RNA-seq is now the technology of choice for genome-wide differential gene expression experiments, but it is not clear how many biological replicates are needed to ensure valid biological interpretation of the results or which statistical tools are best for analyzing the data. An RNA-seq experiment with 48 biological replicates in each of two conditions was performed to answer these questions and provide guidelines for experimental design. With three biological replicates, nine of the 11 tools evaluated found only 20%-40% of the significantly differentially expressed (SDE) genes identified with the full set of 42 clean replicates. This rises to >85% for the subset of SDE genes changing in expression by more than fourfold. To achieve >85% for all SDE genes regardless of fold change requires more than 20 biological replicates. The same nine tools successfully control their false discovery rate at ≲5% for all numbers of replicates, while the remaining two tools fail to control their FDR adequately, particularly for low numbers of replicates. For future RNA-seq experiments, these results suggest that at least six biological replicates should be used, rising to at least 12 when it is important to identify SDE genes for all fold changes. If fewer than 12 replicates are used, a superior combination of true positive and false positive performances makes edgeR and DESeq2 the leading tools. For higher replicate numbers, minimizing false positives is more important and DESeq marginally outperforms the other tools.

Using whole-cell phenotypic assays, the GlaxoSmithKline high-throughput screening (HTS) diversity set of 1.8 million compounds was screened against the three kinetoplastids most relevant to human disease, i.e. Leishmania donovani, Trypanosoma cruzi and Trypanosoma brucei. Secondary confirmatory and orthogonal intracellular anti-parasiticidal assays were conducted, and the potential for non-specific cytotoxicity determined. Hit compounds were chemically clustered and triaged for desirable physicochemical properties. The hypothetical biological target space covered by these diversity sets was investigated through bioinformatics methodologies. Consequently, three anti-kinetoplastid chemical boxes of ~200 compounds each were assembled. Functional analyses of these compounds suggest a wide array of potential modes of action against kinetoplastid kinases, proteases and cytochromes as well as potential host–pathogen targets. This is the first published parallel high throughput screening of a pharma compound collection against kinetoplastids. The compound sets are provided as an open resource for future lead discovery programs, and to address important research questions.

It has recently been shown that RNA 3′ end formation plays a more widespread role in controlling gene expression than previously thought. In order to examine the impact of regulated 3′ end formation genome-wide we applied direct RNA sequencing to A. thaliana. Here we show the authentic transcriptome in unprecedented detail and how 3′ end formation impacts genome organization. We reveal extreme heterogeneity in RNA 3′ ends, discover previously unrecognized non-coding RNAs and propose widespread re-annotation of the genome. We explain the origin of most poly(A)+ antisense RNAs and identify cis-elements that control 3′ end formation in different registers. These findings are essential to understand what the genome actually encodes, how it is organized and the impact of regulated 3′ end formation on these processes.

BackgroundAtopic dermatitis (AD; eczema) is characterized by a widespread abnormality in cutaneous barrier function and propensity to inflammation. Filaggrin is a multifunctional protein and plays a key role in skin barrier formation. Loss-of-function mutations in the gene encoding filaggrin (FLG) are a highly significant risk factor for atopic disease, but the molecular mechanisms leading to dermatitis remain unclear.ObjectiveWe sought to interrogate tissue-specific variations in the expressed genome in the skin of children with AD and to investigate underlying pathomechanisms in atopic skin.MethodsWe applied single-molecule direct RNA sequencing to analyze the whole transcriptome using minimal tissue samples. Uninvolved skin biopsy specimens from 26 pediatric patients with AD were compared with site-matched samples from 10 nonatopic teenage control subjects. Cases and control subjects were screened for FLG genotype to stratify the data set.ResultsTwo thousand four hundred thirty differentially expressed genes (false discovery rate, P < .05) were identified, of which 211 were significantly upregulated and 490 downregulated by greater than 2-fold. Gene ontology terms for “extracellular space” and “defense response” were enriched, whereas “lipid metabolic processes” were downregulated. The subset of FLG wild-type cases showed dysregulation of genes involved with lipid metabolism, whereas filaggrin haploinsufficiency affected global gene expression and was characterized by a type 1 interferon–mediated stress response.ConclusionThese analyses demonstrate the importance of extracellular space and lipid metabolism in atopic skin pathology independent of FLG genotype, whereas an aberrant defense response is seen in subjects with FLG mutations. Genotype stratification of the large data set has facilitated functional interpretation and might guide future therapy development.

