Computational methods for RNA integrative biology

Abstract

Ribonucleic acid (RNA) is an essential molecule, which carries out a wide variety of functions within the cell, from its crucial involvement in protein synthesis to catalysing biochemical reactions and regulating gene expression. Such diverse functional repertoire is indebted to complex structures that RNA can adopt and its flexibility as an interacting molecule. It has become possible to experimentally measure these two crucial aspects of RNA regulatory role with such technological advancements as next-generation sequencing (NGS). NGS methods can rapidly obtain the nucleotide sequence of many molecules in parallel. Designing experiments, where only the desired parts of the molecule (or specific parts of the transcriptome) are sequenced, allows to study various aspects of RNA biology. Analysis of NGS data is insurmountable without computational methods. One such experimental method is RNA structure probing, which aims to infer RNA structure from sequencing chemically altered transcripts. RNA structure probing data is inherently noisy, affected both by technological biases and the stochasticity of the underlying process. Most existing methods do not adequately address the issue of noise, resorting to heuristics and limiting the informativeness of their output. In this thesis, a statistical pipeline was developed for modelling RNA structure probing data, which explicitly captures biological variability, provides automated bias-correcting strategies, and generates a probabilistic output based on experimental measurements. The output of our method agrees with known RNA structures, can be used to constrain structure prediction algorithms, and remains robust to reduced sequence coverage, thereby increasing sensitivity of the technology. Another recent experimental innovation maps RNA-protein interactions at very high temporal resolution, making it possible to study rapid binding events happening on a minute time scale. In this thesis, a non-parametric algorithm was developed for identifying significant changes in RNA-protein binding time-series between different conditions. The method was applied to novel yeast RNA-protein binding time-course data to study the role of RNA degradation in stress response. It revealed pervasive changes in the binding to the transcriptome of the yeast transcription termination factor Nab3 and the cytoplasmic exoribonuclease Xrn1 under nutrient stress. This challenged the common assumption of viewing transcriptional changes as the major driver of changes in RNA expression during stress and highlighted the importance of degradation. These findings inspired a dynamical model for RNA expression, where transcription and degradation rates are modelled using RNA-protein binding time-series data.