Interferon inducible pseudouridine modification in human mRNA by quantitative nanopore profiling

Pseudouridine (Ψ) is an abundant mRNA modification in mammalian transcriptome, but its functions have remained elusive due to the difficulty of transcriptome-wide mapping. We develop a nanopore native RNA sequencing method for quantitative Ψ prediction (NanoPsu) that utilizes native content training, machine learning modeling, and single-read linkage analysis. Biologically, we find interferon inducible Ψ modifications in interferon-stimulated gene transcripts which are consistent with a role of Ψ in enabling efficacy of mRNA vaccines.

A convNet based multi label microRNA sub cellular location predictor, by incorporating k-mer positional encoding

ABSTRACTMicroRNAs are special RNA sequences containing 22 nucleotides and are capable of regulating almost 60% of highly complex mammalian transcriptome. Presently, there exists very limited approaches capable of visualizing miRNA locations inside cell to reveal the hidden pathways, and mechanisms behind miRNA functionality, transport, and biogenesis. State-of-the-art miRNA sub-cellular location prediction MIRLocatar approach makes use of sequence to sequence model along with pre-train k-mer embeddings. Existing pre-train k-mer embedding generation methodologies focus on the extraction of semantics of k-mers. In RNA sequences, rather than semantics, positional information of nucleotides is more important because distinct positions of four basic nucleotides actually define the functionality of RNA molecules. Considering the dynamicity and importance of nucleotides positions, instead of learning representation on the basis of k-mers semantics, we propose a novel kmerRP2vec feature representation approach that fuses positional information of k-mers to randomly initialized neural k-mer embeddings. Effectiveness of proposed feature representation approach is evaluated with two deep learning based convolutional neural network CNN and recurrent neural network RNN methodologies using 8 evaluation measures. Experimental results on a public benchmark miRNAsubloc dataset prove that proposed kmerRP2vec approach along with a simple CNN model outperforms state-of-the-art MirLocator approach with a significant margin of 18% and 19% in terms of precision and recall.

Integrated analysis of directly captured microRNA targets reveals the impact of microRNAs on mammalian transcriptome

MicroRNA (miRNA)-mediated regulation is widespread, relatively mild but functionally important. Despite extensive efforts to identify miRNA targets, it remains unclear how miRNAs bind to mRNA targets globally and how changes in miRNA levels affects the transcriptome. Here we apply an optimized method for simultaneously capturing miRNA and targeted RNA sites to wildtype, miRNA knockout and induced epithelial cells. We find that abundantly expressed miRNAs can bind to thousands of different transcripts and many different miRNAs can regulate the same gene. Although mRNA sites that are bound by miRNAs and also contain matches to seed sequences confer the strongest regulation, ∼50% of miRNAs bind to RNA regions without seed matches. In general, these bindings have little impact on mRNA levels and reflect a scanning activity of miRNAs. In addition, different miRNAs have different preferences to seed matches and 3’end base-pairing. For a single miRNA, the effectiveness of mRNA regulation is highly correlated with the number of captured miRNA:RNA fragments. Notably, elevated miRNA expression effectively represses existing targets with little impact on newly recognized targets. Global analysis of directly captured mRNA targets reveals pathways that are involved in cancer, cell adhesion and signaling pathways are highly regulated by many different miRNAs in epithelial cells. Comparison between experimentally captured and TargetScan predicted targets indicates that our approach is more effective to identify bona fide targets by reducing false positive and negative predictions. This study reveals the global binding landscape and impact of miRNAs on mammalian transcriptome.