FORENSIC: an Online Platform for Fecal Source Identification

ABSTRACT Sewage overflows, agricultural runoff, and stormwater discharges introduce fecal pollution into surface waters. Distinguishing these sources is critical for evaluating water quality and formulating remediation strategies. With the falling costs of sequencing, microbial community-based water...

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Autores principales:	Adélaïde Roguet, Özcan C. Esen, A. Murat Eren, Ryan J. Newton, Sandra L. McLellan
Formato:	article
Lenguaje:	EN
Publicado:	American Society for Microbiology 2020
Materias:	microbial source tracking 16S rRNA gene high-throughput sequencing Bacteroidales Clostridiales random forest classification Microbiology QR1-502
Acceso en línea:	https://doaj.org/article/9a6d8a770d1f45e0a5d41fa02b998b1c
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spelling	oai:doaj.org-article:9a6d8a770d1f45e0a5d41fa02b998b1c2021-12-02T18:44:39ZFORENSIC: an Online Platform for Fecal Source Identification10.1128/mSystems.00869-192379-5077https://doaj.org/article/9a6d8a770d1f45e0a5d41fa02b998b1c2020-04-01T00:00:00Zhttps://journals.asm.org/doi/10.1128/mSystems.00869-19https://doaj.org/toc/2379-5077ABSTRACT Sewage overflows, agricultural runoff, and stormwater discharges introduce fecal pollution into surface waters. Distinguishing these sources is critical for evaluating water quality and formulating remediation strategies. With the falling costs of sequencing, microbial community-based water quality assessment tools are under development. However, their application is limited by the need to build reference libraries, which requires extensive sampling of sources and bioinformatic expertise. Here, we introduce FORest Enteric Source IdentifiCation (FORENSIC; https://forensic.sfs.uwm.edu/), an online, library-independent source tracking platform based on random forest classification and 16S rRNA gene amplicon sequences to identify in environmental samples common fecal contamination sources, including humans, domestic pets, and agricultural animals. FORENSIC relies on a broad reference signature database of Bacteroidales and Clostridiales, two predominant bacterial groups that have coevolved with their hosts. As a result, these groups demonstrate cohesive and reliable assemblage patterns within mammalian species or among species sharing the same diet/physiology. We created a scalable and extensible platform that we tested for global applicability using samples collected in distant geographic locations. This Web application offers a fast and intuitive approach for fecal source identification, particularly in sewage-contaminated waters. IMPORTANCE FORENSIC is an online platform to identify sources of fecal pollution without the need to create reference libraries. FORENSIC is based on the ability of random forest classification to extract cohesive source microbial signatures to create classifiers despite individual variability and to detect the signatures in environmental samples. We primarily focused on defining sewage signals, which are associated with a high human health risk in polluted waters. To test for fecal contamination sources, the platform only requires paired-end reads targeting the V4 or V6 regions of the 16S rRNA gene. We demonstrated that we could use V4V5 reads trimmed to the V4 positions to generate the reference signature. The systematic workflow we describe to create and validate the signatures could be applied to many disciplines. With the increasing gap between advancing technology and practical applications, this platform makes sequence-based water quality assessments accessible to the public health and water resource communities.Adélaïde RoguetÖzcan C. EsenA. Murat ErenRyan J. NewtonSandra L. McLellanAmerican Society for Microbiologyarticlemicrobial source tracking16S rRNA genehigh-throughput sequencingBacteroidalesClostridialesrandom forest classificationMicrobiologyQR1-502ENmSystems, Vol 5, Iss 2 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	microbial source tracking 16S rRNA gene high-throughput sequencing Bacteroidales Clostridiales random forest classification Microbiology QR1-502
spellingShingle	microbial source tracking 16S rRNA gene high-throughput sequencing Bacteroidales Clostridiales random forest classification Microbiology QR1-502 Adélaïde Roguet Özcan C. Esen A. Murat Eren Ryan J. Newton Sandra L. McLellan FORENSIC: an Online Platform for Fecal Source Identification
description	ABSTRACT Sewage overflows, agricultural runoff, and stormwater discharges introduce fecal pollution into surface waters. Distinguishing these sources is critical for evaluating water quality and formulating remediation strategies. With the falling costs of sequencing, microbial community-based water quality assessment tools are under development. However, their application is limited by the need to build reference libraries, which requires extensive sampling of sources and bioinformatic expertise. Here, we introduce FORest Enteric Source IdentifiCation (FORENSIC; https://forensic.sfs.uwm.edu/), an online, library-independent source tracking platform based on random forest classification and 16S rRNA gene amplicon sequences to identify in environmental samples common fecal contamination sources, including humans, domestic pets, and agricultural animals. FORENSIC relies on a broad reference signature database of Bacteroidales and Clostridiales, two predominant bacterial groups that have coevolved with their hosts. As a result, these groups demonstrate cohesive and reliable assemblage patterns within mammalian species or among species sharing the same diet/physiology. We created a scalable and extensible platform that we tested for global applicability using samples collected in distant geographic locations. This Web application offers a fast and intuitive approach for fecal source identification, particularly in sewage-contaminated waters. IMPORTANCE FORENSIC is an online platform to identify sources of fecal pollution without the need to create reference libraries. FORENSIC is based on the ability of random forest classification to extract cohesive source microbial signatures to create classifiers despite individual variability and to detect the signatures in environmental samples. We primarily focused on defining sewage signals, which are associated with a high human health risk in polluted waters. To test for fecal contamination sources, the platform only requires paired-end reads targeting the V4 or V6 regions of the 16S rRNA gene. We demonstrated that we could use V4V5 reads trimmed to the V4 positions to generate the reference signature. The systematic workflow we describe to create and validate the signatures could be applied to many disciplines. With the increasing gap between advancing technology and practical applications, this platform makes sequence-based water quality assessments accessible to the public health and water resource communities.
format	article
author	Adélaïde Roguet Özcan C. Esen A. Murat Eren Ryan J. Newton Sandra L. McLellan
author_facet	Adélaïde Roguet Özcan C. Esen A. Murat Eren Ryan J. Newton Sandra L. McLellan
author_sort	Adélaïde Roguet
title	FORENSIC: an Online Platform for Fecal Source Identification
title_short	FORENSIC: an Online Platform for Fecal Source Identification
title_full	FORENSIC: an Online Platform for Fecal Source Identification
title_fullStr	FORENSIC: an Online Platform for Fecal Source Identification
title_full_unstemmed	FORENSIC: an Online Platform for Fecal Source Identification
title_sort	forensic: an online platform for fecal source identification
publisher	American Society for Microbiology
publishDate	2020
url	https://doaj.org/article/9a6d8a770d1f45e0a5d41fa02b998b1c
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FORENSIC: an Online Platform for Fecal Source Identification

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