Alternative splicing (AS) can critically affect gene function and disease, yet mapping splicing variations remains a challenge. Here, we propose a new approach to define and quantify mRNA splicing in units of local splicing variations (LSVs). LSVs capture previously defined types of alternative splicing as well as more complex transcript variations. Building the first genome wide map of LSVs from twelve mouse tissues, we find complex LSVs constitute over 30% of tissue dependent transcript variations and affect specific protein families. We show the prevalence of complex LSVs is conserved in humans and identify hundreds of LSVs that are specific to brain subregions or altered in Alzheimer's patients. Amongst those are novel isoforms in the Camk2 family and a novel poison exon in Ptbp1, a key splice factor in neurogenesis. We anticipate the approach presented here will advance the ability to relate tissue-specific splice variation to genetic variation, phenotype, and disease.DOI: http://dx.doi.org/10.7554/eLife.11752.001

Purpose: RNA-seq is a promising approach to improve diagnoses by detecting pathogenic aberrations in RNA splicing that are missed by DNA sequencing. RNA-seq is typically performed on clinically-accessible tissues (CATs) from blood and skin. RNA tissue-specificity makes it difficult to identify aberrations in relevant but non-accessible tissues (non-CATs). We determined how RNA-seq from CATs represent splicing in and across genes and non-CATs. Methods:We quantified RNA splicing in 801 RNA-seq samples from 56 different adult and fetal tissues from GTEx and ArrayExpress. We identified genes and splicing events in each non-CAT and determined when RNA-seq in each CAT would inadequately represent them. We developed an online resource, MAJIQ-CAT, for exploring our analysis for specific genes and tissues. Results:In non-CATs, 40.2% of genes have splicing that is inadequately represented by at least one CAT. 6.3% of genes have splicing inadequately represented by all CATs. A majority (52.1%) of inadequately represented genes are lowly expressed in CATs (TPM < 1), but 5.8% are inadequately represented despite being well expressed (TPM > 10). Conclusion:Many splicing events in non-CATs are inadequately evaluated using RNA-seq from CATs. MAJIQ-CAT allows users to explore which accessible tissues, if any, best represent splicing in genes and tissues of interest.

Transcriptome complexity and its relation to numerous diseases underpins the need to predict in silico splice variants and the regulatory elements that affect them. Building upon our recently described splicing code, we developed AVISPA, a Galaxy-based web tool for splicing prediction and analysis. Given an exon and its proximal sequence, the tool predicts whether the exon is alternatively spliced, displays tissue-dependent splicing patterns, and whether it has associated regulatory elements. We assess AVISPA's accuracy on an independent dataset of tissue-dependent exons, and illustrate how the tool can be applied to analyze a gene of interest. AVISPA is available at http://avispa.biociphers.org.

PurposeTo create an interactive web-based tool for the Prediction of Risk of Metastasis in Uveal Melanoma (PRiMeUM) that can provide a personalized risk estimate of developing metastases within 48 months of primary uveal melanoma (UM) treatment. The model utilizes routinely collected clinical and tumor characteristics on 1227 UM, with the option of including chromosome information when available.MethodsUsing a cohort of 1227 UM cases, Cox proportional hazard modeling was used to assess significant predictors of metastasis including clinical and chromosomal characteristics. A multivariate model to predict risk of metastasis was evaluated using machine learning methods including logistic regression, decision trees, survival random forest, and survival-based regression models. Based on cross-validation results, a logistic regression classifier was developed to compute an individualized risk of metastasis based on clinical and chromosomal information.ResultsThe PRiMeUM model provides prognostic information for personalized risk of metastasis in UM. The accuracy of the risk prediction ranged between 80% (using chromosomal features only), 83% using clinical features only (age, sex, tumor location, and size), and 85% (clinical and chromosomal information). Kaplan-Meier analysis showed these risk scores to be highly predictive of metastasis (P < 0.0001).ConclusionsPRiMeUM provides a tool for predicting an individual's personal risk of metastasis based on their individual and tumor characteristics. It will aid physicians with decisions concerning frequency of systemic surveillance and can be used as a criterion for entering clinical trials for adjuvant therapies.

Supplementary data are available at Bioinformatics online.