Deriving accurate microbiota profiles from human samples with low bacterial content through post-sequencing processing of Illumina MiSeq data
Open accessAbstract
BACKGROUND: The rapid expansion of 16S rRNA gene sequencing in challenging clinical contexts has resulted in a growing body of literature of variable quality. To a large extent, this is due to a failure to address spurious signal that is characteristic of samples with low levels of bacteria and high levels of non-bacterial DNA. We have developed a workflow based on the paired-end read Illumina MiSeq-based approach, which enables significant improvement in data quality, post-sequencing. We demonstrate the efficacy of this methodology through its application to paediatric upper-respiratory samples from several anatomical sites. RESULTS: A workflow for processing sequence data was developed based on commonly available tools. Data generated from different sample types showed a marked variation in levels of non-bacterial signal and 'contaminant' bacterial reads. Significant differences in the ability of reference databases to accurately assign identity to operational taxonomic units (OTU) were observed. Three OTU-picking strategies were trialled as follows: de novo, open-reference and closed-reference, with open-reference performing substantially better. Relative abundance of OTUs identified as potential reagent contamination showed a strong inverse correlation with amplicon concentration allowing their objective removal. The removal of the spurious signal showed the greatest improvement in sample types typically containing low levels of bacteria and high levels of human DNA. A substantial impact of pre-filtering data and spurious signal removal was demonstrated by principal coordinate and co-occurrence analysis. For example, analysis of taxon co-occurrence in adenoid swab and middle ear fluid samples indicated that failure to remove the spurious signal resulted in the inclusion of six out of eleven bacterial genera that accounted for 80% of similarity between the sample types. CONCLUSIONS: The application of the presented workflow to a set of challenging clinical samples demonstrates its utility in removing the spurious signal from the dataset, allowing clinical insight to be derived from what would otherwise be highly misleading output. While other approaches could potentially achieve similar improvements, the methodology employed here represents an accessible means to exclude the signal from contamination and other artefacts.
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Sequences from contaminants generally have frequencies inversely correlated with sample analyte concentration.
“Additional putative external contamination was identified on the basis that sequences from contaminants generally have frequencies that are inversely correlated with sample analyte concentration16,38.”
From RETRACTED ARTICLE: Microbiome analyses of blood and tissues suggest cancer diagnostic approach (2020) · cites this paper as [38]
All OTUs were tested for correlation between proportional abundance and post-PCR amplicon concentration, and OTUs with a significant negative correlation were considered contaminants and removed.
“As an additional quality control step, all OTUs were tested for correlations between the proportional abundance of the OTU and the post-PCR amplicon concentration of a sample using the method developed by Jervis-Brady et al. [90] as implemented in the contam_test program for R (https://github.com/eclarke/eclectic).”
From Population structure of human gut bacteria in a diverse cohort from rural Tanzania and Botswana (2019) · cites this paper as [90]
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MethodSelf-citation
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The respiratory microbiome: an underappreciated player in the human response to inhaled pollutants?
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“As contamination is mostly expected to influence the results of low abundance samples, a linear regression and Spearman’s rho test (ρ) between amplicon concentration and relative bacterial abundance would give a significantly negative correlation [39,40].”
