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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Guo, Z.: Genome-wide survey of tissue-specific microRNA and transcription factor regulatory networks in 12 tissues. Sci. Rep. 4, 5150 (2014)
Davis, B.N.: Regulation of MicroRNA biogenesis: a miRiad of mechanisms. Cell Commun. Signal 10, 7–18 (2014)
Bartel, D.P.: MicroRNAs: target recognition and regulatory functions. Cell 136, 215–233 (2009)
Plaisier, C.L.: A miRNA-regulatory network explains how dysregulated miRNAs perturb oncogenic processes across diverse cancers. Genome Res. 22, 2302–2314 (2012)
Esquela-Kerscher, A.: Oncomirs - microRNAs with a role in cancer. Nat. Rev. Cancer 6, 259–269 (2006)
Takahashi, R.U.: The role of microRNAs in the regulation of cancer stem cells. Front Genet 4, 295 (2014)
Davidson-Moncada, J.: MicroRNAs of the immune system: roles in inflammation and cancer. Ann. N. Y. Acad. Sci. 1183, 183–194 (2010)
Ma, X.: MicrorNAs in NF-kappaB signaling. J. Mol. Cell Biol. 3, 159–166 (2011)
Lewis, B.P.: Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. Cell 120, 15–20 (2005)
Betel, D.: Comprehensive modeling of microRNA targets predicts functional non-conserved and non-canonical sites. Genome Biol. 11, R90 (2010)
Sandelin, A.: JASPAR: an open-access database for eukaryotic transcription factor binding profiles Nucl. Acids Res. 32 D91–D94 (2004)
Krol, J.: The widespread regulation of microRNA biogenesis, function and decay. Nat. Rev. Genet. 11, 597–610 (2010)
Fickett, J.: Eukaryotic promoter recognition. Genome Res. 7, 861–878 (1997)
Marsico, A.: PROmiRNA: a new miRNA promoter recognition method uncovers the complex regulation of intronic miRNAs. Genome Biol. 14, R84 (2013)
Monteys, A.M.: Structure and activity of putative intronic miRNA promoters. RNA 16, 495–505 (2010)
Hinske, L.C.: A potential role for intragenic miRNAs on their hosts’ interactome. BMC Genomics 11, 533 (2010)
Fujita, S.: Putative promoter regions of miRNA genes involved in evolutionarily conserved regulatory systems among vertebrates. Bioinformatics 24, 303–308 (2008)
Kozomara, A.: miRBase: integrating microRNA annotation and deep-sequencing data. Nucleic Acids Res. 39, D152-D157 (2011)
Kozomara, A.: ChIP-seq: advantages and challenges of a maturing technology. Nat. Rev. Genet. 10, 669–680 (2009)
de Hoon, M.: Deep cap analysis gene expression (CAGE): genome-wide identification of promoters, quantification of their expression, and network inference. Biotechniques 44, 627–628 (2008)
Core, L.J.: Nascent RNA sequencing reveals widespread pausing and divergent initiation at human promoters. Science 322, 1845–1848 (2008)
Georgakilas, G.: microTSS: accurate microRNA transcription start site identification reveals a significant number of divergent pri-miRNAs. Nat. Commun. 10, 5700 (2014)
Barski, A.: Connecting microRNA genes to the core transcriptional regulatory circuitry of embryonic stem cells. Cell 134, 521–533 (2008)
Barski, A.: Chromatin poises miRNA- and protein-coding genes for expression. Genome Res. 19, 1742–1751 (2009)
Ozsolak, F.: Chromatin structure analyses identify miRNA promoters. Gene Dev. 22, 3172–3183 (2008)
Chien, C.H.: Identifying transcriptional start sites of human microRNAs based on high-throughput sequencing data. Nucleic Acids Res. 39, 9345–9356 (2011)
Megraw, M.: A transcription factor affinity-based code for mammalian transcription initiation. Genome Res. 19, 644–656 (2009)
Eis, P.: Accumulation of miR-155 and BIC RNA in human B cell lymphomas. Proc. Natl. Acad. Sci. 102, 3627–3632 (2003)
Thomas-Chollier, M.: Transcription factor binding predictions using TRAP for the analysis of ChIP-seq data and regulatory SNPs. Nat. Protoc. 102, 3627–3632 (2003)
Uhlen, M.: Tissue-based map of the human proteome. Science 347, 1260419 (2015)
Szklarczyk, D.: The STRING database in 2011: functional interaction networks of proteins, globally integrated and scored. Nucleic Acid Res. 39, D561–568 (2011)
Ha, M.: Regulation of microRNA biogenesis. Nat. Rev. Mol. Cell Biol. 15, 509–524 (2014)
Auyeung, V.C.: Beyond secondary structure: primary-sequence determinants license pri-miRNA hairpins for processing. Cell 152, 844–858 (2013)
Conrad, T.: Microprocessor activity controls differential miRNA biogenesis in vivo. Cell Rep. 9, 542–554 (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Caffrey, B., Marsico, A. (2015). Computational Modeling of miRNA Biogenesis. In: Zazzu, V., Ferraro, M., Guarracino, M. (eds) Mathematical Models in Biology. Springer, Cham. https://doi.org/10.1007/978-3-319-23497-7_6
Download citation
DOI: https://doi.org/10.1007/978-3-319-23497-7_6
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-23496-0
Online ISBN: 978-3-319-23497-7
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)