Computational Modeling of miRNA Biogenesis

Brian Caffrey () and Annalisa Marsico ()
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Brian Caffrey: Max Planck Institute for Molecular Genetics
Annalisa Marsico: Max Planck Institute for Molecular Genetics

A chapter in Mathematical Models in Biology, 2015, pp 85-98 from Springer

Abstract: Abstract Over the past few years it has been observed, thanks in no small part to high-throughput methods, that a large proportion of the human genome is transcribed in a tissue- and time-specific manner. Most of the detected transcripts are non-coding RNAs and their functional consequences are not yet fully understood. Among the different classes of non-coding transcripts, microRNAs (miRNAs) are small RNAs that post-transcriptionally regulate gene expression. Despite great progress in understanding the biological role of miRNAs, our understanding of how miRNAs are regulated and processed is still developing. High-throughput sequencing data have provided a robust platform for transcriptome-level, as well as gene-promoter analyses. In silico predictive models help shed light on the transcriptional and post-transcriptional regulation of miRNAs, including their role in gene regulatory networks. Here we discuss the advances in computational methods that model different aspects of miRNA biogeneis, from transcriptional regulation to post-transcriptional processing. In particular, we show how the predicted miRNA promoters from PROmiRNA, a miRNA promoter prediction tool, can be used to identify the most probable regulatory factors for a miRNA in a specific tissue. As differential miRNA post-transcriptional processing also affects gene-regulatory networks, especially in diseases like cancer, we also describe a statistical model proposed in the literature to predict efficient miRNA processing from sequence features.

Keywords: Mirna regulation; Promoter prediction; Mirna processing; Gene regulatory networks (search for similar items in EconPapers)
Date: 2015
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-23497-7_6

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DOI: 10.1007/978-3-319-23497-7_6

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