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.

We present the ggtreeExtra package for visualizing heterogeneous data with a phylogenetic tree (https://www.bioconductor.org/packages/ggtreeExtra). It supports more data types and visualization methods than other tools and has many features that are not available elsewhere. The ggtreeExtra package is a universal tool for tree data visualization. It extends the applications of phylogenetic tree in different disciplines by making more domain specific data to be available to visualize and interpret on the evolutionary context.

The data output from microbiome research is growing at an accelerating rate, yet mining the data quickly and efficiently remains difficult. There is still a lack of effective data structure to represent and manage data, as well as flexible and composable analysis methods. In response to these two issues, we designed and developed the MicrobiotaProcess package. It provides a comprehensive data structure, MPSE, to better integrate the primary and intermediate data, which improves the integration and exploration of the downstream data. Around this data structure, the downstream analysis tasks are decomposed and a set of functions are designed under the tidy framework. These functions independently perform simple tasks and can be combined to perform complex tasks. This gives users the ability to explore data, conduct personalized analysis and develop analysis workflows. Moreover, MicrobiotaProcess can interoperate with other packages in the R community, which further expands its analytical capabilities. This article demonstrates the MicrobiotaProcess for analyzing microbiome data as well as other ecological data through several examples. It connects upstream data, provides flexible downstream analysis components, and provides visualization methods to assist in presenting and interpreting results.

The complete chloroplast genome of Ranunculus cantoniensis was determined. Total chloroplast genome was 155,119 bp in lenth, displayed a typical quadripartite structure, including a large single copy (LSC) region of 84,634 bp and a small single copy region (SSC) of 18,879 bp, separated by a pair of inverted repeats (IRs) of 25,803 bp. A total of 131 genes, including 84 protein-coding genes, 37 tRNA genes, and 8 rRNA genes were indentified. Among them, 15 genes have one intron each and 3 genes contain two introns. The overall GC content was 37.9%, while the corresponding values of LSC, SSC, and IR regions were 36.0, 31.1, and 43.5%, respectively. Phylogenetic relationship analysis showed that R. cantoniensis was closely related to R. macranthus.