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Benchmarking MicrobIEM – a user-friendly tool for decontamination of microbiome sequencing data

BACKGROUND: Microbiome analysis is becoming a standard component in many scientific studies, but also requires extensive quality control of the 16S rRNA gene sequencing data prior to analysis. In particular, when investigating low-biomass microbial environments such as human skin, contaminants disto...

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Autores principales: Hülpüsch, Claudia, Rauer, Luise, Nussbaumer, Thomas, Schwierzeck, Vera, Bhattacharyya, Madhumita, Erhart, Veronika, Traidl-Hoffmann, Claudia, Reiger, Matthias, Neumann, Avidan U.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10666409/
https://www.ncbi.nlm.nih.gov/pubmed/37996810
http://dx.doi.org/10.1186/s12915-023-01737-5
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author Hülpüsch, Claudia
Rauer, Luise
Nussbaumer, Thomas
Schwierzeck, Vera
Bhattacharyya, Madhumita
Erhart, Veronika
Traidl-Hoffmann, Claudia
Reiger, Matthias
Neumann, Avidan U.
author_facet Hülpüsch, Claudia
Rauer, Luise
Nussbaumer, Thomas
Schwierzeck, Vera
Bhattacharyya, Madhumita
Erhart, Veronika
Traidl-Hoffmann, Claudia
Reiger, Matthias
Neumann, Avidan U.
author_sort Hülpüsch, Claudia
collection PubMed
description BACKGROUND: Microbiome analysis is becoming a standard component in many scientific studies, but also requires extensive quality control of the 16S rRNA gene sequencing data prior to analysis. In particular, when investigating low-biomass microbial environments such as human skin, contaminants distort the true microbiome sample composition and need to be removed bioinformatically. We introduce MicrobIEM, a novel tool to bioinformatically remove contaminants using negative controls. RESULTS: We benchmarked MicrobIEM against five established decontamination approaches in four 16S rRNA amplicon sequencing datasets: three serially diluted mock communities (10(8)–10(3) cells, 0.4–80% contamination) with even or staggered taxon compositions and a skin microbiome dataset. Results depended strongly on user-selected algorithm parameters. Overall, sample-based algorithms separated mock and contaminant sequences best in the even mock, whereas control-based algorithms performed better in the two staggered mocks, particularly in low-biomass samples (≤ 10(6) cells). We show that a correct decontamination benchmarking requires realistic staggered mock communities and unbiased evaluation measures such as Youden’s index. In the skin dataset, the Decontam prevalence filter and MicrobIEM’s ratio filter effectively reduced common contaminants while keeping skin-associated genera. CONCLUSIONS: MicrobIEM’s ratio filter for decontamination performs better or as good as established bioinformatic decontamination tools. In contrast to established tools, MicrobIEM additionally provides interactive plots and supports selecting appropriate filtering parameters via a user-friendly graphical user interface. Therefore, MicrobIEM is the first quality control tool for microbiome experts without coding experience. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12915-023-01737-5.
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spelling pubmed-106664092023-11-23 Benchmarking MicrobIEM – a user-friendly tool for decontamination of microbiome sequencing data Hülpüsch, Claudia Rauer, Luise Nussbaumer, Thomas Schwierzeck, Vera Bhattacharyya, Madhumita Erhart, Veronika Traidl-Hoffmann, Claudia Reiger, Matthias Neumann, Avidan U. BMC Biol Research Article BACKGROUND: Microbiome analysis is becoming a standard component in many scientific studies, but also requires extensive quality control of the 16S rRNA gene sequencing data prior to analysis. In particular, when investigating low-biomass microbial environments such as human skin, contaminants distort the true microbiome sample composition and need to be removed bioinformatically. We introduce MicrobIEM, a novel tool to bioinformatically remove contaminants using negative controls. RESULTS: We benchmarked MicrobIEM against five established decontamination approaches in four 16S rRNA amplicon sequencing datasets: three serially diluted mock communities (10(8)–10(3) cells, 0.4–80% contamination) with even or staggered taxon compositions and a skin microbiome dataset. Results depended strongly on user-selected algorithm parameters. Overall, sample-based algorithms separated mock and contaminant sequences best in the even mock, whereas control-based algorithms performed better in the two staggered mocks, particularly in low-biomass samples (≤ 10(6) cells). We show that a correct decontamination benchmarking requires realistic staggered mock communities and unbiased evaluation measures such as Youden’s index. In the skin dataset, the Decontam prevalence filter and MicrobIEM’s ratio filter effectively reduced common contaminants while keeping skin-associated genera. CONCLUSIONS: MicrobIEM’s ratio filter for decontamination performs better or as good as established bioinformatic decontamination tools. In contrast to established tools, MicrobIEM additionally provides interactive plots and supports selecting appropriate filtering parameters via a user-friendly graphical user interface. Therefore, MicrobIEM is the first quality control tool for microbiome experts without coding experience. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12915-023-01737-5. BioMed Central 2023-11-23 /pmc/articles/PMC10666409/ /pubmed/37996810 http://dx.doi.org/10.1186/s12915-023-01737-5 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research Article
Hülpüsch, Claudia
Rauer, Luise
Nussbaumer, Thomas
Schwierzeck, Vera
Bhattacharyya, Madhumita
Erhart, Veronika
Traidl-Hoffmann, Claudia
Reiger, Matthias
Neumann, Avidan U.
Benchmarking MicrobIEM – a user-friendly tool for decontamination of microbiome sequencing data
title Benchmarking MicrobIEM – a user-friendly tool for decontamination of microbiome sequencing data
title_full Benchmarking MicrobIEM – a user-friendly tool for decontamination of microbiome sequencing data
title_fullStr Benchmarking MicrobIEM – a user-friendly tool for decontamination of microbiome sequencing data
title_full_unstemmed Benchmarking MicrobIEM – a user-friendly tool for decontamination of microbiome sequencing data
title_short Benchmarking MicrobIEM – a user-friendly tool for decontamination of microbiome sequencing data
title_sort benchmarking microbiem – a user-friendly tool for decontamination of microbiome sequencing data
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10666409/
https://www.ncbi.nlm.nih.gov/pubmed/37996810
http://dx.doi.org/10.1186/s12915-023-01737-5
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