MiRLog and dbmiR: Prioritization and functional annotation tools to study human microRNA sequence variants

Area of Science:

• Genomics
• Molecular Biology
• Bioinformatics

Background:

• Noncoding variants are increasingly recognized as pathogenic, potentially explaining many undiagnosed genetic disorders.
• MicroRNAs (miRNAs) are key regulators of gene expression implicated in various biological processes and diseases.
• Existing computational tools for predicting noncoding variant effects lack integration and concordance, hindering variant interpretation.

Purpose of the Study:

• To develop and integrate a novel meta-predictor (MiRLog) and a comprehensive database (dbmiR) for predicting the functional impact of miRNA single nucleotide variants (SNVs).
• To analyze miRNA genetic variability in a large human genome dataset (gnomAD) to identify potentially deleterious variants.

Main Methods:

• Implementation of MiRLog, a meta-predictor for miRNA SNV functional effect.
• Development of dbmiR, a database integrating miRNA SNVs with nucleotide- and miRNA-level biological annotations.
• Analysis of 15,708 human genomes from the gnomAD project using MiRLog and dbmiR.

Main Results:

• Identification of several ultra-rare miRNA SNVs with potentially deleterious effects on miRNA biogenesis and function.
• dbmiR provides comprehensive biological annotations for all possible miRNA allelic SNVs.
• MiRLog and dbmiR facilitate the exploration of miRNA genetic variability and its functional consequences.

Conclusions:

• MiRLog and dbmiR represent a significant advancement in predicting the functional impact of miRNA SNVs.
• The identified ultra-rare variants are putative contributors to human phenotypes and warrant further investigation.
• These tools can aid in diagnosing genetic disorders potentially caused by noncoding variants in miRNAs.