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Updated: May 18, 2026
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A Complete Pipeline for Isolating and Sequencing MicroRNAs, and Analyzing Them Using Open Source Tools
Published on: August 21, 2019
A user-friendly computational workflow for the analysis of microRNA deep sequencing data
Anna Majer1, Kyle A Caligiuri, Stephanie A Booth
1Department of Medical Microbiology and Infectious Diseases, and Molecular PathoBiology, University of Manitoba, Molecular PathoBiology, National Microbiology Laboratory, Public Health Agency of Canada, Winnipeg, MB, Canada.
Methods in Molecular Biology (Clifton, N.J.)
|September 26, 2012
Summary
This study introduces a user-friendly computational workflow for analyzing microRNA (miRNA) sequencing data from the central nervous system. It simplifies identifying and quantifying miRNA expression and predicting targets.
Area of Science:
- Neuroscience
- Genomics
- Bioinformatics
Background:
- Second-generation high-throughput sequencing is a cost-effective method for studying microRNA (miRNA) expression in the central nervous system.
- This technique identifies known and novel miRNAs and quantifies their levels in biological samples.
- Analyzing deep sequencing data often requires command-line interface proficiency, posing a barrier for some researchers.
Purpose of the Study:
- To present a user-friendly computational workflow for analyzing miRNA deep sequencing data.
- To guide researchers from raw sequencing files to miRNA identification and differential expression analysis.
- To highlight tools for predicting miRNA targets.
Main Methods:
- Development of a computational workflow for processing FASTQ sequencing files.
- Integration of bioinformatic tools for miRNA identification and quantification.
- Incorporation of differential expression analysis and miRNA target prediction modules.
Main Results:
- The workflow facilitates the identification of known and novel microRNAs from sequencing data.
- It enables the assessment of differential miRNA expression between experimental conditions.
- Associated programs for predicting miRNA targets are also presented.
Conclusions:
- The described workflow democratizes miRNA sequencing data analysis for central nervous system research.
- It simplifies the complex process of identifying, quantifying, and analyzing miRNA expression and targets.
- This approach supports broader adoption of high-throughput sequencing in neuroscience studies.
