Web-based NGS data analysis using miRMaster: a large-scale meta-analysis of human miRNAs

Area of Science:

• Genomics
• Bioinformatics
• Molecular Biology

Background:

• Analyzing small RNA next-generation sequencing (NGS) data and discovering novel small RNAs are significant challenges in life sciences.
• Existing tools may lack the speed, accuracy, or comprehensiveness required for large-scale small RNA analysis.

Purpose of the Study:

• To develop and present miRMaster, a fast, accurate, and comprehensive web-based tool for the analysis of small RNA NGS data.
• To facilitate the quantification, discovery, and characterization of microRNAs (miRNAs) and other non-coding RNAs, including isomiRs, mutations, exogenous RNAs, and motifs.

Main Methods:

• Implementation of miRMaster, a web-based toolbox with modules for various small RNA analysis tasks.
• Processing of large-scale datasets (hundreds of samples, up to 20 billion reads) using miRMaster.
• Integrative analysis of 1836 small RNA datasets to identify novel and context-specific miRNAs.

Main Results:

• MiRMaster processes hundreds of samples in under 5 hours with 99.4% accuracy.
• Analysis of 1836 datasets revealed that context-specific miRNAs are largely undiscovered, while broadly expressed miRNAs are mostly known.
• Identified nearly 22,000 precursor candidates with mature forms, leading to the design of a custom microarray for validation.

Conclusions:

• MiRMaster is a user-friendly tool for comprehensive and rapid analysis of miRNA NGS data.
• The predicted miRNA candidates and custom microarray enable in-depth validation of potential miRNA discoveries.
• The study highlights the ongoing potential for discovering novel, tissue-specific miRNAs.