Prediction of RNA-RNA Interaction slides by Mathias M ohl and Rolf - - PowerPoint PPT Presentation

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Prediction of RNA-RNA Interaction slides by Mathias M ohl and Rolf - - PowerPoint PPT Presentation

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Prediction of RNA-RNA Interaction slides by Mathias M ohl and Rolf Backofen ohl M.M c 1 RNA-RNA interaction (Waters and Storz, Cell 2009) ohl M.M c 2 RNA-RNA interaction U U G U G C G C C C C U C G U U U U

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SLIDE 1

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Prediction of RNA-RNA Interaction

slides by Mathias M¨

  • hl and Rolf Backofen
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SLIDE 2

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RNA-RNA interaction

(Waters and Storz, Cell 2009)

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SLIDE 3

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RNA-RNA interaction

U G C C U C G U U A U G C A U C A U U A G C U G U U C C U C A A U A C G A A C G A G G C U A C A C A A G C A A U C A U G U A G C U A C A A C U A G C U G U U C C U C A A U A C G A G U C U U C C G U G C U G C C U C G U U A C G U A C A C A U C G G A G C A A U C A U U A G C U G U U C C U C A A U A C G A U A C A C A U C G G A G C A U G C U C C G U A U A C G U C A U U A G C U G U U C C U C A A U A C G A

(Waters and Storz, Cell 2009)

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SLIDE 4

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How is a mRNA-target recognized?

  • idea 1: only hybridization energy counts
  • pt

    

A C A C A U U C G G G A G G C C C C A A A U U U U C C A U U A G G C U G U U U C C U C A A U A C G A

    

  • many approaches build on that: RNAhybrid, RNAplex, TargetRNA etc.
  • problem: structure

A C A C A U U C G G G A G G C C C C A A A U U U U C C A U U A G G C U G U U U C C U C A A U A C G A

versus

A C A C A U U C G G G A G G C A A A U C A U U U A G G C C C C C U G U U U U U C C U C A A U A C G A

  • hence: additional information in accessibility of site

remark: accessibility = single-strandedness

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Approaches to Target Detection

approach 1: maximize duplex energy (RNAhybrid, RNAplex, etc.)

  • pt

    

A C A C A U U C G G G A G G C C C C A A A U U U U C C A U U A G G C U G U U U C C U C A A U A C G A

     problem:

A C A C A U U C G G G A G G C A A A U C A U U U A G G C C C C C U G U U U U U C C U C A A U A C G

approach 2: common structure by concatenation: (RNAcofold, PairFold)

  • given: two sequences

GGUGUUGGGAUUGUCAG CUUACACAUCGGAGCAAUCAUUAGCUGUUCCUCAAUACGA

  • concatenate them

GGUGUUGGGAUUGUCAG CUUACACAUCGGAGCAAUCAUUAGCUGUUCCUCAAUACGA

  • use Zuker’s algorithm

G G U G U U G G G A U U G U C A G C U U A C A C A U C G G A G C A A U C A U U A G C U G U U C C U C A A U A C G A

problem:

U U U G G A A C A C A U C G G A G C A A U C A U U A G C C U G U U C C U C A A A A A U A C G A

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SLIDE 6

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Approaches to Target Detection

approach 3: RNAup/IntaRNA:

  • determine probability for region i..j being unpaired
  • calculate ensemble energy from probability
  • hybridize unpaired region with second RNA

Comparison:

  • approach 3: just one interaction possible
  • approach 2: more than one interaction possible, but only

external interactions (no pseudoknots in concatenated structure) RNAup RNAcofold

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The Idea of IntaRNA

IntaRNA = Interacting RNA similar to RNAup, but much faster (optimized for scanning genomes) k k’ i i’ mRNA ncRNA E = E hybrid + EDmRNA

i,i′

+ EDncRNA

k,k′

↓ ↓ ↓ as in RNAhybrid RNAplfold RNAplfold

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Effificient Unpaired Probabilities (RNAplfold)

