Characterization of mammalian Lipocalin UTRs in silico: Predictions for their role in posttranscriptional regulation

The Lipocalin family is a group of homologous proteins characterized by its big array of functional capabilities. As extracellular proteins, they can bind small hydrophobic ligands through a well-conserved β-barrel folding. Lipocalins evolutionary history sprawls across many different taxa and shows great divergence even within chordates. This variability is also found in their heterogeneous tissue expression pattern. Although a handful of promoter regions have been previously described, studies on UTR regulatory roles in Lipocalin gene expression are scarce. Here we report a comprehensive bioinformatic analysis showing that complex post-transcriptional regulation exists in Lipocalin genes, as suggested by the presence of alternative UTRs with substantial sequence conservation in mammals, alongside a high diversity of transcription start sites and alternative promoters. Strong selective pressure could have operated upon Lipocalins UTRs, leading to an enrichment in particular sequence motifs that limit the choice of secondary structures. Mapping these regulatory features to the expression pattern of early and late diverging Lipocalins suggests that UTRs represent an additional phylogenetic signal, which may help to uncover how functional pleiotropy originated within the Lipocalin family.Ministerio de Ciencia e Innovación BFU2015-68149-RMinisterio de Economía, Industria y Competitividad, Gobierno de España BFU2011-2397

The impact of age, biogenesis, and genomic clustering on Drosophila microRNA evolution

The molecular evolutionary signatures of miRNAs inform our understanding of their emergence, biogenesis, and function. The known signatures of miRNA evolution have derived mostly from the analysis of deeply conserved, canonical loci. In this study, we examine the impact of age, biogenesis pathway, and genomic arrangement on the evolutionary properties of Drosophila miRNAs. Crucial to the accuracy of our results was our curation of high-quality miRNA alignments, which included nearly 150 corrections to ortholog calls and nucleotide sequences of the global 12-way Drosophilid alignments currently available. Using these data, we studied primary sequence conservation, normalized free-energy values, and types of structure-preserving substitutions. We expand upon common miRNA evolutionary patterns that reflect fundamental features of miRNAs that are under functional selection. We observe that melanogaster-subgroup-specific miRNAs, although recently emerged and rapidly evolving, nonetheless exhibit evolutionary signatures that are similar to well-conserved miRNAs and distinct from other structured noncoding RNAs and bulk conserved non-miRNA hairpins. This provides evidence that even young miRNAs may be selected for regulatory activities. More strikingly, we observe that mirtrons and clustered miRNAs both exhibit distinct evolutionary properties relative to solo, well-conserved miRNAs, even after controlling for sequence depth. These studies highlight the previously unappreciated impact of biogenesis strategy and genomic location on the evolutionary dynamics of miRNAs, and affirm that miRNAs do not evolve as a unitary class

Computational analysis of noncoding RNAs

Noncoding RNAs have emerged as important key players in the cell. Understanding their surprisingly diverse range of functions is challenging for experimental and computational biology. Here, we review computational methods to analyze noncoding RNAs. The topics covered include basic and advanced techniques to predict RNA structures, annotation of noncoding RNAs in genomic data, mining RNA-seq data for novel transcripts and prediction of transcript structures, computational aspects of microRNAs, and database resources.Austrian Science Fund (Schrodinger Fellowship J2966-B12)German Research Foundation (grant WI 3628/1-1 to SW)National Institutes of Health (U.S.) (NIH award 1RC1CA147187

In silico modelling of RNA-RNA dimer and its application for rational siRNA design and ncRNA target search

Non-protein coding region, which constitutes 98.5% of the human genome, were long depreciated as evolutive relict. It is only recently that the biological relevance of\ud the non-coding RNAs associated with these non-coding regions was recognized. The development of experimental and bioinformatical methods aimed at detecting these non-coding RNAs (ncRNAs) lead to the discovery of more than 29,000,000 sequences, grouped into more than 1300 families. More often than not these ncRNAs function by binding to other RNAs, either pro- tein coding or non-protein coding. Compared to the number of tools to detect and classify ncRNAs, the number of tools to search for putative RNA binding partners is negligible. This leads to the actual situation where the function of the majority of the annotated ncRNAs genes is completely unknown. The aim of this work is to assess the function of different families of ncRNAs by developing new algorithms and methods to study RNA-RNA interactions. These new methods are extensions of RNA-folding algorithms applied to the problem of RNA- RNA interactions. Depending on the class of ncRNA studied, different methods were developed and tested. This work shows that the development of RNA-folding algorithms to study RNA- RNA interactions is a promising way to functionally annotate ncRNAs. Still other factors like RNA-proteins interaction, RNA-concentration or RNA-expression, play an important role in the process of RNA hybridization and will have to be taken into account in future works in order to achieve reliable prediction of RNA binding partners.Non-protein coding region, which constitutes 98.5% of the human genome, were long depreciated as evolutive relict. It is only recently that the biological relevance of the non-coding RNAs associated with these non-coding regions was recognized. The development of experimental and bioinformatical methods aimed at detecting these non-coding RNAs (ncRNAs) lead to the discovery of more than 29,000,000 sequences, grouped into more than 1300 families. More often than not these ncRNAs function by binding to other RNAs, either pro- tein coding or non-protein coding. Compared to the number of tools to detect and classify ncRNAs, the number of tools to search for putative RNA binding partners is negligible. This leads to the actual situation where the function of the majority of the annotated ncRNAs genes is completely unknown. The aim of this work is to assess the function of different families of ncRNAs by developing new algorithms and methods to study RNA-RNA interactions. These new methods are extensions of RNA-folding algorithms applied to the problem of RNA- RNA interactions. Depending on the class of ncRNA studied, different methods were developed and tested. This work shows that the development of RNA-folding algorithms to study RNA- RNA interactions is a promising way to functionally annotate ncRNAs. Still other factors like RNA-proteins interaction, RNA-concentration or RNA-expression, play an important role in the process of RNA hybridization and will have to be taken into account in future works in order to achieve reliable prediction of RNA binding partners

