Implementation of computational analyses to identify novel biomarkers for molecular diagnosis of pancreatic tumors using next generation sequencing data

Abstract

The application of new massive sequencing technologies (NGS) have a enormous potential to contribute to the identification of somatic and transcriptional alterations present in human neoplasias. Nevertheless, the vast volume and complexity of data generated by these techniques represent an obstacle for the extraction of information with biologic and clinic relevance. This project intend to establish a bioinformatics framework to facilitate the processing, analysis and storage of the genomic data obtained from exome and transcriptome sequencing of clinical samples of pancreatic adenocarcinoma, pre-malignant lesions and non tumor pancreatic tissues, that will be produced in our laboratory. Analytical routines will be implemented for the identification of somatic alterations in the exome (SNPs, indels, CNVs) and transcriptome, including long noncoding RNAs, in pancreatic tissue samples with different degrees of malignancy. Integrated data analysis will allow the identification of genes and functional modules affected during pancreatic adenocarcinoma progression, facilitating the prioritization of candidates for experimental validation as molecular markers for an early diagnosis of the disease or therapeutic targets. (AU)