Dong Xueyi, Tian Luyi, Gouil Quentin, Kariyawasam Hasaru, Su Shian, De Paoli-Iseppi Ricardo, Prawer Yair David Joseph, Clark Michael B, Breslin Kelsey, Iminitoff Megan, Blewitt Marnie E, Law Charity W, Ritchie Matthew E
Epigenetics and Development Division, The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, Victoria 3052, Australia.
Centre for Stem Cell Systems, Department of Anatomy and Neuroscience, The University of Melbourne, Parkville, Victoria 3010, Australia.
NAR Genom Bioinform. 2021 Apr 26;3(2):lqab028. doi: 10.1093/nargab/lqab028. eCollection 2021 Jun.
Application of Oxford Nanopore Technologies' long-read sequencing platform to transcriptomic analysis is increasing in popularity. However, such analysis can be challenging due to the high sequence error and small library sizes, which decreases quantification accuracy and reduces power for statistical testing. Here, we report the analysis of two nanopore RNA-seq datasets with the goal of obtaining gene- and isoform-level differential expression information. A dataset of synthetic, spliced, spike-in RNAs ('sequins') as well as a mouse neural stem cell dataset from samples with a null mutation of the epigenetic regulator was analysed using a mix of long-read specific tools for preprocessing together with established short-read RNA-seq methods for downstream analysis. We used to perform differential gene expression analysis, and the novel pipeline to perform isoform identification and quantification, followed by and (with ) to perform differential transcript usage analysis. We compared results from the sequins dataset to the ground truth, and results of the mouse dataset to a previous short-read study on equivalent samples. Overall, our work shows that transcriptomic analysis of long-read nanopore data using long-read specific preprocessing methods together with short-read differential expression methods and software that are already in wide use can yield meaningful results.
牛津纳米孔技术公司的长读长测序平台在转录组分析中的应用越来越普遍。然而，由于序列错误率高和文库规模小，这种分析可能具有挑战性，这会降低定量准确性并减少统计检验的效力。在此，我们报告了对两个纳米孔RNA测序数据集的分析，目的是获得基因和异构体水平的差异表达信息。使用长读长特有的预处理工具组合以及用于下游分析的既定短读长RNA测序方法，分析了合成的、剪接的、掺入的RNA（“测序标准品”）数据集以及来自表观遗传调控因子无效突变样本的小鼠神经干细胞数据集。我们使用 进行差异基因表达分析，并使用新颖的 流程进行异构体鉴定和定量，随后使用 和 （搭配 ）进行差异转录本使用分析。我们将测序标准品数据集的结果与真实情况进行比较，并将小鼠数据集的结果与之前对等效样本的短读长研究结果进行比较。总体而言，我们的工作表明，使用长读长特有的预处理方法以及已广泛使用的短读长差异表达方法和软件对长读长纳米孔数据进行转录组分析可以产生有意义的结果。
