Identification of novel ΔNp63α-regulated miRNAs using an optimized small RNA-Seq analysis pipeline

Area of Science:

• Genomics
• Bioinformatics
• Molecular Biology

Background:

• High-throughput sequencing enables microRNA (miRNA) profiling, but a standardized analysis pipeline is lacking.
• Existing methods often require complex scripting, posing a barrier to widespread adoption.

Purpose of the Study:

• To develop and optimize a user-friendly analysis pipeline for small RNA sequencing data.
• To evaluate the impact of different alignment references, normalization methods, and statistical models on miRNA quantification and differential expression analysis.
• To identify novel miRNA targets regulated by ΔNp63α in skin cancer cells.

Main Methods:

• Development of a small RNA sequencing analysis pipeline in Partek Flow.
• Evaluation of alignment to miRBase, Trimmed Mean of M-values (TMM) normalization, and a lognormal with shrinkage statistical model.
• Application of the pipeline to HaCaT cells transfected with siRNA against ΔNp63α.

Main Results:

• Alignment to miRBase and TMM normalization were identified as robust methods.
• The lognormal with shrinkage model effectively identified differentially expressed miRNAs.
• The pipeline revealed previously unrecognized regulation of specific miRNAs (miR-149-5p, 18a-5p, 19b-1-5p, 20a-5p, 590-5p, 744-5p, 93-5p) by ΔNp63α.
• RT-qPCR validated the identified miRNA regulations.

Conclusions:

• The developed pipeline provides a robust and accessible method for small RNA sequencing analysis.
• Optimized parameters ensure accurate miRNA quantification and differential expression analysis.
• The identified miRNA regulations offer new insights into ΔNp63α's role in non-melanoma skin cancer progression.