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MicroRNAs01:22

MicroRNAs

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MicroRNA (miRNA) are short, regulatory RNA transcribed from introns—non-coding regions of a gene—or intergenic regions—stretches of DNA present between genes. Several processing steps are required to form biologically active, mature miRNA. The initial transcript, called primary miRNA (pri-mRNA), base-pairs with itself forming a stem-loop structure. Within the nucleus, an endonuclease enzyme, called Drosha, shortens the stem-loop structure into hairpin-shaped pre-miRNA. After...
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MicroRNA (miRNA) are short, regulatory RNA transcribed from introns (non-coding regions of a gene) or intergenic regions (stretches of DNA present between genes). Several processing steps are required to form biologically active, mature miRNA. The initial transcript, called primary miRNA (pri-mRNA), base-pairs with itself, forming a stem-loop structure. Within the nucleus, an endonuclease enzyme, called Drosha, shortens the stem-loop structure into hairpin-shaped pre-miRNA. After the pre-miRNA...
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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
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A Bioinformatics Pipeline to Accurately and Efficiently Analyze the MicroRNA Transcriptomes in Plants
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Analyzing the microRNA Transcriptome in Plants Using Deep Sequencing Data.

Xiaozeng Yang1, Lei Li2

  • 1Department of Biology, University of Virginia, Charlottesville VA 22904, USA. xy2y@virginia.edu.

Biology
|May 17, 2014
PubMed
Summary
This summary is machine-generated.

MicroRNAs (miRNAs) are small RNA molecules regulating gene expression. This review covers computational tools for analyzing miRNA sequencing data, focusing on Arabidopsis thaliana.

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Area of Science:

  • Molecular Biology
  • Genomics
  • Bioinformatics

Background:

  • MicroRNAs (miRNAs) are small, endogenous RNA molecules that regulate gene expression post-transcriptionally.
  • Identifying and profiling miRNA expression is crucial for understanding gene regulation.
  • Next-generation sequencing (NGS) technologies enable ultra-deep sampling of miRNA populations.

Purpose of the Study:

  • To review computational tools for analyzing miRNA sequencing data.
  • To highlight applications in profiling miRNA transcriptomes, particularly in Arabidopsis thaliana.
  • To discuss insights into miRNA biology gained from deep sequencing data analysis.

Main Methods:

  • Review of recently developed computational tools for miRNA analysis.
  • Focus on ultra-deep sequencing data from low-molecular-weight RNA libraries.
  • Application of these tools to model plant systems, specifically Arabidopsis thaliana.

Main Results:

  • Computational tools are essential for deconvoluting complex miRNA sequencing data.
  • These tools provide comprehensive information on miRNAs, precursors, and gene expression profiles.
  • Analysis of deep sequencing data offers valuable insights into miRNA biology.

Conclusions:

  • Effective computational analysis is key to unlocking the potential of miRNA deep sequencing data.
  • The reviewed tools facilitate reliable miRNA transcriptome profiling.
  • Advancements in computational methods enhance our understanding of miRNA functions and regulation.