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Acta Biochim Biophys Sin (Shanghai). 2016 Jun;48(6):544-53. doi: 10.1093/abbs/gmw037. Epub 2016 May 12.

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

Author information

1
Key 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.
2
Key Laboratory of Non-Coding RNA & State Key Laboratory of Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China.
3
Beijing Chest Hospital, Capital Medical University, Beijing 101149, China.
4
BGI-Shenzhen, Shenzhen 518083, China.
5
Key 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.
6
Key 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.

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.

KEYWORDS:

Mycobacterium tuberculosis; RNA-Seq; non-coding RNA; transcriptome

PMID:
27174874
PMCID:
PMC4913526
DOI:
10.1093/abbs/gmw037
[Indexed for MEDLINE]
Free PMC Article

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