Predicting RNA secondary structure using a stochastic conjunctive grammar
| dc.contributor.author | Zier-Vogel, Ryan | |
| dc.contributor.examiningcommittee | Durocher, Stephane (Computer Science) McKenna, Sean (Chemistry) | en_US |
| dc.contributor.supervisor | Domaratzki, Michael (Computer Science) | en_US |
| dc.date.accessioned | 2012-08-22T20:17:24Z | |
| dc.date.available | 2012-08-22T20:17:24Z | |
| dc.date.issued | 2012-08-22 | |
| dc.degree.discipline | Computer Science | en_US |
| dc.degree.level | Master of Science (M.Sc.) | en_US |
| dc.description.abstract | In this thesis I extend a class of grammars called conjunctive grammars to a stochastic form called stochastic conjunctive grammars. This extension allows the grammars to predict pseudoknotted RNA secondary structure. Since observing sec- ondary structure is hard and expensive to do with today's technology, there is a need for computational solutions to this problem. A conjunctive grammar can handle pseudoknotted structure because of the way one sequence is generated by combining multiple parse trees. I create several grammars that are designed to predict pseudoknotted RNA sec- ondary structure. One grammar is designed to predict all types of pseudoknots and the others are made to only predict a pseudoknot called H-type. These grammars are trained and tested and the results are collected. I am able to obtain a sensitivity of over 75% and a speci city of over 89% on H-type pseudoknots | en_US |
| dc.description.note | October 2012 | en_US |
| dc.identifier.uri | http://hdl.handle.net/1993/8453 | |
| dc.language.iso | eng | en_US |
| dc.rights | open access | en_US |
| dc.subject | pseudoknot | en_US |
| dc.subject | RNA | en_US |
| dc.subject | grammar | en_US |
| dc.title | Predicting RNA secondary structure using a stochastic conjunctive grammar | en_US |
| dc.type | master thesis | en_US |