BackgroundSelective targeting of skin pathobionts and inflammation with topically applied lactobacilli
Lebeer, S., Oerlemans, E. F. M., Claes, I., Henkens, T., Delanghe, L., Wuyts, S., Spacova, I., van den Broek, M. F. L., Tuyaerts, I., Wittouck, S., et al. (2022) · Cell Reports Medicine · doi:10.1016/j.xcrm.2022.100521 · Cited by 95 · see citing works
DNA extraction approaches substantially influence the assessment of the human breast milk microbiome
Douglas, C. A., Ivey, K. L., Papanicolas, L. E., Best, K. P., Mühlhäusler, B. S., & Rogers, G. B. (2020) · Scientific Reports · doi:10.1038/s41598-019-55568-y · Cited by 89 · see citing works
Self-citationPiphillin predicts metagenomic composition and dynamics from DADA2-corrected 16S rDNA sequences
Narayan, N., Weinmaier, T., Mendieta, E. J. L., Claesson, M. J., Shanahan, F., Dabbagh, K., Iwai, S., & DeSantis, T. Z. (2020) · BMC Genomics · doi:10.1186/s12864-019-6427-1 · Cited by 85 · see citing works
Sequence-Based Characterization of Intratumoral Bacteria—A Guide to Best Practice
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Influence of sampling technique and bedding type on the milk microbiota: Results of a pilot study
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The microbiome of otitis media with effusion
Chan, C. L., Wabnitz, D. A. M., Jervis-Bardy, J., Bassiouni, A., Wormald, P., Vreugde, S., & Psaltis, A. J. (2016) · The Laryngoscope · doi:10.1002/lary.26128 · Cited by 74 · see citing works
“OTUs with cumulative relative abundance ranked in the top 10 of all OTUs in the dataset were filtered from the data if the relative abundance distribution was inversely correlated with amplicon concentration.(12)”
MethodSelf-citationMaternal and early life exposures and their potential to influence development of the microbiome
Bolte, E., Moorshead, D., & Aagaard, K. M. (2022) · Genome Medicine · doi:10.1186/s13073-021-01005-7 · Cited by 74 · see citing works
Plummer, E. L., Vodstrcil, L. A., Danielewski, J., Murray, G. L., Fairley, C. K., Garland, S. M., Hocking, J. S., Tabrizi, S. N., & Bradshaw, C. S. (2018) · PLoS ONE · doi:10.1371/journal.pone.0190199 · Cited by 70 · see citing works
The microbiome of the invertebrate model host Galleria mellonella is dominated by Enterococcus
Allonsius, C. N., Beeck, W. V., Boeck, I. D., Wittouck, S., & Lebeer, S. (2019) · Animal Microbiome · doi:10.1186/s42523-019-0010-6 · Cited by 70 · see citing works
Lazarević, V., Gaïa, N., Girard, M., & Schrenzel, J. (2016) · BMC Microbiology · doi:10.1186/s12866-016-0689-4 · Cited by 68 · see citing works
“Inverse correlation of a taxon relative abundance with bacterial load as an indicator of a possible reagent contaminant was initially described for bacterial genera in a mock community [4] and subsequently confirmed at the OTU level in cultures and ‘real’ microbiota samples [2, 3, 5, 11].”
BackgroundImpact of a lactobacilli-containing gel on vulvovaginal candidosis and the vaginal microbiome
Oerlemans, E. F. M., Bellen, G., Claes, I., Henkens, T., Allonsius, C. N., Wittouck, S., van den Broek, M. F. L., Wuyts, S., Kiekens, F., Donders, G. G. G., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-64705-x · Cited by 66 · see citing works
The microbiome in urogenital schistosomiasis and induced bladder pathologies
Adebayo, A. S., Suryavanshi, M. V., Bhute, S. S., Agunloye, A. M., Isokpehi, R. D., Anumudu, C. I., & Shouche, Y. S. (2017) · PLoS neglected tropical diseases · doi:10.1371/journal.pntd.0005826 · Cited by 65 · see citing works
The Blood Microbiome and Health: Current Evidence, Controversies, and Challenges
Cheng, H. S., Tan, S. P., Wong, D. M. K., Koo, W. L. Y., Wong, S. H., & Tan, N. S. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24065633 · Cited by 68 · see citing works
The bacterial microbiota in first-void urine from men with and without idiopathic urethritis
Frølund, M., Wikström, A., Lidbrink, P., Al‐Soud, W. A., Larsen, N., Harder, C. B., Sørensen, S. J., Jensen, J. S., & Ahrens, P. (2018) · PLoS ONE · doi:10.1371/journal.pone.0201380 · Cited by 63 · see citing works
Metzger, S. A., Hernandez, L. L., Skarlupka, J. H., Walker, T. M., Suen, G., & Ruegg, P. L. (2018) · Frontiers in Veterinary Science · doi:10.3389/fvets.2018.00247 · Cited by 61 · see citing works
The excreted microbiota of bats: evidence of niche specialisation based on multiple body habitats
Dietrich, M., Kearney, T., Seamark, E. C. J., & Markotter, W. (2016) · FEMS Microbiology Letters · doi:10.1093/femsle/fnw284 · Cited by 61 · see citing works
“The inclusion of controls and accurate post-processing of sequences is thus highly recommended (Jervis-Bardy et al. 2015) in order to identify and filter these background bacterial contaminants from datasets, but this has been rarely applied to date (but see Meadow, Altrichter and Green 2014;…”
mbImpute: an accurate and robust imputation method for microbiome data
Jiang, R., Li, W. V., & Li, J. J. (2021) · Genome biology · doi:10.1186/s13059-021-02400-4 · Cited by 63 · see citing works
Wang, N., Wu, S., Huang, L., Hu, Y., He, X., He, J., Hu, B., Xu, Y., Rong, Y., Yuan, C., et al. (2025) · Journal of Biomedical Science · doi:10.1186/s12929-025-01117-x · Cited by 62 · see citing works
“First, the bacterial biomass in the tumor specimen was low; therefore, the bacterial genomic content was likely overwhelmed by the host/tumor genomic content, resulting in low PCR efficiency and inaccurate results [176].”
