Post-transcriptional knowledge in pathway analysis increases the accuracy of phenotypes classification
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Salvatore Alaimo2, Rosalba Giugno1, Mario Acunzo3, Dario Veneziano3, Alfredo Ferro2, Alfredo Pulvirenti2
1Department of Computer Science, University of Verona, Verona, Italy
2Department of Clinical and Experimental Medicine, University of Catania, Catania, Italy
3Department of Molecular Virology, Immunology and Medical Genetics, Comprehensive Cancer Center, The Ohio State University, Columbus, OH, USA
Alfredo Pulvirenti, email: firstname.lastname@example.org
Keywords: pathway analysis, microRNAs, phenotype classification, RNA-Seq
Received: October 21, 2015 Accepted: May 11, 2016 Published: June 02, 2016
Motivation: Prediction of phenotypes from high-dimensional data is a crucial task in precision biology and medicine. Many technologies employ genomic biomarkers to characterize phenotypes. However, such elements are not sufficient to explain the underlying biology. To improve this, pathway analysis techniques have been proposed. Nevertheless, such methods have shown lack of accuracy in phenotypes classification.
Results: Here we propose a novel methodology called MITHrIL (Mirna enrIched paTHway Impact anaLysis) for the analysis of signaling pathways, which extends the work of Tarca et al., 2009. MITHrIL augments pathways with missing regulatory elements, such as microRNAs, and their interactions with genes. The method takes as input the expression values of genes and/or microRNAs and returns a list of pathways sorted according to their degree of deregulation, together with the corresponding statistical significance (p-values). Our analysis shows that MITHrIL outperforms its competitors even in the worst case. In addition, our method is able to correctly classify sets of tumor samples drawn from TCGA.
Availability: MITHrIL is freely available at the following URL: http://alpha.dmi.unict.it/mithril/
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