1,720,977 research outputs found
Advancements in RNASeqGUI towards a Reproducible Analysis of RNA-Seq Experiments
Full text linkWe present the advancements and novelties recently introduced in RNASeqGUI, a graphical user interface that helps biologists to handle and analyse large data collected in RNA-Seq experiments. This work focuses on the concept of reproducible research and shows how it has been incorporated in RNASeqGUI to provide reproducible (computational) results. The novel version of RNASeqGUI combines graphical interfaces with tools for reproducible research, such as literate statistical programming, human readable report, parallel executions, caching, and interactive and web-explorable tables of results. These features allow the user to analyse big datasets in a fast, efficient, and reproducible way. Moreover, this paper represents a proof of concept, showing a simple way to develop computational tools for Life Science in the spirit of reproducible research
Reproducible Research in the era of Next Generation Sequencing: current ap- proaches, examples and future perspectives
No full textAdvantages and limits in the adoption of Reproducible Research inside R-tools for the analysis of omic data
No full textEasyreporting simplifies the implementation of Reproducible Research layers in R software.
Full text linkDuring last years "irreproducibility" became a general problem in omics data analysis due to the use of sophisticated and poorly described computational procedures. For avoiding misleading results, it is necessary to inspect and reproduce the entire data analysis as a unified product. Reproducible Research (RR) provides general guidelines for public access to the analytic data and related analysis code combined with natural language documentation, allowing third-parties to reproduce the findings. We developed easyreporting, a novel R/Bioconductor package, to facilitate the implementation of an RR layer inside reports/tools. We describe the main functionalities and illustrate the organization of an analysis report using a typical case study concerning the analysis of RNA-seq data. Then, we show how to use easyreporting in other projects to trace R functions automatically. This latter feature helps developers to implement procedures that automatically keep track of the analysis steps. Easyreporting can be useful in supporting the reproducibility of any data analysis project and shows great advantages for the implementation of R packages and GUIs. It turns out to be very helpful in bioinformatics, where the complexity of the analyses makes it extremely difficult to trace all the steps and parameters used in the study
robin2: accelerating single-cell data clustering evaluation
No full textMotivation: The rapid expansion of single-cell RNA sequencing (scRNA-seq) technologies has increased the need for robust and scalable clustering evaluation methods. To address these challenges, we developed robin2, an optimized version of our R package robin. It introduces enhanced computational efficiency, support for high-dimensional datasets, and harmonious integration with R's base functionalities for robust network analysis. Results: robin2 offers improved functionality for clustering stability validation and enables systematic evaluation of community detection algorithms across various resolutions and pipelines. The application to Tabula Muris and PBMC scRNA-seq datasets confirmed its ability to identify biologically meaningful cell subpopulations with high statistical significance. The new version reduces computational time by 9-fold on large-scale datasets using parallel processing. Availability and implementation: The robin2 package is freely available on CRAN at https://CRAN.R-project.org/package=robin. Comprehensive documentation and a detailed analysis vignette are available on GitHub at https://drighelli.github.io/scrobinv2/index.html
ROBustness In Network (robin): an R Package for Comparison and Validation of Communities
Full text linkIn network analysis, many community detection algorithms have been developed. However, their implementation leaves unaddressed the question of the statistical validation of the results. Here, we present robin (ROBustness In Network), an R package to assess the robustness of the community structure of a network found by one or more methods to give indications about their reliability. The procedure initially detects if the community structure found by a set of algorithms is statistically significant and then compares two selected detection algorithms on the same graph to choose the one that better fits the network of interest. We demonstrate the use of our package on the American College Football benchmark dataset
Rates of depression and anxiety in Italian patients with cystic fibrosis and parent caregivers: Implementation of the Mental Health Guidelines
