Computational Challenges in miRNA Target Predictions: To Be or Not to Be a True Target?

Barbato, Christian; Arisi, Ivan; Frizzo, Marcos Emílio dos Santos; Brandi, Rossella; Sacco, Letizia Da; Masotti, Andrea

doi:10.1155/2009/803069

Cited by 66 publications

(69 citation statements)

References 48 publications

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“…Measuring target prediction performance has been recently addressed in few literature reviews. Most of these reviews compared target prediction approaches either from algorithmic point of view [1,13], or using the estimated false positive rates [12] or using small numbers of experimentally validated miRNA targets [21]. However, using only false positive or true positive rates is not sufficient to indicate the prediction performance.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

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Comparison and Integration of Target Prediction Algorithms for microRNA Studies

Zhang

Verbeek

2010

Journal of Integrative Bioinformatics

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SummarymicroRNAs are short RNA fragments that have the capacity of regulating hundreds of target gene expression. Currently, due to lack of high-throughput experimental methods for miRNA target identification, a collection of computational target prediction approaches have been developed. However, these approaches deal with different features or factors are weighted differently resulting in diverse range of predictions. The prediction accuracy remains uncertain. In this paper, three commonly used target prediction algorithms are evaluated and further integrated using algorithm combination, ranking aggregation and Bayesian Network classification. Our results revealed that each individual prediction algorithm displays its advantages as was shown on different test data sets. Among different integration strategies, the application of Bayesian Network classifier on the features calculated from multiple prediction methods significantly improved target prediction accuracy.

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Section: Conclusion and Discussionmentioning

confidence: 99%

“…microRNAs (miRNAs) are a class of novel post-transcriptional gene expression regulators which are involved in a variety of developmental, physiological or disease-associated cellular processes [1]. They bind to their targets, messenger RNAs (mRNAs).…”

Section: Introductionmentioning

confidence: 99%

Comparison and Integration of Target Prediction Algorithms for microRNA Studies

Zhang

Verbeek

2010

Journal of Integrative Bioinformatics

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“…10,11 Often a single approach is not sufficient to give a comprehensive picture of hepatic dysfunctions occurring in NAFLD, and somewhat, a multiple-approach combination of different techniques is recommended. 12 Calvert et al 13 showed a good example of integration of system biology with reverse-phase protein microarray analysis to define genetic architecture of NAFLD. Very recently, also microRNA (miRNA) expression profiling has provided important insights into molecular mechanisms involved in the development of NAFLD.…”

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confidence: 99%

Mirnome analysis reveals novel molecular determinants in the pathogenesis of diet-induced nonalcoholic fatty liver disease

Alisi

Sacco

Bruscalupi

et al. 2011

Laboratory Investigation

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Nonalcoholic fatty liver disease (NAFLD) is an emerging disease with a broad spectrum of liver conditions. The complex molecular pathogenesis of NAFLD is still unclear. In this study, we conducted an analysis of microRNA (miRNA) expression profiles in liver of rats made NAFLD by different diets. To this aim, Sprague-Dawley rats were fed ad libitum for 3 months with different diets: standard diet (SD), diet enriched in fats and low in carbohydrates (HFD), SD with high fructose (SD-HF) and diet with high levels of fats and fructose (HFD-HF). Our results demonstrated that the treatment with different dietetic regimens caused a significant increase of the body weight and the alteration of some metabolic parameters compared with control animals, as well as various liver injuries. The miRNAs analysis showed the significant downregulation of three miRNAs (miR-122, miR-451 and miR-27) and the upregulation of miR-200a, miR-200b and miR-429 in HFD, SD-HF and HFD-HF rats. Besides, miR-21 expression was significantly decreased only in fructose-enriched diets. These miRNAs target molecules involved in the control of lipid and carbohydrate metabolism, signal transduction, cytokine and chemokine-mediated signaling pathway and apoptosis. Western blot analysis of PKCd, LITAF, ALDOLASE-A, p38MAPK, PTEN, LIPIN1, EPHRIN-A1, EPHA2 and FLT1 showed a diet-induced deregulation of all these proteins. Interestingly, the expression pattern of LITAF, PTEN, LIPIN1, EPHRIN-A1, EPHA2 and FLT1 might be well explained by the trend of their specific mRNAs, by potentially regulatory miRNAs, or both. In conclusion, we highlight for the first time the potential involvement of novel determinants (miRNAs and proteins) in the molecular pathogenesis of diet-induced NAFLD.

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“…Various computational algorithms are currently used to predict the target genes of microRNAs. However, since a single microRNA can directly target Ͼ200 genes and each mRNA may be regulated by several microRNAs (6,40), computational challenges in microRNA-mediated regulation persist. As a result, there have been several approaches taken to analyze microRNA and gene expression data, such as performing cluster analysis or computing correlation coefficients for microRNA and mRNA target expression (34,37,50,68).…”

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confidence: 99%