mirTarRnaSeq: An R/Bioconductor Statistical Package for miRNA-mRNA Target Identification and Interaction Analysis

Abstract

We introduce mirTarRnaSeq, an R/Bioconductor package for quantitative assessment of miRNA-mRNA relationships within sample cohorts. mirTarRnaSeq is a statistical package to explore predicted or pre-hypothesized miRNA-mRNA relationships following target prediction.We present two use cases applying mirTarRnaSeq. First, to identify miRNA targets, we examined EBV miRNAs for interaction with human and virus transcriptomes of stomach adenocarcinoma. This revealed enrichment of mRNA targets highly expressed in CD105+ endothelial cells, monocytes, CD4+ T cells, NK cells, CD19+ B cells, and CD34 cells. Next, to investigate miRNA-mRNA relationships in SARS-CoV-2 (COVID-19) infection across time, we used paired miRNA and RNA sequenced datasets of SARS-CoV-2 infected lung epithelial cells across three time points (4, 12, and 24 hours post-infection). mirTarRnaSeq identified evidence for human miRNAs targeting cytokine signaling and neutrophil regulation immune pathways from 4 to 24 hours after SARS-CoV-2 infection. Confirming the clinical relevance of these predictions, three of the immune specific mRNA-miRNA relationships identified in human lung epithelial cells after SARS-CoV-2 infection were also observed to be differentially expressed in blood from patients with COVID-19. Overall, mirTarRnaSeq is a robust tool that can address a wide-range of biological questions providing improved prediction of miRNA-mRNA interactions.

Identifying Key Regulators of Keratinization in Lung Squamous Cell Cancer Using Integrated TCGA Analysis

“…Then, we performed differential expression analyses (DEA) of the transcriptomic, methylomic, and miRNA profiles of these groups. Finally, we identified the key regulatory genes, transcription factors (TFs), and miRNAs using network analysis [ 9 , 13 ], the ELMER algorithm [ 14 ], and miRNA–mRNA correlation analysis [ 11 ], respectively.…”

“…The absolute log fold change ≥1 and FDR < 0.01 were used as the thresholds to select significantly differentially expressed miRNAs. We inputted the log fold change of significant differentially expressed genes from previous DEG analysis and miRNAs to the R Bioconductor package mirTarRnaSeq to identify some significant miRNA–mRNA correlations [ 11 ]. The mirTarRnaSeq approach estimated the difference between the miRNA–mRNA fold change, followed by the generation of a background distribution that represents random differences in fold chance.…”

“…We categorized the samples based on their keratinization signature scores. Then, we compared the groups using three different workflows: the differentially expressed gene network [ 9 ], Enhancer Linking by Methylation/Expression Relationships (ELMER) [ 10 ], and mirTarRNASeq [ 11 ] to identify the important genes, binding motifs, transcription factors, and miRNAs involved in the LUSC keratinization process. The results of our study may provide a basis for the identification of novel biomarkers and facilitate a deeper understanding of the LUSC keratinization process.…”

Osteoclast-Derived Exosomal miR-5134-5p Interferes with Alveolar Bone Homeostasis by Targeting the JAK2/STAT3 Axis

“…Micro-ribonucleic acids (RNAs) (miRNAs/miRs) are non-coding single-stranded RNAs comprising approximately 22 nucleotides that can identify and band to the 3'-untranslated region of target messenger RNAs (mRNAs) and reverse their expression by incomplete base pairing. [6][7][8][9] However, miRNAs are susceptible to degradation owing to their instability in the extracellular space. The exosomes incorporate, transfer, stabilize, and protect themselves from degradation.…”