Variations in sample quality are frequently encountered in small RNA-sequencing experiments, and pose a major challenge in a differential expression analysis. Removal of high variation samples reduces noise, but at a cost of reducing power, thus limiting our ability to detect biologically meaningful changes. Similarly, retaining these samples in the analysis may not reveal any statistically significant changes due to the higher noise level. A compromise is to use all available data, but to down-weight the observations from more variable samples. We describe a statistical approach that facilitates this by modelling heterogeneity at both the sample and observational levels as part of the differential expression analysis. At the sample level this is achieved by fitting a log-linear variance model that includes common sample-specific or group-specific parameters that are shared between genes. The estimated sample variance factors are then converted to weights and combined with observational level weights obtained from the mean–variance relationship of the log-counts-per-million using ‘voom’. A comprehensive analysis involving both simulations and experimental RNA-sequencing data demonstrates that this strategy leads to a universally more powerful analysis and fewer false discoveries when compared to conventional approaches. This methodology has wide application and is implemented in the open-source ‘limma’ package.

Protein kinase-like domains that lack conserved residues known to catalyse phosphoryl transfer, termed pseudokinases, have emerged as important signalling domains across all kingdoms of life. Although predicted to function principally as catalysis-independent protein-interaction modules, several pseudokinase domains have been attributed unexpected catalytic functions, often amid controversy. We established a thermal-shift assay as a benchmark technique to define the nucleotide-binding properties of kinase-like domains. Unlike in vitro kinase assays, this assay is insensitive to the presence of minor quantities of contaminating kinases that may otherwise lead to incorrect attribution of catalytic functions to pseudokinases. We demonstrated the utility of this method by classifying 31 diverse pseudokinase domains into four groups: devoid of detectable nucleotide or cation binding; cation-independent nucleotide binding; cation binding; and nucleotide binding enhanced by cations. Whereas nine pseudokinases bound ATP in a divalent cation-dependent manner, over half of those examined did not detectably bind nucleotides, illustrating that pseudokinase domains predominantly function as non-catalytic protein-interaction modules within signalling networks and that only a small subset is potentially catalytically active. We propose that henceforth the thermal-shift assay be adopted as the standard technique for establishing the nucleotide-binding and catalytic potential of kinase-like domains.

Structural maintenance of chromosomes flexible hinge domain containing 1 (Smchd1) is an epigenetic repressor with described roles in X inactivation and genomic imprinting, but Smchd1 is also critically involved in the pathogenesis of facioscapulohumeral dystrophy. The underlying molecular mechanism by which Smchd1 functions in these instances remains unknown. Our genome-wide transcriptional and epigenetic analyses show that Smchd1 binds cis-regulatory elements, many of which coincide with CCCTC-binding factor (Ctcf) binding sites, for example, the clustered protocadherin (Pcdh) genes, where we show Smchd1 and Ctcf act in opposing ways. We provide biochemical and biophysical evidence that Smchd1-chromatin interactions are established through the homodimeric hinge domain of Smchd1 and, intriguingly, that the hinge domain also has the capacity to bind DNA and RNA. Our results suggest Smchd1 imparts epigenetic regulation via physical association with chromatin, which may antagonize Ctcf-facilitated chromatin interactions, resulting in coordinated transcriptional control.Smchd1 | epigenetic control | clustered protocadherins | Ctcf