This article proposes to use an RNA graph similarity metric, based on the MCES resolution problem, to compare the occurrences of specific complex motifs in RNA graphs, according to their context represented as subgraph. We rely on a new modeling by graphs of these contexts, at two different levels of granularity, and obtain a classification of these graphs, which is consistent with the RNA 3D structure. RNA many non-translational functions, as a ribozyme, riboswitch, or ribosome, require complex structures. Those are composed of a rigid skeleton, a set of canonical interactions called the secondary structure. Decades of experimental and theoretical work have produced precise thermodynamic parameters and efficient algorithms to predict, from sequence, the secondary structure of RNA molecules. On top of the skeleton, the nucleotides form an intricate network of interactions that are not captured by present thermodynamic models. This network has been shown to be composed of modular motifs, that are linked to function, and have been leveraged for better prediction and design. A peculiar subclass of complex structural motifs are those connecting RNA regions far away in the secondary structure. They are crucial to predict since they determine the global shape of the molecule, therefore important for the function. In this paper, we show by using our graph approach that the context is important for the formation of conserved complex structural motifs. We furthermore show that a natural classification of structural variants of the motifs emerges from their context. We explore the cases of three known motif families and we exhibit their experimentally emerging classification.

RNA tertiary structure is crucial to its many non-coding molecular functions. RNA architecture is shaped by its secondary structure composed of stems, stacked canonical base pairs, enclosing loops. While stems are precisely captured by free-energy models, loops composed of non-canonical base pairs are not. Nor are distant interactions linking together those secondary structure elements (SSEs). Databases of conserved 3D geometries (a.k.a. modules) not captured by energetic models are leveraged for structure prediction and design, but the computational complexity has limited their study to local elements, loops. Representing the RNA structure as a graph has recently allowed to expend this work to pairs of SSEs, uncovering a hierarchical organization of these 3D modules, at great computational cost. Systematically capturing recurrent patterns on a large scale is a main challenge in the study of RNA structures. In this paper, we present an efficient algorithm to compute maximal isomorphisms in edge colored graphs. We extend this algorithm to a framework well suited to identify RNA modules, and fast enough to considerably generalize previous approaches. To exhibit the versatility of our framework, we first reproduce results identifying all common modules spanning more than 2 SSEs, in a few hours instead of weeks. The efficiency of our new algorithm is demonstrated by computing the maximal modules between any pair of entire RNA in the non-redundant corpus of known RNA 3D structures. We observe that the biggest modules our method uncovers compose large shared sub-structure spanning hundreds of nucleotides and base pairs between the ribosomes of Thermus thermophilus, Escherichia Coli, and Pseudomonas aeruginosa.

Massive biological datasets are available in public databases and can be queried using portals with keyword queries. Ranked lists of answers are obtained by users. However, properly querying such portals remains difficult since various formulations of the same query can be considered (e.g., using synonyms). Consequently, users have to manually combine several lists of hundreds of answers into one list. Rank aggregation techniques are particularly well-fitted to this context as they take in a set of ranked elements (rankings) and provide a consensus, that is, a single ranking which is the "closest" to the input rankings. However, the problem of rank aggregation is NP-hard in most cases. Using an exact algorithm is currently not possible for more than a few dozens of elements. A plethora of heuristics have thus been proposed which behaviour are, by essence, difficult to anticipate: given a set of input rankings, one cannot guarantee how far from an exact solution the consensus ranking provided by an heuristic will be. The two challenges we want to tackle in this paper are the following: (i) providing an approach based on a pre-process to decompose large data sets into smaller ones where high-quality algorithms can be run and (ii) providing information to users on the robustness of the positions of elements in the consensus ranking produced. Our approach not only lies in mathematical bases, offering guarantees on the result computed but it has also been implemented in a real system available to life science community and tested on various real use cases.

BACKGROUND: In systems biology, there is an acute need for integrative approaches in heterogeneous network mining in order to exploit the continuous flux of genomic data. Simultaneous analysis of the metabolic pathways and genomic context of a given species leads to the identification of patterns consisting in reaction chains catalyzed by products of neighboring genes. Similar such patterns across several species can reveal their mode of conservation throughout the tree of life. RESULTS: We present CoMetGeNe (COnserved METabolic and GEnomic NEighborhoods), a novel method that identifies metabolic and genomic patterns consisting in maximal trails of reactions being catalyzed by products of neighboring genes. Patterns determined by CoMetGeNe in one species are subsequently employed in order to reflect their degree of conservation across multiple prokaryotic species. These interspecies comparisons help to improve genome annotation and can reveal putative alternative metabolic routes as well as unexpected gene ordering occurrences. CONCLUSIONS: CoMetGeNe is an exploratory tool at both the genomic and the metabolic levels, leading to insights into the conservation of functionally related clusters of neighboring enzyme-coding genes. The open-source CoMetGeNe pipeline is freely available at https://cometgene.lri.fr .

