A contig-based strategy for the genome-wide discovery of microRNAs without complete genome resources

MicroRNAs (miRNAs) are important regulators of many cellular processes and exist in a wide range of eukaryotes. High-throughput sequencing is a mainstream method of miRNA identification through which it is possible to obtain the complete small RNA profile of an organism. Currently, most approaches t...

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Bibliographic Details
Published in:PloS one 2014-02, Vol.9 (2), p.e88179-e88179
Main Authors: Wen, Jun-Zhi, Liao, Jian-You, Zheng, Ling-Ling, Xu, Hui, Yang, Jian-Hua, Guan, Dao-Gang, Zhang, Si-Min, Zhou, Hui, Qu, Liang-Hu
Format: Article
Language:English
Subjects:
Analysis
Biological evolution
Biology
Breeding
Databases, Genetic
Economic models
Education
Engineering
Epigenetics
Eukaryotes
Evolution
Evolution, Molecular
Expressed sequence tags
Gene expression
Gene sequencing
Genome, Plant
Genomes
Genomics
Identification
Laboratories
Life sciences
Mathematical models
MicroRNA
MicroRNAs
MicroRNAs - genetics
miRNA
Musa - genetics
Musa acuminata
Next-generation sequencing
Phylogeny
Plant biology
Predictions
Regulators
Ribonucleic acid
RNA
Sequence Analysis, RNA
Species
Strategy
Studies
Trypanosoma brucei
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Summary:MicroRNAs (miRNAs) are important regulators of many cellular processes and exist in a wide range of eukaryotes. High-throughput sequencing is a mainstream method of miRNA identification through which it is possible to obtain the complete small RNA profile of an organism. Currently, most approaches to miRNA identification rely on a reference genome for the prediction of hairpin structures. However, many species of economic and phylogenetic importance are non-model organisms without complete genome sequences, and this limits miRNA discovery. Here, to overcome this limitation, we have developed a contig-based miRNA identification strategy. We applied this method to a triploid species of edible banana (GCTCV-119, Musa spp. AAA group) and identified 180 pre-miRNAs and 314 mature miRNAs, which is three times more than those were predicted by the available dataset-based methods (represented by EST+GSS). Based on the recently published miRNA data set of Musa acuminate, the recall rate and precision of our strategy are estimated to be 70.6% and 92.2%, respectively, significantly better than those of EST+GSS-based strategy (10.2% and 50.0%, respectively). Our novel, efficient and cost-effective strategy facilitates the study of the functional and evolutionary role of miRNAs, as well as miRNA-based molecular breeding, in non-model species of economic or evolutionary interest.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0088179