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An unbiased adaptive sampling algorithm for the exploration of RNA mutational landscapes under evolutionary pressure Jérôme Waldispühl, PhD School of Computer.

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Presentation on theme: "An unbiased adaptive sampling algorithm for the exploration of RNA mutational landscapes under evolutionary pressure Jérôme Waldispühl, PhD School of Computer."— Presentation transcript:

1 An unbiased adaptive sampling algorithm for the exploration of RNA mutational landscapes under evolutionary pressure Jérôme Waldispühl, PhD School of Computer Science, McGill Centre for Bioinformatics, McGill University, Canada Yann Ponty, PhD Laboratoire d’informatique (LIX), École Polytechnique, France

2 Philippe Flajolet (1948 – 2011)

3 RNAmutants: Algorithms to explore the RNA mutational landscape Overview Understanding how mutations influence RNA secondary structures AND how structures influence mutations (Waldispühl et al., PLoS Comp Bio, 2008).

4 Sampling k-mutants CAGUGAUUGCAGUGCGAUGC (-1.20)..((.(((((...))))))) Classic: 0 mutation CAGUGAUUGCAGUGCGAUcC (-3.40)..(.((((((...))))))) CAGUGAUUGCAGUGCGgUGC (-0.30) ((.((....)).))...... CAGUGAUcGCAGUGCGAUGC (-3.10).....(((((...))))).. RNAmutants: 1 mutation uAGcGccgGgAGacCGgcGC (-18.00)..(((((((....))))))) CccUGgccGCAagGCcAgGg (-20.40) ((((((((....)))))))) CcGUGgccGCgagGCcAcGg (-19.10) ((((((((....)))))))) RNAmutants: 10 mutations Seed Sample k mutations increasing the folding energy Consequence: it increases the C+G content

5 Objectives How to efficiently sample sequences at arbitrary C+G contents … without bias! C+G Content (%) Sample frequency Target C+G content

6 Outline Background: RNAmutants in a nutshell Algorithms to sample RNA secondary structures and mutations. Our approach: Adaptive sampling Uniformly shifting the distribution of samples. Results: Evolutionary studies Insights on the evolutionary pressure stemming from an optimization of the thermodynamical stability.

7 Outline Background: RNAmutants in a nutshell Algorithms to sample RNA secondary structures and mutations. Our approach: Adaptive sampling Uniformly shifting the distribution of samples. Results: Evolutionary studies Insights on the evolutionary pressure stemming from an optimization of the thermodynamical stability.

8 RNA secondary structure The secondary structure is the ensemble of base-pairs in the structure. Bracket notation: ((((((((…)))..((((….)))).(((…)))..)))))

9 Loop decomposition Stacking pairs

10 UUUACGGCUAGC Parameterization of the mutational landscape UCUGAAACCCGU UUUACGGCCAGC Sequence ensemble Structure ensemble CCUCAACGAAGC UCUACGGCCAGC UUUAAGGCCAGC 1-neighborhood (1 mutations)

11 Classical Recursions (Zuker & Stiegler, McCaskill) Enumerate all secondary structures

12 RNAmutants Generalize Classical Algorithms Enumerate all secondary structures over all mutants (Waldispuhl et al., ECCB, 2002)

13 Our approach  Explore the complete mutation landscape.  Polynomial time and space algorithm.  Compute the partition function for all sequences:  Sample by backtracking the dynamic prog. tables. RNAmutants (Waldispuhl et al., PLoS Comp Bio, 2008) RNAmutants: Single sequence:

14 Sampling k-mutants CAGUGAUUGCAGUGCGAUGC (-1.20)..((.(((((...))))))) Classic: 0 mutation CAGUGAUUGCAGUGCGAUcC (-3.40)..(.((((((...))))))) CAGUGAUUGCAGUGCGgUGC (-0.30) ((.((....)).))...... CAGUGAUcGCAGUGCGAUGC (-3.10).....(((((...))))).. RNAmutants: 1 mutation uAGcGccgGgAGacCGgcGC (-18.00)..(((((((....))))))) CccUGgccGCAagGCcAgGg (-20.40) ((((((((....)))))))) CcGUGgccGCgagGCcAcGg (-19.10) ((((((((....)))))))) RNAmutants: 10 mutations Seed Sample k mutations increasing the folding energy Consequence: it increases the C+G content

15 Outline Background: RNAmutants in a nutshell Algorithms to sample RNA secondary structures and mutations. Our approach: Adaptive sampling Uniformly shifting the distribution of samples. Results: Evolutionary studies Insights on the evolutionary pressure stemming from an optimization of the thermodynamical stability.

16 UUUAAGGCUAGC Our approach: Weighting mutations UCUGAAACCCGU UUUAAGGCCAGC Sequence ensemble Structure ensemble CCUCAACGAAGC UAUAAGGCCAGC UUUAGGGCCAGC w -1 1 w Z w -1. Z C9U 1. Z U2A w. Z A5G Weighted by partition function value Promote A+U content Penalize C+G content No change

17 Weighting recursive equations ) × W(i,x) × W(j,y) ( × W(j,y)

18 C+G Content (%) Effect of weighted sampling Unweighted sampling weighted ( w =1/2) weighted ( w =2) Frequency of samples

19 Sampling pipe-line Keep all samples at the target C+G and reject others. Update w at each iteration using a bisection method. Stop when enough samples have been stored.

20 Some features After rejection, the weighted schema only impact the performance, not the probability. This is unbiased. Partition function can be written as a polynom: After n iterations we can to calculate all a i and inverse the polynom to compute the optimal weight w. Remark: In practice, less interations are necessary

21 Example: 40 nt., 10000 samples, 30 mutations, 70% C+G content Cumulative distribution

22 Outline Background: RNAmutants in a nutshell Algorithms to sample RNA secondary structures and mutations. Our approach: Adaptive sampling Uniformly shifting the distribution of samples. Results: Evolutionary studies Insights on the evolutionary pressure stemming from an optimization of the thermodynamical stability.

23 20 nucleotides40 nucleotides Low C+G-contents favor structural diversity Simulation at fixed G+C content from random seeds 10% 30% 50% 70% 90%

24 Low C+G contents favor internal loop insertion 10% 30% 50% 70% 90% Number of Internal Loops

25 20 nucleotides40 nucleotides High G+C-contents reduce evolutionary accessibility Simulation at fixed G+C content from random seeds 10% 30% 50% 70% 90%

26 Perspectives More studies of Sequence-Structure maps. Applications to RNA design. Same techniques can be applied to other parameters (e.g. number of base pairs). Can be generalized to multiple parameters.

27 Acknowledgments Ecole Polytechnique Jean-Marc Steyaert Boston College Peter Clote INRIA Philippe Flajolet MIT Bonnie Berger Srinivas Devadas Mieszko Lis Alex Levin Charles W. O’Donnell Google Inc. Behshad Behzadi Yann Ponty CNRS at LIX, École Polytechnique, France. University of Paris 6 Olivier Bodini University of Paris 11 Alain Denise

28 Would you like to know more?  O. Bodini, and Y. Ponty Multi-dimensional Boltzmann Sampling of Languages, Proceedings of AOFA'10, 49--64, 2010  J. Waldispühl, S. Devadas, B. Berger and P. Clote, Efficient Algorithms for Probing the RNA Mutation Landscape, Plos Computational Biology, 4(8):e1000124, 2008. http://csb.cs.mcgill.ca/RNAmutants

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