Biology Ph.D. Dissertations


RNA 3D Motifs: Identification, Clustering, and Analysis

Date of Award


Document Type


Degree Name

Doctor of Philosophy (Ph.D.)


Biological Sciences

First Advisor

Neocles Leontis, PhD

Second Advisor

Craig Zirbel, PhD (Committee Member)

Third Advisor

Paul Morris, PhD (Committee Member)

Fourth Advisor

Scott Rogers, PhD (Committee Member)

Fifth Advisor

Raymond Larsen, PhD (Committee Member)


Many hairpin and internal RNA 3D motif structures are recurrent, occurring in various types of RNA molecules, not necessarily homologs. Although usually drawn as single-strand “loops” in RNA 2D diagrams, recurrent motifs share a common 3D structure, but can vary in sequence. It is essential to understand the sequence variability of RNA 3D motifs in order to advance the RNA 2D and 3D structure prediction and ncRNA discovery methods, to interpret mutations that affect ncRNAs, and to guide experimental functional studies.

The dissertation is organized into two parts as follows. First, the development of a new online resource called RNA 3D Hub is described, which is intended to provide a useful resource for structure modeling and prediction. It houses non-redundant sets of RNA-containing 3D structures, RNA 3D motifs extracted from all RNA 3D structures, and the RNA 3D Motif Atlas, a representative collection of RNA 3D motifs. Unique and stable ids are assigned to all non-redundant equivalence classes of structure files, to all motifs, and to all motif instances. RNA 3D Hub is updated automatically on a regular schedule and is available at

In the second part of the dissertation, the development of WebFR3D (, a new webserver for finding and aligning RNA 3D motifs, is described and its use in a biologically relevant context is then illustrated using two RNA 3D motifs. The first motif was predicted in Potato Spindle Tuber Viroid (PSTVd), and the prediction was supported by functional evidence. The second motif had previously been undescribed, although it is found in multiple 3D structures.

RNA 3D Hub, RNA 3D Motif Atlas, and the bioinformatic techniques discussed in this dissertation lay the groundwork for further research into RNA 3D motif prediction starting from sequence and provide useful online resources for the scientific community worldwide.