| Type of Document |
Master's Thesis |
| Author |
Bhattacharjee, Puranjoy
|
| Author's Email Address |
puran@cs.vt.edu |
| URN |
etd-08192009-013737 |
| Title |
Correlation Between Computed Equilibrium Secondary Structure Free Energy and siRNA Efficiency |
| Degree |
Master of Science |
| Department |
Computer Science |
| Advisory Committee |
| Advisor Name |
Title |
| Onufriev, Alexey V. |
Committee Chair |
| Heath, Lenwood S. |
Committee Member |
| Ramakrishnan, Naren |
Committee Member |
|
| Keywords |
- RNAi efficiency
- RNA interference(RNAi)
- RNAi equilibrium thermodynamics
- Support Vector Machine
- RNA secondary structure
|
| Date of Defense |
2009-08-06 |
| Availability |
unrestricted |
Abstract
We have explored correlations between the measured efficiency of the RNAi process and
several computed signatures that characterize equilibrium secondary structure of the partic-
ipating mRNA, siRNA, and their complexes. A previously published data set of 609 exper-
imental points was used for the analysis. While virtually no correlation with the computed
structural signatures are observed for individual data points, several clear trends emerge
when the data is averaged over 10 bins of N ∼ 60 data points per bin.
The strongest trend is a positive linear (r 2 = 0.87) correlation between ln(remaining mRNA)
and ∆Gms , the combined free energy cost of unraveling the siRNA and creating the break
in the mRNA secondary structure at the complementary target strand region. At the same
time, the free energy change ∆Gtotal of the entire process mRNA + siRNA → (mRNA −
siRNA)complex is not correlated with RNAi efficiency, even after averaging. These general
findings appear to be robust to details of the computational protocols. The correlation be-
tween computed ∆Gms and experimentally observed RNAi efficiency can be used to enhance
the ability of a machine learning algorithm based on a support vector machine (SVM) to
predict effective siRNA sequences for a given target mRNA. Specifically, we observe modest,
3 to 7%, but consistent improvement in the positive predictive value (PPV) when the SVM
training set is pre- or post-filtered according to a ∆Gms threshold.
|
| Files |
| Filename |
Size |
Approximate Download Time
(Hours:Minutes:Seconds)
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| 28.8 Modem |
56K Modem |
ISDN (64 Kb) |
ISDN (128 Kb) |
Higher-speed Access |
| |
Puranjoy_ETD_Revised2.pdf |
439.14 Kb |
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