Towards an Analysis of Post-Transcriptional Gene Regulation in Psoriasis via microRNAs using Machine Learning Algorithms

Single Nucleotide Polymorphisms (SNPs) are the most common inter-individual variations in the human being. They gained popularity with the irruption of Next Generation Sequencing (NGS) as disease biomarkers for diagnosis and/or prognosis using Genome-Wide Association Study. They are along the genome but mostly in the non-coding regions. In these cases, SNPs may affect regulatory regions, such as promoters, enhancers or microRNA (miRNA) binding sites. miRNAs are short non-coding RNAs, that are estimated to regulate up to 60% of gene expression at the post-transcriptional level. It is well known they are implied in many diseases by misregulating the expression of genes. New computational technologies allow extracting more information from RNA-Seq data, being able not only to measure the gene expression but also mapping SNPs on the genome. To understand and model the effects of this type of RNAs in disease phenotype, machine learning algorithms will be trained using SNPs located in the 3'UTR (UnTranslated Region) of deregulated genes to find biomarkers and describe the mechanism of action.