Given RNA sequence S[1..n], compute probability Pr[x..yunpaired|S] that positions x..y of S are unpaired. Recall matrices of McCaskill: Q, Qb, Qm, Qm1, and introduce “outside” matrices ˆ Q, ˆ Qb Pr[x..y unpaired|S] = Qu(x, y)/Q(1, n) Cases I x..y external, O(1) II x..y in hairpin, closed by (i, j), naive O(n2) III x..y in internal loop, closed by (i, j), 5’ or 3’ of inner base pair (k,l), O(1) (internal loop size restricted) IV x..y in multiloop, closed by (i, j), 5’, 3’, or between inner base pairs, naive O(n3) RNA Accessibility in cubic time Stephan H. Bernhart, Ulrike M¨ uckstein, Ivo L. Hofacker. AMB 2011

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SLIDE 9

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Two Parts of One Problem ?

RNAup/IntaRNA RNAcofold

  • Number of Interaction Sites

1 2

Type of Interactions intern extern

  • however: there are more complex

structures

  • double kissing hairpins
  • . . .
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SLIDE 10

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Example

  • more than one internal interaction site
  • example: OxyS-fhlA interaction

(Argamana and Altuviaa: JMB 2000) predicted complex [Alkan et al: JCB 2006]

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Generalized Problem

approach 4: predict joint mfe structure of two sequences

  • like Zuker on two sequences at the same time, including loops

between the sequences

  • no pseudoknots, no crossing interaction
  • proven to be NP-complete
  • NP-completeness because of ZIG-ZAG structure
  • without ZIG-ZAGs polynomial algorithm
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SLIDE 12

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Generalized Problem

approach 4: predict joint mfe structure of two sequences

  • like Zuker on two sequences at the same time, including loops

between the sequences

  • no pseudoknots, no crossing interaction
  • proven to be NP-complete
  • NP-completeness because of ZIG-ZAG structure
  • without ZIG-ZAGs polynomial algorithm
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Efficient Base Pair Maximization without Zig-Zag

Given sequences R and S, compute the maximal number of intramolecular and intermolecular base pairs Fragments/subproblems R[i..j], S[k..l] Decomposition cases I unpaired base at either end of one sequence II closed structure at either end of one sequence III base pair enclosing either sequence IV interaction between left or right ends of sequences V decomposition at i ≤ i′ ≤ j, k ≤ k′ ≤ l into two subproblems R[i..i′], S[k..k′] and R[i′ + 1..j], S[k′ + 1..l] Complexity O(n6) time / O(n4) space. Why no zig-zags?

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RNA-RNA interaction: literature 1/2

approach 1: Hakim Tafer, Ivo L. Hofacker, RNAplex: a fast tool for RNA-RNA interaction search, Bioinformatics 2008 approach 2: Stephan H. Bernhart, Hakim Tafer, Ulrike M¨ uckstein, Christoph Flamm, Peter F. Stadler, Ivo L. Hofacker, Partition function and base pairing probabilities of RNA heterodimers, Algorithms Mol Biol 2006

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RNA-RNA interaction: literature 2/2

approach 3: Ulrike M¨ uckstein, Hakim Tafer, Jorg Hackermuller, Stephan H. Bernhart, Peter F. Stadler, Ivo L. Hofacker, Thermodynamics

  • f RNA-RNA binding, Bioinformatics 2006

Anke Busch, Andreas S. Richter, Rolf Backofen , IntaRNA: efficient prediction of bacterial sRNA targets incorporating target site accessibility and seed regions approach 4: Hamidreza Chitsaz, Raheleh Salari, S. Cenk Sahinalp, Rolf Backofen, A partition function algorithm for interacting nucleic acid strands, Bioinformatics 2009 Raheleh Salari, Mathias M¨

  • hl, Sebastian Will, S. Cenk

Sahinalp, Rolf Backofen, Time and space efficient RNA-RNA interaction prediction via sparse folding, RECOMB 2010