Biology subjects

Andrei Zinovyev

Publications and source records attributed to Andrei Zinovyev.

3 recordsLinked to original sources

bioRxiv2016

Calculating biological module enrichment or depletion and visualizing data on large-scale molecular maps with ACSNMineR and RNaviCell R packages

Biological pathways or modules represent sets of interactions or functional relationships occurring at the molecular level in living cells. A large body of knowledge on pathways is organized in public databases such as the KEGG, Reactome, or in more specialized repositories, such as the Atlas of Cancer Signaling Network (ACSN). All these open biological databases facilitate analyses, improving our understanding of cellular systems. We hereby describe the R package ACSNMineR for calculation of enrichment or depletion of lists of genes of interest in biological pathways. ACSNMineR integrates ACSN molecular pathways, but can use any molecular pathway encoded as a GMT file, for instance sets of genes available in the Molecular Signatures Database (MSigDB). We also present the R package RNaviCell, that can be used in conjunction with ACSNMineR to visualize different data types on web-based, interactive ACSN maps. We illustrate the functionalities of the two packages with biological data taken from large-scale cancer datasets.

Bioinformatics↗

bioRxiv2015

Predicting genetic interactions from Boolean models of biological networks

Genetic interaction can be defined as a deviation of the phenotypic quantitative effect of a double gene mutation from the effect predicted from single mutations using a simple (e.g., multiplicative or linear additive) statistical model. Experimentally characterized genetic interaction networks in model organisms provide important insights into relationships between different biological functions. We describe a computational methodology allowing to systematically and quantitatively characterize a Boolean mathematical model of a biological network in terms of genetic interactions between all loss of function and gain of function mutations with respect to all model phenotypes or outputs. We use the probabilistic framework defined in MaBoSS software, based on continuous time Markov chains and stochastic simulations. In addition, we suggest several computational tools for studying the distribution of double mutants in the space of model phenotype probabilities. We demonstrate this methodology on three published models for each of which we derive the genetic interaction networks and analyze their properties. We classify the obtained interactions according to their class of epistasis, dependence on the chosen initial conditions and phenotype. The use of this methodology for validating mathematical models from experimental data and designing new experiments is discussed.{dagger}

Genetics↗

bioRxiv2015

DeDaL: Cytoscape 3.0 app for producing and morphing data-driven and structure-driven network layouts

BackgroundVisualization and analysis of molecular profiling data together with biological networks are able to provide new mechanistical insights into biological functions. Currently, high-throughput data are usually visualized on top of predefined network layouts which are not always adapted to a given data analysis task. We developed a Cytoscape app which allows to construct biological network layouts based on the data from molecular profiles imported as values of nodes attributes.\n\nResultsDeDaL is a Cytoscape 3.0 app which uses linear and non-linear algorithms of dimension reduction to produce data-driven network layouts based on multidimensional data (typically gene expression). DeDaL implements several data pre-processing and layout post-processing steps such as continuous morphing between two arbitrary network layouts and aligning one network layout with respect to another one by rotating and mirroring. Combining these possibilities facilitates creating insightful network layouts representing both structural network features and the correlation patterns in multivariate data.\n\nConclusionsDeDaL is the first method allowing to construct biological network layouts from high-throughput data. DeDaL is freely available for downloading together with step-by-step tutorial at http://bioinfo-out.curie.fr/projects/dedal/.

Systems Biology↗