Shape based indexing for faster search of RNA family databases

Janssen S, Reeder J, Giegerich R. Shape based indexing for faster search of RNA family databases. BMC Bioinformatics. 2008;9(1):131.Background: Most non-coding RNA families exert their function by means of a conserved, common secondary structure. The Rfam data base contains more than five hundred structurally annotated RNA families. Unfortunately, searching for new family members using covariance models (CMs) is very time consuming. Filtering approaches that use the sequence conservation to reduce the number of CM searches, are fast, but it is unknown to which sacrifice. Results: We present a new filtering approach, which exploits the family specific secondary structure and significantly reduces the number of CM searches. The filter eliminates approximately 85% of the queries and discards only 2.6% true positives when evaluating Rfam against itself. First results also capture previously undetected non-coding RNAs in a recent human RNAz screen. Conclusion: The RNA shape index filter (RNAsifter) is based on the following rationale: An RNA family is characterised by structure, much more succinctly than by sequence content. Structures of individual family members, which naturally have different length and sequence composition, may exhibit structural variation in detail, but overall, they have a common shape in a more abstract sense. Given a fixed release of the Rfam data base, we can compute these abstract shapes for all families. This is called a shape index. If a query sequence belongs to a certain family, it must be able to fold into the family shape with reasonable free energy. Therefore, rather than matching the query against all families in the data base, we can first (and quickly) compute its feasible shape(s), and use the shape index to access only those families where a good match is possible due to a common shape with the query

Sequence–structure relationships in RNA loops: establishing the basis for loop homology modeling

The specific function of RNA molecules frequently resides in their seemingly unstructured loop regions. We performed a systematic analysis of RNA loops extracted from experimentally determined three-dimensional structures of RNA molecules. A comprehensive loop-structure data set was created and organized into distinct clusters based on structural and sequence similarity. We detected clear evidence of the hallmark of homology present in the sequence–structure relationships in loops. Loops differing by <25% in sequence identity fold into very similar structures. Thus, our results support the application of homology modeling for RNA loop model building. We established a threshold that may guide the sequence divergence-based selection of template structures for RNA loop homology modeling. Of all possible sequences that are, under the assumption of isosteric relationships, theoretically compatible with actual sequences observed in RNA structures, only a small fraction is contained in the Rfam database of RNA sequences and classes implying that the actual RNA loop space may consist of a limited number of unique loop structures and conserved sequences. The loop-structure data sets are made available via an online database, RLooM. RLooM also offers functionalities for the modeling of RNA loop structures in support of RNA engineering and design efforts

Lost in folding space? Comparing four variants of the thermodynamic model for RNA secondary structure prediction

Janssen S, Schudoma C, Steger G, Giegerich R. Lost in folding space? Comparing four variants of the thermodynamic model for RNA secondary structure prediction. BMC Bioinformatics. 2011;12(1): 429.BACKGROUND:Many bioinformatics tools for RNA secondary structure analysis are based on a thermodynamic model of RNA folding. They predict a single, "optimal" structure by free energy minimization, they enumerate near-optimal structures, they compute base pair probabilities and dot plots, representative structures of different abstract shapes, or Boltzmann probabilities of structures and shapes. Although all programs refer to the same physical model, they implement it with considerable variation for different tasks, and little is known about the effects of heuristic assumptions and model simplifications used by the programs on the outcome of the analysis.RESULTS:We extract four different models of the thermodynamic folding space which underlie the programs RNAfold, RNAshapes, and RNAsubopt. Their differences lie within the details of the energy model and the granularity of the folding space. We implement probabilistic shape analysis for all models, and introduce the shape probability shift as a robust measure of model similarity. Using four data sets derived from experimentally solved structures, we provide a quantitative evaluation of the model differences.CONCLUSIONS:We find that search space granularity affects the computed shape probabilities less than the over- or underapproximation of free energy by a simplified energy model. Still, the approximations perform similar enough to implementations of the full model to justify their continued use in settings where computational constraints call for simpler algorithms. On the side, we observe that the rarely used level 2 shapes, which predict the complete arrangement of helices, multiloops, internal loops and bulges, include the "true" shape in a rather small number of predicted high probability shapes. This calls for an investigation of new strategies to extract high probability members from the (very large) level 2 shape space of an RNA sequence. We provide implementations of all four models, written in a declarative style that makes them easy to be modified. Based on our study, future work on thermodynamic RNA folding may make a choice of model based on our empirical data. It can take our implementations as a starting point for further program development

