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.

With the rapid increase of large-scale datasets, biomedical data visualization is facing challenges. The data may be large, have different orders of magnitude, contain extreme values, and the data distribution is not clear. Here we present an R package ggbreak that allows users to create broken axes using ggplot2 syntax. It can effectively use the plotting area to deal with large datasets (especially for long sequential data), data with different magnitudes, and contain outliers. The ggbreak package increases the available visual space for a better presentation of the data and detailed annotation, thus improves our ability to interpret the data. The ggbreak package is fully compatible with ggplot2 and it is easy to superpose additional layers and applies scale and theme to adjust the plot using the ggplot2 syntax. The ggbreak package is open-source software released under the Artistic-2.0 license, and it is freely available on CRAN (https://CRAN.R-project.org/package=ggbreak) and Github (https://github.com/YuLab-SMU/ggbreak).

The identification of the conserved and variable regions in the multiple sequence alignment (MSA) is critical to accelerating the process of understanding the function of genes. MSA visualizations allow us to transform sequence features into understandable visual representations. As the sequence–structure–function relationship gains increasing attention in molecular biology studies, the simple display of nucleotide or protein sequence alignment is not satisfied. A more scalable visualization is required to broaden the scope of sequence investigation. Here we present ggmsa, an R package for mining comprehensive sequence features and integrating the associated data of MSA by a variety of display methods. To uncover sequence conservation patterns, variations and recombination at the site level, sequence bundles, sequence logos, stacked sequence alignment and comparative plots are implemented. ggmsa supports integrating the correlation of MSA sequences and their phenotypes, as well as other traits such as ancestral sequences, molecular structures, molecular functions and expression levels. We also design a new visualization method for genome alignments in multiple alignment format to explore the pattern of within and between species variation. Combining these visual representations with prime knowledge, ggmsa assists researchers in discovering MSA and making decisions. The ggmsa package is open-source software released under the Artistic-2.0 license, and it is freely available on Bioconductor (https://bioconductor.org/packages/ggmsa) and Github (https://github.com/YuLab-SMU/ggmsa).

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

While phylogenetic trees and associated data have been getting easier to generate, it has been difficult to reuse, combine, and synthesize the information they provided, because published trees are often only available as image files and associated data are often stored in incompatible formats. To increase the reproducibility and reusability of phylogenetic data, the ggtree object was designed for storing phylogenetic tree and associated data, as well as visualization directives. The ggtree object itself is a graphic object and can be rendered as a static image. More importantly, the input tree and associated data that are used in visualization can be extracted from the graphic object, making it an ideal data structure for publishing tree (image, tree, and data in one single object) and thus enhancing data reuse and analytical reproducibility, as well as facilitating integrative and comparative studies. The ggtree package is freely available at https://www.bioconductor.org/packages/ggtree.

Background Emerging evidence suggests that epithelial mesenchymal transition (EMT) and epigenetic mechanisms promote metastasis. Histone deacetylases (HDACs) and noncoding RNAs (ncRNAs) are important epigenetic regulators. Here, we elucidated a novel role of histone deacetylase 2 (HDAC2) in regulating EMT and CRC metastasis via ncRNA. Methods The expression of HDACs in CRC was analyzed using the public databases and matched primary and metastatic tissues, and CRC cells with different metastatic potentials (DLD1, HCT116, SW480 and SW620). Microarray analysis was used to identify differential genes in parental and HDAC2 knockout CRC cells. EMT and histone modifications were determined using western blot and immunofluorescence. Migration ability was assessed by transwell assay, and metastasis was assessed in vivo using a tail vain injection. Gene expression and regulation was assessed by RT-PCR, chromatin immunoprecipitation and reporter assays. Protein interaction was assessed by immunoprecipitation. Specific siRNAs targeting H19, SP1 and MMP14 were used to validate their role in HDAC2 loss induced EMT and metastasis. Results Reduced HDAC2 expression was associated with poor prognosis in CRC patients and found in CRC metastasis. HDAC2 deletion or knockdown induced EMT and metastasis by upregulating the long noncoding RNA H19 (LncRNA H19). HDAC2 inhibited LncRNA H19 expression by histone H3K27 deacetylation in its promoter via binding with SP1. LncRNA H19 functioned as a miR-22-3P sponge to increase the expression of MMP14. HDAC2 loss strongly promoted CRC lung metastasis, which was suppressed LncRNA H19 knockdown. Conclusion Our study supports HDAC2 as a CRC metastasis suppressor through the inhibition of EMT and the expression of H19 and MMP14.

Background:Clear cell renal cell carcinoma (ccRCC) is the most common form of kidney cancer in adults, and patients with advanced ccRCC have a 5-year survival rate of <30%. The poor prognosis of ccRCC is closely related to its lacking of potential therapeutic and prognostic biomarkers. This meta-analysis aimed to elucidate the precise prognostic value of long non-coding RNAs (lncRNAs) in patients with ccRCC.Methods:A literature search was performed in related databases up to January 31, 2019. Hazard ratios (HRs) and corresponding 95% confidence intervals (CIs) were calculated to explore the relationship between special lncRNAs expression and survival in patients with ccRCC.Results:After literature researching, a total of 16 studies, including 13 lncRNAs were identified. The data from studies that investigated the association between lncRNA expression and survival outcomes in patients with ccRCC were extracted. Results revealed that lncRNAs expression was significantly associated with poor overall survival (OS) outcome in patients with ccRCC (HR = 1.71, 95%CI = 1.40–2.01 in up-regulated subgroup; HR = 0.53, 95% CI = 0.25–0.80 in down-regulated subgroup). The overexpression of PVT1 was significantly associated with poor OS in ccRCC (HR = 1.51, 95% CI = 1.02–2.00). Meanwhile, up-regulation of LUCAT1 was significantly related to worse OS in ccRCC patients (HR = 1.51, 95% CI = 1.01–2.00).Conclusions:These results suggest that lncRNAs could be used to predict unfavorable prognosis and function as potential prognostic biomarkers in ccRCC.