bioRxivGenomicsPreprint
Comparison of Two Illumina Whole Transcriptome RNA Sequencing Library Preparation Methods Using Human FFPE Specimens
Danyi WangiD, Alex RolfeiD, Dorothee Foernzler, Dennis O’RourkeiD, Sheng ZhaoiD, Juergen Scheuenpflug, Zheng FengiD
Abstract
RNA extraction and library preparation from formalin-fixed, paraffin-embedded (FFPE) samples are crucial pre-analytical steps towards achieving optimal downstream RNA Sequencing (RNASeq) results. We assessed the Illumina TruSeq Stranded Total RNA library preparation method and the Illumina TruSeq RNA Access library preparation method for RNA-Seq analysis using 25 FFPE samples from human cancer indications (NSCLC, CRC, RC, BC and HCC) at two independent vendors. These FFPE samples covered a wide range of sample storage durations (3-25 years-old), sample qualities, and specimen types (resection vs. core needle biopsy). Our data showed that TruSeq RNA Access libraries yield over 80% exonic reads across different quality samples, indicating higher selectivity of the exome pull down by the capture approach compared to the random priming of the TruSeq Stranded Total kit. The overall QC data for FFPE RNA extraction, library preparation, and sequencing generated by the two vendors are comparable, and downstream gene expression quantification results show high concordance as well. With the TruSeq Stranded Total kit, the average Spearman correlation between vendors was 0.87 and the average Pearson correlation was 0.76. With the TruSeq RNA Access kit, the average Spearman correlation between vendors was 0.89 and the average Pearson correlation was 0.73. Interestingly, examination of the crossvendor correlations compared to various common QC statistics suggested that library concentration is better correlated with consistency between vendors than is the RNA quantity. Our analyses provide evidence to guide selection of sequencing methods for FFPE samples in which the sample quality may be severely compromised.
- organism: human
- assay: bulk RNA-seq
- research focus: benchmarking
- research focus: differential expression
- research focus: gene regulatory network inference
- research focus: variant calling
- approach: statistical model
- Citations
- 0
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