Analysis of coding gene expression from small RNA sequencing.

Area of Science:

• Genomics
• Molecular Biology
• Bioinformatics

Background:

• Thousands of small RNA sequencing (sRNA-seq) studies exist, but often lack matched total RNA sequencing data.
• This data gap hinders comprehensive analysis of microRNA-gene regulatory networks.

Purpose of the Study:

• To investigate the feasibility of quantifying protein-coding gene expression directly from sRNA-seq data.
• To assess the reliability of this approach for microRNA-mRNA interaction analysis.

Main Methods:

• Analyzed matched total RNA-seq and sRNA-seq data from four human tissues.
• Recovered and quantified protein-coding gene transcripts from sRNA-seq datasets.
• Validated inferred coding gene expression against qPCR data in breast cancer datasets.

Main Results:

• Protein-coding gene expression levels from sRNA-seq were comparable to total RNA-seq (R² 0.33–0.76).
• The approach showed consistent correlations across multiple tissues and species.
• Demonstrated inverse correlation between microRNA and mRNA expression profiles, confirming known interactions.
• Achieved 75% recall and 64% accuracy in breast cancer data analysis.

Conclusions:

• Quantifying mRNA fragments from sRNA-seq is a reliable method for studying microRNA-mRNA interactions when total RNA-seq is unavailable.
• Recommended sequencing the ≥25 nucleotide fraction at ≥5 million reads for dual mRNA/miRNA profiling.
• This approach offers a valuable tool for genomic and transcriptomic research, particularly in cancer studies.