1. Quality control of microbiota metagenomics by k-mer analysis
- Author
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Plaza Onate, Florian, Batto, Jean-Michel, Juste, Catherine, Fadlallah, Jehane, Fougeroux, Cyrielle, Gouas, Doriane, Pons, Nicolas, Kennedy, Sean, Levenez, Florence, Dore, Joel, Dusko Ehrlich, S., Gorochov, Guy, Larsen, Martin, INRA US1367 MetaGenoPolis, MICrobiologie de l'ALImentation au Service de la Santé (MICALIS), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Service d'immunologie [CHU Pitié-Salpétrière], Assistance publique - Hôpitaux de Paris (AP-HP) (APHP)-CHU Pitié-Salpêtrière [APHP], Centre d'Immunologie et de Maladies Infectieuses (CIMI), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), MetaGenoPolis, Institut National de la Recherche Agronomique (INRA), Service d'Immunologie [CHU Pitié-Salpétrière], CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Pierre et Marie Curie - Paris 6 (UPMC), The study was funded by INSERM, the University Pierre et Marie Curie ËMERGENCE' program, Fondation pour l’Aide a la Recherche sur la Sclerose En Plaques (ARSEP), ARTHRITIS Fondation COURTIN and Agence nationale de la recherché (ANR)., The authors acknowledge the funding agencies and the volunteers providing samples for the study., Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), and Administateur, HAL Sorbonne Université
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Sample size limits ,Quality control ,Sampling bias ,Metagenomics ,Next generation sequencing ,MESH: Bacteria/genetics ,MESH: Quality Control ,Médecine humaine et pathologie ,MESH: Metagenomics/standards ,MESH: Genome, Bacterial ,Sensitivity and Specificity ,Feces ,[SDV.BBM.GTP]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN] ,Genetics ,Cluster Analysis ,Humans ,MESH: Gastrointestinal Tract/microbiology ,[SDV.MHEP] Life Sciences [q-bio]/Human health and pathology ,MESH: Humans ,Bacteria ,Methodology Article ,Microbiota ,MESH: Feces/microbiology ,MESH: Metagenome ,MESH: Microbiota ,MESH: Cluster Analysis ,MESH: Sensitivity and Specificity ,MESH: Metagenomics/methods ,Gastrointestinal Tract ,MESH: Bacteria/classification ,[SDV.BBM.GTP] Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN] ,Metagenome ,Human health and pathology ,Genome, Bacterial ,[SDV.MHEP]Life Sciences [q-bio]/Human health and pathology ,Biotechnology - Abstract
Background The biological and clinical consequences of the tight interactions between host and microbiota are rapidly being unraveled by next generation sequencing technologies and sophisticated bioinformatics, also referred to as microbiota metagenomics. The recent success of metagenomics has created a demand to rapidly apply the technology to large case–control cohort studies and to studies of microbiota from various habitats, including habitats relatively poor in microbes. It is therefore of foremost importance to enable a robust and rapid quality assessment of metagenomic data from samples that challenge present technological limits (sample numbers and size). Here we demonstrate that the distribution of overlapping k-mers of metagenome sequence data predicts sequence quality as defined by gene distribution and efficiency of sequence mapping to a reference gene catalogue. Results We used serial dilutions of gut microbiota metagenomic datasets to generate well-defined high to low quality metagenomes. We also analyzed a collection of 52 microbiota-derived metagenomes. We demonstrate that k-mer distributions of metagenomic sequence data identify sequence contaminations, such as sequences derived from “empty” ligation products. Of note, k-mer distributions were also able to predict the frequency of sequences mapping to a reference gene catalogue not only for the well-defined serial dilution datasets, but also for 52 human gut microbiota derived metagenomic datasets. Conclusions We propose that k-mer analysis of raw metagenome sequence reads should be implemented as a first quality assessment prior to more extensive bioinformatics analysis, such as sequence filtering and gene mapping. With the rising demand for metagenomic analysis of microbiota it is crucial to provide tools for rapid and efficient decision making. This will eventually lead to a faster turn-around time, improved analytical quality including sample quality metrics and a significant cost reduction. Finally, improved quality assessment will have a major impact on the robustness of biological and clinical conclusions drawn from metagenomic studies. Electronic supplementary material The online version of this article (doi:10.1186/s12864-015-1406-7) contains supplementary material, which is available to authorized users.
- Published
- 2015