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RNA Biol. 2020 Jan;17(1):75-86. doi: 10.1080/15476286.2019.1667741. Epub 2019 Sep 27.

Systematic assessment of commercially available low-input miRNA library preparation kits.

Author information

1
Department of Medical Genetics, Oslo University Hospital and University of Oslo, Oslo, Norway.
2
Department of Biostatistics, Oslo Centre for Biostatistics and Epidemiology, University of Oslo, Oslo, Norway.
3
Norwegian Institute for Bioeconomy Research, National Forest Inventory, Ås, Norway.
4
Takara Bio USA, Inc., Mountain View, CA, USA.
5
TriLink Biotechnologies LLC, San Diego, CA, USA.
6
QIAGEN Sciences, Frederick, MD, USA.
7
Lexogen GmbH, Vienna, Austria.
8
SeqMatic, LLC, Fremont, CA, USA.

Abstract

High-throughput sequencing is increasingly favoured to assay the presence and abundance of microRNAs (miRNAs) in biological samples, even from low RNA amounts, and a number of commercial vendors now offer kits that allow miRNA sequencing from sub-nanogram (ng) inputs. Although biases introduced during library preparation have been documented, the relative performance of current reagent kits has not been investigated in detail. Here, six commercial kits capable of handling <100ng total RNA input were used for library preparation, performed by kit manufactures, on synthetic miRNAs of known quantities and human total RNA samples. We compared the performance of miRNA detection sensitivity, reliability, titration response and the ability to detect differentially expressed miRNAs. In addition, we assessed the use of unique molecular identifiers (UMI) sequence tags in one kit. We observed differences in detection sensitivity and ability to identify differentially expressed miRNAs between the kits, but none were able to detect the full repertoire of synthetic miRNAs. The reliability within the replicates of all kits was good, while larger differences were observed between the kits, although none could accurately quantify the relative levels of the majority of miRNAs. UMI tags, at least within the input ranges tested, offered little advantage to improve data utility. In conclusion, biases in miRNA abundance are heavily influenced by the kit used for library preparation, suggesting that comparisons of datasets prepared by different procedures should be made with caution. This article is intended to assist researchers select the most appropriate kit for their experimental conditions.

KEYWORDS:

MicroRNA; NGS; UMI; library preparation; low RNA input; miRNA; next generation sequencing; sequencing bias; small RNA-seq

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