SingmiR: a single-cell miRNA alignment and analysis tool.

Area of Science:

• Genomics and Molecular Biology
• Bioinformatics and Computational Biology

Background:

• Single-cell RNA sequencing (RNA-seq) has advanced cell biology but leaves microRNA (miRNA) regulatory processes largely unexplored at the single-cell level.
• Analyzing single-cell miRNA sequencing (miRNA-seq) data requires advanced computational skills, and a dedicated, user-friendly workflow is lacking.

Purpose of the Study:

• To present SingmiR, a novel web server designed for the rapid preprocessing and quantification of human single-cell miRNA-seq data.
• To provide biologists with an accessible computational tool to facilitate the analysis of miRNA expression and regulation in individual cells.

Main Methods:

• Developed a web server implementing a comprehensive workflow for single-cell miRNA-seq data.
• Included functionalities for read trimming, automated quality control, and feature normalization.
• Integrated standard and advanced analytical modules like dimension reduction, heatmaps, and differential expression analysis.

Main Results:

• SingmiR enables efficient preprocessing and quantification of human miRNAs from single-cell samples.
• The server provides automated quality control reports and normalized count files.
• Offers diverse analytical tools to expedite the prototyping of single-cell miRNA-seq protocols for small to medium datasets.

Conclusions:

• SingmiR addresses the need for a specialized, user-friendly computational tool for single-cell miRNA-seq data analysis.
• The platform aims to accelerate research into post-transcriptional gene regulation by miRNAs at the single-cell level.
• SingmiR is freely accessible, promoting wider adoption and advancement in the field.