Evidence map›Paper›PMID 28468647›Full record

ArticleBMC genomics2017

Detecting RNA-RNA interactions in E. coli using a modified CLASH method.

Tao Liu, Kaiyu Zhang, Song Xu, Zheng Wang, Hanjiang Fu, Baolei Tian, Xiaofei Zheng, Wuju Li

Open access · goldAbstract read
In one paragraph

Article in BMC genomics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
1.3field-weighted citation impact, top 20% 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

9 citing papers in PubMed, 19 citations in OpenAlex.

  1. Review
  2. CsrA Shows Selective Regulation of sRNA-mRNA Networks.bioRxiv : the preprint server for biology · 2023
    Article
  3. CsrA selectively modulates sRNA-mRNA regulator outcomes.Frontiers in molecular biosciences · 2023
    Article
  4. Thirty Years of sRNA-Mediated Regulation inInternational journal of molecular sciences · 2022
    Review
  5. Article
  6. Article
  7. Article
  8. Review
  9. Discovering RNA-Based Regulatory Systems forFrontiers in cellular and infection microbiology · 2018
    Review
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

8 authors at 2 institutions in 1 country.

Tao LiuBeijing Institute of Basic Medical Sciences, Taiping Road 27, Haidian district, Beijing, 100850, China.
Kaiyu ZhangBeijing Institute of Basic Medical Sciences, Taiping Road 27, Haidian district, Beijing, 100850, China.
Song XuBeijing Institute of Basic Medical Sciences, Taiping Road 27, Haidian district, Beijing, 100850, China.
Zheng WangBeijing Institute of Basic Medical Sciences, Taiping Road 27, Haidian district, Beijing, 100850, China.
Hanjiang FuBeijing Institute of Radiation Medicine, Taiping Road 27, Haidian district, Beijing, 100850, China.
Baolei TianBeijing Institute of Radiation Medicine, Taiping Road 27, Haidian district, Beijing, 100850, China.
Xiaofei ZhengBeijing Institute of Radiation Medicine, Taiping Road 27, Haidian district, Beijing, 100850, China. xfzheng100@126.com.
Wuju LiBeijing Institute of Basic Medical Sciences, Taiping Road 27, Haidian district, Beijing, 100850, China. wujuchina@126.com.
Beijing Institute of Education · CNInstitute of Basic Medical Sciences of the Chinese Academy of Medical Sciences

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundBacterial small regulatory RNAs (sRNAs) play important roles in sensing environment changes through sRNA-target mRNA interactions. However, the current strategy for detecting sRNA-mRNA interactions usually combines bioinformatics prediction and experimental verification, which is hampered by low prediction accuracy and low-throughput. Additionally, among the 4736 sequenced bacterial genomes, only about 2164 sRNAs from 319 strains have been described. Furthermore, target mRNAs of only 157 sRNAs have been uncovered. Obviously, highly efficient methods were required to detect sRNA-mRNA interactions in the sequenced genomes. This study aimed to apply a modified CLASH (cross-linking, ligation and sequencing hybrids) method to detect RNA-RNA interactions in E. coli, a model bacterial organism.

resultsStatistically significant interactions were detected in 29 transcript pairs. To the best of our knowledge, 24 pairs were reported for the first time and were novel RNA interactions, including tRNA-tRNA, tRNA-ncRNA (non-coding RNA), tRNA-rRNA, rRNA-mRNA, rRNA-ncRNA, rRNA-rRNA, rRNA-IGT (intergenic transcript), and tRNA-IGT interactions.

conclusionsDiscovery of novel RNA-RNA interactions in the present study demonstrates that RNA-RNA interactions might be far more complicated than ever expected. New methods may be required to help discover more novel RNA-RNA interactions. The present work describes a high-throughput protocol not only for discovering new RNA interactions, but also directly obtaining base-pairing sequences, which should be useful in assessing RNA structure and interactions.

Indexed as

Computational BiologyEscherichia coli K12RNA, BacterialThermodynamicsUltraviolet RaysRNA, BacterialBacteriaHigh-throughput sequencingRNA-RNA interaction

Identifiers

PMID28468647
PMCPMC5415748
OpenAlexW2611378212

What OpenQuestion holds

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LicenceCC BY
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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.