Evidence map›Paper›PMID 33462291›Full record

ArticleScientific reports2021

The effect of taxonomic classification by full-length 16S rRNA sequencing with a synthetic long-read technology.

Jinuk Jeong, Kyeongeui Yun, Seyoung Mun, Won-Hyong Chung, Song-Yi Choi, Young-do Nam, Mi Young Lim, Chang Pyo Hong, ChanHyeok Park, Yong Ju Ahn and 1 more

Erratum issuedAbstract read
In one paragraph

Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 84 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
84citing papers in PubMed, 3 pooled it
–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

84 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Pooled it
  2. Meta-analysis of caries microbiome studies can improve upon disease prediction outcomes.APMIS : acta pathologica, microbiologica, et immunologica Scandinavica · 2022
    Pooled it
  3. Pooled it
  4. Five-Day Treatment withNutrients · 2025
    Trial
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  17. Differences in cell-associated and cell-free microbial DNA in blood.bioRxiv : the preprint server for biology · 2025
    Article
  18. Review
  19. Article
  20. Laboratory and In-Field Metagenomics for Environmental Monitoring.Methods in molecular biology (Clifton, N.J.) · 2025
    Article

24 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

11 authors.

Jinuk Jeong *Department of Nanobiomedical Science, Dankook University, Cheonan, 31116, Republic of Korea.
Kyeongeui Yun *Microbiome Division, Theragen Bio Co., Ltd, Seongnam-si, Gyeonggi-do, 13488, Republic of Korea.
Seyoung MunDepartment of Nanobiomedical Science, Dankook University, Cheonan, 31116, Republic of Korea.
Won-Hyong ChungResearch Group of Healthcare, Korea Food Research Institute, Wanju, 55365, Republic of Korea.
Song-Yi ChoiDepartment of Pathology, School of Medicine, Chungnam National University, Daejeon, 35015, Republic of Korea.
Young-do NamResearch Group of Healthcare, Korea Food Research Institute, Wanju, 55365, Republic of Korea.
Mi Young LimResearch Group of Healthcare, Korea Food Research Institute, Wanju, 55365, Republic of Korea.
Chang Pyo HongMicrobiome Division, Theragen Bio Co., Ltd, Seongnam-si, Gyeonggi-do, 13488, Republic of Korea.
ChanHyeok ParkMicrobiome Division, Theragen Bio Co., Ltd, Seongnam-si, Gyeonggi-do, 13488, Republic of Korea.
Yong Ju AhnMicrobiome Division, Theragen Bio Co., Ltd, Seongnam-si, Gyeonggi-do, 13488, Republic of Korea. yongju77.ahn@gmail.com.
Kyudong HanCenter for Bio-Medical Engineering Core Facility, Dankook University, Cheonan, 31116, Republic of Korea. kyudong.han@gmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Characterizing the microbial communities inhabiting specimens is one of the primary objectives of microbiome studies. A short-read sequencing platform for reading partial regions of the 16S rRNA gene is most commonly used by reducing the cost burden of next-generation sequencing (NGS), but misclassification at the species level due to its length being too short to consider sequence similarity remains a challenge. Loop Genomics recently proposed a new 16S full-length-based synthetic long-read sequencing technology (sFL16S). We compared a 16S full-length-based synthetic long-read (sFL16S) and V3-V4 short-read (V3V4) methods using 24 human GUT microbiota samples. Our comparison analyses of sFL16S and V3V4 sequencing data showed that they were highly similar at all classification resolutions except the species level. At the species level, we confirmed that sFL16S showed better resolutions than V3V4 in analyses of alpha-diversity, relative abundance frequency and identification accuracy. Furthermore, we demonstrated that sFL16S could overcome the microbial misidentification caused by different sequence similarity in each 16S variable region through comparison the identification accuracy of Bifidobacterium, Bacteroides, and Alistipes strains classified from both methods. Therefore, this study suggests that the new sFL16S method is a suitable tool to overcome the weakness of the V3V4 method.

Indexed as

BacteriaFecesGastrointestinal MicrobiomeHigh-Throughput Nucleotide SequencingHumansMetagenomeMicrobiotaPhylogenyRNA, Ribosomal, 16SSequence Analysis, DNARNA, Ribosomal, 16S

Identifiers

PMID33462291
PMCPMC7814050

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Registered trials

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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.