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mirMachine: A One-Stop Shop for Plant miRNA Annotation.

Authors
  • Cagirici, H Busra1
  • Sen, Taner Z1
  • Budak, Hikmet2
  • 1 U.S. Department of Agriculture - Agricultural Research Service, Western Regional Research Center, Crop Improvement and Genetics Research Unit, CA, USA.
  • 2 Montana BioAgriculture Inc., Missoula, MT, USA; [email protected]
Type
Published Article
Journal
Journal of Visualized Experiments
Publisher
MyJoVE Corporation
Publication Date
May 01, 2021
Issue
171
Identifiers
DOI: 10.3791/62430
PMID: 33999024
Source
Medline
Language
English
License
Unknown

Abstract

Of different types of noncoding RNAs, microRNAs (miRNAs) have arguably been in the spotlight over the last decade. As post-transcriptional regulators of gene expression, miRNAs play key roles in various cellular pathways, including both development and response to a/biotic stress, such as drought and diseases. Having high-quality reference genome sequences enabled identification and annotation of miRNAs in several plant species, where miRNA sequences are highly conserved. As computational miRNA identification and annotation processes are mostly error-prone processes, homology-based predictions increase prediction accuracy. We developed and have improved the miRNA annotation pipeline, SUmir, in the last decade, which has been used for several plant genomes since then. This study presents a fully automated, new miRNA pipeline, mirMachine (miRNA Machine), by (i) adding an additional filtering step on the secondary structure predictions, (ii) making it fully automated, and (iii) introducing new options to predict either known miRNA based on homology or novel miRNAs based on small RNA sequencing reads using the previous pipeline. The new miRNA pipeline, mirMachine, was tested using The Arabidopsis Information Resource, TAIR10, release of the Arabidopsis genome and the International Wheat Genome Sequencing Consortium (IWGSC) wheat reference genome v2.

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