MOTIVATION: Allelic imbalance (AI), i.e. the unequal expression of the alleles of the same gene in a single cell, affects a subset of genes in diploid organisms. One prominent example of AI is parental genomic imprinting, which results in parent-of-origin-dependent, mono-allelic expression of a limited number of genes in metatherian and eutherian mammals and in angiosperms. Currently available methods for identifying AI rely on data modeling and come with the associated limitations. RESULTS: We have designed ISoLDE (Integrative Statistics of alleLe Dependent Expression), a novel nonparametric statistical method that takes into account both AI and the characteristics of RNA-seq data to infer allelic expression bias when at least two biological replicates are available for reciprocal crosses. ISoLDE learns the distribution of a specific test statistic from the data and calls genes 'allelically imbalanced', 'bi-allelically expressed' or 'undetermined'. Depending on the number of replicates, predefined thresholds or permutations are used to make calls. We benchmarked ISoLDE against published methods, and showed that ISoLDE compared favorably with respect to sensitivity, specificity and robustness to the number of replicates. Using ISoLDE on different RNA-seq datasets generated from hybrid mouse tissues, we did not discover novel imprinted genes (IGs), confirming the most conservative estimations of IG number. AVAILABILITY AND IMPLEMENTATION: ISoLDE has been implemented as a Bioconductor package available at http://bioconductor.org/packages/ISoLDE/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: In the available databases, biological processes are described from molecular and cellular points of view, but these descriptions are represented with text annotations that make it difficult to handle them for computation. Consequently, there is an obvious need for formal descriptions of biological processes. RESULTS: We present a formalism that uses the BioPsi concepts to model biological processes from molecular details to networks. This computational approach, based on elementary bricks of actions, allows us to calculate on biological functions (e.g. process comparison, mapping structure-function relationships, etc.). We illustrate its application with two examples: the functional comparison of proteases and the functional description of the glycolysis network. This computational approach is compatible with detailed biological knowledge and can be applied to different kinds of systems of simulation. AVAILABILITY: www.sysdiag.cnrs.fr/publications/supplementary-materials/BioPsi_Manager/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Synthetic biology studies how to design and construct biological systems with functions that do not exist in nature. Biochemical networks, although easier to control, have been used less frequently than genetic networks as a base to build a synthetic system. To date, no clear engineering principles exist to design such cell-free biochemical networks. RESULTS: We describe a methodology for the construction of synthetic biochemical networks based on three main steps: design, simulation and experimental validation. We developed BioNetCAD to help users to go through these steps. BioNetCAD allows designing abstract networks that can be implemented thanks to CompuBioTicDB, a database of parts for synthetic biology. BioNetCAD enables also simulations with the HSim software and the classical Ordinary Differential Equations (ODE). We demonstrate with a case study that BioNetCAD can rationalize and reduce further experimental validation during the construction of a biochemical network. AVAILABILITY AND IMPLEMENTATION: BioNetCAD is freely available at http://www.sysdiag.cnrs.fr/BioNetCAD. It is implemented in Java and supported on MS Windows. CompuBioTicDB is freely accessible at http://compubiotic.sysdiag.cnrs.fr/.