Turbo-Decoding of RNA Secondary Structure
Event details
| Date | 18.06.2013 |
| Hour | 14:00 › 15:00 |
| Speaker | Prof. Gaurav Sharma, University of Rochester |
| Location | |
| Category | Conferences - Seminars |
RNA has recently emerged as an important molecule in cellular biology with several direct functional roles in addition to its traditionally known role as an intermediary carrier of the code for protein synthesis. For RNAs that serve these direct noncoding roles, knowledge of the molecular structure is fundamental to understanding their function, to finding new RNA genes, and to the design of targeted therapeutics. Due to the difficulty and cost associated 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 structure prediction – the first step in the hierarchy of RNA structure estimation that predicts the folding configuration of a linear RNA chain. Specifically, we consider methods that predict secondary structure for multiple RNA homologs by combining intra-sequence folding information and inter-sequence alignment information. The comparative analysis implicit in these multi-sequence methods provides significant improvements in accuracy over single sequence prediction methods but the resulting computational complexity is often prohibitive. We present our recent work that addresses this challenge 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 probabilities by combining intrinsic information, derived from the sequence itself via a thermodynamic model, with extrinsic information, derived from the other sequences in the input set. This process yields updated estimates of base pairing probability, which are in turn used to recompute the extrinsic information for subsequent interations, resulting in the overall iterative estimation procedure that defines TurboFold. We benchmark TurboFold against alternative methods and highlight several of its advantages. Finally, we explore connections with turbo decoding in digital communications, which inspired TurboFold, and outline our continuing research that seeks to develop algorithms for estimating RNA structure from multiple homologs with high accuracy and low computational cost by using iterative algorithms for approximate maximum a posteriori estimation of RNA structure.
Links
Practical information
- General public
- Free
Organizer
- SuRI 2013
Contact
- Simone Muller