A metagenomic DNA sequencing assay that is robust against environmental DNA contamination

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作者
Omary Mzava
Alexandre Pellan Cheng
Adrienne Chang
Sami Smalling
Liz-Audrey Kounatse Djomnang
Joan Sesing Lenz
Randy Longman
Amy Steadman
Luis G. Gómez-Escobar
Edward J. Schenck
Mirella Salvatore
Michael J. Satlin
Manikkam Suthanthiran
John R. Lee
Christopher E. Mason
Darshana Dadhania
Iwijn De Vlaminck
机构
[1] Cornell University,Nancy E. and Peter C. Meinig School of Biomedical Engineering
[2] Weill Cornell Medicine,Division of Gastroenterology and Hepatology
[3] Jill Roberts Center for IBD,Division of Pulmonary and Critical Care Medicine, Department of Medicine
[4] Global Health Labs,Divisionof Public Health Programs, Department of Medicine
[5] Weill Cornell Medicine,Division of Infectious Diseases, Department of Medicine
[6] Weill Cornell Medicine,Division of Nephrology and Hypertension, Department of Medicine
[7] Weill Cornell Medicine,Department of Transplantation Medicine
[8] Weill Cornell Medicine,Department of Physiology and Biophysics
[9] New York Presbyterian Hospital–Weill Cornell Medical Center,undefined
[10] Weill Cornell Medical College,undefined
[11] WorldQuant Initiative for Quantitative Prediction,undefined
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摘要
Metagenomic DNA sequencing is a powerful tool to characterize microbial communities but is sensitive to environmental DNA contamination, in particular when applied to samples with low microbial biomass. Here, we present Sample-Intrinsic microbial DNA Found by Tagging and sequencing (SIFT-seq) a metagenomic sequencing assay that is robust against environmental DNA contamination introduced during sample preparation. The core idea of SIFT-seq is to tag the DNA in the sample prior to DNA isolation and library preparation with a label that can be recorded by DNA sequencing. Any contaminating DNA that is introduced in the sample after tagging can then be bioinformatically identified and removed. We applied SIFT-seq to screen for infections from microorganisms with low burden in blood and urine, to identify COVID-19 co-infection, to characterize the urinary microbiome, and to identify microbial DNA signatures of sepsis and inflammatory bowel disease in blood.
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