A genome-wide survey of sRNAs in the symbiotic nitrogen-fixing alpha-proteobacterium Sinorhizobium meliloti

Schlüter J-P, Reinkensmeier J, Daschkey S, et al. A genome-wide survey of sRNAs in the symbiotic nitrogen-fixing alpha-proteobacterium Sinorhizobium meliloti. BMC Genomics. 2010;11(1): 245.BACKGROUND: Small untranslated RNAs (sRNAs) are widespread regulators of gene expression in bacteria. This study reports on a comprehensive screen for sRNAs in the symbiotic nitrogen-fixing alpha-proteobacterium Sinorhizobium meliloti applying deep sequencing of cDNAs and microarray hybridizations. RESULTS: A total of 1,125 sRNA candidates that were classified as trans-encoded sRNAs (173), cis-encoded antisense sRNAs (117), mRNA leader transcripts (379), and sense sRNAs overlapping coding regions (456) were identified in a size range of 50 to 348 nucleotides. Among these were transcripts corresponding to 82 previously reported sRNA candidates. Enrichment for RNAs with primary 5'-ends prior to sequencing of cDNAs suggested transcriptional start sites corresponding to 466 predicted sRNA regions. The consensus sigma70 promoter motif CTTGAC-N17-CTATAT was found upstream of 101 sRNA candidates. Expression patterns derived from microarray hybridizations provided further information on conditions of expression of a number of sRNA candidates. Furthermore, GenBank, EMBL, DDBJ, PDB, and Rfam databases were searched for homologs of the sRNA candidates identified in this study. Searching Rfam family models with over 1,000 sRNA candidates, re-discovered only those sequences from S. meliloti already known and stored in Rfam, whereas BLAST searches suggested a number of homologs in related alpha-proteobacteria. CONCLUSIONS: The screening data suggests that in S. meliloti about 3% of the genes encode trans-encoded sRNAs and about 2% antisense transcripts. Thus, this first comprehensive screen for sRNAs applying deep sequencing in an alpha-proteobacterium shows that sRNAs also occur in high number in this group of bacteria

Graphical methods in RNA structure matching

Eukaryotic genomes are pervasively transcribed; almost every base can be found in an RNA transcript. This is a surprising observation since most of the genome does not encode proteins. This RNA must serve an important regulatory function – important because producing non-coding RNA is an energy intensive process, and in the absence of strong selection one would expect it to disappear. RNA families with common functions have specifically conserved structural motifs, which are directly related to the functional roles of RNA in catalysis and regulation. Because the conserved structures depend on base-pairing, similar RNA structures may have little or no detectable sequence similarity, making the identification of conserved RNAs difficult. This is a particularly serious problem when studying regulatory structures in RNA. In many cases, such as that of cellular internal ribosome entry sites, although we can identify RNAs that have similar regulatory responses, it is difficult to tell whether the RNAs have common structural features using current methods. Available tools for identifying common structures based on RNA sequence suffer from one or more of the following problems: they do not consider pseudoknots, which are important in many catalytic and regulatory structures; they do not consider near minimum free energy structures, which is important as many RNAs exist as an ensemble of structures of nearly equal energy; they require many examples of known structures in order to train a computational model; they require impractical amounts of computational time, precluding their use on long sequences or genomic scale; or they use a similarity function that cannot identify RNAs as having similar structure, even when they are from one of the well characterized known classes. The approach presented here has the potential to address all of these issues, allowing novel RNA structures that are shared between RNAs with little or no sequence similarity to be discovered. This provides a powerful tool to investigate and explain the pervasive transcription observed in eukaryotic genomes

