Variability and bias in microbiome metagenomic sequencing: an interlaboratory study comparing experimental protocols.

Autor: Forry SP; Complex Microbial Systems Group, National Institute of Standards and Technology (NIST), Gaithersburg, MD, USA. sam.forry@nist.gov., Servetas SL; Complex Microbial Systems Group, National Institute of Standards and Technology (NIST), Gaithersburg, MD, USA., Kralj JG; Complex Microbial Systems Group, National Institute of Standards and Technology (NIST), Gaithersburg, MD, USA., Soh K; Novo Nordisk, Copenhagen, Denmark., Hadjithomas M; LifeMine Therapeutics, Cambridge Discovery Park, 30 Acorn Park Drive, Cambridge, MA, 02140, USA., Cano R; The BioCollective, LLC, 5650 Washington Street, Suite C9, Denver, CO, 80216, USA., Carlin M; The BioCollective, LLC, 5650 Washington Street, Suite C9, Denver, CO, 80216, USA., Amorim MG; Laboratory of Medical Genomics, A. C. Camargo Cancer Center, Sao Paulo, SP, 01508-010, Brazil., Auch B; University of Minnesota Genomics Center, Minneapolis, MN, 55455, USA., Bakker MG; Department of Microbiology, University of Manitoba, Winnipeg, MB, R3T 2N2, Canada., Bartelli TF; Laboratory of Medical Genomics, A. C. Camargo Cancer Center, Sao Paulo, SP, 01508-010, Brazil., Bustamante JP; Laboratorio de Investigación, Desarrollo y Transferencia de la Facultad de Ingeniería de la Universidad Austral (LIDTUA), CIC-Austral, Pilar, Argentina.; Instituto de Investigación y Desarrollo en Bioingeniería y Bioinformática (IBB), CONICET-UNER, Oro Verde, Argentina.; Facultad de Ingeniería, Universidad Nacional de Entre Ríos, Concepción del Uruguay, Argentina., Cassol I; Laboratorio de Investigación, Desarrollo y Transferencia de la Facultad de Ingeniería de la Universidad Austral (LIDTUA), CIC-Austral, Pilar, Argentina., Chalita M; CJ Bioscience, Seoul, South Korea., Dias-Neto E; Laboratory of Medical Genomics, A. C. Camargo Cancer Center, Sao Paulo, SP, 01508-010, Brazil., Duca AD; OMX Advisors, Inc., Ottawa, Canada., Gohl DM; University of Minnesota Genomics Center, Minneapolis, MN, 55455, USA.; Department of Genetics, Cell Biology, and Development, University of Minnesota, Minneapolis, MN, 55455, USA., Kazantseva J; Center of Food and Fermentation Technologies (TFTAK), Mäealuse 2/4, 12618, Tallinn, Estonia., Haruna MT; Bioenvironmental Program, Morgan State University, Baltimore, MD, USA., Menzel P; Labor Berlin Charité Vivantes GmbH, Sylter Str. 2, 13353, Berlin, Germany., Moda BS; Laboratory of Medical Genomics, A. C. Camargo Cancer Center, Sao Paulo, SP, 01508-010, Brazil.; Laboratory of Computational Biology and Bioinformatics, A.C. Camargo Cancer Center, Sao Paulo, SP, 01508-010, Brazil., Neuberger-Castillo L; Integrated Biobank of Luxembourg (IBBL), Luxembourg Institute of Health (LIH), Dudelange, Luxembourg., Nunes DN; Laboratory of Medical Genomics, A. C. Camargo Cancer Center, Sao Paulo, SP, 01508-010, Brazil., Patel IR; Center for Food Safety and Applied Nutrition, Office of Applied Research and Safety Assessment, U. S. Food and Drug Administration, Laurel, MD, 20708, USA., Peralta RD; Laboratorio de Investigación, Desarrollo y Transferencia de la Facultad de Ingeniería de la Universidad Austral (LIDTUA), CIC-Austral, Pilar, Argentina.; Facultad de Ingeniería, Universidad Nacional de Entre Ríos, Concepción del Uruguay, Argentina., Saliou A; OMICS Hub, BIOASTER, Microbiology Research Institute, Lyon, France., Schwarzer R; Labor Berlin Charité Vivantes GmbH, Sylter Str. 2, 13353, Berlin, Germany., Sevilla S; Center for Cancer Research, CCR Collaborative Bioinformatics Resource, National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA.; Advanced Biomedical Computational Sciences, Frederick National Laboratory for Cancer Research, Leidos Biomedical Research, Inc., Frederick, MD, 21701, USA., Takenaka IKTM; Laboratory of Medical Genomics, A. C. Camargo Cancer Center, Sao Paulo, SP, 01508-010, Brazil., Wang JR; Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA., Knight R; Departments of Pediatrics, Bioengineering and Computer Science & Engineering, and Center for Microbiome Innovation, University of California at San Diego, 9500 Gilman Drive, MC 0763, La Jolla, CA, 92093-0763, USA., Gevers D; Seed Health, 2100 Abbot Kinney Blvd, Venice, CA, 90291-7003, USA., Jackson SA; Complex Microbial Systems Group, National Institute of Standards and Technology (NIST), Gaithersburg, MD, USA.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2024 Apr 29; Vol. 14 (1), pp. 9785. Date of Electronic Publication: 2024 Apr 29.
DOI: 10.1038/s41598-024-57981-4
Abstrakt: Several studies have documented the significant impact of methodological choices in microbiome analyses. The myriad of methodological options available complicate the replication of results and generally limit the comparability of findings between independent studies that use differing techniques and measurement pipelines. Here we describe the Mosaic Standards Challenge (MSC), an international interlaboratory study designed to assess the impact of methodological variables on the results. The MSC did not prescribe methods but rather asked participating labs to analyze 7 shared reference samples (5 × human stool samples and 2 × mock communities) using their standard laboratory methods. To capture the array of methodological variables, each participating lab completed a metadata reporting sheet that included 100 different questions regarding the details of their protocol. The goal of this study was to survey the methodological landscape for microbiome metagenomic sequencing (MGS) analyses and the impact of methodological decisions on metagenomic sequencing results. A total of 44 labs participated in the MSC by submitting results (16S or WGS) along with accompanying metadata; thirty 16S rRNA gene amplicon datasets and 14 WGS datasets were collected. The inclusion of two types of reference materials (human stool and mock communities) enabled analysis of both MGS measurement variability between different protocols using the biologically-relevant stool samples, and MGS bias with respect to ground truth values using the DNA mixtures. Owing to the compositional nature of MGS measurements, analyses were conducted on the ratio of Firmicutes: Bacteroidetes allowing us to directly apply common statistical methods. The resulting analysis demonstrated that protocol choices have significant effects, including both bias of the MGS measurement associated with a particular methodological choices, as well as effects on measurement robustness as observed through the spread of results between labs making similar methodological choices. In the analysis of the DNA mock communities, MGS measurement bias was observed even when there was general consensus among the participating laboratories. This study was the result of a collaborative effort that included academic, commercial, and government labs. In addition to highlighting the impact of different methodological decisions on MGS result comparability, this work also provides insights for consideration in future microbiome measurement study design.
(© 2024. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)
Databáze: MEDLINE
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