ExpressionDB: An open source platform for distributing genome-scale datasets

bigPint: A Bioconductor visualization package that makes big data pint-sized

Interactive data visualization is imperative in the biological sciences. The development of independent layers of interactivity has been in pursuit in the visualization community. We developed bigPint, a data visualization package available on Bioconductor under the GPL-3 license (https://bioconductor.org/packages/release/bioc/html/bigPint.html). Our software introduces new visualization technology that enables independent layers of interactivity using Plotly in R, which aids in the exploration of large biological datasets. The bigPint package presents modernized versions of scatterplot matrices, volcano plots, and litre plots through the implementation of layered interactivity. These graphics have detected normalization issues, differential expression designation problems, and common analysis errors in public RNA-sequencing datasets. Researchers can apply bigPint graphics to their data by following recommended pipelines written in reproducible code in the user manual. In this paper, we explain how we achieved the independent layers of interactivity that are behind bigPint graphics. Pseudocode and source code are provided. Computational scientists can leverage our open-source code to expand upon our layered interactive technology and/or apply it in new ways toward other computational biology tasks.

Biological disciplines face the challenge of increasingly large and complex data. One necessary approach toward eliciting information is data visualization. Newer visualization tools incorporate interactive capabilities that allow scientists to extract information more efficiently than static counterparts. In this paper, we introduce technology that allows multiple independent layers of interactive visualization written in open-source code. This technology can be repurposed across various biological problems. Here, we apply this technology to RNA-sequencing data, a popular next-generation sequencing approach that provides snapshots of RNA quantity in biological samples at given moments in time. It can be used to investigate cellular differences between health and disease, cellular changes in response to external stimuli, and additional biological inquiries. RNA-sequencing data is large, noisy, and biased. It requires sophisticated normalization. The most popular open-source RNA-sequencing data analysis software focuses on models, with little emphasis on integrating effective visualization tools. This is despite sound evidence that RNA-sequencing data is most effectively explored using graphical and numerical approaches in a complementary fashion. The software we introduce can make it easier for researchers to use models and visuals in an integrated fashion during RNA-sequencing data analysis.

StructuRly: A novel shiny app to produce comprehensive, detailed and interactive plots for population genetic analysis

Population genetics focuses on the analysis of genetic differences within and between-group of individuals and the inference of the populations' structure. These analyses are usually carried out using Bayesian clustering or maximum likelihood estimation algorithms that assign individuals to a given population depending on specific genetic patterns. Although several tools were developed to perform population genetics analysis, their standard graphical outputs may not be sufficiently informative for users lacking interactivity and complete information. StructuRly aims to resolve this problem by offering a complete environment for population analysis. In particular, StructuRly combines the statistical power of the R language with the friendly interfaces implemented using the shiny libraries to provide a novel tool for performing population clustering, evaluating several genetic indexes, and comparing results. Moreover, graphical representations are interactive and can be easily personalized. StructuRly is available either as R package on GitHub, with detailed information for its installation and use and as shinyapps.io servers for those users who are not familiar with R and the RStudio IDE. The application has been tested on Linux, macOS and Windows operative systems and can be launched as a shiny app in every web browser.

Combined overexpression of SIRT1 and knockout of GCN5 in adult skeletal muscle does not affect glucose homeostasis or exercise performance in mice

Sirtuin 1 (SIRT1) and general control of amino acid synthesis 5 (GCN5) regulate mitochondrial biogenesis via opposing modulation of peroxisome proliferator-activated receptor-γ coactivator 1α (PGC-1α) acetylation status and activity. However, the combined contribution of SIRT1 and GCN5 to skeletal muscle metabolism and endurance performance in vivo is unknown. In this study, we investigated the impact of combined skeletal muscle-specific overexpression of SIRT1 and deletion of GCN5 on glucose homeostasis, skeletal muscle mitochondrial biogenesis and function, and metabolic adaptation to endurance exercise training in mice. We generated mice with combined and tamoxifen-inducible skeletal muscle-specific overexpression of SIRT1 and knockout of GCN5 (dTG) and floxed [wild type (WT)] littermates using a Cre-LoxP approach. All mice were treated with tamoxifen at 5-6 wk of age, and 4-7 wk later glucose homeostasis, skeletal muscle contractile function, mitochondrial function, and the effects of 14 days of voluntary wheel running on expression of metabolic proteins and exercise capacity were assessed. There was no difference in oral glucose tolerance, skeletal muscle contractile function, mitochondrial abundance, or maximal respiratory capacity between dTG and WT mice. Additionally, there were no genotype differences in exercise performance and markers of mitochondrial biogenesis after 14 days of voluntary wheel running. These results demonstrate that combined overexpression of SIRT1 and loss of GCN5 in vivo does not promote metabolic remodeling in skeletal muscle of sedentary or exercise-trained mice.

WIlsON: Web-based Interactive Omics VisualizatioN

Motivation

High throughput (HT) screens in the omics field are typically analyzed by automated pipelines that generate static visualizations and comprehensive spreadsheet data for scientists. However, exploratory and hypothesis driven data analysis are key aspects of the understanding of biological systems, both generating extensive need for customized and dynamic visualization.

