Computational and experimental identification of tissue-specific MicroRNA targets

Amirkhah, Raheleh, Meshkin, Hojjat Naderi, Farazmand, Ali, Rasko, John E.J., and Schmitz, Ulf (2017) Computational and experimental identification of tissue-specific MicroRNA targets. In: Dalmay, Tamas, (ed.) MicroRNA Detection and Target Identification: methods and protocols. Methods in Molecular Biology, 1580 . Humana Press Inc., New York, NY, USA, pp. 127-147.

[img] PDF (Accepted Author Manuscript) - Accepted Version
Restricted to Repository staff only

[img] PDF (Published Version) - Published Version
Restricted to Repository staff only

View at Publisher Website:


In this chapter we discuss computational methods for the prediction of microRNA (miRNA) targets. More specifically, we consider machine learning-based approaches and explain why these methods have been relatively unsuccessful in reducing the number of false positive predictions. Further we suggest approaches designed to improve their performance by considering tissue-specific target regulation. We argue that the miRNA targetome differs depending on the tissue type and introduce a novel algorithm that predicts miRNA targets specifically for colorectal cancer. We discuss features of miRNAs and target sites that affect target recognition, and how next-generation sequencing data can support the identification of novel miRNAs, differentially expressed miRNAs and their tissue-specific mRNA targets. In addition, we introduce some experimental approaches for the validation of miRNA targets as well as web-based resources sharing predicted and validated miRNA target interactions.

Item ID: 68985
Item Type: Book Chapter (Research - B1)
ISBN: 978-1-4939-6866-4
Keywords: MicroRNA; Computational target prediction; Machine learning; Next-generation sequencing; Cross-linking and immunoprecipitation
Copyright Information: © Springer Science+Business Media LLC 2017
Date Deposited: 13 Jul 2022 03:43
FoR Codes: 31 BIOLOGICAL SCIENCES > 3102 Bioinformatics and computational biology > 310201 Bioinformatic methods development @ 50%
49 MATHEMATICAL SCIENCES > 4905 Statistics > 490502 Biostatistics @ 50%
SEO Codes: 28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280102 Expanding knowledge in the biological sciences @ 100%
Downloads: Total: 9
More Statistics

Actions (Repository Staff Only)

Item Control Page Item Control Page