Identification de motifs au sein des structures biologiques arborescentes

Avec l explosion de la quantité de données biologiques disponible, développer de nouvelles méthodes de traitements efficaces est une problématique majeure en bioinformatique. De nombreuses structures biologiques sont modélisées par des structures arborescentes telles que les structures secondaires d ARN et l architecture des plantes. Ces structures contiennent des motifs répétés au sein même de leur structure mais également d une structure à l autre. Nous proposons d exploiter cette propriété fondamentale afin d améliorer le stockage et le traitement de tels objets.En nous inspirant du principe de filtres sur les séquences, nous définissons dans cette thèse une méthode de filtrage sur les arborescences ordonnées permettant de rechercher efficacement dans une base de données un ensemble d arborescences ordonnées proches d une arborescence requête. La méthode se base sur un découpage de l arborescence en graines et sur une recherche de graines communes entre les structures. Nous définissons et résolvons le problème de chainage maximum sur des arborescences. Nous proposons dans le cas des structures secondaires d ARN une définition de graines (l d) centrées.Dans un second temps, en nous basant sur des techniques d instanciations utilisées, par exemple, en infographie et sur la connaissance des propriétés de redondances au sein des structures biologiques, nous présentons une méthode de compression permettant de réduire l espace mémoire nécessaire pour le stockage d arborescences non-ordonnées. Après une détermination des redondances nous utilisons une structure de données plus compacte pour représenter notamment l architecture de la plante, celle-ci pouvant contenir des informations topologiques mais également géométriques.The explosion of available biological data urges the need for bioinformatics methods. Manybiological structures are modeled by tree structures such as RNA secondary structure and plantsarchitecture. These structures contain repeating units within their structure, but also betweendifferent structures. We propose to exploit this fundamental property to improve storage andtreatment of such objects.Following the principle of sequence filtering, we define a filtering method on ordered treesto efficiently retrieve in a database a set of ordered trees close from a query. The method isbased on a decomposition of the tree into seeds and the detection of shared seeds between thesestructures. We define and solve the maximum chaining problem on trees. We propose for RNAsecondary structure applications a definition of (l d) centered seed.Based on instantiation techniques used for instance in computer graphics and the repetitivenessof biological structures, we present a compression method which reduces the memoryspace required for plant architecture storage. A more compact data structure is used in order torepresent plant architecture. The construction of this data structure require the identification ofinternal redundancies and taking into account both topological and geometrical informations.BORDEAUX1-Bib.electronique (335229901) / SudocSudocFranceF

Kisses, ambivalent models and more: Contributions to the analysis of RNA secondary structure.

Janssen S. Kisses, ambivalent models and more: Contributions to the analysis of RNA secondary structure. Bielefeld: Universitätsbibliothek; 2014.The full functional role of RNA in all domains of life is yet to be explored. Deep sequencing technologies generate massive data about RNA transcripts with functional potential. To decipher this information, bioinformatics methods for structural analysis are in demand. With this thesis at hand, we want to improve current secondary structure prediction in different respects. The introductory chapter explains ADP with a focus on its comfortable, but atypical style of specifying algorithms. Then, we present five contributions to the analysis of RNA secondary structures. 1. It is the nature of models to abstract and simplify reality in order to master its complexity. Chapter 3 is an in depth analysis of four popular computational models of RNA secondary structure (Programs RNAshapes and RNAalishapes). 2. The secondary structure of RNA is too dynamic to be described by a single structure and in turn, there is no single optimal secondary structure. Thus, we compute the most likely abstract shape of a given RNA sequence. Improvements of the algorithms for computing the likelihood of abstract shapes are discussed in Chapter 4, specifically with regards to computational speed (Program RapidShapes). 3. For computational complexity reasons, models of RNA structures commonly exclude crossing base-pairs, the so-called "pseudoknots", from the secondary structure. In Chapter 5, we introduce a heuristic for mastering a frequent type of pseudoknots: "kissing-hairpins" (Program pKiss). 4. In Chapter 6 we revisit the old algorithmic idea of outside-in computation for the new programming framework Bellman’s GAP. This broadens the arsenal of rapid prototyping algorithms for RNA and other sequential problems. It adds "outside" and "MEA" functionality to RNAshapes and RNAalishapes. 5. Covariance Models representing RNA families assume a single consensus secondary structure for a set of related RNAs and serve as statistical tools to search for additional members. In Chapter 7, we evaluate CM scorings that are more structurespecific than the standard sequence-to-model alignments. Furthermore, we introduce a technique to incorporate "ambivalent" consensus structures into covariance models (Program aCMs). The results of this work are available at the Bielefeld Bioinformatic Server. The RNA Studio (http://bibiserv.cebitec.uni-bielefeld.de/rna) supports ready to use web-submissions, web-services and cloud computing for the programs developed in this thesis. debian packages foster a simple way to install our software on your local machine. Developers can benefit from our algorithmic analyses or use our sources for rapid prototyping as a primer for new implementations: http://bibiserv.cebitec.uni-bielefeld.de/fold-grammars