Improved differential expression analysis of miRNA-seq data by modeling competition to be counted

Abstract MicroRNAs play a central role in regulating gene expression and modulating diseases. Despite the importance of microRNAs, statistical methods for analyzing them have received far less attention compared to messenger RNAs. Commonly, messenger RNA-seq methods are applied to microRNA-seq data, which may produce erroneous results due to the highly competitive nature of microRNA sequencing. This study critically examines and challenges the assumptions of messenger RNA-seq methods when applied to microRNA-seq data. We propose a Negative Binomial Softmax Regression (NBSR) method to model the unique characteristics of microRNA-seq data. On both simulated and experimental datasets, NBSR outperforms existing methods and offers a new perspective for analyzing microRNA-seq data. NBSR is implemented in Python and freely available as open-source software.