Next-Generation Sequencing (NGS) is a high-throughput technology widely applied to genome sequencing and transcriptome profiling. RNA-Seq uses NGS to reveal RNA identities and quantities in a given sample. However, it produces a huge amount of raw data that need to be preprocessed with fast and effective computational methods. RNA-Seq can look at different populations of RNAs, including ncRNAs. Indeed, in the last few years, several ncRNAs pipelines have been developed for ncRNAs analysis from RNA-Seq experiments. In this paper, we analyze eight recent pipelines (iSmaRT, iSRAP, miARma-Seq, Oasis 2, SPORTS1.0, sRNAnalyzer, sRNApipe, sRNA workbench) which allows the analysis not only of single specific classes of ncRNAs but also of more than one ncRNA classes. Our systematic performance evaluation aims at guiding users to select the appropriate pipeline for processing each ncRNA class, focusing on three key points: (i) accuracy in ncRNAs identification, (ii) accuracy in read count estimation and (iii) deployment and ease of use.

BACKGROUND: Kinase over-expression and activation as a consequence of gene amplification or gene fusion events is a well-known mechanism of tumorigenesis. The search for novel rearrangements of kinases or other druggable genes may contribute to understanding the biology of cancerogenesis, as well as lead to the identification of new candidate targets for drug discovery. However this requires the ability to query large datasets to identify rare events occurring in very small fractions (1-3 %) of different tumor subtypes. This task is different from what is normally done by conventional tools that are able to find genes differentially expressed between two experimental conditions. RESULTS: We propose a computational method aimed at the automatic identification of genes which are selectively over-expressed in a very small fraction of samples within a specific tissue. The method does not require a healthy counterpart or a reference sample for the analysis and can be therefore applied also to transcriptional data generated from cell lines. In our implementation the tool can use gene-expression data from microarray experiments, as well as data generated by RNASeq technologies. CONCLUSIONS: The method was implemented as a publicly available, user-friendly tool called KAOS (Kinase Automatic Outliers Search). The tool enables the automatic execution of iterative searches for the identification of extreme outliers and for the graphical visualization of the results. Filters can be applied to select the most significant outliers. The performance of the tool was evaluated using a synthetic dataset and compared to state-of-the-art tools. KAOS performs particularly well in detecting genes that are overexpressed in few samples or when an extreme outlier stands out on a high variable expression background. To validate the method on real case studies, we used publicly available tumor cell line microarray data, and we were able to identify genes which are known to be overexpressed in specific samples, as well as novel ones.

We propose a framework to design metabolic pathways in which many objectives are optimized simultaneously. This allows to characterize the energy signature in models of algal and mitochondrial metabolism. The optimal design and assessment of the model is achieved through a multi-objective optimization technique driven by epsilon-dominance and identifiability analysis. A faster convergence process with robust candidate solutions is permitted by a relaxed Pareto dominance, regulating the granularity of the approximation of the Pareto front. Our framework is also suitable for black-box analysis, enabling to investigate and optimize any biological pathway modeled with ODEs, DAEs, FBA and GPR.

In low and high eukaryotes, energy is collected or transformed in compartments, the organelles. The rich variety of size, characteristics, and density of the organelles makes it difficult to build a general picture. In this paper, we make use of the Pareto-front analysis to investigate the optimization of energy metabolism in mitochondria and chloroplasts. Using the Pareto optimality principle, we compare models of organelle metabolism on the basis of single- and multiobjective optimization, approximation techniques (the Bayesian Automatic Relevance Determination), robustness, and pathway sensitivity analysis. Finally, we report the first analysis of the metabolic model for the hydrogenosome of Trichomonas vaginalis, which is found in several protozoan parasites. Our analysis has shown the importance of the Pareto optimality for such comparison and for insights into the evolution of the metabolism from cytoplasmic to organelle bound, involving a model order reduction. We report that Pareto fronts represent an asymptotic analysis useful to describe the metabolism of an organism aimed at maximizing concurrently two or more metabolite concentrations.

MOTIVATION: Metabolic engineering algorithms provide means to optimize a biological process leading to the improvement of a biotechnological interesting molecule. Therefore, it is important to understand how to act in a metabolic pathway in order to have the best results in terms of productions. In this work, we present a computational framework that searches for optimal and robust microbial strains that are able to produce target molecules. Our framework performs three tasks: it evaluates the parameter sensitivity of the microbial model, searches for the optimal genetic or fluxes design and finally calculates the robustness of the microbial strains. We are capable to combine the exploration of species, reactions, pathways and knockout parameter spaces with the Pareto-optimality principle. RESULTS: Our framework provides also theoretical and practical guidelines for design automation. The statistical cross comparison of our new optimization procedures, performed with respect to currently widely used algorithms for bacteria (e.g. Escherichia coli) over different multiple functions, reveals good performances over a variety of biotechnological products. AVAILABILITY: http://www.dmi.unict.it/nicosia/pathDesign.html. CONTACT: [email protected] or [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.