An overview of the role of context-sensitive HMMs in the prediction of ncRNA genes

Non-coding RNAs (ncRNA) are RNA molecules that function in the cells without being translated into proteins. In recent years, much evidence has been found that ncRNAs play a crucial role in various biological processes. As a result, there has been an increasing interest in the prediction of ncRNA genes. Due to the conserved secondary structure in ncRNAs, there exist pairwise dependencies between distant bases. These dependencies cannot be effectively modeled using traditional HMMs, and we need a more complex model such as the context-sensitive HMM (csHMM). In this paper, we overview the role of csHMMs in the RNA secondary structure analysis and the prediction of ncRNA genes. It is demonstrated that the context-sensitive HMMs can serve as an efficient framework for these purposes

Evolutionary rates vary among rRNA structural elements

Understanding patterns of rRNA evolution is critical for a number of fields, including structure prediction and phylogeny. The standard model of RNA evolution is that compensatory mutations in stems make up the bulk of the changes between homologous sequences, while unpaired regions are relatively homogeneous. We show that considerable heterogeneity exists in the relative rates of evolution of different secondary structure categories (stems, loops, bulges, etc.) within the rRNA, and that in eukaryotes, loops actually evolve much faster than stems. Both rates of evolution and abundance of different structural categories vary with distance from functionally important parts of the ribosome such as the tRNA path and the peptidyl transferase center. For example, fast-evolving residues are mainly found at the surface; stems are enriched at the subunit interface, and junctions near the peptidyl transferase center. However, different secondary structure categories evolve at different rates even when these effects are accounted for. The results demonstrate that relative rates and patterns of evolution are lineage specific, suggesting that phylogenetically and structurally specific models will improve evolutionary and structural predictions

A data science approach to pattern discovery in complex structures with applications in bioinformatics

Pattern discovery aims to find interesting, non-trivial, implicit, previously unknown and potentially useful patterns in data. This dissertation presents a data science approach for discovering patterns or motifs from complex structures, particularly complex RNA structures. RNA secondary and tertiary structure motifs are very important in biological molecules, which play multiple vital roles in cells. A lot of work has been done on RNA motif annotation. However, pattern discovery in RNA structure is less studied. In the first part of this dissertation, an ab initio algorithm, named DiscoverR, is introduced for pattern discovery in RNA secondary structures. This algorithm works by representing RNA secondary structures as ordered labeled trees and performs tree pattern discovery using a quadratic time dynamic programming algorithm. The algorithm is able to identify and extract the largest common substructures from two RNA molecules of different sizes, without prior knowledge of locations and topologies of these substructures. One application of DiscoverR is to locate the RNA structural elements in genomes. Experimental results show that this tool complements the currently used approaches for mining conserved structural RNAs in the human genome. DiscoverR can also be extended to find repeated regions in an RNA secondary structure. Specifically, this extended method is used to detect structural repeats in the 3\u27-untranslated region of a protein kinase gene

MAGNOLIA: multiple alignment of protein–coding and structural RNA sequences

MAGNOLIA is a new software for multiple alignment of nucleic acid sequences, which are recognized to be hard to align. The idea is that the multiple alignment process should be improved by taking into account the putative function of the sequences. In this perspective, MAGNOLIA is especially designed for sequences that are intended to be either protein-coding or structural RNAs. It extracts information from the similarities and differences in the data, and searches for a specific evolutionary pattern between sequences before aligning them. The alignment step then incorporates this information to achieve higher accuracy. The website is available at http://bioinfo.lifl.fr/magnolia

RNA structure analysis : algorithms and applications

In this doctoral thesis, efficient algorithms for aligning RNA secondary structures and mining unknown RNA motifs are presented. As the major contribution, a structure alignment algorithm, which combines both primary and secondary structure information, can find the optimal alignment between two given structures where one of them could be either a pattern structure of a known motif or a real query structure and the other be a subject structure. Motivated by widely used algorithms for RNA folding, the proposed algorithm decomposes an RNA secondary structure into a set of atomic structural components that can be further organized in a tree model to capture the structural particularities. The novel structure alignment algorithm is implemented using dynamic programming techniques coupled by position-independent scoring matrices. The algorithm can find the optimal global and local alignments between two RNA secondary structures at quadratic time complexity. When applied to searching a structure database, the algorithm can find similar RNA substructures and therefore can be used to identify functional RNA motifs. Extension of the algorithm has also been accomplished to deal with position-dependent scoring matrix in the purpose of aligning multiple structures. All algorithms have been implemented in a package under the name RSmatch and applied to searching mRNA UTR structure database and mining RNA motifs. The experimental results showed high efficiency and effectiveness of the proposed techniques

