A-to-I RNA editing promotes developmental stage-specific gene and lncRNA expression.

A-to-I RNA editing is a conserved widespread phenomenon in which adenosine (A) is converted to inosine (I) by adenosine deaminases (ADARs) in double-stranded RNA regions, mainly noncoding. Mutations in ADAR enzymes in Caenorhabditis elegans cause defects in normal development but are not lethal as in human and mouse. Previous studies in C. elegans indicated competition between RNA interference (RNAi) and RNA editing mechanisms, based on the observation that worms that lack both mechanisms do not exhibit defects, in contrast to the developmental defects observed when only RNA editing is absent. To study the effects of RNA editing on gene expression and function, we established a novel screen that enabled us to identify thousands of RNA editing sites in nonrepetitive regions in the genome. These include dozens of genes that are edited at their 3' UTR region. We found that these genes are mainly germline and neuronal genes, and that they are down-regulated in the absence of ADAR enzymes. Moreover, we discovered that almost half of these genes are edited in a developmental-specific manner, indicating that RNA editing is a highly regulated process. We found that many pseudogenes and other lncRNAs are also extensively down-regulated in the absence of ADARs in the embryo but not in the fourth larval (L4) stage. This down-regulation is not observed upon additional knockout of RNAi. Furthermore, levels of siRNAs aligned to pseudogenes in ADAR mutants are enhanced. Taken together, our results suggest a role for RNA editing in normal growth and development by regulating silencing via RNAi.

Function of cancer associated genes revealed by modern univariate and multivariate association tests.

Copy number variation (CNV) plays a role in pathogenesis of many human diseases, especially cancer. Several whole genome CNV association studies have been performed for the purpose of identifying cancer associated CNVs. Here we undertook a novel approach to whole genome CNV analysis, with the goal being identification of associations between CNV of different genes (CNV-CNV) across 60 human cancer cell lines. We hypothesize that these associations point to the roles of the associated genes in cancer, and can be indicators of their position in gene networks of cancer-driving processes. Recent studies show that gene associations are often non-linear and non-monotone. In order to obtain a more complete picture of all CNV associations, we performed omnibus univariate analysis by utilizing dCov, MIC, and HHG association tests, which are capable of detecting any type of association, including non-monotone relationships. For comparison we used Spearman and Pearson association tests, which detect only linear or monotone relationships. Application of dCov, MIC and HHG tests resulted in identification of twice as many associations compared to those found by Spearman and Pearson alone. Interestingly, most of the new associations were detected by the HHG test. Next, we utilized dCov's and HHG's ability to perform multivariate analysis. We tested for association between genes of unknown function and known cancer-related pathways. Our results indicate that multivariate analysis is much more effective than univariate analysis for the purpose of ascribing biological roles to genes of unknown function. We conclude that a combination of multivariate and univariate omnibus association tests can reveal significant information about gene networks of disease-driving processes. These methods can be applied to any large gene or pathway dataset, allowing more comprehensive analysis of biological processes.