Summary Functional enrichment analysis is pivotal for interpreting high-throughput omics data in life science. It is crucial for this type of tool to use the latest annotation databases for as many organisms as possible. To meet these requirements, we present here an updated version of our popular Bioconductor package, clusterProfiler 4.0. This package has been enhanced considerably compared with its original version published 9 years ago. The new version provides a universal interface for functional enrichment analysis in thousands of organisms based on internally supported ontologies and pathways as well as annotation data provided by users or derived from online databases. It also extends the dplyr and ggplot2 packages to offer tidy interfaces for data operation and visualization. Other new features include gene set enrichment analysis and comparison of enrichment results from multiple gene lists. We anticipate that clusterProfiler 4.0 will be applied to a wide range of scenarios across diverse organisms.

We present the ggtreeExtra package for visualizing heterogeneous data with a phylogenetic tree in a circular or rectangular layout (https://www.bioconductor.org/packages/ggtreeExtra). The package supports more data types and visualization methods than other tools. It supports using the grammar of graphics syntax to present data on a tree with richly annotated layers and allows evolutionary statistics inferred by commonly used software to be integrated and visualized with external data. GgtreeExtra is a universal tool for tree data visualization. It extends the applications of the phylogenetic tree in different disciplines by making more domain-specific data to be available to visualize and interpret in the evolutionary context.

In many aspects of life, epigenetics, or the altering of phenotype without changes in sequences, play an essential role in biological function. A vast number of epigenomic datasets are emerging as a result of the advent of next‐generation sequencing. Annotation, comparison, visualization, and interpretation of epigenomic datasets remain key aspects of computational biology. ChIPseeker is a Bioconductor package for performing these analyses among variable epigenomic datasets. The fundamental functions of ChIPseeker, including data preparation, annotation, comparison, and visualization, are explained in this article. ChIPseeker is a freely available open‐source package that may be found at https://www.bioconductor.org/packages/ChIPseeker. © 2022 Wiley Periodicals LLC. Basic Protocol 1: ChIPseeker and epigenomic dataset preparation Basic Protocol 2: Annotation of epigenomic datasets Basic Protocol 3: Comparison of epigenomic datasets Basic Protocol 4: Visualization of annotated results Basic Protocol 5: Functional analysis of epigenomic datasets Basic Protocol 6: Genome‐wide and locus‐specific distribution of epigenomic datasets Basic Protocol 7: Heatmaps and metaplots of epigenomic datasets

To provide convenient access to epidemiological data on the coronavirus outbreak, we developed an R package, nCov2019 (https://github.com/GuangchuangYu/nCov2019). Besides detailed real-time statistics, it offers access to three data sources with detailed daily statistics from December 1, 2019, for 43 countries and more than 500 Chinese cities. We also developed a web app (http://www.bcloud.org/e/) with interactive plots and simple time-series forecasts. These analytics tools could be useful in informing the public and studying how this and similar viruses spread in populous countries.

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