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Medigue, C.

Publications and source records attributed to Medigue, C..

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PlaScope: a targeted approach to assess the plasmidome of Escherichia coli strains

Plasmid prediction may be of great interest when studying bacteria such as Enterobacteriaceae. Indeed many resistance and virulence genes are located on such replicons and can have major impact in terms of pathogenicity and spreading capacities. Beyond strains outbreak, plasmids outbreaks have been reported especially for some extended-spectrum beta-lactamase or carbapenemase producing Enterobacteriaceae. Several tools are now available to explore the \"plasmidome\" from whole-genome sequence data, with many interesting and various approaches. However recent benchmarks have highlighted that none of them succeed to combine high sensitivity and specificity. With this in mind we developed PlaScope, a targeted approach to recover plasmidic sequences in Escherichia coli. Based on Centrifuge, a metagenomic classifier, and a custom database containing complete sequences of chromosomes and plasmids from various curated databases, it performs a classification of contigs from an assembly according to their predicted location. Compared to other plasmid classifiers, Plasflow and cBar, it achieves better recall (0.87), specificity (0.99), precision (0.96) and accuracy (0.98) on a dataset of 70 genomes containing plasmids. Finally we tested our method on a dataset of E. coli strains exhibiting an elevated rate of extended-spectrum beta-lactamase coding gene chromosomal integration, and we were able to identify 20/21 of these events. Moreover virulence genes and operons predicted locations were also in agreement with the literature. Similar approaches could also be developed for other well-characterized bacteria such as Klebsiella pneumoniae.\n\nData summaryO_LIAll the genomes were downloaded from the National Center for Biotechnology Information Sequence Read Archive and Genome database (Supplementary table 1 and 2).\nC_LIO_LIThe source code of PlaScope is available on Github (https://github.com/GuilhemRoyer/PlaScope).\nC_LI\n\nImportancePlasmid exploration could be of great interest since these replicons are pivotal in the adaptation of bacteria to their environment. They are involved in the exchange of many genes within and between species, with a significant impact on antibiotic resistance and virulence in particular. However, plasmid characterization has been a laborious task for many years, requiring complex conjugation or electroporation manipulations for example. With the advent of whole genome sequencing techniques, access to these sequences is now potentially easier provided that appropriate tools are available. Many softwares have been developed to explore the plasmidome of a large variety of bacteria, but they rarely managed to combine sensitivity and specificity. Here, we focus on a single species, E. coli, and we use the many data available to overcome this problem. With our tool called PlaScope, we achieve high performance compared with two other classifiers, Plasflow and cBar, and we demonstrate the utility of such an approach to determine the location of virulence or resistance genes. We think that PlaScope could be very useful in the analysis of specific and well-known bacteria.

bioinformatics

GROOLS: reactive graph reasoning for genome annotation through biological processes

BackgroundHigh quality functional annotation is essential for understanding the phenotypic consequences encoded in a genome. Despite improvements in bioinformatics methods, millions of sequences in databanks are not assigned reliable functions. The curation of protein functions in the context of biological processes is a way to evaluate and improve their annotation.\n\nResultsWe developed an expert system using paraconsistent logic, named GROOLS (Genomic Rule Object-Oriented Logic System), that evaluates the completeness and the consistency of predicted functions through biological processes like metabolic pathways. Using a generic and hierarchical representation of knowledge, biological processes are modeled in a graph from which observations (i.e. predictions and expectations) are propagated by rules. At the end of the reasoning, conclusions are assigned to biological process components and highlight uncertainties and inconsistencies. Results on 14 microbial organisms are presented.\n\nConclusionsGROOLS software is designed to evaluate the overall accuracy of functional unit and pathway predictions according to organism experimental data like growth phenotypes. It assists biocurators in the functional annotation of proteins by focusing on missing or contradictory observations.

bioinformatics