Accepted_test

Transcriptomics analysis of various small RNA biotypes is a new and rapidly developing field. However, the bioinformatic analysis of NGS data for sRNA is prone to many challenges and not yet well established. Here, we attempt to identify the optimal pipeline configurations for each step of the sRNA analysis of human data, including read trimming, filtering, mapping, transcript abundance quantification, and differential expression (DE) analysis. Also we calculated the quality of obtained DE analysis signatures to estimate robustness of the obtained gene signature with robust and efficient rank statistics based approach.

The effects of various factors that impact on the expression analysis of human sRNA at different stages of data processing were investigated. The optimal pipeline setup parameters were suggested, and an optimized pipeline for setting and running sRNA expression analysis was proposed. Assessing the resulting expression signatures with rank-statistics-based inference suggests a way to estimate the quality of resulting signatures and performance of bioinformatic analysis for particular biological data.