Cited by 192Genomics and Phylogenetic StudiesGut microbiota and healthMicrobial Community Ecology and Physiology

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Deriving accurate microbiota profiles from human samples with low bacterial content through post-sequencing processing of Illumina MiSeq data

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Jervis-Bardy, Jake, Leong, Lex E X, Marri, Shashikanth, Smith, Renee J, Choo, Jocelyn M, Smith-Vaughan, Heidi C, Nosworthy, Elizabeth, Morris, Peter S, O’Leary, Stephen, Rogers, Geraint B, Marsh, Robyn L·2015·Microbiome

DOI 10.1186/s40168-015-0083-8PMC PMC4428251

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Abstract

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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Figure 1 Workflow of bioinformatic and biostatistical analysis.CC BY · PubMed Central

Figure 2 Proportion of sequence reads removed from adenoid swab compared to adenoid biopsy. (A) Proportion of reads removed by pre-filtering human OTUs in adenoid swab (n = 11) and adenoid biopsies (n = 11). ***P < 0.002, Mann–Whitney test. (B) Relative abundance of sequences identified as artefact in adenoid swab and adenoid biopsies. *P < 0.032, Mann–Whitney test.CC BY · PubMed Central

Figure 3 Relative abundance of OTUs identified as potential sequencing artefact plotted against amplicon concentration following library preparation. Spearman’s rho (ρ) and significance of correlation are shown.CC BY · PubMed Central

Figure 4 Relative abundance of presumed contamination and genuine signal plotted against amplicon concentration. Relative abundance of OTUs of sequencing artefacts (A-C) and non-artefacts (D-F) plotted against amplicon concentration following library preparation. Spearman’s rho (ρ) and significance of correlation are shown.CC BY · PubMed Central

Figure 5 Proportion of sequence reads removed by pre-filtration or when identified as contaminants. The proportion of the total number of sequence reads obtained that were removed at the pre-filtering step or after being identified as potential contamination are shown for each of the four sample types. Also shown, the proportion of total sequence reads that were representative of the bacterial signal. Proportions do not sum up to 100% in each sample type as the reads removed as contaminants are shown as a proportion of the filtered reads.CC BY · PubMed Central

Figure 6 Principal coordinate plots based on a BC similarity matrix of bacterial community sequence data. Distribution of adenoid swabs, middle ear fluid and nasopharyngeal swabs are shown before (A) and after (B) removal of signal derived from contaminants. Panel A is based on 11 adenoid swabs, 13 middle ear fluid and 11 N swabs, with panel B based on 10 adenoid swabs, 11 middle ear fluid and 11 nasopharyngeal swabs.CC BY · PubMed Central

Figure 7 Co-occurrence plots for genera detected in adenoid swabs, middle ear fluid, and nasopharyngeal swabs. Plots are shown for data before (A) and after (B) removal of the signal derived from contaminants. Genera detected in both adenoid and MEF are highlighted. Note that Achromobacter was not removed based upon spurious distribution. However, following the removal of all contaminant OTUs and rarefaction to 400 reads, the sample with Achromobacter at >1.5% relative abundance was removed due to low sequencing depth. The removal based on spurious distribution of a Rhizobium OTU, detected in both NP and MEF, is also shown.CC BY · PubMed Central

