Saba Anwar, Matthew Lamaudiere, 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 Kohl, David Berry, Sarah J Vancuren, Emma Allen-Vercoe, Nynne Nielsen, Nikolaj Sørensen, Aron 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 Soberon, Eugenia Silva-Herzog, Joost A M Verlouw, Pascal Arp, Mila Jhamai, Robert Kraaij, Anoecim R Geelen, Quinten R Ducarmon, Wiep Klaas Smits, Ed J Kuijper, Romy D Zwittink, Niels van Best, John Penders, Giang Le, Christel Driessen, Jolanda Kool, Sudarshan A Shetty, Susana Fuentes, Mehmet Demirci, Akin Yigin, Celina Whalley, Andrew D Beggs, Christopher Quince, Rob James, Sebastien Raguideau, Martin Gordon, Ryan Mate, Martin Fritzsche, Nathan P Danckert, Jesus Miguens 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 J Butler, Christopher Mozsary, Jonathan Foox, Namita Damle, Aidan Resh, Amanda Busswitz, Peter Lenz, Shane Sontag, Andrew Cross, Christian Sanchez, Mingsheng Guo, Kayla Olson, Eric A Smith, Alex J La Reau, Tonya Ward, Scott Kuersten, Fred Hyde, Irina Khrebtukova, Gary Schroth, Sjoerd Rijpkema, Gregory C A Amos, Chrysi Sergaki
{"title":"DNA参考试剂在微生物组分析中分离偏差:一项全球多实验室研究。","authors":"Saba Anwar, Matthew Lamaudiere, 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 Kohl, David Berry, Sarah J Vancuren, Emma Allen-Vercoe, Nynne Nielsen, Nikolaj Sørensen, Aron 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 Soberon, Eugenia Silva-Herzog, Joost A M Verlouw, Pascal Arp, Mila Jhamai, Robert Kraaij, Anoecim R Geelen, Quinten R Ducarmon, Wiep Klaas Smits, Ed J Kuijper, Romy D Zwittink, Niels van Best, John Penders, Giang Le, Christel Driessen, Jolanda Kool, Sudarshan A Shetty, Susana Fuentes, Mehmet Demirci, Akin Yigin, Celina Whalley, Andrew D Beggs, Christopher Quince, Rob James, Sebastien Raguideau, Martin Gordon, Ryan Mate, Martin Fritzsche, Nathan P Danckert, Jesus Miguens 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 J Butler, Christopher Mozsary, Jonathan Foox, Namita Damle, Aidan Resh, Amanda Busswitz, Peter Lenz, Shane Sontag, Andrew Cross, Christian Sanchez, Mingsheng Guo, Kayla Olson, Eric A Smith, Alex J La Reau, Tonya Ward, Scott Kuersten, Fred Hyde, Irina Khrebtukova, Gary Schroth, Sjoerd Rijpkema, Gregory C A Amos, Chrysi Sergaki","doi":"10.1128/msystems.00466-25","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":18819,"journal":{"name":"mSystems","volume":" ","pages":"e0046625"},"PeriodicalIF":4.6000,"publicationDate":"2025-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"DNA reference reagents isolate biases in microbiome profiling: a global multi-lab study.\",\"authors\":\"Saba Anwar, Matthew Lamaudiere, 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 Kohl, David Berry, Sarah J Vancuren, Emma Allen-Vercoe, Nynne Nielsen, Nikolaj Sørensen, Aron 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 Soberon, Eugenia Silva-Herzog, Joost A M Verlouw, Pascal Arp, Mila Jhamai, Robert Kraaij, Anoecim R Geelen, Quinten R Ducarmon, Wiep Klaas Smits, Ed J Kuijper, Romy D Zwittink, Niels van Best, John Penders, Giang Le, Christel Driessen, Jolanda Kool, Sudarshan A Shetty, Susana Fuentes, Mehmet Demirci, Akin Yigin, Celina Whalley, Andrew D Beggs, Christopher Quince, Rob James, Sebastien Raguideau, Martin Gordon, Ryan Mate, Martin Fritzsche, Nathan P Danckert, Jesus Miguens 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 J Butler, Christopher Mozsary, Jonathan Foox, Namita Damle, Aidan Resh, Amanda Busswitz, Peter Lenz, Shane Sontag, Andrew Cross, Christian Sanchez, Mingsheng Guo, Kayla Olson, Eric A Smith, Alex J La Reau, Tonya Ward, Scott Kuersten, Fred Hyde, Irina Khrebtukova, Gary Schroth, Sjoerd Rijpkema, Gregory C A Amos, Chrysi Sergaki\",\"doi\":\"10.1128/msystems.00466-25\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>When profiling the human gut microbiome, technical biases introduced by analytical approaches impede translational research, reducing data reliability and study comparability. 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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.</p>\",\"PeriodicalId\":18819,\"journal\":{\"name\":\"mSystems\",\"volume\":\" \",\"pages\":\"e0046625\"},\"PeriodicalIF\":4.6000,\"publicationDate\":\"2025-10-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"mSystems\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://doi.org/10.1128/msystems.00466-25\",\"RegionNum\":2,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MICROBIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"mSystems","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1128/msystems.00466-25","RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MICROBIOLOGY","Score":null,"Total":0}
DNA reference reagents isolate biases in microbiome profiling: a global multi-lab study.
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.
mSystemsBiochemistry, Genetics and Molecular Biology-Biochemistry
CiteScore
10.50
自引率
3.10%
发文量
308
审稿时长
13 weeks
期刊介绍:
mSystems™ will publish preeminent work that stems from applying technologies for high-throughput analyses to achieve insights into the metabolic and regulatory systems at the scale of both the single cell and microbial communities. The scope of mSystems™ encompasses all important biological and biochemical findings drawn from analyses of large data sets, as well as new computational approaches for deriving these insights. mSystems™ will welcome submissions from researchers who focus on the microbiome, genomics, metagenomics, transcriptomics, metabolomics, proteomics, glycomics, bioinformatics, and computational microbiology. mSystems™ will provide streamlined decisions, while carrying on ASM''s tradition of rigorous peer review.