Biology subjects

Bouchier, C.

Publications and source records attributed to Bouchier, C..

4 recordsLinked to original sources

bioRxiv2018

Generating genomic platforms to study Candida albicans pathogenesis

The advent of the genomic era has made elucidating gene function at large scale a pressing challenge. ORFeome collections, whereby almost all ORFs of a given species are cloned and can be subsequently leveraged in multiple functional genomic approaches, represent valuable resources towards this endeavor. Here we provide novel, genome-scale tools for the study of Candida albicans, a commensal yeast that is also responsible for frequent superficial and disseminated infections in humans. We have generated an ORFeome collection composed of 5,102 ORFs cloned in a Gateway donor vector, representing 83% of the currently annotated coding sequences of C. albicans. Sequencing data of the cloned ORFs are available in the CandidaOrfDB database at http://candidaorfeome.eu. We also engineered 49 expression vectors with a choice of promoters, tags, and selection markers and demonstrated their applicability to the study of target ORFs transferred from the C. albicans ORFeome. In addition, the use of the ORFeome in the detection of protein-protein interaction was demonstrated. Mating-compatible strains as well as Gateway-compatible two-hybrid vectors were engineered, validated and used in a proof of concept experiment. These unique and valuable resources should greatly facilitate future functional studies in C. albicans and the elucidation of mechanisms that underlie its pathogenicity.

genomics↗

bioRxiv2017

Diverse laboratory colonies of Aedes aegypti harbor the same adult midgut bacterial microbiome

BackgroundHost-associated microbes, collectively known as the microbiota, play an important role in the biology of multicellular organisms. In mosquito vectors of human pathogens, the gut bacterial microbiota influences vectorial capacity and has become the subject of intense study. In laboratory studies of vector biology, genetic effects are often inferred from differences between geographically and genetically diverse colonies of mosquitoes that are reared in the same insectary. It is unclear, however, to what extent genetic effects can be confounded by uncontrolled differences in the microbiota composition among mosquito colonies. To address this question, we used 16S metagenomics to compare the midgut bacterial microbiome of six recent laboratory colonies of Aedes aegypti representing the geographical range and genetic diversity of the species.\n\nResultsWe found that the diversity, abundance, and community structure of the midgut bacterial microbiome was remarkably similar among the six different colonies of Ae. aegypti, regardless of their geographic origin. We also confirmed the relatively low complexity of bacterial communities inhabiting the mosquito midgut.\n\nConclusionsOur finding that geographically diverse colonies of Ae. aegypti reared in the same insectary harbor a similar gut bacterial microbiome supports the conclusion that the gut microbiota of adult mosquitoes is environmentally determined regardless of the host genotype. Thus, uncontrolled differences in microbiota composition are unlikely to represent a significant confounding factor in genetic studies of vector biology.

genetics↗

bioRxiv2017

Sequanix: A Dynamic Graphical Interface for Snakemake Workflows

SummaryWe designed a PyQt graphical user interface - Sequanix - aiming at democratizing the use of Snakemake pipelines. Although the primary goal of Sequanix was to facilitate the execution of NGS Snakemake pipelines available in the Sequana project (http://sequana.readthedocs.io), it can also handle any Snakemake pipelines. Therefore, Sequanix should be useful to all Snakemake developers willing to expose their pipelines to a wider audience.\n\nAvailabilitySource code available on http://github.com/sequana/sequana and standalone on http://bioconda.github.io (sequana package).

bioinformatics↗

bioRxiv2016

Detection and characterization of low and high genome coverage regions using an efficient running median and a double threshold approach.

MotivationNext Generation Sequencing (NGS) provides researchers with powerful tools to investigate both prokaryotic and eukaryotic genetics. An accurate assessment of reads mapped to a specific genome consists of inspecting the genome coverage as number of reads mapped to a specific genome location. Most current methods use the average of the genome coverage (sequencing depth) to summarize the overall coverage. This metric quickly assess the sequencing quality but ignores valuable biological information like the presence of repetitive regions or deleted genes. The detection of such information may be challenging due to a wide spectrum of heterogeneous coverage regions, a mixture of underlying models or the presence of a non-constant trend along the genome. Using robust statistics to systematically identify genomic regions with unusual coverage is needed to characterize these regions more precisely.\n\nResultsWe implemented an efficient running median algorithm to estimate the genome coverage trend. The distribution of the normalized genome coverage is then estimated using a Gaussian mixture model. A z-score statistics is then assigned to each base position and used to separate the central distribution from the regions of interest (ROI) (i.e., under and over-covered regions). Finally, a double threshold mechanism is used to cluster the genomic ROIs. HTML reports provide a summary with interactive visual representations of the genomic ROIs.\n\nAvailabilityAn implementation of the genome coverage characterization is available within the Sequana project. The standalone application is called sequana_coverage. The source code is available on GitHub (http://github.com/sequana/sequana), and documentation on ReadTheDocs (http://sequana.readtheodcs.org). An example of HTML report is provided on http://sequana.github.io.\n\nContactdimitri.desvillechabrol@pasteur.fr, thomas.cokelaer@pasteur.fr

bioinformatics↗