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Additional file 1. Includes additional methods, figures, and tables that complement the analysis and results presented in the main manuscript.

Additional file 1. Additional tables, text and figures.

From creation to its degradation, the RNA molecule is the action field of many binding proteins with different roles in regulation and RNA metabolism. Since these proteins are involved in a large number of processes, a variety of diseases are related to abnormalities occurring within the binding mechanisms. One of the experimental methods for detecting the binding sites of these proteins is PAR-CLIP built on the next generation sequencing technology. Due to its size and intrinsic noise, PAR-CLIP data analysis requires appropriate pre-processing and thorough statistical analysis. The present work has two main goals. First, to develop a modular pipeline for preprocessing PAR-CLIP data and extracting necessary signals for further analysis. Second, to devise a novel statistical model in order to carry out inference about presence of protein binding sites based on the signals extracted in the pre-processing step.

Enterococcus faecalis is one of the most controversial commensal bacteria of the human intestinal flora that is also responsible for lethal nosocomial infections. Determining the factors that influence its pathogenicity is at present a great challenge. Cutting-edge approaches analyze the E. faecalis bacterium trough the next generation RNA-sequencing technology. Since next generation sequencing is recent and yields a large amount of data, there is a continuous need for appropriate statistical methods to interpret its output. We propose an approach based on hidden Markov models to explore RNA-seq data and show an example of how we can apply this statistical tool to detect transcription start sites. We compare this application with a previously developed method based on signal processing. Individual Project in Computational Biology. QC 20130911

Throughout the HIV-1 replication cycle, complex host-pathogen interactions take place in the infected cell, leading to the production of new virions. The virus modulates the host cellular machinery in order to support its life cycle, while counteracting intracellular defense mechanisms. We investigated the dynamic host response to HIV-1 infection by systematically measuring transcriptomic, proteomic, and phosphoproteomic expression changes in infected and uninfected SupT1 CD4+ T cells at five time points of the viral replication process. By means of a Gaussian mixed-effects model implemented in the new R/Bioconductor package TMixClust, we clustered host genes based on their temporal expression patterns. We identified a proteo-transcriptomic gene expression signature of 388 host genes specific for HIV-1 replication. Comprehensive functional analyses of these genes confirmed the previously described roles of some of the genes and revealed novel key virus-host interactions affecting multiple molecular processes within the host cell, including signal transduction, metabolism, cell cycle, and immune system. The results of our analysis are accessible through a freely available, dedicated and user-friendly R/Shiny application, called PEACHi2.0. This resource constitutes a catalogue of dynamic host responses to HIV-1 infection that provides a basis for a more comprehensive understanding of virus-host interactions., Scientific Reports, 9, ISSN:2045-2322