The growing application of gene expression profiling demands powerful yet user-friendly bioinformatics tools to support systems-level data understanding. NetworkAnalyst was first released in 2014 to address the key need for interpreting gene expression data within the context of protein-protein interaction (PPI) networks. It was soon updated for gene expression meta-analysis with improved workflow and performance. Over the years, NetworkAnalyst has been continuously updated based on community feedback and technology progresses. Users can now perform gene expression profiling for 17 different species. In addition to generic PPI networks, users can now create cell-type or tissue specific PPI networks, gene regulatory networks, gene co-expression networks as well as networks for toxicogenomics and pharmacogenomics studies. The resulting networks can be customized and explored in 2D, 3D as well as Virtual Reality (VR) space. For meta-analysis, users can now visually compare multiple gene lists through interactive heatmaps, enrichment networks, Venn diagrams or chord diagrams. In addition, users have the option to create their own data analysis projects, which can be saved and resumed at a later time. These new features are released together as NetworkAnalyst 3.0, freely available at https://www.networkanalyst.ca.

Mercury pollution and its impacts on human health is of global concern. The authors of this paper were members of the Plenary Panel on Human Health in the 12th International Conference on Mercury as a Global Pollutant held in Korea in June 2015. The Panel was asked by the conference organizers to address two questions: what is the current understanding of the impacts of mercury exposure on human health and what information is needed to evaluate the effectiveness of the Minamata Convention in lowering exposure and preventing adverse effects. The authors conducted a critical review of the literature published since January 2012 and discussed the current state-of-knowledge in the following areas: environmental exposure and/or risk assessment; kinetics and biomonitoring; effects on children development; effects on adult general populations; effects on artisanal and small-scale gold miners (ASGM); effects on dental workers; risk of ethylmercury in thimerosal-containing vaccines; interactions with nutrients; genetic determinants and; risk communication and management. Knowledge gaps in each area were identified and recommendations for future research were made. The Panel concluded that more knowledge synthesis efforts are needed to translate the research results into management tools for health professionals and policy makers.

The wide applications of liquid chromatography - mass spectrometry (LC-MS) in untargeted metabolomics demand an easy-to-use, comprehensive computational workflow to support efficient and reproducible data analysis. However, current tools were primarily developed to perform specific tasks in LC-MS based metabolomics data analysis. Here we introduce MetaboAnalystR 4.0 as a streamlined pipeline covering raw spectra processing, compound identification, statistical analysis, and functional interpretation. The key features of MetaboAnalystR 4.0 includes an auto-optimized feature detection and quantification algorithm for LC-MS1 spectra processing, efficient MS2 spectra deconvolution and compound identification for data-dependent or data-independent acquisition, and more accurate functional interpretation through integrated spectral annotation. Comprehensive validation studies using LC-MS1 and MS2 spectra obtained from standards mixtures, dilution series and clinical metabolomics samples have shown its excellent performance across a wide range of common tasks such as peak picking, spectral deconvolution, and compound identification with good computing efficiency. Together with its existing statistical analysis utilities, MetaboAnalystR 4.0 represents a significant step toward a unified, end-to-end workflow for LC-MS based global metabolomics in the open-source R environment.