DNA reference reagents isolate biases in microbiome profiling: a global multi-lab study
Saba Anwar, Matthew T. F. Lamaudière, Jack Hassall, Jacob Dehinsilu, Ravneet K. Bhuller, Georgina L. Hold, Xabier Vázquez-Campos, Alexander Mahnert, Christine Moissl‐Eichinger, Birgit Gallé, Gudrun Kainz, Petra Pjevac, Bela Hausmann, Jasmin Schwarz, Gudrun Köhl, David Berry, Sarah J. Vancuren, Emma Allen‐Vercoe, Nynne Nielsen, Nikolaj Sørensen, Aron C. Eklund, Henrik Bjørn Nielsen, René Riedel, Jannike Lea Krause, Hyun‐Dong Chang, Suenie Park, Ho-Yeon Song, Hoonhee Seo, Asad Ul-Haq, Sukyung Kim, Yongbin Kwon, Sunwha Park, Xavier Soberón, Eugenia Silva‐Herzog, Joost Verlouw, Pascal Arp, Mila Jhamai, Robert Kraaij, Anoecim Robecca Geelen, Quinten R. Ducarmon, Wiep Klaas Smits, Ed J. Kuijper, Romy D. Zwittink, Niels van Best, John Penders, Giang Truong Le, Christel Driessen, Jolanda Kool, Sudarshan A. Shetty, Susana Fuentes, Mehmet Demırci, Akın Yiğin, Celina Whalley, Andrew D. Beggs, Christopher Quince, Rob S. James, Sébastien Raguideau, Martin Gordon, Ryan Mate, Martin Fritzsche, Nathan Danckert, Jesús Miguéns Blanco, Julian R. Marchesi, Marcus Rauch, R. Anthony Williamson, Angélique B. van ’t Wout, Angelika Kritz, Stephan Rosecker, Richard Stevens, Lynette Laws, Lizbeth Sayavedra, Stefano Romano, Andrea Telatin, David Baker, Arjan Narbad, Stephanie L. Servetas, Jason G. Kralj, Samuel P. Forry, Monique E. Hunter, Jennifer N. Dootz, Scott A. Jackson, Christopher E. Mason, Daniel Butler, Christopher Mozsary, Jonathan Foox, Namita Damle, Aidan Resh, Amanda Busswitz, Peter Lenz, Shane Sontag, Andrew Cross, Christian A. Sanchez, Mingsheng Guo, Kayla Olson, Eric Alden Smith, Alex J. La Reau, Tonya Ward, Scott Kuersten, F W Hyde, Irina Khrebtukova
Abstract
When profiling the human gut microbiome, technical biases introduced by analytical approaches impede translational research, reducing data reliability and study comparability. Here, through a global study involving 23 labs, we analyzed a wide range of sequencing and bioinformatic approaches for the taxonomic profiling of two well-defined DNA reference reagents (RRs) comprised of 20 common gut bacteria. Through both shotgun and 16S rRNA gene amplicon sequencing, we aimed to isolate sources of bias and understand their impact on microbiome profiling accuracy. Importantly, minimum quality criteria (MQC) were established and are used to evaluate profiling performance. We found that the variability of shotgun sequencing data sets was greater than that of 16S rRNA gene amplicon sequencing and isolated sources of bias in wet and dry lab steps, such as sequencing depth, primer and database choices, rarefaction, and 16S copy number adjustment. This study presents well-defined RRs and MQC to combat technical bias, paving the way for reliable and comparable microbiome research.IMPORTANCEThis benchmark paper highlights the true level of variability in microbiome data across the world and across sectors, underscoring the critical need for the use of WHO International DNA Gut Reference Reagents (RRs) to elevate the quality of data in microbiome research. This global study is the first of its kind, revealing the reality of the bias in the field, comprehensively testing methodologies used by leading laboratories across the world, but also providing avenues for workflow optimization, to accelerate innovation and translational research and move the field forward.