Analysis of RNA Energy Folding Landscapes

Student thesis: Doctoral ThesisDoctor of Philosophy


Over recent years there has been explosive growth in genomic sequence data largely due to rapid advances in sequencing techniques and decreasing costs. It is widely assumed that decoding genomic sequence data will lead to significant advances in our understanding of disease pathogenesis and thus open up new possibilities to tackle diseases. The functional importance of ribonucleic acids (RNAs) as regulatory molecules in normal and abnormal biology has grown considerably. In comparison to proteins, one area of research where there has been slower progress is experimental determination of RNA structure. Based on the fundamental idea that sequence determines a molecule’s structure which in turn provides important insights into its biological functions, knowledge of RNA structure is growing in importance.

In this thesis, RNA secondary structures and their folding landscapes are analysed by means of computational techniques. Firstly, we analyse the accessibility of microRNA binding sites over metastable conformations in the context of single nucleotide polymorphisms (SNPs). We developed a tool, MSbind, to analyse features of metastable SNP/miRNA binding sites and discovered three parameters that distinguish between alleles. We then incorporated our findings into a new miRNA target site prediction tool, RNAStrucTar, which takes into consideration metastable target site accessibility and found, for 16 of 20 [mRNA/3’ UTR; SNP; miRNA] instances, RNAStrucTar supports experimental findings.

Secondly, we compared random and deterministic descent strategies in the context of RNA folding landscapes. We analyse the speed-up achievable by randomised descent in attraction basins in the context of large sample sets. For the two nongradient methods we analysed for partial energy landscapes induced by ten different RNA sequences; we obtained that the number of observed local minima is on average larger by 7.3% and 3.5%, respectively. The run-time improvement is approximately 16.6% and 6.8% on average over the ten partial energy landscapes. Finally, we propose a new heuristic method based on the general framework devised by Garnier and Kallel to approximate the number of local minima states within partial RNA energy folding landscapes. Our heuristic method achieves for best approximations on average a deviation below 3.0% from the true number of local minima.
Date of Award2017
Original languageEnglish
Awarding Institution
  • King's College London
SupervisorKathleen Steinhofel (Supervisor) & Tomasz Radzik (Supervisor)

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