PARiS: Probabilistic Assignment and Repartitioning of isomiR Sequences: A data-driven method for denoising isomiR read count data

MicroRNAs (miRNAs) are non-coding RNAs, approximately 18–24 nucleotides in length, with important gene regulatory functions. In small RNA sequencing (sRNA-seq), observed isoforms of miRNA, called isomiRs, arise from my biological and technical processes. Alterations in isomiR expression has been linked to a wide variety of human diseases, from cancers to neurological diseases. However, it is difficult to distinguish between technical and biological isomiRs. We present PARiS, an algorithm for the Probabilistic Assignment and Repartitioning of isomiR Sequences, that identifies technical error isomiRs in sRNA-seq data and reassigns them to their most likely biological source. We assess the ability of PARiS to identify and remove error isomiR sequences in a realistic simulation study. Additionally, we compare PARiS to alternative approaches, focusing on downstream miRNA-level differential expression analysis in a variety of settings, including a set of simulated datasets, an experimental benchmark dataset, and three colorectal adenocarcinoma cell lines.