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  Computational modeling of microRNA Biogenesis

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.

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Caffrey.pdf (Publisher version), 439KB
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© 2015 Springer International Publishing AG. Part of Springer Nature
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 Creators:
Caffrey, Brian1, Author           
Marsico, Annalisa1, Author           
Affiliations:
1RNA Bioinformatics (Annalisa Marsico), Independent Junior Research Groups (OWL), Max Planck Institute for Molecular Genetics, Max Planck Society, ou_2117285              

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Free keywords: Mirna regulation, Promoter prediction, Mirna processing, Gene regulatory networks
 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.

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Language(s): eng - English
 Dates: 2015
 Publication Status: Published in print
 Pages: 14
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1007/978-3-319-23497-7_6
 Degree: -

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Title: Mathematical Models in Biology
Source Genre: Book
 Creator(s):
Zazzu, Valeria, Editor
Ferraro, Maria Brigida, Editor
Guarracino , Mario R., Author
Affiliations:
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Publ. Info: Springer International Publishing
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 85 - 98 Identifier: ISBN: 978-3-319-23496-0