sncRNAP: Prediction and profiling of full sncRNA repertoires from sRNAseq data
MotivationNon-coding RNAs (ncRNAs), which include long non-coding RNAs (lncRNAs) and small non-coding RNAs (sncRNAs), have been shown to play essential roles in various biological processes. Over the past few years, a group of sncRNA identification tools have been developed but none has shown the capacity to fully profile and accurately identify those that are differentially expressed in control vs treated samples. Therefore, a tool that fully profiles and identifies differentially expressed sncRNAs in group comparisons is required. ResultsWe developed sncRNAP, a Nextflow pipeline for the profiling and identification of differentially abundant sncRNAs from sRNAseq datasets. sncRNAP primary use case is the comparison of multiple small RNA-seq datasets belonging to two conditions such as the comparison of treatment (T) and control (C) cohorts. sncRNAP can be used to analyze human, mouse, and rat datasets. The pipeline carries out all the steps required to assess raw sequencing data, performs differential gene expression (DE) analysis, profiles sncRNAs in each sample, and outputs TXT, PDF, CSV, and interactive HTML files for the quality score and the top identified sncRNA candidates. We verified sncRNAP on publicly available sRNAseq datasets in chronic hepatitis-infected liver tissue and pancreatic ductal adenocarcinoma (PDAC) datasets. Our results support the identification of Val[C/A]AC in hepatitis patients and miR135b in PDAC as potential disease biomarkers. Furthermore, we applied sncRNAP on mouse samples from control and Opa1 mouse mutants and identified AspGTC, ValAAC, SerTGA, and AspGTC as the top DE tsRNAs. In addition, sncRNAP identified mmu-miR-136-5p, mmu-miR-10b-5p, mmu-miR-351-5p, and mmu-miR-6390 as the top DE miRNA candidates.