title: Assessment of Common and Emerging Bioinformatics Pipelines for Targeted Metagenomics.
abstract: Targeted metagenomics, also known as metagenetics, is a high-throughput sequencing application focusing on a nucleotide target in a microbiome to describe its taxonomic content. A wide range of bioinformatics pipelines are available to analyze sequencing outputs, and the choice of an appropriate tool is crucial and not trivial. No standard evaluation method exists for estimating the accuracy of a pipeline for targeted metagenomics analyses. This article proposes an evaluation protocol containing real and simulated targeted metagenomics datasets, and adequate metrics allowing us to study the impact of different variables on the biological interpretation of results. This protocol was used to compare six different bioinformatics pipelines in the basic user context: Three common ones (mothur, QIIME and BMP) based on a clustering-first approach and three emerging ones (Kraken, CLARK and One Codex) using an assignment-first approach. This study surprisingly reveals that the effect of sequencing errors has a bigger impact on the results that choosing different amplified regions. Moreover, increasing sequencing throughput increases richness overestimation, even more so for microbiota of high complexity. Finally, the choice of the reference database has a bigger impact on richness estimation for clustering-first pipelines, and on correct taxa identification for assignment-first pipelines. Using emerging assignment-first pipelines is a valid approach for targeted metagenomics analyses, with a quality of results comparable to popular clustering-first pipelines, even with an error-prone sequencing technology like Ion Torrent. However, those pipelines are highly sensitive to the quality of databases and their annotations, which makes clustering-first pipelines still the only reliable approach for studying microbiomes that are not well described.
authors.affiliation: Gènes Diffusion, Douai, France.|CRIStAL (UMR CNRS 9189 University of Lille, Centre de Recherche en Informatique, Signal et Automatique de Lille) and Inria, Villeneuve d'Ascq, France.|Univ. Lille, CNRS, Inserm, CHU Lille, Institut Pasteur de Lille, U1019 - UMR 8204 - CIIL - Centre d'Infection et d'Immunité de Lille, Lille, France.|PEGASE-Biosciences, Institut Pasteur de Lille, Lille, France.
authors.forename: Léa
authors.initials: L
authors.lastname: Siegwald
authors.affiliation: CRIStAL (UMR CNRS 9189 University of Lille, Centre de Recherche en Informatique, Signal et Automatique de Lille) and Inria, Villeneuve d'Ascq, France.
authors.forename: Hélène
authors.initials: H
authors.lastname: Touzet
authors.affiliation: Univ. Lille, CNRS, Inserm, CHU Lille, Institut Pasteur de Lille, U1019 - UMR 8204 - CIIL - Centre d'Infection et d'Immunité de Lille, Lille, France.|PEGASE-Biosciences, Institut Pasteur de Lille, Lille, France.
authors.forename: Yves
authors.initials: Y
authors.lastname: Lemoine
authors.affiliation: Univ. Lille, CNRS, Inserm, CHU Lille, Institut Pasteur de Lille, U1019 - UMR 8204 - CIIL - Centre d'Infection et d'Immunité de Lille, Lille, France.|PEGASE-Biosciences, Institut Pasteur de Lille, Lille, France.
authors.forename: David
authors.initials: D
authors.lastname: Hot
authors.affiliation: Gènes Diffusion, Douai, France.|PEGASE-Biosciences, Institut Pasteur de Lille, Lille, France.
authors.forename: Christophe
authors.initials: C
authors.lastname: Audebert
authors.affiliation: Univ. Lille, CNRS, Inserm, CHU Lille, Institut Pasteur de Lille, U1019 - UMR 8204 - CIIL - Centre d'Infection et d'Immunité de Lille, Lille, France.|PEGASE-Biosciences, Institut Pasteur de Lille, Lille, France.
authors.forename: Ségolène
authors.initials: S
authors.lastname: Caboche
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D030561:Databases, Nucleic Acid; D014644:Genetic Variation; D059014:High-Throughput Nucleotide Sequencing; D056186:Metagenomics / Q000379:methods*