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SUMMARY:Turbo-Decoding of RNA Secondary Structure
DTSTART:20130618T140000
DTEND:20130618T150000
DTSTAMP:20260502T120559Z
UID:2344540dec60ac752015ca1e2adc9fd60066b66b596f854a8e6ae918
CATEGORIES:Conferences - Seminars
DESCRIPTION:Prof. Gaurav Sharma\, University of Rochester\nRNA has recentl
 y emerged as an important molecule in cellular biology with several direct
  functional roles in addition to its traditionally known role as an interm
 ediary carrier of the code for protein synthesis. For RNAs that serve thes
 e direct noncoding roles\, knowledge of the molecular structure is fundame
 ntal to understanding their function\, to finding new RNA genes\, and to t
 he design of targeted therapeutics. Due to the difficulty and cost associa
 ted with experimental procedures for determining structure\, computational
  methods for predicting structure are of significant research interest. In
  this talk\, we focus on computational techniques for RNA secondary struct
 ure prediction – the first step in the hierarchy of RNA structure estima
 tion that predicts the folding configuration of a linear RNA chain. Specif
 ically\, we consider methods that predict secondary structure for multiple
  RNA homologs by combining intra-sequence folding information and inter-se
 quence alignment information. The comparative analysis implicit in these m
 ulti-sequence methods provides significant improvements in accuracy over s
 ingle sequence prediction methods but the resulting computational complexi
 ty is often prohibitive. We present our recent work that addresses this ch
 allenge via a novel iterative algorithm\, TurboFold. TurboFold formulates 
 RNA folding in a probabilistic framework as the problem of estimating base
  pairing probabilities and iteratively re-computes these base pairing prob
 abilities by combining intrinsic information\, derived from the sequence i
 tself via a thermodynamic model\, with extrinsic information\, derived fro
 m the other sequences in the input set. This process yields updated estima
 tes of base pairing probability\, which are in turn used to recompute the 
 extrinsic information for subsequent interations\, resulting in the overal
 l iterative estimation procedure that defines TurboFold. We benchmark Turb
 oFold against alternative methods and highlight several of its advantages.
  Finally\, we explore connections with turbo decoding in digital communica
 tions\, which inspired TurboFold\, and outline our continuing research tha
 t seeks to develop algorithms for estimating RNA structure from multiple h
 omologs with high accuracy and low computational cost by using iterative a
 lgorithms for approximate maximum a posteriori estimation of RNA structure
 .
LOCATION:BC 420 https://plan.epfl.ch/?room==BC%20420
STATUS:CONFIRMED
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