Strategies for measuring evolutionary conservation of RNA secondary structures

<p>Abstract</p> <p>Background</p> <p>Evolutionary conservation of RNA secondary structure is a typical feature of many functional non-coding RNAs. Since almost all of the available methods used for prediction and annotation of non-coding RNA genes rely on this evolutionary signature, accurate measures for structural conservation are essential.</p> <p>Results</p> <p>We systematically assessed the ability of various measures to detect conserved RNA structures in multiple sequence alignments. We tested three existing and eight novel strategies that are based on metrics of folding energies, metrics of single optimal structure predictions, and metrics of structure ensembles. We find that the folding energy based SCI score used in the RNAz program and a simple base-pair distance metric are by far the most accurate. The use of more complex metrics like for example tree editing does not improve performance. A variant of the SCI performed particularly well on highly conserved alignments and is thus a viable alternative when only little evolutionary information is available. Surprisingly, ensemble based methods that, in principle, could benefit from the additional information contained in sub-optimal structures, perform particularly poorly. As a general trend, we observed that methods that include a consensus structure prediction outperformed equivalent methods that only consider pairwise comparisons.</p> <p>Conclusion</p> <p>Structural conservation can be measured accurately with relatively simple and intuitive metrics. They have the potential to form the basis of future RNA gene finders, that face new challenges like finding lineage specific structures or detecting mis-aligned sequences.</p

A bioinformatics framework for RNA structure mining, motif discovery and polyadenylation analysis

The RNA molecules play various important roles in the cell and their functionality depends not only on the sequence information but to a large extent on their structure. The development of computational and predictive approaches to study RNA molecules is extremely valuable. In this research, a tool named RADAR was developed that provides a multitude of functionality for RNA data analysis and research. It aligns structure annotated RNA sequences so that both the sequence as well as structure information is taken into consideration. This tool is capable of performing pair-wise structure alignment, multiple structure alignment, database search and clustering. In addition, it provides two salient features: (i) constrained alignment of RNA secondary structures, and (ii) prediction of consensus structure for a set of RNA sequences. This tool is also hosted on the web and can be freely accessed and the software can be downloaded from http://datalab.njitedu/biodata/rna/RSmatch/server.htm . The RADAR software has been applied to various datasets (genomes of various mammals, viruses and parasites) and our experimental results show that this approach is capable of detecting functionally important regions. As an application of RADAR, a systematic data mining approach was developed, termed GLEAN-UTR, to identify small stem loop RNA structure elements in the Untranslated regions (UTRs) that are conserved between human and mouse orthologs and exist in multiple genes with common Gene Ontology terms. This study resulted in 90 distinct RNA structure groups containing 748 structures, with 3\u27 Histone stem loop (HSL3) and Iron Response element (IRE) among the top hits. Further, the role played by structure in mRNA polyadenylation was investigated. Polyadenylation is an important step towards the maturation of almost all cellular mRNAs in eukaryotes. Studies have identified several cis-elements besides the widely known polyadenylation signal (PAS) element (AATAAA or ATTAAA or a close variant) which may have a role to play in poly(A) site identification. In this study the differences in structural stability of sequences surrounding poly(A) sites was investigated and it was found that for the genes containing single poly(A) site, the surrounding sequence is most stable as compared with the surrounding sequences for alternative poly(A) sites. This indicates that structure may be providing a evolutionary advantage for single poly(A) sites that prevents multiple poly(A) sites from arising. In addition the study found that the structural stability of the region surrounding a polyadenylation site correlates with its distance from the next gene. The shortest distance corresponding to a greater structural stability