No full textBackground: Individuals with chronic respiratory conditions are at-risk for depression and anxiety. In the largest mental health screening study of over 6000 people with cystic fibrosis (CF) and 4000 parent caregivers (TIDES, 2014), rates of symptomatology were two to three times higher than in the general population. International guidelines recommend annual screening of mental health. This is the first study to implement these guidelines in one of the largest CF Centers in Italy.Methods: All individuals with CF, 12 and older (n = 167) and caregivers of children with CF (n = 186), birth to 18, were screened. Health outcome data were also collected (i.e FEV1, BMI, pulmonary exacerbations, CF-related diabetes). Prevalence data and associations between psychological symptoms and health outcomes were examined.Results: A high percentage of patients and parent caregivers reported scored above the clinical cut-off for depression and anxiety (37%-48% of adolescents, 45%-46% of adults, 49%-66% of mothers and fathers). Most scores fell in the mild range, however, over 30% were in the moderate to severe range. Elevations in depression and anxiety were correlated. Adolescents who had more pulmonary exacerbations reported higher anxiety. Adults with recent events of hemoptysis reported higher symptoms of depression.Conclusions: Symptoms of depression and anxiety were elevated in both individuals with CF and parents. Implementation of mental health screening was critical for identifying those in need of psychological interventions. These results strongly suggest that mental health should be integrated into physical health care for those with complex, chronic respiratory conditions, including COPD, PCD
Effects of Lumacaftor/Ivacaftor on physical activity and exercise tolerance in three adults with cystic fibrosis
No full textThe combination of the corrector lumacaftor with the potentiator ivacaftor has been approved for treatment of cystic fibrosis (CF) patients homozygous for the Phe508del CFTR mutation. There are no reports detailing the effect of lumacaftor–ivacaftor on physical activity (PA) and exercise tolerance. We performed incremental cardiopulmonary exercise testing (CPET) and we assessed PA pre- and post 2 years initiation of lumacaftor–ivacaftor in three CF adults. PA of mild intensity improved by +13% in patient 1, + 84% in patients 2 and + 89% in patient 3. Oxygen uptake increased both at anaerobic threshold and at peak exercise (patient 1 + 33%, patient 2 + 42% and patient 3 + 20%). Daily physical activities and exercise tolerance improved after two years of lumacaftor–ivacaftor therapy
Distinct antigen delivery systems induce dendritic cells’ divergent transcriptional response: New insights from a comparative and reproducible computational analysis
Full text linkVaccination is the most successful and cost-effective method to prevent infectious diseases. However, many vaccine antigens have poor in vivo immunogenic potential and need adjuvants to enhance immune response. The application of systems biology to immunity and vaccinology has yielded crucial insights about how vaccines and adjuvants work. We have previously characterized two safe and powerful delivery systems derived from non-pathogenic prokaryotic organisms: E2 and fd filamentous bacteriophage systems. They elicit an in vivo immune response inducing CD8+ T-cell responses, even in absence of adjuvants or stimuli for dendritic cells’ maturation. Nonetheless, a systematic and comparative analysis of the complex gene expression network underlying such activation is missing. Therefore, we compared the transcriptomes of ex vivo isolated bone marrow-derived dendritic cells exposed to these antigen delivery systems. Significant differences emerged, especially for genes involved in innate immunity, co-stimulation, and cytokine production. Results indicate that E2 drives polarization toward the Th2 phenotype, mainly mediated by Irf4, Ccl17, and Ccr4 over-expression. Conversely, fd-scαDEC-205 triggers Th1 T cells’ polarization through the induction of Il12b, Il12rb, Il6, and other molecules involved in its signal transduction. The data analysis was performed using RNASeqGUI, hence, addressing the increasing need of transparency and reproducibility of computational analysis
HiCeekR: A Novel Shiny App for Hi-C Data Analysis
Full text linkThe High-throughput Chromosome Conformation Capture (Hi-C) technique combines the power of the Next Generation Sequencing technologies with chromosome conformation capture approach to study the 3D chromatin organization at the genome-wide scale. Although such a technique is quite recent, many tools are already available for pre-processing and analyzing Hi-C data, allowing to identify chromatin loops, topological associating domains and A/B compartments. However, only a few of them provide an exhaustive analysis pipeline or allow to easily integrate and visualize other omic layers. Moreover, most of the available tools are designed for expert users, who have great confidence with command-line applications. In this paper, we present HiCeekR (https://github.com/lucidif/HiCeekR), a novel R Graphical User Interface (GUI) that allows researchers to easily perform a complete Hi-C data analysis. With the aid of the Shiny libraries, it integrates several R/Bioconductor packages for Hi-C data analysis and visualization, guiding the user during the entire process. Here, we describe its architecture and functionalities, then illustrate its capabilities using a publicly available dataset
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