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Cytoscape: A software Environment for integrated models of biomolecular interaction networks
Shannon, P., Markiel, A., Ozier, O., Baliga, N. S., Wang, J. T., Ramage, D., Amin, N., Schwikowski, B., & Ideker, T. (2003) · Genome Research · doi:10.1101/gr.1239303 · Cited by 51,888 · cited 1×
“2 [26] was used to create a co-occurrence model.”
Method
QIIME allows analysis of high-throughput community sequencing data
Caporaso, J. G., Kuczynski, J., Stombaugh, J., Bittinger, K., Bushman, F. D., Costello, E. K., Fierer, N., Peña, A. G., Goodrich, J. K., Gordon, J. I., et al. (2010) · Nature Methods · doi:10.1038/nmeth.f.303 · Cited by 34,887 · cited 12×
“Keywords: 16S rRNA, Respiratory, MiSeq, Contamination, Pair-end reads, QIIME, Otitis media * Correspondence: jakejervisbardy@hotmail.com †Equal contributors 1Menzies School of Health Research, Child Health Division, Charles Darwin University, Darwin, NT, Australia 2School of Medicine, Flinders University, Bedford Park…”
MethodInfluential
The SILVA ribosomal RNA gene database project: improved data processing and web-based tools
Quast, C., Pruesse, E., Yilmaz, P., Gerken, J., Schweer, T., Yarza, P., Peplies, J., & Glöckner, F. O. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks1219 · Cited by 33,055 · cited 2×
“8) [9] and SILVA (v111) [10], to assign identities to OTUs generated from a multi-template control comprised of known species at defined relative abundance.”
BackgroundMethod
Search and clustering orders of magnitude faster than BLAST
Edgar, R. C. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq461 · Cited by 21,098
Introducing mothur: Open-Source, Platform-Independent, Community-Supported Software for Describing and Comparing Microbial Communities
Schloss, P. D., Westcott, S. L., Ryabin, T., Hall, J. R., Hartmann, M., Hollister, E. B., Lesniewski, R. A., Oakley, B. B., Parks, D. H., Robinson, C. J., et al. (2009) · Applied and Environmental Microbiology · doi:10.1128/aem.01541-09 · Cited by 19,549
UCHIME improves sensitivity and speed of chimera detection
Edgar, R. C., Haas, B. J., Clemente, J. C., Quince, C., & Knight, R. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr381 · Cited by 15,114
An improved Greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea
McDonald, D. T., Price, M. N., Goodrich, J. K., Nawrocki, E. P., DeSantis, T. Z., Probst, A. J., Andersen, G. L., Knight, R. D., & Hugenholtz, P. (2011) · The ISME Journal · doi:10.1038/ismej.2011.139 · Cited by 5,144 · cited 1×
“8) [9] and SILVA (v111) [10], to assign identities to OTUs generated from a multi-template control comprised of known species at defined relative abundance.”
Background
Kraken: ultrafast metagenomic sequence classification using exact alignments
Wood, D. E., & Salzberg, S. L. (2014) · Genome Biology · doi:10.1186/gb-2014-15-3-r46 · Cited by 4,521 · cited 7×
“We note there are a variety of stand-alone methods available to eliminate this type of non-bacterial signal, such as DeconSeq [12], Kraken [11] and FastQ Screen (Babraham Bioinformatics, Babraham Institute, UK).”
MethodInfluential
PEAR: a fast and accurate Illumina Paired-End reAd mergeR
Zhang, J., Kobert, K., Flouri, T., & Stamatakis, A. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btt593 · Cited by 4,497 · cited 2×
“5 [18] was used to pair the forward and reverse reads of sequences in each sample and discard all sequences less than 450 bp and/or with a Phred score <33.”
Method
Reagent and laboratory contamination can critically impact sequence-based microbiome analyses
Salter, S. J., Cox, M. J., Turek, E. M., Calus, S. T., Cookson, W. O., Moffatt, M. F., Turner, P., Parkhill, J., Loman, N. J., & Walker, A. W. (2014) · BMC Biology · doi:10.1186/s12915-014-0087-z · Cited by 3,463 · cited 5×
“Such a spurious signal can substantially distort community profiles from samples with low bacterial load [4-6].”
BackgroundInfluential

Show all 25 references

Research works printed but not linked: 2

The National Center for Biotechnology Information Sequence Read Archive. http://www.ncbi.nlm.nih.gov/sra/ (accessed 011014)
The National Center for Biotechnology Information Basic Local Alignment Search Tool. http://blast.ncbi.nlm.nih.gov/Blast.cgi (accessed 011014)

