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le jeudi 22 mai à 11:00

Robert Giegerich (Bielefeld University, Germany)

Salle de formation du PRABI - 2ème étage

par Vincent Daubin - 22 mai 2008

Shape Abstraction in RNA Folding and Family Modelling

When humans reason about functional structures of RNA, they
speak of long hairpins with miRNA precursors, of clover leaf
structures with tRNA, of neighouring hairpins with attenuators,
and so on. Most of the time, we do not care about individual
base pairs or helix sizes, while the overall arrangment of helices
and loops really matters.

RNA folding programs, however, used to be ignorant of abstraction
in RNA, deceiving us with a single, minimum free energy structure,
or overwhelming us with a plethora of near-optimal structures,
most of which are quite similar and therefore redundant.

RNA shape abstraction alleviates this situation. RNA shapes are
abstract structure images, retaining adjacency and nesting of
structural features, but disregarding size information. Shape
abstraction integrates well with dynamic programming algorithms,
and hence it can be applied during structure prediction rather than
afterwards. This avoids exponential explosion in the near-optimal
folding space, and provides a non-heuristic and complete account
of an RNA molecule’s structural inclinations. Quite magically, some
long-studied problems become easy.

In the presentation, I will shortly review the notion of abstract
shapes. I will then discuss several applications of this concept,
including a highly effective filtering method when working with
structural classes of RNA described by covariance models,
such as provided by the Rfam data base.