Motivation: High-throughput RNA sequencing (RNA-seq) is now the standard method to determine differential gene expression. Identifying differentially expressed genes crucially depends on estimates of read-count variability. These estimates are typically based on statistical models such as the negative binomial distribution, which is employed by the tools edgeR, DESeq and cuffdiff. Until now, the validity of these models has usually been tested on either low-replicate RNA-seq data or simulations.Results: A 48-replicate RNA-seq experiment in yeast was performed and data tested against theoretical models. The observed gene read counts were consistent with both log-normal and negative binomial distributions, while the mean-variance relation followed the line of constant dispersion parameter of ∼0.01. The high-replicate data also allowed for strict quality control and screening of ‘bad’ replicates, which can drastically affect the gene read-count distribution.Availability and implementation: RNA-seq data have been submitted to ENA archive with project ID PRJEB5348.Contact: g.j.barton@dundee.ac.uk

Alternative cleavage and polyadenylation influence the coding and regulatory potential of mRNAs and where transcription termination occurs. Although widespread, few regulators of this process are known. The Arabidopsis thaliana protein FPA is a rare example of a trans-acting regulator of poly(A) site choice. Analysing fpa mutants therefore provides an opportunity to reveal generic consequences of disrupting this process. We used direct RNA sequencing to quantify shifts in RNA 3′ formation in fpa mutants. Here we show that specific chimeric RNAs formed between the exons of otherwise separate genes are a striking consequence of loss of FPA function. We define intergenic read-through transcripts resulting from defective RNA 3′ end formation in fpa mutants and detail cryptic splicing and antisense transcription associated with these read-through RNAs. We identify alternative polyadenylation within introns that is sensitive to FPA and show FPA-dependent shifts in IBM1 poly(A) site selection that differ from those recently defined in mutants defective in intragenic heterochromatin and DNA methylation. Finally, we show that defective termination at specific loci in fpa mutants is shared with dicer-like 1 (dcl1) or dcl4 mutants, leading us to develop alternative explanations for some silencing roles of these proteins. We relate our findings to the impact that altered patterns of 3′ end formation can have on gene and genome organisation.

RNA-binding proteins (RBPs) play an important role in plant host-microbe interactions. In this study, we show that the plant RBP known as FPA, which regulates 3′-end mRNA polyadenylation, negatively regulates basal resistance to bacterial pathogen Pseudomonas syringae in Arabidopsis. A custom microarray analysis reveals that flg22, a peptide derived from bacterial flagellins, induces expression of alternatively polyadenylated isoforms of mRNA encoding the defence-related transcriptional repressor ETHYLENE RESPONSE FACTOR 4 (ERF4), which is regulated by FPA. Flg22 induces expression of a novel isoform of ERF4 that lacks the ERF-associated amphiphilic repression (EAR) motif, while FPA inhibits this induction. The EAR-lacking isoform of ERF4 acts as a transcriptional activator in vivo and suppresses the flg22-dependent reactive oxygen species burst. We propose that FPA controls use of proximal polyadenylation sites of ERF4, which quantitatively limit the defence response output.

The reference annotations made for a genome sequence provide the framework for all subsequent analyses of the genome. Correct and complete annotation in addition to the underlying genomic sequence is particularly important when interpreting the results of RNA-seq experiments where short sequence reads are mapped against the genome and assigned to genes according to the annotation. Inconsistencies in annotations between the reference and the experimental system can lead to incorrect interpretation of the effect on RNA expression of an experimental treatment or mutation in the system under study. Until recently, the genome-wide annotation of 3′ untranslated regions received less attention than coding regions and the delineation of intron/exon boundaries. In this paper, data produced for samples in Human, Chicken and A. thaliana by the novel single-molecule, strand-specific, Direct RNA Sequencing technology from Helicos Biosciences which locates 3′ polyadenylation sites to within +/− 2 nt, were combined with archival EST and RNA-Seq data. Nine examples are illustrated where this combination of data allowed: (1) gene and 3′ UTR re-annotation (including extension of one 3′ UTR by 5.9 kb); (2) disentangling of gene expression in complex regions; (3) clearer interpretation of small RNA expression and (4) identification of novel genes. While the specific examples displayed here may become obsolete as genome sequences and their annotations are refined, the principles laid out in this paper will be of general use both to those annotating genomes and those seeking to interpret existing publically available annotations in the context of their own experimental data.