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Helminth Microbiota Profiling Using Bacterial 16S rRNA Gene Amplicon Sequencing: From Sampling to Sequence Data Mining
Formenti, F., Rinaldi, G., Cantacessi, C., & Cortés, A. (2021) · Methods in molecular biology · doi:10.1007/978-1-0716-1681-9_15 · Cited by 4
Genome-wide mapping of gene-microbe interactions in the murine lung microbiota based on quantitative microbial profiling
Chung, C. J., Hermes, B. M., Gupta, Y., Ibrahim, S. M., Belheouane, M., & Baines, J. F. (2023) · Animal Microbiome · doi:10.1186/s42523-023-00250-y · Cited by 5
A Method for Targeted 16S Sequencing of Human Milk Samples
Tobin, N. H., Woodward, C. L., Zabih, S., Lee, D., Li, F., & Aldrovandi, G. M. (2018) · Journal of Visualized Experiments · doi:10.3791/56974 · Cited by 4
16S rDNA Sequencing for Bacterial Identification in Preterm Infants with Suspected Early-Onset Neonatal Sepsis
Agudelo‐Pérez, S., Moreno, A. M., Garro, J. M. M., Flórez, J. E. S., Lopez, R., Becerra, M. P., & Sánchez, R. G. P. (2024) · Tropical Medicine and Infectious Disease · doi:10.3390/tropicalmed9070152 · Cited by 3
“Several statistical approaches have been developed based on the correlation between the relative abundance of samples and the sequencing depth [35].”
Background
Detection of lumpy skin disease virus reads in the human upper respiratory tract microbiome requires further investigation
Tomar, S. S., & Khairnar, K. (2024) · Journal of Medical Virology · doi:10.1002/jmv.29829 · Cited by 3
Data analysis workflow for the detection of canine vector-borne pathogens using 16 S rRNA Next-Generation Sequencing
Vasconcelos, E. J. R., Roy, C., Geiger, J., Oney, K. M., Koo, M., Ren, S., Oakley, B. B., & Diniz, P. P. V. D. P. (2021) · BMC Veterinary Research · doi:10.1186/s12917-021-02969-9 · Cited by 3
“The background diversity is expected to be present in every sample and our previous data and other studies have shown that the frequency of contamination is directly related to the sample DNA abundance within total DNA content [44, 57, 58].”
Protocol for CHIEF (cochlear implants and inner ear inflammation) study; an observational, cross-sectional, study of children and young people undergoing cochlear implantationPreprint
Hough, K., Nichani, J., Findlay, C., Bruce, I. A., & Newman, T. A. (2024) · medRxiv · doi:10.1101/2024.11.25.24317767 · Cited by 3
“A known issue with using a non-culture method for the identification of bacterial strains and species is an increased likelihood of false-positive results 36 .”
State-of-the-art approaches in the investigation of human seminal bacteriome using metagenomic methods
Hofman, J., Brenerová, P., & Linhartová, P. B. (2025) · Frontiers in Reproductive Health · doi:10.3389/frph.2025.1557912 · Cited by 2
Diversity and Roles of Mycorrhizal Fungi in the Bee Orchid Ophrys apifera
Abdullah, W. (2018) · University of Liverpool · doi:10.17638/03022579 · Cited by 2
Metagenomic evidence for a polymicrobial signature of sepsisPreprint
Tan, C. C. S., Acman, M., van Dorp, L., & Balloux, F. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.04.07.028837 · Cited by 2
Patterns of microbiome variation among infrapopulations of permanent bloodsucking parasitesPreprint
Doña, J., Herrera, S. V., Nyman, T., Kunnasranta, M., & Johnson, K. P. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.05.27.118331 · Cited by 2
Bacterial communities of Aedes aegypti mosquitoes differ between crop and midgut tissuesPreprint
Villegas, L. E. M., Radl, J., Dimopoulos, G., & Short, S. M. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.08.31.506054 · Cited by 2
Unlocking pathogens from preserved tissues: challenges and limitations of microbial retrieval from formalin-fixed paraffin-embedded (FFPE) samples in clinical diagnostics
Avendano-Viloria, A., Paniz-Mondolfi, A. E., Brody, R., Cordon‐Cardo, C., & Said, M. A. (2026) · Diagnostic histopathology · doi:10.1016/j.mpdhp.2026.06.012 · Cited by 1
