Title page for ETD etd-08192009-013737

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
  • RNAi efficiency
  • RNA interference(RNAi)
  • RNAi equilibrium thermodynamics
  • Support Vector Machine
  • RNA secondary structure
Date of Defense 2009-08-06
Availability unrestricted
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.

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