Investigate the functional importance of RNA secondary structure with computational biology approaches

Yang, Bibo (2025) Investigate the functional importance of RNA secondary structure with computational biology approaches. Doctoral thesis, University of East Anglia.

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Abstract

RNA structures are fundamental regulators of diverse biological processes, yet the identification of functional structures remains a major challenge. Advances in deep sequencing–based probing have generated large-scale, high-precision datasets, reshaping the field and providing a strong foundation for computational approaches to discover the functional importance of RNA structures. This thesis presents three computational biology studies addressing RNA structural features and their functional roles from two complementary perspectives: structure motifs and structure dynamics.

The first project focused on i-motifs (iMs), a known cytosine-rich structural motif. A machine learning framework, iM-Seeker, was developed using iM-specific datasets to identify putative iM-forming sequences in DNA and RNA, predict folding status, and estimate folding stability. Application across over 400 plant transcriptomes revealed strong enrichment of RNA iMs in 5′UTRs, particularly in monocots, with positive correlation to environmental temperature. Translatome analyses indicated that 5′UTR iMs act as translational repressors, independent of folding stability.

The second project investigated RNA structure motifs underlying RNA stability in wheat. RNA structure was identified as the dominant determinant of stability. The functional structure discovery pipeline pyTEISER (python-implemented Tool for Eliciting Informative Structural Elements in RNA) was optimized by expanding motif seeds and improving computational efficiency, enabling the identification of stability-associated motifs. Subgenome-specific preferences highlighted the regulatory role of structural motifs in polyploid plants.

The third project addressed RNA structural dynamics during tomato fruit development. High-resolution structuromes were generated at two developmental stages, and RSDE (RNA Structural Dynamic Elements)-Tool was introduced to integrate ensemble-based structure analysis, statistics, and machine learning. Over 12,000 RSDEs were identified, predominantly in 5′UTRs and CDS regions, many significantly associated with translational efficiency changes in tomato development.

Collectively, these studies establish computational strategies to investigate crucial RNA structures, highlighting their diverse and critical regulatory roles in biological systems.

Item Type:	Thesis (Doctoral)
Faculty \ School:	Faculty of Science > School of Biological Sciences
Depositing User:	Chris White
Date Deposited:	16 Feb 2026 11:40
Last Modified:	16 Feb 2026 11:40
URI:	https://ueaeprints.uea.ac.uk/id/eprint/101945
DOI:

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