Bowling Green State University
Roll, James Elwood.
Inferring RNA 3D Motifs from Sequence.
Degree: PhD, Statistics, 2019, Bowling Green State University
An outstanding problem in molecular biology is the
prediction of the 3D structure of RNA molecules based on the
sequence of the RNA. An important step toward prediction of full
RNA 3D structures from sequence is predicting the 3D structures of
the non-helical regions, which are often referred to as loop
regions. We have developed a methodology for modeling the sequence
variability of known RNA 3D loop structures, using data from the
RNA 3D Motif Atlas. Our models are stochastic context free grammars
(SCFGs) that utilize Markov random fields (MRFs) where necessary.
The models are parameterized based on the geometry of the pairwise
interactions in the loop 3D structure as well as the sequences that
have been observed making the structure in 3D, with the result that
a reasonable model can be generated using only one sequence variant
observed forming the 3D loop structure. Work has also been done to
measure and compare how these sequence variability models overlap
in sequence space.We have developed a software package in which
these models for the sequence variability of RNA 3D loop structures
can be quickly and automatically generated. The software, called
JAR3D, is available on Github for download, and a web server and a
command line tool by the same name is publicly available. There are
a variety of applications for the JAR3D package. It can be used to
align loop sequences to a particular known 3D loop geometry, as
well as accept or reject a loop sequence as a viable candidate to
form a particular geometry. JAR3D can also be used to address a
matching problem: given a novel loop sequence, which known 3D
geometry, if any, is the sequence likely to form? This matching
problem use case is not addressed by current tools for RNA 3D
structure prediction, and is a new addition to the
Advisors/Committee Members: Zirbel, Craig (Advisor).
Subjects/Keywords: Bioinformatics; Statistics; RNA 3D structure prediction; stochastic context free gramars; markov random fields
to Zotero / EndNote / Reference
APA (6th Edition):
Roll, J. E. (2019). Inferring RNA 3D Motifs from Sequence. (Doctoral Dissertation). Bowling Green State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1557482505513958
Chicago Manual of Style (16th Edition):
Roll, James Elwood. “Inferring RNA 3D Motifs from Sequence.” 2019. Doctoral Dissertation, Bowling Green State University. Accessed September 19, 2019.
MLA Handbook (7th Edition):
Roll, James Elwood. “Inferring RNA 3D Motifs from Sequence.” 2019. Web. 19 Sep 2019.
Roll JE. Inferring RNA 3D Motifs from Sequence. [Internet] [Doctoral dissertation]. Bowling Green State University; 2019. [cited 2019 Sep 19].
Available from: http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1557482505513958.
Council of Science Editors:
Roll JE. Inferring RNA 3D Motifs from Sequence. [Doctoral Dissertation]. Bowling Green State University; 2019. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1557482505513958