Computational Investigation of the Switching Mechanism in Riboswitches

Gene Regulation is one of the most important mechanisms at the basis of the widespread diffusion and versatility of organism. It provides the cell with an effective control means over structure and functions, with a direct connection to cellular differentiation, morphogenesis, and adaptability. Until recently it has been common belief that only proteins were involved in gene regulation; later, regulatory functions have been discovered also for non-coding RNA; in particular, since the discovery of riboswitches such a simplistic perception has changed. Riboswitches are regulatory elements, usually found in 5' untranslated regions of bacterial mRNA, directly interacting with metabolites as a means of regulating expression of the coding region via a secondary structural switch. Modelling riboswitch structural rearrangement would be a greater advance, not only for understanding key processes of this RNA functional elements, but will also speed up riboswitches engineering leading to a huge improvement in biotechnological application of these genetic switches

A specific box switches the cell fate determining activity of XOTX2 and XOTX5b in the Xenopus retina

<p>Abstract</p> <p>Background</p> <p><it>Otx </it>genes, orthologues of the <it>Drosophila orthodenticle </it>gene (<it>otd</it>), play crucial roles in vertebrate brain development. In the <it>Xenopus </it>eye, <it>Xotx2 </it>and <it>Xotx5b </it>promote bipolar and photoreceptor cell fates, respectively. The molecular basis of their differential action is not completely understood, though the carboxyl termini of the two proteins seem to be crucial. To define the molecular domains that make the action of these proteins so different, and to determine whether their retinal abilities are shared by <it>Drosophila </it>OTD, we performed an <it>in vivo </it>molecular dissection of their activity by transfecting retinal progenitors with several wild-type, deletion and chimeric constructs of <it>Xotx2</it>, <it>Xotx5b </it>and <it>otd</it>.</p> <p>Results</p> <p>We identified a small 8–10 amino acid divergent region, directly downstream of the homeodomain, that is crucial for the respective activities of XOTX2 and XOTX5b. In lipofection experiments, the exchange of this 'specificity box' completely switches the retinal activity of XOTX5b into that of XOTX2 and <it>vice versa</it>. Moreover, the insertion of this box into <it>Drosophila </it>OTD, which has no effect on retinal cell fate, endows it with the specific activity of either XOTX protein. Significantly, in cell transfection experiments, the diverse ability of XOTX2 and XOTX5b to synergize with NRL, a cofactor essential for vertebrate rod development, to transactivate the rhodopsin promoter is also switched depending on the box. We also show by GST-pull down that XOTX2 and XOTX5b differentially interact with NRL, though this property is not strictly dependent on the box.</p> <p>Conclusion</p> <p>Our data provide molecular evidence on how closely related homeodomain gene products can differentiate their functions to regulate distinct cell fates. A small 'specificity box' is both necessary and sufficient to confer on XOTX2 and XOTX5b their distinct activities in the developing frog retina and to convert the neutral orthologous OTD protein of <it>Drosophila </it>into a positive and specific XOTX-like retinal regulator. Relatively little is known of what gives developmental specificity to homeodomain regulators. We propose that this box is a major domain of XOTX proteins that provides them with the appropriate developmental specificity in retinal histogenesis.</p

Extensive assessment of metrics on RNA secondary structures and relative ensembles

The ability to properly compare RNA secondary conformations is of prominent importance in many RNA structural investigations, and particularly in the study of non-coding RNAs (ncRNAs). Several different metrics have been proposed to catch topological dissimilarities between RNA secondary structures, but so far specific metric features have not been assessed yet against extensive datasets. The choice of proper metrics is also crucial for the characterization of structural ensembles, so to be able to proficiently analyze them. The current availability of large ncRNA databases has made it possible to perform an extensive comparison of different metrics. Correlation analysis has uncovered the relative descriptive power of such metrics, providing indications on their possible practical use in different contexts

Interpretable CNV-based tumour classification using fuzzy rule based classifiers

DNA copy number variations (CNVs) are widespread structural variants in the genome that are believed to play an important role in tumour evolution. Although CNVs affect a greater fraction of the genome than single nucleotide polymorphism (SNPs), so far they have received much less attention, and their contribution in cancer genomics is not still fully understood. In this work we propose a novel pipeline to support tumour type classification and rule extraction based on somatic CNV data. The pipeline outputs an interpretable Fuzzy Rule Based Classifier (FRBC), on which inference can be made. The pipeline benchmarking is performed over a set of samples of kidney cancer from TCGA. The results show the potential application of the approach: The method is able to classify between three kidney tumour types, with an accuracy of ∼ 93%, using a compact set of ∼ 50 interpretable rules

Multi-class boosting with fuzzy decision trees

Boosting is a simple and effective procedure that combines several weak learners with the aim of generating a strong classifier. Multi-class boosting has been only recently studied in the context of crisp classifiers, showing encouraging performances. In this paper, we propose FDT-Boost, a boosting approach shaped according to the multi-class SAMME-AdaBoost scheme, that employs size-constrained fuzzy binary decision trees as weak classifiers. We test FDT-Boost on twenty-three classification benchmarks. By comparing our approach with FURIA, one of the most popular fuzzy classifiers, and with a fuzzy binary decision tree, we show that our approach is accurate, yet keeping low the model complexity in terms of total number of leaf nodes

Implicitly Distributed Fuzzy Random Forests

Big Data Mining, the availability of effective and efficient classifiers is a prime concern. Accurate classification results can be obtained with sophisticated models, e.g. using ensembling approaches and exploiting concepts of fuzzy set theory, but with an high computational cost. The quest for efficiency leads to the adoption of distributed versions of classification algorithms, and in this effort the support of proper cluster computing frameworks can be fundamental. In this paper it is proposed DFRF, a novel distributed fuzzy random forest induction algorithm, based on a fuzzy discretizer for continuous attributes. The described approach, although shaped on the MapReduce programming model, takes advantage of the implicit distribution of the computation provided by the Apache Spark framework. An extensive experimental characterization of the algorithm over Big Datasets, along with a comparison with other state-of-the-art fuzzy classification algorithms, shows that DFRF provides very competitive results; moreover, a scalability study carried out on a small computer cluster shows that the approach is well behaved with respect to an increment in the number of available computing units

Optimizing Partition Granularity, Membership Function Parameters, and Rule Bases of Fuzzy Classifiers for Big Data by a Multi-objective Evolutionary Approach

Classical data mining algorithms are considered inadequate to manage the volume, variety, velocity, and veracity aspects of big data. The advent of a number of open-source cluster-computing frameworks has opened new interesting perspectives for handling the volume and velocity features. In this context, thanks to their capability of coping with vague and imprecise information, distributed fuzzy models appear to be particularly suitable for handling the variety and veracity features of big data. Moreover, the interpretability of fuzzy models may assume a particular relevance in the context of big data mining. In this work, we propose a novel approach for generating, out of big data, a set of fuzzy rule–based classifiers characterized by different optimal trade-offs between accuracy and interpretability. We extend a state-of-the-art distributed multi-objective evolutionary learning scheme, implemented under the Apache Spark environment. In particular, we exploit a recently proposed distributed fuzzy decision tree learning approach for generating an initial rule base that serves as input to the evolutionary process. Furthermore, we integrate the evolutionary learning scheme with an ad hoc strategy for the granularity learning of the fuzzy partitions, along with the optimization of both the rule base and the fuzzy set parameters. Experimental investigations show that the proposed approach is able to generate fuzzy rule–based classifiers that are significantly less complex than the ones generated by the original multi-objective evolutionary learning scheme, while keeping the same accuracy levels