Provenance in bioinformatics workflows

In this work, we used the PROV-DM model to manage data provenance in workflows of genome projects. This provenance model allows the storage of details of one workflow execution, e.g., raw and produced data and computational tools, their versions and parameters. Using this model, biologists can access details of one particular execution of a workflow, compare results produced by different executions, and plan new experiments more efficiently. In addition to this, a provenance simulator was created, which facilitates the inclusion of provenance data of one genome project workflow execution. Finally, we discuss one case study, which aims to identify genes involved in specific metabolic pathways of Bacillus cereus, as well as to compare this isolate with other phylogenetic related bacteria from the Bacillus group. B. cereus is an extremophilic bacteria, collectemd in warm water in the Midwestern Region of Brazil, its DNA samples having been sequenced with an NGS machine

An analysis of cosmological perturbations in hydrodynamical and field representations

Density fluctuations of fluids with negative pressure exhibit decreasing time behaviour in the long wavelength limit, but are strongly unstable in the small wavelength limit when a hydrodynamical approach is used. On the other hand, the corresponding gravitational waves are well behaved. We verify that the instabilities present in density fluctuations are due essentially to the hydrodynamical representation; if we turn to a field representation that lead to the same background behaviour, the instabilities are no more present. In the long wavelength limit, both approachs give the same results. We show also that this inequivalence between background and perturbative level is a feature of negative pressure fluid. When the fluid has positive pressure, the hydrodynamical representation leads to the same behaviour as the field representation both at the background and perturbative levels.Comment: Latex file, 18 page

PlantRNA_sniffer : a SVM-based workflow to predict long intergenic non-coding RNAs in plants

Non-coding RNAs (ncRNAs) constitute an important set of transcripts produced in the cells of organisms. Among them, there is a large amount of a particular class of long ncRNAs that are difficult to predict, the so-called long intergenic ncRNAs (lincRNAs), which might play essential roles in gene regulation and other cellular processes. Despite the importance of these lincRNAs, there is still a lack of biological knowledge and, currently, the few computational methods considered are so specific that they cannot be successfully applied to other species different from those that they have been originally designed to. Prediction of lncRNAs have been performed with machine learning techniques. Particularly, for lincRNA prediction, supervised learning methods have been explored in recent literature. As far as we know, there are no methods nor workflows specially designed to predict lincRNAs in plants. In this context, this work proposes a workflow to predict lincRNAs on plants, considering a workflow that includes known bioinformatics tools together with machine learning techniques, here a support vector machine (SVM). We discuss two case studies that allowed to identify novel lincRNAs, in sugarcane (Saccharum spp.) and in maize (Zea mays). From the results, we also could identify differentially-expressed lincRNAs in sugarcane and maize plants submitted to pathogenic and beneficial microorganisms

Roles of non-coding RNA in sugarcane-microbe interaction

Studies have highlighted the importance of non-coding RNA regulation in plant-microbe interaction. However, the roles of sugarcane microRNAs (miRNAs) in the regulation of disease responses have not been investigated. Firstly, we screened the sRNA transcriptome of sugarcane infected with Acidovorax avenae. Conserved and novel miRNAs were identified. Additionally, small interfering RNAs (siRNAs) were aligned to differentially expressed sequences from the sugarcane transcriptome. Interestingly, many siRNAs aligned to a transcript encoding a coppertransporter gene whose expression was induced in the presence of A. avenae, while the siRNAs were repressed in the presence of A. avenae. Moreover, a long intergenic non-coding RNA was identified as a potential target or decoy of miR408. To extend the bioinformatics analysis, we carried out independent inoculations and the expression patterns of six miRNAs were validated by quantitative reverse transcription-PCR (qRT-PCR). Among these miRNAs, miR408—a copper- microRNA—was downregulated. The cleavage of a putative miR408 target, a laccase, was confirmed by a modified 50RACE (rapid amplification of cDNA ends) assay. MiR408 was also downregulated in samples infected with other pathogens, but it was upregulated in the presence of a beneficial diazotrophic bacteria. Our results suggest that regulation by miR408 is important in sugarcane sensing whether microorganisms are either pathogenic or beneficial, triggering specific miRNA-mediated regulatory mechanisms accordingly

Los elementos subjetivos del tipo legal [2.ed.]

Divulgação dos SUMÁRIOS das obras recentemente incorporadas ao acervo da Biblioteca Ministro Oscar Saraiva do STJ. Em respeito à lei de Direitos Autorais, não disponibilizamos a obra na íntegra.Localização na estante: 343(82) P766i 2.ed

A Probabilistic View of Datalog Parallelization

We explore an approach to developing Datalog parallelization strategies that aims at good expected rather than worst-case performance. To illustrate, we consider a very simple parallelization strategy that applies to all Datalog programs. We prove that this has very good expected performance under equal distribution of inputs. This is done using an extension of 0-1 laws adapted to this context. The analysis is confirmed by experimental results on randomly generated data. 1 Introduction The performance requirements of databases for advanced applications, and the increased availability of cheap parallel processing, have naturally lend great importance to the development of parallel processing techniques for databases. Much of the existing research in this direction has focused on parallelization of Datalog queries. In this paper we investigate parallel processing of Datalog from a probabilistic viewpoint. In contrast to existing work, we propose to guide the design and evaluation of para..

An Object-Oriented Framework for the Parallel Join Operation

We propose an object-oriented framework for one of the most frequent and costly operations in parallel database systems: the parallel join. The framework independently captures a great variety of parameters, such as different load balancing procedures and different synchronization disciplines. The framework addresses DBMS flexibility, configuration and extensibility issues, via the instantiation of known algorithms and facilities for the introduction of new ones. The framework can also be used to compare algorithms and to determine the execution scenario an algorithm is best suited for. Related algorithms are grouped in families, suggesting a taxonomy. 1. Introduction Since the introduction of parallel processing architectures, a great deal of effort has been spent on the development of algorithms for relational operators that support intra-operation parallelism -- one of the available techniques for DBMS parallelization [1]. Particularly, the join operator gained much attention due ..

Index Self-tuning with Agent-based Databases

The use of software agents as Database Management System components lead to database systems that may be configured and extended to support new requirements. We focus here with the self-tuning feature, which demands a somewhat intelligent behavior that agents could add to traditional DBMS modules. We propose in this paper an agent-based database architecture to deal with automatic index creation. Implementation issues are also discussed, for a built-in agents and DBMS integration architecture