Book contents
- Frontmatter
- Contents
- Contributors
- Introduction
- Part A Horizontal Meta-Analysis
- Part B Vertical Integrative Analysis (General Methods)
- Part C Vertical Integrative Analysis (Methods Specialized to Particular Data Types)
- 12 eQTL and Directed Graphical Model
- 13 MicroRNAs: Target Prediction and Involvement in Gene Regulatory Networks
- 14 Integration of Cancer Omics Data into a Whole-Cell Pathway Model for Patient-Specific Interpretation
- 15 Analyzing Combinations of Somatic Mutations in Cancer Genomes
- 16 A Mass-Action-Based Model for Gene Expression Regulation in Dynamic Systems
- 17 From Transcription Factor Binding and Histone Modification to Gene Expression: Integrative Quantitative Models
- 18 Data Integration on Noncoding RNA Studies
- 19 Drug-Pathway Association Analysis: Integration of High-Dimensional Transcriptional and Drug Sensitivity Profile
- Index
- Color plates
13 - MicroRNAs: Target Prediction and Involvement in Gene Regulatory Networks
from Part C - Vertical Integrative Analysis (Methods Specialized to Particular Data Types)
Published online by Cambridge University Press: 05 September 2015
- Frontmatter
- Contents
- Contributors
- Introduction
- Part A Horizontal Meta-Analysis
- Part B Vertical Integrative Analysis (General Methods)
- Part C Vertical Integrative Analysis (Methods Specialized to Particular Data Types)
- 12 eQTL and Directed Graphical Model
- 13 MicroRNAs: Target Prediction and Involvement in Gene Regulatory Networks
- 14 Integration of Cancer Omics Data into a Whole-Cell Pathway Model for Patient-Specific Interpretation
- 15 Analyzing Combinations of Somatic Mutations in Cancer Genomes
- 16 A Mass-Action-Based Model for Gene Expression Regulation in Dynamic Systems
- 17 From Transcription Factor Binding and Histone Modification to Gene Expression: Integrative Quantitative Models
- 18 Data Integration on Noncoding RNA Studies
- 19 Drug-Pathway Association Analysis: Integration of High-Dimensional Transcriptional and Drug Sensitivity Profile
- Index
- Color plates
Summary
Abstract
MicroRNAs (miRNAs), the small, noncoding RNA molecules, were first discovered by Victor Ambros in 1993 as negative regulators of C. elegans gene lin-4. They were later identified as key regulators of gene expression in plants and animals. Their role in disease mechanisms and disease prognosis and diagnosis as well as in the precise regulation of the developmental program of many animals has been undisputable. In this chapter, we discuss two key issues related to their function: (1)miRNA:mRNA targeting and (2) miRNA involvement in gene regulatory networks. We present the fundamental principles on which the major computational algorithms are based for predictingmiRNA targets and inferring miRNA-involving regulatory networks.
Introduction
MicroRNAs (miRNAs) are small, noncoding RNAs that act as regulators of gene expression. Their central role in development and disease [1] has been well established [1–3]. As biomarkers, miRNAs can be more accurate than mRNAs because they constitute the final, fully functional product and not some intermediate state. With the discovery that miRNAs are circulating in the blood plasma [4–6], there was an explosion of research in this area, the results of which tie miRNAs to cell-cell communication [7, 8] as well as diseases like myocardial injury [9] and cardiovascular diseases [10–12], pulmonary hypertension [13], interstitial fibrosis [14], and cancer [15–21], to name a few.
In the genome, they are found in the introns of protein coding genes or in the intergenic regions. They are transcribed by their own promoter or by the promoter of the host gene. Interestingly, as many as 25% of the intronic miRNAs have their own promoter too [22, 23].After transcription, they undergo a number of processing steps that result in a single-stranded, mature RNA of 20–22 nts, which is loaded to the Argonaute protein, an RNAse H enzyme. Functional miRNAs, located in small introns, have been also reported that bypass the initial step of miRNA processing [24–26]. In complex with the Argonaute protein miRNAs form duplexes with their targets on mRNAs via Watson- Crick RNA base pairing. The way the miRNAs are attached to Argonaute implies that their 5′-end sequence (especially nucleotides 2–7 or 2–8, also named “seed sequence”) initiates the binding, and near-perfect matching is required for this region [27].
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- Integrating Omics Data , pp. 291 - 309Publisher: Cambridge University PressPrint publication year: 2015