Overview of Computational and Experimental Methods to Identify Tissue-Specific MicroRNA Targets

Schmitz, Ulf (2023) Overview of Computational and Experimental Methods to Identify Tissue-Specific MicroRNA Targets. In: Dalmay, Tamas, (ed.) MicroRNA Detection and Target Identification: Methods and Protocols. Methods in Molecular Biology, 2630 . Humana Press, New York, NY, USA, pp. 155-177.

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As ubiquitous posttranscriptional regulators of gene expression, microRNAs (miRNAs) play key roles in cell physiology and function across taxa. In the last two decades, we have gained a good understanding about miRNA biogenesis pathways, modes of action, and consequences of miRNA-mediated gene regulation. More recently, research has focused on exploring causes for miRNA dysregulation, miRNA-mediated crosstalk between genes and signaling pathways, and the role of miRNAs in disease.This chapter discusses methods for the identification of miRNA-target interactions and causes for tissue-specific miRNA-target regulation. Computational approaches for predicting miRNA target sites and assessing tissue-specific target regulation are discussed. Moreover, there is an emphasis on features that affect miRNA target recognition and how high-throughput sequencing protocols can help in assessing miRNA-mediated gene regulation on a genome-wide scale. In addition, this chapter introduces some experimental approaches for the validation of miRNA targets as well as web-based resources sharing predicted and validated miRNA-target interactions.

Item ID: 78554
Item Type: Book Chapter (Research - B1)
ISBN: 978-1-0716-2982-6
ISSN: 1940-6029
Keywords: CLIP-Seq, Machine learning, MiRNA biogenesis, MiRNA cooperativity, Regulatory motifs, Synergistic target regulation, Systems biology
Copyright Information: © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2017, 2023.
Date Deposited: 28 May 2024 04:38
FoR Codes: 31 BIOLOGICAL SCIENCES > 3102 Bioinformatics and computational biology > 310204 Genomics and transcriptomics @ 70%
31 BIOLOGICAL SCIENCES > 3101 Biochemistry and cell biology > 310114 Systems biology @ 30%
SEO Codes: 28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280102 Expanding knowledge in the biological sciences @ 100%
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