Abstract
BACKGROUND: mRNA sequencing is a powerful technique, which is used to investigate the transcriptome status of a gene of interest, such as its transcription level and splicing variants. Presently, several RNA sequencing (RNA-Seq) methods have been developed; however, the relative advantage of each method has remained unknown. Here we used three commercially available RNA-Seq library preparation kits; the traditional method (TruSeq), in addition to full-length double-stranded cDNA methods (SMARTer and TeloPrime) to investigate the advantages and disadvantages of these three approaches in transcriptome analysis. RESULTS: We observed that the number of expressed genes detected from the TeloPrime sequencing method was fewer than that obtained using the TruSeq and SMARTer. We also observed that the expression patterns between TruSeq and SMARTer correlated strongly. Alternatively, SMARTer and TeloPrime methods underestimated the expression of relatively long transcripts. Moreover, genes having low expression levels were undetected stochastically regardless of any three methods used. Furthermore, although TeloPrime detected a significantly higher proportion at the transcription start site (TSS), its coverage of the gene body was not uniform. SMARTer is proposed to be yielded for nonspecific genomic DNA amplification. In contrast, the detected splicing event number was highest in the TruSeq. The percent spliced in index (PSI) of the three methods was highly correlated. CONCLUSIONS: TruSeq detected transcripts and splicing events better than the other methods and measured expression levels of genes, in addition to splicing events accurately. However, although detected transcripts and splicing events in TeloPrime were fewer, the coverage at TSS was highest. Additionally, SMARTer was better than TeloPrime with regards to the detected number of transcripts and splicing events among the understudied full-length double-stranded cDNA methods. In conclusion, for short-read sequencing, TruSeq has relative advantages for use in transcriptome analysis.