Two-Target Quantitative PCR To Predict Library Composition for Shallow Shotgun Sequencing

When determining human microbiota composition, shotgun sequencing is a powerful tool that can generate high-resolution taxonomic and functional information at once. However, the technique is limited by missing information about host-to-microbe ratios observed in different body compartments. This lim...

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Autores: Cho, Matthew Y., Oliva, Marc, Spreafico, Anna, Chen, Bo, Wei, Xu, Choi, Yoojin, Kaul, Rupert, Siu, Lillian L., Coburn, Bryan, Schneeberger, Pierre H. H.
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2021
País:España
Institución:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:2445/181363
Acceso en línea:https://hdl.handle.net/2445/181363
Access Level:acceso abierto
Palabra clave:ADN
Metagenòmica
Microbiota
DNA
Metagenomics
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spelling Two-Target Quantitative PCR To Predict Library Composition for Shallow Shotgun SequencingCho, Matthew Y.Oliva, MarcSpreafico, AnnaChen, BoWei, XuChoi, YoojinKaul, RupertSiu, Lillian L.Coburn, BryanSchneeberger, Pierre H. H.ADNMetagenòmicaMicrobiotaDNAMetagenomicsMicrobiotaWhen determining human microbiota composition, shotgun sequencing is a powerful tool that can generate high-resolution taxonomic and functional information at once. However, the technique is limited by missing information about host-to-microbe ratios observed in different body compartments. This limitation makes it difficult to plan shotgun sequencing assays, especially in the context of high sample multiplexing and limited sequencing output and is of particular importance for studies employing the recently described shallow shotgun sequencing technique. In this study, we evaluated the use of a quantitative PCR (qPCR)-based assay to predict host-to-microbe ratio prior to sequencing. Combining a two-target assay involving the bacterial 16S rRNA gene and the human beta-actin gene, we derived a model to predict human-to-microbe ratios from two sample types, including stool samples and oropharyngeal swabs. We then validated it on two independently collected sample types, including rectal swabs and vaginal secretion samples. This assay enabled accurate prediction in the validation set in a range of sample compositions between 4% and 98% nonhuman reads and observed proportions varied between -18.8% and +19.2% from the expected values. We hope that this easy-to-use assay will help researchers to plan their shotgun sequencing experiments in a more efficient way. IMPORTANCE When determining human microbiota composition, shotgun sequencing is a powerful tool that can generate large amounts of data. However, in sample compositions with low or variable microbial density, shallowing sequencing can negatively affect microbial community metrics. Here, we show that variable sequencing depth decreases measured alpha diversity at differing rates based on community composition. We then derived a model that can determine sample composition prior to sequencing using quantitative PCR (qPCR) data and validated the model using a separate sample set. We have included a tool that uses this model to be available for researchers to use when gauging shallow sequencing viability of samples.American Society for Microbiology2021202120212021info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://hdl.handle.net/2445/181363Articles publicats en revistes (Institut d'lnvestigació Biomèdica de Bellvitge (IDIBELL))reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésReproducció del document publicat a: https://doi.org/10.1128/mSystems.00552-21mSystems, 2021, vol. 6, num. 4https://doi.org/10.1128/mSystems.00552-21cc-by (c) Cho, Matthew Y. et al., 2021http://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:recercat.cat:2445/1813632026-05-29T05:05:01Z
dc.title.none.fl_str_mv Two-Target Quantitative PCR To Predict Library Composition for Shallow Shotgun Sequencing
title Two-Target Quantitative PCR To Predict Library Composition for Shallow Shotgun Sequencing
spellingShingle Two-Target Quantitative PCR To Predict Library Composition for Shallow Shotgun Sequencing
Cho, Matthew Y.
ADN
Metagenòmica
Microbiota
DNA
