RNA Bioinformatics pp 207-229

Part of the Methods in Molecular Biology book series (MIMB, volume 1269) | Cite as

Prediction of miRNA Targets

  • Anastasis Oulas
  • Nestoras Karathanasis
  • Annita Louloupi
  • Georgios A. Pavlopoulos
  • Panayiota Poirazi
  • Kriton Kalantidis
  • Ioannis Iliopoulos

Abstract

Computational methods for miRNA target prediction are currently undergoing extensive review and evaluation. There is still a great need for improvement of these tools and bioinformatics approaches are looking towards high-throughput experiments in order to validate predictions. The combination of large-scale techniques with computational tools will not only provide greater credence to computational predictions but also lead to the better understanding of specific biological questions. Current miRNA target prediction tools utilize probabilistic learning algorithms, machine learning methods and even empirical biologically defined rules in order to build models based on experimentally verified miRNA targets. Large-scale protein downregulation assays and next-generation sequencing (NGS) are now being used to validate methodologies and compare the performance of existing tools. Tools that exhibit greater correlation between computational predictions and protein downregulation or RNA downregulation are considered the state of the art. Moreover, efficiency in prediction of miRNA targets that are concurrently verified experimentally provides additional validity to computational predictions and further highlights the competitive advantage of specific tools and their efficacy in extracting biologically significant results. In this review paper, we discuss the computational methods for miRNA target prediction and provide a detailed comparison of methodologies and features utilized by each specific tool. Moreover, we provide an overview of current state-of-the-art high-throughput methods used in miRNA target prediction.

Key words

MiRNA target prediction Computational tools Databases High-throughput methods Biological features of miRNA target recognition 

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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Anastasis Oulas
    • 1
  • Nestoras Karathanasis
    • 2
    • 3
  • Annita Louloupi
    • 4
  • Georgios A. Pavlopoulos
    • 5
  • Panayiota Poirazi
    • 2
  • Kriton Kalantidis
    • 2
    • 3
  • Ioannis Iliopoulos
    • 5
    • 6
  1. 1.Institute of Marine Biology, Biotechnology and Aquaculture-HCMRHeraklionGreece
  2. 2.Institute of Molecular Biology and Biotechnology-FORTHHeraklionGreece
  3. 3.Department of BiologyUniversity of CreteHeraklionGreece
  4. 4.Biomedical Science-Medical BiologyUniversity of AmsterdamAmsterdamNetherlands
  5. 5.Division of Basic SciencesUniversity of Crete Medical SchoolHeraklionGreece
  6. 6.Faculty of Medicine, Division of Basic SciencesUniversity of CreteHeraklionGreece

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