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Chauve, C.

Publications and source records attributed to Chauve, C..

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Leveraging evolutionary relationships to improve Anopheles genome assemblies

While new sequencing technologies have lowered financial barriers to whole genome sequencing, resulting assemblies are often fragmented and far from finished. Subsequent improvements towards chromosomal-level status can be achieved by both experimental and computational approaches. Requiring only annotated assemblies and gene orthology data, comparative genomics approaches that aim to capture evolutionary signals to predict scaffold neighbours (adjacencies) offer potentially substantive improvements without the costs associated with experimental scaffolding or re-sequencing. We leverage the combined detection power of three such gene synteny-based methods applied to 21 Anopheles mosquito assemblies with variable contiguity levels to produce consensus sets of scaffold adjacency predictions. Three complementary validations were performed on subsets of assemblies with additional supporting data: six with physical mapping data; 13 with paired-end RNA sequencing (RNAseq) data; and three with new assemblies based on re-scaffolding or incorporating Pacific Biosciences (PacBio) sequencing data. Improved assemblies were built by integrating the consensus adjacency predictions with supporting experimental data, resulting in 20 new reference assemblies with improved contiguities. Combined with physical mapping data for six anophelines, chromosomal positioning of scaffolds improved assembly anchoring by 47% for A. funestus and 38% A. stephensi. Reconciling an A. funestus PacBio assembly with synteny-based and RNAseq-based adjacencies and physical mapping data resulted in a new 81.5% chromosomally mapped reference assembly and cytogenetic photomap. While complementary experimental data are clearly key to achieving high-quality chromosomal-level assemblies, our assessments and validations of gene synteny-based computational methods highlight the utility of applying comparative genomics approaches to improve community genomic resources.

genomics

The Integrated Rapid Infectious Disease Analysis (IRIDA) Platform

Whole genome sequencing (WGS) is a powerful tool for public health infectious disease investigations owing to its higher resolution, greater efficiency, and cost-effectiveness over traditional genotyping methods. Implementation of WGS in routine public health microbiology laboratories is impeded by a lack of user-friendly automated and semi-automated pipelines, restrictive jurisdictional data sharing policies, and the proliferation of non-interoperable analytical and reporting systems. To address these issues, we developed the Integrated Rapid Infectious Disease Analysis (IRIDA) platform (irida.ca), a user-friendly, decentralized, open-source bioinformatics and analytical web platform to support real-time infectious disease outbreak investigations using WGS data. Instances can be independently installed on local high-performance computing infrastructure, enabling private and secure data management and analyses according to organizational policies and governance. IRIDAs data management capabilities enable secure upload, storage and sharing of all WGS data and metadata. The core platform currently includes pipelines for quality control, assembly, annotation, variant detection, phylogenetic analysis, in silico serotyping, multi-locus sequence typing, and genome distance calculation. Analysis pipeline results can be visualized within the platform through dynamic line lists and integrated phylogenomic clustering for research and discovery, and for enhancing decision-making support and hypothesis generation in epidemiological investigations. Communication and data exchange between instances are provided through customizable access controls. IRIDA complements centralized systems, empowering local analytics and visualizations for genomics-based microbial pathogen investigations. IRIDA is currently transforming the Canadian public health ecosystem and is freely available at https://github.com/phac-nml/irida and www.irida.ca.\n\nImpact StatementWhole genome sequencing (WGS) is revolutionizing infectious disease analysis and surveillance due to its cost effectiveness, utility, and improved analytical power. To date, no \"one-size-fits-all\" genomics platform has been universally adopted, owing to differences in national (and regional) health information systems, data sharing policies, computational infrastructures, lack of interoperability and prohibitive costs. The Integrated Rapid Infectious Disease Analysis (IRIDA) platform is a user-friendly, decentralized, open-source bioinformatics and analytical web platform developed to support real-time infectious disease outbreak investigations using WGS data. IRIDA empowers public health, regulatory and clinical microbiology laboratory personnel to better incorporate WGS technology into routine operations by shielding them from the computational and analytical complexities of big data genomics. IRIDA is now routinely used as part of a validated suite of tools to support outbreak investigations in Canada. While IRIDA was designed to serve the needs of the Canadian public health system, it is generally applicable to any public health and multi-jurisdictional environment. IRIDA enables localized analyses but provides mechanisms and standard outputs to enable data sharing. This approach can help overcome pervasive challenges in real-time global infectious disease surveillance, investigation and control, resulting in faster responses, and ultimately, better public health outcomes.\n\nDATA SUMMARYO_LIData used to generate some of the figures in this manuscript can be found in the NCBI BioProject PRJNA305824.\nC_LI

bioinformatics

MentaLiST - A fast MLST caller for large wgMLST schemes

MLST (multi-locus sequence typing) is a classic technique for genotyping bacteria, widely applied for pathogen outbreak surveillance. Traditionally, MLST is based on identifying sequence types from a small number of housekeeping genes. With the increasing availability of whole-genome sequencing (WGS) data, MLST methods have evolved toward larger typing schemes, based on a few hundred genes (core genome MLST, cgMLST) to a few thousand genes (whole genome MLST, wgMLST). Such large-scale MLST schemes have been shown to provide a finer resolution and are increasingly used in various contexts such as hospital outbreaks or foodborne pathogen outbreaks. This methodological shift raises new computational challenges, especially given the large size of the schemes involved. Very few available MLST callers are currently capable of dealing with large MLST schemes.\n\nWe introduce MentaLiST, a new MLST caller, based on a k-mer voting algorithm and written in the Julia language, specifically designed and implemented to handle large typing schemes. We test it on real and simulated data to show that MentaLiST is faster than any other available MLST caller while providing the same or better accuracy, and is capable of dealing with MLST scheme with up to thousands of genes while requiring limited computational resources. MentaLiST source code and easy installation instructions using a Conda package are available at https://github.com/WGS-TB/MentaLiST.

bioinformatics

MaxTiC: Fast Ranking Of A Phylogenetic Tree By Maximum Time Consistency With Lateral Gene Transfers

Lateral gene transfers between ancient species contain information about the relative timing of species diversification. Specifically, the ancestors of a donor species must have existed before the descendants of the recipient species. Hence, the detection of a transfer event can be translated into a time constraint between nodes of a phylogeny if the donor and recipient can be identified. When a set of transfers is detected by interpreting the phylogenetic discordance between gene trees and a species tree, the set of all deduced time constraints can be used to rank the species tree, i.e. order totally its internal nodes. Unfortunately lateral gene transfer detection is challenging and current methods produce a significant proportion of false positives. As a result, often, no ranking of the species tree is compatible with the full set of time constraints deduced from predicted transfers. Here we propose a method, implemented in a software called MaxTiC (Maximum Time Consistency), which takes as input a species tree and a series of (possibly inconsistent) time constraints between its internal nodes, weighted by confidence scores. MaxTiC outputs a ranked species tree compatible with a subset of constraints with maximum cumulated confidence score. We extensively test the method on simulated datasets, under a wide range of conditions that we compare to measures on biological datasets. In most conditions the obtained ranked tree is very close to the real one, confirming the potential of dating the history of life with transfers by maximizing time consistency. MaxTiC is freely available, distributed along with a documentation and several examples: https://github.com/ssolo/ALE/tree/master/maxtic.

evolutionary biology