Metagenomics
Microbiota
title_short Two-Target Quantitative PCR To Predict Library Composition for Shallow Shotgun Sequencing
title_full Two-Target Quantitative PCR To Predict Library Composition for Shallow Shotgun Sequencing
title_fullStr Two-Target Quantitative PCR To Predict Library Composition for Shallow Shotgun Sequencing
title_full_unstemmed Two-Target Quantitative PCR To Predict Library Composition for Shallow Shotgun Sequencing
title_sort Two-Target Quantitative PCR To Predict Library Composition for Shallow Shotgun Sequencing
dc.creator.none.fl_str_mv Cho, Matthew Y.
Oliva, Marc
Spreafico, Anna
Chen, Bo
Wei, Xu
Choi, Yoojin
Kaul, Rupert
Siu, Lillian L.
Coburn, Bryan
Schneeberger, Pierre H. H.
author Cho, Matthew Y.
author_facet Cho, Matthew Y.
Oliva, Marc
Spreafico, Anna
Chen, Bo
Wei, Xu
Choi, Yoojin
Kaul, Rupert
Siu, Lillian L.
Coburn, Bryan
Schneeberger, Pierre H. H.
author_role author
author2 Oliva, Marc
Spreafico, Anna
Chen, Bo
Wei, Xu
Choi, Yoojin
Kaul, Rupert
Siu, Lillian L.
Coburn, Bryan
Schneeberger, Pierre H. H.
author2_role author
author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv ADN
Metagenòmica
Microbiota
DNA
Metagenomics
Microbiota
topic ADN
Metagenòmica
Microbiota
DNA
Metagenomics
Microbiota
description When determining human microbiota composition, shotgun sequencing is a powerful tool that can generate high-resolution taxonomic and functional information at once. However, the technique is limited by missing information about host-to-microbe ratios observed in different body compartments. This limitation makes it difficult to plan shotgun sequencing assays, especially in the context of high sample multiplexing and limited sequencing output and is of particular importance for studies employing the recently described shallow shotgun sequencing technique. In this study, we evaluated the use of a quantitative PCR (qPCR)-based assay to predict host-to-microbe ratio prior to sequencing. Combining a two-target assay involving the bacterial 16S rRNA gene and the human beta-actin gene, we derived a model to predict human-to-microbe ratios from two sample types, including stool samples and oropharyngeal swabs. We then validated it on two independently collected sample types, including rectal swabs and vaginal secretion samples. This assay enabled accurate prediction in the validation set in a range of sample compositions between 4% and 98% nonhuman reads and observed proportions varied between -18.8% and +19.2% from the expected values. We hope that this easy-to-use assay will help researchers to plan their shotgun sequencing experiments in a more efficient way. IMPORTANCE When determining human microbiota composition, shotgun sequencing is a powerful tool that can generate large amounts of data. However, in sample compositions with low or variable microbial density, shallowing sequencing can negatively affect microbial community metrics. Here, we show that variable sequencing depth decreases measured alpha diversity at differing rates based on community composition. We then derived a model that can determine sample composition prior to sequencing using quantitative PCR (qPCR) data and validated the model using a separate sample set. We have included a tool that uses this model to be available for researchers to use when gauging shallow sequencing viability of samples.
publishDate 2021
dc.date.none.fl_str_mv 2021
2021
2021
2021
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/2445/181363
url https://hdl.handle.net/2445/181363
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Reproducció del document publicat a: https://doi.org/10.1128/mSystems.00552-21
mSystems, 2021, vol. 6, num. 4
https://doi.org/10.1128/mSystems.00552-21
dc.rights.none.fl_str_mv cc-by (c) Cho, Matthew Y. et al., 2021
http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv cc-by (c) Cho, Matthew Y. et al., 2021
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv American Society for Microbiology
publisher.none.fl_str_mv American Society for Microbiology
dc.source.none.fl_str_mv Articles publicats en revistes (Institut d'lnvestigació Biomèdica de Bellvitge (IDIBELL))
reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,811543

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