Biology subjects

Emmanuel Barillot

Publications and source records attributed to Emmanuel Barillot.

4 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↗

bioRxiv2016

Clonal assessment of functional mutations in cancer based on a genotype-aware method for clonal reconstruction

In cancer, clonal evolution is characterized based on single nucleotide variants and copy number alterations. Nonetheless, previous methods failed to combine information from both sources to accurately reconstruct clonal populations in a given tumor sample or in a set of tumor samples coming from the same patient. Moreover, previous methods accepted as input all variants predicted by variant-callers, regardless of differences in dispersion of variant allele frequencies (VAFs) due to uneven depth of coverage and possible presence of strand bias, prohibiting accurate inference of clonal architecture. We present a general framework for assignment of functional mutations to specific cancer clones, which is based on distinction between passenger variants with expected low dispersion of VAF versus putative functional variants, which may not be used for the reconstruction of cancer clonal architecture but can be assigned to inferred clones at the final stage. The key element of our framework is QuantumClone, a method to cluster variants into clones, which we have thoroughly tested on simulated data. QuantumClone takes into account VAFs and genotypes of corresponding regions together with information about normal cell contamination. We applied our framework to whole genome sequencing data for 19 neuroblastoma trios each including constitutional, diagnosis and relapse samples. We discovered specific pathways recurrently altered by deleterious mutations in different clonal populations. Some such pathways were previously reported (e.g., MAPK and neuritogenesis) while some were novel (e.g., epithelial-mesenchymal transition, cell survival and DNA repair). Most pathways and their modules had more mutations at relapse compared to diagnosis.

Cancer Biology↗

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↗