Bisgin Halil, Gong Binsheng, Wang Yuping, Tong Weida
Department of Computer Science, Engineering, and Physics, University of Michigan-Flint, Flint, MI, United States.
Division of Bioinformatics and Biostatistics, National Center for Toxicological Research (FDA), Jefferson, AR, United States.
Front Genet. 2018 Feb 6;9:22. doi: 10.3389/fgene.2018.00022. eCollection 2018.
MicroRNAs (miRNAs) are key post-transcriptional regulators that affect protein translation by targeting mRNAs. Their role in disease etiology and toxicity are well recognized. Given the rapid advancement of next-generation sequencing techniques, miRNA profiling has been increasingly conducted with RNA-seq, namely miRNA-seq. Analysis of miRNA-seq data requires several steps: (1) mapping the reads to miRBase, (2) considering mismatches during the hairpin alignment (windowing), and (3) counting the reads (quantification). The choice made in each step with respect to the parameter settings could affect miRNA quantification, differentially expressed miRNAs (DEMs) detection and novel miRNA identification. Furthermore, these parameters do not act in isolation and their joint effects impact miRNA-seq results and interpretation. In toxicogenomics, the variation associated with parameter setting should not overpower the treatment effect (such as the dose/time-dependent effect). In this study, four commonly used miRNA-seq analysis tools (i.e., miRDeep2, miRExpress, miRNAkey, sRNAbench) were comparatively evaluated with a standard toxicogenomics study design. We tested 30 different parameter settings on miRNA-seq data generated from thioacetamide-treated rat liver samples for three dose levels across four time points, followed by four normalization options. Because both miRExpress and miRNAkey yielded larger variation than that of the treatment effects across multiple parameter settings, our analyses mainly focused on the side-by-side comparison between miRDeep2 and sRNAbench. While the number of miRNAs detected by miRDeep2 was almost the subset of those detected by sRNAbench, the number of DEMs identified by both tools was comparable under the same parameter settings and normalization method. Change in the number of nucleotides out of the mature sequence in the hairpin alignment (window option) yielded the largest variation for miRNA quantification and DEMs detection. However, such a variation is relatively small compared to the treatment effect when the study focused on DEMs that are more critical to interpret the toxicological effect. While the normalization methods introduced a large variation in DEMs, toxic behavior of thioacetamide showed consistency in the trend of time-dose responses. Overall, miRDeep2 was found to be preferable over other choices when the window option allowed up to three nucleotides from both ends.
微小RNA（miRNA）是关键的转录后调节因子，通过靶向mRNA影响蛋白质翻译。它们在疾病病因学和毒性方面的作用已得到充分认识。鉴于下一代测序技术的快速发展，越来越多地使用RNA测序（即miRNA测序）进行miRNA谱分析。miRNA测序数据分析需要几个步骤：（1）将读数映射到miRBase，（2）在发夹比对过程中考虑错配（加窗），以及（3）计算读数（定量）。在每个步骤中关于参数设置所做的选择可能会影响miRNA定量、差异表达miRNA（DEM）检测和新miRNA鉴定。此外，这些参数并非孤立起作用，它们的联合效应会影响miRNA测序结果和解释。在毒理基因组学中，与参数设置相关的变异不应超过处理效应（如剂量/时间依赖性效应）。在本研究中，采用标准毒理基因组学研究设计对四种常用的miRNA测序分析工具（即miRDeep2、miRExpress、miRNAkey、sRNAbench）进行了比较评估。我们在硫代乙酰胺处理的大鼠肝脏样本生成的miRNA测序数据上测试了30种不同的参数设置，涉及四个时间点的三个剂量水平，随后采用四种标准化选项。由于miRExpress和miRNAkey在多个参数设置下产生的变异均大于处理效应，我们的分析主要集中在miRDeep2和sRNAbench的并列比较上。虽然miRDeep2检测到的miRNA数量几乎是sRNAbench检测到的miRNA数量的子集，但在相同参数设置和标准化方法下，两种工具鉴定出的DEM数量相当。发夹比对（加窗选项）中成熟序列外核苷酸数量的变化对miRNA定量和DEM检测产生的变异最大。然而，当研究聚焦于对解释毒理效应更为关键的DEM时，与处理效应相比，这种变异相对较小。虽然标准化方法在DEM中引入了较大变异，但硫代乙酰胺的毒性行为在时间 - 剂量反应趋势上表现出一致性。总体而言，当加窗选项允许两端最多三个核苷酸时，发现miRDeep2比其他选择更可取。
