GiANT: gene set uncertainty in enrichment analysis.

UNLABELLED: Over the past years growing knowledge about biological processes and pathways revealed complex interaction networks involving many genes. In order to understand these networks, analysis of differential expression has continuously moved from single genes towards the study of gene sets. Various approaches for the assessment of gene sets have been developed in the context of gene set analysis (GSA). These approaches are bridging the gap between raw measurements and semantically meaningful terms.We present a novel approach for assessing uncertainty in the definition of gene sets. This is an essential step when new gene sets are constructed from domain knowledge or given gene sets are suspected to be affected by uncertainty. Quantification of uncertainty is implemented in the R-package GiANT. We also included widely used GSA methods, embedded in a generic framework that can readily be extended by custom methods. The package provides an easy to use front end and allows for fast parallelization. AVAILABILITY AND IMPLEMENTATION: The package GiANT is available on CRAN. CONTACTS: hans.kestler@leibniz-fli.de or hans.kestler@uni-ulm.de.

Predicting Variabilities in Cardiac Gene Expression with a Boolean Network Incorporating Uncertainty.

Gene interactions in cells can be represented by gene regulatory networks. A Boolean network models gene interactions according to rules where gene expression is represented by binary values (on / off or {1, 0}). In reality, however, the gene's state can have multiple values due to biological properties. Furthermore, the noisy nature of the experimental design results in uncertainty about a state of the gene. Here we present a new Boolean network paradigm to allow intermediate values on the interval [0, 1]. As in the Boolean network, fixed points or attractors of such a model correspond to biological phenotypes or states. We use our new extension of the Boolean network paradigm to model gene expression in first and second heart field lineages which are cardiac progenitor cell populations involved in early vertebrate heart development. By this we are able to predict additional biological phenotypes that the Boolean model alone is not able to identify without utilizing additional biological knowledge. The additional phenotypes predicted by the model were confirmed by published biological experiments. Furthermore, the new method predicts gene expression propensities for modelled but yet to be analyzed genes.

Site-specific methylation of Notch1 controls the amplitude and duration of the Notch1 response.

Physiologically, Notch signal transduction plays a pivotal role in differentiation; pathologically, Notch signaling contributes to the development of cancer. Transcriptional activation of Notch target genes involves cleavage of the Notch receptor in response to ligand binding, production of the Notch intracellular domain (NICD), and NICD migration into the nucleus and assembly of a coactivator complex. Posttranslational modifications of the NICD are important for its transcriptional activity and protein turnover. Deregulation of Notch signaling and stabilizing mutations of Notch1 have been linked to leukemia development. We found that the methyltransferase CARM1 (coactivator-associated arginine methyltransferase 1; also known as PRMT4) methylated NICD at five conserved arginine residues within the C-terminal transactivation domain. CARM1 physically and functionally interacted with the NICD-coactivator complex and was found at gene enhancers in a Notch-dependent manner. Although a methylation-defective NICD mutant was biochemically more stable, this mutant was biologically less active as measured with Notch assays in embryos of Xenopus laevis and Danio rerio. Mathematical modeling indicated that full but short and transient Notch signaling required methylation of NICD.