Abstract
The computational prediction of novel microRNAs (miRNAs) within a full genome involves identifying sequences having the highest chance of being bona fide miRNA precursors (pre-miRNAs). These sequences are usually named candidates to miRNA. The well-known pre-miRNAs are usually only a few in comparison to the hundreds of thousands of potential candidates to miRNA that have to be analyzed. Although the selection of positive labeled examples is straightforward, it is very difficult to build a set of negative examples in order to obtain a good set of training samples for a supervised method. In this chapter we describe an approach to this problem, based on the unsupervised clustering of unlabeled sequences from genome-wide data, and the well-known miRNA precursors for the organism under study. Therefore, the protocol developed allows for quick identification of the best candidates to miRNA as those sequences clustered together with known precursors.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Command-line examples for Ubuntu Linux.
- 2.
A modified version of the script is also provided in the utils folder of miRNA-SOM version 23.
- 3.
References
Bartel DP (2004) MicroRNAs: genomics, biogenesis, mechanism, and function. Cell 116:281–297
Esquela-Kerscher A, Slack FJ (2006) Oncomirs - microRNAs with a role in cancer. Nat Rev Cancer 6(1):259–269
Lecellier CH, Dunoyer P, Arar K, Lehmann-Che J, Eyquem S, Himber C, Saib A, Voinnet O (2005) A cellular MicroRNA mediates antiviral defense in human cells. Science 308(5721):557–560
Rosenzvit M, Cucher M, Kamenetzky L, Macchiaroli N, Prada L, Camicia F (2013) MicroRNAs in endoparasites. Nova Science Publishers, New York
Li L, Xu J, Yang D, Tan X, Wang H (2010) Computational approaches for microRNA studies: a review. Mamm Genome 21(1):1–12
Lopes I de ON, Schliep A, de Carvalho A (2014) The discriminant power of RNA features for pre-miRNA recognition. BMC Bioinformatics 15(1):124+
Liu B, Li J, Cairns M (2014) Identifying mirnas, targets and functions. Brief Bioinform 15(1):1–19
Lorenz R, Bernhart S, zu Siederdissen CH, Tafer H, Flamm C, Stadler P, Hofacker I (2011) ViennaRNA Package 2.0. Algorithms Mol Biol 6(1):26–36
Xue C, Li F, He T, Liu GP, Li Y, Zhang X (2005) Classification of real and pseudo microRNA precursors using local structure-sequence features and support vector machine. BMC Bioinform 6(1):310
Zuker M, Stiegler P (1981) Optimal computer folding of large RNA sequences using thermodynamics and auxiliary information. Nucleic Acids Res 9(1):133–148
Yones C, Stegmayer G, Kamenetzky L, Milone D (2015) miRNAfe: a comprehensive tool for feature extraction in microRNA prediction. BioSystems 238:1–5
Altschul S, Gish W, Miller W, Myers E, Lipman D (1990) Basic local alignment search tool. J Mol Biol 215(1):403–410
Kamenetzky L, Stegmayer G, Maldonado L, Macchiaroli N, Yones C, Milone D (2016) MicroRNA discovery in the human parasite echinococcus multilocularis from genome-wide data. Genomics 107(6):274–280
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer Science+Business Media LLC
About this protocol
Cite this protocol
Stegmayer, G., Yones, C., Kamenetzky, L., Macchiaroli, N., Milone, D.H. (2017). Computational Prediction of Novel miRNAs from Genome-Wide Data. In: Kaufmann, M., Klinger, C., Savelsbergh, A. (eds) Functional Genomics. Methods in Molecular Biology, vol 1654. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7231-9_3
Download citation
DOI: https://doi.org/10.1007/978-1-4939-7231-9_3
Published:
Publisher Name: Humana Press, New York, NY
Print ISBN: 978-1-4939-7230-2
Online ISBN: 978-1-4939-7231-9
eBook Packages: Springer Protocols