Genome Biology

Understanding the regulation of gene expression, including transcription start site usage, alternative splicing, and polyadenylation, requires accurate quantification of expression levels down to the level of individual transcript isoforms. To comparatively evaluate the accuracy of the many methods that have been proposed for estimating transcript isoform abundance from RNA sequencing data, we have used both synthetic data as well as an independent experimental method for quantifying the abundance of transcript ends at the genome-wide level. We found that many tools have good accuracy and yield better estimates of gene-level expression compared to commonly used count-based approaches, but they vary widely in memory and runtime requirements. Nucleotide composition and intron/exon structure have comparatively little influence on the accuracy of expression estimates, which correlates most strongly with transcript/gene expression levels. To facilitate the reproduction and further extension of our study, we provide datasets, source code, and an online analysis tool on a companion website, where developers can upload expression estimates obtained with their own tool to compare them to those inferred by the methods assessed here. As many methods for quantifying isoform abundance with comparable accuracy are available, a user’s choice will likely be determined by factors such as the memory and runtime requirements, as well as the availability of methods for downstream analyses. Sequencing-based methods to quantify the abundance of specific transcript regions could complement validation schemes based on synthetic data and quantitative PCR in future or ongoing assessments of RNA-seq analysis methods.

The intestinal epithelium separates us from the microbiota but also interacts with it and thus affects host immune status and physiology. Previous studies investigated microbiota-induced responses in the gut using intact tissues or unfractionated epithelial cells, thereby limiting conclusions about regional differences in the epithelium. Here, we sought to investigate microbiota-induced transcriptional responses in specific fractions of intestinal epithelial cells. To this end, we used microarray analysis of laser capture microdissection (LCM)-harvested ileal and colonic tip and crypt epithelial fractions from germ-free and conventionally raised mice and from mice during the time course of colonization. We found that about 10% of the host’s transcriptome was microbially regulated, mainly including genes annotated with functions in immunity, cell proliferation, and metabolism. The microbial impact on host gene expression was highly site specific, as epithelial responses to the microbiota differed between cell fractions. Specific transcriptional regulators were enriched in each fraction. In general, the gut microbiota induced a more rapid response in the colon than in the ileum. Our study indicates that the microbiota engage different regulatory networks to alter host gene expression in a particular niche. Understanding host-microbiota interactions on a cellular level may facilitate signaling pathways that contribute to health and disease and thus provide new therapeutic strategies.

The increasing availability of sequence data for many viruses provides power to detect regions under unusual evolutionary constraint at a high resolution. One approach leverages the synonymous substitution rate as a signature to pinpoint genic regions encoding overlapping or embedded functional elements. Protein-coding regions in viral genomes often contain overlapping RNA structural elements, reading frames, regulatory elements, microRNAs, and packaging signals. Synonymous substitutions in these regions would be selectively disfavored and thus these regions are characterized by excess synonymous constraint. Codon choice can also modulate transcriptional efficiency, translational accuracy, and protein folding. We developed a phylogenetic codon model-based framework, FRESCo, designed to find regions of excess synonymous constraint in short, deep alignments, such as individual viral genes across many sequenced isolates. We demonstrated the high specificity of our approach on simulated data and applied our framework to the protein-coding regions of approximately 30 distinct species of viruses with diverse genome architectures. FRESCo recovers known multifunctional regions in well-characterized viruses such as hepatitis B virus, poliovirus, and West Nile virus, often at a single-codon resolution, and predicts many novel functional elements overlapping viral genes, including in Lassa and Ebola viruses. In a number of viruses, the synonymously constrained regions that we identified also display conserved, stable predicted RNA structures, including putative novel elements in multiple viral species.

During the type-setting of the final version of the article [1] some of the additional files were swapped. The correct files are republished in this Erratum.