Abstract
Predicting the secondary structure of RNA molecules from the knowledge of the primary structure (the sequence of bases) is still a challenging task. There are algorithms that provide good results e.g. based on the search for an energetic optimal configuration. However the output of such algorithms does not always give the real folding of the molecule and therefore a feature to judge the reliability of the prediction would be appreciated. In this paper we present results on the expected structural behavior of LSU rRNA derived using a stochastic context-free grammar and generating functions. We show how these results can be used to judge the predictions made for LSU rRNA by any algorithm. In this way it will be possible to identify those predictions which are close to the natural folding of the molecule with a probability of 97% of success.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.