Protocol for CHIEF (cochlear implants and inner ear inflammation) study; an observational, cross-sectional study of children and young people undergoing cochlear implantation.
Hough, K., Nichani, J., Findlay, C., Bruce, I. A., & Newman, T. A. (2025) · NIHR Open Research · doi:10.3310/nihropenres.13879.1 · Cited by 3
Protocol for CHIEF (cochlear implants and inner ear inflammation) study; an observational, cross-sectional study of children and young people undergoing cochlear implantation.
Hough, K., Nichani, J., Findlay, C., Bruce, I. A., & Newman, T. A. (2025) · NIHR Open Research · doi:10.3310/nihropenres.13879.2 · Cited by 1
An atlas of bacterial and viral associations in cancerPreprint
Borchmann, S. (2019) · bioRxiv · doi:10.1101/773200 · Cited by 2
Microbial and Proteomic Signature of Acute Cellular Rejection in Lung Transplants
Tanes, C., Mezochow, G. A., Brown, M., McGinniss, J. E., Bittinger, K. L., Anderson, M. R., Bushman, F. D., Christie, J. D., Collman, R. G., & Diamond, J. M. (2019) · Transplantation Proceedings · doi:10.1016/j.transproceed.2026.08.015
Microbiota development and mucosal IgA responses during childhood in health and allergic disease
Dzidic, M. (2019) · RiuNet (Universitat Politècnica de València) · doi:10.4995/thesis/10251/125479
Evaluating Historical Paradigms Of Sterility In Perinatal Microbiology And Ramifications For Pregnancy Outcomes
Greenberg, J. M. (2020) · Human Biology
Optimized Analysis of the Lung Allograft Microbiota from Bronchoalveolar Lavage Fluid
Prescod, J. (2018) · TSpace (University of Toronto)
The Rest Is Noise: Finding Signals in Lung Microbiome Data Analysis
Boix-Amorós, A., Lee, A., & Clemente, J. C. (2022) · Respiratory medicine · doi:10.1007/978-3-030-87104-8_3
Peer Review #2 of "Challenging a bioinformatic tool’s ability to detect microbial contaminants using in silico whole genome sequencing data (v0.1)"
(2017) · PeerJ · doi:10.7287/peerj.3729v0.1/reviews/2
Faculty Opinions recommendation of Human placenta has no microbiome but can contain potential pathogens.
Hall, L. J. (2020) · Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature · doi:10.3410/f.736297589.793574631
Peer Review #1 of "Challenging a bioinformatic tool’s ability to detect microbial contaminants using in silico whole genome sequencing data (v0.1)"
(2017) · PeerJ · doi:10.7287/peerj.3729v0.1/reviews/1
Peer Review #1 of "Challenging a bioinformatic tool’s ability to detect microbial contaminants using in silico whole genome sequencing data (v0.2)"
(2017) · PeerJ · doi:10.7287/peerj.3729v0.2/reviews/1
Faculty Opinions recommendation of Human placenta has no microbiome but can contain potential pathogens.
Larsen, B. (2019) · Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature · doi:10.3410/f.736297589.793564076
Faculty Opinions recommendation of Human placenta has no microbiome but can contain potential pathogens.
Germain, R. N., & Khan, M. M. (2019) · Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature · doi:10.3410/f.736297589.793566710
Peer Review #2 of "Challenging a bioinformatic tool’s ability to detect microbial contaminants using in silico whole genome sequencing data (v0.2)"
(2017) · PeerJ · doi:10.7287/peerj.3729v0.2/reviews/2
Profiling the Faecal Microbiome and its Modulators Across the Lifespan in the NetherlandsPreprint
Boverhoff, D., Kool, J., Pijnacker, R., Ducarmon, Q. R., Zeller, G., Shetty, S. A., Sie, S., Mulder, A. C., van der Klis, F. R. M., Franz, E., et al. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4757043
Changes in Microbial Communities Throughout the Body Decomposition Process and Its Potential Application in Forensic Casework
Adserias‐Garriga, J., Yaroshuk, T., & Zapico, S. C. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-13320-6.00002-0
Microbiota Profiling Techniques: Recent Advancement
