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Ripan, R. C.

Publications and source records attributed to Ripan, R. C..

2 recordsLinked to original sources

Novel features of miRNA and isomiR-mRNA interactions

Studying the interactions between microRNAs/isomiRs and mRNAs is critical due to their fundamental roles in gene regulation and their involvement in various diseases. Although many isomiRs have been identified, the analysis of their interactions with mRNAs remains in its early stages. In this study, we compiled available human chimeric read data, each comprising a microRNA or isomiR segment paired with an mRNA fragment and identified 1,747 isomiRs and over 5 million microRNA/isomiR-mRNA interactions. We observed that microRNAs with higher adenine and thymine content, and lower cytosine content, tend to have more isomiRs and more mRNA targets. Notably, an average of 18.9% of mRNA targets were bound exclusively by isomiRs, not by their microRNA counterparts. Furthermore, isomiRs sharing the same seed sequences as their reference microRNAs may bind to different targets from their microRNAs, highlighting functional divergence. Interestingly, 20.0% of microRNAs and 8.2% of isomiRs appear to bind mRNAs independently of their seed regions. Among those that do utilize seed regions, 94.5% of microRNAs and 95.7% of isomiRs also engage non-seed regions, suggesting a broader and more complex binding behavior. Our findings offer new insights into microRNA/isomiR-mRNA interactions.

bioinformatics↗

Deep learning inference of miRNA expression from bulk and single-cell mRNA expression

Understanding the activity of miRNA in individual cells presents a challenge due to the limitations of single-cell technologies in capturing miRNAs. To tackle this obstacle, we introduce two deep learning models: Cross-Modality (CM) and Single-Modality (SM). These models utilize encoder-decoder architectures to predict miRNA expression at the bulk and single-cell levels from mRNA data. We compared CM and SM with a state-of-the-art approach, miRSCAPE, using both bulk and single-cell datasets. We found that both CM and SM outperformed miRSCAPE in terms of accuracy. We also observed that integrating miRNA target information led to a significant enhancement in performance compared to using all genes. These models offer valuable tools for predicting miRNA expression from single-cell mRNA data.

bioinformatics↗