UMIche: A platform for robust UMI-centric simulation and analysis in bulk and single-cell sequencing

Abstract

Unique molecular identifiers (UMIs) have actively been utilised by various RNA sequencing (RNA-seq) protocols and technologies to remove polymerase chain reaction (PCR) duplicates, thus enhancing counting accuracy. However, errors during sequencing processes often compromise the precision of UMI-assisted quantification. To overcome this, various computational methods have been proposed for UMI error correction. Despite these advancements, the absence of a unified benchmarking and validation framework for UMI deduplication methods hinders the systematic evaluation and optimisation of these methods. Here, we present UMIche, an open-source, UMI-centric computational platform designed to improve molecular quantification by providing a systematic, integrative, and extensible framework for UMI analysis. Additionally, it supports the development of more effective UMI deduplication strategies. We show that through its integration of a broad spectrum of UMI deduplication methods and computational workflows, UMIche significantly advances the accuracy of molecular quantification and facilitates the generation of high-fidelity gene expression profiles.