Evidence map›Paper›PMID 35171049›Full record

ArticleMicrobiology spectrum2022

Effects of Waterlogging on Soybean Rhizosphere Bacterial Community Using V4, LoopSeq, and PacBio 16S rRNA Sequence.

Taobing Yu, Lang Cheng, Qi Liu, Shasha Wang, Yuan Zhou, Hongbin Zhong, Meifang Tang, Hai Nian, Tengxiang Lian

Open access · goldAbstract read
In one paragraph

Article in Microbiology spectrum, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed
8.6field-weighted citation impact, top 2% of its field
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

21 citing papers in PubMed, 45 citations in OpenAlex.

  1. Article
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  13. Utility Analyses of AVITI Sequencing Chemistry.bioRxiv : the preprint server for biology · 2024
    Article
  14. Review
  15. Article
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  17. Article
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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 at 3 institutions in 1 country.

Taobing YuThe State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, South China Agricultural Universitygrid.20561.30, Guangzhou, People's Republic of China.
Lang ChengThe State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, South China Agricultural Universitygrid.20561.30, Guangzhou, People's Republic of China.
Qi LiuThe State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, South China Agricultural Universitygrid.20561.30, Guangzhou, People's Republic of China.
Shasha WangThe State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, South China Agricultural Universitygrid.20561.30, Guangzhou, People's Republic of China.
Yuan ZhouBGI Genomics, BGI-Shenzhen, Shenzhen, China.
Hongbin ZhongBGI Genomics, BGI-Shenzhen, Shenzhen, China.
Meifang TangBGI Genomics, BGI-Shenzhen, Shenzhen, China.
Hai NianThe State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, South China Agricultural Universitygrid.20561.30, Guangzhou, People's Republic of China.
Tengxiang LianThe State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, South China Agricultural Universitygrid.20561.30, Guangzhou, People's Republic of China.
South China Agricultural University · CNBGI GenomicsBGI Group (China) · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Waterlogging causes a significant reduction in soil oxygen levels, which in turn negatively affects soil nutrient use efficiency and crop yields. Rhizosphere microbes can help plants to better use nutrients and thus better adapt to this stress, while it is not clear how the plant-associated microbes respond to waterlogging stress. There are also few reports on whether this response is influenced by different sequencing methods and by different soils. In this study, using partial 16S rRNA sequencing targeting the V4 region and two full-length 16S rRNA sequencing approaches targeting the V1 to V9 regions, the effects of waterlogging on soybean rhizosphere bacterial structure in two types of soil were examined. Our results showed that, compared with the partial 16S sequencing, full-length sequencing, both LoopSeq and Pacific Bioscience (PacBio) 16S sequencing, had a higher resolution. On both types of soil, all the sequencing methods showed that waterlogging significantly affected the bacterial community structure of the soybean rhizosphere and increased the relative abundance of

Indexed as

RhizosphereSoil MicrobiologyBacteriaGlycine maxHigh-Throughput Nucleotide SequencingMicrobiotaPhylogenyRNA, Ribosomal, 16SSoilRNA, Ribosomal, 16SSoilLoopSeqPacBiorhizosphere bacterial communitysoybeanV4 16S rRNA sequencewaterlogging

Identifiers

PMID35171049
PMCPMC8849089
OpenAlexW4213284747

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.