A Database and Evaluation for Classification of RNA Molecules Using Graph Methods

In this paper, we introduce a new graph dataset based on the representation of RNA. The RNA dataset includes 3178 RNA chains which are labelled in 8 classes according to their reported biological functions. The goal of this database is to provide a platform for investigating the classication of RNA using graph-based methods. The molecules are represented by graphs representing the sequence and base-pairs of the RNA, with a number of labelling schemes using base labels and local shape. We report the results of a number of state-of-the-art graph based methods on this dataset as a baseline comparison and investigate how these methods can be used to categorise RNA molecules on their type and functions. The methods applied are Weisfeiler Lehman and optimal assignment kernels, shortest paths kernel and the all paths and cycle methods. We also compare to the standard Needleman-Wunsch algorithm used in bioinformatics for DNA and RNA comparison, and demonstrate the superiority of graph kernels even on a string representation. The highest classication rate is obtained by the WL-OA algorithm using base labels and base-pair connections

Representation, searching and discovery of patterns of bases in complex RNA structures

We describe a graph theoretic method designed to perform efficient searches for substructural patterns in nucleic acid structural coordinate databases using a simplified vectorial representation. Two vectors represent each nucleic acid base and the relative positions of bases with respect to one another are described in terms of distances between the defined start and end points of the vectors on each base. These points comprise the nodes and the distances the edges of a graph, and a pattern search can then be performed using a subgraph isomorphism algorithm. The minimal representation was designed to facilitate searches for complex patterns but was first tested on simple, well-characterised arrangements of bases such as base pairs and GNRA-tetraloop receptor interactions. The method performed very well for these interaction types. A survey of side-by-side base interactions, of which the adenosine platform is the best known example, also locates examples of similar base rearrangements that we consider to be important in structural regulation. A number of examples were found, with GU platforms being particularly prevalent. A GC platform in the RNA of the Thermus thermophilus small ribosomal subunit is in an analogous position to an adenosine platform in other species. An unusual GG platform is also observed close to one of the substrate binding sites in Haloarcula marismortui large ribosomal subunit RNA

McGenus: A Monte Carlo algorithm to predict RNA secondary structures with pseudoknots

We present McGenus, an algorithm to predict RNA secondary structures with pseudoknots. The method is based on a classification of RNA structures according to their topological genus. McGenus can treat sequences of up to 1000 bases and performs an advanced stochastic search of their minimum free energy structure allowing for non trivial pseudoknot topologies. Specifically, McGenus employs a multiple Markov chain scheme for minimizing a general scoring function which includes not only free energy contributions for pair stacking, loop penalties, etc. but also a phenomenological penalty for the genus of the pairing graph. The good performance of the stochastic search strategy was successfully validated against TT2NE which uses the same free energy parametrization and performs exhaustive or partially exhaustive structure search, albeit for much shorter sequences (up to 200 bases). Next, the method was applied to other RNA sets, including an extensive tmRNA database, yielding results that are competitive with existing algorithms. Finally, it is shown that McGenus highlights possible limitations in the free energy scoring function. The algorithm is available as a web-server at http://ipht.cea.fr/rna/mcgenus.php .Comment: 6 pages, 1 figur

Automatic generation of pseudoknotted RNAs taxonomy

Background: The ability to compare RNA secondary structures is important in understanding their biological function and for grouping similar organisms into families by looking at evolutionarily conserved sequences such as 16S rRNA. Most comparison methods and benchmarks in the literature focus on pseudoknot-free structures due to the difficulty of mapping pseudoknots in classical tree representations. Some approaches exist that permit to cluster pseudoknotted RNAs but there is not a general framework for evaluating their performance. Results: We introduce an evaluation framework based on a similarity/dissimilarity measure obtained by a comparison method and agglomerative clustering. Their combination automatically partition a set of molecules into groups. To illustrate the framework we define and make available a benchmark of pseudoknotted (16S and 23S) and pseudoknot-free (5S) rRNA secondary structures belonging to Archaea, Bacteria and Eukaryota. We also consider five different comparison methods from the literature that are able to manage pseudoknots. For each method we clusterize the molecules in the benchmark to obtain the taxa at the rank phylum according to the European Nucleotide Archive curated taxonomy. We compute appropriate metrics for each method and we compare their suitability to reconstruct the taxa

Chemoinformatics Research at the University of Sheffield: A History and Citation Analysis

This paper reviews the work of the Chemoinformatics Research Group in the Department of Information Studies at the University of Sheffield, focusing particularly on the work carried out in the period 1985-2002. Four major research areas are discussed, these involving the development of methods for: substructure searching in databases of three-dimensional structures, including both rigid and flexible molecules; the representation and searching of the Markush structures that occur in chemical patents; similarity searching in databases of both two-dimensional and three-dimensional structures; and compound selection and the design of combinatorial libraries. An analysis of citations to 321 publications from the Group shows that it attracted a total of 3725 residual citations during the period 1980-2002. These citations appeared in 411 different journals, and involved 910 different citing organizations from 54 different countries, thus demonstrating the widespread impact of the Group's work

Post-transcriptional knowledge in pathway analysis increases the accuracy of phenotypes classification

Motivation: Prediction of phenotypes from high-dimensional data is a crucial task in precision biology and medicine. Many technologies employ genomic biomarkers to characterize phenotypes. However, such elements are not sufficient to explain the underlying biology. To improve this, pathway analysis techniques have been proposed. Nevertheless, such methods have shown lack of accuracy in phenotypes classification. Results: Here we propose a novel methodology called MITHrIL (Mirna enrIched paTHway Impact anaLysis) for the analysis of signaling pathways, which has built on top of the work of Tarca et al., 2009. MITHrIL extends pathways by adding missing regulatory elements, such as microRNAs, and their interactions with genes. The method takes as input the expression values of genes and/or microRNAs and returns a list of pathways sorted according to their deregulation degree, together with the corresponding statistical significance (p-values). Our analysis shows that MITHrIL outperforms its competitors even in the worst case. In addition, our method is able to correctly classify sets of tumor samples drawn from TCGA. Availability: MITHrIL is freely available at the following URL: http://alpha.dmi.unict.it/mithril