Anand, A., Manjula, S. N., & Mruthunjaya, K. (2025) · Interdisciplinary biotechnological advances · doi:10.1007/978-981-95-2104-3_2
Additional file 8 of mbImpute: an accurate and robust imputation method for microbiome data
Jiang, R., Li, W. V., & Li, J. J. (2021) · Figshare · doi:10.6084/m9.figshare.14870492
Additional file 1 of Family matters: skin microbiome reflects the social group and spatial proximity in wild zebra finches
Engel, K., Pankoke, H., Jünemann, S., Brandl, H. B., Sauer, J., Griffith, S. C., Kalinowski, J., & Caspers, B. A. (2020) · Figshare · doi:10.6084/m9.figshare.13237570
Additional file 4 of An atlas of the tissue and blood metagenome in cancer reveals novel links between bacteria, viruses and cancer
Borchmann, S. (2021) · Figshare · doi:10.6084/m9.figshare.14471615
Additional file 1 of Human liver microbiota modeling strategy at the early onset of fibrosis
Champion, C., Neagoe, R. M., Effernberger, M., Sala, D. T., Servant, F., Christensen, J. E., Arnoriaga‐Rodríguez, M., Amar, J., Lelouvier, B., Loubières, P., et al. (2023) · Figshare · doi:10.6084/m9.figshare.26564922
Additional file 2 of An atlas of the tissue and blood metagenome in cancer reveals novel links between bacteria, viruses and cancer
Borchmann, S. (2021) · Figshare · doi:10.6084/m9.figshare.14471609
Additional file 1 of mbImpute: an accurate and robust imputation method for microbiome data
Jiang, R., Li, W. V., & Li, J. J. (2021) · Figshare · doi:10.6084/m9.figshare.14870471
Additional file 5 of Optimizing 16S rRNA gene profile analysis from low biomass nasopharyngeal and induced sputum specimens
Claassen-Weitz, S., Gardner‐Lubbe, S., Mwaikono, K. S., du Toit, E., Zar, H. J., & Nicol, M. P. (2020) · Figshare · doi:10.6084/m9.figshare.12292046
Additional file 2 of Multi-factorial examination of amplicon sequencing workflows from sample preparation to bioinformatic analysis
Wolfe, T. J. D., & Wright, E. S. (2023) · Figshare · doi:10.6084/m9.figshare.22662419
Additional file 1 of Differential response of digesta- and mucosa-associated intestinal microbiota to dietary insect meal during the seawater phase of Atlantic salmon
Li, Y., Bruni, L., Jaramillo-Torres, A., Gajardo, K., Kortner, T. M., & Krogdahl, Å. (2021) · Figshare · doi:10.6084/m9.figshare.13545373
Additional file 6 of Optimizing 16S rRNA gene profile analysis from low biomass nasopharyngeal and induced sputum specimens
Claassen-Weitz, S., Gardner‐Lubbe, S., Mwaikono, K. S., du Toit, E., Zar, H. J., & Nicol, M. P. (2020) · Figshare · doi:10.6084/m9.figshare.12292049
LungMicroHostR: an R package for integrated host–microbiome analysis of bronchoalveolar lavage fluid metagenomic sequencing data
Li, N., Hu, J., Tong, W., Liu, C., Ding, Y., Li, N., & Cai, Z. (2026) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2026.1906036
The human oral and airway viral genome catalog from metagenomes enables virome characterization informing respiratory health
Zou, X., Ni, Y., Zhang, Q., Chang, K., Li, S., Zhang, Y., Yu, H., Wang, C., Yao, X. H., Chen, S., et al. (2026) · Med · doi:10.1016/j.medj.2026.101269 · Cited by 1

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: Leong, Lex E X
Marri, Shashikanth
Smith, Renee J
Choo, Jocelyn M
Morris, Peter S
O’Leary, Stephen
Rogers, Geraint B
Marsh, Robyn L
Venue: Microbiome
Publisher: Springer Science and Business Media LLC
Published: 2015-05-05
Volume: 3
Issue: 1
Pages: 19-19
ISSN: 2049-2618 (ISSN-L)
DOI: 10.1186/s40168-015-0083-8
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
Licence: cc-by
creativecommons.org/licenses/by/4.0 (unspecified)
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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Deriving accurate microbiota profiles from human samples with low bacterial content through post-sequencing processing of Illumina MiSeq data

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