Bioinformatics Forum: Algorithmic Techniques for RNA Secondary Structure Prediction

Speaker:
Shay Zakov (Computer Science and Engineering, UCSD)
Date:
Tuesday, 11.3.2014, 13:30
Place:
Taub 701

The main role of RNA as a mediator in the process of protein construction out of DNA information, as expressed in Crick's "central dogma of molecular biology", is challenged by recent discoveries regarding the amount of non-protein-coding RNA (ncRNA) being transcribed, and the diverse functionalities of some of these molecules. Some functional RNA molecules are known for several decades now (e.g. tRNA and ribosomal RNA), and other were more recently discovered (e.g. riboswitches), yet it is speculated that many additional ncRNA molecules carry functionalities which are still unknown.

As for proteins, information regarding the structure of RNA molecules is valuable when studying their functional roles. Unfortunately, traditional wet-lab structural inference techniques, such as X-ray crystallography and NMR, are more difficult to apply to RNA than to proteins, and to date only a relatively small number of RNA structures were determined using these techniques. On the other hand, current sequencing technologies can determine RNA sequences in a relatively accurate, fast, and cheap manners. This motivates the development of computational tools that predict RNA structures, given RNA sequences.

In this talk, we will present some of the main computational concepts in the domain of RNA secondary structure prediction, and recent advanced algorithmic techniques that may be applied in order to improve algorithms for several related problem variants. As much as time permits, we will discuss feature-modeling and machine-learning techniques to improve the accuracy of RNA structure prediction, and sparsification and other techniques to improve both time and space complexities of the standard RNA folding algorithms.

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