The microbiome of otitis media with effusion in Indigenous Australian children
Jervis-Bardy, J., Rogers, G. B., Morris, P. S., Smith‐Vaughan, H., Nosworthy, E., Leong, L. E. X., Smith, R. J., Weyrich, L. S., Haan, J. D., Carney, A. S., et al. (2015) · International Journal of Pediatric Otorhinolaryngology · doi:10.1016/j.ijporl.2015.07.013 · Cited by 60 · see citing works
Self-citationIntricacies of assessing the human microbiome in epidemiologic studies
Robinson, C. K., Brotman, R. M., & Ravel, J. (2016) · Annals of Epidemiology · doi:10.1016/j.annepidem.2016.04.005 · Cited by 61 · see citing works
Laboratory contamination in airway microbiome studies
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Understanding the Milk Microbiota
Metzger, S. A., Hernandez, L. L., Suen, G., & Ruegg, P. L. (2018) · Veterinary Clinics of North America Food Animal Practice · doi:10.1016/j.cvfa.2018.06.003 · Cited by 53 · see citing works
Ciric, M., Waite, D. W., Draper, J. L., & Jones, J. B. (2019) · Archives of Biological Sciences · doi:10.2298/abs190402040c · Cited by 52 · see citing works
“Removal of OTUs associated with negative controls or with distributions inversely correlated to amplicon yield [35] improved the fit of the data (Fig.”
ResultHow low can we go? The implications of low bacterial load in respiratory microbiota studies
Marsh, R. L., Nelson, M. T., Pope, C. E., Leach, A. J., Hoffman, L. R., Chang, A. B., & Smith‐Vaughan, H. (2018) · Pneumonia · doi:10.1186/s41479-018-0051-8 · Cited by 50 · see citing works
“[56] excluded OTUs detected in negative controls or clinical specimens when a strong negative correlation was observed between the relative abundance and the bacterial load.”
BackgroundSelf-citationPeleg, A. Y., Choo, J. M., Langan, K. M., Edgeworth, D. M., Keating, D., Wilson, J. W., Rogers, G. B., & Kotsimbos, T. C. (2017) · Journal of Cystic Fibrosis · doi:10.1016/j.jcf.2017.08.002 · Cited by 48 · see citing works
Self-citationCharacterisation of the nasal microbiota in granulomatosis with polyangiitis
Rhee, R. L., Sreih, A. G., Najem, C. E., Grayson, P. C., Zhao, C., Bittinger, K. L., Collman, R. G., & Merkel, P. A. (2018) · Annals of the Rheumatic Diseases · doi:10.1136/annrheumdis-2018-213645 · Cited by 48 · see citing works
Considerations For Optimizing Microbiome Analysis Using a Marker Gene
de la Cuesta‐Zuluaga, J., & Escobar, J. S. (2016) · Frontiers in Nutrition · doi:10.3389/fnut.2016.00026 · Cited by 46 · see citing works
Choo, J. M., Kanno, T., Zain, N. M. M., Leong, L. E. X., Abell, G. C. J., Keeble, J. E., Bruce, K. D., Mason, A. J., & Rogers, G. B. (2017) · mSphere · doi:10.1128/msphere.00005-17 · Cited by 46 · see citing works
“8.0) (63) using a bioinformatics pipeline described previously (64).”
