Evidence map›Paper›PMID 27174874›Full record

ArticleActa biochimica et biophysica Sinica2016

An automated approach for global identification of sRNA-encoding regions in RNA-Seq data from Mycobacterium tuberculosis.

Ming Wang, Joy Fleming, Zihui Li, Chuanyou Li, Hongtai Zhang, Yunxin Xue, Maoshan Chen, Zongde Zhang, Xian-En Zhang, Lijun Bi

Abstract readValidation Study
In one paragraph

Article in Acta biochimica et biophysica Sinica, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing 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

18 citing papers in PubMed.

  1. Article
  2. Identification of Small RNAs inInternational journal of molecular sciences · 2024
    Article
  3. Article
  4. Article
  5. Article
  6. Heliyon · 2023
    Article
  7. Article
  8. Article
  9. Article
  10. Review
  11. Article
  12. Article
  13. On a stake-out:Non-coding RNA research · 2019
    Review
  14. Review
  15. Small RNA profiling inProceedings of the National Academy of Sciences of the United States of America · 2018
    Article
  16. Article
  17. Article
  18. 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

10 authors.

Ming WangKey Laboratory of Non-Coding RNA & State Key Laboratory of Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China University of Chinese Academy of Sciences, Beijing 100049, China.
Joy FlemingKey Laboratory of Non-Coding RNA & State Key Laboratory of Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China.
Zihui LiBeijing Chest Hospital, Capital Medical University, Beijing 101149, China.
Chuanyou LiBeijing Chest Hospital, Capital Medical University, Beijing 101149, China.
Hongtai ZhangKey Laboratory of Non-Coding RNA & State Key Laboratory of Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China.
Yunxin XueKey Laboratory of Non-Coding RNA & State Key Laboratory of Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China.
Maoshan ChenBGI-Shenzhen, Shenzhen 518083, China.
Zongde ZhangBeijing Chest Hospital, Capital Medical University, Beijing 101149, China.
Xian-En ZhangKey Laboratory of Non-Coding RNA & State Key Laboratory of Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China blj@sun5.ibp.ac.cn zhangxe@sun5.ibp.ac.cn.
Lijun BiKey Laboratory of Non-Coding RNA & State Key Laboratory of Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China Guangdong Province Key Laboratory of TB Systems Biology and Translational Medicine, Foshan 528000, China blj@sun5.ibp.ac.cn zhangxe@sun5.ibp.ac.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Deep-sequencing of bacterial transcriptomes using RNA-Seq technology has made it possible to identify small non-coding RNAs, RNA molecules which regulate gene expression in response to changing environments, on a genome-wide scale in an ever-increasing range of prokaryotes. However, a simple and reliable automated method for identifying sRNA candidates in these large datasets is lacking. Here, after generating a transcriptome from an exponential phase culture of Mycobacterium tuberculosis H37Rv, we developed and validated an automated method for the genome-wide identification of sRNA candidate-containing regions within RNA-Seq datasets based on the analysis of the characteristics of reads coverage maps. We identified 192 novel candidate sRNA-encoding regions in intergenic regions and 664 RNA transcripts transcribed from regions antisense (as) to open reading frames (ORF), which bear the characteristics of asRNAs, and validated 28 of these novel sRNA-encoding regions by northern blotting. Our work has not only provided a simple automated method for genome-wide identification of candidate sRNA-encoding regions in RNA-Seq data, but has also uncovered many novel candidate sRNA-encoding regions in M. tuberculosis, reinforcing the view that the control of gene expression in bacteria is more complex than previously anticipated.

Indexed as

Automation, LaboratoryChromosome MappingGenome, BacterialHigh-Throughput Nucleotide SequencingMycobacterium tuberculosisRNA, BacterialRNA, Small UntranslatedRNA, TransferSequence Analysis, RNATranscriptomeRNA, BacterialRNA, Small UntranslatedRNA, TransferMycobacterium tuberculosisnon-coding RNARNA-Seqtranscriptome

Identifiers

PMID27174874
PMCPMC4913526

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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.