Improved Workflows for RNA Homology Search

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: Discussio