Evidence map›Paper›PMID 38038441›Full record

ArticlemSystems2023

Effects of error, chimera, bias, and GC content on the accuracy of amplicon sequencing.

Yujia Qin, Liyou Wu, Qiuting Zhang, Chongqin Wen, Joy D Van Nostrand, Daliang Ning, Lutgarde Raskin, Ameet Pinto, Jizhong Zhou

Abstract read
In one paragraph

Article in mSystems, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

16 citing papers in PubMed.

  1. Article
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  15. Targeted 16S rRNA Gene Sequencing for Water Samples.Methods in molecular biology (Clifton, N.J.) · 2025
    Article
  16. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Yujia Qin *Department of Microbiology and Plant Biology, Institute for Environmental Genomics, University of Oklahoma, Norman, Oklahoma, USA.
Liyou Wu *Department of Microbiology and Plant Biology, Institute for Environmental Genomics, University of Oklahoma, Norman, Oklahoma, USA.ORCID 0000-0003-3048-6876
Qiuting Zhang *Department of Microbiology and Plant Biology, Institute for Environmental Genomics, University of Oklahoma, Norman, Oklahoma, USA.
Chongqin Wen *Department of Microbiology and Plant Biology, Institute for Environmental Genomics, University of Oklahoma, Norman, Oklahoma, USA.
Joy D Van NostrandDepartment of Microbiology and Plant Biology, Institute for Environmental Genomics, University of Oklahoma, Norman, Oklahoma, USA.
Daliang NingDepartment of Microbiology and Plant Biology, Institute for Environmental Genomics, University of Oklahoma, Norman, Oklahoma, USA.
Lutgarde RaskinDepartment of Civil and Environmental Engineering, University of Michigan, Ann Arbor, Michigan, USA.
Ameet PintoSchool of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA.ORCID 0000-0003-1089-5664
Jizhong ZhouDepartment of Microbiology and Plant Biology, Institute for Environmental Genomics, University of Oklahoma, Norman, Oklahoma, USA.ORCID 0000-0003-2014-0564

Funding

National Sleep Foundation (NSF) EF-2025558, DEB-2129235U.S. Department of Energy (DOE) DE-AC02-05CH11231U.S. Department of Energy (DOE) DE-SC0014079, DE-SC0004601, DE-SC0010715, DE-SC0014079
6 · The paper itself

Abstract

importanceAmplicon sequencing of targeted genes is the predominant approach to estimate the membership and structure of microbial communities. However, accurate reconstruction of community composition is difficult due to sequencing errors, and other methodological biases and effective approaches to overcome these challenges are essential. Using a mock community of 33 phylogenetically diverse strains, this study evaluated the effect of GC content on sequencing results and tested different approaches to improve overall sequencing accuracy while characterizing the pros and cons of popular amplicon sequence data processing approaches. The sequencing results from this study can serve as a benchmarking data set for future algorithmic improvements. Furthermore, the new insights on sequencing error, chimera formation, and GC bias from this study will help enhance the quality of amplicon sequencing studies and support the development of new data analysis approaches.

Indexed as

DNA Barcoding, TaxonomicMicrobiotaBase CompositionBiasSequence Analysis, DNAbiaschimeric sequenceMiSeqsequence errorsequence GC contenttargeted gene amplicon sequencing

Identifiers

PMID38038441
PMCPMC10734440

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.