Despite the many approaches to study differential splicing from RNA-seq, many challenges remain unsolved, including computing capacity and sequencing depth requirements. Here we present SUPPA2, a new method that addresses these challenges, and enables streamlined analysis across multiple conditions taking into account biological variability. Using experimental and simulated data, we show that SUPPA2 achieves higher accuracy compared to other methods, especially at low sequencing depth and short read length. We use SUPPA2 to identify novel Transformer2-regulated exons, novel microexons induced during differentiation of bipolar neurons, and novel intron retention events during erythroblast differentiation.Electronic supplementary materialThe online version of this article (10.1186/s13059-018-1417-1) contains supplementary material, which is available to authorized users.

Aims/hypothesisCurrent genetic tests for diagnosing monogenic diabetes rely on selection of the appropriate gene for analysis according to the patient’s phenotype. Next-generation sequencing enables the simultaneous analysis of multiple genes in a single test. Our aim was to develop a targeted next-generation sequencing assay to detect mutations in all known MODY and neonatal diabetes genes.MethodsWe selected 29 genes in which mutations have been reported to cause neonatal diabetes, MODY, maternally inherited diabetes and deafness (MIDD) or familial partial lipodystrophy (FPLD). An exon-capture assay was designed to include coding regions and splice sites. A total of 114 patient samples were tested—32 with known mutations and 82 previously tested for MODY (n = 33) or neonatal diabetes (n = 49) but in whom a mutation had not been found. Sequence data were analysed for the presence of base substitutions, small insertions or deletions (indels) and exonic deletions or duplications.ResultsIn the 32 positive controls we detected all previously identified variants (34 mutations and 36 polymorphisms), including 55 base substitutions, ten small insertions or deletions and five partial/whole gene deletions/duplications. Previously unidentified mutations were found in five patients with MODY (15%) and nine with neonatal diabetes (18%). Most of these patients (12/14) had mutations in genes that had not previously been tested.Conclusions/interpretationOur novel targeted next-generation sequencing assay provides a highly sensitive method for simultaneous analysis of all monogenic diabetes genes. This single test can detect mutations previously identified by Sanger sequencing or multiplex ligation-dependent probe amplification dosage analysis. The increased number of genes tested led to a higher mutation detection rate.Electronic supplementary materialThe online version of this article (doi:10.1007/s00125-013-2962-5) contains peer-reviewed but unedited supplementary material, which is available to authorised users.

Prostate is the most frequent cancer in men. Prostate cancer progression is driven by androgen steroid hormones, and delayed by androgen deprivation therapy (ADT). Androgens control transcription by stimulating androgen receptor (AR) activity, yet also control pre-mRNA splicing through less clear mechanisms. Here we find androgens regulate splicing through AR-mediated transcriptional control of the epithelial-specific splicing regulator ESRP2. Both ESRP2 and its close paralog ESRP1 are highly expressed in primary prostate cancer. Androgen stimulation induces splicing switches in many endogenous ESRP2-controlled mRNA isoforms, including splicing switches correlating with disease progression. ESRP2 expression in clinical prostate cancer is repressed by ADT, which may thus inadvertently dampen epithelial splice programmes. Supporting this, treatment with the AR antagonist bicalutamide (Casodex) induced mesenchymal splicing patterns of genes including FLNB and CTNND1. Our data reveals a new mechanism of splicing control in prostate cancer with important implications for disease progression.

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Genome-wide association studies (GWAS) have identified variation at >65 genomic loci associated with susceptibility to type 2 diabetes, but little progress has been made in elucidating the molecular mechanisms behind most of these associations. Using samples heterozygous for transcribed single nucleotide polymorphisms (SNPs), allelic expression profiling is a powerful technique for identifying cis-regulatory variants controlling gene expression. In this study, exonic SNPs, suitable for measuring mature mRNA levels and in high linkage disequilibrium with 65 lead type 2 diabetes GWAS SNPs, were identified and allelic expression determined by real-time PCR using RNA and DNA isolated from islets of 36 white nondiabetic donors. A significant allelic expression imbalance (AEI) was identified for 7/14 (50%) genes tested (ANPEP, CAMK2B, HMG20A, KCNJ11, NOTCH2, SLC30A8, and WFS1), with significant AEI confirmed for five of these genes using other linked exonic SNPs. Lastly, results of a targeted islet expression quantitative trait loci experiment support the AEI findings for ANPEP, further implicating ANPEP as the causative gene at its locus. The results of this study support the hypothesis that changes to cis-regulation of gene expression are involved in a large proportion of SNP associations with type 2 diabetes susceptibility.