henhaobio
Sign in

bioRxivGenomicsPreprint

Comprehensive analysis of RNA-seq kits for standard, low and ultra-low quantity samples

Marie-Ange Palomares, Cyril Dalmasso, Éric BonnetiD, Céline Derbois, Solène BrohardiD, Christophe AmbroiseiD, Christophe BattailiD, Jean‐François DeleuzeiD, Robert OlasoiD

PDFDOI

Abstract

High-throughput RNA-sequencing has become the gold standard method for whole-transcriptome gene expression analysis, and is widely used in numerous applications to study cell and tissue transcriptomes. It is also being increasingly used in a number of clinical applications, including expression profiling for diagnostics and alternative transcript detection. However, despite its many advantages, RNA sequencing can be challenging in some situations, for instance in cases of low input amounts or degraded RNA samples. Several protocols have been proposed to overcome these challenges, and many are available as commercial kits. In this study, we comprehensively test three recent commercial technologies for RNA-seq library preparation (TruSeq, SMARTer and SMARTer Ultra-Low) on human reference tissue preparations, using standard (1g), low (100 and 10 ng) and ultra-low (< 1 ng) input amounts, and for mRNA and total RNA, stranded or unstranded. The results are analyzed using read quality and alignment metrics, gene detection and differential gene expression metrics. Overall, we show that the TruSeq kit performs well with an input amount of 100 ng, while the SMARTer kit shows degraded performance for inputs of 100 and 10 ng, and the SMARTer Ultra-Low kit performs relatively well for input amounts < 1 ng. All the results are discussed in detail, and we provide guidelines for biologists for the selection of a RNA-seq library preparation kit.

Citations
1

Reproducibility ledger

Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.

Sign in to file a reproducibility report.

Similarity Map
SelectedSame fieldCross-field
THISHeinicke '19Combs '14Herbert '18Pallares '19Baran-Gale '13Kottapalli '25
67% semantic matchBioinformatics
Fatima Heinicke, Xiangfu Zhong, Manuela Zucknick+18 more·RNA Biology·Sep 27, 2019· 45 citations