Identification of Alternative Splicing Characteristic Associated with ClearCell Ovarian Cancer from Paired Normal and Tumor Tissues Alternative splicing of messenger RNAs (mRNAs) is a common and conserved biological process in eukaryotes. The aberrancy or disruption of different alternative splicing forms may cause alterations of cell functions and result in diseases. It is proposed that alternative splicing may play a critical role in the mechanisms of carcinogenesis. By studying a large dataset in The Cancer Genome Atlas database, a recent study showed that alternative splicing, particularly exonexclusion, is a powerful prognosis factor in serious subtype of ovarian cancer. However, the characteristics of alternative splicing has not been studied in other subtypes. In this study, we focus on the alternative splicing events, i.e. single exoninclusion or exclusion, in clearcell ovarian cancer subtype. The subtype appears to have particularly high incidence in Asians comparing to Europeans and Americans and tend to be drug resistant. Transcriptomes were obtained from tumors and their pairednormal tissues from five patients. PSIvalues, which represent the proportions of alternative splicing events of an exon, were calculated in both tumor and pairednormal tissues. Differences of PSIvalues between tumors and pairednormal were examined by a significant test based on Conditional Beta Regression model. In total, we identified 200 exons covering 52 genes with significant differences between cancer and pairednormal tissue (p u003c 0.001) including gene ERP29 and PAM which were previously identified in serous subtypes.

Differential splicing analysis based on isoforms expression with NBSplice Abstract   Alternative splicing alterations have been widely related to several human diseases revealing the importance of their study for the success of translational medicine. Differential splicing (DS) occurrence has been mainly analyzed through exonbased approaches over RNAseq data. Although these strategies allow identifying differentially spliced genes, they ignore the identity of the affected gene isoforms which is crucial to understand the underlying pathological processes behind alternative splicing changes. Moreover, despite several isoform quantification tools for RNAseq data have been recently developed, DS tools have not taken advantage of them.  Here, the NBSplice R package for differential splicing analysis by means of isoform expression data is presented. It estimates differences on relative expressions of gene transcripts between experimental conditions to infer changes in gene alternative splicing patterns. The developed tool was evaluated using a synthetic RNAseq dataset with controlled differential splicing. NBSplice accurately predicted DS occurrence, outperforming current methods in terms of accuracy, sensitivity, Fscore, and false discovery rate control. The usefulness of our development was demonstrated by the analysis of a real cancer dataset, revealing new differentially spliced genes that could be studied pursuing new colorectal cancer biomarkers discovery.