InfluentialSelf-citationPlummer, E. L., Vodstrcil, L. A., Fairley, C. K., Tabrizi, S. N., Garland, S. M., Law, M. G., Hocking, J. S., Fethers, K. A., Bulach, D., Murray, G. L., et al. (2019) · Scientific Reports · doi:10.1038/s41598-019-55929-7 · Cited by 46 · see citing works
Biomass-degrading glycoside hydrolases of archaeal origin
Suleiman, M., Krüger, A., & Antranikian, G. (2020) · Biotechnology for Biofuels · doi:10.1186/s13068-020-01792-y · Cited by 46 · see citing works
“With sinking costs for NGS, this technology became the gold standard in all areas of life sciences, reaching from the analysis of human microbiome consortia [107] to microbial communities in extreme hydrothermal ecosystems [108].”
BackgroundDuodenal Dysbiosis and Relation to the Efficacy of Proton Pump Inhibitors in Functional Dyspepsia
Wauters, L. I., Tito, R. Y., Ceulemans, M., Lambaerts, M., Accarie, A., Rymenans, L., Verspecht, C., Tóth, J. G., Mols, R., Augustijns, P., et al. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms222413609 · Cited by 46 · see citing works
Hilmi, M., Kamal, M. T., Vacher, S., Dupain, C., Ibadioune, S., Halladjian, M., Sablin, M. P., Marret, G., Ajgal, Z. C., Nijnikoff, M., et al. (2023) · European Journal of Cancer · doi:10.1016/j.ejca.2023.01.024 · Cited by 45 · see citing works
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Stygofauna enhance prokaryotic transport in groundwater ecosystems
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Self-citationThe airway microbiota in early cystic fibrosis lung disease
Frayman, K. B., Armstrong, D., Grimwood, K., & Ranganathan, S. (2017) · Pediatric Pulmonology · doi:10.1002/ppul.23782 · Cited by 42 · see citing works
Engel, M., Endesfelder, D., Schloter-Hai, B., Kublik, S., Granitsiotis, M. S., Boschetto, P., Stendardo, M., Barta, I., Döme, B., Deleuze, J., et al. (2017) · PLoS ONE · doi:10.1371/journal.pone.0180859 · Cited by 39 · see citing works
Zhang, M., Zou, Y., Xiao, S., & Hou, J. (2023) · Marine Pollution Bulletin · doi:10.1016/j.marpolbul.2023.115430 · Cited by 41 · see citing works
Mottawea, W., Butcher, J., Li, J. S. Y., Abujamel, T. S., Manoogian, J., Mack, D. R., & Stintzi, A. P. (2019) · Pediatric Research · doi:10.1038/s41390-019-0326-7 · Cited by 39 · see citing works
Changes in 16s RNA Gene Microbial Community Profiling by Concentration of Prokaryotic DNA
Glassing, A., Dowd, S. E., Galandiuk, S., Davis, B. R., Jorden, J. R., & Chiodini, R. J. (2015) · Journal of Microbiological Methods · doi:10.1016/j.mimet.2015.11.001 · Cited by 37 · see citing works
“Although it is common to exclude bacteria containing a relative prevalence less than 1% of a microbial community (Jervis-Bardy et al., 2015), organisms at very low prevalence are capable of causing chronic disease (Silva et al., 2015) and it may be erroneous to perform such arbitrary exclusions.”
