Flush With Data (or) Optimizing and Validating the Efficacy of Free and Computationally Simple 16S Metabarcoding Approaches for Use in Wastewater Surveillance.
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| Title: | Flush With Data (or) Optimizing and Validating the Efficacy of Free and Computationally Simple 16S Metabarcoding Approaches for Use in Wastewater Surveillance. |
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| Authors: | Berta, Joe1 (AUTHOR) cberta@tulane.edu, Rowe, Lori A.2 (AUTHOR), Multala, Evan3 (AUTHOR), Garry, Robert F.4 (AUTHOR) |
| Source: | Environmental Microbiology. May2026, Vol. 28 Issue 5, p1-15. 15p. |
| Subjects: | Bacteria classification, Bioinformatics, Microbial diversity, Mathematical optimization, Genetic barcoding, Sewage microbiology |
| Abstract: | We propose free and low‐computationally complex methods of 16S rRNA metabarcoding analysis, then optimized and validate their accuracy for wastewater bacterial surveillance. Three taxonomic analysis pipelines were augmented: NCBI BLAST subsampling, Kraken 2/Bracken and QIIME 2/DADA 2. Our optimization strategies for the high complexity of wastewater samples raised QIIME 2/DADA 2's sensitivity to species‐level taxa by 240.5%, while they increased the species‐level selectivity of Kraken 2/Bracken and NCBI BLAST subsampling by 18.7% and 79.1%, respectively. Optimization vastly lowered the read mapping error for BLAST subsampling and Kraken 2/Bracken, by 42.0% and 11.4%, respectively. Microbial community diversity estimates were also improved through our optimization strategies. Richness measurements for BLAST subsampling became 95.6% more accurate, while Kraken 2/Bracken and QIIME 2/DADA 2 improved by 2.2% and 37.8%. Shannon entropy estimates by BLAST subsampling increased in accuracy by 17.4%, while for Kraken 2/Bracken and QIIME 2/DADA 2 they increased by 19.7% and 41.4%. For beta diversity, Bray–Curtis dissimilarity estimates by QIIME 2/DADA 2 increased in accuracy by 8.5% and by Kraken 2/Bracken by 174.3%. [ABSTRACT FROM AUTHOR] |
| Copyright of Environmental Microbiology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 194013068 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Flush With Data (or) Optimizing and Validating the Efficacy of Free and Computationally Simple 16S Metabarcoding Approaches for Use in Wastewater Surveillance. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Berta%2C+Joe%22">Berta, Joe</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> cberta@tulane.edu</i><br /><searchLink fieldCode="AR" term="%22Rowe%2C+Lori+A%2E%22">Rowe, Lori A.</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Multala%2C+Evan%22">Multala, Evan</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Garry%2C+Robert+F%2E%22">Garry, Robert F.</searchLink><relatesTo>4</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Environmental+Microbiology%22">Environmental Microbiology</searchLink>. May2026, Vol. 28 Issue 5, p1-15. 15p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Bacteria+classification%22">Bacteria classification</searchLink><br /><searchLink fieldCode="DE" term="%22Bioinformatics%22">Bioinformatics</searchLink><br /><searchLink fieldCode="DE" term="%22Microbial+diversity%22">Microbial diversity</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Genetic+barcoding%22">Genetic barcoding</searchLink><br /><searchLink fieldCode="DE" term="%22Sewage+microbiology%22">Sewage microbiology</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: We propose free and low‐computationally complex methods of 16S rRNA metabarcoding analysis, then optimized and validate their accuracy for wastewater bacterial surveillance. Three taxonomic analysis pipelines were augmented: NCBI BLAST subsampling, Kraken 2/Bracken and QIIME 2/DADA 2. Our optimization strategies for the high complexity of wastewater samples raised QIIME 2/DADA 2's sensitivity to species‐level taxa by 240.5%, while they increased the species‐level selectivity of Kraken 2/Bracken and NCBI BLAST subsampling by 18.7% and 79.1%, respectively. Optimization vastly lowered the read mapping error for BLAST subsampling and Kraken 2/Bracken, by 42.0% and 11.4%, respectively. Microbial community diversity estimates were also improved through our optimization strategies. Richness measurements for BLAST subsampling became 95.6% more accurate, while Kraken 2/Bracken and QIIME 2/DADA 2 improved by 2.2% and 37.8%. Shannon entropy estimates by BLAST subsampling increased in accuracy by 17.4%, while for Kraken 2/Bracken and QIIME 2/DADA 2 they increased by 19.7% and 41.4%. For beta diversity, Bray–Curtis dissimilarity estimates by QIIME 2/DADA 2 increased in accuracy by 8.5% and by Kraken 2/Bracken by 174.3%. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Environmental Microbiology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1111/1462-2920.70276 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 15 StartPage: 1 Subjects: – SubjectFull: Bacteria classification Type: general – SubjectFull: Bioinformatics Type: general – SubjectFull: Microbial diversity Type: general – SubjectFull: Mathematical optimization Type: general – SubjectFull: Genetic barcoding Type: general – SubjectFull: Sewage microbiology Type: general Titles: – TitleFull: Flush With Data (or) Optimizing and Validating the Efficacy of Free and Computationally Simple 16S Metabarcoding Approaches for Use in Wastewater Surveillance. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Berta, Joe – PersonEntity: Name: NameFull: Rowe, Lori A. – PersonEntity: Name: NameFull: Multala, Evan – PersonEntity: Name: NameFull: Garry, Robert F. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 14622912 Numbering: – Type: volume Value: 28 – Type: issue Value: 5 Titles: – TitleFull: Environmental Microbiology Type: main |
| ResultId | 1 |
