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.

Long-read sequencing technologies are overcoming early limitations in accuracy and throughput, broadening their application domains in genomics. Dedicated analysis tools that take into account the characteristics of long-read data are thus required, but the fast pace of development of such tools can be overwhelming. To assist in the design and analysis of long-read sequencing projects, we review the current landscape of available tools and present an online interactive database, long-read-tools.org, to facilitate their browsing. We further focus on the principles of error correction, base modification detection, and long-read transcriptomics analysis and highlight the challenges that remain.

SUMMARY Activated caspases are a hallmark of apoptosis induced by the intrinsic pathway, but they are dispensable for cell death and the apoptotic clearance of cells in vivo. This has led to the suggestion that caspases are activated not just to kill but to prevent dying cells from triggering a host immune response. Here, we show that the caspase cascade suppresses type I interferon (IFN) production by cells undergoing Bak/Bax-mediated apoptosis. Bak and Bax trigger the release of mitochondrial DNA. This is recognized by the cGAS/STING-dependent DNA sensing pathway, which initiates IFN production. Activated caspases attenuate this response. Pharmacological caspase inhibition or genetic deletion of caspase-9, Apaf-1, or caspase-3/7 causes dying cells to secrete IFN-β. In vivo, this precipitates an elevation in IFN-β levels and consequent hematopoietic stem cell dysfunction, which is corrected by loss of Bak and Bax. Thus, the apoptotic caspase cascade functions to render mitochondrial apoptosis immunologically silent.