Biology subjects

Rousset, F.

Publications and source records attributed to Rousset, F..

3 recordsLinked to original sources

bioRxiv2018

Genome-wide CRISPR-dCas9 screens in E. coli identify essential genes and phage host factors

High-throughput genetic screens are powerful methods to identify genes linked to a given phenotype. The catalytic null mutant of the Cas9 RNA-guided nuclease (dCas9) can be conveniently used to silence genes of interest in a method also known as CRISPRi. Here, we report a genome-wide CRISPR-dCas9 screen using a pool of ~ 92,000 sgRNAs which target random positions in the chromosome of E. coli. We first investigate the utility of this method for the prediction of essential genes and various unusual features in the genome of E. coli. We then apply the screen to discover E. coli genes required by phages {lambda}, T4 and 186 to kill their host. In particular, we show that colanic acid capsule is a barrier to all three phages. Finally, cloning the library on a plasmid that can be packaged by {lambda} enables to identify genes required for the formation of functional {lambda} capsids. This study demonstrates the usefulness and convenience of pooled genome-wide CRISPR-dCas9 screens in bacteria in order to identify genes linked to a given phenotype.

microbiology↗

bioRxiv2017

Modelling isoscapes using mixed models

AbstractIsoscapes are maps depicting the continuous spatial (and sometimes temporal) variation in isotope composition. They have various applications ranging from the study of isotope circulation in the main earth systems to the determination of the provenance of migratory animals. Isoscapes can be produced from the fit of statistical models to observations originating from a set of discrete locations. Mixed models are powerful tools for drawing inferences from correlated data. While they are widely used to study non-spatial variation, they are often overlooked in spatial analyses. In particular, they have not been used to study the spatial variation of isotope composition. Here, we introduce this statistical framework and illustrate the methodology by building isoscapes of the isotope composition of hydrogen (measured in{delta} 2H) for precipitation water in Europe. For this example, the approach based on mixed models presents a higher predictive power than a widespread alternative approach. We discuss other advantages offered by mixed models including: the ability to model the residual variance in isotope composition, the quantification of prediction uncertainty, and the simplicity of model comparison and selection using an adequate information criterion: the conditional AIC (cAIC). We provide all source code required for the replication of the results of this paper as a small R package to foster a transparent comparison between alternative frameworks used to model isoscapes.\n\nAbbreviations used in this paper O_LIAIC: Akaike Information Criterion\nC_LIO_LIBLUP: Best Linear Unbiased Predictor\nC_LIO_LIBWR: a method for building isoscape introduced by Bowen and Wilkinson (2002) and Bowen and Revenaugh (2003)\nC_LIO_LIcAIC: conditional Akaike Information Criterion\nC_LIO_LIDHGLM: Double Hierarchical Generalised Linear Model\nC_LIO_LIGLM: Generalised Linear Model\nC_LIO_LIGLMM: Generalised Linear Mixed-effects Model\nC_LIO_LIGNIP: Global Network for Isotopes in Precipitation\nC_LIO_LILM: Linear Model\nC_LIO_LILMM: Linear Mixed-effects Model\nC_LIO_LIMAE: Mean Absolute Error\nC_LIO_LIML: Maximum Likelihood\nC_LIO_LIREML: Restricted Maximum Likelihood\nC_LIO_LIRMSE: Root Mean Squared Error\nC_LI

ecology↗

bioRxiv2017

Likelihood analysis of population genetic data under coalescent models: computational and inferential aspects

Likelihood methods are being developed for inference of migration rates and past demographic changes from population genetic data. We survey an approach for such inference using sequential importance sampling techniques derived from coalescent and diffusion theory. The consistent application and assessment of this approach has required the re-implementation of methods often considered in the context of computer experiments methods, in particular of Kriging which is used as a smoothing technique to infer a likelihood surface from likelihoods estimated in various parameter points, as well as reconsideration of methods for sampling the parameter space appropriately for such inference. We illustrate the performance and application of the whole tool chain on simulated and actual data, and highlight desirable developments in terms of data types and biological scenarios.\n\nResumeDiverses approches ont ete developpees pour linference des taux de migration et des changements demo-graphiques passes a partir de la variation genetique des populations. Nous decrivons une de ces approches utilisant des techniques dechantillonnage pondere sequentiel, fondees sur la modelisation par approches de coalescence et de diffusion de levolution de ces polymorphismes. Lapplication et levaluation systematique de cette approche ont requis la re-implementation de methodes souvent considerees pour lanalyse de fonctions simulees, en particulier le krigeage, ici utilise pour inferer une surface de vraisemblance a partir de vraisemblances estimees en differents points de lespace des parametres, ainsi que des techniques dechantillonage de ces points. Nous illustrons la performance et lapplication de cette serie de methodes sur donnees simulees et reelles, et indiquons les ameliorations souhaitables en termes de types de donnees et de scenarios biologiques.\n\nMots-cleshistoire demographique, processus de coalescence, importance sampling, genetic polymorphism\n\nAMS 2000 subject classifications92D10, 62M05, 65C05

evolutionary biology↗