Evidence map›Paper›PMID 41675542›Full record

ReviewiMetaOmics2024

Long journey of 16S rRNA-amplicon sequencing toward cell-based functional bacterial microbiota characterization.

Jianshi Jin, Xiongduo Liu, Katsuyuki Shiroguchi

Abstract readReview
In one paragraph

Review in iMetaOmics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
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  10. Article
  11. Influence of cervicovaginal microbiota onMicrobial cell (Graz, Austria) · 2025
    Review
  12. 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

3 authors.

Jianshi JinState Key Laboratory of Integrated Management of Pest Insects and Rodents, Institute of Zoology Chinese Academy of Sciences Beijing China.ORCID https://orcid.org/0000-0003-4697-8760
Xiongduo LiuState Key Laboratory of Integrated Management of Pest Insects and Rodents, Institute of Zoology Chinese Academy of Sciences Beijing China.
Katsuyuki ShiroguchiLaboratory for Prediction of Cell Systems Dynamics RIKEN Center for Biosystems Dynamics Research (BDR) Osaka Japan.ORCID 0000-0003-4025-8789

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Bacteria often exist and function as a community, known as the bacterial microbiota, which consists of vast numbers of bacteria belonging to many bacterial species (taxa). Characterizing the bacterial microbiota needs high-throughput approaches that enable the identification and quantification of many bacterial cells, and such approaches have been under development for more than 30 years. In this review, we describe the history of high-throughput technologies based on 16S ribosomal RNA (rRNA) gene-amplicon sequencing for the characterization of bacterial microbiotas. Then, we summarize the features and applications of current 16S rRNA gene-amplicon sequencing approaches, including a recent achievement that enables the identification of individual cells with single-base accuracy for 16S rRNA genes and the quantification of many identified cells. Furthermore, we present the prospects for further technical development, including the combined use of high-throughput methods and other informative analyses, such as whole-genome sequencing in the common unit of the cell, which enables bacterial microbiota characterization based on both the number of cells and their functions.

Indexed as

16S rRNA‐amplicon sequencingabsolute quantificationbacterial microbiotahigh‐throughput measurementsingle cell analysis

Identifiers

PMID41675542
PMCPMC12806536

What OpenQuestion holds

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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.