Evaluation of Bioinformatics Approaches for Next-Generation Sequencing Analysis of microRNAs with a Toxicogenomics Study Design

Abstract

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.

Evaluation and application of tools for the identification of known microRNAs in plants

“…These tools include various types of algorithms and functions, with approximately 77% having been developed for the study of miRNAs in animals rather than plants (Akhtar et al, 2015; Morgado and Johannes, 2019). Additionally, most comprehensive tests and evaluations of these tools have been performed on animals (Li et al, 2012; Bisgin et al, 2018) and are lacking in plants (Srivastava, 2014). In particular, the detection of known miRNAs in species with varying genome sizes has not yet been conducted, making it difficult for researchers to select optimal software (Fig.…”

“…Most previous studies evaluating miRNA analysis software have focused on running time, sensitivity, and accuracy (Li et al, 2012; Srivastava, 2014; Bisgin et al, 2018; Ou et al, 2019). In this study, we not only evaluated the eight tools in terms of these three factors, but also compared the number of known miRNAs predicted by each of them, as well as the maximum memory (random access memory [RAM]) cost when the software is running.…”

Comparison of RNA-Seq and Microarray Gene Expression Platforms for the Toxicogenomic Evaluation of Liver From Short-Term Rat Toxicity Studies

“…This implies that RNA-Seq can help identify more differentially modulated transcripts of toxicological relevance, splice variants, and non-coding transcripts [e.g., microRNA (miRNA), long non-coding RNA (lncRNA), pseudogenes] and that these additional data may be informative for toxicity prediction, mechanistic investigations or biomarker discovery (Wang et al, 2010; Iyer et al, 2015; Li et al, 2015; Yan et al, 2015). Due to these advantages and general advancement of the field, there has been an increasing interest in using RNA-Seq platforms for toxicogenomic studies (Chen et al, 2012; Bisgin et al, 2018).…”

Cell‐cycle dependence of transcriptome gene modules: comparison of regression lines

“…Their analysis, which should convert the heaps of numbers into biological meaning, is a challenging task. The bioinformatic stage is believed to be a bottleneck of transcriptomic studies [1][2][3]. A usual dimension-reduction step is enrichment analysis of differentially expressed genes (DEG) in molecular pathways, Gene Ontology (GO) categories, network clusters, and other gene signatures (gene sets related to various cell features).…”