TY - JOUR T1 - Comprehensive assessment of multiple biases in small RNA sequencing reveals significant differences in the performance of widely used methods JF - bioRxiv DO - 10.1101/445437 SP - 445437 AU - Carrie Wright AU - Anandita Rajpurohit AU - Emily E. Burke AU - Courtney Williams AU - Leonardo Collado-Torres AU - Martha Kimos AU - Nicholas J. Brandon AU - Alan J. Cross AU - Andrew E. Jaffe AU - Daniel R. Weinberger AU - Joo Heon Shin Y1 - 2018/01/01 UR - http://biorxiv.org/content/early/2018/10/30/445437.abstract N2 - 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 have been limited. Furthermore, little work has evaluated quantifications of isomiRs (miRNA isoforms) 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 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. ER -