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Turning Data to Knowledge: Online Tools, Databases, and Resources in microRNA Research

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Systems Biology of MicroRNAs in Cancer

Part of the book series: Advances in Experimental Medicine and Biology ((AEMB,volume 1385))

Abstract

MicroRNAs (miRNAs) provide a fundamental layer of regulation in cells. miRNAs act posttranscriptionally through complementary base-pairing with the 3′-UTR of a target mRNA, leading to mRNA degradation and translation arrest. The likelihood of forming a valid miRNA-target duplex within cells was computationally predicted and experimentally monitored. In human cells, the miRNA profiles determine their identity and physiology. Therefore, alterations in the composition of miRNAs signify many cancer types and chronic diseases. In this chapter, we introduce online functional tools and resources to facilitate miRNA research. We start by introducing currently available miRNA catalogs and miRNA-gateway portals for navigating among different miRNA-centric online resources. We then sketch several realistic challenges that may occur while investigating miRNA regulation in living cells. As a showcase, we demonstrate the utility of miRNAs and mRNAs expression databases that cover diverse human cells and tissues, including resources that report on genetic alterations affecting miRNA expression levels and alteration in binding capacity. Introducing tools linking miRNAs with transcription factor (TF) networks reveals miRNA regulation complexity within living cells. Finally, we concentrate on online resources that analyze miRNAs in human diseases and specifically in cancer. Altogether, we introduce contemporary, selected resources and online tools for studying miRNA regulation in cells and tissues and their utility in health and disease.

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Abbreviations

CAGE:

Cap-based expression analysis

ceRNA:

Competing endogenous RNA

ChIP:

Chromatin immunoprecipitation

circRNA:

Circular RNA

CLASH:

Cross linking, ligation and sequencing of hybrids

CLIP:

Cross-linking immunoprecipitation

CNV:

Copy number variation

DRV:

Disease-related variation

FFL:

Feed-forward loop

GEO:

Gene expression omnibus

GO:

Gene ontology

GWAS:

Genome wide association study

HTP:

High throughput

KEGG:

Kyoto encyclopedia of genes and genomes

lncRNA:

Long non-coding RNAs

LTP:

Low throughput

MBS:

miRNA-binding sites

miRNA:

microRNA

ML:

Machine learning

mRNA:

Messenger RNA

MS:

Mass spectrometry

MTI:

miRNA-target interaction

RISC:

RNA-induced silencing complex

RPM:

Reads per million

Seq:

Sequencing

smRNA:

Small RNA

SNV:

Single nucleotide variation

SVM:

Support vector machine

TCGA:

The Cancer Genome Atlas

TF:

Transcription factor

TFBS:

TF binding sites

TSS:

Transcription start sites

UTR:

Untranslated region

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Authors and Affiliations

  1. The Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel

    Ido Blass

  2. Department of Biological Chemistry, Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel

    Keren Zohar & Michal Linial

Authors
  1. Ido Blass
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  2. Keren Zohar
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  3. Michal Linial
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Corresponding author

Correspondence to Michal Linial .

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Editors and Affiliations

  1. Department of Molecular and Cell Biology, Centre for Tropical Bioinformatics and Molecular Biology, James Cook University, Townsville, QLD, Australia

    Ulf Schmitz

  2. Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, Germany

    Olaf Wolkenhauer

  3. Department of Dermatology, Friedrich-Alexander-Universität Erlangen-Nürnberg and University Hospital Erlangen, Erlangen, Bayern, Germany

    Julio Vera-González

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Blass, I., Zohar, K., Linial, M. (2022). Turning Data to Knowledge: Online Tools, Databases, and Resources in microRNA Research. In: Schmitz, U., Wolkenhauer, O., Vera-González, J. (eds) Systems Biology of MicroRNAs in Cancer. Advances in Experimental Medicine and Biology, vol 1385. Springer, Cham. https://doi.org/10.1007/978-3-031-08356-3_5

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