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Related Concept Videos

RNA-seq03:21

RNA-seq

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. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while microarray-based...

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Related Experiment Video

Updated: May 15, 2026

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

Published on: May 1, 2021

Automatically clustering large-scale miRNA sequences: methods and experiments.

Linxia Wan1, Jiandong Ding, Ting Jin

  • 1School of Computer Science, Fudan University, Shanghai 200433, China.

BMC Genomics
|January 4, 2013
PubMed
Summary
This summary is machine-generated.

miRCluster automatically groups microRNAs (miRNAs) into families, improving the classification of unannotated miRNAs and aiding in the discovery of novel miRNA functions.

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Last Updated: May 15, 2026

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MicroRNA Amplification and Recognition through Locked-nucleic-acid In situ Hybridization as a Novel Detection and Quantification Method
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A Complete Pipeline for Isolating and Sequencing MicroRNAs, and Analyzing Them Using Open Source Tools

Published on: August 21, 2019

Area of Science:

  • Bioinformatics
  • Genomics
  • Molecular Biology

Background:

  • MicroRNAs (miRNAs) regulate gene expression post-transcriptionally.
  • Current research often focuses on individual miRNA functions.
  • Understanding miRNA families can reveal shared biological roles and targets.

Purpose of the Study:

  • To develop an unsupervised method for automatic miRNA family identification.
  • To improve the classification of unannotated and novel miRNAs.

Main Methods:

  • Developed miRCluster, an unsupervised clustering-based method.
  • Utilized data sets from the miRBase database for evaluation.

Main Results:

  • miRCluster accurately identified 354 miRNA families in miRBase 16 (92.08% accuracy).
  • Successfully recognized 9 out of 10 newly added families in miRBase 17.
  • Classified over 85% of previously unclassified miRNAs, identifying ~300 novel families.

Conclusions:

  • miRCluster is an efficient, automatic miRNA family identification tool.
  • Requires no prior knowledge, facilitating the exploration of novel miRNA functions.