Comprehensive assessment of multiple biases in small RNA sequencing reveals significant differences in the performance of widely used methods

Abstract

High-throughput sequencing offers advantages over other quantification methods for microRNA (miRNA), yet numerous biases make reliable quantification challenging. Previous evaluations of reverse transcription or amplification bias in small RNA sequencing has been limited. Furthermore, little work has evaluated isomiR (miRNA isoforms) quantifications or the influence of starting amount on performance. We therefore evaluated quantifications of canonical miRNA and isomiRs using four library preparation kits, with various starting amounts (100ng to 2000ng), as well as quantifications following the removal of duplicate reads using unique molecular identifiers (UMIs) to mitigate reverse transcription and amplification biases. Randomized adapter and adapter-free methods mitigated bias; however, the adapter-free method was especially prone to false isomiR detection. We demonstrate that using UMIs improves accuracy and we provide a guide for input amounts to improve consistency. Our data show differences and limitations of current methods, thus raising concerns about the validity of quantification of miRNA and isomiRs.

Publication
bioRxiv 445437
Date