Post-transcriptional knowledge in pathway analysis increases the accuracy of phenotypes classification.
BACKGROUND
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.


BACKGROUND
MITHrIL is freely available at the following URL: http://alpha.dmi.unict.it/mithril/.
Department of Clinical and Experimental Medicine, University of Catania, Catania, Italy.
Salvatore
S
Alaimo
Department of Computer Science, University of Verona, Verona, Italy.
Rosalba
R
Giugno
Department of Molecular Virology, Immunology and Medical Genetics, Comprehensive Cancer Center, The Ohio State University, Columbus, OH, USA.
Mario
M
Acunzo
Department of Molecular Virology, Immunology and Medical Genetics, Comprehensive Cancer Center, The Ohio State University, Columbus, OH, USA.
Dario
D
Veneziano
Department of Clinical and Experimental Medicine, University of Catania, Catania, Italy.
Alfredo
A
Ferro
Department of Clinical and Experimental Medicine, University of Catania, Catania, Italy.
Alfredo
A
Pulvirenti
D020869:Gene Expression Profiling; D053263:Gene Regulatory Networks; D006801:Humans; D035683:MicroRNAs / Q000502:physiology; D009369:Neoplasms / Q000235:genetics*; D010641:Phenotype; D057285:Precision Medicine; D015398:Signal Transduction
D035683:MicroRNAs
RNA-Seq; microRNAs; pathway analysis; phenotype classification