CoChat
Sign Up
Cited by 2,112Genomics and Phylogenetic StudiesMicrobial Community Ecology and PhysiologyEnvironmental DNA in Biodiversity Studies

The DOI matches its Crossref record; 47 of the 48 references it cites were found in a registry or an index.

Reducing the Effects of PCR Amplification and Sequencing Artifacts on 16S rRNA-Based Studies

Open access
Schloss, Patrick D., Gevers, Dirk, Westcott, Sarah L.·2011·PLoS ONE
DOI 10.1371/journal.pone.0027310PMC PMC3237409
Open this paper in CoChat — free accountOpen PDF

Abstract

The advent of next generation sequencing has coincided with a growth in interest in using these approaches to better understand the role of the structure and function of the microbial communities in human, animal, and environmental health. Yet, use of next generation sequencing to perform 16S rRNA gene sequence surveys has resulted in considerable controversy surrounding the effects of sequencing errors on downstream analyses. We analyzed 2.7×10(6) reads distributed among 90 identical mock community samples, which were collections of genomic DNA from 21 different species with known 16S rRNA gene sequences; we observed an average error rate of 0.0060. To improve this error rate, we evaluated numerous methods of identifying bad sequence reads, identifying regions within reads of poor quality, and correcting base calls and were able to reduce the overall error rate to 0.0002. Implementation of the PyroNoise algorithm provided the best combination of error rate, sequence length, and number of sequences. Perhaps more problematic than sequencing errors was the presence of chimeras generated during PCR. Because we knew the true sequences within the mock community and the chimeras they could form, we identified 8% of the raw sequence reads as chimeric. After quality filtering the raw sequences and using the Uchime chimera detection program, the overall chimera rate decreased to 1%. The chimeras that could not be detected were largely responsible for the identification of spurious operational taxonomic units (OTUs) and genus-level phylotypes. The number of spurious OTUs and phylotypes increased with sequencing effort indicating that comparison of communities should be made using an equal number of sequences. Finally, we applied our improved quality-filtering pipeline to several benchmarking studies and observed that even with our stringent data curation pipeline, biases in the data generation pipeline and batch effects were observed that could potentially confound the interpretation of microbial community data.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1371/journal.pone.0027310

Cites

47 of 47 references verified0 citation errors · 0 not checkable

Statistics

Figures

Figure 1 Effect of various sequence culling, trimming, and de-noising strategies on the number of non-chimeric reads that were longer than 200 bp and their error rates using sequence data generated by sequencing the 16S rRNA gene sequence of the same mock community. The horizontal lines represent the average across all samples within that treatment.
Figure 1 Effect of various sequence culling, trimming, and de-noising strategies on the number of non-chimeric reads that were longer than 200 bp and their error rates using sequence data generated by sequencing the 16S rRNA gene sequence of the same mock community. The horizontal lines represent the average across all samples within that treatment.CC BY · PubMed Central
Figure 2 A typical error profile (accession SRX020131, V35, replicate 1) generated by sequencing the mock community without any sequence curation measures.
Figure 2 A typical error profile (accession SRX020131, V35, replicate 1) generated by sequencing the mock community without any sequence curation measures.CC BY · PubMed Central
Figure 3 Effect of trimming sequences to the same alignment coordinates and using the pre-clustering or shhh.seqs algorithm on sequencing error rates when used within the sliding window and shhh.flows pipelines. The horizontal lines represent the average error rate across all samples within that treatment.
Figure 3 Effect of trimming sequences to the same alignment coordinates and using the pre-clustering or shhh.seqs algorithm on sequencing error rates when used within the sliding window and shhh.flows pipelines. The horizontal lines represent the average error rate across all samples within that treatment.CC BY · PubMed Central
Figure 4 Differences in relative abundance of each sequence type in the raw unprocessed reads, following the Basic sequence curation steps, and at the end of the two pipelines. Each bar represents the average relative abundance for 30 sequencing collections.
Figure 4 Differences in relative abundance of each sequence type in the raw unprocessed reads, following the Basic sequence curation steps, and at the end of the two pipelines. Each bar represents the average relative abundance for 30 sequencing collections.CC BY · PubMed Central
Figure 5 Non-metric multidimensional scaling (NMDS) plot generated using ThetaYC distances between mock community sequencing data after standardizing the number of sequences per sample. Points with the same color and shape originated from the same sequencing run.
Figure 5 Non-metric multidimensional scaling (NMDS) plot generated using ThetaYC distances between mock community sequencing data after standardizing the number of sequences per sample. Points with the same color and shape originated from the same sequencing run.CC BY · PubMed Central
Figure 6 NMDS plot generated using ThetaYC distances between sequencing replicates of the same stool sample after standardizing the number of sequences per sample. Points with the same color and shape originated from the same sequencing run.
Figure 6 NMDS plot generated using ThetaYC distances between sequencing replicates of the same stool sample after standardizing the number of sequences per sample. Points with the same color and shape originated from the same sequencing run.CC BY · PubMed Central
Figure 7 Pairwise ThetaYC distances between different DNA samples that were PCR amplified and sequenced at two different sequencing centers using the V13 region. The uneven distribution of points across body sites is due to the inability of the sequencing centers to generate more than 2,000 sequence reads for that sample.
Figure 7 Pairwise ThetaYC distances between different DNA samples that were PCR amplified and sequenced at two different sequencing centers using the V13 region. The uneven distribution of points across body sites is due to the inability of the sequencing centers to generate more than 2,000 sequence reads for that sample.CC BY · PubMed Central

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 48 references

Research works printed but not linked: 1

  1. [7]Haenel H Schmidt EF Feldheim G 1959 Fecal dysbiosis in infancy. Z Kinderheilkd 82 595 603 13830809

Cited by

Sorted by citations

Items 301–400 of 1,002

Related

See all

Metadata

Authors
Schloss, P. D., Gevers, D., & Westcott, S. L.
Affiliations
  • University of Michigan (US)
  • Broad Institute (US)
Corresponding author
Schloss, Patrick D.
ORCID
Venue
PLoS ONE
Publisher
Public Library of Science (PLoS)
Published
2011-12-14
Volume
6
Issue
12
Pages
e27310
ISSN
1932-6203 (ISSN-L)
DOI
10.1371/journal.pone.0027310
PMC
PMC3237409
OpenAlex
W2103412200
Cited by
2,112
References
48
Citations by year
2026: 34 · 2025: 58 · 2024: 77 · 2023: 69 · 2022: 115 · 2021: 139 · 2020: 153 · 2019: 188 · 2018: 224 · 2017: 208 · 2016: 238 · 2015: 231 · 2014: 190 · 2013: 130 · 2012: 56
FWCI
49.00
Type
journal article
Language
en
Open access
Licence
Locations
  • PLoS ONE (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc)
Funders
  • National Science Foundation (0743432)
  • University of Michigan
  • National Human Genome Research Institute (R01HG005975, U54HG004969, NIHU54HG004969)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
  • Physical Sciences › Environmental Science › Ecology › Microbial Community Ecology and Physiology
  • Physical Sciences › Environmental Science › Ecology › Environmental DNA in Biodiversity Studies
Keywords
16S rRNA gene sequencing, sequencing errors, mock microbial community, error rate, quality filtering, operational taxonomic units, batch effects, microbial community analysis
MeSH terms
Algorithms, Base Sequence, DNA, Bacterial, Humans, Phylogeny, RNA, Ribosomal, 16S, Cluster Analysis, Polymerase Chain Reaction, Sequence Alignment, Artifacts, Sequence Analysis, DNA, DNA Primers, Biodiversity, Metagenome

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review