Dataset Information

Exprtarget: an integrative approach to predicting human microRNA targets.

ABSTRACT: Variation in gene expression has been observed in natural populations and associated with complex traits or phenotypes such as disease susceptibility and drug response. Gene expression itself is controlled by various genetic and non-genetic factors. The binding of a class of small RNA molecules, microRNAs (miRNAs), to mRNA transcript targets has recently been demonstrated to be an important mechanism of gene regulation. Because individual miRNAs may regulate the expression of multiple gene targets, a comprehensive and reliable catalogue of miRNA-regulated targets is critical to understanding gene regulatory networks. Though experimental approaches have been used to identify many miRNA targets, due to cost and efficiency, current miRNA target identification still relies largely on computational algorithms that aim to take advantage of different biochemical/thermodynamic properties of the sequences of miRNAs and their gene targets. A novel approach, ExprTarget, therefore, is proposed here to integrate some of the most frequently invoked methods (miRanda, PicTar, TargetScan) as well as the genome-wide HapMap miRNA and mRNA expression datasets generated in our laboratory. To our knowledge, this dataset constitutes the first miRNA expression profiling in the HapMap lymphoblastoid cell lines. We conducted diagnostic tests of the existing computational solutions using the experimentally supported targets in TarBase as gold standard. To gain insight into the biases that arise from such an analysis, we investigated the effect of the choice of gold standard on the evaluation of the various computational tools. We analyzed the performance of ExprTarget using both ROC curve analysis and cross-validation. We show that ExprTarget greatly improves miRNA target prediction relative to the individual prediction algorithms in terms of sensitivity and specificity. We also developed an online database, ExprTargetDB, of human miRNA targets predicted by our approach that integrates gene expression profiling into a broader framework involving important features of miRNA target site predictions.

SUBMITTER: Gamazon ER

PROVIDER: S-EPMC2958831 | biostudies-literature | 2010 Oct

REPOSITORIES: biostudies-literature

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Exprtarget: an integrative approach to predicting human microRNA targets.

Gamazon Eric R ER Im Hae-Kyung HK Duan Shiwei S Lussier Yves A YA Cox Nancy J NJ Dolan M Eileen ME Zhang Wei W

PloS one 20101021 10

Variation in gene expression has been observed in natural populations and associated with complex traits or phenotypes such as disease susceptibility and drug response. Gene expression itself is controlled by various genetic and non-genetic factors. The binding of a class of small RNA molecules, microRNAs (miRNAs), to mRNA transcript targets has recently been demonstrated to be an important mechanism of gene regulation. Because individual miRNAs may regulate the expression of multiple gene targe ...[more]

PMID: 20975837

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Project description:BACKGROUND: Elucidation of the direct/indirect protein interactions and gene associations is required to fully understand the workings of the cell. This can be achieved through the use of both low- and high-throughput biological experiments and in silico methods. We present GAP (Gene functional Association Predictor), an integrative method for predicting and characterizing gene functional associations. GAP integrates different biological features using a novel taxonomy-based semantic similarity measure in predicting and prioritizing high-quality putative gene associations. The proposed similarity measure increases information gain from the available gene annotations. The annotation information is incorporated from several public pathway databases, Gene Ontology annotations as well as drug and disease associations from the scientific literature. RESULTS: We evaluated GAP by comparing its prediction performance with several other well-known functional interaction prediction tools over a comprehensive dataset of known direct and indirect interactions, and observed significantly better prediction performance. We also selected a small set of GAP's highly-scored novel predicted pairs (i.e., currently not found in any known database or dataset), and by manually searching the literature for experimental evidence accessible in the public domain, we confirmed different categories of predicted functional associations with available evidence of interaction. We also provided extra supporting evidence for subset of the predicted functionally-associated pairs using an expert curated database of genes associated to autism spectrum disorders. CONCLUSIONS: GAP's predicted "functional interactome" contains ?1M highly-scored predicted functional associations out of which about 90% are novel (i.e., not experimentally validated). GAP's novel predictions connect disconnected components and singletons to the main connected component of the known interactome. It can, therefore, be a valuable resource for biologists by providing corroborating evidence for and facilitating the prioritization of potential direct or indirect interactions for experimental validation. GAP is freely accessible through a web portal: http://ophid.utoronto.ca/gap.

Dataset Information

Exprtarget: an integrative approach to predicting human microRNA targets.

Publications

Exprtarget: an integrative approach to predicting human microRNA targets.

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