Edges are weighted with the number of such paths they
belong to.• Remove greedily starting from the largest weight until al
paths are disrupted.
Bypassed !!!
Step 3: Edge insertion
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Purpose: Make sure that critical are connected + noncritical genes are consistent
Receptor Reportervi-1 vi+1vi
Insert an edge from vi-1 to vi if 1. There is no path from vi-1 to vi.2. There is a noncritical gene on all paths from vi-1
to vi.
Overview
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GR = (VR, ER)
Reference network
Constraints
1 10 0 0
GT = (VT, ET)
Target network
1 0
SiNeC (Signal Network Constructor)
S-SiNeC (Scalable Signal Network Constructor)
Giv
en Find
Finding all the paths can be to
o time
consuming for la
rge networks
S-SiNeC algorithm
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Edge insertion0 0 0 None
0 0 1 None
0 1 0 None
0 1 1 A1
1 0 0 A2 + A3 + A4
1 0 1 A2 + A4
1 1 0 A3 + A4
1 1 1 A4
Critic
alLe
ft rea
chab
le
Rig
ht rea
chab
le
Edge deletion
Reference network
vs vtvi
S-SiNeC: Edge insertion (A1)
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Reference network
vs vtvi
L R
Purpose: Make sure that noncritical genes are consistent
S-SiNeC: Edge insertion (A2)
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Reference network
vs vtvi
L R
Purpose: Make sure that critical genes are left reachable
S-SiNeC: Edge insertion (A3)
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Reference network
vs vtvi
L R
Purpose: Make sure that critical genes are right reachable
S-SiNeC: Edge insertion
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L/R e1 e2 e3
1 X
2 X X
3 X X
4 X X
5 X X
6 X X
7 X
8 X
S-SiNeC: Edge deletion (A4)
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Reference network
vs vtvi
L R
Purpose: Make sure that no detours exist around critical genes
Solve minimum cut between L &
R
Dataset
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• Reference networks are obtained by random edge shuffling at 5% to 40% mutation rates.
• 200 references per target network & per mutation rate.
Average distance to the true network
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Accuracy based on edge class
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vs vt
Hot
Cold
Running time results
22SiNeC > 1 hour per reference network.
Success rate on constraints
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Accuracy
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Functional Enrichment of the Pathway
Last Remarks
• Constructing very large signaling networks from RNAi data is possible in practical running time.
• Both SiNeC and S-SiNeC are robust to errors in reference network.
• We recommend
– S-SiNeC for very large OR dense networks.
– SiNeC otherwise.26
Acknowledgements
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CCF - 0829867 IIS - 0845439260429
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