MOTIVATION: Predicting the 3D structure of RNA molecules is a key feature towards predicting their functions. Methods which work at atomic or nucleotide level are not suitable for large molecules. In these cases, coarse-grained prediction methods aim to predict a shape which could be refined later by using more precise methods on smaller parts of the molecule. RESULTS: We developed a complete method for sampling 3D RNA structure at a coarse-grained model, taking a secondary structure as input. One of the novelties of our method is that a second step extracts two best possible structures close to the native, from a set of possible structures. Although our method benefits from the first version of GARN, some of the main features on GARN2 are very different. GARN2 is much faster than the previous version and than the well-known methods of the state-of-art. Our experiments show that GARN2 can also provide better structures than the other state-of-the-art methods. AVAILABILITY AND IMPLEMENTATION: GARN2 is written in Java. It is freely distributed and available at http://garn.lri.fr/. CONTACT: [email protected] or [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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We present a new approach for the prediction of the coarse-grain 3D structure of RNA molecules. We model a molecule as being made of helices and junctions. Those junctions are classified into topological families that determine their preferred 3D shapes. All the parts of the molecule are then allowed to establish long-distance contacts that induce a 3D folding of the molecule. An algorithm relying on game theory is proposed to discover such long-distance contacts that allow the molecule to reach a Nash equilibrium. As reported by our experiments, this approach allows one to predict the global shape of large molecules of several hundreds of nucleotides that are out of reach of the state-of-the-art methods.

We describe a theoretical unifying framework to express the comparison of RNA structures, which we call alignment hierarchy. This framework relies on the definition of common supersequences for arc-annotated sequences and encompasses the main existing models for RNA structure comparison based on trees and arc-annotated sequences with a variety of edit operations. It also gives rise to edit models that have not been studied yet. We provide a thorough analysis of the alignment hierarchy, including a new polynomial-time algorithm and an NP-completeness proof. The polynomial-time algorithm involves biologically relevant edit operations such as pairing or unpairing nucleotides. It has been implemented in a software, called gardenia, which is available at the Web server http://bioinfo.lifl.fr/RNA/gardenia.

DESCRIPTION: VARNA is a tool for the automated drawing, visualization and annotation of the secondary structure of RNA, designed as a companion software for web servers and databases. FEATURES: VARNA implements four drawing algorithms, supports input/output using the classic formats dbn, ct, bpseq and RNAML and exports the drawing as five picture formats, either pixel-based (JPEG, PNG) or vector-based (SVG, EPS and XFIG). It also allows manual modification and structural annotation of the resulting drawing using either an interactive point and click approach, within a web server or through command-line arguments. AVAILABILITY: VARNA is a free software, released under the terms of the GPLv3.0 license and available at http://varna.lri.fr. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Abstract Summary: GenRGenS is a software tool dedicated to randomly generating genomic sequences and structures. It handles several classes of models useful for sequence analysis, such as Markov chains, hidden Markov models, weighted context-free grammars, regular expressions and PROSITE expressions. GenRGenS is the only program that can handle weighted context-free grammars, thus allowing the user to model and to generate structured objects (such as RNA secondary structures) of any given desired size. GenRGenS also allows the user to combine several of these different models at the same time. Availability: Source and executable files of GenRGenS (in Java) and the complete user's manual are freely available at Contact: [email protected]

This paper addresses the problem of selecting finite test sets and automating this selection. Among these methods, some are deterministic and some are statistical. The kind of statistical testing we consider has been inspired by the work of Thevenod-Fosse and Waeselynck. There, the choice of the distribution on the input domain is guided by the structure of the program or the form of its specification. In the present paper, we describe a new generic method for performing statistical testing according to any given graphical description of the behavior of the system under test. This method can be fully automated. Its main originality is that it exploits recent results and tools in combinatorics, precisely in the area of random generation of combinatorial structures. Uniform random generation routines are used for drawing paths from the set of execution paths or traces of the system under test. Then a constraint resolution step is performed, aiming to design a set of test data that activate the generated paths. This approach applies to a number of classical coverage criteria. Moreover, we show how linear programming techniques may help to improve the quality of test, i.e. the probabilities for the elements to be covered by the test process. The paper presents the method in its generality. Then, in the last section, experimental results on applying it to structural statistical software testing are reported.

MOTIVATION: Unconventional decoding events are now well acknowledged, but not yet well formalized. In this study, we present a bioinformatics analysis of eukaryotic -1 frameshifting, in order to model this event. RESULTS: A consensus model has already been established for -1 frameshifting sites. Our purpose here is to provide new constraints which make the model more precise. We show how a machine learning approach can be used to refine the current model. We identify new properties that may be involved in frameshifting. Each of the properties found was experimentally validated. Initially, we identify features of the overall model that are to be simultaneously satisfied. We then focus on the following two components: the spacer and the slippery sequence. As a main result, we point out that the identity of the primary structure of the so-called spacer is of great importance. AVAILABILITY: Sequences of the oligonucleotides in the functional tests are available at http://www.igmors.u-psud.fr/rousset/bioinformatics/.

We propose a novel way of automating statistical structural testing of software, based on the combination of uniform generation of combinatorial structures, and of randomized constraint solving techniques. More precisely, we show how to draw test cases which balance the coverage of program structures according to structural testing criteria. The control flow graph is formalized as a combinatorial structure specification. This provides a way of uniformly drawing execution paths which have suitable properties. Once a path has been drawn, the predicate characterizing those inputs which lead to its execution is solved using a constraint solving library. The constraint solver is enriched with powerful heuristics in order to deal with resolution failures and random choice strategies.