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BMC Genomics. 2019 Feb 13;20(1):133. doi: 10.1186/s12864-019-5478-7.

PmiRDiscVali: an integrated pipeline for plant microRNA discovery and validation.

Author information

1
College of Life and Environmental Sciences, Hangzhou Normal University, Xuelin Street 16#, Xiasha, Hangzhou, 310036, People's Republic of China.
2
Faculty of Science, Hokkaido University, Kita10 Nishi8, Kita-ku, Sapporo, Hokkaido, 060-0810, Japan.
3
Holistic Integrative Pharmacy Institutes, Hangzhou Normal University, Wenyixi Road 1378#, Hangzhou, 311121, People's Republic of China. xbs@hznu.edu.cn.
4
Key Laboratory of microbiological technology and Bioinformatics in Zhejiang Province, Hangzhou, 310036, People's Republic of China.
5
College of Life Sciences, Huzhou University, Huzhou, 313000, People's Republic of China.
6
College of Life and Environmental Sciences, Hangzhou Normal University, Xuelin Street 16#, Xiasha, Hangzhou, 310036, People's Republic of China. mengyijun@zju.edu.cn.

Abstract

BACKGROUND:

MicroRNAs (miRNAs) constitute a well-known small RNA (sRNA) species with important regulatory roles. To date, several bioinformatics tools have been developed for large-scale prediction of miRNAs based on high-throughput sequencing data. However, some of these tools become invalid without reference genomes, while some tools cannot supply user-friendly outputs. Besides, most of the current tools focus on the importance of secondary structures and sRNA expression patterns for miRNA prediction, while they do not pay attention to miRNA processing for reliability check.

RESULTS:

Here, we reported a pipeline PmiRDiscVali for plant miRNA discovery and partial validation. This pipeline integrated the popular tool miRDeep-P for plant miRNA prediction, making PmiRDiscVali compatible for both reference-based and de novo predictions. To check the prediction reliability, we adopted the concept that the miRNA processing intermediates could be tracked by degradome sequencing (degradome-seq) during the development of PmiRDiscVali. A case study was performed by using the public sequencing data of Dendrobium officinale, in order to show the clear and concise presentation of the prediction results.

CONCLUSION:

Summarily, the integrated pipeline PmiRDiscVali, featured with degradome-seq data-based validation and vivid result presentation, should be useful for large-scale identification of plant miRNA candidates.

KEYWORDS:

Conservation; Degradome sequencing (degradome-seq); Graphic outputs; Plant microRNA; Processing; Secondary structure

PMID:
30760208
PMCID:
PMC6375137
DOI:
10.1186/s12864-019-5478-7
[Indexed for MEDLINE]
Free PMC Article

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