Results

Here we describe WIlsON, an interactive workbench for analysis and visualization of multi-omics data. It is primarily intended to empower screening platforms to offer access to pre-calculated HT screen results to the non-computational scientist. Facilitated by an open file format, WIlsON supports all types of omics screens, serves results via a web-based dashboard, and enables end users to perform analyses and generate publication-ready plots.

Availability and implementation

We implemented WIlsON in R with a focus on extensibility using the modular Shiny and Plotly frameworks. A demo of the interactive workbench without limitations may be accessed at http://loosolab.mpi-bn.mpg.de. A standalone Docker container as well as the source code of WIlsON are freely available from our Docker hub https://hub.docker. com/r/loosolab/wilson, CRAN https://cran.r-project.org/web/packages/wilson/, and GitHub repository https://github.molgen.mpg.de/loosolab/wilson-apps, respectively.

Rapid Analysis of Diagnostic and Antimicrobial Patterns in R (RadaR): Interactive Open-Source Software App for Infection Management and Antimicrobial Stewardship

BACKGROUND

Analyzing process and outcome measures for all patients diagnosed with an infection in a hospital, including those suspected of having an infection, requires not only processing of large datasets but also accounting for numerous patient parameters and guidelines. Substantial technical expertise is required to conduct such rapid, reproducible, and adaptable analyses; however, such analyses can yield valuable insights for infection management and antimicrobial stewardship (AMS) teams.

OBJECTIVE

The aim of this study was to present the design, development, and testing of RadaR (Rapid analysis of diagnostic and antimicrobial patterns in R), a software app for infection management, and to ascertain whether RadaR can facilitate user-friendly, intuitive, and interactive analyses of large datasets in the absence of prior in-depth software or programming knowledge.

METHODS

RadaR was built in the open-source programming language R, using Shiny, an additional package to implement Web-app frameworks in R. It was developed in the context of a 1339-bed academic tertiary referral hospital to handle data of more than 180,000 admissions.

RESULTS

RadaR enabled visualization of analytical graphs and statistical summaries in a rapid and interactive manner. It allowed users to filter patient groups by 17 different criteria and investigate antimicrobial use, microbiological diagnostic use and results including antimicrobial resistance, and outcome in length of stay. Furthermore, with RadaR, results can be stratified and grouped to compare defined patient groups on the basis of individual patient features.

CONCLUSIONS

AMS teams can use RadaR to identify areas within their institutions that might benefit from increased support and targeted interventions. It can be used for the assessment of diagnostic and therapeutic procedures and for visualizing and communicating analyses. RadaR demonstrated the feasibility of developing software tools for use in infection management and for AMS teams in an open-source approach, thus making it free to use and adaptable to different settings.

Transcriptional profiling reveals extraordinary diversity among skeletal muscle tissues

Skeletal muscle comprises a family of diverse tissues with highly specialized functions. Many acquired diseases, including HIV and COPD, affect specific muscles while sparing others. Even monogenic muscular dystrophies selectively affect certain muscle groups. These observations suggest that factors intrinsic to muscle tissues influence their resistance to disease. Nevertheless, most studies have not addressed transcriptional diversity among skeletal muscles. Here we use RNAseq to profile mRNA expression in skeletal, smooth, and cardiac muscle tissues from mice and rats. Our data set, MuscleDB, reveals extensive transcriptional diversity, with greater than 50% of transcripts differentially expressed among skeletal muscle tissues. We detect mRNA expression of hundreds of putative myokines that may underlie the endocrine functions of skeletal muscle. We identify candidate genes that may drive tissue specialization, including Smarca4, Vegfa, and Myostatin. By demonstrating the intrinsic diversity of skeletal muscles, these data provide a resource for studying the mechanisms of tissue specialization.

About 40% of our weight is formed of skeletal muscles, the hundreds of muscles in our bodies that can be voluntarily controlled by our nervous system. At the moment, the research community largely sees all these muscles as a single group whose tissues are virtually interchangeable.

Yet, skeletal muscles have highly diverse origins, shapes and roles. For example, our diaphragm is a long muscle that contracts slowly and rhythmically so we can draw breaths, while tiny muscles in our eyes generate the short and precise movements of our eyeballs. Different skeletal muscles also respond in distinct ways to injuries, drugs and diseases. This suggests that these muscles may be diverse at the genetic level.

While all the cells in our body have the same genetic information, exactly which genes are turned on and off (or ‘expressed’) changes between types of cells. On top of this ‘on or off’ regulation, the level of expression of a gene – how active it is – can also differ. However, the studies that examine the differences in gene expression between tissues usually overlook skeletal muscles.

Here, Terry et al. use genetic techniques to measure how genes are expressed in over 20 types of muscle in mice and rats. The results show that the expression levels of over 50% of all the animals’ genes vary between muscles. In fact, any two types of muscles express on average 13% of their genes differently from each other. The analyses yield further unexpected findings. For example, the expression levels in a muscle in the foot that helps to flex the rodents’ toes are more similar to those found in eye muscles than to the ones observed in limb muscles. These conclusions indicate that skeletal muscles are a widely diverse family of tissues.

The research community will be able to use the data collected by Terry et al. to explore further the origins and the consequences of the differences between skeletal muscles. This could help researchers to understand why specific groups of muscles are more susceptible to disease, or react differently to a drug. This knowledge could also be exploited to refine approaches in tissue engineering, which aims to replace damaged muscles in the body.