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Homology vs Analogy via Motifs Benasque 2012 – RNA Motifs Session Manuel Lladser & Rob Knight PowerPoint PPT Presentation


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Homology vs Analogy via Motifs Benasque 2012 – RNA Motifs Session Manuel Lladser & Rob Knight. Single Adenine: produced independently. What happens in between?. Ribosomal RNA: Evolved from one common ancestor. Motifs in random sequences.

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Homology vs Analogy via Motifs Benasque 2012 – RNA Motifs Session Manuel Lladser & Rob Knight

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Homology vs analogy via motifs benasque 2012 rna motifs session manuel lladser rob knight

Homology vs Analogy via Motifs

Benasque 2012 – RNA Motifs Session

Manuel Lladser & Rob Knight


Homology vs analogy via motifs benasque 2012 rna motifs session manuel lladser rob knight

Single Adenine: produced independently

What happensin between?

Ribosomal RNA:

Evolved from one commonancestor


Homology vs analogy via motifs benasque 2012 rna motifs session manuel lladser rob knight

Motifs in random sequences.

[Kennedy, Lladser, Wu, Zhang, Yarus, De Sterck& Knight (2010).]


Homology vs analogy via motifs benasque 2012 rna motifs session manuel lladser rob knight

Probability of modular correlated pattern given Memorylessor Markovian background : Embeddingsusing automata.

What’s the probability that 1a#b1 occurs in a random binarytext of a’s and b’s of length n? [Lladser, Betterton & Knight (2008)]


What about more complicated motifs

What about more complicated motifs?


Homology vs analogy via motifs benasque 2012 rna motifs session manuel lladser rob knight

Natural & artificial RNAs occupy the same restricted region of sequence

space: Neutral network hypothesis.

Many natural (left) and artificial (middle) aptamers and ribozymes occupy the same (right)

restricted region of sequence space. The maximum function probability is achieved with the

CompositionA ∼ 30%, C ∼ 15%, G ∼ 30% and U ∼ 25%.

[Kennedy, Lladser, Wu, Zhang, Yarus,DeSterck and Knight (2010)]


So far we have looked in random seqs but what if seqs are known

So far we have looked in random seqs, but what if seqs are known?

  • Example: the hammerhead ribozyme. We know it evolved at least three times…

  • Modular nature of the motif greatly complicates its analysis and increases its chance of occurring: need Pr(seqs|model)


Pr seqs model for different models

Pr(seqs|model) for different models

iid (or any Markovian model)

Infernal (once model is trained – need to take care for overfitting)

SCFG

One tree

(but how to align modules?)

Optimize each tree for each origin? Lots of ways to break up tree…

Many trees?


Open questions

Open questions?

  • What’s the right way to compute scores?

  • Non-nested pairing (pseudoknots and tertiary motifs)

  • Noncanonical interactions

  • Computational complexity of handling multiple origins

Your thoughts?


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