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Related Concept Videos

Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...

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Related Experiment Video

Updated: May 15, 2026

mirMachine: A One-Stop Shop for Plant miRNA Annotation
06:16

mirMachine: A One-Stop Shop for Plant miRNA Annotation

Published on: May 1, 2021

Detecting miRNAs in deep-sequencing data: a software performance comparison and evaluation.

Vernell Williamson1, Albert Kim, Bin Xie

  • 1Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Medical College of Virginia of Virginia Commonwealth University, Richmond, VA, USA. vswilliamson@vcu.edu

Briefings in Bioinformatics
|January 22, 2013
PubMed
Summary
This summary is machine-generated.

Comparing miRNA detection software revealed that while programs like miRDeep, miRanalyzer, and DSAP identify similar microRNA (miRNA) candidates, using multiple tools increases confidence in novel findings.

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A Bioinformatics Pipeline to Accurately and Efficiently Analyze the MicroRNA Transcriptomes in Plants
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A Bioinformatics Pipeline to Accurately and Efficiently Analyze the MicroRNA Transcriptomes in Plants

Published on: January 21, 2020

Related Experiment Videos

Last Updated: May 15, 2026

mirMachine: A One-Stop Shop for Plant miRNA Annotation
06:16

mirMachine: A One-Stop Shop for Plant miRNA Annotation

Published on: May 1, 2021

A Bioinformatics Pipeline to Accurately and Efficiently Analyze the MicroRNA Transcriptomes in Plants
06:34

A Bioinformatics Pipeline to Accurately and Efficiently Analyze the MicroRNA Transcriptomes in Plants

Published on: January 21, 2020

Area of Science:

  • Bioinformatics
  • Genomics
  • Molecular Biology

Background:

  • Deep sequencing is a key technology for discovering novel microRNAs (miRNAs).
  • However, the interpretation of deep sequencing data for miRNA detection is still evolving.
  • Several bioinformatics tools exist for analyzing this data, each with different algorithms and stringency levels.

Purpose of the Study:

  • To critically evaluate the performance of popular miRNA detection software using deep sequencing data.
  • To compare the ability of miRDeep (v1, 2), miRanalyzer, and DSAP in identifying known and novel miRNAs.
  • To propose an optimized approach for novel miRNA candidate selection.

Main Methods:

  • Analysis of seven datasets (six biological, one simulated) using three distinct miRNA prediction programs: miRDeep (v1, 2), miRanalyzer, and DSAP.
  • Comparison of predicted known and novel miRNA sets across different software.
  • Initial validation of novel miRNA predictions using real-time PCR.

Main Results:

  • All evaluated programs identified a largely overlapping set of known and novel miRNA predictions, despite variations in stringency.
  • Differences in predicted miRNA numbers appear related to the read mapping algorithms employed by each program.
  • An intersection approach across multiple programs identified 12 novel miRNA candidates, with six being previously unreported.

Conclusions:

  • The choice of miRNA detection software and its stringency settings can significantly impact the number of novel candidates identified.
  • An intersection strategy using multiple bioinformatics tools is recommended for robust novel miRNA candidate selection.
  • This approach enhances the efficiency of downstream functional validation, saving time and resources.