Pathway-level multi-omics analysis of the molecular mechanisms underlying the toxicity of long-term tacrolimus exposure

limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.

KEGG (Kyoto Encyclopedia of Genes and Genomes) is a knowledge base for systematic analysis of gene functions, linking genomic information with higher order functional information. The genomic information is stored in the GENES database, which is a collection of gene catalogs for all the completely sequenced genomes and some partial genomes with up-to-date annotation of gene functions. The higher order functional information is stored in the PATHWAY database, which contains graphical representations of cellular processes, such as metabolism, membrane transport, signal transduction and cell cycle. The PATHWAY database is supplemented by a set of ortholog group tables for the information about conserved subpathways (pathway motifs), which are often encoded by positionally coupled genes on the chromosome and which are especially useful in predicting gene functions. A third database in KEGG is LIGAND for the information about chemical compounds, enzyme molecules and enzymatic reactions. KEGG provides Java graphics tools for browsing genome maps, comparing two genome maps and manipulating expression maps, as well as computational tools for sequence comparison, graph comparison and path computation. The KEGG databases are daily updated and made freely available (http://www. genome.ad.jp/kegg/).

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.