Directed acyclic graph kernels for structural RNA analysis

<p>Abstract</p> <p>Background</p> <p>Recent discoveries of a large variety of important roles for non-coding RNAs (ncRNAs) have been reported by numerous researchers. In order to analyze ncRNAs by kernel methods including support vector machines, we propose stem kernels as an extension of string kernels for measuring the similarities between two RNA sequences from the viewpoint of secondary structures. However, applying stem kernels directly to large data sets of ncRNAs is impractical due to their computational complexity.</p> <p>Results</p> <p>We have developed a new technique based on directed acyclic graphs (DAGs) derived from base-pairing probability matrices of RNA sequences that significantly increases the computation speed of stem kernels. Furthermore, we propose profile-profile stem kernels for multiple alignments of RNA sequences which utilize base-pairing probability matrices for multiple alignments instead of those for individual sequences. Our kernels outperformed the existing methods with respect to the detection of known ncRNAs and kernel hierarchical clustering.</p> <p>Conclusion</p> <p>Stem kernels can be utilized as a reliable similarity measure of structural RNAs, and can be used in various kernel-based applications.</p

Systematic computational analysis of potential RNA interference regulation in Toxoplasma gondii

Thesis (Master)--Izmir Institute of Technology, Molecular Biology and Genetics, Izmir, 2009Includes bibliographical references (leaves: 58-73)Text in English; Abstract: Turkish and Englishx, 79 leavesRNA-mediated silencing was first described in plants and became famous by studies in Caenorhabditis elegans. RNA interference (RNAi) is the mechanism through which an RNA interferes with the production of other RNAs in a sequence specific manner. MiRNAs are a type of RNA which originate from the genome with their active form being ss-RNAs of 21-23 nucleotides in length. They are being transcribed as primiRNAs then processed in the nucleus by Drosha to pre-miRNAs with a stem-loop structure and 70 nucleotides in length. This stem-loop containing pre-miRNAs is then processed in the cytoplasm to ds-RNA one strand of which will serve as interfering RNA. Toxoplasma gondii is a species of parasitic protozoa which causes several diseases. T.gondii emerges as a good candidate for computational efforts with its small genome size, publicly available genome files and extensive information about its gene structure, either based on experimental data or the prediction with several gene finders in parallel. Therefore, it seems important to establish the regulatory network composed of RNAi which may be beneficial for the Toxoplasma community. Within this context the pool of possible stem-loop constitutive transcripts are produced, further analysis of this pool for desired 2D structure is integrated and mapping of possible RNAi regulation to T.gondii.s genome is established. In connection with computational assessment and mapping, the derived information is provided as a database for quick lookup using a convenient web interface for experimental studies of RNAi regulation in Toxoplasma, thus reduce time and money costs in such studies

Computational and transcriptional evidence for microRNAs in the honey bee genome

A total of 68 non-redundant candidate honey bee miRNAs were identified computationally; several of them appear to have previously unrecognized orthologs in the Drosophila genome. Several miRNAs showed caste- or age-related differences in transcript abundance and are likely to be involved in regulating honey bee development

High-Throughput Sequencing of RNA Silencing-Associated Small RNAs in Olive (Olea europaea L.)

Small RNAs (sRNAs) of 20 to 25 nucleotides (nt) in length maintain genome integrity and control gene expression in a multitude of developmental and physiological processes. Despite RNA silencing has been primarily studied in model plants, the advent of high-throughput sequencing technologies has enabled profiling of the sRNA component of more than 40 plant species. Here, we used deep sequencing and molecular methods to report the first inventory of sRNAs in olive (Olea europaea L.). sRNA libraries prepared from juvenile and adult shoots revealed that the 24-nt class dominates the sRNA transcriptome and atypically accumulates to levels never seen in other plant species, suggesting an active role of heterochromatin silencing in the maintenance and integrity of its large genome. A total of 18 known miRNA families were identified in the libraries. Also, 5 other sRNAs derived from potential hairpin-like precursors remain as plausible miRNA candidates. RNA blots confirmed miRNA expression and suggested tissue- and/or developmental-specific expression patterns. Target mRNAs of conserved miRNAs were computationally predicted among the olive cDNA collection and experimentally validated through endonucleolytic cleavage assays. Finally, we use expression data to uncover genetic components of the miR156, miR172 and miR390/TAS3-derived trans-acting small interfering RNA (tasiRNA) regulatory nodes, suggesting that these interactive networks controlling developmental transitions are fully operational in olive