BackgroundMarsh, R. L., Aho, C., Beissbarth, J., Bialasiewicz, S., Binks, M., Cervin, A., Kirkham, L. S., Lemon, K. P., Slack, M., & Smith‐Vaughan, H. (2019) · International Journal of Pediatric Otorhinolaryngology · doi:10.1016/j.ijporl.2019.109836 · Cited by 37 · see citing works
Self-citationChoo, J. M., Tran, C. D., Luscombe‐Marsh, N. D., Stonehouse, W., Bowen, J., Johnson, N. A., Thompson, C. H., Watson, E.-J., Brinkworth, G. D., & Rogers, G. B. (2020) · Nutrition Research · doi:10.1016/j.nutres.2020.11.005 · Cited by 37 · see citing works
Self-citationGaleano Niño, J. L., Wu, H., LaCourse, K. D., Srinivasan, H., Fitzgibbon, M., Minot, S. S., Sather, C., Johnston, C. D., & Bullman, S. (2023) · Nature Protocols · doi:10.1038/s41596-023-00888-7 · Cited by 36 · see citing works
16S metagenomics for diagnosis of bloodstream infections: opportunities and pitfalls
Rutanga, J. P., Puyvelde, S. V., Heroes, A., Muvunyi, C. M., Jacobs, J. A., & Deborggraeve, S. (2018) · Expert Review of Molecular Diagnostics · doi:10.1080/14737159.2018.1498786 · Cited by 36 · see citing works
“Another interesting approach was recently reported by Jervis-Bardy and colleagues who applied a statistical method to distinguish true hits from environmental contaminations based on a correlation between the relative abundance across samples and sequencing depths [68].”
BackgroundCastellano‐Hinojosa, A., & Strauss, S. L. (2021) · Scientific Reports · doi:10.1038/s41598-021-89569-7 · Cited by 36 · see citing works
Liang, Q., Li, J., Zou, Y., Hu, X., Deng, X., Zou, B., Liu, Y., Wei, L., Liang, L., & Wen, X. (2021) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2021.731867 · Cited by 36 · see citing works
“We further excluded the species whose relative abundance was greater than 0.05% in at least one negative blank control and the species whose relative abundance showed a significant inverse correlation with DNA concentrations (ρ < −0.2, p < 0.05, Spearman’s correlation) (Jervis-Bardy et al., 2015).”
BackgroundInfluentialQuin, C., & Gibson, D. L. (2020) · Gut Microbes · doi:10.1080/19490976.2020.1736973 · Cited by 35 · see citing works
“Still, others maintain that any DNA detected using DNA-based methods may represent contaminants.(12) Whether infants are colonized prior to birth or not remains unclear, most agree that mode of birth, vaginal or Cesarean section (C-section), impacts infant gut microbial communities.”
BackgroundClaassen-Weitz, S., Gardner‐Lubbe, S., Mwaikono, K. S., du Toit, E., Zar, H. J., & Nicol, M. P. (2020) · BMC Microbiology · doi:10.1186/s12866-020-01795-7 · Cited by 34 · see citing works
Bacterial communities of Aedes aegypti mosquitoes differ between crop and midgut tissues
Villegas, L. E. M., Radl, J., Dimopoulos, G., & Short, S. M. (2023) · PLoS neglected tropical diseases · doi:10.1371/journal.pntd.0011218 · Cited by 33 · see citing works
Examining the Evidence for an Adult Healthy Middle Ear Microbiome
Jervis-Bardy, J., Leong, L. E. X., Papanicolas, L. E., Ivey, K. L., Chawla, S., Woods, C. M., Frauenfelder, C., Ooi, E. H., & Rogers, G. B. (2019) · mSphere · doi:10.1128/msphere.00456-19 · Cited by 33 · see citing works
Self-citationThe Influence of DNA Extraction and Lipid Removal on Human Milk Bacterial Profiles
Okunola, A. O., Claassen-Weitz, S., Mwaikono, K. S., Gardner‐Lubbe, S., Zar, H. J., Nicol, M. P., & du Toit, E. (2020) · Methods and Protocols · doi:10.3390/mps3020039 · Cited by 31 · see citing works
Borgognone, A., Serna, G., Noguera-Julián, M., Alonso, L., Parera, M., Català‐Moll, F., Sanchez, L. R., Fasani, R., Paredes, R., & Nuciforo, P. G. (2021) · Cancers · doi:10.3390/cancers13215421 · Cited by 31 · see citing works
“Additionally, a high host to bacterial DNA ratio, as found in our samples, potentially derived from the patient, hospital and/or lab personnel can lead to PCR biases, thus reducing amplification efficiency and validity of results [51].”
