RNA molecules interact by forming inter-molecular base pairs that compete with the intra-molecular base pairs of their secondary structures. Here we investigate the patterns of neutral mutations in RNAs whose function is the interaction with other RNAs, i.e., the co-folding with one or more other RNA molecules. We find that (1) the degree of neutrality is much smaller in interacting RNAs compared to RNAs that just have to coform to a single externally prescribed target structure, and (2) strengthening this contraint to the conservation of the co-folded structure with two or more partners essentially eliminates neutrality. It follows that RNAs whose function depends on the formation of a specific interaction complex with a target RNA molecule will evolve much more slowly than RNAs with a function depending only on their own structure.

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Non-protein-coding RNAs play critical regulatory roles in cellular life. Many ncRNAs fold into specific structures in order to perform their biological functions. Some of the RNAs, such as riboswitches, can even fold into alternative structural conformations in order to participate in different biological processes. In addition, these RNAs can transit dynamically between different functional structures along folding pathways on their energy landscapes. These alternative functional structures are usually energetically favored and are stable in their local energy landscapes. Moreover, conformational transitions between any pair of alternate structures usually involve high energy barriers, such that RNAs can become kinetically trapped by these stable and local optimal structures. We have proposed a suite of computational approaches for analyzing and discovering regulatory RNAs through studying folding pathways, alternative structures and energy landscapes associated with conformational transitions of regulatory RNAs. First, we developed an approach, RNAEAPath, which can predict low-barrier folding pathways between two conformational structures of a single RNA molecule. Using RNAEAPath, we can analyze folding iii pathways between two functional RNA structures, and therefore study the mechanism behind RNA functional transitions from a thermodynamic perspective. Second, we introduced an approach, RNASLOpt, for finding all the stable and local optimal structures on the energy landscape of a single RNA molecule. We can use the generated stable and local optimal structures to represent the RNA energy landscape in a compact manner. In addition, we applied RNASLOpt to several known riboswitches and predicted their alternate functional structures accurately. Third, we integrated a comparative approach with RNASLOpt, and developed RNAConSLOpt, which can find all the consensus stable and local optimal structures that are conserved among a set of homologous regulatory RNAs. We can use RNAConSLOpt to predict alternate functional structures for regulatory RNA families. Finally, we have proposed a pipeline making use of RNAConSLOpt to computationally discover novel riboswitches in bacterial genomes. An application of the proposed pipeline to a set of bacteria in Bacillus genus results in the re-discovery of many known riboswitches, and the detection of several novel putative riboswitch elements.

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Non-coding RNAs are the most abundant class of RNAs found throughout genomes. These RNAs are key players of gene regulation and thus, the func- tion of whole organisms. Numerous methods have been developed so far for detecting novel classes of ncRNAs or finding homologs to the known ones. Because of their abundance, the sequence availability of these RNAs is rapidly increasing, as is the case for example for microRNAs. However, for classes of them, still only incomplete information is available, invertebrates 7SK snRNA for instance. Consequently, a lot of false positive outputs are produced in the former case, and more accurate annotation methods are needed for the latter cases to improve derivable knowledge. This makes the accuracy of gathering correct homologs a challenging task and it leads directly to a not less important problem, the curation of these data. Finding solutions for the aforementioned problems is more complex than one would expect as these RNAs are characterized not only by sequences informa- tion but also structure information, in addition to distinct biological features. In this work, data curation methods and sensitive homology search are shown as complementary methods to solve these problems. A careful curation and annotation method revealed new structural information in the invertebrates 7SK snRNA, which pushes the investigation in the area forward. This has been reflected by detecting new high potential 7SK RNA genes in different invertebrates groups. Moreover, the gaps between homology search and well- curated data on the one side, and between experimental and computational outputs on the other side, are closed. These gaps were bridged by a curation method applied to the microRNA data, which was then turned into a com- prehensive workflow implemented into an automated pipeline. MIRfix is a microRNA curation pipeline considering the detailed sequence and structure information of the metazoan microRNAs, together with biological features related to the microRNA biogenesis. Moreover, this pipeline can be integrated into existing methods and tools related to microRNA homology search and data curation. The application of this pipeline on the biggest open source microRNA database revealed its high capacity in detecting wrong annotated pre-miRNA, eventually improving alignment quality of the majority of the available data. Additionally, it was tested with artificial datasets highlighting the high accuracy in predicting the pre-miRNA components, miRNA and miRNA*.:Chapter 1: Introduction Chapter 2: Biological and Computational background 2.1 Biology 2.1.1 Non-coding RNAs 2.1.2 RNA secondary structure 2.1.3 Homology versus similarity 2.1.4 Evolution 2.2 The role of computational biology 2.2.1 Alignment 2.2.1.1 Pairwise alignment 2.2.1.2 Multiple sequence alignment (MSA) 2.2.2 Homology search 2.2.2.1 Sequence-based 2.2.2.2 Structure-based 2.2.3 RNA secondary structure prediction Chapter 3: Careful curation for snRNA 3.1 Biological background 3.2 Introduction to the problem 3.3 Methods 3.3.1 Initial seeds and models construction 3.3.2 Models anatomy then merging 3.4 Results 3.4.1 Refined model of arthropod 7SK RNA 3.4.1.1 5’ Stem 3.4.1.2 Extension of Stem A 3.4.1.3 Novel stem B in invertebrates 3.4.1.4 3’ Stem 3.4.2 Invertebrates model conserves the HEXIM1 binding site 3.4.3 Computationally high potential 7SK RNA candidate . 3.4.4 Sensitivity of the final proposed model 3.5 Conclusion Chapter 4: Behind the scenes of microRNA driven regulation 4.1 Biological background 4.2 Databases and problems 4.3 MicroRNA detection and curation approaches Chapter 5: Initial microRNA curation 5.1 Introduction 5.2 Methods 5.2.1 Data pre-processing 5.2.2 Initial seeds creation 5.2.3 Main course 5.3 Results and discussion 5.4 Conclusion Chapter 6: MIRfix pipeline 6.1 Introduction 6.2 Methods 6.2.1 Inputs and Outputs 6.2.2 Prediction of the mature sequences 6.2.3 The original precursor and its alternative 6.2.4 The validation of the precursor 6.2.5 Alignment processing 6.3 Results and statistics 6.4 Applications 6.4.1 Real life examples and artificial data tests 6.4.2 miRNA and miRNA* prediction 6.4.3 Covariance models 6.5 Conclusion Chapter 7: Discussion

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