Improving small RNA-seq by using a synthetic spike-in set for size-range quality control together with a set for data...

Area of Science:

• Molecular Biology
• Genomics
• Bioinformatics

Background:

• Complementing RNA sequencing (RNA-seq) with small RNA (sRNA) expression data offers a comprehensive transcriptome view.
• sRNA sequencing (sRNA-seq) faces challenges in size distribution assessment and inter-sample data normalization due to protocol sensitivity and low data complexity.
• Addressing these technical hurdles is crucial for accurate sRNA-seq analysis.

Purpose of the Study:

• To develop and validate synthetic RNA spike-in sets for enhancing sRNA-seq experiments.
• To provide tools for monitoring size selection and normalizing data in sRNA-seq.
• To improve the technical reproducibility of sRNA-seq studies.

Main Methods:

• Introduction of two distinct synthetic RNA spike-in sets: Size-Range Quality Control (SRQC) and External Reference for Data-Normalization (ERDN).
• SRQC set (11 oligoribonucleotides, 10-70 nt) tested by manipulating size-selection protocols and comparative experiments.
• ERDN set (19 oligoribonucleotides) applied for sample-to-sample normalization in differential expression analysis.

Main Results:

• The SRQC set effectively and reproducibly identifies biases in sRNA-seq size selection.
• The ERDN set enables reproducible detection of differential expression across a dynamic range of 2^18.
• Biological variations in sRNA composition are preserved while technical variations are minimized.

Conclusions:

• The developed SRQC and ERDN spike-in sets significantly enhance the technical reproducibility of sRNA-seq.
• These tools address key experimental challenges in sRNA-seq, facilitating more reliable transcriptome analysis.
• The synthetic spike-ins offer a robust solution for quality control and data normalization in small RNA research.