Allelic imbalance metre (Allim), a new tool for measuring allele‐specific gene expression with RNA‐seq data

Estimating differences in gene expression among alleles is of high interest for many areas in biology and medicine. Here, we present a user‐friendly software tool, Allim, to estimate allele‐specific gene expression. Because mapping bias is a major problem for reliable estimates of allele‐specific gene expression using RNA‐seq, Allim combines two different strategies to account for the mapping biases. In order to reduce the mapping bias, Allim first generates a polymorphism‐aware reference genome that accounts for the sequence variation between the alleles. Then, a sequence‐specific simulation tool estimates the residual mapping bias. Statistical tests for allelic imbalance are provided that can be used with the bias corrected RNA‐seq data.     

Single cell RNA‐seq in the sea urchin embryo show marked cell‐type specificity in the Delta/Notch pathway

Sea urchin embryos are excellent for in vivo functional studies because of their transparency and tractability in manipulation. They are also favorites for pharmacological approaches since they develop in an aquatic environment and addition of test substances is straightforward. A concern in many pharmacological tests though is the potential for pleiotropic effects that confound the conclusions drawn from the results. Precise cellular interpretations are often not feasible because the impact of the perturbant is not known. Here we use single‐cell mRNA (messenger RNA) sequencing as a metric of cell types in the embryo and to determine the selectivity of two commonly used inhibitors, one each for the Wnt and the Delta‐Notch pathways, on these nascent cell types. We identified 11 distinct cell types based on mRNA profiling, and that the cell lineages affected by Wnt and Delta/Notch inhibition were distinct from each other. These data support specificity and distinct effects of these signaling pathways in the embryo and illuminate how these conserved pathways selectively regulate cell lineages at a single cell level. Overall, we conclude that single cell RNA‐seq analysis in this embryo is revealing of the cell types present during development, of the changes in the gene regulatory network resulting from inhibition of various signaling pathways, and of the selectivity of these pathways in influencing developmental trajectories.     

Whole‐transcriptome analysis reveals genetic factors underlying flowering time regulation in rapeseed (Brassica napus L.)

Rapeseed (Brassica napus L.), one of the most important sources of vegetable oil and protein‐rich meals worldwide, is adapted to different geographical regions by modification of flowering time. Rapeseed cultivars have different day length and vernalization requirements, which categorize them into winter, spring, and semiwinter ecotypes. To gain a deeper insight into genetic factors controlling floral transition in B. napus, we performed RNA sequencing (RNA‐seq) in the semiwinter doubled haploid line, Ningyou7, at different developmental stages and temperature regimes. The expression profiles of more than 54,000 gene models were compared between different treatments and developmental stages, and the differentially expressed genes were considered as targets for association analysis and genetic mapping to confirm their role in floral transition. Consequently, 36 genes with association to flowering time, seed yield, or both were identified. We found novel indications for neofunctionalization in homologs of known flowering time regulators like VIN3 and FUL. Our study proved the potential of RNA‐seq along with association analysis and genetic mapping to identify candidate genes for floral transition in rapeseed. The candidate genes identified in this study could be subjected to genetic modification or targeted mutagenesis and genotype building to breed rapeseed adapted to certain environments.     

Pan‐cancer RNA‐seq data stratifies tumours by some hallmarks of cancer

Numerous genetic and epigenetic alterations cause functional changes in cell biology underlying cancer. These hallmark functional changes constitute potentially tissue‐independent anticancer therapeutic targets. We hypothesized that RNA‐Seq identifies gene expression changes that underly those hallmarks, and thereby defines relevant therapeutic targets. To test this hypothesis, we analysed the publicly available TCGA‐TARGET‐GTEx gene expression data set from the University of California Santa CruzToil recompute project using WGCNA to delineate co‐correlated ‘modules’ from tumour gene expression profiles and functional enrichment of these modules to hierarchically cluster tumours. This stratified tumours according to T cell activation, NK‐cell activation, complement cascade, ATM, Rb, angiogenic, MAPK, ECM receptor and histone modification signalling. These correspond to the cancer hallmarks of avoiding immune destruction, tumour‐promoting inflammation, evading growth suppressors, inducing angiogenesis, sustained proliferative signalling, activating invasion and metastasis, and genome instability and mutation. This approach did not detect pathways corresponding to the cancer enabling replicative immortality, resisting cell death or deregulating cellular energetics hallmarks. We conclude that RNA‐Seq stratifies tumours along some, but not all, hallmarks of cancer and, therefore, could be used in conjunction with other analyses collectively to inform precision therapy.