Nottingham Ryan M, Wu Douglas C, Qin Yidan, Yao Jun, Hunicke-Smith Scott, Lambowitz Alan M
Institute for Cellular and Molecular Biology, University of Texas at Austin, Austin, Texas, 78712, USA Department of Molecular Biosciences, University of Texas at Austin, Austin, Texas, 78712, USA.
Institute for Cellular and Molecular Biology, University of Texas at Austin, Austin, Texas, 78712, USA.
RNA. 2016 Apr;22(4):597-613. doi: 10.1261/rna.055558.115. Epub 2016 Jan 29.
Next-generation RNA sequencing (RNA-seq) has revolutionized our ability to analyze transcriptomes. Current RNA-seq methods are highly reproducible, but each has biases resulting from different modes of RNA sample preparation, reverse transcription, and adapter addition, leading to variability between methods. Moreover, the transcriptome cannot be profiled comprehensively because highly structured RNAs, such as tRNAs and snoRNAs, are refractory to conventional RNA-seq methods. Recently, we developed a new method for strand-specific RNA-seq using thermostable group II intron reverse transcriptases (TGIRTs). TGIRT enzymes have higher processivity and fidelity than conventional retroviral reverse transcriptases plus a novel template-switching activity that enables RNA-seq adapter addition during cDNA synthesis without using RNA ligase. Here, we obtained TGIRT-seq data sets for well-characterized human RNA reference samples and compared them to previous data sets obtained for these RNAs by the Illumina TruSeq v2 and v3 methods. We find that TGIRT-seq recapitulates the relative abundance of human transcripts and RNA spike-ins in ribo-depleted, fragmented RNA samples comparably to non-strand-specific TruSeq v2 and better than strand-specific TruSeq v3. Moreover, TGIRT-seq is more strand specific than TruSeq v3 and eliminates sampling biases from random hexamer priming, which are inherent to TruSeq. The TGIRT-seq data sets also show more uniform 5' to 3' gene coverage and identify more splice junctions, particularly near the 5' ends of mRNAs, than do the TruSeq data sets. Finally, TGIRT-seq enables the simultaneous profiling of mRNAs and lncRNAs in the same RNA-seq experiment as structured small ncRNAs, including tRNAs, which are essentially absent with TruSeq.
新一代RNA测序(RNA-seq)彻底改变了我们分析转录组的能力。当前的RNA-seq方法具有高度可重复性,但每种方法都存在因RNA样本制备、逆转录和接头添加方式不同而产生的偏差,导致不同方法之间存在变异性。此外,转录组无法得到全面分析,因为诸如tRNA和snoRNA等高度结构化的RNA对传统RNA-seq方法具有抗性。最近,我们开发了一种使用热稳定II组内含子逆转录酶(TGIRT)进行链特异性RNA-seq的新方法。TGIRT酶比传统逆转录病毒逆转录酶具有更高的持续合成能力和保真度,以及一种新型的模板转换活性,能够在cDNA合成过程中添加RNA-seq接头,而无需使用RNA连接酶。在此,我们获得了特征明确的人类RNA参考样本的TGIRT-seq数据集,并将其与之前通过Illumina TruSeq v2和v3方法获得的这些RNA的数据集进行了比较。我们发现,与非链特异性TruSeq v2相当且优于链特异性TruSeq v3,TGIRT-seq在核糖体去除且片段化的RNA样本中概括了人类转录本和RNA加标对照的相对丰度。此外,TGIRT-seq比TruSeq v3具有更强的链特异性,并消除了TruSeq固有的随机六聚体引物导致的抽样偏差。TGIRT-seq数据集还显示出从5'到3'的基因覆盖更均匀,并且比TruSeq数据集识别出更多的剪接接头,特别是在mRNA的5'端附近。最后与TruSeq基本不存在的情况相比,TGIRT-seq能够在与包括tRNA在内的结构化小ncRNA相同的RNA-seq实验中同时分析mRNA和lncRNA。