A Verified Workflow for MiRNA-Seq Data Processing and Bioinformatics Analysis Using R

Summary

Here, we present a protocol to analyze miRNA-Seq data using R. The workflow empowers researchers to explore miRNA regulated networks, and their significance in diverse biological and clinical questions. This work intends to serve as a practical guide for both novice and experienced researchers in the field of miRNA bioinformatics.

Abstract

MicroRNAs (miRNAs) are critical post-transcriptional regulators that influence a wide range of physiological and pathological processes. With the advancement of high-throughput sequencing technologies, miRNA-Seq has emerged as a powerful tool for profiling miRNA expression patterns. However, reliable interpretation of such data requires a standardized and reproducible analysis pipeline. Here, we present a verified workflow for miRNA-Seq data processing and bioinformatics analysis using R. This protocol encompasses all essential steps, including raw data preprocessing, quality control, alignment, quantification, normalization, differential expression analysis, target prediction, functional enrichment, and regulatory network construction. Designed for flexibility and transparency, the workflow integrates widely adopted R packages and supports species-specific annotation and modular customization. Additionally, users are guided to conduct downstream biological interpretation by leveraging curated databases and visualization tools such as Cytoscape. This protocol not only supports robust statistical analysis but also enables meaningful insights into miRNA-mRNA interactions and their roles in disease mechanisms. It is particularly well-suited for both novice and experienced researchers conducting miRNA biomarker discovery, disease modeling, or integrative multi-omics studies.

Introduction

MicroRNAs (miRNAs) are short non-coding RNA molecules that significantly influence gene expression by acting at the post-transcriptional stage¹. They typically function by binding to complementary sequences in the 3' untranslated regions (UTRs) of target messenger RNAs (mRNAs), leading to mRNA degradation or translational repression¹. Over the past two decades, miRNAs have been increasingly recognized as central regulators of various biological processes, including cell proliferation, differentiation, apoptosis, immune responses, and organ development². Moreover, dysregulation of miRNA expression ....

Protocol

NOTE: Materials with software links are listed in the Table of Materials.

1. Prepare RNA samples and sequence libraries

NOTE: Perform RNA extraction and sequencing outside this computational workflow. There is more than one way to analyze miRNA-sequencing data. This section provides the context of one practical.

1. Extract total RNA: Extract total RNA from the biological samples using a kit optimized for small RNA isolation (e.g., miRNA isolation kit). Follow the manufacturer's protocol carefully. Ensure that you use RNase-free consumables and keep samples on ice to ....

Results

We downloaded the microRNA expression matrix from GSE133530 and conducted differential expression analysis directly. We provided an example analytical R script for the dataset in Supplementary File 1. The dataset performed global miRNA profiling on 16 renal cysts of different sizes (minimal cysts: less than 1-5 mL, n = 10; medium cysts: between 10-25 mL, n = 4; large cysts: greater than 50 mL, n = 4) and minimally cystic tissue (MCT, n = 7, including 1 replicate) from fou.......

Discussion

The analysis of miRNA-Seq data presents distinct challenges due to the small size and redundancy of reads, making rigorous quality control and preprocessing critical. One of the most important steps in the workflow is adapter trimming. Because miRNAs are approximately 22 nucleotides long, adapter sequences can easily dominate the reads if not properly removed. Failure to perform accurate trimming can result in misalignment and inflation of false-positive reads. Similarly, quality filtering should be implemented before al.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Agilent-021827 Human miRNA Microarray	Agilent	/	A commercial array for profiling microRNAs of human samples
Bowtie	Johns Hopkins University	http://bowtie-bio.sourceforge.net/index.shtml	A software tool for aligning sequencing reads to long reference sequences
clusterProfiler (R package)	Bioconductor	https://bioconductor.org/packages/clusterProfiler/	An R package designed for functional enrichment analysis and visualization of high-throughput biological data.
Cutadapt	Open Source	https://cutadapt.readthedocs.io	A command-line tool that removes adapter sequences, primers, poly-A tails and other unwanted fragments from high-throughput sequencing reads.
Cytoscape	Cytoscape Consortium	https://cytoscape.org/	An open-source software platform designed for the visualization and analysis of complex biological networks.
DESeq2 (R package)	Bioconductor	https://bioconductor.org/packages/DESeq2/	An R package designed for differential gene expression analysis of count data
EnhancedVolcano (R package)	Bioconductor	https://bioconductor.org/packages/EnhancedVolcano/	An R package designed to create publication-quality volcano plots.
FastQC	Babraham Bioinformatics	https://www.bioinformatics.babraham.ac.uk/projects/fastqc/	An open-source quality-control tool for high-throughput sequencing data.
featureCounts	Subread / SourceForge	http://subread.sourceforge.net/	A program used for counting reads mapped to genomic features
HTSeq-count	Python Package	https://htseq.readthedocs.io	A command-line tool that counts how many aligned high-throughput sequencing reads overlap genomic features such as genes or exons. I
Illumina Human v2 MicroRNA expression beadchip	Illumina	/	A commercial array for profiling microRNAs of human samples
multiMiR (R package)	Bioconductor	https://bioconductor.org/packages/multiMiR/	An R package that provides the largest integrated collection of predicted and experimentally validated microRNA–target interactions together with their associations to diseases and drugs.
org.Hs.eg.db (R package)	Bioconductor	https://bioconductor.org/packages/org.Hs.eg.db/	An annotation package designed for human (Homo sapiens) genomics research.
R Software	R Project	https://www.r-project.org/	An open-source Project for Statistical Computing
Rstudio	Posit PBC	/	An integrated development environment helps to be more productive with R and Python
SAMtools	Open Source	http://www.htslib.org/	A software package for manipulating next-generation sequencing (NGS) data.