Optimization of miRNA-seq data preprocessing.

Area of Science:

• Genomics
• Molecular Biology
• Bioinformatics

Background:

• MicroRNAs (miRNAs) are key regulators of biological processes and disease biomarkers.
• High-throughput profiling, especially next-generation sequencing, is vital for miRNA discovery and quantification.
• Standardized data preprocessing methods for miRNA sequencing are lacking, impacting downstream analysis reliability.

Purpose of the Study:

• To evaluate the impact of various data preprocessing steps on miRNA sequencing data.
• To identify optimal aligners and normalization methods for accurate miRNA analysis.
• To provide practical recommendations for miRNA count data extraction and interpretation.

Main Methods:

• Utilized a spike-in dilution study to assess different general-purpose aligners (BWA, Bowtie, Bowtie 2, Novoalign).
• Evaluated multiple normalization methods including counts-per-million, total count scaling, upper quartile scaling, Trimmed Mean of M, DESeq, linear regression, cyclic loess, and quantile.
• Assessed the effects on miRNA count data distribution, variance, bias, and differential expression analysis accuracy.

Main Results:

• Different aligners and normalization methods significantly impact miRNA count data distribution and variance.
• Suboptimal preprocessing can introduce bias and reduce the accuracy of differential expression analysis.
• Specific combinations of aligners and normalization methods demonstrated superior performance in preserving data integrity.

Conclusions:

• Data preprocessing is a critical, often overlooked, step in small RNA sequencing analysis.
• The choice of aligner and normalization method directly influences the reliability of miRNA quantification.
• Recommendations are provided for optimal preprocessing to enhance the accuracy of miRNA biomarker discovery and biological interpretation.