English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Book Chapter

Computational modeling of microRNA Biogenesis

MPS-Authors
/persons/resource/persons194969

Caffrey,  Brian
RNA Bioinformatics (Annalisa Marsico), Independent Junior Research Groups (OWL), Max Planck Institute for Molecular Genetics, Max Planck Society;

/persons/resource/persons50424

Marsico,  Annalisa
RNA Bioinformatics (Annalisa Marsico), Independent Junior Research Groups (OWL), Max Planck Institute for Molecular Genetics, Max Planck Society;

External Resource
No external resources are shared
Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)

Caffrey.pdf
(Publisher version), 439KB

Supplementary Material (public)
There is no public supplementary material available
Citation

Caffrey, B., & Marsico, A. (2015). Computational modeling of microRNA Biogenesis. In V. Zazzu, & M. B. Ferraro (Eds.), Mathematical Models in Biology (pp. 85-98). Springer International Publishing.


Cite as: https://hdl.handle.net/11858/00-001M-0000-002D-4D5F-7
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.