Biology 2012, 1(2), 297-310; doi:10.3390/biology1020297
Review

Analyzing the microRNA Transcriptome in Plants Using Deep Sequencing Data

Received: 24 July 2012; in revised form: 3 August 2012 / Accepted: 9 August 2012 / Published: 15 August 2012
(This article belongs to the Special Issue Next Generation Sequencing Approaches in Biology)
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract: MicroRNAs (miRNAs) are 20- to 24-nucleotide endogenous small RNA molecules emerging as an important class of sequence-specific, trans-acting regulators for modulating gene expression at the post-transcription level. There has been a surge of interest in the past decade in identifying miRNAs and profiling their expression pattern using various experimental approaches. In particular, ultra-deep sampling of specifically prepared low-molecular-weight RNA libraries based on next-generation sequencing technologies has been used successfully in diverse species. The challenge now is to effectively deconvolute the complex sequencing data to provide comprehensive and reliable information on the miRNAs, miRNA precursors, and expression profile of miRNA genes. Here we review the recently developed computational tools and their applications in profiling the miRNA transcriptomes, with an emphasis on the model plant Arabidopsis thaliana. Highlighted is also progress and insight into miRNA biology derived from analyzing available deep sequencing data.
Keywords: microRNA; miRDeep-P; next-generation sequencing; small RNA; plant
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MDPI and ACS Style

Yang, X.; Li, L. Analyzing the microRNA Transcriptome in Plants Using Deep Sequencing Data. Biology 2012, 1, 297-310.

AMA Style

Yang X, Li L. Analyzing the microRNA Transcriptome in Plants Using Deep Sequencing Data. Biology. 2012; 1(2):297-310.

Chicago/Turabian Style

Yang, Xiaozeng; Li, Lei. 2012. "Analyzing the microRNA Transcriptome in Plants Using Deep Sequencing Data." Biology 1, no. 2: 297-310.

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