BackgroundNext Generation Sequencing and the Microbiome of Chronic Rhinosinusitis
Jervis-Bardy, J., & Psaltis, A. J. (2016) · Annals of Otology Rhinology & Laryngology · doi:10.1177/0003489416641429 · Cited by 30 · see citing works
Self-citationAkorli, J., Namaali, P., Ametsi, G., Egyirifa, R. K., & Pels, N. A. (2019) · Parasites & Vectors · doi:10.1186/s13071-019-3287-0 · Cited by 30 · see citing works
The bovine milk microbiome – an evolving science
Ruegg, P. L. (2021) · Domestic Animal Endocrinology · doi:10.1016/j.domaniend.2021.106708 · Cited by 30 · see citing works
Hu, Y., & Satten, G. A. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac399 · Cited by 30 · see citing works
Challenges for case-control studies with microbiome data
Brooks, J. P. (2016) · Annals of Epidemiology · doi:10.1016/j.annepidem.2016.03.009 · Cited by 30 · see citing works
Boelsen, L. K., Dunne, E. M., Mika, M., Eggers, S., Nguyen, C. D., Ratu, F. T., Russell, F. M., Mulholland, K., Hilty, M., & Satzke, C. (2019) · Microbiome · doi:10.1186/s40168-019-0716-4 · Cited by 29 · see citing works
“The exception is Pseudomonas, which is not commonly found in the nasopharynx, but has been identified as a potential contaminant in several studies [51, 54].”
BackgroundTotal bacterial load, inflammation, and structural lung disease in paediatric cystic fibrosis
Taylor, S., Leong, L. E. X., Ivey, K. L., Wesselingh, S. L., Grimwood, K., Wainwright, C. E., & Rogers, G. B. (2020) · Journal of Cystic Fibrosis · doi:10.1016/j.jcf.2020.03.008 · Cited by 28 · see citing works
Self-citationEngel, K., Pankoke, H., Jünemann, S., Brandl, H. B., Sauer, J., Griffith, S. C., Kalinowski, J., & Caspers, B. A. (2020) · BMC Ecology · doi:10.1186/s12898-020-00326-2 · Cited by 28 · see citing works
Topical cream with live lactobacilli modulates the skin microbiome and reduce acne symptoms
Lebeer, S., Oerlemans, E., Claes, I., Wuyts, S., Henkens, T., Spacova, I., van den Broek, M., Tuyaerts, I., Wittouck, S., De Boeck, I., et al. (2018) · doi:10.1101/463307 · Cited by 29 · see citing works
- Authors
- Jervis-Bardy, J., Leong, L. E. X., Marri, S., Smith, R. J., Choo, J. M., Smith-Vaughan, H. C., Nosworthy, E., Morris, P. S., O’Leary, S., Rogers, G. B., & Marsh, R. L.
- Affiliations
- Flinders University (AU)
- South Australian Health and Medical Research Institute (AU)
- Charles Darwin University (AU)
- The University of Melbourne (AU)
- Corresponding author
- Jervis-Bardy, Jake
- ORCID
- Venue
- Microbiome
- Published
- 2015-05-05
- Volume
- 3
- Issue
- 1
- Pages
- 19-19
- ISSN
- 2049-2618 (ISSN-L)
- PMC
- PMC4428251
- OpenAlex
- W2063276097
- Cited by
- 192
- References
- 25
- Citations by year
- 2026: 3 · 2025: 6 · 2024: 10 · 2023: 20 · 2022: 14 · 2021: 23 · 2020: 25 · 2019: 27 · 2018: 23 · 2017: 20 · 2016: 18 · 2015: 3
- FWCI
- 9.27
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Microbiome (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Minerva Access (University of Melbourne) (submittedVersion, cc-by)
- Adelaide Research & Scholarship (AR&S) (University of Adelaide) (submittedVersion, cc-by)
- Griffith Research Online (submittedVersion, cc-by)
- Funders
- Medical Research Council
- National Health and Medical Research Council (1007641)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gut microbiota and health
- Physical Sciences › Environmental Science › Ecology › Microbial Community Ecology and Physiology
- Keywords
- 16S rRNA gene sequencing, microbiota profiling, reagent contamination, operational taxonomic units, paired-end reads, middle ear fluid, reference database
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