Moyamoya angiopathy (MMA) is a cerebral angiopathy affecting the terminal part of internal carotid arteries. Its prevalence is 10 times higher in Japan and Korea than in Europe. In East Asian countries, moyamoya is strongly associated to the R4810K variant in the RNF213 gene that encodes for a protein containing a RING-finger and two AAA+ domains. This variant has never been detected in Caucasian MMA patients, but several rare RNF213 variants have been reported in Caucasian cases. Using a collapsing test based on exome data from 68 European MMA probands and 573 ethnically matched controls, we showed a significant association between rare missense RNF213 variants and MMA in European patients (odds ratio (OR)=2.24, 95% confidence interval (CI)=(1.19-4.11), P=0.01). Variants specific to cases had higher pathogenicity predictive scores (median of 24.2 in cases versus 9.4 in controls, P=0.029) and preferentially clustered in a C-terminal hotspot encompassing the RING-finger domain of RNF213 (P<10). This association was even stronger when restricting the analysis to childhood-onset and familial cases (OR=4.54, 95% CI=(1.80-11.34), P=1.1 × 10). All clinically affected relatives who were genotyped were carriers. However, the need for additional factors to develop MMA is strongly suggested by the fact that only 25% of mutation carrier relatives were clinically affected.

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Intron retention (IR) occurs when a complete and unspliced intron remains in mature mRNA. An increasing body of literature has demonstrated a major role for IR in numerous biological functions, including several that impact human health and disease. Although experimental technologies used to study other forms of mRNA splicing can also be used to investigate IR, a specialized downstream computational analysis is optimal for IR discovery and analysis. Here we provide a review of IR and its biological implications, as well as a practical guide for how to detect and analyze it. Several methods, including long read third generation direct RNA sequencing, are described. We have developed an R package, FakIR, to facilitate the execution of the bioinformatic tasks recommended in this review and a tutorial on how to fit them to users aims. Additionally, we provide guidelines and experimental protocols to validate IR discovery and to evaluate the potential impact of IR on gene expression and protein output. This article is categorized under: RNA Evolution and Genomics > Computational Analyses of RNA RNA Processing > Splicing Regulation/Alternative Splicing RNA Methods > RNA Analyses in vitro and In Silico K E Y W O R D S intron retention, nonsense mediated decay, post-transcriptional gene regulation, RNA export, RNA processing 1 | INTRODUCTION Alternative splicing of messenger RNA (mRNA) is responsible for much of the proteome complexity in mammals (Berget, Moore, & Sharp, 2000; Nilsen & Graveley, 2010). We now know that at least 90% of all mammalian genes can undergo some form of alternative splicing, often generating multiple protein isoforms with sometimes disparate biological functions (Wang et al., 2008). Intron retention (IR) is a type of alternative splicing that is gaining increased interest in human health and disease research. Originally described in plants and viruses, IR has now been shown to be a common form of alternative David F. Grabski, Lucile Broseus, and Bandana Kumari contributed equally to this study.

Comparative analysis of high throughput sequencing data between multiple conditions often involves mapping of sequencing reads to a reference and downstream bioinformatics analyses. Both of these steps may introduce heavy bias and potential data loss. This is especially true in studies where patient transcriptomes or genomes may vary from their references, such as in cancer. Here we describe a novel approach and associated software that makes use of advances in genetic algorithms and feature selection to comprehensively explore massive volumes of sequencing data to classify and discover new sequences of interest without a mapping step and without intensive use of specialized bioinformatics pipelines. We demonstrate that our approach called GECKO for GEnetic Classification using k-mer Optimization is effective at classifying and extracting meaningful sequences from multiple types of sequencing approaches including mRNA, microRNA, and DNA methylome data.