Measuring and mitigating PCR bias in microbiota datasets.

PCR amplification plays an integral role in the measurement of mixed microbial communities via high-throughput DNA sequencing of the 16S ribosomal RNA (rRNA) gene. Yet PCR is also known to introduce multiple forms of bias in 16S rRNA studies. Here we present a paired modeling and experimental approa...

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Autores principales:	Justin D Silverman, Rachael J Bloom, Sharon Jiang, Heather K Durand, Eric Dallow, Sayan Mukherjee, Lawrence A David
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/571577d34c1f4bfa89db5707c223535e
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id	oai:doaj.org-article:571577d34c1f4bfa89db5707c223535e
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spelling	oai:doaj.org-article:571577d34c1f4bfa89db5707c223535e2021-12-02T19:57:24ZMeasuring and mitigating PCR bias in microbiota datasets.1553-734X1553-735810.1371/journal.pcbi.1009113https://doaj.org/article/571577d34c1f4bfa89db5707c223535e2021-07-01T00:00:00Zhttps://doi.org/10.1371/journal.pcbi.1009113https://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358PCR amplification plays an integral role in the measurement of mixed microbial communities via high-throughput DNA sequencing of the 16S ribosomal RNA (rRNA) gene. Yet PCR is also known to introduce multiple forms of bias in 16S rRNA studies. Here we present a paired modeling and experimental approach to characterize and mitigate PCR NPM-bias (PCR bias from non-primer-mismatch sources) in microbiota surveys. We use experimental data from mock bacterial communities to validate our approach and human gut microbiota samples to characterize PCR NPM-bias under real-world conditions. Our results suggest that PCR NPM-bias can skew estimates of microbial relative abundances by a factor of 4 or more, but that this bias can be mitigated using log-ratio linear models.Justin D SilvermanRachael J BloomSharon JiangHeather K DurandEric DallowSayan MukherjeeLawrence A DavidPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 17, Iss 7, p e1009113 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Justin D Silverman Rachael J Bloom Sharon Jiang Heather K Durand Eric Dallow Sayan Mukherjee Lawrence A David Measuring and mitigating PCR bias in microbiota datasets.
description	PCR amplification plays an integral role in the measurement of mixed microbial communities via high-throughput DNA sequencing of the 16S ribosomal RNA (rRNA) gene. Yet PCR is also known to introduce multiple forms of bias in 16S rRNA studies. Here we present a paired modeling and experimental approach to characterize and mitigate PCR NPM-bias (PCR bias from non-primer-mismatch sources) in microbiota surveys. We use experimental data from mock bacterial communities to validate our approach and human gut microbiota samples to characterize PCR NPM-bias under real-world conditions. Our results suggest that PCR NPM-bias can skew estimates of microbial relative abundances by a factor of 4 or more, but that this bias can be mitigated using log-ratio linear models.
format	article
author	Justin D Silverman Rachael J Bloom Sharon Jiang Heather K Durand Eric Dallow Sayan Mukherjee Lawrence A David
author_facet	Justin D Silverman Rachael J Bloom Sharon Jiang Heather K Durand Eric Dallow Sayan Mukherjee Lawrence A David
author_sort	Justin D Silverman
title	Measuring and mitigating PCR bias in microbiota datasets.
title_short	Measuring and mitigating PCR bias in microbiota datasets.
title_full	Measuring and mitigating PCR bias in microbiota datasets.
title_fullStr	Measuring and mitigating PCR bias in microbiota datasets.
title_full_unstemmed	Measuring and mitigating PCR bias in microbiota datasets.
title_sort	measuring and mitigating pcr bias in microbiota datasets.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/571577d34c1f4bfa89db5707c223535e
work_keys_str_mv	AT justindsilverman measuringandmitigatingpcrbiasinmicrobiotadatasets AT rachaeljbloom measuringandmitigatingpcrbiasinmicrobiotadatasets AT sharonjiang measuringandmitigatingpcrbiasinmicrobiotadatasets AT heatherkdurand measuringandmitigatingpcrbiasinmicrobiotadatasets AT ericdallow measuringandmitigatingpcrbiasinmicrobiotadatasets AT sayanmukherjee measuringandmitigatingpcrbiasinmicrobiotadatasets AT lawrenceadavid measuringandmitigatingpcrbiasinmicrobiotadatasets
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Measuring and mitigating PCR bias in microbiota datasets.

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